EFFECT OF SUSPECT RACE ON OFFICERS

EFFECT OF SUSPECT RACE ON OFFICERS’
ARREST DECISIONS∗
TAMMY RINEHART KOCHEL
Department of Criminology and Criminal Justice
Southern Illinois University
DAVID B.WILSON
STEPHEN D. MASTROFSKI
Department of Criminology, Law and Society
George Mason University
KEYWORDS: race, arrest decisions, meta-analysis, police discretion,
biased policing
Many respondents to opinion surveys say that the citizen’s race influences
how police officers treat the public, yet recent expert socialscience
panels have declared that research findings are too contradictory
to form a conclusion on whether American police are biased against
racial minorities. We perform a meta-analysis of quantitative research
that estimates the effect of race on the police decision to arrest. Screening
nearly 4,500 potential sources, we analyze the results based on 27 independent
data sets that generated 40 research reports (both published and
unpublished) that permitted an estimate of the effect size of the suspect’s
race on the probability of arrest. The meta-analysis shows with strong
consistency that minority suspects are more likely to be arrested than
White suspects. Depending on the method of estimation, the effect size
of race varied between 1.32 and 1.52. Converting the race effect size to
probabilities shows that compared with the average probability in these
studies of a White being arrested (.20), the average probability for a
non-White was calculated at .26. The significant race effect persists when
taking into account the studies’ variations in research methods and the
nature of explanatory models used in the studies. Implications for future
research are presented.
∗ Direct correspondence to Tammy Rinehart Kochel, Department of Criminology
and Criminal Justice, Southern Illinois University, Carbondale, IL 62901
(e-mail: tkochel@siu.edu).
C  2011 American Society of Criminology doi: 10.1111/j.1745-9125.2011.00230.x
CRIMINOLOGY Volume 49 Number 2 2011 473
474 KOCHEL, WILSON & MASTROFSKI
Making an arrest is a relatively rare event in the daily work of the
average police officer (Bittner, 1970; Black, 1980; Brown, 1981; Klinger,
1996), but it is one of the most consequential ways that the State restricts
the liberty of the public. Are American police influenced by a suspect’s
race when deciding whether to make an arrest? Significant portions of the
public are inclined to believe that police discretion is influenced by race.
Public opinion surveys have shown that three fourths of the general public
regard racial profiling by police as a problem, and that low ratings of police
fairness are especially concentrated among Blacks, with only slightly more
than one third indicating that police in their community treat all races
fairly (Gallagher et al., 2001: 59–72). Even three in ten White respondents
declined to rate the police in their community as fair to all races (Gallagher
et al., 2001: 60). But when blue-ribbon academic panels convened in recent
years to review the research-based evidence, they judged the findings to
be so mixed that they have been unable to draw a definitive conclusion.
Examining research on a range of police practices that included arrest, the
National Research Council’s Committee to Review Research on Police Policy
and Practices concluded, “the evidence is mixed, ranging from findings
that indicate bias against racial minorities, findings of bias in favor of racial
minorities, and findings of no race effect” (Skogan and Frydl, 2004: 122–3).
A few years later, an American Sociological Association report produced
by a working group of 45 social scientists found conflicting results within the
existing research on racial bias in criminal justice processing and was unable
to draw firm conclusions (Rosich, 2007: 22). Both expert groups called for
continued research to improve understanding of whether, and under what
circumstances, police behave in a racially biased manner.
There is obvious tension between a significant public belief that policing
is influenced by the citizen’s race and the received academic wisdom that
the evidence is mixed. During the last 50 years, a body of quantitative
research on police practices, and especially the decision to arrest, has
emerged; yet expert reviews of that literature have led to results that are
deemed inconclusive, requiring more and better research. Our purpose is
to submit that body of research to a rigorous meta-analysis that will allow
a more conclusive interpretation, one that points the way toward a better
understanding of whether, when, and why race may influence police arrest
practices.
Although our analytic method removes us from the streets where police
officers practice their occupation, we begin with a consideration of the
event-by-event, suspect-by-suspect world where the arrest decision is made.
We then move to a brief discussion of the quantitative research literature assessing
the effects of race on arrest before presenting our ownmeta-analytic
assessment of the extant literature. Our study produces a clearer picture
than prior academic analyses have suggested. Focusing on a comparison of
EFFECT OF RACE ON ARREST DECISIONS 475
the treatment of Black and White suspects, we find a strong and strikingly
consistent pattern of race effects. In most studies that satisfy our criteria
for consideration, the suspect’s race does increase the probability of arrest,
taking into account other factors that have been shown to influence arrest
as well. This pattern of results is robust across a wide range of assumptions.
We submit the data on the studies to analyses that might account for the
variation that we do observe in the scope of race effects, and we offer
suggestions for improving future research.
ANALYZING THE DECISION TO ARREST
Although police detectives define success almost solely in terms of making
arrests, most arrests in America are made by the ordinary patrol officer
in the course of his or her everyday work on the streets, and the bulk of
these arrests are made without the use of a warrant, essentially a legal
mandate that at least in theory eliminates the discretion of the officer.
Therefore, it is impossible to observe on a case-by-case basis the true
scope of influence the suspect’s race may exert on the arrest decision. To
the extent that race is a conscious factor, nowadays we have many social
and legal disincentives for American police to manifest obvious indications
(e.g., uttering a racial epithet) that race influences their decisions: civil
lawsuits and penalties, internal discipline, and bad publicity. Similarly, they
are unlikely to reveal any such motivation in the official documents they
complete or even in confidential interviews. That is not to say that such
events seldom occur1 but only that their absence is no reliable indicator of
a lack of an effect of race on arrest. Race may not even be a conscious factor
in decision making, but it could still be a powerful one.
The recent, highly publicized arrest of Harvard Professor Henry Louis
Gates, Jr. for disorderly conduct is a good example of the difficulty of
determining the influence of race on an individual case basis (Cambridge
Review Committee, 2010). Professor Gates, a Black man, was questioned
by police when a neighbor phoned in a report of a possible break-in. In
fact, Professor Gates and a friend had been forcing open a stuck door in
Professor Gates’s own home. The police report indicated that in the course
of the questioning process, the professor acted inappropriately toward an
officer who had requested identification from the professor and had asked
him to step outside, and after warning him, the officer arrested him for
disorderly conduct. The professor claimed that he was treated inappropriately
and that it was because of his race. The difficulty with establishing the
1. See Kennedy (1997: 113–25) for several contemporary examples of where police
have exhibited strong indicators that racial prejudice influenced their actions
toward Blacks.
476 KOCHEL, WILSON & MASTROFSKI
veracity of this claim is that we have no obvious indicator that race played
a role in this situation, such as a racial epithet. Would the officers have
treated a White man in a similar fashion? The officer involved claims he
would have. As extensive social-psychological research has shown, race may
have affected the officer’s decision in subtle ways of which he was unaware,
but that hardly makes it easier to discern whether that was so in this case.
This case illustrates the difficulties in determining the existence of racism in
individual cases. However, if we could compare several similar situations,
some with Black suspects and some with White suspects, we could make an
evidence-based judgment about whether the department shows a pattern of
arresting Blacks more frequently than Whites under similar circumstances.
That is in fact what many social-science studies have tried to do, producing
an array of effects that social-science expert panels have found perplexing.
We take the next step and synthesize this array of findings with a metaanalysis.
Many studies observe what most of the public seems to believe—that
minorities are arrested at a higher rate than Whites—but demonstrating
this difference does not establish the extent to which the suspect’s race is
responsible for the observed differences. Researchers have appropriately
argued that many other factorsmay influence the arrest decision, and unless
their effects are taken into account, we cannot say with confidence just how
influential is the suspect’s race.
