IN THE UNITED STATES DISTRICT COURT
FOR THE EASTERN DISTRICT OF PENNSYLVANIA
Mahari Bailey, et al.,
Plaintiffs
v.
City of Philadelphia, et al.,
Defendants

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C.A. No. 10-5952

PLAINTIFFS’ SIXTH REPORT TO COURT AND MONITOR
ON STOP AND FRISK PRACTICES: FOURTH AMENDMENT ISSUES
I. Introduction
This Sixth Report to the Court and Monitor provides an update on stops and frisk
practices for 2015 and sets forth plaintiffs’ monitoring and enforcement plans for 2016.
As with the previous Reports, this submission presents compelling evidence that
the City has failed to remedy serious flaws in the Police Department’s stop and frisk
practices. The Consent Decree was intended to ensure that stops and frisks are conducted
only where there is the requisite “reasonable suspicion” of criminal conduct and to ensure
that any racial disparities in stops and frisks are not the result of impermissible bias. On
reasonable suspicion issues, the data continues to show very high numbers of illegal stops
and frisks.

For the First and Second Quarters, 2015, plaintiffs found that 33% of all

stops and 42% of all frisks were without reasonable suspicion. The City reports even
higher rates: for the Second Quarter, 62% of all stops and 53% of all frisks were without
reasonable suspicion.

Moreover, using benchmarks agreed upon by the parties,

including regression analysis, there is strong evidence that the large difference in stop and

 frisk rates by race are not explained by non-racial factors.
Over the past four years the City has asserted that compliance with the Consent
Decree could not be achieved until there were new police directives on stop and frisk
practices, a reliable electronic data base, re-training of officers, and internal
accountability measures.

As of 2015, the City had implemented all of these measures

and, therefore, continued non-compliance cannot be excused on the need for further
training or supervision.

We understand that Mayor Kenney has recognized the serious

problems in stop and frisk practices and we are willing to work with his Administration
in implementing the necessary remedial steps. However, given the four year history of
non-compliance, there is an immediate need for the Mayor and Police Commissioner to
ensure substantial improvements.
II. Procedural History
On June 21, 2011, the Court approved a Settlement Agreement, Class
Certification, and Consent Decree (“Agreement”). On February 6, 2012, plaintiffs
submitted their First Report which analyzed stop and frisk data for the first two quarters
of 2011. The First Report focused on Fourth Amendment issues, and specifically whether
there was sufficient cause for the stops and frisks reported by the Philadelphia Police
Department (“PPD”). The audits showed that over 50% of stops and frisks were
undertaken without reasonable suspicion.
Plaintiffs’ Second Report was submitted in July 2012, and included (1) a Fourth
Amendment analysis of the Third Quarter 2011 stop and frisk data, (2) a racial analysis of
the data for the First and Second Quarters, 2011, and (3) a racial analysis of arrests for
possession of small amounts of marijuana for the period September 15-November 15,

2

 2011. Plaintiffs reported continued high rates of stops and frisks without reasonable
suspicion (over 40% in both categories). On the question of racial disparities, plaintiffs’
expert, Professor David Abrams, conducted a series of regression analyses and concluded
that the racial disparities in stops and frisks (numbers by race compared to census data)
were not fully explainable by non-racial factors. Further, the analysis of marijuana arrests
showed even more pronounced disparities, with Blacks and Latinos constituting over
90% of all marijuana arrests.
Plaintiffs’ Third Report focused on stop and frisk practices for the first two
quarters of 2012 and analyzed marijuana arrests for the period September 15-November
15, 2012. Plaintiffs again found a 40% rate of non-compliance with Fourth Amendment
standards, and racial minorities constituted over 90% of arrests for small amounts of
marijuana. In response, the City stated that the PPD was providing additional training,
issuing revised auditing protocols, and instituting new accountability measures.
The Fourth Report, filed in December, 2013, analyzed stops and frisks in 2012
and 2013, on both Fourth and Fourteenth Amendment grounds. Pedestrian stops were
made without reasonable suspicion in 43% of the cases reviewed, and frisks were
conducted without reasonable suspicion in over 50% of the cases. There continued to be
very low “hit-rates,” with only 3 guns recovered in over 1100 stops (0.27%). Overall,
contraband of any kind was recovered in only 3% of the stops. We also noted the
relatively low number of frisks reported, with only 20% of stops resulting in frisks, and
numerous stops based on allegations of violent crime or weapon possession, where no
frisk was conducted.

3

 The stops and frisks continued to be racially disproportionate with statistically
significant disparities that were not explained by non-racial factors (e.g., crime rates,
demographics of police districts, age, and gender). The rate of stops without reasonable
suspicion for Blacks was 6.5 percentage points higher than the rate for Whites,
demonstrating that police were using a higher threshold of “reasonable suspicion” for
stops of White suspects.
On the Fourteenth Amendment issue, the City’s expert, Dr. Ralph Taylor of
Temple University, using benchmarks that differed in part from those agreed upon by the
parties, disputed certain of plaintiffs’ findings of racial bias. To ensure that the experts
were in accord on the proper benchmarks, the parties met and conferred with the Court
Monitor, Dean Joanne Epps.

These meetings and discussions have produced a draft set

of revised benchmarks.1
The Fifth Report covered the first two Quarters of 2014 and showed a rate of
stops without reasonable suspicion of 37%. The rate of frisks without reasonable
suspicion, or as fruits of an impermissible stop, was 53%.2
with 433 frisks yielding only two firearms.

Hit rates remained very low,

Indeed, where officers stated that a “bulge”

justified a frisk, they seized a gun in only 1 of 78 frisks.

On the issue of racial impact,

both experts found statistically significant evidence of racial bias in stops and frisks.

1 The new Benchmark Memorandum is attached as Exhibit A.
2 While the City initially disputed the frisk data, on review it conceded that there were substantial numbers of
frisks without reasonable suspicion.

4

 III.

Review of 75-48a Forms, First Two Quarters, 2015:
Analysis

Fourth Amendment

In this section, plaintiffs set forth their findings on the issue of whether stops and
frisks for the first two quarters of 2015 were supported by the requisite reasonable
suspicion.

As in previous audits, in assessing whether reasonable suspicion existed for

the stop or frisk, we fully credited the narrative information provided by the officer and in
“close” cases credited the assertion of reasonable suspicion.
For the first two quarters of 2015, from the random sample of pedestrian stops
produced by the Police Department, we found 2380 pedestrian stops (as opposed to
arrests, car stops, or contacts with civilians that did not constitute a Terry stop).

Of

these 2380 pedestrian stops, 67% were supported by reasonable suspicion and 33% were
made without reasonable suspicion, an improvement of 4% over the same period in 2014.
Frisks (as opposed to searches) were reported in 326 stops.

Of these, 43% were

made with reasonable suspicion, 42% were made without reasonable suspicion, and 14%
were preceded by a stop without reasonable suspicion (“fruit of the poisonous tree”
category). In contrast to the stop data, this shows a 4% slippage from the 2014
compliance rates.

5

 1. Stop Data
Actual Stops
Reasonable Suspicion
No Reasonable Suspicion

2380
1586
794

67%
33%

Stops: First Half 2015

No Reasonable
Suspicion
33%
Reasonable
Suspicion
67%

6

 2. Frisk Data
326
141
138
47

Frisks
Reasonable Suspicion
No Reasonable Suspicion
Fruit of the Poisonous Tree

43.3%
42.3%
14.4%

Frisks: First Half 2015

Fruit of the
Poisonous Tree
14.4%
Reasonable
Suspicion
43.3%

No Reasonable
Suspicion
42.3%

7

 3. Stop/Frisk Ratio
The 326 frisks represent 13.6% of the 2380 stops.

Stops Followed By Frisk

Frisk
14%

Non-Frisk
86%

8

 4. Contraband Recovered by Stops
Non-Gun Contraband
Guns
No contraband
Total Stops

36
6
2338
2380

Stops Resulting In Recovery Of Contraband
36

6

Non-Gun Contraband
Guns
Nothing

2338

9

 5. Contraband Recovered by Frisks
Non-Gun Contraband
Guns
No contraband
Total Frisks

15
4
307
326

Frisks Resulting In Recovery Of Contraband
15
4

Non-Gun Contraband
Guns
Nothing

307

6. Contraband Recovered By Frisks, With and Without Reasonable Suspicion
Reasonable Suspicion
No Reasonable Suspicion
Fruit of the Poisonous Tree

9
9
1

10

 7. Racial Composition of Philadelphia (2010 Census)
1,517,550 total
White
Black & African American
Hispanic
Asian
American Indian / Pacific Islander / Other

644,395
655,824
128,928
67,654
20,749

42.46%
43.22%
8.50%
4.46%
1.37%

Racial Composition of Philadelphia

Hispanic
9%

Asian
4%

American Indian /
Pacific Islander /
Other
1%

White
43%
Black & African
American
43%

11

 8. Stops by Race
Black
White
Latino
Total

1626
546
208
2380

68.32%
22.94%
8.74%

77.06% minorities

Stops by Race
208

546

Non-Latino Black
Non-Latino White
Latino & Other
1626

12

 9. Stops by Race and Reasonable Suspicion
Reasonable Unreasonable Reasonable %
1065
561
65.50%
375
171
68.68%
146
62
70.19%
1586
794
2380
66.64%
33.36%

Black
White
Latino
Total

Reasonable Basis for Stop
Reasonable

Latino & Other

Non-Latino White

Black

Unreasonable

146 62

375

171

1065

561

13

 10. Frisks By Race
Black
White
Latino
Total

253
36
37
326

77.61%
11.04%
11.35%

88.96% minorities

Frisks by Race
37

36
Black
Non-Latino White
Latino & Other

253

14

 11. Frisks by Race and Reasonable Suspicion
Reasonable
109
19
13
141
43.25%

Black
White
Latino
Total

Unreasonable
FTPT
109
35
12
5
17
7
138
47
42.23% 14.42%

Reasonable %
43.08%
52.78%
35.14%
326

Reasonable Basis For Frisk
Reasonable

Latino & Other

13 17 7

Non-Latino White

19 12 5

Black

Unreasonable

109

109

15

FTPT

35

 12. Frisks by Race and Contraband Recovery
Contraband No Contraband Total % w/ Contraband
13
240
253
5.14%
3
33
36
8.33%
3
34
37
8.11%
19
307
5.83%
94.17%
326

Black
Non-Latino White
Latino & Other

Frisks, Race & Contraband Recovered
Contraband

Latino & Other 3

34

Non-Latino White 3

33

Black

13

No Contraband

240

16

 13. Stops Leading to Frisks by Race

Stops Leading To Frisk
Black
Non-Latino White
Latino & Other

Frisked Unfrisked Stopped
253
1373
1626
36
510
546
37
171
208
326
2054
2380

Stops Leading To Frisk
Frisked

Unfrisked

Latino & Other 37 171

Non-Latino White 36

Black

510

253

1373

17

Frisked %
15.56%
6.59%
17.79%

 Commentary on Fourth Amendment Issues
There are a number of significant findings from the data review.
1.

