b
Massachusetts Chiefs of Police Association
PRESS RELEASE
FOR IMMEDIATE RELEASE
Chief Richard A. Marchese, Ret.
May 4, 2004
Executive Director
508 842-2935
RACIAL PROFILING DATA COLLECTION
Police officers in Massachusetts do a remarkable job at protecting our citizens,
addressing crime, and enforcing the law in an even-handed manner. Putting
their lives on the line daily, police officers deserve our praise and thanks.
Anyone who thinks that the police in this state have a practice of stopping
motorists on the highway based on their race or skin color is sadly misinformed.
The elimination of all forms of race-based enforcement is a commitment shared
by the police chiefs and virtually all police officers across this state.
Unfortunately, the single greatest threat to progress in “color blind policing” is
faulty data collection and analysis that erroneously seeks to label certain
departments as likely to be engaging in racial profiling. The time, energy,
resources and funds expended over the past three years on data collection and
analysis diverted attention from and hindered efforts at addressing the underlying
causes for real or perceived biased policing.
The data on a statewide basis compiled by the Northeastern University’s Institute
on Race and Justice (IRJ) confirms that, overall, Massachusetts police
departments did a remarkable job at enforcing the traffic laws in a fair and
unbiased manner. The data reveals a statewide “racial disparity” of only
2.8%. This is truly a credit to the evenhanded enforcement practices of the
overwhelming majority of our dedicated police officers.
By way of comparison, the data collection projects in other states where racial
profiling was “confirmed,” had disparities of 40-80%! (In some states, e.g.,
Connecticut, a disparity level below 5% resulted in a statewide declaration of “no
profiling.”)
This is not to say that any level of biased policing is acceptable – it is not.
1
Whenever a person believes they have been victimized on the basis of their race,
skin color or nationality, we have a problem. Community policing depends on
creating trust so that we can all address community concerns. However, all the
data available tells us that the problem is not widespread. For example, the state
installed a toll-free hot line three years ago and publicized it with radio ads,
billboards and the like, encouraging citizens to call if they believed they had been
the victim of racial profiling. As was the experience in other states, the phone
hardly ever rang. This corresponds to the experience virtually all police chiefs
have reported. Few, if any, complaints of profiling have been made by minority
drivers to most municipal police departments in this state.
With this said, we have prepared a comprehensive “Action Plan” to further
address real or perceived biased policing in Massachusetts. Since the overall
disparity percentage is so low, it is our hope that the Secretary of Public Safety,
the Attorney General and the legislature will agree that no departments should
be compelled to engage in additional data collection. Rather, the present level
should continue, with frequent (monthly?) user-friendly analysis being provided to
Chiefs to help monitor progress. As more information is available about what type
of data collection is beneficial, the usefulness of this tool could be reconsidered.
The Police Executive Research Forum (PERF) report issued in April 2004
entitled “By the Numbers: A Guide for Analyzing Race Data from Vehicle Stops,”
funded by the US Department of Justice Office of Community Oriented Policing
Services (COPS), concludes by stating:
“ We strongly recommend, however, that agencies focus
not merely on measuring racially biased policing but on
responding to it.”
2
IS DATA COLLECTION WORHT IT?
After spending over three years and possibly upwards of a million dollars
on data collection and analysis, what do we have to show for it? This
Association issued an “Action Plan to Address Racial Profiling” in the year 2000.
It recommended that rather than wasting years of time and scarce tax dollars, we
acknowledge that there is a problem. The report detailed several ways to
address the issue. Enhancement of training, supervision, recruitment and
discipline were suggested. Changes in policies and procedures were also
included. We predicted, correctly as it turns out, that focusing everyone’s
attention on data collection would mean ignoring those steps which could actually
produce the desired result.
If the state insists on more data collection, rather than the other
enhancements, it will defer and even setback the effort at eliminating the
perception and reality of racial profiling.
GOVERNMENT STUDY FAULTS ALL DATA COLLECTION
A report released in April 2004 by the Police Executive Research Forum
(PERF) and funded by the U.S. Department of Justice Office of Community
Oriented Police Services (COPS) entitled “By the Numbers: A Guide for
Analyzing Race Data from Vehicle Stops” helps document the weaknesses in the
Massachusetts project as well as those across the country. While it might be
possible to design a more accurate data collection and analysis effort in the
future, the numerous errors built into the Massachusetts Racial Profiling Data
Collection Project to date have doomed it to failure. Flawed data, erroneous
assumptions, and untested speculation cannot produce reliable conclusions.
The Secretary of Public Safety has a unique opportunity to advance race
relations and effective law enforcement, depending upon how he responds to the
racial profiling data collection report. By bringing together a diverse group of
police professionals, community groups and researchers over the past eight
months, the Secretary has started a process that could help lead the way to real
progress. It will take great courage on the Secretary’ s part to stand up to those
who insist that the police cannot be trusted and that data collection will confirm
their accusations of biased policing.
It is unrealistic to expect police officers to welcome more data collection after
their experience with the past three year’s problems.
3
EXAMPLES OF DATA COLLECTION ERRORS IN MASSACHUSETTS
1. Most police officers received little guidance and no training from the state on
how to complete the new citation forms. No test phase was attempted to work
out the “bugs”. No effort was made to learn from mistakes.
2. Despite a requirement that all police departments receive monthly reports on
the data related to their department – presumably so they could make
corrections and identify problems early - this never happened. Only once
during the past three years was any data sent, and it was in a form that was
unintelligible to most non-research professionals.
3. Officers were instructed to “guess” at a driver’s race. Brazilians, for example,
were classified as Hispanic by some officers (even though they do not speak
Spanish), and as white by others – as well as by the Census? The US
Census allows everyone to “self declare” and often people list themselves as
a certain % of more than one race or nationality. The state refused to address
this and would not put the information on drivers’ licenses, forcing an
unreliable guessing or “unknown” reply by many officers.
4. Despite massive confusion over when to check off the “search” box, no
corrective training or instructions were issued by the state. It has been widely
acknowledged by the researchers and state officials that with the low number
of searches conducted, the errors have irrevocably skewed the results.