FACTORS THAT INFLUENCE THE ARREST DECISION
Researchers interested in assaying the effects of race on police decision
making often attempt to isolate the effects of race alone by controlling for
legal factors that justify an arrest and other legally irrelevant (extralegal)
considerations that might influence the decision (Skogan and Frydl, 2004:
115). Legal considerations are those set forth by law that define the circumstances
under which an arrest is allowable or required (Black, 1980) and
“strategic factors that bear upon the case’s prospects in subsequent legal
proceedings” (Mastrofski, Worden, and Snipes, 1995: 541). Among legal
considerations are the strength of the inculpatory evidence (does it meet
the requirements of probable cause?), the availability of a cooperating complainant
(willing to testify in court), the seriousness of the offense (more
serious offenses presumably being worthy of more severe punishment and
less likely to be dismissed by the prosecutor), the criminal record of the suspect
(the longer the record, the greater the need for the consequences that
arrest can produce), and mandatory arrest policies for particular offenses
(e.g., domestic violence). Extralegal considerations are those features of
the situation that are prohibited or not explicitly authorized by law as
relevant to the arrest decision. The personal characteristics of the suspect
EFFECT OF RACE ON ARREST DECISIONS 477
and victim (e.g., race, sex, age, religion, wealth, and other indicators of
social status) fall into this category, as do certain behaviors, such as showing
a “bad attitude” or disrespect to the officer, or failure to cooperate (in ways
that are not themselves legal violations). They also may include certain
conditions (evidenced by relationships, behavior, or appearances) that may
justify concerns about the need to exert some control, even if arrest is not
justified: intoxication, heightened emotional state, and incoherent or irrational
behavior. Another extralegal consideration is the degree of intimacy
or closeness between antagonists: the closer the relationship (acquaintance
or partner), the lower the inclination to arrest, presumably because of the
greater availability of “sublegal” forms of social control available to parties
in closer relationships (Black, 1971: 1108).2
Most studies attempting to discern the effects of the suspect’s race on
the probability of arrest, especially those based on field observations and
department records, have taken into account at least some legal and extralegal
considerations. Presumably, the more of these other factors that
are included as controls in the analysis, the greater the confidence that
any race effects are not mistaken for these other influences, and that any
masking effects are revealed (Black, 1980: 108). Prior research has shown
fairly consistently that the following variables significantly increase the
likelihood of an arrest: evidence strength, severity of the offense, request
by the victim to make an arrest, and the suspect’s negative demeanor.
Researchers have found that minorities are more likely to show disrespect
toward the police; they are more likely to be suspected of serious offenses;
and they are more likely to ask the police to arrest the suspect (Skogan and
Frydl, 2004: 115–28). If researchers fail to account for the effects of these
influences, then what might otherwise appear as racial bias could be a result
of one or more of these variables. Whether these independent variables
also are subject to police choices born of racial bias is a matter of some
debate not fully resolved (Anderson, 1990, 1999; Reisig et al., 2004), but
it is standard practice to attempt to control for them when attempting to
isolate the effects of race.
Although the above legal and extralegal variables are with fair consistency
the most powerful predictors of the police arrest decision, they do not
exhaust its possible influences. The suspect’s sex, social class, and mental
health have been examined, but the studies are relatively rare and the
findings are mixed as to their impact, not to mention their relevance to the
2. The passage of mandatory arrest legislation for domestic violence in the last
decade or so altered the legal decision calculus for police. Police enjoy the legal
discretion to arrest or not for most misdemeanor offenses, but mandatory arrest
laws eliminate that discretion and require arrest in domestic violence situations
where probable cause requirements are satisfied.
478 KOCHEL, WILSON & MASTROFSKI
effects of race (Skogan and Frydl, 2004: 120–8). So too have some studies
explored the impact of officers’ personal characteristics and attitudes, but
these have generally not proven to be powerful or consistent predictors of
arrest, including the race of the officer (Skogan and Frydl, 2004: 128–52);
however, see Donohue and Levitt (2001) for evidence of an effect of officer
race.
Furthermore, in recent years, a debate has emerged about the extent
to which researchers studying police arrest practices have accurately distinguished
legal from extralegal influences (Skogan and Frydl, 2004: 118).
Klinger (1994, 1996) argues that many studies failed to identify illegal acts
committed by suspects in the course of the encounter, either missing them
entirely or treating them as a part of some putative extralegal variable,
such as demeanor (e.g., physically resisting or acting violently toward the
police officer). Some have responded that reanalyses of the data that correct
at least some of these problems have not altered conclusions about race
effects (Lundman, 1996: 319), whereas others have argued that this is a
problem only if the researcher’s purpose is to observe whether the arrest
can be legally justified post hoc (Worden, Shepard, and Mastrofski, 1996:
327). Nonetheless, it is conceivable that either model misspecification or
measurement errors of this sort could affect the estimation of race effects,
which is a concern that meta-analysis should take into account.
THE INFLUENCE OF RESEARCH CHOICES
Aside from controlling for the effects of other influences, several methodological
features of a study might influence the nature and extent of the
observed race effect. A possible source of variation in results is how the
arrests were observed. One approach is to use participants at the scene as
informants. Police agencies require officers to document certain situations
where arrest is a possible outcome (e.g., during pedestrian or vehicle stops
or contacts with juveniles), so these documents may be used. Surveys of
victims or the general public may also be used, where the respondent’s
account of what transpired during a police contact may be gathered. Yet
another approach is for researchers to train observers to accompany police
and note what transpired. One might expect that official police documents
would render the weakest race effects, inasmuch as there are strong disincentives
in most contemporary agencies to showing racial bias, especially
at a time when police are being scrutinized for racial profiling. However,
research relying on victim surveys might yield stronger race effects because
of patterns in the way that citizens infer or attribute police motives to
racism, minorities being more inclined to attribute negative experiences
with the criminal justice system to their race than Whites (Hagan and
Albonetti, 1982; Henderson et al., 1997; Hurwitz and Peffley, 2005). It is not
EFFECT OF RACE ON ARREST DECISIONS 479
obvious how field observation by disinterested third-party researchers who
guarantee the confidentiality of the police officer’s identity would affect
the strength of race effects in the study. Because accuracy and objectivity
are the goals of selecting and training field researchers, and observation is
their sole function in the field, the accuracy and objectivity of observations
should be much stronger, but officers may react to the observer’s presence,
altering what they do. We have some reason to think that the presence of
an observer causes officers to be less passive and more legalistic and, hence,
less likely to show a race effect (Mastrofski and Parks, 1990; Mastrofski,
Parks, and McCluskey, 2010; Spano, 2007). It also seems that observers (by
their personal characteristics and behavior) can influence patterns of police
behavior (Spano, 2007). Unfortunately, researchers have not explored how
the presence or actions of an observer affect the relationship between race
and arrest, and presumably this could vary depending on the specifics of
who conducted observations and how they conducted themselves. Given
the frequency that field researchers report observing police misbehavior
and abusive practices, it seems likely that the presence of an observer is not
a major deterrent to police behaving in ways that are proscribed culturally
or legally (Gould and Mastrofski, 2004; Mastrofski, Parks, and McCluskey,
2010; Mastrofski et al., 1998; Reiss, 1968, 1971; Terrill and Mastrofski,
2004).
Another factor that could affect the strength of race effects is the nature
of the offense under consideration. One would expect that (at least since the
1960s) police discretion that is the least legally constrained or subject to the
least oversight (by the courts, for example) would be that which demonstrates
the strongest race effects. On the one hand, a study that sampled
misdemeanors or other minor offenses should show stronger race effects
than one focusing on more serious crimes. The latter typically receive far
more scrutiny than the former. On the other hand, where police are charged
with enforcing a mandatory arrest law (such as with misdemeanor domestic
violence cases in many jurisdictions in the last two decades), then race
effects should be lower.
Other theoretically and methodologically relevant factors could affect
the strength of effects found, but those discussed earlier are those we could
assess in the extant data. We will discuss other possible factors in our discussion
of future research possibilities in the concluding section of this article.
METHOD
Meta-analysis was used to synthesize the extant evidence of the relationship
between race and likelihood of an arrest for two main reasons.
First, it provided us with a credible method of examining the distribution
of effects across studies that focused on the magnitude and direction of the
480 KOCHEL, WILSON & MASTROFSKI
effect rather than on the statistical significance. The latter is problematic
when examining conflicting results across studies (for a discussion of this,
see Hedges and Olkin, 1980). Second, it provided statistical methods for
exploring the relationship between study features, such as whether suspect
demeanor was incorporated into the model, and the observed effect. This
allowed for the exploration of the influence on results of theoretically
important substantive, and methodological features of studies.
SELECTION CRITERIA
We developed explicit criteria for establishing which studies would be
included and excluded from the meta-analysis. The goal was to create a
clear demarcation of the boundaries for the review. Applying the criteria
provided in the subsequent discussion yielded a final sample of 40 documents
and 23 research projects that produced 27 independent data sets
usable for meta-analysis.
Research Design
To be included, studies must have examined the relationship between
a citizen’s race/ethnicity and the likelihood of arrest. This required the
following design features: 1) that the design used microlevel data—data
collected at the encounter or suspect level; 2) that arrest versus a less severe
alternative to arrest at the time of the incident was used as a dependent
variable; 3) that citizen race was an independent variable in at least one
analysis; and 4) that the study only included cases where the citizen was
actually present at the time of the police/citizen encounter or the study
controlled for the ability to locate a suspect. We defined arrest as taking
a person into custody for the purpose of charging him/her with a criminal
offense, which would exclude stops in which the most serious possible
consequence was a traffic summons. This is consistent with Terrill, Paoline,
and Manning (2003) but is admittedly conservative, at least by the standards
of United States v. Mendenhall (1980), in which the U.S. Supreme Court
provided a broader and more subjective definition: “only if, in view of all the
circumstances surrounding the incident, a reasonable person would have
believed that he is not free to leave.” In fact, police departments vary in
how they operationalize arrest (Linn, 2005: 10). The definition we selected
fits most closely with popular conceptions of an arrest and is consistent with
the way most empirical studies have defined it.
