Citation
Racial profiling

Material Information

Title:
Racial profiling a study of police practices in traffic enforcement
Creator:
Escarcida, Gilbert Zach
Publication Date:
Language:
English
Physical Description:
vii, 52 leaves : ; 28 cm

Subjects

Subjects / Keywords:
Racial profiling in law enforcement ( lcsh )
Traffic regulations ( lcsh )
Traffic police ( lcsh )
Racial profiling in law enforcement ( fast )
Traffic police ( fast )
Traffic regulations ( fast )
Genre:
bibliography ( marcgt )
theses ( marcgt )
non-fiction ( marcgt )

Notes

Bibliography:
Includes bibliographical references (leaves 50-52).
General Note:
Department of Sociology
Statement of Responsibility:
by Gilbert Zach Escarcida.

Record Information

Source Institution:
|University of Colorado Denver
Holding Location:
Auraria Library
Rights Management:
All applicable rights reserved by the source institution and holding location.
Resource Identifier:
71814413 ( OCLC )
ocm71814413
Classification:
LD1193.L66 2006m E72 ( lcc )

Full Text

RACIAL PROFILING: A STUDY OF POLICE PRACTICES
IN TRAFFIC ENFORCEMENT
Gilbert Zach Escarcida
B. A, University of New Mexico, 2002
A thesis submitted to the
University of Colorado at Denver
in partial fulfillment
of the requirements for the degree of
Master of Arts
Sociology
2006
by


This thesis for the Master of Arts
degree by
Gilbert Zach Escarcida
has been approved
by
f // Date
Andrea Haar


Escarcida, Gilbert Zach (M.A., Sociology)
Racial Profiling and Police Practices
Thesis directed by Assistant Professor Yili Xu
ABSTRACT
The focus of this study was on the significance racial background has on the
likelihood a minority individual will be stopped and searched compared to
his/her White counterpart. The theoretical framework used in this study is
social cognition theory, which describes racial and other prejudices as the
result of cognitive errors during the processing of information. This
framework serves as a guide for the empirical examination of individual
police behavior. Prior research on racial profiling and police practices is
reviewed and its findings are discussed. The 1999 Police-Public Contact
Survey, a nationwide supplemental questionnaire to the National Crime
Victimization Survey, was examined to determine the relative influence of
drivers characteristics over police behavior during traffic stops. In addition
to investigating the occurrence of traffic stops, the quantity of searches of
drivers and/or their vehicles will be assessed. The results from this study
indicate race is a significant factor in being stopped and/or being searched.
However, the results from this study also indicate that race is not the most
influential factor in the decision to stop and/or search. In both instances, age
for the decision to stop and gender for the decision to search, have a greater
influence on a police officers decision making processes regarding who to
stop and to search.


DEDICATION
I dedicate this thesis to my parents, who gave me the motivation to follow my
dreams, my two brothers for being my inspiration, and Dr. Michael Dougher
for his guidance. I also would like to thank my fiance Maeghin, for her
unfaltering support and understanding while I was completing this thesis.


ACKNOWLEDGEMENT
My thanks to my advisor, Yili Xu, for his contribution, guidance, and support
to my research. I also wish to thank all the members of my committee for
their valuable participation and insights.


TABLE OF CONTENTS
Tables...................................................vii
CHAPTER
1. INTRODUCTION............................................1
2. LITERATURE REVIEW.......................................6
Theory..............................................13
3. DATA and METHODS.......................................21
Dependent Variables.................................22
Independent Variables...............................23
Data Limitations....................................26
Statistical Procedures..............................26
4. RESULTS................................................28
General Descriptives................................28
Characteristics of Drivers Stopped/Not Stopped......30
The Decision to Stop................................32
Characteristics of Drivers Searched/Not Searched....34
The Decision to Search..............................36
5. CONCLUSIONS............................................38
Future Research.................................. .48
REFERENCES....................................................50
vi


LIST OF TABLES
Table
1. General descriptives of total sample....................................29
2. Characteristics of drivers stopped/not stopped.........................31
3. Multivariate regression of decision to stop............................33
4. Characteristics of drivers searched....................................35
5. Logistical regression of decision to search............................37
vii


CHAPTER 1
INTRODUCTION
In recent years racial profiling has rapidly emerged as one of the key yet
controversial police-related topics in the academic world (Petrocelli, Piquero,
and Smith, 2003; Engel and Calnon, 2004; Schafer, Carter, and Katz-
Bannister, 2004; Tyler and Wakslak, 2004; Wilson, Dunham, and Alpert,
2004). This growing concern with racial profiling has infiltrated the public
sphere as well, with an outcry of calls coming from many segments of society,
namely minorities who are the usual suspects regarding this phenomenon
(Petrocelli, Piquero, and Smith, 2003; Wilson, Dunham, ancf Alpert, 2004).
The consequences of racial profiling are numerous and detrimental to society
and identifying the factors that help perpetuate this phenomenon can only help
society develop the mechanisms needed to end this discriminate behavior.
Any inaction that allows racial profiling to continue only further compounds
the racism problem that still infects our country. Therefore, by asking critical
questions and attempting to identify the factors associated with racial
profiling, society can take the necessary steps in achieving racial equality, but
more importantly, racial harmony. In tackling this task, there are two
questions that may illuminate our understanding of racial profiling: (1) does
1


racial profiling exist and if so, (2) how prevalent is this behavior in police
practices?
Before attempting to answer these questions, and before discussing prior
research in this area, it would be beneficial to operationally define racial
profiling to minimize variability regarding this term. Prior research has
defined racial profiling in different terms. Withrow (2004) defines racial
profiling as a practice by which a police officer makes an enforcement
decision, such as a decision to stop an individual, solely on the basis of a
citizens race or ethnicity. Tyler and Wakslak (2004) define racial profiling as
situations in which legal authorities may be acting, as least in part, based on
the race of the person in question. Schafer, Carter, and Katz-Bannister (2004),
drawing on the work of Ekstrand (2000), define racial profiling as situations
in which police use race or the perception of a drivers race as a key factor in
deciding whether to make a traffic stop. Furthermore, its definition describes
any police-initiated behaviors that are the primary product of a citizens
perceived race or ethnicity rather than behavioral or legal cues (Ramirez,
McDevitt, and Farrell, 2000 in Schafer, Carter, and Katz-Bannister, 2004). I
believe the latter provides the most complete definition regarding racial
profiling. In addition to maintaining that race is one possible factor in racial
profiling, it also encompasses possible police practices which are being
disregarded in place of racial profiling, in this case the behavioral or legal
2


cues that could possibly signify criminal behavior. In essence, rather than
looking for criminal behavior as a key indicator for making arrests or stopping
crime, police officers are targeting individuals from a particular race, namely
African Americans and Hispanics, and expecting them to engage or be
engaged in criminal activities. This is the crux of the problem regarding racial
profiling.
Researchers in this area have previously and routinely focused their efforts
on traffic stops and the frequency at which Whites, African Americans, and
Hispanics get stopped and searched. Prior studies have taken place in many
different parts of the United States and have varied from questionnaires to
observations and interviews (Petrocelli, Piquero, and Smith, 2003; Engel and
Calnon, 2004; Schafer, Carta-, and Katz-Bannister, 2004; Tyler and Wakslak,
2004; Wilson, Dunham, and Alpert, 2004). Additionally, traffic stop data
recorded by police have been used in various instances and they provide
researchers with relatively accurate data that is efficiently collected during the
traffic stop encounter, which is not the case with questionnaires or interviews.
The advantage of using self-reported information is including
individuals (police officers) who are in direct contact and in direct knowledge
of what transpired during an encounter. Self-reported information can also
produce a high volume of cases in contrast to other methods, such as
observational research. However, self-reported information is not free from
3


error and can be of questionable reliability and validity. Police officers
participating in the study might purposefully or erroneously omit or distort
what transpired during a traffic stop encounter and/or fail to report an
encounter altogether (Schafer, Carter, and Katz-Bannister, 2004).
Questionnaires and public polls run the risk of asking the wrong people or
asking the wrong questions regarding the desired focus and hence are prone to
reliability and validity concerns as well. Observations and interviews are
lower in number than usual quantitative methods and are usually subjective in
nature, thus incorporating questions regarding the measures reliability.
Therefore, for this literature review I have chosen research that represents a
wide array of research methods in hopes of illustrating that regardless of
methodology, research on police racial profiling has yielded consistent results.
The focus of this study is the effect racial background has on the
likelihood that a minority individual will be stopped and searched compared
to his/her White counterpart. The theoretical framework is social cognition
theory (Howard and Renfrow, 2003), which describes racial and other
prejudices as the result of cognitive errors during information processing.
This framework serves as a guide for the empirical examination of policing
practices. As mentioned above, prior research on racial profiling and police
practices is reviewed and its findings are discussed. The 1999 Police-Public
Contact Survey, a nationwide supplemental questionnaire to the National
4


