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Disproportionate Use of Lethal Force in Policing Is Associated With Regional Racial Biases of Residents


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Due to a lack of data, the demographic and psychological factors associated with lethal force by police officers have remained insufficiently explored. We develop the first predictive models of lethal force by integrating crowd-sourced and fact-checked lethal force databases with regional demographics and measures of geolocated implicit and explicit racial biases collected from 2,156,053 residents across the United States. Results indicate that only the implicit racial prejudices and stereotypes of White residents, beyond major demographic covariates, are associated with disproportionally more use of lethal force with Blacks relative to regional base rates of Blacks in the population. Thus, the current work provides the first macropsychological statistical models of lethal force, indicating that the context in which police officers work is significantly associated with disproportionate use of lethal force.
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Disproportionate Use of Lethal Force
in Policing Is Associated With Regional
Racial Biases of Residents
Eric Hehman
, Jessica K. Flake
, and Jimmy Calanchini
Due to a lack of data, the demographic and psychological factors associated with lethal force by police officers have remained
insufficiently explored. We develop the first predictive models of lethal force by integrating crowd-sourced and fact-checked
lethal force databases with regional demographics and measures of geolocated implicit and explicit racial biases collected from
2,156,053 residents across the United States. Results indicate that only the implicit racial prejudices and stereotypes of White
residents, beyond major demographic covariates, are associated with disproportionally more use of lethal force with Blacks
relative to regional base rates of Blacks in the population. Thus, the current work provides the first macropsychological statistical
models of lethal force, indicating that the context in which police officers work is significantly associated with disproportionate use
of lethal force.
intergroup dynamics, racial bias, stereotypes, prejudice, lethal force, police
Minorities killed by police officers in the United States is an
issue that regularly garners national attention. The extent to
which it is occurring and the role that racial prejudice might
play are regular questions in the discourse following these inci-
dents. However, because the U.S. government does not man-
date reporting of lethal force (Byers & Moskop, 2014), it has
been difficult to empirically investigate associated factors.
More recently, the Guardian news agency developed a data-
base of U.S. individuals killed by police. Integrating traditional
reporting with police reports, fact-checked witness state-
ments, monitoring of regional news, and other open-sourced
police fatality databases (Swaine, Laughland, & Lartey,
2015), it is currently the most comprehensive and reliable
database of individuals killed by police.
To examine what
factors might be associated with Black and White Americans
being disproportionately killed by police relative to their pres-
ence in the population, the current research integrated use of
lethal force data with demographics and a large database of
implicit and explicit biases.
Racial bias can take many forms. Prejudice refers to a
valenced evaluation (e.g., good, bad) of a group, and stereo-
types refer to mental associations between a group (e.g.,
Blacks) and attributes (e.g., threat). These distinct forms of bias
can be measured relatively directly or indirectly. For example,
prejudice can be measured directly through explicit questions
(e.g., “How warmly or coldly do you feel toward Black peo-
ple?”) or indirectly through so-called implicit tasks that infer
bias from the speed or accuracy with which a response is made,
rather than from the contents of the response itself (Fazio, Jack-
son, Dunton, & Williams, 1995; Greenwald, McGhee, &
Schwartz, 1998). Biases measured explicitly are assumed to
reflect relatively deliberate and conscious mental processes,
often predicting intentional judgments and behaviors. In con-
trast, implicit biases have traditionally been conceptualized
as reflecting less intentional or controlled processes (Dovidio,
Kawakami, & Gaertner, 2002; Gawronski, Peters, Brochu, &
Strack, 2008) that can influence judgments and behaviors out-
side of conscious awareness.
Rather than examine the racial biases of police officers
directly as in previous work (Correll et al., 2007; Sim, Correll,
& Sadler, 2013; Terrill & Reisig, 2003), we instead examined
the context in which officers operate. Specifically, we used
regional demographic factors and the racial biases of residents
to capture that context and tested the relationships between
racial demographics, biases, and lethal force. Context and
behavior are closely linked (Asch, 1946; Barden, Maddux,
Petty, & Brewer, 2004) because environmental factors (e.g.,
Department of Psychology, Ryerson University, Toronto, ON, Canada
Department of Psychology, York University, Toronto, ON, Canada
Department of Psychology, University of California, Davis, CA, USA
Corresponding Author:
Eric Hehman, Department of Psychology, Ryerson University, 350 Victoria
Street, Toronto, Ontario, Canada M5B 2K3.
Social Psychological and
Personality Science
ªThe Author(s) 2017
Reprints and permission:
DOI: 10.1177/1948550617711229
social norms, institutions) shape decisions made within that
environment. Thus, there are several reasons police officers
operating in racially biased contexts may be more likely to use
lethal force.
How might the biases of many people in a region translate
into disproportionate lethal force? Macropsychological factors
such as the prevailing attitudes and beliefs within a region
might shape the manner in which police encounters unfold.