Geographic Location
We restricted our search to studies that collected data in the United
States. The nature of this relationship is likely to vary across countries, and
EFFECT OF RACE ON ARREST DECISIONS 481
the focus of this review is the nature of this relationship within the United
States.
Statistical Data
To be included, a study had to report the results on the relationship
between race and arrest in a way that permitted the computation of an
effect size and its associated standard error. The effect size index for this
meta-analysis was the odds ratio, with analyses performed on the logged
odds ratio (see subsequent discussion for more information on the coding
and analysis of effect sizes). Effect sizes were coded directly from logistic
regression models or converted from probit regression models and some
ordinary least-squares (OLS) regression models. Although a few publications
were excluded because of insufficient information to enable the
computation of an effect size and standard error, most identified data
sets are represented. A few minor data sets (e.g., a traffic stop study in
Savannah, GA) were excluded because no available manuscript provided
sufficient statistical information to compute an effect size of the race and
arrest relationship.
Timeframe
We did not limit the review to studies based on data collected after a
specific date. We had several reasons for this decision. First, we wanted
to examine the full spectrum of evidence addressing this research question
that also met our design criteria. These studies are difficult and time consuming
to conduct, and we therefore anticipated that the total number of
available studies would be modest. Second, we were interested in exploring
whether any observed relationship between race and likelihood of arrest
had changed over time. We began with the expectation that by the 1960s,
the Civil Rights Movement had set in motion a series of legal and social
changes that might reduce the effects of race on police decision making,
and that over time we would see a decline in the strength of any observed
relationship between race and the probability of arrest (Skogan and Frydl,
2004: 283). The studies meeting the inclusion criteria ranged from 1966 to
2004, which is a span of nearly four decades.
Publication Status
We did not exclude studies based on publication status. That is, both published
and unpublished studies were eligible for inclusion. This has become
standard practice within meta-analysis and helps guard against publicationselection
bias or the tendency of the published literature to be biased in
favor of results that are statistically significant (see Gerber and Malhotra,
2008; Lipsey and Wilson, 2001a; Rothstein, Sutton, and Borenstein, 2005).
482 KOCHEL, WILSON & MASTROFSKI
SEARCH STRATEGY
Our search strategy attempted to identify all studies meeting these criteria.
It is possible that we missed at least one publication or manuscript,
but we are confident that this approach identified all major data sets that
examined this relationship. We searched electronic databases; examined
the bibliographies of relevant articles, books, and reviews; reviewed American
Society of Criminology Annual Conference agendas from 2000 through
2007; and browsed recent relevant journals. The databases searched were as
follows: Criminal Justice Abstracts, Criminal Justice Periodical Index, Dissertation
Abstracts International, National Criminal Justice Reference Service
(NCJRS), National Technical Information Service (NTIS), ProQuest
Research Library, ProQuest Social Science Journals, PsycINFO, Public
Affairs Information Service, Sociological Abstracts, Social Science Citation
Index, Social Services Abstracts, and Worldwide Political Sciences Abstracts.
The search terms used included race, racial, ethnicity, discriminate,
discriminatory, discriminated, bias, biased, disparity, disparate, disparities,
minority, African American, or Black in combination with arrest not cardiac,
arrest and decision, police and (encounter, discretion, or decision),
officer and (encounter, discretion, or decision), or juvenile and referral.
We also searched for police stop data because of an abundance of racial
profiling studies conducted in recent years.
The electronic search produced more than 4,200 hits, including duplicates.
A relatively small number of additional documents resulted from the
other types of searches. From nearly 4,500 potential sources, we selected
601 documents for retrieval that seemed from their title or abstract to meet
our eligibility criteria. Of those documents, we retrieved 566. Thirteen of
the 35 documents that could not be retrieved seemed to be alternative
versions of documents that were retrieved. Furthermore, another of the 35
documents was determined to be preliminary thoughts for a paper and not
an actual manuscript. Based on a review of the full documents by the first
author with ongoing communication with the second author, 40 documents
met the eligibility criteria. These 40 documents are based on 23 unique data
sets.
STUDY CODING AND EFFECT SIZE COMPUTATION
We developed a coding protocol designed to capture various pieces of
information about the study, including the data collection method, sampling
strategy, sample size, level of analysis (suspect/encounter/other), geographic
locations and years of the data collection, crime type, suspect age
and sex of the samples, the data source, document type, type of statistical
analyses applied, independent variables included, and the effect sizes for
race on arrest, among other things.We created a data structure that allowed
EFFECT OF RACE ON ARREST DECISIONS 483
us to handle the complex hierarchical nature of the data. Most studies
reported multiple analyses of the race/arrest relationship, and multiple
publications or manuscripts reported many of the data sets.
The effect size index used in this meta-analysis was the odds ratio. The
odds ratio is ideal for relationships between two binary or dichotomous
variables (see Fleiss, 1994; Fleiss and Berlin, 2009; Lipsey and Wilson,
2001a), such as arrest (yes/no) and race (minority/nonminority or
Black/White, etc.). The regression coefficient for race from studies that
reported the results from logistic regression models is a logged odds ratio,
and the antilogarithm is the odds ratio. As such, the effect size from logistic
regression models was coded directly from these results. Furthermore,
logistic regression models were the most common form of analysis across
studies. A handful of studies reported probit regression models. The unstandardized
regression coefficient for race from a probit model reflects the
predicted difference in the likelihood of arrest on a normal distribution,
rather than a logistic distribution, as it is in a logistic regression model.
These probit regression coefficients were transformed into an approximate
logged odds ratio through multiplication by π/
√
3. This provides a good approximation
between the normal and logistic distributions (see Hasselblad
and Hedges, 1995). The standard error was transformed by first converting
it to a variance, then multiplying it by π2/3, and then taking the square
root. Computing an odds-ratio effect size and related standard error from
the results from OLS regression models (or related statistical methods)
was more difficult. The unstandardized regression coefficient for race from
these models represents the predicted difference in the proportion arrested
versus not arrested. Combining this with descriptive statistics on the proportion
of the overall sample arrested, it was possible through simple algebra
to reconstruct a 2 × 2 contingency table representing the arrest by race
relationship, adjusted for the variables represented in the model. The odds
ratio and associated standard error were then computed from these values.
Although this method of computing the effect size is less precise than the
results from a logistic regression model, using this approximation allowed
us to include these studies in the meta-analysis. This only affected two data
sets in the final analyses. Although these OLS models produced somewhat
smaller effect sizes, sensitivity analyses showed that the results were roughly
the same whether these studies were included or excluded.
Effect sizes were coded for each reported analysis with one exception.
We did not code effect sizes from a regression model that included both
a main and an interaction effect involving race unless centered versions
of the relevant variables were used. The main effects in models with an
interaction term are not interpretable in unbalanced designs without centering.
We coded the direction of all effects such that positive effects (odds
ratios higher than 1 or positive logged odds ratios) indicated that Blacks or
484 KOCHEL, WILSON & MASTROFSKI
minorities had a greater odds of arrest and a negative effect (odds ratios less
than 1 or negative logged odds ratios) indicated that Blacks or minorities
had a lower odds of arrest.
To ensure reliability of coding, all studies were double-coded and differences
between the two coders were resolved by the first author with
consultation from the second author. We also assessed the reliability of the
coded items to identify potentially problematic items. Overall, inter-rater
reliabilities were good: 59 of 66 substantive coding items (i.e., those not
related to tracking identifiers, etc.) had reliabilities greater than 80 percent,
with 42 items with reliabilities greater than 90 percent. Only two variables
had reliabilities less than 70 percent. These were the number of independent
variables in the model and the variable related to the method of
data collection. The former variable suffered from an unclear decision rule
regarding how to count dummy coded variables. Recall, however, that all
differences between coders were resolved.
STATISTICAL APPROACH
This meta-analysis used the inverse-variance weight method of metaanalysis
(see Fleiss and Berlin, 2009; Hedges and Olkin, 1985; Lipsey
and Wilson, 2001a; Shadish and Haddock, 1994). This approach provides
greater weight to effect sizes from larger studies, although the actual weight
is a function of the standard error of the effect. Thus, more precise estimates
are provided great weight in determining the mean effect size.
The analyses were performed on the logged odds ratios, but the final
results were converted back into odds ratios for ease of interpretation.
We assumed a random-effects model a priori. That is, we assumed that
the true underlying population race/arrest effect estimated by the studies
varied because of genuine study differences. In addition to examining
the overall mean effects, we directly explored variability in effects
across studies through moderator analyses. Both analog-to-the-analysis-ofvariance
(ANOVA) and meta-analytic regression methods were used (see
Lipsey and Wilson, 2001a). All analyses were performed using Stata macros
(StataCorp, College Station, TX) written by David B. Wilson, available at
http://mason.gmu.edu/∼dwilsonb/ma.html.