Crime Victimization Survey, is examined to determine the relative influence
of drivers characteristics over police behavior during traffic stops. In
addition to investigating the occurrence of traffic stops, the quantity of
searches of drivers and/or their vehicles will be assessed. Finally, the results
of the research and its implications are discussed.
5


CHAPTER 2
LITERATURE REVIEW
Schafer, Carter, and Katz-Bannister (2004) studied traffic stop encounters
in a Midwestern town of400,000 people. The information source was a
voluntary data collection project that was initiated to better understand officer
decision making behaviors during traffic enforcement encounters (Schafer,
Carter, and Katz-Bannister, 2004). During the one-year study, police officers
used a form to record the context of every traffic stop (date, time, and
location), the reason for the stop, driver demographics (gender, race/ethnicity,
and age), any actions taken during the stop (searches, search authority, search
outcome), and traffic stop disposition (citation, arrest, warning, or report)
(Schafer, Carter, and Katz-Bannister, 2004). Results from the study indicated
in terms of their proportion of all drivers stopped, searches of African
American, Hispanic, male, and younger drivers were disproportionately high
(Schafer, Carter, and Katz-Bannister, 2004). For example, African Americans
comprised 26 percent of all drivers stopped, but they accounted for 42.2
percent of all searches (Schafer, Carter, and Katz-Bannister, 2004). Yet, and
despite the higher percentage of searches, African Americans were no more
likely than Whites or Hispanics to be in the possession of contraband
(Schafer, Carter, and Katz-Bannister, 2004).
6


Withrow (2004) investigated whether a differential enforcement pattern
(police racial profiling) exists in the city of Wichita, Kansas. Withrow (2004)
posited that at some level, the race or ethnicity of an individual is an
important, even critical component of the police decision-making process.
Furthermore, race and ethnicity are often included among a series of other
identifiers that assist police officers to identify and arrest individuals who are
suspected of criminal activity (Withrow, 2004). The study was done by the
Wichita Police Department during the first half of2001 (Withrow, 2004).
Police officers were trained to collect data and used qualitative bubble
forms to document the traffic stop (Withrow, 2004). Additionally,
demographic information regarding the police officers were obtained, which
included factors of race/ethnicity, age, years of service, and gender of the
police officers involved in the study (Withrow, 2004). Withrow (2004) found
that African Americans were more disproportionately represented in traffic
stops than Whites, with Hispanics also showing a higher percentage of traffic
stops than Whites but at a lower percentage than African Americans.
Specifically, African Americans were more likely to be stopped, searched,
stopped for a longer period of time, and these searches involved more officers
present than any of the other races (Withrow, 2004). The searching of African
Americans occurred much more often than Whites and yet the percentage of
searches that revealed contraband was nearly identical (Withrow, 2004).
7


Additionally, African Americans were much more likely to be issued citations
and/or be arrested than non-African Americans (Withrow, 2004). Finally, for
African Americans these percentages of all the traffic stop characteristics
increase as it became later at night (Withrow, 2004).
Lundman and Kaufman (2003), using the Contacts between Police and
the Public: Findings from the 1999 National Survey, aimed to answer a series
of questions regarding racial profiling. For example, researchers posed
questions such as, are African American men as compared with White men
more likely to self-report being stopped by police for traffic law violations or
are African American and Hispanic drivers less likely to perceive the stop as
pretextual and less likely to report that police had a legitimate reason for
stopping them (Lundman and Kaufman, 2003, p. 195). In general,
researchers found that in addition to other explanatory variables, their research
indicates that African American males were disproportionately stopped
compared to White drivers (Lundman and Kaufman, 2003). Furthermore,
results indicated that there was a similar pattern of stops for African American
women when compared to White women, albeit at a lower overall percentage
than their male counterparts (Lundman and Kaufman, 2003). African
Americans and Hispanic drivers were significantly less likely to report that
police had a legitimate reason for making the stop and significantly less likely
to report that police acted properly (Lundman and Kaufman, 2003). Lundman
8


and Kaufman (2003) also reported that the beliefs in the legitimacy and
propriety of police actions differ starkly between African Americans and
Whites, indicating that Whites are more likely to believe the police act
properly when making a traffic stop. This finding supports much of the prior
research regarding public perceptions and police legitimacy, with minorities
on one end of the continuum (the non-trusting police end) and Whites on the
other (the trusting end).
Reitzel, Rice, and Piquero (2004) focused their research on the
Hispanic experience with regards to police racial profiling. Reitzel, Rice, and
Piquero (2004) placed their attention on the Hispanic population because they
feel there has been a void in the research linking the Hispanic population and
perceived racial profiling. The authors used a public poll from the New York
Times and the CBS broadcasting company, and results from the study
indicated that race, gender, age, education and political affiliation all
correlated with perceptions of police racial profiling, most of them at
significant levels (Reitzel, Rice, and Piquero, 2004). In general, Hispanics fell
between the position of blacks and whites regarding racial profiling issues
(Reitzel, Rice, and Piquero, 2004). For instance, Hispanics were more likely
than Whites but less likely than African Americans to believe racial profiling
was widespread (Reitzel, Rice, and Piquero, 2004).
9


The work of Rojek, Rosenfeld, and Decker (2004) focused on the
influence of the drivers race on being stopped in Missouri. The sample in
this study stemmed from a state-wide bill that mandated the collection of
traffic stop data for the last four months of2000 by police departments across
the state (Rojek, Rosenfeld, and Decker, 2004). Information yielded just
fewer than 200,000 stops from 92 municipalities, which was a quarter of all
the municipalities in Missouri (Rojek, Rosenfeld, and Decker, 2004). Rojek,
Rosenfeld, and Decker (2004) found that African Americans were 17 percent
more likely to be pulled over by police than Whites, and 55 percent more
likely to be pulled over than Hispanics (the discrepancy between African
Americans and Hispanics was noted as unanticipated). However, data showed
that African Americans and Hispanics were approximately twice as likely to
be searched and arrested as Whites (Rojek, Rosenfeld, and Decker, 2004). In
their conclusion, the authors affirmed with confidence that African American
drivers attract more police attention on the nations roadways than do Whites,
and if stopped African Americans and Hispanics are much more likely to face
serious sanctions (Rojek, Rosenfeld, and Decker, 2004).
Research by Petrocelli, Piquero, and Smith (2003) investigated the
incidence of racial profiling in police traffic stops in an East coast city. In
doing so, the researchers employed conflict theory as the basis for providing
possible relationships between police officer practices and citizen contact. To
10


measure the variables in the study researchers used traffic stop data from a ten
week period that resulted in 6,699 traffic stops (Petrocelli, Piquero, and Smith,
2003). Police officers recorded pre-selected information on the driver and the
stop itself directly into a police computer, which was eventually matched with
demographic data from the officers (Petrocelli, Piquero, and Smith, 2003).
The results from the study revealed that police stops were significantly higher
in areas with higher crime rates, and the percentage of Blacks in the
population was positively and significantly related to the percentage of total
stops that resulted in searches (Petrocelli, Piquero, and Smith, 2003). Using
the backdrop of conflict theory, researchers analyses led to three conclusions:
first, the total number of stops by the police in this city was determined solely
by the crime rate of the neighborhood (Petrocelli, Piquero, and Smith, 2003).
Second, the percentage of stops that resulted in a search was determined by
only one characteristic: the percentage of Blacks in the population (Petrocelli,
Piquero, and Smith, 2003). Third, the increased percent of Blacks in the
population as well as an increased crime rate served to decrease the
percentage of police stops that ended in an arrest/summons (Petrocelli,
Piquero, and Smith, 2003). I find the third finding rather intriguing and I
believe the reason why the percentage of Blacks in the population and the
higher crime rate lower the percentages of police stops that end in
arrest/summons is because there are more cases in which the police stop
11