Models of attitude spread hold that individuals can be influ-
enced by the attitudes of others in their communities and that
such biases can be “contagious” (Rentfrow, Gosling, & Potter,
2008; Weisbuch & Pauker, 2011). Attitudes and stereotypes
can spread through explicit conversations, as well as
through nonverbal vectors such as observing facial expres-
sions and body language (Weisbuch, Pauker, & Ambady,
2009). To the extent that police officers are exposed to the
biases of their fellow residents in their region, they may
adopt those same attitudes themselves. Accordingly, one
possibility is that prevailing regional biases might shape
police officers’ own attitudes, and their behaviors on the job
are a result of these attitudes.
For instance, lab-based research at the individual level has
revealed that attitudes and stereotypes can influence perceptual
decisions (e.g., whether a person is holding a wallet or a gun),
particularly when such decisions must be made rapidly (Kubota
& Ito, 2014; Payne, 2001). Common individual-level factors,
such as mental stressors or fatigue, exacerbate the influence
of attitudes and stereotypes on judgments and behaviors (Ma
et al., 2013; Payne, 2006). Thus, it is reasonable that the demo-
graphics and/or biases of a region might create a context that
influences police officers, as they make challenging, split-
second, life-and-death decisions in the line of duty.
Alternatively, the opposite causal direction is equally plau-
sible that disproportionate lethal force might contribute to
regional racial biases. Individuals being killed by police fre-
quently receive media attention. If minorities being killed by
police are given selective media attention, it may create or
strengthen links between racial groups and crime or threat in
the minds of residents. Therefore, there are multiple plausible
mechanisms by which we might expect a relationship between
regional biases and police behavior. Consequently, the analyti-
cal focus of the current research lies not on police officers’
individual demographics or personality factors but, instead,
on the broad contextual factors present in the environments
in which police officers live and work.
In summary, we examined associations between regional
bias and use of lethal force. Moreover, there is ample evi-
dence from across the social sciences that both explicit and
implicit biases and stereotypes can jointly influence judg-
ments and behaviors (Dovidio & Gaertner, 2000; Gawronski
& Creighton, 2013). Accordingly, we investigated both possi-
bilities in the current research: Analysis 1 examined the pos-
sible influence of racial prejudice (i.e., positive or negative
evaluations of racial groups), and Analysis 2 examined the
possible influence of racial stereotypes (i.e., threat-related
beliefs about racial groups).
Analysis 1: Prejudice
The most widely used method of assessing implicit biases is the
implicit association test (IAT; Greenwald et al., 1998), a
speeded dual-categorization task in which participants must
simultaneously respond to social targets (e.g., White, Black)
and attributes (e.g., good, bad) by timed computer-key press.
The speed and/or accuracy with which participants respond
to one set of target-attribute pairings (e.g., White–Good) than
another set of pairings (e.g., White-–Bad) is assumed to reflect
the strength with which the target is associated with one attri-
bute relative to the other. Project Implicit (implicit.harvar has been collecting various IATs and measures of expli-
cit bias over the Internet since 2002. By geolocating respon-
dents, we used this data set (Xu, Nosek, & Greenwald, 2014)
to compute point estimates of implicit and explicit biases by
region. We did so at the level of core-based statistical areas
(CBSAs), a geographic area defined by the U.S. Office of Man-
agement and Budget of at least 10,000 people and adjacent
areas that are socioeconomically tied to a metropolitan center
by commuting. Importantly, Project Implicit data are not sys-
tematic samples of CBSAs: Although the percentage of Black
and White Project Implicit respondents in each CBSA corre-
lates strongly with the racial demographics of each CBSA as
reported by the U.S. Census (r¼.931, p< .001), Project Impli-
cit data differ from the general population on other demo-
graphic factors and may not be representative.
To test whether Blacks were being killed by police officers
at a rate disproportionate to their CBSA populations, the per-
centage of Blacks living in each CBSA was subtracted from the
percentage of Blacks killed in each CBSA relative to the total
amount of individuals killed by police officers. Individuals
were not killed by police in every CBSA, and CBSAs could
only be included in analyses if at least one individual in the
region (Black or White) had been killed by police. Population
data were obtained from the 2010 census (U.S. Census Bureau,
2010) and lethal force data from the Guardian (Swaine et al.,
2015). A higher score on this variable reflected greater usage
of lethal force with Blacks than would be expected based on the
CBSA population. An identical score was calculated for White/
non-Hispanic individuals to test whether Whites were being
disproportionally killed by police.
Racial Prejudice IAT. To create CBSA-level implicit and explicit
prejudice scores of respondents to Project Implicit (Xu et al.,
2014), we used only those that were U.S.-based, had CBSA-
level geographic information included, and implicit and expli-
cit data. We focused on Black and White participants only, as
sufficient data were not available for reliable estimates from
other groups. These criteria left 1,860,818 (out of a total of
4,023,404) respondents collected between 2003 and 2013 from
which to calculate point estimates of CBSA-level implicit and
explicit biases. We created variables reflecting the biases of
Black and White respondents separately to assess the unique
contribution of each group to the overall context and outcomes.