An important statistical issue in meta-analysis is handling the statistical
dependencies of multiple effect sizes generated from a single sample. The
typical study in this area reported the results from multiple statistical models.
Because these multiple effect sizes are based on the same data set, they
are statistically dependent and cannot be treated as independent estimates
of the relationship between race and arrest. To complicate matters even
more, multiple publications report on the results based on a common data
set. We restricted any given analysis of effect sizes to a single effect size
EFFECT OF RACE ON ARREST DECISIONS 485
per data set unless the effect sizes were based on completely independent
subsets of the data. For example, Wordes and Bynum (1995) reported
all analyses separately for juvenile felonies, juvenile misdemeanors, and
juvenile status offenses. Because these represent independent data sets,
one effect size from each was allowed in any given analysis. Thus, the data
include 23 studies with 27 independent effect sizes for any given analysis.
To ensure that our results were not being overly influenced by the specific
effect size selected from a given data set, we ran several analyses with
different selection rules. These analyses included calculating the average
effect size within each data set, selecting the smallest effect size within each
data set, selecting the largest effect size within each data set, and selecting
the effect size that met explicit criteria that we believe represent the best
effect size for that data set. The selection criteria for the best effect size
within a data set gave preference to 1) the effect sizes based on a logistic
regression model rather than on an OLS or probit regression model; 2)
the standard error that was reported directly and not imputed from other
information, such as sample size; 3) the effect sizes that were based on
the full sample rather than on a subset or the effect size with the largest
sample size; 4) the models where the operationalization of the dependent
variable was clearly arrest versus no arrest rather than on some other
less severe alternative sanction (e.g., Crawford, 2000; Patnoe, 1990); 5) the
race variable that represented Black versus White rather on than minority
versus nonminority orWhite versus non-White; 6) the statistical model that
included demeanor as an independent variable; 7) the statistical model that
included officer characteristics; and 8) the effect size that was based on a
model with the largest number of independent variables. If multiple effect
sizes remained after applying these selection criteria, the median effect size
was chosen. This only occurred for Lundman (1996).
RESULTS
The search strategy resulted in the identification of 40 documents, 23
unique data sets, and 27 independent effects. Although we did not limit
the dates of publication or of data collection, the earliest eligible study
was published in 1977 and most studies were written or published in the
1980s through the 2000s. More than half (22 of 40) were published in 2000
through 2007. Three data sets represent data collected in the 1960s or 1970s.
Two data sets began data collection in 1966 and 1968 (Black and Reiss’s
collection in Boston, Washington, and Chicago [Friedrich, 1977] and the
1958 Philadelphia birth cohort [Sealock and Simpson, 1998], respectively).
The Midwest City study (Lundman, 1994, 1996) began data collection in
1970, and the police services study (Smith and Klein, 1984; Smith, Visher,
and Davidson, 1984) began data collection in 1977. The limited number
486 KOCHEL, WILSON & MASTROFSKI
of eligible pre-1980 studies limits our ability to examine changes in the
relationship between race and arrest occurring during or immediately after
the Civil Rights Movement.3
Thirty-five percent of these data sets draw on observations of police by
observers as they conduct business, and an additional 35 percent rely on
police recording their experiences when they stop a motorist or pedestrian,
which often are logged to allow assessments about racial profiling. The
remaining studies include documentation from routine police records: incident
reports or referrals of juveniles (26 percent), and interviews or surveys
of citizens who have been involved in police encounters as either a victim
or a suspect (4 percent). Table 1 displays the characteristics of the 23 data
sets included in our synthesis.
We coded 146 effect sizes across the 23 data sets and 40 documents.
Twenty-six of these effect sizes were based on a race variable that contrasted
a non–African American minority group with Caucasians. These
were not used in the analyses that follow. Fifty-four effect sizes were
based on race defined as White/Black, 34 as White/non-White, 29 as non-
Black/Black, and 3 as Nonminority/Minority. Across all 23 samples, the
percentage of non-Black minorities was low. Consequently, this sample of
studies compares mostly African Americans to White Americans. Among
the independent set of effect sizes used in table 2, 19 were based on race
defined as White/Black, 5 asWhite/non-White, 2 as non-Black/Black, and 1
as Nonminority/Minority.
OVERALL RESULTS
Table 2 presents the results of the meta-analysis of the average, smallest,
largest, and best effect size within a data set. The sample size for these
analyses is 27 due to two studies contributing three independent effect sizes
to each aggregate analysis. Wordes and Bynum (1995) separately analyzed
juvenile felony, misdemeanor, and status offenses. From the Midwest City
data set, Lundman (1994) separately analyzed a subset of juvenile encounters
and public drunkenness (nontraffic) encounters and Lundman (1996)
analyzed drunk driving encounters. Each of the four methods of selecting
one effect size per independent data set produces an overall mean effect
size that was statistically significant under a random-effects model, which
is more conservative than a fixed-effects model. The range in the mean
odds ratio across selection criteria was 1.32 for the smallest effect size per
data set and 1.52 for the largest effect size per data set. Using the average
3. Early studies, published in the 1960s to 1970s, tended to use descriptive statistics
rather than regression-based or multivariate models, and therefore, as many as ten
studies published in the 1960s and 1970s could not be included in our sample.
EFFECT OF RACE ON ARREST DECISIONS 487
Table 1. Descriptive Information on the 23 Data Sets Included in This Meta-Analysis
Collection Full Suspect
Data Set Dates Location Studies Sample Source Age
Albert Reiss Police—Citizen
Encounters
1966 Boston, MA;
Washington,
DC; Chicago, IL
Friederich (1977) 3,955 Observation Both
1958 Philadelphia Birth
Cohort
1968–1975 Philadelphia, PA Sealock and Simpson (1968) 27,160 Police
contact data
Juvenile
Sykes and Clark Midwest City
Police–Citizen Encounters
1970–1971 Midwest City Lundman (1994 and 1996) 2,000 Observation Both
Police Services Study (PSS) 1977 St. Louis, MO;
Rochester, NY;
Tampa and St.
Petersburg, FL
Engel, Sobol, and Worden (2000); Smith
(1984, 1986, and 1987); Smith and Klein
(1984); Smith and Visher (1981); Smith,
Visher, and Davidson (1984); Visher
(1983); Worden and Shepard (1996)
5,688 Observation Both
Pima County Police Referrals 1984 Pima County,
AZ
Patnoe (1990) 6,126 Police
referrals
Juvenile
Metro Dade Police
Department Dispute Study
1985–1986 Metro Dade
County, FL
Klinger (1996) 245 Observation Both
Role of Alcohol Use in
Breaches of Peace and Crime
1986–1987 Chicago, IL Freeman (1992) 2,365 Observation Both
Richmond Bureau of Police
Police–Citizen Encounters
1992 Richmond, VA Mastrofski, Worden, and Snipes (1995) 1,630 Observation Both
Incident Data Police Agencies
in Los Angeles County
1995–1998 Los Angeles
County, CA
Viehe (2003) 1,040 Incident
reports
Both
Project on Policing
Neighborhoods (POPN)
1996–1997 Indianapolis, IN
St. Petersburg,
FL
Engel (2000); Myers (2002); Spano (2002
and 2003)
7,443 Observation Both
Cincinnati Police Department
Community Policing Study
1997–1998 Cincinnati, OH Brown and Frank (2005 and 2006);
Novak (1999); Novak et al. (2002);
Ratansi (2005)
2,671 Observation Both
488 KOCHEL, WILSON & MASTROFSKI
Table 1. Continued
Collection Full Suspect
Data Set Dates Location Studies Sample Source Age
Police Records in Nine
Michigan Jurisdictions
1990 9 Michigan
Jurisdictions
Wordes and Bynum (1995) 2,845 Police
records
Juvenile
Domestic Violence Incidents 1997–1998 City in Michigan Robinson and Chandek (2000) 471 Supplemental
incident data
Both
Midwest City Noise Complaint
Incidents
1998–1999 Midwest City Crawford (2000) 594 Incident data Both
BJS Police-Public Contact
Survey NCVS Supplement
1999 Nationwide
(U.S.)
Engel and Calnon (2004) 80,543 Citizen
survey
Both
Domestic Violence Incidents 2000–2001 Niagara Falls,
NY
Gibbs (2003) 1,401 Incident data Both
Miami Dade County Police
Stop Study
2001 Miami Dade
County, FL
Alpert Group (2004); Smith, Makarios,
and Alpert (2006)
86,232 Police
contact data
Both
Wichita Stop Study 2001 Wichita, KS Withrow (2004) 37,454 Police
contact data
Both
Las Vegas Stop Study 2002 Las Vegas, NV Doran (2007) 167,432 Police
contact data
Both
Eugene Police Department
Vehicle Stop Study
2002–2003 Eugene, OR Gumbhir (2005) 36,011 Police
contact data
Both
Pennsylvania State Police Stop
Study
2002–2003 Pennsylvania Engel et al. (2004) 327,120 Police
contact data
Both
Pennsylvania State Police Stop
Study
2003–2004 Pennsylvania Engel et al. (2005) 315,705 Police
contact data
Both
Los Angeles Stop Study 2003–2004 Los Angeles, CA Alpert et al. (2006) 814,492 Police
contact data
Both
EFFECT OF RACE ON ARREST DECISIONS 489
Table 2. Meta-analysis Results of Odds-Ratio for the Effects
of Race on Arrest
95% C.I.