African Americans and more cases in which the police search persons or
vehicles that do not warrant such a measure, hence the lower percentage of
arrests.
Similar to Petrocelli, Piquero, and Smith (2003), Weitzer and Tuch (2005)
also employed conflict theory as the basis of their research. Using
questionnaires distributed to a representative sample from metropolitan areas
in the United States, results of their study indicated that African Americans
and Hispanics held perceptions of racial profiling by law enforcement
agencies in the United States (Weitzer and Tuch, 2005). The Whites in the
study who did believe racial profiling was practiced by police officers were
much less likely to believe that racial profiling exists in their neighborhoods
(Weitzer and Tuch, 2005). African Americans and Hispanics were much
more likely to believe that police treat African Americans and Hispanics
worse than they treat Whites (Weitzer and Tuch, 2005). By contrast, Whites
overwhelmingly believed that police in their city treat Whites and the other
two minority groups equally (Weitzer and Tuch, 2005). These results are
indicative of what other studies have illustrated for the held perceptions of
racial profiling by minorities in this country (Weitzer and Tuch, 2005).
Finally, Engel and Calnon (2004), using the same 1999 Police-Public
Contact Survey as in this study, examined the influence of drivers
characteristics during traffic stop encounters with police. Engel and Calnon
12


(2004) discussed the war on drugs and its implications on racial profiling. In
short, the authors contend the war on drugs, which focuses strongly on
reducing the supply, distribution, and use of illicit drugs, has provided a forum
which allows law enforcement agencies to legitimize the targeting of young
minority males, those believed to be the most likely perpetrators of these
crimes (Engel and Calnon, 2004). Furthermore, this legitimation of the
racial profiling tactics has transferred into more everyday situations, namely
traffic stop encounters. Engel and Calnon (2004) found that, in general, the
coercive actions toward drivers during traffic stops (i.e., citation, search,
arrest, and use of force) show consistent and substantial differences that are
based, in part, on drivers characteristics (p. 77). For example, the authors
found that African American drivers are 1.5 times more likely to be issued a
citation, 1.5 times more likely to be searched, 1.8 times more likely to be
arrested, and 2.1 times more likely to be exposed to the use of force (Engel
and Calnon, 2004). For Hispanics drivers, the rates are similar to that of
African Americans (Engel and Calnon, 2004).
Theory
Conflict theory, as seen with the Petrocelli, Piquero, and Smith (2003) and
Weitzer and Tuch (2005) studies, has been the theory of choice when a
theoretical framework has been incorporated into racial profiling research.
Conflict theory is clearly an advantageous perspective in regards to the
13


unequal treatment and incarceration of those without power, namely
minorities. Researchers maintained that the development and use of racial
profiling is the product of an unequal system that stipulates the affluent can
and will devise mechanisms for maintaining their elite status (Petrocelli,
Piquero, and Smith, 2003). Petrocelli, Piquero, and Smith (2003) further state
that in essence laws are made which serve the interests of the privileged and
the police are used to suppress and control any segment of society that poses a
threat to the status quo (Black, 1976; Dahrendorf, 1959; Quinney, 1970;
Turk, 1969; Void, 1958; cited in Petrocelli, Piquero, and Smith, 2003, p. 2).
Weitzer and Tuch (2005) also argued that the racial profiling by law
enforcement agencies sustains the power struggle between Whites and
minorities, with Whites clearly maintaining their advantage in todays society.
In this study, I have taken a different theoretical route than researchers of
previous studies. I have employed social cognition theory as the basis for my
research. Social cognition is a social psychological perspective that in
addition to accounting for macro social factors in decision making, social
cognition theory also incorporates a micro-sociological perspective that
focuses on individual factors associated with decision making (Howard and
Renffow, 2003). Conflict theory stipulates that those in power develop
mechanisms to maintain and increase power, in this case, keeping minorities
from accumulating wealth and status by impeding their ability to move up the
14


social ladder (Petrocelli, Piquero, and Smith, 2003). Furthermore, conflict
theory maintains that the developing of mechanisms includes the building of
social structures and institutions that serve to keep the affluent in power,
which would include government institutions and law enforcement agencies
(Petrocelli, Piquero, and Smith, 2003). Conflict theory clearly emphasizes
social systems and their structures and institutions, which in turn focuses their
attention on the influence these structures and institutions have on controlling
societys behavior. This element of conflict theory suppresses or eliminates
all individual discretion, in a sense making the everyday citizen a drone or
pawn of the larger social structure, the unintentional and unknowing
compliance in their own demise. Conversely, some credence needs to be
given to the autonomy of the social actor, that people make individual choices
based on their own perceptions and are not merely pawns of the larger social
structure. Therefore, the use of social cognition theory in place of conflict
theory provides this study a theoretical perspective that accounts for
individual discretion based on personal perceptions regarding the phenomenon
of racial profiling.
Social cognition can be defined as referring to structures of knowledge,
the interpersonal processes of knowledge creation and dissemination, the
actual content of this knowledge, and the shaping of each of these aspects of
cognition by social forces (Howard and Renffow, 2003). Cognitive structures
15


therefore allow information to be represented in some mental, sensoiy, verbal,
or iconic form (Howard and Renffow, 2003). Cognitive structures are
believed to have limits, which maintains that it is impossible to process all
incoming information in a given situation and the result is the development of
systems of categorization (Howard and Renfrew, 2003). These structures are
created through multiple experiences and provide interpretive frameworks for
the processing of new information (Howard and Renfrew, 2003).
Information processing is premised in cognitive inferences that require the
evaluation of social information according to some set of rules and the
formation of social judgment (Howard and Renfrew, 2003). Deficiencies may
develop during this process of information integration and the result may be
the development of heuristics or cognitive shortcuts (Howard and Renfrew,
2003). Heuristics simplify complex problem solving into more easily
manageable mental tasks. This type of cognitive functioning, or cognitive
errors, is one of the bases of stereotyping (Howard and Renffow, 2003). The
formation of heuristics, such as causal or trait inferences, follows the
principles of attribution (Howard and Renfrew, 2003). Individuals create
prototypes (trait inferences) by synthesizing their experiences with members
of a social category into an average abstract account of characteristics
associated with that particular group (Howard and Renffow, 2003). In short,
prototypes can be defined as the central tendency of characteristics associated
16