5. The lack of any funding from the state meant that the money for data analysis
had to be diverted from federal grants by the Executive Office of Public
Safety. For the small amount involved (reportedly in the vicinity of $100,000),
the IRJ has done a remarkable amount of work. Since funds were scarce,
several “shortcuts” were attempted, often with disastrous results. For
example, rather than conducting on-site traffic studies as the rest of the
country has done, the IRJ team resorted to modifying the “Dominos Pizza”
delivery computer program to come up with an “estimated driving population.”
They estimated wrong in every case that we tested. After putting observers
on the roadways where most tickets were issued, the percentage of non-white
drivers actually on the road was nearly identical to the percentage of nonwhite drivers cited. This varied markedly from what the researchers used as
an estimate and upon which they based a major portion of their report. (To
show the absurdity of the results, the map the researchers produced for
drivers likely to frequent Cambridge includes those from Attleborough – near
the Rhode Island border!)
6. The only time the researchers made any on-road observations was in
connection with trying to determine an estimated driving population for the
State Police. They picked out a sample roadway in each barracks region and
sent some college students in a car to make observations of the race or
nationality of drivers. As an acknowledgement of the difficulty of identifying
non-white drivers before they are pulled over, the researchers only made
observations during the daytime (and with three people in each car!)
4
Getting Data Collection Right
The Police Executive Research Forum (PERF) issued the first ten
chapters of a report in April 2004 entitled “By the Numbers: A Guide for
Analyzing Race Data from Vehicle Stops.” Funded by the U.S. Department of
Justice Office of Community Oriented Policing Services (COPS), the report is
intended as a guide for law enforcement agencies, municipal officials, citizens
and community groups on how to analyze, interpret and understand vehicle stop
data being collected on drivers’ race. (The second volume, containing chapters
11-13, will be issued in August 2004.)
Unfortunately, the information contained in this report was not available in
2000 when Massachusetts enacted its Racial and Gender Profiling Data
Collection law. In nearly every area, the Massachusetts effort falls short of
the report’s recommendations on how to properly collect and analyze data.
This is not from a lack of effort or professionalism on the part of the dedicated
researchers at Northeastern University’s Institute on Race and Justice (IRJ). As
the IRJ’s “preliminary Tabulations” report issued in January 2004 stated:
From the outset it is important to note that aggregate
data, such as the data presented in this preliminary
report can indicate patterns of disparate traffic citation
activity in a department but cannot identify the motives
involved in individual traffic stop, citation or other
enforcement decisions. Therefore, this preliminary
report should not be read as an indication of racial
profiling by any Massachusetts law enforcement
agency. Social science cannot provide reliable
explanations for what individual officers are thinking
when they decided to stop or cite a particular motorist.
Social science can, however, help to identify whether
certain groups are disproportionately targeted for
enforcement practices. (emphasis added)
Research on racial profiling in traffic stops is a relatively
new area of inquiry. Although numerous studies have
begun to address questions of differential treatment in
traffic stops, no absolute consensus exists about the
best way to determine disparities. Racial disparities
in citations can result from a number of factors that
social scientists are just beginning to understand.
Bias on the part of an individual officer is one of several
possible explanations for disparities in citations. For
example, certain department enforcement strategies or
5
allocation of patrol resources. while perhaps race
neutral on their face may, result in the disparate
treatment of particular racial groups. In some
communities, police commanders may assign a larger
number of officers to a particular neighborhood
because that neighborhood has more crime and thus
an increased need for police services. It may then be
the case that police assigned to this high crime area
engage in traffic enforcement as part of their normal
patrol activities and since there are more police working
in this neighborhood, individuals who live, work or drive
through this neighborhood are more likely to be stopped
and cited than individuals who live in other
neighborhoods. If the neighborhoods where police
assign additional patrols are neighborhoods where
people of color are more likely to live, then the
deployment decision may result in racial disparities in
traffic citations. (emphasis added)
The following excerpts from the PERF report are helpful in understanding
that the Massachusetts traffic stop data cannot identify instances of racial
profiling:
It is not difficult to measure whether there is disparity between
racial/ethnic groups in stops made by police; the difficulty comes in
identifying the causes for any disparity. For instance, a jurisdiction might
compare the demographic profile of people stopped by police to the
demographic profile of residents as measured by the census. The results
might show “disparity”; that is, the results might show that some groups
are stopped disproportionate to their representation in the residential
population. The jurisdiction, cannot, however, identify the causes of that
disparity using this measure. Only after controlling for driving quantity,
driving quality, and driving location, can a researcher who finds that
minorities are disproportionately represented among drivers stopped by
police conclude with reasonable confidence that the disparity reflects
police bias in decision making. (emphasis added)
Although jurisdictions nationwide have invested considerable resources to
collect race data from vehicle stops, most jurisdictions do not know how to
analyze the collected data properly. They are either ill-equipped to do the
analysis, or they are misinformed about what should be done. An overwhelming
majority of the data analyses reviewed by PERF staff for this project were based
on substandard methods. Most agencies are using models for their analyses that
fall far short of minimal social science standards. In jurisdictions across the
country, reports prepared by agencies or external groups (for example, some civil
6
rights groups) draw conclusions wholly unsupported by the data. Other reports
indicate that despite all the efforts and resources that were dedicated to the data
collection, no conclusions can be drawn. These failures can largely be explained
by the complexity of the task of measuring whether policing in a jurisdiction is
racially biased. A tremendous number of factors other than bias can legitimately
influence police decisions to stop drivers, and these “alternative hypotheses”
must be ruled out before the “bias hypothesis” can be tested. A lack of
understanding about which benchmarking methods will yield the most valid
interpretations of the data is hindering agencies’ efforts to reach valid,
responsible conclusions.
A key aspect of analyzing vehicle stop data is to determine whether the
driver’s race/ethnicity has an impact on police stopping decisions. In order to
assess whether there is an impact, however, we must exclude or “control for”
factors other than race/ethnicity that might legitimately explain police stopping
decisions.
In developing “benchmarks,” the researcher is attempting to construct a
comparison group that represents the drivers at risk of being stopped by police—
absent bias. This group is compared to the group of drivers actually stopped to
help determine whether racial bias may have been a factor in police officers’
decision-making process. The variation in quality across benchmarks is directly
related to how closely each benchmark represents the group of people who
should be at risk of being stopped by police if no bias exists. The strongest
benchmarks take into consideration variations in driving quality, driving quantity,
and driving location.