Analysis Mean Lower Upper z p Q k
Average 1.45 1.28 1.64 5.76 .0000 57.030∗ 27
Smallest 1.32 1.16 1.50 4.34 .0000 43.580∗ 27
Largest 1.52 1.32 1.74 5.95 .0000 48.190∗ 27
Best 1.38 1.24 1.53 5.82 .0000 39.270∗ 27
Data Source .4000 .7100
Researcher 1.39 1.09 1.75 2.68 .0074 10
Officer 1.36 1.20 1.55 4.71 .0000 16
Victim Survey 1.79 .96 3.32 1.83 .0670 1
Model type .1430 3.8800
Logistic 1.39 1.27 1.52 7.14 .0000 23
Probit 1.63 1.13 2.37 2.58 .0098 2
OLS 1.15 .95 1.40 1.41 .1591 2
Sample Age .7760 .0800
Juvenile 1.42 1.13 1.79 2.96 .0030 5
Mixed 1.37 1.20 1.55 4.84 .0000 22
Amount of Evidence in Model .7100 .1370
Yes 1.35 1.15 1.58 3.77 .0002 10
No 1.41 1.20 1.65 4.28 .0000 17
Crime Type .6400 .2200
No crime type distinction 1.43 1.21 1.69 4.25 .0000 16
Traffic related 1.32 1.10 1.59 3.02 .0025 7
Domestic 1.27 .88 1.84 1.28 .2008 3
Noise 1.78 .91 3.48 1.69 .0918 1
Crime During Encounter in Model .9840 .0004
Yes 1.38 1.13 1.68 3.12 .0019 9
No 1.38 1.21 1.58 4.73 .0000 18
Demeanor in Model .3500 .8900
Yes 1.49 1.22 1.82 3.93 .0001 12
No 1.33 1.17 1.52 4.29 .0000 15
Seriousness of Offense in Model .6100 .2600
Yes 1.34 1.14 1.57 3.59 .0003 16
No 1.42 1.22 1.65 4.52 .0000 11
Suspect on Drugs or Alcohol in Model .5600 .3400
Yes 1.49 1.12 2.00 2.69 .0071 7
No 1.36 1.21 1.53 5.10 .0000 20
Suspect Prior Record in Model .2400 1.3600
Yes 1.52 1.25 1.84 4.19 .0000 9
No 1.32 1.16 1.50 4.17 .0000 18
Victim Requested Arrest in Model .9480 .0040
Yes 1.37 1.09 1.72 2.68 .0074 6
No 1.38 1.22 1.57 5.01 .0000 21
Witness in Model .9100 .0140
Yes 1.39 1.17 1.65 3.71 .0002 9
No 1.37 1.19 1.58 4.28 .0000 18
490 KOCHEL, WILSON & MASTROFSKI
Table 2. Continued
95% C.I.
Analysis Mean Lower Upper z p Q k
Year of Data Collection (Median Year) .9710 .0014
1966–1975 1.33 .99 1.76 1.93 .0533 5
1976–1985 1.25 .89 1.73 1.30 .1926 3
1986–1995 1.66 1.19 2.34 2.94 .0033 5
1996–2004 1.38 1.19 1.58 4.47 .0000 14
NOTE: All analyses based on a random-effects model with the random effects variance
component estimated via the method-of-moments estimator.
ABBREVIATIONS: C. I. = confidence interval; OLS = ordinary least squares.
∗p < = .05
and best (see the Method section) effect size within each data set produced
very similar results (1.45 and 1.38, respectively). The 95 percent confidence
interval ranged from a low of 1.16 for the analysis using the smallest effect
size per data set to a high of 1.74 for the analysis using the largest effect
size per data set. These results suggest that minorities have a higher odds
of arrest than nonminorities in a police/citizen encounter. The different
selection models show that our results are robust to which effect size is
selected from each data set.
Figures 1–4 present these data graphically. These forest plots show the
observed effect size and 95 percent confidence interval for the effect selected
for each data set. Examining figure 1 shows that only 4 of the 27
odds ratios were in the negative direction with Blacks or minorities having
smaller odds of arrest. We would expect a few negative effects just by
chance. What is of greater interest is the pattern of results across studies.
Across all four figures, we observe a clear pattern of evidence consistent
with the hypothesis that minorities and Blacks have higher odds of arrest.
Even when we took the most conservative effect size produced across
all available analyses from each data set, we still observed a statistically
significant increase in the odds of arrest for minorities and Blacks relative
to Whites.
By conventional standards, this is a small effect. To interpret the magnitude
of the effect in meaningful terms, we converted the overall mean
odds ratio of 1.38 (model based on the best effect size within each data
set) into the probability of arrest for minorities and Blacks relative to
Whites. This conversion is based on an assumed arrest rate of 20 percent
for Whites, roughly the average across all samples. Using this value as the
benchmark, an odds ratio of 1.38 is equivalent to an arrest probability of .26
for minorities and Blacks and of .20 for Whites. We believe this difference
is large enough to be of practical concern.
EFFECT OF RACE ON ARREST DECISIONS 491
Figure 1. Forest Plot of the Average Effect Size Within Each
Data Set
Independent Data Set Sample Size (average) Whites Higher Arrest Rate Blacks/Minorities Higher Arrest Rate
Richmond Observation Study 1992 451
Midwest City 1970 Public Drunkenness 195
103236
245
1401
60839
Pima County AZ 1984 Juveniles 5915
Reiss Data in Boston, Chicago, and DC 1966 1917
32592
9 Michigan Cities 1990 Juvenile Felonies 691
306602
Las Vegas Stop Study 2002 167432
15662
2175
9 Michigan Cities 1990 Juvenile Status Offense 516
229
327120
1040
594
NCVS Police Public Contact Survey 1999 7054
Wichita Stop Data 2001 37454
423
Police Services Study 1977 723
9 Michigan Cities 1990 Juvenile Misdemeanors 961
Midwest City 1970 Juveniles 200
157
Midwest City 1970 Drunk Driver 100
Overall
.5 1. 0 1.5 2 .0 4. 0 10.0 50.0
Although the overall mean results suggest that minorities and Black
suspects have a higher probability of arrest than White suspects, the results
were heterogeneous across studies. The Q values shown in table 2 are
tests of effect size homogeneity, that is, variability in excess of what would
be expected from sampling error alone. Across all four models, Q was
significant, indicating statistically significant heterogeneity across data sets.
Thus, some studies observed larger effects of race on arrest than others.
The moderator analyses provided in the subsequent discussion explored
potential explanations of this excess variability based on substantive and
methodological study features.
MODERATOR ANALYSES
We performed two types of moderator analyses: theoretically based and
methodologically based explanations of why some models may produce
larger or smaller effects. These analyses are based on a single effect size
per independent data set using the effect size selected as the best.
We first examine methodological study characteristics. Studies differed
in the method of observation. Ten independent effect sizes were based on
data obtained from field researchers observing police/citizen interactions;
16 were from data generated by police officers, most of which are data
492 KOCHEL, WILSON & MASTROFSKI
Figure 2. Forest Plot of the Smallest Effect Size Within Each
Data Set
Independent Data Set Sample Size Whites Higher Arrest Rate Blacks/Minorities Higher Arrest Rate
Police Services 1977 Style of PD 288
Richmond VA 1992 Community Policing Negative Officers 192
Midwest City 1970 Public Drunkenness 195
103236
7035
245
Reiss Data 1966 by City 683
1401
Pima County AZ 1984 Juveniles 5915
e 1487
32592
9 Michigan Cities 1990 Juvenile Felonies 691
306602
3222
Las Vegas Stop Study 2002 167432
103
9 Michigan Cities 1990 Juvenile Status Offense 516
229
327120
586
1040
NCVS Police Public Contact Survey 1999 7054
Wichita Stop Data 2001 37454
9 Michigan Cities 1990 Juvenile Misdemeanors 961
Midwest City 1970 Juveniles 200
157
Midwest City 1970 Drunk Driver 100
Overall
.5 1.0 1.5 2.0 4.0 10.0 50.0
routinely collected as part of vehicle or pedestrian stops; some are data
collected through incident reports; one data set was generated from juvenile
referral records; and one was based on a victim survey. The test of the
difference between these means (a Q test similar to a one-way F) was
not statistically significant (Q = .71, df = 2, p = .400). The effects are
not significantly different across studies based on who collected the data.