with members of a social group created from experience (Howard and
Renfrew, 2003).
While prototypes are derival from a more psychological perspective,
schemas are the sociological adaptation of prototypes to the real world
(Howard and Renfrew, 2003). Schemas are utilized as everyday theories that
shape and mold how people use information (Howard and Renfrew, 2003).
They are both abstract cognitive structures that represent organized
knowledge about a concept or stimulus as well as mechanisms used in
information processing (DiMaggio, 1997; cited in Howard and Renfrew,
2003, p. 263). Furthermore, schemas allow individuals to apply social
knowledge and to exert a certain degree of control over the social world by
guiding an individuals perceptions, memory, and inference processes
(Howard and Renfrew, 2003). Person schemas specifically address the
phenomenon of racial profiling because person schemas organize knowledge
about certain individuals or specific groups of people, usually emphasizing
traits or personality categories (Howard and Renfrew, 2003).
One final component of social cognition that warrants attention is
assessing the human thinker (Howard and Renfrew, 2003). Some in the field
argue social actors are methodical, rational thinkers while others portray
individuals as efficient, although flawed, information processors (Howard and
Renfrew, 2003). Cognitive errors, such as misattributions, produce inaccurate
17


predictions or judgments that depart from known facts (Howard and Renfrew,
2003). Conversely, cognitive biases, such as cognitive errors, are more
systematic misrepresentations of otherwise plausible or logical attributions
(Howard and Renfrew, 2003).
All the social cognition factors listed above provide a basis for how racial
biases are derived and manifested in individuals. I believe racial biases are
developed in individuals due to the abundance of misinformation regarding
minorities and crime. Crime is a function of poverty, not race (Weitzer and
Tuch, 2005). Yet, poor people are disproportionately represented by
minorities in this country and hence the association. Misinformation of this
sort results in cognitive errors in which inaccurate stereotypes and prejudice
attitudes arise and contribute to police officers schema, or tendency to look
for suspicious activity when in contact with minorities. These schemas are
what influence a police officers decision making process, which ultimately
guides their behavior.
Finally, it is not the intent of this study to determine if prejudice
attitudes are the result of misinformation or institutional policies (conflict
theory), but rather the intent of this study is to determine what role race plays
in racial profiling. Although the term racial profiling implies a racial
component, is race itself the most significant factor regarding minorities being
stopped and searched? As mentioned earlier, prior research has routinely used
18


conflict theory as the theoretical framework guiding their respective inquiries
into the phenomenon of racial profiling. In prior research it was the authors
presumption that institutional policies structured law enforcement practices to
target minorities in search for criminal activities. However, the goal of prior
research was rarely to confirm or deny institutional policy as the cause of
racial profiling. Any such action would be a bold claim difficult to
substantiate regardless of statistical findings. Therefore, it is my presumption
that social cognition errors or misinformation, and not institutional policies,
are the causes for prejudice attitudes and the tendencies for police officers to
target minorities.
In this study I will test the following three hypotheses. (1) Minorities
are being overrepresented during traffic stop enforcement, (2) minorities are
being searched more often than Whites, and (3) the environmental context, or
the location of the traffic stop, will have a significant influence regarding the
first two hypotheses. The first two hypotheses serve to answer the two
questions posed in the introduction of this paper [(1) does police racial
profiling exist and if so, (2) how prevalent is this behavior in police practices],
I believe by investigating traffic enforcement, a minor law infraction that
encompasses nearly every individual, the prevalence of racial profiling in
police practices can be determined. The third hypothesis serves to determine
whether the environment of the traffic stop location is of any significance
19


regarding possible actions taken by the police officers during the traffic stop
encounter. In simpler terms, if a minority individual is treated the same, good
or bad, searched or not searched, in a poor or an affluent neighborhood, then
the environmental context of the traffic stop encounter does not have any
significance regarding the outcome. It is my belief that the contrary is true,
that the environmental context of the traffic stop encounter will have a
significant influence regarding the outcome of the traffic stop.
20


CHAPTER 3
DATA AND METHODS
The primary source of data used in this study was the 1999 Police-Public
Contact Survey (PPCS). The PPCS was a Bureau of Justice Statistics (BJS)
sponsored national survey of citizens that was designed to examine the
publics interactions with police. A pilot test was administered in 1996 and a
revised version of the survey was administered to die public as a supplemental
questionnaire to the National Crime Victimization Survey (NCVS) during the
second half of 1999 (U.S. Department, of Justice, 2001). As a supplement to
the NCVS, the PPCS was a complex survey design that utilized a multi-stage
cluster sampling method (U.S. Department, of Justice, 2001). Specifically,
the NCVS sample included 94,717 respondents aged 16 or older (U.S.
Department, of Justice, 2001). Of these respondents, 85% (80,543) completed
questions that were included in the PPCS (U.S. Department, of Justice, 2001).
The 85% response rate from the PPCS was comparable to the overall response
rate of 89% for the NCVS (U.S. Department, of Justice, 2001). The overall
sample size for the PPCS was 15,786 respondents, a sample that weights to a
national estimate of 209,350,600 persons aged 16 or older (U.S. Department,
of Justice, 2001). The national estimate of209,350,600 people is very
comparable to the Census Bureau projection of210,604,000 at the time of the
21


survey completion (U S. Department, of Justice, 2001). The specific focus in
this study was on the questions which are intended to determine traffic stop
information, race/ethnicity and other demographic information of the
individual. Multivariate analysis was used to determine the significance of the
variables utilized for the stop variable, while logistical regression was used for
the search variable.
Dependent Variables
There are two dependent variables in this study. For the first dependent
variable, two like variables were manipulated to create one continuous
variable that encompasses the total number of vehicle stops. The first variable
used asked if the vehicle was stopped by the police (responses were 1 Once;
2 - More than once; and 3 Not at all). The second variable gave detailed
information regarding category 2 (stopped more than once) of the first
variable. The second variable asked for the number of vehicle stops
(responses for the second variable were 0 Missing; 2 Two times; 3 Three
times; 4 Four times; up to 41 Forty one times). The newly computed
variable, STOPN, gave a more complete picture of the number of vehicle
stops (response categories were 0 Not stopped; 1 One Time; 2-Two times;
3 - Three times; 4 Four times; 5 Five times; and 6 Stopped by police more
than five times). For the descriptive statistics, the newly converted variable
22


was then converted into a separate dichotomous variable, 0 Not stopped and
1 Stopped, for statistical purposes.
The second dependent variable asked the respondent if they were
searched, both person and automobile, during their traffic stop. Originally the
search variable was coded as 1 Yes and 2 No. However, due to the use of
logistical regression for the search variable, SEARCH was recoded into 0 -
Not Searched and 1 Searched.
Independent Variables
A number of independent variables were used to predict each of the two
outcome (dependent) variables. These variables were drawn from the results
emerging from prior citizen perception and racial profiling studies. The
independent variables are listed below.
Race
Race is the most crucial variable of any racial profiling study. It helps
identify the racial background of those who are stopped for traffic violations
and for those who are being searched. Originally race was broken down into
four categories. 1 White; 2 Black; 3 Other; and 4 Hispanic. However,
in order to categorize race into a variable that could more accurately measure
what a police officer would be able to discern from a patrolling situation, race
was recoded into two distinct categories: all racial categories other than White
23


were recoded 0 Non-White, while the White racial category was recoded 1 -
White.
Age
The variable of age is an important component in identifying which age
group is being stopped and searched more often. Age was a continuous
variable ranging from youngest possible driving age 16, to as high as 90.
Sex
The variable of sex is important because of prior research indicating a
discrepancy in how often each gender is being stopped for traffic violations
and being searched. The sex of the respondent was recoded from 1 Male
and 2 Female into 0 Female and 1 Male for statistical purposes.
Income
The variable income was used in hopes of obtaining an indication of the
social class, and hence the type of vehicle, of the respondents who were
stopped for a traffic violation. Incorporating income as a social class indicator
was the only feasible alternative because the data set did not obtain the year
and make of the vehicle stopped, which could have a significant influence on
who gets stopped and who gets searched. The yearly income of the
respondent was coded into three categories: 1 Less than $20,000; 2 -
$20,000-49,999; 3 $50,000 or more.
24