We emphasize that an agency should, if feasible, select a plan for
analyzing the data at the same time that the decision makers decide what stops
to target and what information to collect on stops.1
We recommend that decision makers select all traffic stops or all vehicle stops,
and not a subset of these categories as defined by their outcomes (for example,
citations, arrests). Some jurisdictions (indeed, some entire states) are collecting
data only on subsets of stops, such as traffic stops that result in a citation. In
Chapter 3 we explain why this practice produces substandard data for analysis.
In Chapter 3 we also encourage agencies to involve residents and agency
personnel from all levels in planning data collection and analysis.
We start by explaining how the data that have been collected from officers
can be checked for quality, an important first step in any type of social science
research and not unique to the analysis of police-citizen contact data. Although
there is no cost-effective way to ensure that the data are 100 percent accurate,
1
For information on what stops to target for data collection and what information to obtain for each stop,
see PERF’s first report on racial profiling entitled Racially Biased Policing: A Principled Response (Fridell
et al. 2001, Chap. 8).
7
the methods described in the chapter can help the researcher check for and
enhance the quality of their data. A range of methods can be used to ascertain
whether officers are submitting forms to the agency for each and every stop
targeted for data collection.
Additionally, there are methods for assessing the level and source of
missing data, errors, and intentional misstatements of facts. When selecting
reference periods we recommend that, if economically and politically feasible,
agencies collect one year of data before analyzing it. Agencies are advised to
delay the start of the reference period for several months after data collection
begins. In the first few months, officers can become accustomed to the data
collection process, and their data should be reviewed to identify particular
problems (such as large amounts of missing data on certain variables or missing
forms). Once the problems appear to be resolved, the reference period should
begin.
For many reasons, it is appropriate for agencies to analyze subsets of
their police-citizen contact data. In Chapter 4 we describe why a researcher
might choose not to analyze all of the data submitted during the reference period
but only a portion, and how and why a researcher might conduct separate,
multiple analyses using subsets of the data. For example, the researcher might
choose to analyze for his or her report only proactive stops (stops in which police
have discretion regarding whom to stop); then the researcher might choose to
conduct separate analyses of these data within geographic sub-areas of the
jurisdiction. We discuss subsets based on (1) whether stops are proactive or
reactive, (2) whether the officer could discern the driver’s race/ethnicity, (3)
whether the driver appears in the database once or multiple times, (4)
geographic locations of stops (to allow for analyses within sub-areas of the
jurisdiction), and (5) whether the stops are for
traffic violations or for the purpose of investigating crime.
The final section of Chapter 4 explains the need for comparability of the
stop data and benchmarking data or what we call “matching the numerator and
the denominator.” The “numerator” refers to the data collected on stops by the
police, and the “denominator” refers to the data collected to produce the
comparison group, or benchmark. To “match the numerator and the
denominator” the researcher adjusts the stop data to correspond to any limiting
parameters of the benchmark or vice versa. For instance, in the observation
benchmarking method, researchers collect data from the field regarding the
race/ethnicity of drivers. Placed at various locations, the observers count the
drivers in different race/ethnicity categories. This process produces a
racial/ethnic profile of drivers observed at these locations that can be compared
to the people who are stopped by police. Since the “denominator” (observation
data) pertains only to certain areas, the relevant analysis will only include in the
“numerator” the police stops in that area. Using this method, the researcher will
compare the demographics of the people who are observed driving through
8
Intersection A, for example, to the demographics of the people stopped by police
in and around Intersection A. (This type of analysis will be conducted separately
for each intersection.)
The numerator and denominator must be matched with regard to other
parameters as well. For example, if observation data were collected from
January through May 2002, the analysis should involve only police stops that
occurred during roughly that same time period. If the researchers collected
observation data only during daylight hours because of visibility issues, then the
analysis should include in the numerator only those stops that occurred during
daylight hours.
Chapters 5 through 10 target some of the mistakes often made when
comparing stop data to commonly used benchmarks. For example, many law
enforcement agencies and outside analysts will compare the percentage of stops
that involve African Americans or other minorities to the racial make-up of the
residents of a particular area as measured by census data. More often than not,
the mass media, civic groups, and citizens draw conclusions from this
comparison regarding the existence or lack of racially biased policing in the
jurisdiction; these conclusions are wholly unsupportable using this method of
analysis. Frequently, no mention is made of non-race-related explanations for the
disparity between the census population and the population of stopped drivers,
explanations that relate to driving quantity, driving quality, and driving location.
These are all factors that legitimately affect stopping behavior by police.
Despite the weaknesses of using census data as a diagnostic tool, some
jurisdictions (limited by resources or time) may have no option other than to use
this method. This will be particularly true of researchers charged with analyzing
data for an entire state. The obligation of the researcher in this position is to
ensure that the results are conveyed in a responsible fashion. In fact, this
obligation falls to all stakeholders, including concerned citizens, civil rights
groups, and the media. No one interpreting results based on census
benchmarking—even adjusted census benchmarking—can claim they have
proved the existence or lack of racially biased policing.
This caveat is not unique to adjusted census benchmarking, and the
inability to identify a causal connection between driver race/ethnicity and police
decisions does not mean that data collection is without value. Even if the results
from data collection do not provide definitive conclusions, they can serve as a
basis for constructive discussions between police and citizens regarding ways to
reduce racial bias and/or perceptions of racial bias.
The chapter also explains how social scientists have addressed these
questions in the context of their research. A key point of controversy is whether
to use as a benchmark all drivers at the selected site or only traffic law-violating
drivers at the site. We recommend that the observation benchmark be based on
law-violating drivers, not all drivers, because this model encompasses the fact
9
that drivers who drive poorly are at greater risk of being stopped by police. (We
present the alternative viewpoint in an appendix.)
Chapter 13, the final chapter in By the Numbers, will discuss how law
enforcement agencies can use the results from data collection to achieve reform.
Even results based on weak benchmarking methods can stimulate productive
discussions between police and residents about the issues of racially biased
policing and the perceptions of its practice. The chapter suggests how these
discussions can be structured to produce action plans for reform. We strongly
recommend, however, that agencies focus not merely on measuring racially
biased policing but on responding to it. Varied responses to racially biased
policing are set forth in PERF’s first DOJ COPS-funded report, Racially Biased
Policing: A Principled Response, available on the PERF Web site. They can be
grouped in the following areas: supervision/accountability, policy,
recruitment/hiring, training/education, and outreach to diverse communities.