Arguably the first of these (researcher as observer) is the most credible
of these research designs, and this design produced a result that was only
slightly smaller than the overall result. Thus, the source of data does not
seem to account for meaningful differences across studies.
Another methodological difference across the studies was the type of
statistical analysis or modeling method. Most effect sizes (23 of 27) are
based on the results from logistic regression models. Two of these 27 effect
sizes were based on probit regression models, and two were based on OLStype
models. The mean for the probit models was higher, and the mean for
the OLS models was lower relative to the logistic models, although not by
enough to account for a significant amount of variability in effect sizes (Q =
3.88, df = 2, p = .143).
Studies restricted to juveniles observed slightly higher effects, albeit not
statistically significant (Q = .08, df = 1, p = .776). It is worth noting that
EFFECT OF RACE ON ARREST DECISIONS 493
Figure 3. Forest Plot of the Largest Effect Size Within Each
Data Set
Independent Data Set Sample Size Whites Higher Arrest Rate Blacks/Minorities Higher Arrest Rate
103236
Midwest City 1970 Public Drunkenness 195
245
1401
Pima County AZ 1984 Juveniles 5915
32592
9 Michigan Cities 1990 Juvenile Felonies 691
Richmond VA 1992 Community Policing Positive Officers 259
15662
Las Vegas Stop Study 2002 167432
9 Michigan Cities 1990 Juvenile Status Offense 516
2286
229
327120
Reiss Data 1966 by City 761
1040
NCVS Police Public Contact Survey 1999 7054
Wichita Stop Data 2001 37454
221331
9 Michigan Cities 1990 Juvenile Misdemeanors 961
3723
Midwest City 1970 Juveniles 200
157
142
233
Midwest City 1970 Drunk Driver 100
e 252
Overall
.5 1 .0 1.5 2.0 4 .0 10.0 50.0 300.0
three of the five juvenile data sets are from a single study (Wordes and
Bynum, 1995), reducing the strength of any inference that might be drawn
from this difference.
In addition to examining potential methodological explanations for our
findings, we also examined several theoretical explanations, both legal and
extralegal. Scholars have distinguished between disparity and discrimination.
The logic is that disparate experiences by race when explained away by
legal factors present during the encounter are not discriminatory (Skogan
and Frydl, 2004: 124). Therefore, if a difference in the odds of arrest by race
dissipates when accounting for mandatory arrest policies, the seriousness
of the offense, amount of evidence against the suspect, or the presence
of a victim supportive of arrest, we would conclude that the uncontrolled
difference is not discriminatory. Rather, it demonstrates disparate legally
relevant circumstances by race. Other potentially legally relevant factors
include the presence of witnesses on the scene who may be able to provide
eyewitness testimony; prior record of the suspect (particularly if known by
the officer on the scene at the time of the encounter), as an indicator that
the criminal justice system may need to take more focused action to break
a pattern of behavior; the suspect being under the influence of drugs or
alcohol as an indicator of his/her potential immediate threat to him/herself
494 KOCHEL, WILSON & MASTROFSKI
Figure 4. Forest Plot of the Best Effect Size Within Each
Data Set
Independent Data Set Sample Size Whites Higher Arrest Rate Blacks/Minorities Higher Arrest Rate
Richmond Observation Study 1992 451
103236
Midwest City 1970 Public Drunkenness 195
245
1401
60254
Pima County AZ 1984 Juveniles 5915
Reiss Data in Boston, Chicago, and DC 1966 1917
1487
32592
9 Michigan Cities 1990 Juvenile Felonies 691
306602
Las Vegas Stop Study 2002 167432
15662
9 Michigan Cities 1990 Juvenile Status Offense 516
229
327120
643
1040
594
NCVS Police Public Contact Survey 1999 7054
Wichita Stop Data 2001 37454
9 Michigan Cities 1990 Juvenile Misdemeanors 961
614
Midwest City 1970 Juveniles 200
157
Midwest City 1970 Drunk Driver 100
Overall
.5 1 .0 1.5 2 .0 4 .0 10.0 50.0
or others; or the observance or discovery of additional criminal acts during
the course of an officer–citizen interaction (e.g., when a citizen assaults an
officer).
We anticipated that the race/arrest relationship would be smaller for
domestic violence crimes because of the proliferation of mandatory arrest
policies for domestic violence during the last three decades. That is,
since the proliferation of misdemeanor mandatory arrest laws for domestic
violence that began in the 1980s, officer discretion has been substantially
reduced for domestic violence cases. This should manifest itself as
a smaller relationship between race and arrest when examining domestic
violence cases, given the restrictions these laws place on police discretion.4
Table 2 shows only limited support for this hypothesis.5 The mean odds
ratio for the three studies that only examined domestic cases (data collected
in 1995–1998, 1997–1998, and 2000–2001) was the lowest of the four
4. Sherman (1992) reported that by 1989, 84 percent of urban police agencies had
instituted preferred or mandatory arrest policies for domestic violence. By 1991,
15 states and the District of Columbia had mandatory arrest statutes for domestic
violence.
5. Because there are only three studies of domestic violence cases, the statistical
power of this moderator analysis is poor.
EFFECT OF RACE ON ARREST DECISIONS 495
categories of crime type measured (1.27) and not statistically significant.
For studies limited to domestic violence incidents, we did not find strong
evidence showing that race influences arrest decisions. However, among
studies where we expected fewer limits on officer discretion—traffic encounters
and studies that included all crime types—we did find support that
race influences arrest decisions. However, a test of the difference between
the mean effect size for the domestic violence studies and the other crime
types was not statistically significant (Q = .22, df = 1, p = .64).
Other legal considerations showed no effects. Controlling for the seriousness
of the offense did not meaningfully reduce the race/arrest relationship
(Q = .26, df = 1, p = .61). Controlling for whether the victim requested an
arrest also was unrelated to the size of the effect (Q = .004, df = 1, p =
.95). Additionally, controlling for the presence of witnesses (Q = .014, df =
1, p = .91), the quantity of evidence at the scene (Q = .137, df = 1, p = .71),
the suspect being under the influence of drugs or alcohol (Q = .34, df = 1,
p = .56), and the prior record of the suspect (Q = 1.36, df = 1, p = .24)
each did not significantly reduce the race/arrest relationship. Furthermore,
we found no significant difference in the mean effect size of studies that
controlled for the occurrence or discovery of a new criminal offense during
the course of the police/citizen encounter relative to those that did not (Q =
.0004, df = 1, p = .98).
Because of the failure of each of these potentially legally relevant factors
to explain away the relationship between race and arrest, we also explored
whether including greater numbers of legally relevant controls in a study
may reduce the size of the race effect. We constructed a variable that
measured the degree to which a model addressed legally relevant factors. It
ranged from zero to three, with zero reflecting no legally relevant controls
and three reflecting a model that controlled for seriousness of offense,
amount of evidence at the scene, and whether the victim requested an
arrest.6 These three factors were described as legally relevant by Skogan
and Frydl (2004). The results of a meta-analytic, random-effects regression
analysis showed that as the numbers of legally relevant variables in the
model increased, the effect size decreased by a very small, nonsignificant
amount (B = –.0326, p = .08). The difference between the high and low
6. In constructing the scale, studies that included a measure of victim injury but
did not also include a separate measure of the seriousness of the offense were
given a point on the legal relevance scale to reflect the seriousness of the offense.
We deemed it reasonable to assume that officers perceived a greater level of
seriousness when the victim was injured than when he/she was not. Also, studies
that controlled for the presence of witnesses, but did not also control for the
amount of evidence at the scene, were given a point on the legal relevance scale to
reflect amount of evidence because the presence of witnesses provides police with
more evidence (witness testimony) than the absence of witnesses on scene.
496 KOCHEL, WILSON & MASTROFSKI
levels of this scale are trivial, and the race effect remains even at the high
end. Controlling for legally relevant variables does not noticeably reduce or
explain the relationship between suspect race and arrest.We note Klinger’s
(1996: 335) claim that no extant study had comprehensively controlled for
the illegality of a suspect’s behavior, including such behavior in the presence
of the police. We accept that there is always room for improvement in
the measurement of such variables, but we would also expect that if this
consideration really bears on the race–arrest relationship, it would have
surfaced in the variation we observed across the studies in the extent to
which legal factors were taken into account. As it did not, we submit that
there are solid reasons to have confidence in the validity of the race effect
illuminated by the meta-analysis.