Employment
The variable employment, like income, serves as indicator of wealth. In
addition to income, the employment status of the respondent aids in
identifying the possible social class of the respondent and the type and make
of the vehicle the respondent may drive. As mentioned above, the appearance
and type of vehicle may influence a police officers decision to stop and/or
search. The employment variable asked if the respondent worked last week,
and responses were coded 1 Yes and 2 No.
Population Size (POPSIZE)
The location of the traffic stop and/or search was not recorded in this data
set so identifying the environmental context, or the social class of the
neighborhood where the stop and/or search occurred, makes it difficult to
measure the amount of influence the environmental context has on the
propensity of being stopped and/or searched. Therefore, the variable
population size is used as a crude indicator of the environmental context of
where the traffic stop and/or search occurred based on where the respondent
was living at the time of the survey. The population of the location where the
traffic stop occurred was categorized into four categories: 1 Under
100,000/not a place; 2 100,000-499,999; 3 500,000-999,999; and 4 1
million or more.
25


Data Limitations
Limitations of this study rest in the use of secondary data analysis. The
analyses that follow are limited because of the structure and measures
included in the data. Clearly the inability to determine the structure in which
questions could be asked and the composition of such questions reduces the
validity of the measures. Additionally, the community measures are only
crude indicators of the types of areas from which the respondents may reside.
Therefore, a comprehensive indicator of neighborhood characteristics is
lacking and difficult to deduct from the given variables. Finally, the reliability
and validity of self-report information possibly places a limitation in the
results if there are systematic biases in responses. For example, different
types of citizens may have differential reporting patterns on some variables
and cause inconsistencies in report data.
Statistical Procedures
Multiple regression was used for the decision to stop model. The recoding
of two stop variables into one continuous variable in this study allowed the
use of multiple regression. One benefit of using multiple regression is its
ability to account for all the variables being considered in a correlation matrix,
in essence accounting for any intercorrelations among the set of independent
or predictor variables, which can affect the results.
26


Unlike the decision to stop model, logistic regression was used for the
decision to search model because the search variable was dichotomous, and it
requires special treatment of the dependent variable and corresponding
analyzing algorism. Logistic regression, like multiple regression, is useful to
predict an outcome or dependent variable from a set of predictor variables.
Finally, for each dependent variable, three separate models were utilized
to test the incremental effects of the predictor variables and the relative
strength and relationships of the independent variables on the dependent
variables. The initial model for each variable contained four independent
variables and each successive model added one independent variable to the
respective model. Additionally, this approach helps identify the amount of
influence the newly added variable has on the dependent variable as well as
on the entire model fit.
27


CHAPTER 4
RESULTS
The following analyses respond to the key issues regarding the racial
profiling and police practices discussion. Most importantly, these analyses are
designed to determine the significance racial background has on the likelihood
that a minority individual will be stopped and searched compared to his/her
White counterpart. The analyses feature a series of multivariate regression
models. These models are presented to identify the factors affecting the race
or ethnicity of drivers stopped and those drivers who are searched. Each of
these separate analyses pays particular attention to the influence racial
characteristics, among others, has over the decision to be stopped or the
decision to be searched.
General Descriptives
The total size of the sample that was used for this study was 15,786 (Table
1). There were 8,186 respondents who were stopped at least once in the year
prior to the survey and 429 searched. The gender breakdown of the sample
measured at 49.7 percent female and 50.3 percent male.
28


TABLE 1: General Descriptives of Total Sample
GENERAL
POPULATION
STOPPED
Stopped
Not stopped
SEARCHED
Searched
Not searched
AGE
Avg. age (mean)
SEX
Female
Male
RACE
Non-White
White
POPSIZE
Less than 100,000
100.000- 499,999
500.000- 999,999
1 Million or more
EMPLOYMENT
Yes
No
INCOME
Less than $20,000
$20,000-49,999
FREQ %
8,186 51.9%
7,600 48.1%
429 6.2%
6,541 93.8%
40.5
7,839 49.7%
7,947 50.3%
3230 20.5%
12,556 79.5%
12,000 76.0%
2,271 14.4%
646 4.1%
869 5.5%
11,704 74.1%
3,960 25.1%
4,694 29.7%
5,258 33.3%
5,834 37.0%
$50,000 or more
Notes: N=15,786
29


Whites comprised 79.5 percent of the sample, with non-Whites comprising
20.5 percent, respectively. The average age of the sample was 40.5 years old.
Thirty-seven percent of the respondents made $50,000 or more, with 33.3
percent making between $20,000-49,999, and 29.7 percent making less than
$20,000. In terms of employment, 74.1 percent worked in the week preceding
the survey, with 25.1 percent not working (there was .8 percent missing).
Finally, 76 percent of the respondents lived in a location under 100,000, with
14.4 living in a location between 100,000-499,999,4.1 percent between
500,000-999,999, and 5.5 percent living in a location of one million or more.
Characteristics of Drivers Stopped/Not Stopped
Table 2 indicates the average age of all the drivers stopped was 37 years
old, while the average age was 42 years old for all the drivers not stopped. Of
all the males in the study, 59.5 percent had been stopped at least once, with
46.4 percent of all women being stopped. Approximately 56 percent of all
non-Whites had been stopped at least once, while approximately 50 percent of
all Whites had been stopped at least once. In terms of population size, 53.1
percent of all those who lived in a location with a population under 100,000
people were stopped, with 49.4 percent for populations between 100,GOO-
499,999, 44.3 percent for populations between 500,000-999,999, and 47.5
percent for populations of 1 million or more had been stopped.
30


TABLE 2: Characteristics of Drivers Stopped/Not Stopped
STOPPED N< STOl DT PED
FREQ % FREQ %
AGE
Avg. age (mean) 37.0 42.2
SEX
Female 3,639 46.4% 4,200 53.6%
Male 4,547 59.5% 3,400 40.5%
RACE
Non-White 1,812 56.1% 1,418 43.9%
White 6,374 50.1% 6,182 49.9%
POPSIZE
Less than 100,000 6,366 53.1% 5,634 46.9%
100,000-499,999 1,121 49.4% 1,150 50.6%
500,000-999,999 286 44.3% 360 55.7%
1 Million or more 413 47.5% 456 52.5%
EMPLOYMENT
Yes 5,321 45.5% 6,383 54.5%
No 2,235 56.4% 1,725 43.6%
INCOME
Less than $20,000 2,250 47.9% 2,444 52.1%
$20,000-49,999 2,784 52.9% 2,474 47.1%
$50,000 or more 3,152 54.0% 2,682 46.0%
Notes: N=15,786 (Stopped=8186 and Not Stoppet =7600)
All percentages in table were calculated percentage across
31


In regards to employment, 45.5 percent of those who worked had been
stopped at least once, while the percentage was 56.4 for those who did not
work. Finally, for those respondents who made less than $20,000,45.3
percent were stopped at least once. For those respondents who made between
$20,000-49,999 and $50,000 or more, 52.9 and 54 percent had been stopped at
least once in the previous year, respectively.
The Decision to Stop
Looking at the factors that possibly influence a police officers decision to
stop sheds light on some clear relationships. Of the three models used in this
analysis, Model 3 has the all variables included in its analysis and accounts for
the most variability in the decision to stop. By looking at Table 3 it is evident
the most influential factor or variable in the decision to stop was age. Sex was
the second most influential factor, followed by population size, race, and
finally employment status. The variables age, sex, race, and population size
were all significant at the a = .001 levels. The variable employment was also
found to be significant, but at the a = .05 level, while the income variable was
not found to be significant.
32


TABLE 3: Multivariate Regression of Decision to Stop
VARIABLES MODEL I MODEL 2 MODEL 3
B Beta B Beta B Beta
AGE - 011*** -.192*** _ Oio*** -.186*** -010*** -.186***
SEX .201*** .117*** 197*** .115*** 197*** 114***
RACE -.073*** -.034*** -.077*** -.036*** -.080*** -.038***
POPSIZE - 045*** _042*** -.045*** -.043*** -.045*** -.042***
EMPLOYMENT -.041* -.021* -.037* -.019*
INCOME .011 .010
CONSTANT 1.114 1.157 1.133
F-test 227.748*** 182.413*** 152.277***
df=4 df=5 df=6
R2 .055 .055 .055
Notes: N=15,786 (Stopped=8,186 and Not stopped=7,600)
*: p < .05; **p < .01; ***p < .001
33