MCOPA’s Recommendations
The cost of data collection and analysis in Massachusetts has not
proven to be worth the effort. At a time when police budgets are being
cut, training is being reduced, and layoffs are being implemented, it is
irresponsible to divert hundreds of thousands, if not another million or so
dollars, to study a problem rather than working on the solution. If there is
money left over after this state funds training, policy development,
supervision enhancements, community policing models, and improve
recruitment, hiring and discipline, data collection might be considered; but
next time, try and do it right!
10
ACTION PLAN TO COMBAT RACIAL PROFILING
The first and most important step in addressing the issue of racial profiling
in traffic stops is to acknowledge that a problem exists. The use of racial or
ethnic stereotypes has no place in law enforcement. The goal of this Action Plan
is to condemn and eliminate such use.
The Board of Directors of this Association joined with the Colonel of the
State Police and a group of municipal chiefs during June 1999 in issuing a
“Resolution” to condemn the practice of racial profiling or stereotyping. As a
follow-up, the Association’s Traffic and Highway Safety Committee conducted a
study of the problem and made recommendations. Based on the
recommendations of that group, the Board of Directors at its meeting, on June 8,
2000, unanimously voted to adopt an Action Plan. The following represents an
updating of said Plan, incorporating recommendations from national experts, and
groups such as NOBLE (National Association of Black Law Enforcement
Executives), the Police Executive Research Forum (PERF) and even the ACLU!
GOAL IDENTIFICATION
The police throughout the state should commit themselves to raising the
level of public trust and confidence in the law enforcement community. The use
of racial profiling or stereotyping is morally and legally wrong and has no place in
modern policing efforts. It corrodes the presumption of innocence to which all
our citizens are entitled. It also alienates law-abiding citizens and undermines
the effectiveness of community policing efforts.
Given the proper support, the police departments in Massachusetts are
capable of identifying the problem and eliminating it. A statewide effort,
coordinated by the Secretary of Public Safety, is recommended. We are
convinced that through improvements in policy, training, supervision and
discipline, we can achieve these noble goals.
11
POLICIES & PROCEDURES
The “Policies and Procedures” utilized by police agencies should reflect
the philosophy that racial profiling and stereotyping is prohibited. The Municipal
Police Institute, Inc. (MPI) is the private, non-profit research and training affiliate
of the Massachusetts Chiefs of Police Association. Its sample manual has
served as the basis for the “Policies & Procedures” currently in use by the
majority of police departments in this state. We have reviewed, updated and,
where appropriate, prepared new samples addressing the issue of racial profiling
in connection with traffic stops. He will pay particular attention to discipline for
violations.
TRAINING
We have asked our representatives on the Massachusetts
Municipal Police Training Committee (MPTC) to work with the Executive
Office of Public Safety to identify funding to ensure that all future police
training has a component to deal with the Issue of racial profiling. We
intend this to include.
 Basic training for recruits;
 In-service training for veteran officers;
 Supervisory training for all superior officers
from Sergeant to Chief; and
 Dispatcher and Communications Officer training.
We recommend that the Training Council produce a video
concerning racial profiling and provide copies to all departments in the
state. The video should be suitable in length for use at roll-call and should
be updated periodically as appropriate. (Making training available on a
“distance learning” basis, as the Governor’s Crime Commission recently
recommended, would be the most appropriate way to assure all officers
are trained. Local budgets for training overtime and travel require that
internet-based options be encouraged.)
The efforts of the State Police and individual police departments
and regional training groups over the past few years should be reviewed
and incorporated into all MPTC programs.
PUBLIC AWARENESS
Focusing solely on officer training and discipline is not enough. A
statewide public relations campaign is needed. Citizens should be
sensitized to the need to identify unlawful behavior, and not simply the
12
race of an unknown person in their neighborhood, before calling to report
a suspicious person. A request form the Governor, Attorney General and
Secretary of Public Safety would be appropriate. The media responded
well in a similar situation involving hate crimes several years ago.
Chiefs should encourage local cable TV providers to get involved
as well.
Providing speakers to local, civic, school and professional groups
will also help spread the word.
Some effort should also be made to inform drivers of what to expect
and do.
DISCIPLINE
Police departments should work toward adopting a “zero tolerance”
policy for intentional racial profiling offenses. Once proper policies and
procedures are in place, and officers receive adequate training in what is
expected, discipline for racial profiling should be severe.
This will involve working with unions to secure their understanding and
cooperation. An effort must also be undertaken to educate arbitrators, the Civil
Service Commission and judges of the seriousness of these offenses.
Summary of Massachusetts Chiefs of Police Association’s Action Plan
Recommendations
1.
Acknowledge that, while not widespread, there is a problem of
racial profiling by some Massachusetts police officers. The
perception of the problem by some citizens means that it must be
addressed.
2.
The use of racial profiling and stereotyping is condemned.
3.
The police throughout this state are committed to raising the level
of citizens’ trust and confidence in the law enforcement community.
4.
Given the proper support, the police departments in Massachusetts
are capable of identifying the problem and eliminating it. Racebased enforcement is neither taught nor tolerated in this state.
13
5.
A statewide effort, coordinated by the Secretary of Public Safety,
aimed at addressing everyone’s legitimate concerns is
recommended.
6.
Sample “Policies and Procedures” such as those developed by the
Municipal Police Institute for use by police departments across the
state should be reviewed and updated regularly. These should
address the proper policy for traffic stops, appropriate discipline,
and a uniform procedure whenever a motor vehicle is subjected to
a consent search.
7.
The Municipal Police Training Committee – with the necessary
funding to do the job right - should review all existing procedures
and curriculum and offer training in racial profiling issues at all
levels including:
 basic training for recruits;
 in-service training for veteran officers;
 supervisory training for all superior officers, from
Sergeants to Chiefs; and
 Dispatcher and Communication Officer training.
8.
The MPTC should produce a roll-call training video on racial
profiling and make such training available on a “distance learning”
basis to all officers in the state..
9.
The Governor, Attorney General and Secretary of Public Safety
should encourage the media to conduct a statewide public
education effort, similar to the one done several years ago
concerning hate crimes. Chiefs and the State Police should
support this effort and enhance it with the use of local cable TV and
meetings with civic, school and professional groups.
10.
The Registry should include in new driver training programs
information about what motorists should expect and do during a
traffic stop.