Prior research has found that a major extralegal influence on the arrest
decision is the suspect’s demeanor, although there is also some evidence
that the scope of this effect declines or disappears when disrespectful behavior
that is illegal (e.g., physical resistance) is distinguished from that
which is not (e.g., using insulting language) (Klinger, 1994). Regardless,
researchers have suggested that officers making a decision to arrest may
not be reacting to a suspect’s race but to his or her disrespectful behavior,
with Black suspects showing a greater tendency to exhibit disrespectful
behaviors during encounters (Skogan and Frydl, 2004: 124). If demeanor
accounts for the race/arrest relationship, then the effect size for race from
models that control for demeanor should be smaller than from models that
do not. However, statistical models that adjusted for demeanor produced
effect sizes roughly comparable with models that did not (Q = .89, df =
1, p = .35). Studies that controlled for demeanor and studies that did not
control for demeanor both showed an effect of race on arrest.
A final moderator analysis examined whether the race/arrest relationship
has changed over time. We anticipated that societal and legal changes over
time may reduce the inclination for police to be influenced by race in
making the arrest decision. Most of these data sets were conducted only
during a 1-year (11 of 27) or 2-year period (14 of 27), but two studies
spanned more than 2 years (see table 1). To examine the relationship
between year and effect size, we used the median year of data collection.
Contrary to expectation, we did not observe any decrease in effect size
over time. A random-effects, meta-analytic regression model showed a
near-zero coefficient between mid-year of data collection and logged odds
ratio (β = .0001, p = .97). Table 2 shows the mean odds ratio for mid-year
categories (1966–1975, 1976–1985, 1986–1995, and 1996–2004) and shows
no clear pattern over time. Unfortunately, this is a weak test of the race and
arrest relationship given that so few studies were based on data collected
before 1980.
EFFECT OF RACE ON ARREST DECISIONS 497
A complication with moderator analyses is that study features often are
confounded, making it difficult to interpret observed differences across
studies properly (see Lipsey and Wilson, 2001b). We performed a sensitivity
analysis to help ensure that meaningful moderator effects were not
being masked by study feature confounding. Using only those studies for
which the effect size was based on a logistic or probit regression model
and excluding studies that examined domestic violence or noise complaints
(k = 20), we performed a meta-analytic regression analysis examining the
relationship between the inclusion in the statistical model of the following
independent variables and the effect size: suspect demeanor, seriousness of
offense, and victim requested an arrest. None of the regression coefficients
were statistically significant. Furthermore, the observed direction of effect
was counter to expectation for suspect demeanor and victim requested
an arrest. Thus, incorporating into the statistical models measures of demeanor,
seriousness of offense, and whether victims requested arrest did
not significantly influence the observed relationship between suspect race
and the decision to arrest.
PUBLICATION-SELECTION BIAS
Publication-selection bias is a serious concern when conducting a metaanalysis
(Rothstein, Sutton, and Borenstein, 2005). To mitigate the possible
effects of this potential bias, we explicitly sought to include unpublished
studies in our meta-analysis. Using the distribution of best effect size,
we compared the results of the 11 effect sizes from unpublished documents
(dissertations and government reports) with the 16 effect sizes
from published documents (journal articles and book chapters). The difference
showed only a slight bias toward more positive results in published
manuscripts (1.44 vs. 1.23, respectively,Q = 3.26, df = 1, p = .07).ADuvall
and Tweedie trim-and-fill analysis suggested that four small or negative
results might be unobserved and produced an adjusted mean odds ratio of
1.35, slightly less than the mean for our analysis based on the smallest effect
within each data set. This mean was still statistically significant under the
trim-and-fill analysis. Given the great deal of effort it takes to collect the
data used in the studies included in this meta-analysis, it seems unlikely
that many such data sets have been created that have not become part
of the discoverable literature. However, it is possible that the statistical
models that are reported are those that tend to produce significant results.
Our analysis based on the smallest effect size within each data set provides
some assurance that even if this were the case, the overall effect would still
be positive and statistically significant. Publication-selection bias may have
affected the overall results, but this evidence suggests that it is likely that
498 KOCHEL, WILSON & MASTROFSKI
the magnitude of any such bias is modest and would not change the overall
conclusions of this synthesis.
CONCLUSION
By focusing solely on arrest and doing so systematically and quantitatively,
we help to fill the gap in knowledge reported by the National
Research Council and by the American Sociological Association. From our
findings, we can conclude more definitively than prior nonsystematic reviews
that racial minority suspects experience a higher probability of arrest
than do Whites. We report with confidence that the results are not mixed.
Race matters. Our finding is consistent with what most of the American
public perceives, and that finding holds over time, research site, across data
collection methods, and across publication types. Furthermore, controlling
for demeanor, offense severity, presence of witnesses, quantity of evidence
at the scene, the occurrence or discovery of a new criminal offense during
the encounter, the suspect being under the influence of drugs or alcohol,
prior record of the suspect, or requests to arrest by victims does not significantly
reduce the strength of the relationship between suspect race and
arrest. It remains possible that unaccounted for legal aspects of the police–
citizen encounter could explain the race–arrest relationship, reducing the
observed effect even to zero. However, it seems unlikely that improvements
in the measurement of legally relevant factors will meaningfully change the
strength of the observed relationship, given the robustness of the evidence
examined in this meta-analysis to existing attempts at accounting for these
factors. Thus, the most credible conclusion based on the evidence examined
is that race does affect the likelihood of an arrest.
Statistically, the effect is clearly significant, but interpreting the effect
size requires broader contextual considerations. On average, the chances
of a minority suspect being arrested were found to be 30 percent greater
than a White suspect (rising from the sample average of .20 for Whites
to .26 for minorities). This finding is larger than most race effects found
in a meta-analysis of court sentencing, with the exception of non-federal
drug offenses and federal property offenses (Mitchell, 2005). Several of the
overall mean effects in the court sentencing area were substantially smaller,
such as an odds ratio of 1.09 for nonfederal courts’ sentencing of property
offenses and 1.08 federal courts’ sentencing of drug offenses. Because of the
interconnectedness of decisions made in the criminal justice system, even
small racial differences that occur at many points in the criminal justice
process will compound and produce profound effects further along in the
system (Kempf-Leonard, 2007). Arrest occurs at a relatively early stage in
the process. Holding all other situational factors constant, the arrest risk on
average is 30 percent higher for racial minorities than for Whites; thus, it
EFFECT OF RACE ON ARREST DECISIONS 499
would not be surprising to observe that even more modest effects during
the court processing stages would still produce the level of impressive
differences that are observed at the punishment end of the system.7
The extant research does not demonstrate the causes of this racial disparity,
nor does it point to a clear policy response for dealing with it. What it
does establish is that where there is smoke, there is indeed fire regarding
racial disparity in the arrest practices of American police. This certainly
shows that future efforts to delineate the legal and ethical implications of
racially differentiated policing will be based on a solid empirical foundation.
And it should stimulate criminologists to develop empirical research that
moves beyond just testing for race effects to research that accounts for
variation in them. What follows are some suggestions to further that line
of inquiry.
Even with the striking clarity of findings in our sample, we have noted
substantial heterogeneity in the strength of effects across studies. Our moderator
analysis did not reveal obvious substantive sources of this variation,
but we speculate on some that might be incorporated into future research
on the effects of suspect race on police discretion. Notable in this body of
studies was the rarity with which researchers explicitly took into account the
ecology of police decision making. The theoretical importance of the police
environment for explaining police practices was articulated four decades
ago (Reiss and Bordua, 1967), but it remains an underdeveloped aspect
of empirical research on police discretion (Klinger, 2004). Following the
argument that the exercise of officer discretion is not immune to external influences,
we anticipate that more theoretically useful and more empirically
powerful accounts of the effects of race will take four kinds of contexts into
account. First is the character of the organizational environment in which
individual police officers operate—the policies, structures, and cultural
features that might influence the strength of the race–arrest relationship.
Second is the decision context that clarifies what is at stake in the arrest
decision—explicitly who or what is being served by the arrest. Third is the
socioeconomic and cultural context of the police–citizen encounter, usually
represented spatially as the “neighborhood” in which it occurs. Fourth is
the larger context of political power or lack of power that a given minority
group may have in the police jurisdiction.
By and large, studies of police arrest discretion that we reviewed paid
little attention to the particulars of the organizational environment in
which police officers were making the arrest decision. How actively the
department attended to unequal treatment of citizens according to their
7. In 2008, Black males were incarcerated in the United States at more than six times
the rate of White males (3,161 per 100,000 versus 487 per 100,000, respectively
[Sabol, West, and Cooper, 2009]).
500 KOCHEL, WILSON & MASTROFSKI
race (through training and disciplinary practices, for example) would be
logical starting points. Broader structural features, such as the degree of
professionalism and bureaucracy, also have been hypothesized to affect
race–arrest patterns (Wilson, 1968). An exceptional study in this regard
explored the implications of the police department’s professionalization
and bureaucratization, finding no significant differences in the effects of
suspect race on arrest probabilities according to type of department (Smith,
1984).Given that formal organizations exist principally to shape and control
the choices and performance of their members, it seems a striking omission
that so few studies seeking to assess race’s impact on arrest attend to this
aspect of discretion control (Klinger, 2004).