33% of all stops were made without the requisite reasonable suspicion.

Significantly, the PPD audits show even higher rates of stops without reasonable
suspicion: the audits for the first two quarters of 2015 by the PPD show stops without
reasonable suspicion at 36% and 62%, respectively.

In light of the over 200,000

pedestrian stops for 2015, tens of thousands of persons in Philadelphia continue to be
stopped each year without reasonable suspicion.
2.

42% of all frisks were made without reasonable suspicion and an

additional 14% were made in cases where the stop itself was not supported by reasonable
suspicion (“fruit of the poisonous tree”). The PPD audit for the Second Quarter (the first
audit conducted after significant changes were made in the standards for assessing frisks)
show frisks without reasonable suspicion at a comparable rate of 53%.3
3.

As with previous data analysis, the number of reported frisks is quite low,

with only 13.6% of stops recording a frisk (and an additional 3.5% resulting in a search).
There is good reason to believe that officers have not been reporting many frisks.

For

example, in stops based on suspicion of gun possession or a violent crime, the police
frequently report no frisk of the suspect. Of the 159 stops in which guns or gun-related
activity are referenced as a basis for the stop, there were no frisks recorded on 55 stops,
or 35% of the total.

It is simply not plausible to suggest that frisks are not conducted in

these situations.
4.

The very low “hit-rate” of stops and frisks is further cause for serious

3 The Department’s First Quarter frisk analysis, which showed a very low rate of frisks without reasonable
suspicion, was conducted with what the Department concedes was flawed metrics.

18

 concern. Contraband of any kind was recovered in only 42 stops (with 54 seizures of
different types of contraband (2.3 % of all stops) and 6 guns were seized (0.25 % of all
stops), but 2 of these seizures were the result of searches, not frisks.

We recognize that

some legitimate stops are not likely to disclose contraband or lead to an arrest, but such
low hit rates are troubling and are likely the product of the high rate of stops without
reasonable suspicion.
By contrast, hit-rates for frisks are a highly reliable metric as officers must have
reasonable suspicion that the suspect is armed and dangerous before a frisk can be
conducted. Thus, we would expect that seizure of weapons or other contraband would
be made in a significant number of these cases if the officers are accurately reporting
facts that establish reasonable suspicion. Yet, the rate of recovery is vanishingly small.
Of 326 frisks, only 4 firearms were seized (98.8% of all frisks yielded no weapons) and
contraband other than weapons was seized in only 15 other frisks (a 95% rate of no
contraband or weapon seizure).

And it is highly likely that the hit-rates are even lower,

given the fact that police reported no frisks in 80 stops involving violent crimes or reports
of weapons.
Moreover, the data raise serious questions regarding the justifications for many
frisks. Most frisk reports assert that the suspect has a “bulge” in a pocket, refuses to take
his hands out of his pockets, does not cooperate with police, or that the stop was based on
a report of a gun or violent crime. “Bulges” inevitably turn out to be cell phones or
wallets and the other triggering factors are very weak indicators of criminal activity.
Thus, in 38 cases in which police conducted a frisk based on a “bulge,” not a single
weapon was detected.

The fact that so few frisks lead to the recovery of a weapon raises

19

 serious questions as to whether the police are accurately reporting what they observe and
whether the reasons generally provided for frisks are appropriate indicators of weapon
possession.
5.

Analyzing improper stops and frisks by category, there continue to be

significant numbers of stops for conduct which the Agreement and federal and state case
law make clear are not justifiable grounds for stops or frisks. These include:


loitering (or persons hanging out; congregating)4



person involved in a “disturbance”



single person “obstructing” the sidewalk



anonymous information (e.g., man with gun; man with drugs)5



person on steps of or near “abandoned” property



person involved in “verbal dispute” (non-domestic)6



high crime area/roll call complaints



panhandling



suspicion of narcotics activity
As for frisks, problematic grounds include:



frisk for “officer protection”



frisk based on “narcotics investigation”

4 The PPD has instructed officers that “loitering” is not a valid basis for a stop. And while the number of such
stops has decreased, the PPD has recognized that officers are using other vague narratives (e.g., blocking
buildings) to justify stops.
5 Notwithstanding clear case law, e.g. Florida v. J.L, 529 U.S. 266 (2000) and the terms of the Consent Decree, the
Police Department was until this year training officers to make a stop and frisk based on anonymous information
only.
6 We credit reports of “domestic” disputes.

20

 

frisk because suspect stopped in “high crime” or high drug area
6.

Although the continued high rates of impermissible stops and frisks are

the result of several factors, we believe that the primary cause at this point is the lack of
accountability of officers and their supervisors for violations of the Consent Decree.
The Police Department delayed implementation of the accountability process until 2015
(following establishment of the electronic data base, re-training of officers with respect to
stop and frisk practices, and the institution of an internal auditing process).

These

accountability measures are set forth in the Department’s Directive on stop and frisk
practices (currently Directive 12.11, Appendix B), and include:
1. Under Section 7, patrol supervisors must review each 75-48a, send incomplete
forms back to the officer, and note what actions were taken where the officer did
not provide sufficient reasons for the stop or frisk.
2. Under Section 8, Commanding Officers must take necessary actions to correct
errors in stop and frisk practices including the identification of officers who fail to
state reasonable suspicion and they are accountable for officers and their
supervisors who repeatedly engage in impermissible stops or frisks. The
Commanding Officers must submit memorandum on a periodic basis detailing
corrective actions taken.
3. Under Section 9, Special Unit Inspectors must complete audits of randomly
selected stop and frisk reports, provide Commanding Officers under their
supervision and command with memorandum detailing errors and deficiencies in
these reports, review responses by the Commanding Officers as to remedial
actions taken by the Commanding Officers, and to forward all findings and
actions taken to the Chief Inspector, Office of Standards and Accountability.
4. Under Section 9, the Office of Standards and Accountability must ensure
departmental compliance with stop and frisk procedures under the Directive
(including reports on any racially biased or other discriminatory patterns), and
provide quarterly audits of stop and frisk reports to various officials and offices
within the Police Department, including the Police Commissioner, Deputy Police
Commissioner and all Inspectors.
We have reviewed the reports generated pursuant to this accountability process for
the Third Quarter, 2015 and, as we have already informed the City, that process has failed
21

 to produce anything near the level of supervision, accountability, or remedial measures
required by the Directive, and which are essential to compliance with the Consent
Decree.

A few Commanders have acknowledged Fourth Amendment and Consent

Decree compliance issues, but overall there is only minimal recognition of the scope of
the problems and the fact that tens of thousands of persons are being stopped and frisked
each year without reasonable suspicion.

Some Commanders continue to assert that

75-48a reports that do not state reasonable suspicion for stops and frisks are the product
of “incomplete paperwork;” others do not even address the reasons for impermissible
stops. There is no indication of any process for identifying officers (or their supervisors)
who repeatedly engage in stops or frisks without reasonable suspicion or of any specific
retraining, increased supervision, or other remedial action. Nor does there appear to be
a remedial process of accountability in place.
In short, plaintiffs have been more than reasonable in giving the City the means
and the time to implement what the Police Department has insisted are the necessary
measures for compliance. We have also stated from the start that the kind of
organizational/cultural change envisioned by the Consent Decree cannot be achieved
simply by new Directives or training.

Unless officers and supervisors are held

accountable, the current state of affairs will not change. The time for such action cannot
be delayed.

22

 IV. Racial Analysis of Stop and Frisk Practices, January-June, 2015
A. Introduction
This section sets forth a statistical analysis of the “Stop and Frisk” practices of the
PPD for the first half of 2015, conducted by plaintiffs’ expert, Professor David Abrams.
The benchmarks to be used in the analysis are those set forth in a revised Benchmark
Memorandum agreed to by the parties in 2016. See Exhibit A.
In creating benchmarks to measure compliance of the PPD with the terms of the
Agreement, we considered several criteria. First, the benchmarks are designed to be
straightforward in terms of computation and interpretation.

Second, they are designed

to measure characteristics at the core of the Agreement, namely compliance with the
Fourteenth Amendment. Third, they consider other potential explanations for patterns in
the data beyond suspect race. The benchmarks are based on a combination of those
discussed and used in NAACP v. City of Philadelphia, academic literature on the topic,
and those used recently in other jurisdictions. See, e.g., Floyd v. City of New York, 959
F. Supp. 2d 540 (S.D.N.Y. 2013).
B.