2Social Psychological and Personality Science XX(X)
We created CBSA-level implicit prejudice scores by averaging
the IAT Dscores of Black and White respondents (separately)
in each CBSA. CBSA-level explicit prejudice scores come
from responses to two feeling thermometer items, separately
asking how warm or cold participants felt toward both Blacks
and Whites (0 ¼very cold,10¼very warm). Responses to the
Black feeling thermometer were subtracted from responses to
the White feeling thermometer for both Black and White
respondents and averaged for each CBSA. Consequently, pos-
itive scores on the implicit and explicit prejudice measures rep-
resent positive attitudes toward Whites relative to Blacks.
Demographics. We also included additional CBSA-level demo-
graphic variables in the models. Socioeconomic status for
Blacks and Whites in each CBSA was represented by 5-year
estimates of median household income calculated with data
reported in the 2011–2013 American Community Survey
(U.S. Census Bureau, 2010). Two education variables repre-
senting the percentage of Blacks and Whites of the CBSA pop-
ulation who received a high-school or equivalent degree or a
BA or equivalent degree were calculated using 3-year estimates
from data reported in the 2011–2013 American Community
Survey (U.S. Census Bureau, 2010). The residential segrega-
tion of each CBSA was represented by an isolation index cal-
culated with 2010 census data (Glaeser & Vigdor, 2012),
with higher scores representing greater residential segregation
of Blacks from all other racial groups in that CBSA. Population
density is expressed as the average number of people per square
mile, as assessed by 2010 census data (U.S. Census Bureau,
2010). Employment rate was calculated using 3-year estimates
from data reported in the 2011–2013 American Community
Survey (U.S. Census Bureau, 2010). Total lethal force (regard-
less of victim race) was included from the Guardian database
(Swaine et al., 2015). Violent crime rate represents the number
of crimes in this category per 100,000 inhabitants. Rates for
2010–2013 were obtained from the Federal Bureau of Investi-
gations and averaged (Federal Bureau of Investigation, 2015).
When incorporating this large number of covariates from
different databases, many CBSAs had missing data on one or
more covariates. To compensate, all analyses were repeated
using multiple imputation, the best currently available missing
data approach (Enders, 2010). Conclusions based on these
analyses were identical (Supplemental Table S1). In addition,
identical analyses were completed using disproportionate lethal
force calculated both as a risk ratio and as an odds ratio as out-
come measures. Conclusions based on these analyses were
identical (Supplemental Table S2).
From when the Guardian began aggregating the lethal force
database in January 1, 2015, to September 30, 2015, a total
of 875 (M
¼37.3 years, SD ¼13.3, 35 female), individuals
had been confirmed as killed by police officers in the United
States. Across all 196 CBSAs in which lethal force occurred,
Black people represented 22.76%of all deaths, but constituted
only 11.76%of those CBSA populations, indicating that Blacks
are killed by police at a rate roughly double their presence in
the population, t(195) ¼4.46, p< .001, 95%CI [6.14, 15.89]
(Figure 1A). In contrast, the percentage of White deaths
(77.24%of all lethal force) was consistent with the presence
of Whites in those populations (78.70%of CBSA populations),
and the disproportionate lethal force of Whites did not signifi-
cantly differ from zero, t(195) ¼.57, p¼.573, 95%CI
[0.07, 0.04]. This result indicates that Blacks, but not Whites,
are killed by police at rates disproportionate to their presence in
the U.S. population.
Because Blacks are being killed at a rate disproportionate to
their population and Whites are not, we frame the subsequent
models as predicting disproportionate use of lethal force with
Blacks (but note these variables are highly correlated). We
tested statistical models of racial biases to explain variance in
disproportionate lethal force. Because the number of respon-
dents in each CBSA varied significantly (range ¼1–23,753
respondents), we were concerned that a low number of respon-
dents in a CBSA would lead to unstable estimates of CBSAs’
mean bias. To balance this concern with maximizing the num-
ber of CBSAs included in the analysis, we included a CBSA
Figure 1. Disproportionate lethal force (A) and implicit racial prejudice of Whites (B) by core-based statistical area (CBSA). Tick marks on scale
represent zero points in which no disproportion is present. CBSAs are included in analyses if at least one individual had been killed by police.
Hehman et al. 3
only if at least 150 residents (on average, .0005%of a CBSA
population) completed an IAT, resulting in 135 CBSAs
included in the analysis. However, results for Analysis 1 are
identical when using no such threshold and including all
CBSAs. The number of respondents in each CBSA used in each
analysis is reported in Supplemental Table S3.
We regressed disproportionate lethal force on the implicit
and explicit prejudices of White and Black residents (though
the number of Black respondents in each CBSA was frequently
below our threshold of 150 set for White respondents) in each
CBSA in a single linear regression model. Covariates in this
model included Black and White income, education level, resi-
dential segregation, violent crime, unemployment, population
density, and total lethal force (Table 1).