Modeling of the race–arrest linkage also will benefit from a broader
consideration of what is at stake. In attempting to account for the effects
of race on police arrest discretion, researchers have mostly focused on the
question of which race suffers the punishment, whereas only a few have
attended to the question of who benefits. We have reason to expect that, at
least under some circumstances, police decisions about how to exercise their
authority are driven at least as much by who benefits as who suffers (Black,
1976, 1989). For example, Smith (1987) examined a narrowly focused group
of police–citizen encounters involving violence between disputants, both
of whom were present. Under these circumstances, he found that, other
things being equal, the estimated probability of making an arrest when
the disputant dyad was non-White was only 27 percent that of when the
disputants wereWhite,8 showing a propensity towardmediation rather than
toward arrest for non-White combatants. One of the strongest indicators
of arrest was a preference for arrest by the perceived victim, with the
probability of arrest being 65 percent greater when the victim requested
formal action (Smith, 1987: 777). Alternatively, in some police–suspect
encounters, no victim is present to cue a reaction from the officer or to lobby
for an outcome, and in many of these cases, the victim may be unspecified
and left largely to the officer to construct (e.g., traffic stops and other
officer-initiated interventions). Under these circumstances, often dealing
with observed violations, suspicious circumstances, and public disorder, we
might anticipate that any racial biases held by officers would be more likely
to be influenced by the suspect’s race. Our point is that modeling the context
of how officers determine who benefits explicitly in the analysis will help to
account for variation in race effects within and across studies.
A third contextual consideration is the character of the immediate spatial
environment in which the arrest decision occurs. The impact of neighborhood
context on police decision making is of growing interest among
8. Reported probabilities of arrest were .5850 for White disputants and .1158 for
non-White disputants (Smith, 1987: 777).
EFFECT OF RACE ON ARREST DECISIONS 501
scholars. Klinger (1997) presented an ecological framework for explaining
the “vigor” by which police exert their authority by examining the
socioeconomic and crime features of the beat in which the police–citizen
encounter occurs. He argued that the standards of tolerable conduct and
the thresholds for police intervention are respectively lowered and raised
in areas with large amounts of socioeconomic disadvantage and violent
crime. A few studies have taken neighborhood-level effects into account
in predicting various forms of police discretion and have found with some
consistency that disadvantaged and high-crime areas are more likely to
experience punitive, enforcement-oriented policing, with all other things
being equal (Skogan and Frydl, 2004: 189). It is possible that in our sample
of studies, some variation in race effects could be attributable to variation
in the character of the neighborhood samples across studies. Unfortunately,
research reports did not provide sufficient detail in neighborhood sample
composition to conduct more than a crude moderator analysis that showed
no significant effect for studies that oversampled disadvantaged neighborhoods
relative to studies that did not.
More to our point, future research should concentrate on the effects,
if any, of neighborhood characteristics on the size and direction of race
effects. A recent analysis of the arrest experiences of 12–18-year-olds found
that neighborhood context factors do account for some arrest differences
across racial/ethnic groups but that substantial individual differences across
race/ethnic groups still persist (Kirk, 2008), with minorities generally at
greater risk. Interestingly, different minority groups (Mexican vs. Black)
often show consistent patterns of race effect but sometimes do not, depending
on the particular neighborhood characteristic under consideration.
A fourth, and perhaps even more compelling, policy-sensitive explanation
for race effect differences could be the degree of empowerment
that minorities enjoy in the communities under study. The disciplines of
political science and public administration have established an extensive
literature on political empowerment and representative bureaucracy that
proves useful here. This research shows with fair consistency that in communities
and states where racial minorities have achieved certain levels of
power in elective office (e.g., having a minority-race mayor or legislative
body dominated by minorities), minority citizens tend to receive more
favorable treatment from the local government (Bratton, 2002; Mladenka,
1989; Saltzstein, 1989). Where minorities have strong representation as
employees in government bureaucracy, there too they are more likely to
experience favorable results (Hindera, 1993; Hindera and Young, 1998;
Lim, 2006; Selden, 1997). Some go even further, arguing that the effect can
be disaggregated to the individual encounter level (Theobald and Haider-
Markel, 2008). They argue that when persons of the same race serve in both
the officer and citizen roles, more favorable outcomes are likely—at least
502 KOCHEL, WILSON & MASTROFSKI
as perceived by the citizen. Although the evidence in terms of effects at
this level is mixed when the consequences are measured in terms of police
behavior, greater consistency is found when results are measured in terms
of citizens’ perceptions (Theobald and Haider-Markel, 2008).
The dynamics of political empowerment and bureaucratic representation
may vary (Lim, 2006). In a given police–citizen encounter, minority police
officers might be expected to treat suspects of their race or ethnicity more
favorably because of their ability to identify and empathize with minority
citizens. But the likelihood of that dynamic may well depend on the larger
organizational context in which the minority officer operates. The presence
of a large number of minority officers in the department may present a
very different cultural context for favorable police action than one in which
minority officers comprise only a small proportion of the police employees
(Sklansky, 2006). The presence of a large number of minority officers in a
police department might alter the cultural pressures about acceptable practice
within the organization for all officers, both minority and White, having
an indirect but more pervasive effect (Sklansky, 2006). Furthermore, having
politically powerful minority persons serving in executive (e.g., mayor,
city manager, or police chief) and legislative positions of authority (not
to mention prosecutors, judges, and defense attorneys) may signal to the
police that racial disparities unfavorable to minorities are both undesired
and more likely to produce negative consequences for officers who practice
policing of that sort.9 Finally, attempts to assess the consequences of the
political empowerment of minority citizens should consider not only who
holds positions of power, but what actions they have taken that might
influence the nature and extent to which race enters into the calculus of
street-level decision making.10
The studies we reviewed provided no insight into the degree to which
minority citizens were politically empowered in the communities or represented
in police organizations studied, although there have been some ex
post facto attempts to reconcile differences in race effects across studies in
9. Using aggregate data on 125 cities, Parker, Stults, and Rice (2005) found no
significant relationship between the city having a Black mayor and the arrest rates
for Black or White citizens.
10. For example, one study found unexpectedly that White suspects in one city were
more likely to experience disrespect than Black suspects (Mastrofski, Reisig, and
McCluskey, 2002). The authors attributed this to the actions of a new Black police
chief who had a long history in the department of vigorously opposing racist
policing and from the very outset of his administration took considerable effort to
convince his officers that he intended to punish relentlessly officers who practiced
it. The authors speculated that thismay have caused officers to reduce sharply their
disrespectful behavior toward minority suspects in the community, while leaving
largely unaffected past practices in the treatment of Whites.
EFFECT OF RACE ON ARREST DECISIONS 503
these terms (Lundman, 1996: 319). It would be highly valuable for future
studies of race effects on policing to take these considerations into account
explicitly, a practice that would facilitate cross-study comparisons, and that
we expect would go a long way toward accounting for variability in the effects
of race in both cross-sectional and longitudinal terms. Such studies also
would provide more insight into the value of policies, such as affirmative
action hiring, which have increased minority representation in police forces
of cities throughout the United States (Sklansky, 2006). Finally, we note
that we have examined only one of many discretionary choices police make,
choices that are susceptible to disparate racial impact: stop and search, use
of force, procedural justice, assistance to victims—to name a few. As the
body of available quantitative studies on these topics grows, so also does
the opportunity to usemeta-analysis to make sense of what otherwise might
seem as confusing or contradictory findings.
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Tammy Rinehart Kochel is an assistant professor of criminology and
criminal justice at Southern Illinois University, Carbondale. Her research
interests include policing reform, strategies and organization, correlates and
outcomes of institutional legitimacy, and neighborhood collective efficacy.
She has published in Policing, Journal of Crime and Justice, Criminal Justice
Policy Review, and International Review of Victimology. Recent research
projects have been sponsored by the Ministry of National Security for
Trinidad and Tobago and by the U.S. Department of Justice Office of
Community Oriented Policing Services.
David B. Wilson is a professor and chair of the Department of Criminology,
Law and Society at George Mason University. His research interests
are the effectiveness of offender rehabilitation and crime prevention
efforts, program evaluation methodology, meta-analysis, and systematic reviews.
He is an associate editor of the Journal of Experimental Criminology
and editor of Campbell Collaboration Systematic Reviews published by the
Crime and Justice Group.
Stephen D. Mastrofski is a university professor and director of the Center
for Justice Leadership and Management in the Department of Criminology,
Law and Society at George Mason University. His research interests
512 KOCHEL, WILSON & MASTROFSKI
include police discretion, police organizations and their reform, and systematic
field observation methods in criminology. In 2000 he received the
O.W. Wilson Award from the Academy of Criminal Justice Sciences for
education, research, and service on policing. In 2008 he and his coauthors
received the Law and Society Association’s article prize for their article
on Compstat. In 2010 he was elected a Fellow of the American Society of
Criminology