Summary of the Racial Aspects of the Stop and Frisk Data

We examined data from Q1 and Q2 2015 pedestrian stops. A random sample of
the stops was drawn by the Philadelphia Police Department for legal analysis for stop and
frisk sufficiency by the plaintiffs.

In this report we largely focus on an analysis of this

randomly selected sample (see Table 1).7

We also include a description of the full array

of stops (Table 2) at the PSA-race level, which is the way the overall stop rate is analyzed
(Table 5).

7 The Tables and Figures are set forth, infra 35-53.

23

 The sample dataset (Table 1) includes 2,380 total pedestrian stops and the full
data set has 117,559.8 The mean detainee age is 34 and 82% of detainees are male.

The

likelihood of being stopped rises sharply in the late teens and early 20’s (Figure 1), which
is not surprising given the evidence that criminal activity rises sharply at this age.
Blacks account for 69% of those stopped and compared to 2014, Black stop share has
decreased by 3 percentage points.
The data is subdivided into 64 Police Service Areas (PSA). See Table 2.9

There

were an average of 1251 stops of Black pedestrians per PSA in the first half of 2015,
compared with 375 White stops and 136 of Hispanics.

In light of the fact that much of

this variation is due to different resident racial composition, we also report the stop rate
by race per 10,000 residents of the same race. This varies from a low of 583 to
Hispanics, to 747 for Whites and 1,611 stops of Blacks for every 10,000 Black residents.
Below we use a regression framework to determine whether other factors account for
these differences.
The control variables include demographic, economic and crime factors.

The

employment rate varies substantially across PSA’s, from 20% to 67%. The variation in
racial composition is even greater, with the Black residential share ranging from 3% to
98%. To account for higher crime rates among juvenile and young adult males, we
control for the share of males under 24 in some regression specifications. This rate also
varies widely, from 9 to 52 percent, with a mean of 37%. Crime rates are also likely to

The data originally provided contains 300 additional observations that were incorrectly coded as stops. These
observations were dropped from the analysis.
8

Two PSA’s are omitted: 77, which is the airport and has no residential population and 254, due to missing
demographic information.
9

24

 drive stop rates and thus we control for them using three different measures of violent
crime, property crime and overall Part 1 crimes.
Table 3 provides a breakdown of stop, frisk and arrest rates by race. As
mentioned, Blacks account for 69% of stops, and Whites are 23% with Latinos
accounting for 7%.

Minorities account for an even higher share of individuals frisked,

of which 79% are Black, 10% Latino and 11% White.
identical to that in 2014.

This racial composition is

About 1 in 6.4 stops of Black pedestrians result in a frisk, but

the rate is only 1 in 15.2 for Whites. The difference is greater for arrests, with an arrest
of a Black detained resulting from 16.3 stops on average, while for Whites it takes 28.7
stops. The arrest rate for Blacks and Latinos is similar to 2014, while the White arrest
rate dropped somewhat.
The number of stops varies substantially by district, with the 19th, which includes
Overbrook and parts of West Philadelphia, accounting for the most, with 9% (Figure 2).
The fewest stops are in the 7th police district, in Northeast Philadelphia, accounting for
less than 1% of all stops.
C.

Benchmark Applications

1. Stops, Census and Regression Analysis
The question of whether race is impermissibly used as a factor in the decision to
stop and frisk cannot be answered by a simple comparison of stop and frisk rates to
census data. Non-racial factors may explain the disparities. However, the stop
rate/census comparison is the first step in this process. As set forth in Tables 2 and 3, the
stop rate by race in comparison to the census is as follows:

25

 Black stops=69%; Black census=46%
White stops=23%; White census=42%
Latino stops=7%; Latino census=11%

The next step is a cross-PSA comparison of stop rates by Black/Minority
population share. A racial disparity in stops should be expected based on differences in
population composition. It is possible to examine variation in the share of Black and
Latino stops by PSA, as reported in Tables 4A and 4B, respectively. Each row in the
tables represents a PSA (column 1) and the tables are sorted by the Black or Latino share
of the population in the district, as reflected in column 2. The third column reports the
share of stops that are of Black/Latino pedestrians and the fourth is the ratio of
Black/Latino stops to Black/Latino population share. Note that in all but four PSAs,
Blacks account for a higher share of stops than they do in the population; in some PSA’s,
they are stopped at a rate over five times their share of the population. Thus, in PSA 243,
where the population is 3% Black, 24% of the stops were of Blacks and in PSA 63, where
the population is 7% Black, 59% of the stops were of Blacks. By contrast, in the PSA
192, where Blacks make up 96% of the population, the ratio of stops by race was close to
a 1:1 ratio.
This trend of a vastly inflated minority stop rate in heavily White locations can be
seen visually in Figure 3.

If the ratio of minority stops were independent of PSA

minority share, the points should form a horizontal line. The fact that the points in the
left end of the figure (heavily White PSA’s) have much higher Black stop ratios,
reinforces the results from Table 4A.

26

 The last two columns in Tables 4A and 4B report characteristics based on the
census population of the district, not just minorities. Column 5 reports total stops per
capita and Column 6, the violent crime rate in the district (violent crimes per 10,000
residents). Figure 4 visually displays the relationship between overall stop rate and
Black population share.

It shows that areas with a greater Black population experience

a higher stop rate than those with a lower Black population share.
To address non-racial influences, we next move to a multivariate regression
analysis. This approach is more robust than a comparison of averages because it
examines the relationship among multiple variables simultaneously. To determine the
impact of suspect race on the likelihood of a stop or frisk, we control for factors that
include the demographic makeup and crime rate of the neighborhood.
First, we add data collected from the U.S. Census as well as Uniform Crime
Report data on reported crimes, by PSA. We begin by examining differences in overall
stop rates by race in Table 5. This table (and tables 6, 8, 9 and 11) share the same
format: each column in the table reports results from a separate regression that identifies
the relationship between the variables listed in the first column and the dependent
variable, which is the title of the table.

For example, the regression that is reported in

column 4 can be written as:
(1)

𝑆𝑡𝑜𝑝𝑅𝑎𝑡𝑒 = 𝛼 + 𝛽1𝐵𝑙𝑎𝑐𝑘 + 𝛽2 𝐿𝑎𝑡𝑖𝑛𝑜 + 𝛽3 𝑀𝑎𝑙𝑒 + 𝛽4 𝐴𝑔𝑒 + 𝜖

Stop Rate is the number of stops in the sample examined per 10,000 residents in a district
and Black is coded 0 if the detainee is White and 1 if the detainee is Black. Similarly,
Latino is coded 1 if the detainee is Latino and zero otherwise. Male is coded 1 for men
and 0 for women. Age is the detainee’s age in years. By including four variables in the

27

 equation, this regression can better isolate the impact of race and Latino identity on the
likelihood of being stopped, even if sex or age are important factors affecting the stop
rate.
The coefficient on Black found in column 4 is 962.0, which means that in the full
dataset about 962 more Black individuals were stopped than White individuals for every
10,000 residents of a PSA. The standard errors are reported in parentheses below the
coefficient and the double stars on the standard error indicates that this result is
statistically significant at better than the 1% level. This means that there is less than a 1%
chance that the difference in stop rates between Blacks and Whites is zero.
There may be reasons other than race that minorities are stopped at higher rates.
For example, if minorities tend to be younger on average, since more crime is committed
by younger individuals, one might expect a higher stop rate for minorities. We control for
this factor (as in equation 1 above) and others relevant to this issue.

Column 5 adds

controls for the PSA racial composition and Column 6 the share of the male population
under 24 years of age.

Even after adding these controls, the coefficient on Detainee

Black (978.3) is still similar to what it was with no controls. The 7th column includes a
control for whether flash information led to the stop, which does not have a statistically
significant influence on the stop rate. Column 8 adds the PSA employment rate to the
regression.

Not surprisingly, PSA’s with higher employment rates have lower stop

rates, but this control does not have a substantial impact on the race effect.
Columns 9-11 add different controls for PSA crime rates. The crime rates are
based on crimes reported to the police in 2014. It is preferable to use lagged crime
because current crime levels could be influenced by policing policies. In each case,

28

 PSA’s with higher crime rates have more stops, but controlling for crime rates does not
affect the influence of detainee race on stop rate.
The final column reproduces column 10, but includes additional econometric
safeguards.

It controls for other potential differences across districts (district fixed

effects) as well as potential correlations in the errors within a district (clustering standard
errors at the district level). A comparison between columns 10 and 12 shows that the
coefficients on Black and Latino are not greatly impacted by these additions.

The

coefficient on Black is of a similar magnitude, although now significant at the 5% level,
not 1%. All of the regressions reported were run with the addition of district fixed
effects and clustering of standard errors, and the results were not materially changed.
A number of additional specification checks were run to insure the robustness of
the results.

Instead of using stop rate as the outcome, the number of stops was also

examined. The results from these regressions were consistent with those reported.
While the number of stops per PSA is large enough that an ordinary least squares (OLS)
regression is appropriate, we also made use of a negative binomial regression, which is
appropriate for use with count data. Again the results were consistent with those
reported. Next, we varied the types of control variables used, including replacing the
demographic, economic and crime control variables with those provided by the
defendant’s expert. This, too, did not change the results.
Table 6 is analogous to Table 5, but it reports the results of a regression of the
incidence of pedestrian frisks (rather than stops) on detainee race and various controls.
Rather than aggregating data to the PSA-race level, the data in Table 6 is at the stop level
and controls for the quarter of the year.

In each regression, the coefficient on Detainee

29

 Black is statistically significantly different from zero and ranges from about 0.044 –
0.084.

The preferred estimate is .047 and may be found in column 10 and controls for

demographic, economic and crime variables. This means the frisk rate for Black
detainees is 4.7 percentage points higher than for Whites, once controlling for the entire
array of variables described above. Since the frisk rate for Whites is 6.6%, this translates
to an 71% greater likelihood that a Black detainee is frisked compared to a White
detainee.