Characteristics of Drivers Searched/Not Searched
Table 4 indicates the average age was 30 years old for all the drivers
searched and 38 years old for those drivers not searched. While only 2.3
percent of all females were searched, 8.9 percent of all males were searched.
This indicates that men are being searched at a rate that is four times higher
than women. The percentage of all White drivers searched was 5.2 percent,
however, the rate for non-White drivers searched was nearly double that of
Whites, at 9.2 percent. In regards to population size, 5.5 percent of those
drivers who lived in a population under 100,000 were searched. The
percentage rate for those in all the other population categories were 8.8, 8.3,
and 8.1 percent, respectively. In terms of employment, 6 percent of all those
who worked had been searched. For those drivers who did not work, 6.8
percent were searched, indicating only a minimal difference between those
who worked and those who did not. Finally, 9.4 percent of those drivers who
made less than $20,000 were searched, while those drivers who made $50,000
or more were nearly three times less likely to be searched, at 3.7 percent. For
those respondents who made between $20,000-49,999, 6.6 percent were
searched in the previous year.
34


TABLE 4: Characteristics of Drivers Searched
SEARCHED NOT SEARCHED
FREQ % FREQ %
AGE
Avg. age (mean) 29.8 37.9
SEX
Female 66 2.3% 2,847 97.7%
Male 363 8.9% 3,694 91.1%
RACE
Non-White 146 9.4% 1,387 90.6%
White 283 5.2% 5,154 94.8%
POPSIZE
Less than 100,000 296 5.5% 5,126 94.5%
100,000-499,999 84 8.8% 866 91.2%
500,000-999,999 20 8.3% 220 91.7%
1 Million or more 29 8.1% 329 91.9%
EMPLOYMENT
Yes 334 6.0% 5,224 94.0%
No 92 6.8% 1,253 93.2%
INCOME
Less than $20,000 175 9.4% 1,691 90.6%
$20,000-49,999 158 6.6% 2,234 93.4%
$50,000 or more 96 3.7% 2,616 96.3%
Notes: N=6,970 (Searched=429 and Not Searched=6541)
All percentages in table were calculated percentage across


The Decision to Search
The factors that possibly influence a police officers decision to search
indicate an overall similar relationship to being stopped. Like the stop
variable, there were three models used in this analysis. The third model in
Table 5, Model 3, has the most variables included in its analysis and accounts
for the most variability in the decision to search. By looking at the third
model in Table 5, and unlike in the decision to stop, the most influential factor
in the decision to search was sex. Employment status, which was fifth in the
decision to stop model, was second in model 3. Interestingly, the income
variable, which was not found to be significant in the decision to stop model,
was significant in this model and was the third most influential factor. Race
was the fourth most influential variable, similar to the decision to stop model.
Age, which was the most influential factor for the stop variable, is the fifth
most influential factor for the decision to search. The variables sex, age, race,
and employment were all significant at the a = .001 levels, while income was
found to be significant at the a = .05 level. Interestingly, population size,
which was found to be significant in the decision to stop, was not found
significant in any decision to search analysis.
36


TABLE 5: Logistical Regression of Decision to Search
VARIABLES MODEL 1 MODEL2 MODEL 3
B Exp(B) B Exp(B) B Exp(B)
AGE - 049*** 952*** _ 048*** .953*** -.046*** .955***
SEX 1.438*** 4.210*** 1.471*** 4.352*** 1 47g*** 4.383***
RACE -.520*** 594*** -.500*** .607*** - 409*** .664***
POPSIZE .105 1.110 111 1.117 .082 1.086
EMPLOYMENT .423*** 1.526*** .295* 1.343*
INCOME -.418*** .659***
CONSTANT -1.886*** .152*** -2.459*** .085*** -1.585*** 205***
X2-test 323.361*** df=4 330.3 df DO*** =5 369.878*** df=6
R2 (Nagelkerke) .122 .126 .141
R2 (Cox & Snell) .045 .047 .052
Notes: N=6,970 (Searched=429 and Not Searched=6541)
*: p < .05; **p < .01; ***p < .001
37


CHAPTER 5
CONCLUSIONS
Initially we will discuss the descriptives of the study, followed by a more
in-depth analysis of the regression models and their findings. By looking at
all the descriptives and comparing the three groups (the general sample, the
stopped/not stopped sample, and the searched/not searched sample), a general
picture can be made regarding who is getting stopped and who is being
searched. Then by interpreting the findings from the regression models we
can identify any relationships and discuss their relevance.
The average age of the sample was 40.5 years, yet the average age
decreased to 37 years for those stopped and decreased considerably more, to
29.8 years, for those who were searched. Comparing the average age of those
drivers searched, 29.8 years, to the average age of those not stopped, 42.2
years, the difference of 12.4 years indicated that younger drivers are more
likely to be searched and stopped. On the surface this finding appears to be
consistent with prior research (Schafer, Carter, and Katz-Bannister, 2004;
Lundman and Kaufman, 2003; and Engel and Calnon, 2004),
In terms of gender, females and males were approximately equally
represented in the sample, yet 59.5 percent of all the males in the sample were
stopped, as opposed to 46,4 percent of all females, a difference of 13.1
38


percent. In regards to being searched, 2.3 percent of all the females in the
sample were searched, yet for males the percentage is 8.9, a nearly four-fold
increase when compared to the females in the study. The targeting of males
for searches appeared to indicate the greatest discrepancy of all the variables
in the study.
In regards to race, the general breakdown was 79.5 percent White and 20.5
percent non-White. The Whites in the study were proportionally represented
with approximately 50 percent stopped and 50 percent not stopped. However,
56.1 percent of all non-Whites were stopped, indicating a discrepancy in
traffic stop practices. For those individuals searched, 5.2 percent of the all the
Whites were searched, but 9.4 percent of all the non-Whites were searched,
indicating that non-Whites were nearly twice as likely to be searched as
Whites. Finally, and much like the gender variable mentioned above, the race
of the individual appeared to play a significant role regarding who gets
stopped and/or searched. These findings were consistent with the work of
Schafer, Carter, and Katz-Bannister (2004); Withrow (2004); Lundman and
Kaufman (2003); and Engel and Calnon (2004).
The income variable also signified some interesting findings. Thirty-
seven percent of the overall sample indicated making more than $50,000, yet
only 3.7 percent of this group was searched by police. Conversely, 9 .4
percent of those respondents making less than $20,000 were searched; nearly
39


three times the amount of the $50,000 or more group. Clearly the searching of
individuals from the lower social strata was evident in this sample. In regards
to the employment variable, 56.4 percent of those individuals who did not
work were stopped, but only 45.5 percent of those individuals who did work
were stopped, which indicated a difference of 10.9 percent. The population
size variable appeared to only have slight fluctuations between the overall
sample, the stopped/not stopped sample, and the searched/not searched
sample.
The regression analysis for the decision to stop model yielded some
appealing findings regarding the influence of driver characteristics. The most
interesting of these findings was the amount of influence race has on being
stopped. In comparison to the other variables and the amount of influence
they have on the decision to stop, the race of the individual held minimal
influence on being stopped. In Model 1 for the stop variable, race was the
least influential factor of the four, behind age, sex, and population size. In
Model 2, race was the forth out of five factors, only ahead of employment. In
the last model, Model 3, race was slightly behind population size for the third
most influential factor, behind age, sex, and population size but ahead of
employment and income (income was not found to be significant). The
results clearly indicated that race is not the most influential factor in the
decision to stop model and in fact is slightly less significant than the
40