Only the implicit prejudice of Whites (Figure 1B) was asso-
ciated with disproportionate lethal force of Blacks, b¼.354,
p¼.031, 95%CI of B[0.374, 7.885].
As the implicit prejudice
of Whites in a CBSA increased, so too did disproportionate use
of lethal force with Blacks (Figure 2). Overall, this model
explained 14%of the variance in disproportionate lethal force.
Post hoc estimate of the achieved power with White implicit
bias was .67.
There were many CBSAs in which no Black individuals had
been killed by police which contributed to a nonnormal distri-
bution of disproportionate lethal force with Blacks. Accord-
ingly, all analyses for this and the subsequent analysis were
additionally tested by examining 95%confidence intervals
derived from 5,000 bias-corrected bootstraps, a technique that
does not require normally distributed data (Efron & Tibshirani,
1993). All results using this technique were identical (i.e., in
the same direction and significant) to those reported through-
out. Additionally, all results using multiple imputation and cal-
culating outcomes as risk and odds ratios were identical
(Supplemental Tables S1 and S2). Finally, we note this model
is inflated with a large number of covariates and not parsimo-
nious. We have adopted this approach to develop initial
predictive models of lethal force but also used ridge regression
and forward and backward stepwise regression techniques to
converge on a parsimonious model (Cohen, Cohen, West, &
Aiken, 2013). Based on the results of these follow-up analyses,
we present parsimonious models in the supplemental materials
(Supplemental Table S3). Critical to our conclusions, the impli-
cit racial biases of Whites remain the primary predictor in the
most parsimonious model.
Analysis 2: Stereotypes
Racial bias can take many forms. In Analysis 1, we operationa-
lized racial bias in terms of prejudice, that is, as an association
between a group (e.g., White) and an evaluation (e.g., positive).
Another form of racial bias is stereotypes, that is, as an associ-
ation between a group (e.g., Black) and an attribute (e.g., threa-
tening) (Dovidio, Hewstone, Glick, & Esses, 2010). To test
whether specific stereotypes might better predict dispropor-
tionate lethal force than White implicit prejudice, we utilized
a different data set from Project Implicit examining racial
threat stereotypes. Individuals responded to pictures of Black
and White people paired with weapons and harmless objects.
Thus, responses on the Weapons Stereotypes IAT indicate the
strength with which weapons are stereotypically associated
with Blacks relative to Whites. Though this data set is smaller
than the Racial Prejudice IAT data set used in Analysis 1, we
used the same respondent inclusion criteria in Analysis 2,
which gave us 295,235 (out of a total of 631,276) participants
from which to calculate point estimates of CBSA-level associa-
tions. We used the same threshold used in Analysis 1 of
150 respondents per CBSA for inclusion, which left 81 CBSAs
for analysis.
The smaller size of this data set necessarily meant that fewer
CBSAs were included in this analysis. However, all the CBSAs
that met the inclusion threshold of 150 respondents for the
Racial Prejudice data set reported in Analysis 1 also met this
Figure 2. The correlation between the core-based statistical area
(CBSA)-level implicit racial prejudice and disproportionate use of
lethal force with Blacks. Circle size represents the number of
respondents in each CBSA.
Table 1. Full Model of Disproportionate Lethal Force From the Racial
Prejudice Implicit Association Test.
Effect BSEbpValue
White implicit bias 4.129 1.90 .354 .031
White explicit bias 0.519 0.29 .306 .079
Black median income 0.001 0.01 .074 .699
White median income 0.001 0.01 .223 .261
% HS degree Blacks 0.362 1.34 .270 .788
% HS degree Whites 0.681 1.01 .095 .503
% BA degree Blacks 2.613 2.24 .162 .246
% BA degree Whites 1.659 1.49 .153 .268
Segregation 0.040 0.36 .015 .912
Black implicit bias 1.130 0.84 .146 .182
Black explicit bias 0.117 0.14 .089 .392
Violent crime 0.001 0.01 .082 .489
Unemployment 0.013 0.02 .089 .476
Population density 0.001 0.01 .182 .123
Total lethal force rate 0.031 0.02 .181 .077
Note.HS¼high school. BA ¼bachelor of arts. R
4Social Psychological and Personality Science XX(X)
criterion for the Racial Stereotype data set. Consequently, we
were able to fit a model that included both prejudice and stereo-
type estimates for each CBSA, which allowed us to examine
which better explained disproportionate lethal force. Thus,
Analysis 2 simultaneously compared the relationships among
prejudice, stereotypes, and lethal force. We entered the average
Weapons Stereotypes IAT score of White residents in each
CBSA into the full model used in Analysis 1 including all
covariates (Table 2).
In this model, implicit threat stereotypes better predicted
disproportionate lethal force, b¼.390, p¼.001, 95%CI of
B[2.241, 8.752]
(Figure 3), than the implicit racial prejudice
of Whites, b¼.166, p¼.459, 95%CI of B[3.220, 7.050].