This results is statistically significant at the 1% level.

It is robust to the array

of alternative specifications described above for the stop rate regressions.
There are several other interesting results reflected in Table 6.

Latinos are also

far more likely than Whites to be frisked (see second row) as the coefficient of .072
indicates that Latinos have a 7.2 percentage point higher frisk rate than Whites, which is
over twice as high a rate, accounting for all of the control variables. This result is
statistically significant at the 1% level.

Also statistically significant are results for age

and gender: an extra decade of age decreases likelihood of frisk by 2 percentage points
and male detainees are far more likely to be frisked than females. Additionally, if a
detainee matches flash information, that individual is 27 percentage points more likely to
be frisked than otherwise. Overall, in assessing data as to frisks, and controlling for
non-racial factors, there is a substantially higher frisk rate of minorities.

2. Reasonable Suspicion for Stops and Frisks: Racial Analysis
As the previous Reports and Section II of this Report demonstrate, a substantial
number of the pedestrian stops do not meet the reasonable suspicion standard.

Table 7

shows the share of stops without reasonable suspicion across racial and ethnic categories,

30

 at 31% for Whites and Latinos and 35% for Blacks. The average of 33% unfounded
stops is 4 percentage points lower than in 2014.

This is a move in the right direction,

but still an extraordinarily high rate of stops without reasonable suspicion.

The share of

frisks made without reasonable suspicion is far higher, at 56% overall. This is an
increase of 4 percentage points from 2014 and very close to the 55% unfounded frisk rate
in 2012.

The unfounded rate is highest for minorities, making up 62% of Latino frisks

and 57% for Blacks, whereas the rate for Whites is still quite high at 47%.
As with stop rates and frisks, summary statistics can only get you so far, and
regressions are necessary to control for potentially confounding factors. Table 8 reports
results from such regressions, with each column representing a separate regression where
the dependent variable is whether there was reasonable suspicion for the stop. As
before, additional control variables are added in the different columns.

In most of the

columns the coefficient on Detainee Black is between -.019 and -.034 indicating that
reasonable suspicion was found in 1.9 to 3.4 percentage points fewer cases when the
detainee was black. However, none of these differences are statistically significant.
There is also no statistically significant impact of Latino status on likelihood of an
improper stop. The two variables for which there are consistently significant effects are
age and sex. Younger detainees and males are far more likely to be subject to
unfounded stops than older detainees and females.
Table 9 is similar to Table 8 and describes regressions of the rate of reasonable
suspicion, but now for a frisk rather than a stop. The coefficient on Detainee Black covers
a wide range, but as in Table 8, but none of these coefficients are statistically significant.
The same is true for Latino detainees.

Overall there is little evidence that there are

31

 significant disparities in the rates of unfounded frisks, although this is largely due to the
less precise estimates from a smaller sample size.
3. Hit-Rate Analysis
An important measure of the propriety of stops and particularly of frisks is the
rate at which they lead to the discovery of contraband, and particularly weapons, since
frisks are permitted only where the officer reasonably believes that the suspect is armed
and dangerous. Moreover, seizures of weapons are often cited as justification for a robust
stop and frisk program. The rates of discovery of contraband from frisks are reported in
Table 10. Contraband is categorized as firearms, drugs, or other. “Other” includes small
amounts of cash or unspecified materials.
Table 10 reports an overall detection rate for firearms that is extremely low, with
fewer than 1 in 80 pedestrian frisks yielding a firearm. Drugs were by far the most
commonly detected type of contraband, and were found in 1 of every 46 frisks.

Overall,

contraband was found in under 6% of all frisks.
Table 11 is a more sophisticated approach to the firearms hit-rate analysis. The
regressions report the rate of discovery of a firearm in pedestrian frisks. None of the
coefficients on Detainee Black or Detainee Latino are statistically significant, but this is
likely due to the fact that firearms are very rarely discovered and there are just over 300
frisks available for analysis in the sample.
This suggests that the full dataset will be more useful than the sample to
understand the impact of race on contraband hit-rates. These results are presented in
Table 12, which examines 15,821 frisks in Q1 and Q2 of 2015, of which 9.2% resulted in
the recovery of some kind of contraband (the type is not categorized in the full data).

32

 An examination of column 10, one of the most conservative specifications, shows that
Blacks who are frisked are 2.9 percentage points less likely to have contraband, and this
result is statistically significant at the 1% level.

From an overall contraband recovery

rate of 9.2% this means that frisks of Blacks are 31% less productive than those of
Whites, even when controlling for a whole host of variables.
vastly less productive than those of females.

Frisks of males are also

Due to the recent introduction of electronic

data collection by the Philadelphia Police Department, this is the first time the contraband
analysis has been done using all available stops and is thus vastly more powerful than
prior analyses, which were akin to the results shown in Table 11. These new findings
are some of the strongest evidence yet that Blacks are treated differently from Whites, in
ways that cannot be explained by a host of other factors.
4. Marijuana Arrests
In previous Reports to the Court, plaintiffs analyzed arrest data to determine
whether there were racial disparities in cases involving arrests for small amounts of
marijuana. The data from 2011, 2012 and 2013 showed that Blacks and Latinos
accounted for over 90% of those charged. These rates were not explainable by patterns of
use or possession of marijuana, as all reliable data shows that Blacks and Whites use and
possess marijuana at approximately the same rate.
In 2014, an Ordinance sponsored by Mayor (then Council Member) Kenney was
enacted and provides that possession of under 30 grams of marijuana is to be treated as a
Civil Code Violation punishable by a fine. In most circumstances, the offender is not

33

 subject to arrest or prosecution.1 This legislation has had a significant positive impact. In
the period March 1-May 15, 2015, there were 203 possession of marijuana arrests, an
80% decline from previous years during the same time frame. The racial disparities
continue, with over 90% of arrests of Blacks and Latinos, and with no arrests in several
predominantly White police districts. Most (80%) of the arrests were the result of
observed marijuana transactions.
D. Commentary
We have examined the relationship of race to stop and frisk practices from
multiple perspectives, following standard statistical theories.

It is significant that on the

key benchmarks that provide the most reliable measures of racial bias—regression
analysis, comparisons of stops without reasonable suspicion by race, and hit rate
analysis—there is strong evidence that the large difference in stop and frisk rates by race
in Philadelphia are not explained by non-racial factors.

To the contrary, the data show

statistically significant racial disparities that in almost all respects are not explainable by
non-racial factors.

1

Philadelphia Code, Chapter 10-2100.
34

 Number

Figure 1

Detainee Age

 

2000 3000 4000 5000

1000

 

 

35

 

 

90


100

Figure 2

 

36

Ftatio of Black Stop Share to Population Share

 

Figure 3

Black Stop Ftatio vs Black Population Share
By PSA, 2015 ??tun-Illinoi-

I 
.2 .4 .5 .B
PEA Black share

37

Annual Stops ms 

 

Figure 4

Annual Stop Flats vs. Black Population Share
By PEA.E-
PEA Black share

38

Table 1

2015 Q1 & Q2 Random Sample Summary Statistics
VARIABLES

(1)
Mean

(2)
N

Reasonable Suspicion for stop?
Individual Frisked
Reasonable Suspicion for frisk?
Search Made
Arrest Made
Evidence or Contraband Found
Firearm Found
Drugs Found
Detainee Age
Detainee Male
Detainee Black
Detainee Latino

67%
14%
43%
6.4%
5.7%
1.7%
0.26%
0.85%
33.8
82%
69%
8.6%

2,380
2,352
326
2,380
2,352
2,352
2,352
2,352
2,375
2,376
2,342
2,380

Table includes summary statistics from 2015 Q1 & Q2 random sample, excluding 300
observations incorrectly coded as stops.

 1251
375
136
1611
747
583
33.9
82%
23578
46%
42%
11%
5%
40%
37%
272
520
64
687

VARIABLES

Stop of Black Pedestrian
Stop of White Pedestrian
Stop of Hispanic Pedestrian
Stops per 10,000 Black Residents
Stops per 10,000 White Residents
Stops per 10,000 Hispanic Residents
Detainee Age
Detainee Male
PSA Population
PSA Black share
PSA White share
PSA Latino share
PSA Asian share
Employment Rate
Male population under 24
Violent Crime Rate (per 10k residents)
Property Crime Rate (per 10k residents)
Drug Crime Rate (per 10k residents)
UCR Part 1 Crime Rate (per 10k residents)

897
195
24
1258
364
355
33.3
82%
21097
38%
39%
4%
3%
40%
39%
262
455
39
640

(2)
Median

Table includes PSA-level summary statistics from 2015 Q1 & Q2 all stops, excluding PSA 77 and 254.