14
11.
Once proper policies and procedures are in place and training has
been accomplished, departments should adopt a “zero tolerance”
policy for cases of intentional racial profiling. An effort to involve
unions and to educate arbitrators, the Civil Service Commission
and judges will be needed to assure that such discipline is upheld
on appeal.
12.
Regional training seminars on Internal Affairs and racial profiling
should be conducted throughout the State.
13.
Cooperative training efforts with municipal and State Police officers
and command staff should be undertaken.
14.
The use and expansion of the existing data collection and analysis
capabilities of the Registry of Motor Vehicles appears to be far too
expensive for the potential return. Unless and until there is a
national consensus on the best way to collect data on motor vehicle
stops, this state’s limited resources should be spent addressing
rather than studying the problem.
15
RACIAL PROFILING STATISTICS
Of the 351 cities and towns in Massachusetts, only 340 reportedly issued
traffic citations during the 27 months (4/1/01 – 6/30/04) studied. A total of
approximately 1,025,978 citations were issued.
CENSUS COMPARISONS
According to the 2000 U.S. Census, the state’s population, 18 years of
age or older, is comprised of 93.3% whites and 6.7% non-white. During the 27
months of the study, on average, a total of 3,018 citations were issued per
community. Of these, 89.5% were issued to white drivers, while 10.5% went to
non-whites. When the percentage of non-whites in the state’s population (6.7%)
is compared with the percentage of citations given to non-white drivers (10.5%),
this produces what the researchers refer to as the “Non-White Disparity” of 3.8%
(10.5% - 6.7%).
The researchers broke down the Non-White Disparity by police
departments in various population groups, the same size categories as those
used by the FBI in compiling its Uniform Crime Reports (UCR’s). The average
Non-White Disparities by population group, based on the 2000 U.S. Census,
were:
POPULATION
NON-WHITE DISPARITY
100,000+
3.2%
50,000 – 99,999
3.8%
25,000 – 49,999
6.6%
10,000 – 24,999
4.3%
under 10,000
2.7%
16
SEX COMPARISONS
In Massachusetts, 52% of the residents 18 years and older are female and 48%
are male. Of the 1,054,913 citations issued statewide for which the sex of the
driver was determined, 28.7% were issued to females and 71.3% to males. This
produces what the researchers call a “Sex Disparity” of 23.3%. This is derived
by subtracting the percentage of females cited from their percentage in the
census population. The same results are reached by subtracting the male
percentage of the population from the percentage of citations issued to males.
(71.3% - 48.0% = 23.3%)
When the researchers broke down the same information by UCR
population group, the following resulted:
POPULATION
AVERAGE SEX DISPARITY
100,000+
23.0%
50,000 – 99,999
22.6%
25,000 – 49,999
23.1%
10,000 – 24,999
24.3%
under 10,000
23.0%
Clearly, there is very little difference among various sized communities in
the percentage breakdown between male and females that are cited.
While the researchers’ tables do not show a “Racial Disparity” for each of
the five race (or “nationality”) categories, the statewide average figures break
down as follows:
RACE/NATIONALITY
STATEWIDE RACIAL DISPARITY
White
3.8%
Black
-2.7%
Hispanic
-2.7%
Asian
0.2%
Native American
0.2%
17
This means that, on average, across Massachusetts, the percentage of
white drivers cited is 3.8% less than the percentage 18 and older whites
comprise in the state’s population. Blacks and Hispanics are each cited 2.7%
more than their percentage of the state’s 18 and older population. Asians and
Native Americans are cited 0.2% less than their percentage of the 18 and older
state population.
When reviewing the “racial disparity” numbers for the various UCR
population groups, several trends appear.
First, the percentage of white drivers cited increases steadily as the
community’s population size increases, from 61.4% for the largest cities to 93.3%
for the smallest towns. (Note: 93.3% is the exact statewide white population.) It
is often in the smallest towns, especially in the more rural areas of the state,
where the Census population and the driving population are most alike. The
exact opposite is seen when each of the four “non-white” categories are studied.
The largest cities cite the greatest percentages of all non-white categories, and
the figures decrease as the size of the population group decreases. Blacks
receive 17.3% of the citations issued in cities over 100,000 population, yet only
2.7% of those in the smallest towns. Similarly, the figures for Hispanics decrease
steadily from 15.1% for the biggest cities to 3.0% for the smallest towns. Asians
receive 6.1% of the largest cities’ citations, yet only 0.9% of the smallest towns.
And, finally, Native Americans receive 0.3% of the citations written by cities of
over 100,000 population, yet only 0.1% from towns with less than 10,000
residents. Again, in every category, there is a steady, and often uniform drop as
the population category decreases.
The researchers compared the breakdown of cited drivers, by both sex
and either white or not-white, to the census population. On average, across the
state, of those cited,
26.3% were white females;
2.4% were non-white females;
63.2% were white males; and
8.1% were non-white males.
18
SEX DISPARITY
While the tables do not show one, a “Sex Disparity”, derived by comparing
the percentage breakdown of persons receiving citations with their percentage
breakdown in the census population, the results are as follows:
22.4% for white females;
1.0% for non-white females;
-18.6% for white males; and
-4.7% for non-white males.
This means that on average, across the state, females are cited less often
than their proportion of the population, with white females being cited 22.4% less
than their proportionate share and non-white females being cited 1% less than
their proportionate share of the population. Conversely, white males are cited
18.6% more than their share of the population and non-white males are cited
4.7% more often than the percentage of the population they comprise.
When the “Sex Disparity” percentages are looked at for each of the five
UCR population groupings, several trends appear. The disparity figures increase
from 20.0% to 26.7% for white females from the largest to the smallest
communities. They conversely decrease from 10.3% in the biggest cities for
non-white females, to 1.4% in the smallest towns. As for males, the disparity
increases steadily from 41.4% in the largest cities to 66.4% in the smallest
communities, as do figures for non-white males, from 28.3% to 5.5%.
ESTIMATED DRIVING POPULATION
In the next series of charts, the researchers compare the percentage of persons
cited with what they estimated to be the driving population of each community.
When estimating the state’s driving population, one might expect that the overall
breakdowns should be identical to the census population. With one exception,
this was the case. For some reason, the percentage breakdown by race was
19
reportedly 92.3% white in the state’s driving population in Table B2a and 93.3%
in Table A1a. This results in a “Non-White Disparity” of 2.8% for the state’s
“Estimated Driving Population” yet 3.8% for the state’s Census Population.