Moreover, this model explained a substantial 34%of the var-
iance in disproportionate lethal force in these CBSAs (as com-
pared to 14%in Analysis 1). Post hoc estimate of the achieved
power with Black–weapon association was .96.
We find that the implicit racial biases of White residents pre-
dict disproportionate regional use of lethal force with Blacks
by police. This association is robust, reliably emerging across
two conceptually distinct measures of racial bias, multiple
imputations, three different transformations of the outcome
measure, traditional and bootstrapped distributions, and above
and beyond 14 sociodemographic covariates. Though the
implicit prejudice of Whites is sufficient to significantly pre-
dict disproportionate lethal force (Analysis 1), the strongest
predictor of lethal force was the regional implicit stereotypical
association between Blacks and weapons (Analysis 2). These
results also suggest that disproportionate lethal force is not as
strongly related to sociodemographic characteristics of a
region as might be expected. Rather, in the present analyses,
the macropsychological characteristics of residents, operatio-
nalized at the CBSA level, are uniquely associated with mean-
ingful and important behavioral outcomes. Importantly,
CBSA-level effects may be quite different than individual-
level effects (Selvin, 1958). Hence, the research cannot
describe effects associated with racially biased individuals, and
the correct interpretation of these results is that racially biased
contexts are related to disproportionate lethal force.
That demographic covariates were consistently not associ-
ated with patterns of disproportionate lethal force in any anal-
ysis is as compelling as finding associations with bias.
Demographics are associated with a wide variety of important
behaviors and outcomes, and ostensibly might be expected to
be significant predictors in the analyses reported here. It is pos-
sible that the influence of demographic factors may be
obscured at the CBSA-level resolution of the present research.
CBSAs are large geographic units capturing metropolitan
areas, and the multiple communities within a CBSA may be
diverse, varying in important socioeconomic factors such as
wealth or ethnicity. Because these factors were averaged across
CBSAs, one possibility is that this process may have masked
the influence of these factors on lethal force. Because we can-
not draw inferences from null results, future research should
continue to consider these factors. But we can conclude that
in the CBSAs included here, racial prejudices and particularly
Black–weapon stereotypical associations are a stronger
predictor of lethal force than these demographic factors (see
Supplemental Materials for parsimonious models).
That implicit bias was the sole predictor raises some inter-
esting methodological and theoretical questions. Recent debate
has challenged the reliability and predictive validity of the IAT
(Blanton, Jaccard, Strauts, Mitchell, & Tetlock, 2015; Green-
wald, Poehlman, Uhlmann, & Banaji, 2009; Lai, Hoffman, &
Nosek, 2013; Oswald, Mitchell, Blanton, Jaccard, & Tetlock,
2015). Much of this debate has focused on the link between
individual-level IAT bias and behavior. In contrast, the unit
Figure 3. The correlation between the core-based statistical area
(CBSA)-level implicit weapon stereotypes and disproportionate use
of lethal force with Blacks. Circle size represents the number
of respondents in each core-based statistical area.
Table 2. Full Model of Disproportionate Lethal Force From the
Weapons Stereotype Implicit Association Test.
Effect BSEbpValue
Black–weapon association 5.497 1.63 .390 .001
White implicit bias 1.915 2.57 .166 .459
White explicit bias 0.100 0.45 .056 .824
Black implicit bias 1.372 0.123 .145 .267
Black explicit bias 0.105 0.23 .057 .646
Black median income 0.001 .01 .047 .844
White median income 0.001 .01 .163 .451
% HS degree Blacks 0.433 1.71 .042 .800
% HS degree Whites 2.698 1.47 .339 .072
% BA degree Blacks 2.434 2.77 .158 .383
% BA degree Whites 1.674 1.89 .137 .380
Segregation 0.570 0.389 .250 .148
Violent crime 0.001 0.01 .086 .547
Unemployment 0.031 0.03 .186 .196
Population density 0.001 0.01 .189 .196
Total lethal force rate 0.014 0.02 .100 .408
Note. HS ¼high school. BA ¼bachelor of arts. R
Hehman et al. 5
of analysis in the current research is geographic region, rather
than individuals, in which implicit and explicit bias scores are
the aggregate of many individuals. Whether similar psycho-
metric and validity criticisms apply to implicit bias aggregated
at a regional level remains an open question. Nevertheless, the
relationship between implicit bias and lethal force demon-
strated in the current work makes an important contribution
to this conversation.
As seen in Figures 2 and 3, multiple CBSAs do not have dis-
proportionate lethal force, in that no Blacks were killed by
police in these areas which, in turn, creates a nonnormal distri-
bution. Though statistical approaches were used to ensure accu-
rate standard errors for all of our tests, this distribution suggests
that two distinct processes may be driving these data. In other
words, CBSAs with zero disproportionate deaths may be
qualitatively different from those with disproportionate deaths.