(1)
Mean
1127
495
314
1536
980
618
3
5%
10529
34%
32%
16%
5%
11%
11%
134
249
93
314

(3)
SD
50
43
1
113.6
70.95
11.96
28.06
71%
5278
3%
1%
1%
0%
20%
9%
59
196
2
219

(4)
Min

2015 Q1 & Q2 PSA-Level All Stops Summary Statistics

Table 2

5732
3137
1810
9619
5309
3448
40.97
92%
46642
98%
93%
75%
22%
67%
52%
609
1740
647
2155

(5)
Max

64
64
64
64
64
64
64
64
64
64
64
64
64
64
64
64
64
64
64

(6)
Obs

 Table 3
Counts by Race in Random Sample, 2015 Q1 & Q2
Black

Latino

White

Total

Stops

1626

172

546

2344

Stop Share

69%

7%

23%

100%

Frisks

253

32

36

321

Frisk Share

79%

10%

11%

100%

Stops/Frisk

6.4

5.4

15.2

7.3

Searches

107

16

28

151

Stops/Search

15.2

10.8

19.5

15.5

Arrests

100

13

19

132

Stops/Arrest

16.3

13.2

28.7

17.8

Contraband Discovered

28

7

6

41

Frisks/Contraband

9.0

4.6

6.0

7.8

 Table 4A
PSA-Level Statistics, Black Stops 2015 Q1 & Q2

PSA

PSA Black share

Black Share of
Stops

222
124
393
181
192
141
392
182
224
162
142
353
221
122
123
223
193
172
191
121
173
352
351
161
391
144
143
251
61
261
11
151
22
171
21
262
183
242
253
241
252
152

98%
98%
98%
97%
96%
96%
96%
95%
93%
91%
89%
88%
84%
83%
83%
82%
80%
79%
77%
74%
73%
68%
68%
63%
61%
57%
51%
50%
50%
48%
42%
39%
37%
36%
35%
35%
33%
31%
29%
27%
26%
21%

97%
97%
97%
96%
97%
97%
97%
96%
93%
97%
97%
94%
94%
93%
95%
86%
92%
79%
94%
88%
92%
91%
90%
95%
94%
74%
90%
57%
61%
46%
59%
69%
60%
72%
60%
41%
85%
33%
30%
38%
41%
42%

Ratio of Black
Total Stops per
Stop Share to
100 Residents
Population Share
0.99
0.99
1.00
0.99
1.00
1.02
1.01
1.01
1.01
1.07
1.09
1.06
1.12
1.12
1.14
1.06
1.16
1.00
1.22
1.20
1.26
1.35
1.34
1.50
1.53
1.29
1.74
1.13
1.22
0.95
1.40
1.75
1.62
2.00
1.68
1.19
2.55
1.07
1.05
1.40
1.61
2.02

42

23.2
19.9
52.2
27.6
36.0
16.4
29.6
26.4
27.2
30.1
22.9
10.2
36.2
23.3
23.3
12.2
7.8
37.3
16.8
11.0
36.2
21.1
8.0
18.2
15.2
2.3
9.6
8.6
18.3
27.8
25.2
15.4
7.6
9.8
9.3
15.5
8.0
55.5
18.4
25.4
6.0
5.9

Violent Crime
Rate (per 10k
residents)
454
299
609
337
384
261
390
439
606
393
367
261
517
277
411
392
182
461
203
168
263
368
192
311
205
112
196
284
386
441
206
369
211
138
231
260
121
380
332
375
291
281

 Table 4A, continued
PSA-Level Statistics, Black Stops 2015 Q1 & Q2

PSA

81
93
92
32
23
62
31
12
153
33
263
82
63
53
83
72
52
51
71
73
243
91

Ratio of Black Stop Share
Total Stops per 100
Violent Crime Rate (per
PSA Black share Black Share of Stops
to Population Share Residents
10k residents)
21%
16%
14%
14%
13%
12%
12%
9%
8%
8%
8%
8%
7%
6%
6%
5%
5%
4%
4%
4%
3%
3%

24%
72%
68%
41%
39%
50%
47%
32%
30%
40%
20%
20%
59%
38%
20%
21%
28%
21%
20%
13%
24%
50%

1.17
4.56
4.84
2.98
2.93
4.07
3.85
3.56
3.59
4.91
2.44
2.48
7.91
6.09
3.57
4.15
6.15
4.76
4.70
3.34
6.91
16.71

43

2.0
9.5
18.2
18.2
5.2
35.2
11.1
9.0
4.7
12.9
16.7
2.5
16.2
3.9
2.4
1.7
9.9
9.0
2.7
2.7
10.0
12.4

142
180
320
266
122
575
186
100
214
198
260
101
278
62
103
59
141
146
87
83
264
176

 Table 4B
PSA-Level Statistics, Latino Stops 2015 Q1 & Q2

PSA
253
252
242
261
251
241
262
21
352
151
152
22
32
263
33
351
23
31
61
81
93
153
92
83
72
71
62
82
243
73
183
192
191
171
53
143
63
11
144
121
223
91

Ratio of Latino Stop
Total Stops per 100
Violent Crime Rate (per
PSA Latino shareLatino Share of Stops
Share to Population
Residents
10k residents)
Share
75%
58%
52%
50%
48%
46%
37%
20%
20%
19%
14%
14%
14%
12%
11%
11%
10%
9%
9%
8%
8%
7%
7%
6%
6%
5%
5%
5%
5%
4%
4%
4%
4%
4%
4%
3%
3%
3%
3%
3%
3%
3%

44%
39%
24%
33%
29%
23%
15%
21%
5%
8%
12%
20%
7%
14%
8%
5%
13%
3%
6%
6%
4%
9%
2%
3%
6%
8%
6%
4%
16%
2%
1%
1%
0%
4%
4%
1%
2%
2%
0%
1%
2%
1%

0.59
0.68
0.47
0.67
0.59
0.49
0.40
1.07
0.24
0.40
0.81
1.43
0.50
1.11
0.72
0.44
1.35
0.37
0.73
0.78
0.49
1.23
0.23
0.50
1.04
1.48
1.05
0.74
3.37
0.48
0.22
0.15
0.11
1.07
1.09
0.20
0.55
0.68
0.10
0.39
0.83
0.49

44

18.4
6.0
55.5
27.8
8.6
25.4
15.5
9.3
21.1
15.4
5.9
7.6
18.2
16.7
12.9
8.0
5.2
11.1
18.3
2.0
9.5
4.7
18.2
2.4
1.7
2.7
35.2
2.5
10.0
2.7
8.0
36.0
16.8
9.8
3.9
9.6
16.2
25.2
2.3
11.0
12.2
12.4

332
291
380
441
284
375
260
231
368
369
281
211
266
260
198
192
122
186
386
142
180
214
320
103
59
87
575
101
264
83
121
384
203
138
62
196
278
206
112
168
392
176

 Table 4B, continued
PSA-Level Statistics, Latino Stops 2015 Q1 & Q2

PSA
173
161
51
141
123
391
392
221
193
182
122
162
393
142
52
353
222
224
12
181
124
172

Ratio of Latino Stop
Total Stops per 100
Violent Crime Rate (per
PSA Latino shareLatino Share of Stops
Share to Population
Residents
10k residents)
Share
3%
3%
2%
2%
2%
2%
2%
2%
2%
2%
2%
2%
2%
1%
1%
1%
1%
1%
1%
1%
1%
1%

2%
1%
2%
1%
1%
1%
1%
1%
1%
1%
1%
0%
1%
0%
2%
2%
1%
2%
3%
0%
1%
1%

0.58
0.25
0.89
0.27
0.31
0.44
0.46
0.63
0.37
0.32
0.69
0.25
0.66
0.21
1.20
1.45
1.11
1.84
3.82
0.53
0.73
1.92

45

36.2
18.2
9.0
16.4
23.3
15.2
29.6
36.2
7.8
26.4
23.3
30.1
52.2
22.9
9.9
10.2
23.2
27.2
9.0
27.6
19.9
37.3

263
311
146
261
411
205
390
517
182
439
277
393
609
367
141
261
454
606
100
337
299
461

 (1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

(9)

(10)

(11)

(12)

Standard errors in parentheses ** p<0.01, * p<0.05

Observations
R-squared

192
0.140

46

192
0.143

192
0.148

192
0.200

192
0.203

192
0.221

192
0.234

192
0.268

192
0.346

192
0.379

192
0.324

192
0.446

946.4
864.6
781.6
962.0
981.9
978.3
1,079
1,113
1,101
1,104
1,101
1,125
(169.9)** (196.4)** (213.0)** (213.3)** (217.1)** (215.1)** (222.0)** (217.9)** (206.5)** (201.2)** (210.0)** (489.6)*
Detainee Latino
-163.7
-245.2
23.39
45.42
16.97
72.69
101.5
79.16
97.86
75.17
137.0
(196.4)
(212.4)
(220.3)
(225.0)
(223.3)
(224.5)
(220.3)
(208.9)
(203.4)
(212.4)
(316.3)
Detainee Male
1,165
485.3
257.2
-198.8
-257.0
-177.5
-609.9
-492.0
-588.7
-954.9
(1,159)
(1,143)
(1,200)
(1,209)
(1,203)
(1,180)
(1,122)
(1,091)
(1,142)
(1,354)
Detainee Age
80.75
82.96
60.92
62.80
70.43
54.01
65.19
52.53
71.80
(23.17)** (24.20)** (26.18)* (26.07)* (25.69)** (24.60)* (23.74)** (25.18)* (62.37)
PSA Asian share
532.7
1,108
117.1
389.3
-1,267
-870.2
-906.9
-1,887
(1,769)
(1,774)
(1,857)
(1,823)
(1,764)
(1,698)
(1,788)
(1,249)
PSA Black share
34.30
747.9
426.7
12.87
-530.1
-1,072
-279.1
-1,003
(270.7)
(432.9)
(469.8)
(482.2)
(471.7)
(484.3)* (470.7)
(696.6)
PSA Latino share
396.0
1,360
1,135
482.6
-121.6
-474.2
161.6
-1,158
(530.9)
(698.0)
(706.6)
(728.5)
(702.5)
(693.3)
(706.8)
(1,050)
Male population under 24
-2,817
-2,333
-5,611
-3,446
-3,060
-3,960
318.5
(1,341)* (1,364) (1,752)** (1,725)* (1,679)
(1,742)* (2,257)
Detainee Matches Flash Information
-2,306
-2,976
-2,596
-2,414
-2,672
-1,853
(1,348)
(1,342)* (1,274)* (1,243)
(1,295)* (1,201)
Employment Rate
-5,040
-3,932
-2,531
-4,411
-325.8
(1,742)** (1,667)* (1,667) (1,686)** (2,189)
UCR Part 1 Crime Rate (per 10k residents)
1.219
(0.262)**
Violent Crime Rate (per 10k residents)
4.102
4.537
(0.721)**
(0.883)**
Property Crime Rate (per 10k residents)
1.294
(0.334)**
Constant
664.7
746.5
-148.1
-2,471
-2,459
-763.1
-551.4
2,618
1,830
618.1
2,250
-1,440
(98.11)** (138.9)** (900.6)
(1,100)* (1,112)* (1,366)
(1,364)
(1,728)
(1,647)
(1,634)
(1,668)
(2,591)