Presumably the researchers determined that not all persons 18 or over are
drivers.
When comparing the average percentage breakdowns of citations by the
race or nationality of what the researchers estimate to be the state’s driving
population, the race disparity breaks down as follows:
2.8% fewer whites were cited than their percentage of estimated
drivers;
2.3% more of both the Black and Hispanic drivers were cited; and
0.3% fewer of the Asians and
0.2% fewer of the Native Americans were cited.
SEARCH DISPARITIES
It appears that there were over 900,000 citations for which the “search”
box on the citation was checked either “yes” or “no”. The exact number is difficult
to determine from the Preliminary Tabulations. For example, Table C1 shows a
total of 924,411 citations; Table C2 shows 930,805; and Table C3 lists 907,028
total citations issued. Since only about 40 searches on average for each
department were reportedly conducted, the researchers noted that the numbers
were so low that any meaningful analysis on a department level were statistically
impossible.
NON-WHITE SEARCH DISPARITIES
Statewide, 2.0% of the white drivers that were cited were also searched,
while 2.8% of the non-whites cited were searched. This results in what the
researchers label as a “Non-White Disparity” of 0.8%.
There is almost no change in this figure when looking at departments in
various sized groups. The disparity figure ranges from 0.7% to 0.9% for the five
size categories.
20
MALE SEARCH DISPARITIES
Of the forty-one searches conducted by departments on average over the
27 month period of the study, 7 involved female drivers and 34 were conducted
in vehicles driven by male drivers. This means that 2.4% of the male drivers that
were given citations also had some search of their vehicle conducted, while 1.3%
of the cited females were also searched. The researchers compare these
percentages and list a “Male Disparity” of 1.2%.
When looking at the disparity by population groups, the only notable
variances occur among the largest cities and the smallest towns. The cities of
over 100,000 report a “Male Disparity” of 0.6% while towns with less than 10,000
residents have a “Male Disparity” of 2.3%.
Of the 40 persons that were searched on average per department over the
27 months of the study, 2.0% of the white drivers that were cited were also
searched. This figure is 3.1% for Blacks, 3.0% for Hispanics, 1.3% for Asians,
0.9% for Middle Eastern and 3.2% for Native Americans. (The 2000 U.S.
Census classifies Middle-Easterners as white. Therefore, the researchers did not
list this as a category in earlier tables. However, because it was a category that
officers could list on the citation form, it appears here for the first time.)
The bulk of the disparity for Blacks and Hispanics appears to result from
communities of over 50,000 residents.
On average, 2.3% of the white male drivers that were cited were
searched. The number was 3.2% for non-whites, 1.3% for white females and
1.7% for non-white females.
The small numbers of searches made it difficult for the researchers to
conduct any meaningful analysis, especially when looking at a breakdown by
population group. However, for the largest cities, where the largest number of
searchers were conducted, the percentages are virtually identical for white
(0.5%) and non-white (0.7%) females and white males (0.7%). The percentage
of cited non-white males that were searched was 1.6%. This is approximately
half of the statewide average.
21
The researchers looked at those 847,715 cases where drivers were cited and searched, but
not arrested. On average, departments searched 23 persons during the 27 month study period
without arresting the driver. White drivers were searched and given a citation, but not arrested, 1.3%
of the time. For non-white drivers, the figure was 1.9%. This produced what the researchers call a
“Non-White Disparity” of 0.6%.
A review of these figures by the population group of the department shows very little variances
among all but the largest cities. Departments from communities of less than 100,000 residents
searched citied whites without arresting them from 1.1% to 1.4% and non-whites 1.7% to 2.1% of the
time. For the state’s five largest cities, the figures are 0.5% for whites and 0.8% for non-whites, with
a non-white disparity of only 0.4%.
Table A1a: Traffic Citations for White and Non-White Drivers Compared to the
Census Population — STATEWIDE AVERAGES
Citations
Census Benchmark
(Population 18 and Older)
%
%
Non%
Total
Agency
Number
Non% White Non- White
White
Population
White
White Disparity
AVERAGES
3,018
89.5 10.5
14,273
93.3
6.7
3.8
TOTAL
1,025,978
Table A1b: Traffic Citations for White and Non-White Drivers Compared to the
Census Population — STATEWIDE AVERAGES BY POPULATION GROUPS
Citations
Census Benchmark
(Population 18 and Older)
%
%
Non%
Total
Agency
Number
Non% White Non- White
White
Population
White
White Disparity
100,000+
AVERAGE
40,943 61.4 38.6
175,458
64.6
35.4
3.2
50,000-99,999
AVERAGE
9,922
76.4 23.6
54,370
80.7
19.8