Future research might incorporate zero-inflated Poisson
models to address this distinct question, facilitating an
understanding of differences between areas in which individu-
als are and are not killed by police.
The present research has several limitations due to the data and
its sources. First, the approach is correlational in nature, which
limits conclusions. Establishing the causes of disproportionate
use of lethal force with Blacks is important, but establishing
causality requires several steps. One step is demonstrating an
association between two variables, and another step is estab-
lishing clear temporal precedence. Reliable data on lethal force
do not exist prior to 2015 so we are limited in our ability to
establish temporal precedence. Consequently, we can only con-
clude that an association exists between racial biases and lethal
force, and future research can build upon this finding, provid-
ing more evidence of causal relationships.
Second, though we utilized the most comprehensive data-
base of U.S. lethal force currently available, the data rely partly
on crowd-sourcing. Lower population areas have a reduced
media presence, so deaths in these areas may be less likely to
be reported. Thus, a systematic bias toward high-population
areas may restrict the conclusions of the present research to
these areas. We note, however, that most of the U.S. population
resides in the areas covered, which means that our results may
be limited to areas where the majority of U.S. citizens reside
(Figure 1).
Further, these analyses examine data collected through the
Project Implicit website. Though our sample was representa-
tive of CBSA-level racial demographics, it is unlikely to be a
representative sample of residents on all other demographic
factors. That said, responses from this sample are correlated
with serious police outcomes. Moreover, previous research has
used this same data source (i.e., Project Implicit) to predict
other large-scale outcomes associated with the racial biases
in this sample (Leitner, Hehman, Ayduk, & Mendoza-
Denton, 2016a, 2016b). Thus, rather than considering the
representativeness of Project Implicit data, we believe it more
productive to consider why the biases reflected by their visitors
are related to lethal force above all other predictors. To be sure,
Project Implicit respondents differ from the general population
in at least two ways: They have access to the Internet and have
visited a website to learn more about bias. Having Internet
access may be a function of wealth or influence. Thus, one
explanation for the relationship between Project Implicit
responses and police behaviors is that police selectively act
in a manner consistent with the attitudes of the wealthy and
influential residents of the region. Conversely, Internet access
and/or motivation to learn about one’s biases may be character-
istics of people who pay attention to police behavior in their
area, which informs their racial biases. These and other links
might explain why Project Implicit respondents’ bias is related
to the behavior of police in their region. Nevertheless, future
research should examine whether these effects persist when
bias is measured with representative sampling methodology.
In the current research, we operationalize our baseline
against which to compare lethal force with Blacks as the pop-
ulation of Blacks in the United States. Other possible baselines
have been used by other research, including general crime
rates, violent crime rates, police encounters, arrest rates, con-
viction rates, or incarceration rates. However, as discussed
more fully elsewhere (Bayley & Mendelsohn, 1969; Smith,
1986; Terrill & Reisig, 2003), these rates all originate within
the criminal justice system and are therefore unreliable due
to biases that stem from factors such as the disproportionate
policing, behaviors, reporting, and enforcement of lower socio-
economic areas that typically have greater numbers of racial
minorities. For example, police may patrol an area with a
greater proportion of minorities more regularly, such that more
encounters and arrests in this area are likely than in areas with
fewer minorities, even controlling for crime rates. Compound-
ing the issue, the same infractions can result in an arrest in one
neighborhood but not another (Terrill & Reisig, 2003). There-
fore, we utilized general population statistics in the current
analyses to avoid the circularity inherent in using these other
potential baselines.
Another limitation is that our lethal force data is from 2015,
whereas our racial bias data were collected between 2003 and
2013. Other work has reported the stability of U.S. racial biases
over the past decade with this very data set (Schmidt & Nosek,
2010). Our supplementary analyses are consistent with this
conclusion: White implicit bias decreased very slightly by year,
B¼0.000355. Thus, these estimates of bias are extremely
stable and suggest that implicit bias scores collected in 2015
would not vary meaningfully from the data reported here in
their ability to predict disproportionate lethal force. Addition-
ally, we focused on Black/White relations only, because
(a) the most data were available for these groups, (b) Whites are
the largest group in the United States, and (c) Blacks are the
minority group most frequently killed by police. However,
whether these results hold for other minority populations is
an open question.
Finally, in the current work, we report relationships between
lethal force and implicit measures of both prejudice and
6Social Psychological and Personality Science XX(X)
stereotyping. A marginal relationship is additionally found
between explicitly reported prejudice, as measured by the dif-
ference between a feeling thermometer for Blacks and Whites,
and lethal force in Analysis 1 (though this relationship was not
found in Analysis 2). We used this measure of explicit bias
because it was available for the largest number of participants,
but there are limitations of validity and reliability of limited-
item measures (Flake, Pek, & Hehman, 2017; Nunnally,
1978). Thus, examining the relationships between explicit pre-
judice and lethal force with comprehensive, multi-item scales
would be valuable in future work.