Detainee Black

VARIABLES

Table 5
Stop Rate per 10,000 Residents

 2,314
0.007

0.096
(0.013)**

0.061
(0.016)**

(1)

2,314
0.013

0.071
(0.014)**

0.084
(0.017)**
0.11
(0.027)**

(2)

2,310
0.027

-0.0097
(0.020)

0.076
(0.017)**
0.092
(0.027)**
0.11
(0.019)**

(3)

47

(5)

Frisk
(6)

(7)

(8)

(9)

(10)

(11)

(12)

2,305
0.035

2,269
0.044

2,269
0.046

2,269
0.097

2,269
0.098

2,269
0.098

2,269
0.100

2,269
0.099

2,269
0.109

0.075
0.052
0.052
0.044
0.046
0.046
0.047
0.046
0.046
(0.017)** (0.019)** (0.019)** (0.018)*
(0.019)*
(0.019)*
(0.019)*
(0.019)*
(0.014)**
0.085
0.074
0.074
0.071
0.071
0.071
0.072
0.071
0.073
(0.027)** (0.029)** (0.029)** (0.028)*
(0.028)*
(0.028)*
(0.028)** (0.028)*
(0.023)**
0.11
0.11
0.11
0.094
0.094
0.093
0.093
0.093
0.097
(0.019)** (0.019)** (0.019)** (0.018)** (0.018)** (0.018)** (0.018)** (0.018)** (0.018)**
-0.0023
-0.0025
-0.0025
-0.0019
-0.0019
-0.0020
-0.0020
-0.0020
-0.0018
(0.00052)** (0.00053)** (0.00053)** (0.00051)** (0.00051)** (0.00051)** (0.00051)** (0.00051)** (0.00060)**
-0.58
-0.61
-0.50
-0.49
-0.49
-0.48
-0.49
-0.20
(0.17)**
(0.17)**
(0.17)**
(0.17)**
(0.17)**
(0.17)**
(0.17)**
(0.22)
0.020
-0.041
-0.0086
-0.027
-0.032
-0.051
-0.026
-0.0037
(0.031)
(0.043)
(0.042)
(0.045)
(0.046)
(0.047)
(0.045)
(0.097)
-0.027
-0.11
-0.077
-0.10
-0.11
-0.12
-0.10
0.0034
(0.046)
(0.062)
(0.060)
(0.065)
(0.065)
(0.066)
(0.065)
(0.083)
0.24
0.18
0.044
0.074
0.11
0.059
-0.20
(0.12)*
(0.12)
(0.17)
(0.17)
(0.17)
(0.17)
(0.39)
0.27
0.27
0.27
0.27
0.27
0.27
(0.024)** (0.024)** (0.024)** (0.024)** (0.024)** (0.041)**
-0.21
-0.19
-0.11
-0.20
0.015
(0.18)
(0.18)
(0.19)
(0.18)
(0.29)
0.000025
(0.000026)
0.00014
0.00017
(0.000074)
(0.00017)
0.000015
(0.000032)
0.069
0.11
0.059
0.030
0.17
0.14
0.092
0.16
0.092
(0.027)*
(0.035)**
(0.044)
(0.042)
(0.13)
(0.14)
(0.14)
(0.14)
(0.27)

(4)

Standard errors in parentheses ** p<0.01, * p<0.05, All regressions include control for quarter of the year

Observations
R-squared

Constant

Property Crime Rate (per 10k residents)

Violent Crime Rate (per 10k residents)

UCR Part 1 Crime Rate (per 10k residents)

Employment Rate

Detainee Matches Flash Information

Male population under 24

PSA Latino share

PSA Black share

PSA Asian share

Detainee Age

Detainee Male

Detainee Latino

Detainee Black

VARIABLES

Table 6

 Table 7
Reasonable Suspicion by Race in Random Sample, 2015 Q1 & Q2

Stops
Reasonable Suspicion
Share of Stops with
Reasonable
Suspicion
Frisks
Reasonable Suspicion
Share of Frisks with
Reasonable
Suspicion

Black

Latino

White

Total

1626

172

546

2344

1065

119

375

1559

65%

69%

69%

67%

253

32

36

321

109

12

19

140

43%

38%

53%

44%

48

 (8)

(9)

(10)

(11)

2,338
0.008

2,333
0.012

49

Standard errors in parentheses ** p<0.01, * p<0.05, All regressions include control for quarter of the year

2,342
0.002

2,297
0.012

2,297
0.014

2,297
0.014

2,297
0.015

2,297
0.015

2,297
0.016

2,297
0.015

-0.000077
(0.00011)

-0.032
(0.022)
0.029
(0.032)
-0.090
(0.024)**
0.0022
(0.00080)*
-0.66
(0.38)
-0.12
(0.17)
-0.019
(0.15)
-0.17
(0.51)
0.029
(0.046)
-0.00074
(0.45)

(12)

2,297
0.026

(7)

2,342
0.002

(6)

Observations
R-squared

(5)

Reasonable Suspicion for Stop
(4)

0.87
(0.39)*

(3)

-0.023
-0.024
-0.019
-0.019
-0.020
-0.024
-0.024
-0.025
-0.024
(0.023)
(0.023)
(0.026)
(0.026)
(0.026)
(0.026)
(0.026)
(0.026)
(0.026)
Detainee Latino
0.026
0.031
0.028
0.029
0.028
0.028
0.029
0.028
0.029
(0.037)
(0.037)
(0.039)
(0.039)
(0.039)
(0.039)
(0.039)
(0.039)
(0.039)
Detainee Male
-0.096
-0.097
-0.091
-0.092
-0.093
-0.093
-0.093
-0.093
-0.093
(0.026)** (0.026)** (0.026)** (0.026)** (0.026)** (0.026)** (0.026)** (0.026)** (0.026)**
Detainee Age
0.0024
0.0023
0.0023
0.0023
0.0024
0.0024
0.0024
0.0024
(0.00072)** (0.00073)** (0.00073)** (0.00073)** (0.00073)** (0.00073)** (0.00073)** (0.00073)**
PSA Asian share
-0.33
-0.29
-0.28
-0.30
-0.30
-0.31
-0.30
(0.24)
(0.24)
(0.24)
(0.24)
(0.24)
(0.24)
(0.24)
PSA Black share
-0.046
0.043
0.046
0.081
0.083
0.090
0.082
(0.043)
(0.060)
(0.060)
(0.064)
(0.065)
(0.067)
(0.064)
PSA Latino share
-0.072
0.050
0.053
0.11
0.11
0.11
0.11
(0.064)
(0.085)
(0.086)
(0.092)
(0.092)
(0.093)
(0.092)
Male population under 24
-0.35
-0.36
-0.084
-0.094
-0.11
-0.089
(0.16)*
(0.16)*
(0.24)
(0.24)
(0.24)
(0.24)
Detainee Matches Flash Information
0.024
0.027
0.027
0.027
0.027
(0.033)
(0.034)
(0.034)
(0.034)
(0.034)
Employment Rate
0.40
0.39
0.37
0.40
(0.26)
(0.26)
(0.27)
(0.26)
UCR Part 1 Crime Rate (per 10k residents)
-8.8e-06
(0.000036)
Violent Crime Rate (per 10k residents)
-0.000050
(0.00010)
Property Crime Rate (per 10k residents)
-6.9e-06
(0.000046)
Constant
0.68
0.67
0.75
0.67
0.72
0.79
0.79
0.52
0.53
0.55
0.52
(0.020)** (0.022)** (0.029)** (0.038)** (0.050)** (0.061)** (0.061)** (0.19)**
(0.19)**
(0.20)**
(0.19)**

-0.031
(0.023)
0.014
(0.037)

(2)

-0.034
(0.021)

(1)

Detainee Black

VARIABLES

Table 8

 -0.066
(0.083)
-0.094
(0.10)

(2)

321
0.002

321
0.004

0.47
0.52
(0.066)** (0.082)**

-0.024
(0.068)

(1)

320
0.014

0.30
(0.14)*

-0.066
(0.082)
-0.076
(0.10)
0.22
(0.12)

(3)

320
0.017

0.36
(0.16)*

-0.066
(0.082)
-0.077
(0.10)
0.22
(0.12)
-0.0018
(0.0021)

(4)

50

(5)

311
0.037

0.46
(0.19)*

0.0066
(0.088)
-0.065
(0.11)
0.18
(0.12)
-0.0011
(0.0022)
0.44
(0.80)
-0.22
(0.14)
-0.044
(0.21)

Standard errors in parentheses ** p<0.01, * p<0.05, All regressions include control for quarter of the year

Observations
R-squared

Constant

Property Crime Rate (per 10k residents)

Violent Crime Rate (per 10k residents)

UCR Part 1 Crime Rate (per 10k residents)

Employment Rate

Detainee Matches Flash Information

Male population under 24

PSA Latino share

PSA Black share

PSA Asian share

Detainee Age

Detainee Male

Detainee Latino

Detainee Black

VARIABLES

311
0.039

0.55
(0.22)*

0.0065
(0.088)
-0.064
(0.12)
0.19
(0.12)
-0.0011
(0.0022)
0.50
(0.80)
-0.13
(0.18)
0.092
(0.27)
-0.43
(0.52)

(6)