population size of where the respondent lived, giving some credence to the
environmental context of the location of the traffic stop. The population
variable indicated a negative relationship, which meant that the smaller the
population the more likely one would be stopped. Unfortunately, the wealth
of the location could not be obtained in this data set but the relevance of the
variable on the model cannot be ignored. The employment variable indicated
that the more likely an individual worked the more likely they would be
stopped. The result however, could stem from the need of those who are
employed to drive to and from work, which would increase the amount of
driving these individuals engage in and increase their probability of being
stopped. The age variable indicated that younger individuals were more likely
to stopped than older individuals and the sex variable specified that male
individuals as more likely to be stopped compared to women. Results such as
these argue for a more complete analysis of other variables in addition to the
racial characteristics most commonly analyzed in racial profiling research.
One could speculate from this model that young male drivers, regardless of
race, engage in more risky driving behaviors than other groups and are more
prone to police action. While this speculation may appear logical, the results
from this study neither support nor discredit this assumption.
The variables used in the three decision to stop models maintained their
approximate strength relative to how influential each variable was on the
41


dependent variable of being stopped by police. For the decision to stop
model, the results indicated that if you are, in appropriate order, young, male,
live in a smaller population, non-White, and have a job, you are more likely to
be stopped when compared to all other possible groups. This finding held
constant for all three models used and represents findings that are supported
by Schafer, Carter, and Katz-Bannister (2004); Lundman and Kaufman
(2003) ; and Engel and Calnon (2004), but differ from the work of Withrow
(2004) ; Rojek, Rosenfeld, and Decker (2004); Petrocelli, Piquero, and Smith
(2003), which did not focus on the influence of age or gender on racial
profiling.
In general, the regression analysis for the decision to search model
indicated some similar findings to the decision to stop model but also some
differences. Age, sex, and race all showed similar relationships to the
decision to search model as to the decision to stop model, meaning younger
minority males were most at risk of being searched when stopped. In Model
3, the number one predictor in the model was sex, followed by employment
status, income, race, and finally age (population size had no significant effect
on the decision to search). Interestingly, age, which was the most influential
factor in the decision to stop, is the least influential factor in the decision to
search. While this result appears contradictory to the age descriptives of this
study and to the prior research of Schafer, Carter, and Katz-Bannister (2004),
42


which indicated younger (minority) people most at risk of being searched, the
results from the information used in this study indicated the sex, employment
status, income level, and racial category of the respondent all have a more
significant role in determining who is at risk of being searched. Other
differences in the model lie in the relationships some of the variables had on
the dependent variable. In the decision to stop model, population size and
employment had negative relationships to being stopped; meaning the more
likely an individual was in a smaller population and was working, the more
likely the individual would be stopped. However, this relationship was
reversed for the decision to search model, indicating the more likely an
individual was not working the more likely the individual would be searched,
while the population variable was not found to be significant. For the income
variable, which was not found to be significant in the decision to stop model,
indicated the less income one made the more likely he/or would be searched.
While it would be impossible for a police officer to obtain the actual amount
of income the person they search generates, it is logical to assume that those
individuals who are unemployed and are from the lower social strata have a
certain appearance of possessing lesser financial resources to the patrolling
officer. This appearance may be all the initiative police officers need to
search the individual because poor people are commonly believed to be
engaging in more crime (Weitzer and Tuch, 2005). In regards to race, similar
43


to the decision to stop model, was not the most influential factor in being
searched, ranking no higher than fourth in any of the decision to search
models.
Like the decision to stop model, all three models in the decision to search
model maintained their approximate strength relative to how influential each
variable was on the dependent variable of being searched by police. The
results for the decision to search model signified that if you are a male, who is
not employed, have less income, non-White, and young, you are more likely
to be searched when compared to all other possible groups. These findings
are at odds with the work of Schafer, Carter, and Katz-Bannister (2004);
Withrow (2004); Rojek, Rosenfeld, and Decker (2004), who make little or no
reference to driver characteristics other than race, but support the prior work
of Petrocelli, Piquero, and Smith (2003) and Engel and Calnon (2004), who
maintained that other factors in addition to race influence the decision to
search.
As illustrated above, clearly race is a significant factor in being
stopped and/or being searched. What is also clear is that race is not the most
influential factor in either model. In both instances, age for the decision to
stop and gender for the decision to search, each have a greater influence on a
police officers decision making processes regarding who to stop and to
search. Additionally, the results exemplified in this paper may help identify
44


other possible factors influencing a police officers decision to stop and/or
search that are worth investigating. Unmistakably a clearer picture needs to
be painted before grand proclamations can be made claiming race as the sole
factor in law enforcement discrimination.
Initially on the surface race appeared to play the most significant role
in police discrimination, not only in this study but also in the prior research
discussed earlier. While the results from this study dictate otherwise, other
instances in law enforcement and criminal justice bare similar characteristics.
For example, and after discussing the apparent prevalence of racial profiling
regarding the minor offense of a traffic law violation, we can only assume this
police practice is carried into other criminal offenses. Investigating the prison
population and its characteristics would identify if the practice of targeting
minorities for other criminal offenses does indeed occur. The following
provides just a brief glimpse into the demographics of the United States prison
population.
The United States prison population at the end of 2004 was 2,135,901
inmates (Bureau of Justice Statistics (BJS), www.ojp.usdoj.gov/bjs).
Although the prison population increased by 2.6 percent over the previous
year, down from just over 3 percent from 1990-2000, the overall prison
increase indicated another year in which the prison population grew in number
(BJS, www.ojp.usdoj .gov/bjs). In general, more than 60 percent of all the
45


prison inmates in the United States have racial minority backgrounds (BJS,
www.ojp.usdoj.gov/bjs). Specifically, there are 3,218 per every 100,000
African Americans in prison, or 3.2 percent of all African Americans in the
U S. population. In regards to the other major racial categories, there are
1,220 per every 100,000 Hispanics, or 1.2 percent, and 463 per every 100,000
Whites, or 0.4 percent in prison (BJS, www.ojp.usdoj.gov/bjs). This
information on the prison population indicates that African Americans are 8
times more likely to be incarcerated than Whites, and Hispanics are 3 times
more likely to be incarcerated than Whites (BJS, www.ojp.usdoj.gov/bjs).
This type of disparity raises the red flag regarding law enforcement agencies
and their policing practices in that they appear to be unfairly targeting and
subjecting minorities to the criminal justice system much more often than
Whites. However, as seen by the results of this study, race single-handedly
cannot be the lone suspect in identifying the causes for the racial disparity in
traffic enforcement or our prison populations.
Whether on the surface or in-depth, the results from this study may
indicate the attribution of cognitive errors to the judgments or schemas of the
police officers who stopped and/or searched the respondents from this sample.
The discrepancies in the stop and search rates, as well as the influence of
driver characteristics for the decision to stop and/or search, indicated the
schemas of police officers are constructed to actively target younger minority
46


men when searching for crime. What perhaps is more troubling is that many
police officers maintained these schemas upon entrance to their respective law
enforcement agencies, since it is highly unlikely the institution accounted for
all the officers information regarding minorities during training, indicating
that these particular schemas may be more widespread than just to the
individuals of law enforcement agencies. It may prove difficult to discern the
origin of this apparent stereotyping by police officers, be it personal
experience or institutional teaching, the development of inaccurate schemas
apparently may contribute to the problem of racial profiling. Whereas the
results from this study lay claim to other more influential factors, there is no
doubt minorities are bearing the blunt of this cognitive miscalculation.
Finally, returning to the three hypotheses stated earlier, the first two
hypotheses, (1) Minorities are being overrepresented during traffic stop
enforcement and (2) minorities are being searched more often than Whites,
were proven to be true. The third hypothesis, (3) the environmental context,
or the location of the traffic stop, will have a significant influence regarding
the first two hypotheses, was proven to be partially true. While the variable
population size was found to be significant for the decision to stop (hypothesis
1), its influence on the decision to stop was minimal at best. Population size
was not found to be significant for the decision to search (hypothesis 2).
While this finding does not support the original hypothesis, it may indicate a
47