Social scientists have long recognized that context is strongly
associated with behavior (Asch, 1946; Barden et al., 2004; Ter-
rill & Reisig, 2003), and the present research provides evidence
that prevailing racial attitudes and beliefs in a region are related
to life-or-death decisions that police officers make in the line of
duty. To our knowledge, the present work is the first to develop
models of disproportionate lethal force on such a scale, and the
first to implicate psychological processes (beyond socio-
demographic factors) as central to this phenomenon. Though
examined at the CBSA level, our results converge with
research at the individual level in finding that biased racial
associations may influence life-or-death decisions (Correll
et al., 2007; Correll, Crawford, & Sadler, 2015; Correll, Park,
Judd, & Wittenbrink, 2002; Sim et al., 2013). Again, however,
it is critical to avoid the ecological fallacy (Selvin, 1958) when
interpreting these results: The region-level effect may be quite
different than the individual-level effect. Given the correla-
tional nature of the analyses, the causal relationship between
CBSA-level biases and lethal force cannot be determined. One
interpretation of these results is that Whites’ biases create a
racially charged atmosphere that contributes to police killing
Blacks disproportionately. Alternatively, Blacks in some
regions may be more violent when interacting with police,
resulting in more justifiable lethal force, in turn influencing the
prejudice and stereotypes about Blacks held by people in the
region. Importantly, because of the correlational nature of the
analyses, we cannot rule out either interpretation. Moreover,
like all correlational work it is possible that a third, unobserved
variable better explains the relationship between lethal force
and regional biases. Thus, this research represents an important
first step in demonstrating an association between the regional
racial biases of Whites and the disproportionate use of lethal
force with Blacks. With increased data and improved reporting,
understanding the challenging contexts in which police
officers operate and decide to use lethal force will be possible
in future research.
Authors’ Note
All authors designed the study. E.H. and J.C. compiled the data. E.H.
and J.K.F. analyzed the data. All authors contributed to writing
the manuscript.
We thank Katherine Greenaway, Jordan Leitner, Michael Slepian, and
Jeff Sherman for providing feedback on earlier versions of this
Declaration of Conflicting Interests
The author(s) declared no potential conflicts of interest with respect to
the research, authorship, and/or publication of this article.
The author(s) disclosed receipt of the following financial support for
the research, authorship, and/or publication of this article: This
research was partially supported by a SSHRC Institutional Grant and
SSHRC Insight Development Grant (430-2016-00094) to EH, and the
Alexander von Humbolt Post-Doctoral Fellowship to JC.
Supplemental Material
The supplemental material is available in the online version of the
1. The Washington Post and have independently
compiled similar databases. Because the Washington Post’s data-
base includes only deaths by police from firearms (instead of all
deaths caused by police), and is not fact checked
and validated by a reputable source, we opted to analyze the
Guardian’s database.
2. The zero-order correlation between the implicit bias of Whites and
lethal force with Blacks was also significant, b¼.187, p¼.031,
95%CI of B[0.186, 3.793].
3. Implicit association test data for Analysis 2 (n¼295,235) were
more sparse than Analysis 1 (n¼1,860,818). To ensure our results
were not a function of our threshold of 150 respondents per CBSA,
we tested our models using different sample size thresholds.
Results were conceptually identical with the reported results from
thresholds of 100 to our maximum tested threshold of 300 at inter-
vals of 10. When including CBSAs with fewer than 100 respon-
dents, White implicit associations between Blacks and weapons
were marginally related (p< .1) to disproportionate lethal force.
When including CBSAs with fewer than 70 respondents, and at all
lower thresholds, results were nonsignificant (p> .1).
4. The zero-order correlation between implicit threat stereotypes and
lethal force with Blacks was also significant, b¼.297, p¼.006,
95%CI of B[1.249, 7.134].
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Author Biographies
Eric Hehman is an assistant professor at Ryerson University. He
examines how perceptions across group boundaries are formed and
can manifest in outcomes of societal significance.
Jessica K. Flake received her PhD working with Betsy McCoach at
the University of Connecticut and is now a postdoctoral scholar work-
ing with Jolynn Pek and Dave Flora at York University. She is inter-
ested in applications and evaluations of latent variable models.
Jimmy Calanchini received his PhD working with Jeff Sherman at
UC Davis and is now an Alexander von Humboldt postdoctoral scho-
lar working at Albert-Ludwigs-Universita¨t Freiburg. He examines the
malleability, outcomes, and components of implicit attitudes.
Handling Editor: Kate Ratliff
Hehman et al. 9
... For those with societal power, intergroup bias provides the moral justification for perpetuating injustice and inequality, systemic oppression and violence, ethnic cleansing and genocide (Hewstone & Cairns, 2001). For example, it has been found to contribute to racial and ethnic disparities across a range of life outcomes including health care quality (Hall et al., 2015;Williams & Wyatt, 2015), police use of deadly force (Hehman et al., 2018;Ross, 2015), employment decisions (Bertrand & Mullainathan, 2004;Koch et al., 2015), and disciplinary responses in school settings (Chin et al., 2020;Okonofua & Eberhardt, 2015). Beyond race and ethnicity, intergroup biases related to gender, social class, disability, body size, sexual orientation, and so on have also been associated with significant adverse consequences for marginalized groups (Chrisler & Barney, 2017;Hackett et al., 2020;Lott, 2012;Meyer, 2003;Watts & Zimmerman, 2002). ...