311
0.039

0.55
(0.22)*

0.0062
(0.088)
-0.064
(0.12)
0.19
(0.12)
-0.0011
(0.0022)
0.49
(0.81)
-0.13
(0.18)
0.094
(0.27)
-0.44
(0.52)
0.0090
(0.065)

(7)

311
0.043

-0.080
(0.64)

0.0056
(0.088)
-0.055
(0.12)
0.19
(0.12)
-0.00057
(0.0023)
0.43
(0.81)
-0.059
(0.19)
0.19
(0.28)
0.18
(0.79)
0.012
(0.065)
0.88
(0.85)

(8)

Reasonable Suspicion for Frisk

Table 9
(10)

(11)

(12)

311
0.046

311
0.047

311
0.045

311
0.133

-0.0014
0.0023
-0.00083 0.0014
(0.088)
(0.088)
(0.088)
(0.084)
-0.060
-0.052
-0.062
-0.031
(0.12)
(0.12)
(0.12)
(0.12)
0.20
0.19
0.20
0.18
(0.12)
(0.12)
(0.13)
(0.085)*
-0.00085 -0.00081 -0.00078 0.00025
(0.0023) (0.0023) (0.0023) (0.0033)
0.64
0.66
0.58
0.90
(0.83)
(0.83)
(0.83)
(1.36)
-0.032
-0.075
-0.026
0.36
(0.19)
(0.19)
(0.19)
(0.52)
0.26
0.22
0.26
-1.04
(0.29)
(0.28)
(0.29)
(0.37)*
0.095
0.19
0.091
4.03
(0.79)
(0.79)
(0.80)
(1.15)**
-0.00017 0.0058
0.0015
-0.016
(0.066)
(0.065)
(0.066)
(0.068)
0.99
1.21
0.90
2.83
(0.85)
(0.89)
(0.85)
(1.31)*
0.00014
(0.00012)
0.00039
0.000049
(0.00032)
(0.00038)
0.00013
(0.00016)
-0.21
-0.32
-0.15
-2.49
(0.65)
(0.67)
(0.65)
(1.05)*

(9)

 Table 10
Contraband by Race in Random Sample, 2015 Q1 & Q2
Black

Latino

White

Total

Frisks

253

32

36

321

Firearm

4

0

0

4

Drugs

6

1

0

7

Other

2

1

2

5

Any

13

3

3

19

Frisks/Firearm

63

∞

∞

80

Frisks/Drugs

42

32

∞

46

Frisks/Other

127

32

18

64

Frisks/Any

19

11

12

17

51

 321
0.003

Observations
R-squared

321
0.003

0.0021
(0.018)

0.014
(0.018)
-0.0042
(0.023)

(2)

320
0.004

-0.011
(0.032)

0.014
(0.019)
-0.0038
(0.024)
0.013
(0.027)

(3)

52

(5)

(6)

(7)

(8)

(9)

320
0.006

-0.021
(0.035)

311
0.012

0.0073
(0.044)

311
0.036

0.075
(0.050)

311
0.036

0.075
(0.050)

311
0.036

0.12
(0.15)

311
0.039

0.098
(0.15)

0.014
0.013
0.013
0.013
0.013
0.012
(0.019)
(0.020)
(0.020)
(0.020)
(0.020)
(0.020)
-0.0037
0.0049
0.0060
0.0061
0.0054
0.0045
(0.024)
(0.026)
(0.026)
(0.026)
(0.026)
(0.026)
0.012
0.016
0.024
0.024
0.024
0.025
(0.027)
(0.028)
(0.028)
(0.028)
(0.028)
(0.028)
0.00037 0.00028 0.00030 0.00030 0.00027 0.00022
(0.00047) (0.00051) (0.00050) (0.00051) (0.00052) (0.00053)
-0.14
-0.097
-0.098
-0.093
-0.057
(0.18)
(0.18)
(0.18)
(0.18)
(0.19)
-0.028
0.043
0.044
0.039
0.044
(0.031)
(0.040)
(0.041)
(0.043)
(0.044)
-0.058
0.044
0.044
0.037
0.048
(0.048)
(0.060)
(0.060)
(0.064)
(0.066)
-0.32
-0.32
-0.37
-0.38
(0.12)** (0.12)** (0.18)*
(0.18)*
0.0013
0.0010
-0.0012
(0.015)
(0.015)
(0.015)
-0.064
-0.045
(0.19)
(0.19)
0.000024
(0.000028)

(4)

Standard errors in parentheses ** p<0.01, * p<0.05, All regressions include control for quarter of the year

0.00014
(0.015)

0.016
(0.015)

(1)

Constant

Property Crime Rate (per 10k residents)

Violent Crime Rate (per 10k residents)

UCR Part 1 Crime Rate (per 10k residents)

Employment Rate

Detainee Matches Flash Information

Male population under 24

PSA Latino share

PSA Black share

PSA Asian share

Detainee Age

Detainee Male

Detainee Latino

Detainee Black

VARIABLES

Firearm Recovered

Table 11
(11)

311
0.037

0.11
(0.15)

0.000020
(0.000073)

311
0.039

0.000033
(0.000036)
0.10
(0.15)

0.013
0.011
(0.020)
(0.020)
0.0056
0.0036
(0.026)
(0.026)
0.024
0.026
(0.028)
(0.028)
0.00025 0.00022
(0.00052) (0.00053)
-0.082
-0.054
(0.19)
(0.19)
0.038
0.047
(0.043)
(0.044)
0.038
0.054
(0.065)
(0.067)
-0.37
-0.39
(0.18)*
(0.18)*
0.00068 -0.0017
(0.015)
(0.015)
-0.047
-0.058
(0.20)
(0.19)

(10)

311
0.094

0.012
(0.27)

0.000036
(0.000082)

0.0030
(0.0044)
0.0054
(0.0069)
0.028
(0.020)
0.00056
(0.00085)
-0.0055
(0.21)
-0.00042
(0.061)
0.028
(0.048)
-0.15
(0.24)
-0.00036
(0.014)
-0.0025
(0.33)

(12)

 (1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

(9)

(10)

(11)

(12)

15,821
0.007

15,821
0.007

15,814
0.009

15,774
0.009

15,372
0.014

53

Standard errors in parentheses ** p<0.01, * p<0.05, All regressions include control for quarter of the year

Observations
R-squared

15,372
0.014

15,372
0.014

15,372
0.014

15,372
0.014

15,372
0.014

15,372
0.014

15,372
0.021

-0.058
-0.053
-0.050
-0.049
-0.029
-0.030
-0.030
-0.029
-0.029
-0.029
-0.029
-0.023
(0.0056)** (0.0070)** (0.0070)** (0.0071)** (0.0079)** (0.0079)** (0.0079)** (0.0079)** (0.0080)** (0.0080)** (0.0080)** (0.010)*
Detainee Latino
0.013
0.016
0.017
-0.0032
-0.0035
-0.0034
-0.0034
-0.0032
-0.0034
-0.0031 -0.00097
(0.010)
(0.010)
(0.010)
(0.011)
(0.011)
(0.011)
(0.011)
(0.011)
(0.011)
(0.011)
(0.018)
Detainee Male
-0.048
-0.047
-0.048
-0.048
-0.047
-0.048
-0.048
-0.048
-0.048
-0.046
(0.0098)** (0.0099)** (0.0100)** (0.0100)** (0.0100)** (0.0100)** (0.0100)** (0.0100)** (0.0100)** (0.017)*
Detainee Age
0.00033 0.00038 0.00037 0.00036 0.00036 0.00036 0.00036 0.00037 0.00035
(0.00020) (0.00020) (0.00020) (0.00020) (0.00020) (0.00021) (0.00020) (0.00021) (0.00026)
PSA Asian share
-0.011
0.012
0.011
0.022
0.017
0.019
0.016
-0.10
(0.061)
(0.062)
(0.062)
(0.063)
(0.064)
(0.064)
(0.064)
(0.095)
PSA Black share
-0.018
0.0052
0.0049
0.00073 -0.00066 0.00089 -0.0021
-0.048
(0.012)
(0.015)
(0.015)
(0.016)
(0.016)
(0.016)
(0.016)
(0.043)
PSA Latino share
0.098
0.13
0.13
0.12
0.12
0.12
0.12
0.15
(0.016)** (0.020)** (0.020)** (0.022)** (0.023)** (0.023)** (0.024)** (0.040)**
Male population under 24
-0.10
-0.10
-0.14
-0.14
-0.14
-0.14
-0.24
(0.042)* (0.042)* (0.060)* (0.060)* (0.060)* (0.060)*
(0.13)
Detainee Matches Flash Information
-0.0028
-0.0028
-0.0029
-0.0028
-0.0029
-0.0022
(0.0052) (0.0052) (0.0052) (0.0052) (0.0052) (0.011)
Employment Rate
-0.053
-0.061
-0.059
-0.062
-0.20
(0.067)
(0.069)
(0.072)
(0.068)
(0.11)
UCR Part 1 Crime Rate (per 10k residents)
-5.2e-06
(0.000010)
Violent Crime Rate (per 10k residents)
-6.5e-06
0.000028
(0.000028)
(0.000035)
Property Crime Rate (per 10k residents)
-8.9e-06
(0.000013)
Constant
0.13
0.13
0.17
0.16
0.14
0.16
0.16
0.20
0.21
0.20
0.21
0.31
(0.0054)** (0.0069)** (0.011)** (0.013)** (0.016)** (0.019)** (0.019)** (0.048)** (0.051)** (0.053)** (0.051)** (0.091)**

Detainee Black

VARIABLES

Contraband Recovered

Table 12

 Respectfully submitted,

/s/ David Rudovsky, Esquire

/s/ Mary Catherine Roper, Esquire
ACLU of Pennsylvania
PO Box 40008
Philadelphia, PA 19106

/s/ Paul Messing, Esquire
Kairys Rudovsky Messing & Feinberg, LLP
718 Arch Street, Suite 501S
Philadelphia, PA 19106
(215) 925-4400

/s/ Seth Kreimer, Esquire
University of Pennsylvania Law School
3900 Chestnut Street
Philadelphia, PA 19104
Counsel for Plaintiffs10

10

Counsel express their appreciation to Jon Dilks who organized and structured the data collection.