measurement issue associated with the concept of location. Population size
was at best, a crude indicator of the location where the traffic stop occurred
based on where the respondent lived, and yet some significant relationship
was found. A more complete traffic stop location variable could yield fruitful
findings in the realm of racial profiling. Isolating this variable, I believe, can
bring more light on the significance of race, since minorities dominate the
lower classes, and could help discover if patrolling a poorer neighborhood
increases a police officers tendency to target minorities. Discovering
information such as this could help to better explain what is happening on our
streets.
Future Research
Future research on racial profiling should focus on designing surveys that
provide wider coverage and a better measurement of the issues regarding the
traffic stop encounter. Rarely has systematically obtained data provided
sufficient information such as the reason for the stop, or perhaps more
importantly, the demographic information regarding who is not being stopped.
While research on traffic stops has routinely provided the sex, age, and race of
those drivers stopped, rarely is the environmental context of where the stop
occurred recorded. A true and reliable measure of the environmental context
may be difficult to obtain but its relevance is of utmost importance to
determining the prevalence of racial profiling. By knowing this information
48


researchers could state with more confidence why certain groups are being
targeted by law enforcement agencies, which would be more beneficial to
social science and society as a whole.
Future research should also aim to bring social cognition and conflict
theory to together in a joint effort to help perpetuate additional thought and
research ideas. Together, social cognition and conflict theory can provide a
more complete picture of this social phenomenon and help our understanding
of how to combat the issue. Furthermore, the bridging of these two theories
can also initiate a more complete dialogue in developing mechanisms to help
alleviate the targeting of minorities by law enforcement agencies.
Finally, fiiture research should also focus more on the age and gender
variables because, as seen in this research, their significance on being stopped
and searched cannot be ignored. Additionally, determining any interaction
effects regarding men and women should also be addressed. Future
researchers need to focus on these aspects and determine its relevance in
regards to an individual being profiled. Future enlightenments such as these
may lead to racial profiling being converted into age profiling, or gender
profiling, to better suit the information obtained through research. Perhaps
racial profiling, in regards to law enforcement, may not be a label that
accurately portrays what is happening on our streets, highways, and prisons.
49


References:
Bureau of Justice Statistics. (2005). U.S. Department of Justice-Office of
Justice Programs. Retrieved November 15, 2005, from the Bureau of
Justice Statistics website: www.ojp.usdoj.gov/bjs/prisons.htm
Coker, Donna. (2003). Foreword: Addressing the Real World of Racial
Injustice in the Criminal Justice System. Journal of Criminal Law &
Criminology, (93) 4, p827-879, 53p
DAlessio, Steward J. and Stolzenberg. (2003). Race and the Probability of
Arrest. Social Forces, (81) 4, pl381-1397, 16p
Engel, Robin Shepard and Calnon, Jennifer M. (2004). Examining the
Influence of Drivers Characteristics During Traffic Stops With Police:
Results From a National Survey. Justice Quarterly, (21) 1, p49-90, 41p
Gabor, Thomas. (2004). Inflammatory Rhetoric on Racial Profiling Can
Undermine Police Services. Canadian Journal of Criminology &
Criminal Justice, (46) 4, p457-466, lOp
Gold, Alan D. (2003). Media Hype, Racial Profiling, and Good Science.
Canadian Journal of Criminology & Criminal Justice, (45) 3, p391-399,
9p
Howard, Judith A. & Renfrow, Daniel G. (2003). Handbook of Social
Psychology, edited by John Delamater. New York: Kluwer
Academic/Plenum Publishers. 22p, 259-281
Leung, Ambrose; Woolley, Frances; Tremblay, Richard E., and Vitaro, Frank.
(2005). Who Gets Caught? Statistical Discrimination in Law
Enforcement. Journal of Socio-Economics, (34) 3, p289-309, 21p
Lundman, Richard J. and Kaufman, Robert L. (2003). Driving While Black:
Effect of Race, Ethnicity, and Gender on Citizen Self-Reports of Traffic
Stops and Police Actions. Criminology, (41) 1, pl95-221,26p
50


Melchers, Ron. (2003). Do Toronto Police Engage in Racial Profiling?
Canadian Journal of Criminology & Criminal Justice, (45) 3, p347-366,
20p
Petrocelli, Matthew; Piquero, Alex R., and Smith, Michael R. (2003).
Conflict Theory and Racial Profiling: An Empirical Analysis of Police
Traffic Stop Data. Journal of Criminal Justice, (31) 1, pi, 1 Ip
Reitzel, John D.; Rice, Stephen K., and Piquero, Alex R. (2004). Lines and
Shadows: Perceptions of Racial Profiling and the Hispanic Experience.
Journal of Criminal Justice, (32) 6, p607-616, lOp
Roberts, Julian. (2003). Introduction: Commentaries on Policing in Toronto.
Canadian Journal of Criminology & Criminal Justice, (45) 3, p343-346,
5p
Rojek, Jeff; Rosenfeld, Richard; and Decker, Scott. (2004). The Influence of
Drivers Race on Traffic Stops in Missouri. Police Quarterly, (7/1, pi 26-
147, 21p
Schafer, Joseph A. and Mastrofski, Stephen D. (2005). Police Leniency in
Traffic Enforcement Encounters: Exploratory Findings From Observations
and Interviews. Journal of Criminal Justice, (33) 3, p225-238, 14p
Schafer, Joseph A.; Carter, David L., and Katz-Bannister, Andra. (2004).
Studying Traffic Stop Encounters. Journal of Criminal Justice, (32) 2,
pi59, 12p
Tyler, Tom R. and Wakslak, Cheryl J. (2004). Profiling and Police
Legitimacy: Procedural Justice, Attributions of Motive, and Acceptance of
Police Authority. Criminology, (42) 2, p253-281,29p
U S Department of Justice, Bureau of Justice Statistics. (2001). POLICE-
PUBLIC CONTACT SURVEY, 1999: [UNITED STATES] [Computer
file], 2nd ICPSR version. Washington, DC: US. Department of Justice,
Bureau of Justice Statistics [ producer], 1999. Ann Arbor, MI: Inter-
university Consortium for Political and Social Research [distributor],
Weitzer, Ronald and Tuch, Steven A. (2005). Racially Biased Policing:
Determinants of Citizen Perceptions. Social Forces, (83) 3, pi 009-1030,
22p
51


Weitzer, Ronald and Tuch, Steven A. (2002). Perceptions of Racial
Profiling: Race, Class, and Personal Experience. Criminology, (40) 2,
p435, 22p
Wilson, George; Dunham, Roger, and Alpert, Geoffrey. (2004). Prejudice in
Police Profiling: Assessing an Overlooked Aspect in Prior Research.
American Behavioral Scientist, (47) 7, p896-909, 14p
Withrow, Brian L. (2004). Driving While Different: A Potential Theoretical
Explanation for Race-Based Policing. Criminal Justice Policy Review,
(15) 3, p344-364,21p
Withrow, Brian L. (2004). Race-Based Policing. A Descriptive Analysis of
the Wichita Stop Study. Police Practice & Research, (5) 3, p223-240, 18p
Wortley, Scot and Tanner, Julian. (2005). Inflammatory Rhetoric? Baseless
Accusations? A Response to Gabors Critique of Racial Profiling
Research in Canada. Canadian Journal of Criminology & Criminal
Justice, (47) 3, p581-609, 29p
Wortley, Scot and Tanner, Julian. (2003). Data, Denials, and Confusion: The
Racial Profiling Debate in Toronto. Canadian Journal of Criminology &
Criminal Justice, (45) 3, p367-389, 23p
52