3.8
25,000-49,999
AVERAGE
5,218
82.0 18.0
26,113
88.6
11.5
6.6
10,000-24,999
AVERAGE
2,173
90.5 9.5
11,770
94.8
5.2
4.3
Under 10,000
AVERAGE
1,028
93.3 6.7
3,301
96.0
4.0
2.7
22
Table A2a: Traffic Citations for Male and Female Drivers Compared to the
Census Population — STATEWIDE AVERAGES
Citations
Census Benchmark
Sex
(Population 18 and Older)
%
%
Total
%
Agency
Citations
% Female
Disparity
Female Male Population
Male
AVERAGES
3,112
28.7 71.3
14,284
52.0
48.0 23.3
TOTAL
1,054,913
Table A2b: Traffic Citations for Male and Female Drivers Compared to the
Census Population — STATEWIDE AVERAGES Grouped by Population Size
Citations
Census Benchmark
(Population 18 and Older)
%
%
Total
%
Sex
Agency
Citations
% Female
Female Male Population
Male Disparity
100,000+
AVERAGE
43,514
29.6 70.4
175,458
52.6
47.4 23.0
50,000-99,999
AVERAGE
10,219
30.7 69.3
54,259
53.3
46.7 22.6
25,000-49,999
AVERAGE
5,320
30.1 69.9
25,972
53.1
46.9 23.1
10,000-24,999
AVERAGE
2,211
28.3 71.7
11,767
52.6
47.4 24.3
Under 10,000
AVERAGE
1,041
28.2 71.8
3,300
51.2
48.8 23.0
23
Table A3a: Traffic Citations for White, Black, Hispanic, Asian and Native American Drivers
Compared to the Census Polulation — STATEWIDE AVERAGES
Citations
Agency
Number
AVERAGE
3,018
TOTAL
1,025,977
RACIAL DISPARITY
% White
% Black
% Hispanic
% Asian
89.5
4.1
4.9
1.5
3.8
-2.7
-2.7
0.2
Census Benchmark
(Population 18 and Older)
% Native %
%
%
% % Native
American White Black Hispanic Asian American
0.1
93.3 1.4
2.2
1.6
0.3
0.2
Table A3b: Traffic Citations for White, Black, Hispanic, Asian and Native American Drivers
Commpared to the Census Population — STATEWIDE AVERAGES Grouped by Population Size
Citations
Agency
100,000+
AVERAGE
50,000-99,999
AVERAGE
25,000-49,999
AVERAGE
10,000-24,999
AVERAGE
Under 10,000
AVERAGE
Census Benchmark
(Population 18 and Older)
% Native %
%
%
% % Native
American White Black Hispanic Asian American
Number
% White
% Black
% Hispanic
% Asian
40,943
61.4
17.3
15.1
6.1
0.3
64.6 11.4
12.7
8.1
0.2
9,922
76.4
8.2
12.0
3.3
0.1
80.2
3.9
7.9
4.8
0.2
5,120
82.0
6.5
8.9
2.4
0.1
88.9
2.2
4.3
2.9
0.1
2,160
90.6
3.8
4.2
1.3
0.1
94.8
1.0
1.5
1.5
0.2
1,058
93.3
2.7
3.0
0.9
0.1
96.0
0.9
1.1
0.8
0.4
24
Table A4a: Traffic Citations for White Females, Non-White Females, White Males and Non-White Males
Compared to the Census Population — STATEWIDE AVERAGES
Citations
Census Benchmark
(Population 18 and Older)
Agency
AVERAGE
TOTAL
SEX DISPARITY
Number
3,013
1,024,515
% White % Non-White
% White Male
Female
Female
% Non-White
Male
Total
Population
14,246
26.3
2.4
63.2
8.1
22.4
1.0
-18.6
-4.7
%
% % Non- %
NonWhite White White
White
Female Female Male
Male
48.7
3.3
44.6 3.4
Table A4b: Traffic Citations for White Females, Non-White Females, White Males, and Non-White Males
Compared to the Census Population — STATEWIDE AVERAGES Grouped by Population Size
Citations
Census Benchmark
(Population 18 and Older)
Agency
100,000+
AVERAGE
50,000-99,999
AVERAGE
25,000-49,999
AVERAGE
10,000-24,999
AVERAGE
Under 10,000
AVERAGE
Number
% White % Non-White
% White Male
Female
Female
% Non-White
Male
Total
Population
%
% % Non- %
NonWhite White White
White
Female Female Male
Male
40,829
20.0
10.3
41.4
28.3
175,458
33.8
18.7
30.7 16.7
9,905
25.0
5.7
51.4
17.9
54,259
43.0
10.3
37.1
9.6
5,213
25.8
4.2
56.1
13.8
25,972
47.3
5.9
41.3
5.6
2,171
26.2
2.1
64.3
7.4
11,767
50.0
2.6
44.7
2.6
1,030
26.7
1.4
66.4
5.5
3,301
49.3
1.9
46.6
2.2
25
Table B2a: Traffic Citations for White and Non-White Drivers
Compared to the Driving Population Estimate — STATEWIDE AVERAGES
Citations
Driving Population Estimate
Non-White
Agency
Number
% White % Non-White
% White
% Non-White
Disparity
AVERAGE
3,018
89.5
10.5
92.3
7.7
2.8
TOTAL
1,025,977
Table B2b: Traffic Citations for White and Non-White Drivers
Compared to the Driving Population Estimate — STATEWIDE AVERAGES Grouped byPopulation Size
Citations
Driving Population Estimate
Non-White
Agency
Number
% White % Non-White
% White
% Non-White
Disparity
100,000+
AVERAGE
40,943
61.4
38.6
71.5
28.5
10.1
50,000-99,999
AVERAGE
9,922
76.4
23.6
80.7
19.3
4.3
25,000-49,000
AVERAGE
5,218
82.0
18.0
86.7
13.3
4.7
10,000-24,999
AVERAGE
2,173
90.5
9.5
92.6
7.4
2.1
Under 10,000
AVERAGE
1,028
93.3
6.7
95.6
4.4
2.4
26
Table B3a: Traffic Citations for White, Black, Hispanic, Asian and Native American Drivers
Compared to the Driving Population Estimate — STATEWIDE AVERAGES
Citations
Agency
AVERAGE
TOTAL
RACE DISPARITY
Number
% White
% Black
% Hispanic
% Asian
3,018
1,025,977
89.5
4.1
4.9
1.5
2.8
-2.3
-2.3
0.3
Driving Population Estimate
% Native %
%
%
% % Native
American White Black Hispanic Asian American
0.1
92.3 1.7
2.6
1.8
0.3
0.2
Table B3b: Traffic Citations for White, Black, Hispanic, Asian and Native American Drivers
Compared to the Driving Population A182Estimate — STATEWIDE AVERAGES Grouped by Population Size
Citations
Driving Population Estimate
% Native %
%
%
% % Native
Agency
Number
% White
% Black
% Hispanic
% Asian
American White Black Hispanic Asian American
100,000+
AVERAGE
40,943
61.4
17.3
15.1
6.1
0.3
71.5 8.7
10.6
6.5
0.2