54

 EXHIBIT A

 

BENCHMARKS FOR BAILEY V. CITY OF PHILADELPHIA

I.

Introduction

This Memorandum sets forth agreed upon benchmarks for evaluating the compliance of the
Philadelphia Police Department (“PPD”) with the Settlement Agreement, Class Certification and
Consent Decree (“Agreement”).
The Agreement provides for monitoring of the stop and frisk practices of the PPD to measure and
ensure compliance with the terms of the Agreement. The parties agreed to a set of benchmarks in
2010 and plaintiffs have employed those benchmarks in assessing compliance in their Reports to the
Courts. In 2013, the City’s expert, Professor Ralph Taylor, suggested additional benchmarks in his
analysis of the racial distribution of stops and frisks. Thereafter, the parties met and discussed these
issues and as a result of further collaboration between the respective experts, some modifications in the
Benchmark Memorandum have been made. These modified benchmarks are intended to fairly measure
and assess PPD compliance with the provisions of the Agreement under the Fourth and Fourteenth
Amendments.
II.

Fourth Amendment Issues

The major question with respect to Fourth Amendment compliance in pedestrian and car stops
(and related frisks, searches and/or arrests) is whether the PPD is stopping and frisking suspects
with the requisite reasonable suspicion mandated by the Fourth Amendment.

The substantive

standards on reasonable suspicion are set forth in the Agreement and applicable court decisions.
Under the Agreement and Consent Decree, the Court has the power to determine whether the
rate of stops and/or frisks made without reasonable suspicion are grounds for a finding of
non-compliance with the Consent Decree.

To measure compliance, both sides review

randomly selected 75-48a forms by the PPD on a quarterly basis. To ensure the most accurate
analysis, the following procedures will be used.

First, the PPD will ensure that the randomly selected files do not contain more than a single
75-48a from a particular incident.

Where multiple persons are stopped and/or frisked in one

incident, only one of these stops will be part of the randomly selected files for review.

2

 Second, in each quarterly review both sides will screen the reported stops and frisks to separate
out (1) “stops” that are either sight arrests or are otherwise not forcible stops under the Fourth
Amendment and (2) “frisks” that are searches (often incident to arrest).

In these cases, officers

have filed 75-48a reports even though the incident was not within the Departmental definition of a
stop or frisk pursuant to Directive 12.11. These incidents will not be included in the Fourth
Amendment “reasonable suspicion” analysis.
Third, the category of “fruit of an illegal stop” will be used to signify where a frisk, though proper
given the officers observations, was made pursuant to a stop without reasonable suspicion.
Finally, there will be a “hit-rate” analysis on the Fourth Amendment issues. More specifically,
the ratio of “hits” (i.e., finding of weapons or contraband) to the number of frisks will be
tabulated.

In addition, there will be a designation of cases in which the officer marks “no frisk”

in cases in which a frisk was highly likely to have occurred (e.g., stop for a robbery investigation).

III.

Fourteenth Amendment Issues: Racial Fairness

The Agreement requires monitoring and analysis with respect to the question of whether race is
being impermissibly used as a factor in the decision to stop and frisk. Over the years, a number
of “benchmarks” have been used by economists, statisticians, lawyers and courts to measure
possible racial bias in stop and frisk practices. The following benchmarks will be used in Bailey.

A. Regression Analysis Regarding Police Stops and Frisks.
There is no dispute over the fact that racial minorities are stopped and frisked far more
frequently than whites, but this fact alone does not prove race bias, as non-racial factors may be
contributing causal influences.

To assess possible bias, both sides will start with a comparison of actual stop and frisk rates by
race by Police Service Areas (PSA), which are geographical subdivisions of Police Districts in
Philadelphia, to those that would be expected based on census data on the racial composition for

3

 that PSA. This analysis will use race specific data comparing stops and frisks and the census
population by race.

A multivariate regression analysis will then be used to assess the relationship among multiple
variables simultaneously.

To determine the impact of suspect race on the likelihood of a stop or

frisk, it is important to control for factors that include the demography and crime rates of the PSA.
The regressions have been used in all previous reports, but they have been modified by
agreement.

The following regressions will be used, with the dependent variable of number of stops and
frisks.
a. Defendant race;
b. Defendant race, Latino status;
c. Defendant race, Latino status, sex;
d. Defendant race, Latino status, sex, age;
e. Defendant race, Latino status, sex, age, district racial composition;
f. Defendant race, Latino status, sex, age, district racial composition, district age
composition;
g. Defendant race, Latino status, sex, age, district racial composition, district age
composition, defendant matches flash information;
h. Defendant race, Latino status, sex, age, district racial composition, district age
composition, defendant matches flash information, district employment rate;
i. Defendant race, Latino status, sex, age, district racial composition, district age
composition, defendant matches flash information, district employment rate;
district crime rate;
j. Defendant race, Latino status, sex, age, district racial composition, district age
composition, defendant matches flash information, district employment rate;
district violent crime rate;
k. Defendant race, Latino status, sex, age, district racial composition, district age
composition, defendant matches flash information, district employment rate;
district property crime rate;

4

 The above regressions are summarized in the following table:

Defendant Race
Defendant Latino Status
Defendant Sex
Defendant Age
District Racial Composition
District Age Composition
Presence of Flash Information
District Employment Rate
District Crime Rate
District Violent Crime Rate
District Property Crime Rate

a b c d
X X X X
X X X
X X
X

e
X
X
X
X
X

f
X
X
X
X
X
X

g
X
X
X
X
X
X
X

h
X
X
X
X
X
X
X
X

i
X
X
X
X
X
X
X
X
X

j
X
X
X
X
X
X
X
X

k
X
X
X
X
X
X
X
X

X
X

The following protocols will be followed in the regression analysis:

1.

The PSA as opposed to Police Districts will be the geographical areas for data analysis.
The City has agreed to provide to Professor Abrams the relevant PSA population data.

.
2. Crime rates will be measured by the incidence of serious crime (using lagged data, i.e., the
crime rate from the previous Quarter or Year), but the parties will initially use somewhat
different measures of serious crime, including violent and property crimes.

The

operating assumption is that these different metrics will not lead to different conclusions,
but the experts will confer again if there are significant differences reported.
3. Economic and social data will be used as controls and these will account for cross-PSA
variation in economic and demographic circumstances. As with crime rates, there may
be some differences in the regression factors used by each side, and to the degree there
appear to be different conclusions based on different factors, the experts will determine
which are the most relevant and reliable.

5

 4. The data will be presented cumulatively for two or more consecutive quarters and may
also include controls for time period or be broken out by quarter.
5. Professors Abrams and Taylor may perform “robustness” checks beyond the regressions
outlined above. Thereafter, these experts will confer as to their usefulness.

Regression analysis allows for more precise statements about the effects of different variables on
outcomes, but it is also important to determine whether any differences in the racial data is
statistically significant.

If the effect of race is not statistically significant, it means that the effect

of race is lower than the ability of the regression to detect.

This relates to the power of the

regression, which is affected by the sample size. If the sample size is large enough, a
statistically insignificant coefficient indicates that the difference by race is statistically
indistinguishable from zero (i.e., no race effect) to a high degree of accuracy.

Regression

analysis provides measures for determining statistically significant racial disparities and in the
social sciences, the standard significance threshold for the likelihood of finding the same result is
95%.11 For some of the benchmarks, including analysis of gross numbers of stops and frisks
with regression for salient factors, there is no need for sampling as all of this data is available in
the electronic data base.

Even if the coefficient on race is statistically significant, the magnitude may be so small that it is
not meaningful.

For example, if blacks are frisked 10% of the time and whites 10.1% of the

time, even though the difference might be statistically significant, it is not likely to be of great
concern.

For evaluating these regression results, a good rule of thumb for a meaningful amount

of difference is about 20% of the variable mean.

Using the same numbers from the frisk

example above, this means that if the frisk rates differed by more than 20% of the 10% frisk rate
(0.2 * 0.1 = 0.02 or 2 percent) that would represent a meaningful racial disparity.

B. Regression Analysis/Dependent Variable of No Reasonable Suspicion for Stop or
Frisk

11 This corresponds to a p-value of .05

6

 The same set of regressions will be used where the dependent variable is whether there was
reasonable suspicion for the stop or frisk.

Since this variable is only available for the sample of

the data that has been analyzed for reasonable suspicion, it will contain a smaller number of
observations than the regressions described above.

C.

Stops and Frisks by Race, Contraband Recovered, and Racial Characteristics of

PSA

In addition to the regressions described above, other benchmarks will be used to determine
possible racial bias.

First, there will be an analysis of the rate of frisks by race.

Second, a hit-rate analysis will be used to determine possible racial effects of stop and frisk
practices. As an example, if hit-rates for Whites are higher than for Blacks, there is a question as
to whether the police are employing different thresholds for reasonable suspicion. An example
of a hit-rate is the share of frisks that result in a firearm.

Third, there will be a hit-rate analysis at the PSA-level to test for the possibility that stops or
frisks may increase at a disproportionate level for all persons in heavily minority neighborhoods.
In this analysis, there will be calculations of the PSA-level stop and frisk rates per reported crimes
to determine whether these ratios are correlated with PSA racial demographics.

The question in

this context is whether there is a possible racial effect of stop and frisk practices if the crime rate
in PSA-1 is five times the crime rate in PSA-2, but the stop rate is 10 times higher in PSA-1.

7