50,000-99,999
AVERAGE
9,922
76.4
8.2
12.0
3.3
0.1
80.7 4.5
7.4
4.6
0.2
25,000-49,000
AVERAGE
5,218
82.0
6.5
9.0
2.4
0.1
86.7 3.0
5.0
3.3
0.1
10,000-24,999
AVERAGE
2,173
90.5
3.9
4.2
1.3
0.1
92.6 1.7
2.3
1.9
0.2
Under 10,000
AVERAGE
1,031
93.1
2.8
3.1
1.0
0.1
95.8 0.9
1.3
0.9
0.2
27
Table C1a: Traffic Citations for White and Non-White Drivers Which Result in a Search for All Citations —
STATEWIDE AVERAGES
Agency
Citations
Total
White Searches
Non-White Searches Non-White
# Non#
Total # White
White # Searched % Searched # Searched % Searched Searched Disparity
AVERAGE
2,719
2,087
581
40
2.0
30
2.8
10
0.8
TOTAL
924,411
Table C1b: Traffic Citations for White and Non-White Drivers Which Result in a Search for All Citations —
STATEWIDE AVERAGES Grouped by Population Size
Agency
Citations
Total
White Searches
Non-White Searches Non-White
# Non#
Total # White
White # Searched % Searched # Searched % Searched Searched Disparity
100,000+
AVERAGE
34,978 19,077
15,901
286
0.6
107
1.4
178
0.7
50,000-99,999
AVERAGE
8,747
6,359
2,389
141
1.8
89
2.7
52
0.9
25,000-49,999
AVERAGE
5,148
3,876
909
58
1.5
44
2.2
14
0.7
10,000-24,999
AVERAGE
1,892
1,707
185
38
2.3
33
3.2
5
0.9
Under 10,000
AVERAGE
916
841
75
17
2.1
15
2.9
2
0.8
28
Table C2a: Traffic Citations for Male and Female Drivers Which Result in a Search for All Citations
—STATEWIDE AVERAGES
Agency
Citations
Total
Female Searches
Male Searches
Male
Total # Female # Male # Searched % Searched # Searched % Searched # Searched Disparity
AVERAGE
2,738
830
1,908
41
1.3
7
2.4
34
1.2
TOTAL
930,805
Table C2b: Traffic Citations for Male and Female Drivers Which Result in a Search for All Citations
— STATEWIDE AVERAGES Grouped by Population Size
Agency
Citations
Total
Female Searches
Male Searches
Male
Total # Female # Male # Searched % Searched # Searched % Searched # Searched Disparity
100,000+
AVERAGE
37,129 11,327
25,802
329
0.5
63
1.2
266
0.6
50,000-99,999
AVERAGE
8,964
2,845
6,119
148
1.2
23
2.3
124
1.1
25,000-49,999
AVERAGE
4,878
1,505
3,373
60
1.0
10
1.8
50
0.9
10,000-49,999
AVERAGE
1,922
561
1,360
38
1.5
7
2.7
32
1.2
Under 10,000
AVERAGE
926
270
656
17
1.4
3
2.5
14
2.3
29
Table C3a: Traffic Citations for White, Black, Hispanic, Asian, Middle Eastern and Native American Drivers
Which Result in a Search for All Citations — STATEWIDE AVERAGES
Agency
Citations Searches % White
% Black
% Hispanic
% Asian % Middle Eastern % Native Am.
Total
Total Searched Searched
Searched
Searched
Searched
Searched
AVERAGE
2,668
40
2.0
3.1
3.0
1.3
0.9
3.2
TOTAL
907,028
Table C3b: Traffic Citations for White, Black, Hispanic, Asian, Middle Eastern and Native American Drivers
Which Result in a Search for All Citations — STATEWIDE AVERAGES Grouped by Population Size
Agency
Citations Searches % White
% Black
% Hispanic
% Asian % Middle Eastern % Native Am.
Total
Total Searched Searched
Searched
Searched
Searched
Searched
100,000+
AVERAGE
34,980
286
0.6
1.4
1.5
0.6
0.4
4.4
50,000-99,999
AVERAGE
8,748
141
1.8
3.4
2.8
0.7
0.9
1.2
25,000-49,999
AVERAGE
4,785
58
1.5
1.8
2.4
1.7
0.6
1.1
10,000-24,999
AVERAGE
1,892
38
2.3
3.4
3.6
0.9
1.4
1.7
Under 10,000
AVERAGE
914
17
2.1
3.3
2.9
1.6
0.6
7.2
30
Table C4a: Citations for White Male, Non-White Male, White Female and Non-White Female Drivers
Which Result in a Search for All Citations — STATEWIDE AVERAGES
Agency
Citations Searches
Total
Total
STATE AVERAGE 2,665
40
TOTAL
903,482
% White
Male
Searched
2.3
% Non-White
Male
Disparity
Searched
3.2
0.9
% White
Female
Searched
1.3
Table C4b: Citations for White Male, Non-White Male, White Female and Non-White Female Drivers
Which Result in a Search for All Citations — STATEWIDE AVERAGES Grouped by Population Size
% White
% Non-White
% Non-White
Agency
Citations Searches
Male
Male
% White Female
Female
Total
Total
Searched
Searched
Searched
Searched
100,000+
AVERAGE
34,899
284
0.7
1.6
0.5
0.7
50,000-99,999
AVERAGE
8,736
141
2.1
3.2
1.2
1.2
25,000-49,999
AVERAGE
4,789
58
1.7
2.4
1.0
1.4
10,000-24,999
AVERAGE
1,897
38
2.6
3.6
1.5
1.7
Under 10,000
AVERAGE
916
17
2.4
3.3
1.4
1.9
31
% NonWhite
Female Disparity
Searched
1.7
0.4
Table C5a: Traffic Citations for White and Non-White Drivers Which Result in a Search for Only Citations without Arrests
— STATEWIDE AVERAGES
Agency
Citations
Total
White Searches
Non-White Searches Non-White
# Non%
Total
# White
White
# Searched # Searched % Searched # Searched Searched Disparity
STATE AVERAGE 2,523
1,986
544
23
17
1.3
6
1.9
0.6
TOTAL
857,715
Table C5b: Traffic Citations for White and Non-White Drivers Which Result in a Search for Only Citations without Arrests STATEWIDE AVERAGES Grouped by Population Size
Agency
Citations
Total
White Searches
Non-White Searches Non-White
# Non%
Total
# White
White
# Searched # Searched % Searched # Searched Searched Disparity
100,000+
AVERAGE
34,408
19,007
15,401
200
81
0.5
119
0.8
0.4
50,000-99,999
AVERAGE
8,221
6,058
2,163
76
50
1.1
26
1.7
0.6
25,000-49,999
AVERAGE
4,547
3,707
840
35
28
1.1
8
1.4
0.3
10,000-24,999
AVERAGE
1,740
1,572
168
20
18
1.4
3
1.9
0.5
Under 10,000
AVERAGE
856
793
67
10
8
1.4
1
2.1
0.8
32