Whereas mindfulness has been shown to enhance personal well-being, studies suggest it may also benefit intergroup dynamics. Using an integrative conceptual model, this meta-analysis examined associations between mindfulness and (a) different manifestations of bias (implicit/explicit attitudes, affect, behavior) directed toward (b) different bias targets (outgroup or ingroup, e.g., internalized bias), by (c) intergroup orientation (toward bias or anti-bias). Of 70 samples, 42 (N = 3,229) assessed mindfulness-based interventions (MBIs) and 30 (N = 6,002) were correlational studies. Results showed a medium-sized negative effect of MBIs on bias outcomes, g = -0.56, 95% confidence interval [-0.72, -0.40]; I(2;3)2: 0.39; 0.48, and a small-to-medium negative effect between mindfulness and bias for correlational studies, r = -0.17 [-0.27, -0.03]; I(2;3)2: 0.11; 0.83. Effects were comparable for intergroup bias and internalized bias. We conclude by identifying gaps in the evidence base to guide future research.
This article sets out to describe and solve two puzzles that emerge in segregated labour markets (e.g. the USA or Sweden). First, in many hiring contexts people profess to adhere to egalitarian norms, and specifically to a qualification norm according to which job qualification should be the basis of employment. Still there is evidence of frequent norm violations (discrimination). Surprisingly, the norm persists and people do not frequently protest against such norm violations. The second puzzle is that people are suspicious of the hiring of minorities, perceiving such hirings as evidence that a ‘political correctness’ norm has replaced the qualification norm. The article proposes that both puzzles can be solved within a game‐theoretical model of social norm‐following, where implicit bias is introduced into an ‘employment game’. Within this model, implicit bias plays a double role. First, it interferes with employers' hiring decisions regarding ethnic majority and minority members, respectively. This is the standard way of understanding the effects of implicit bias. Second, implicit bias interferes with bystander evaluations of hired candidates' qualifications. This is a hitherto overlooked effect of implicit bias. The article concludes that once we understand the double role of implicit bias, the two puzzles are resolved.
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The spatial patterning of present-day racial bias in Southern states is predicted by the prevalence of slavery in 1860 and the structural inequalities that followed. Here we extend the investigation of the historical roots of implicit bias to areas outside the South by tracing the Great Migration of Black southerners to Northern and Western states. We found that the proportion of Black residents in each county ( N = 1,981 counties) during the years of the Great Migration (1900–1950) was significantly associated with greater implicit bias among White residents today. The association was statistically explained by measures of structural inequalities. Results parallel the pattern seen in Southern states but reflect population changes that occurred decades later as cities reacted to larger Black populations. These findings suggest that implicit biases reflect structural inequalities and the historical conditions that produced them.
Introduction: This project aims to characterize trauma-associated deaths of the American incarcerated population through legal intervention (LI) or death by law enforcement officials while in custody before and during incarceration. We determined the preceding events leading to violent death, including initiation of medical care, use of restraints and force, and demographics of the victims. Methods: We used National Violent Death Reporting System data from the years 2003-2019 to identify deaths that occurred while in custody or incarcerated, including discriminate and narrative data. Event information included weapon type, location of death, incident type, incarceration status, use of restraints, and prone positioning. Results: There were 86 victims who died from LI included in the analysis. Most events occurred after incarceration. All victims in our cohort were male, and race was an associated factor for death by LI. Only 16% of victims had an education level above high school/general educational development. Death by firearm compared to other weapons was significantly more common in the in-custody but not yet incarcerated group (83% versus 42%, P ≤ 0.0001). Other associated factors included a history of mental health, physical confrontations, the belief that the victim had a weapon, and being restrained in prone positioning. Conclusions: Our study shows that racial minority victims are disproportionately affected by LI deaths. Firearms and restraint type were important factors in LI deaths. Our findings suggest that violence prevention in the justice system should focus on prevention and de-escalation across setting with specific attention to use of force and inmate access to the weapons of police, guards, and other law and justice system workers. More transparent quality data is sorely needed to adequately define and address this problem.
Unlabelled: Policy Points Cultural racism-or the widespread values that privilege and protect Whiteness and White social and economic power-permeates all levels of society, uplifts other dimensions of racism, and contributes to health inequities. Overt forms of racism, such as racial hate crimes, represent only the "tip of the iceberg," whereas structural and institutional racism represent its base. This paper advances cultural racism as the "water surrounding the iceberg," allowing it to float while obscuring its base. Considering the fundamental role of cultural racism is needed to advance health equity. Context: Cultural racism is a pervasive social toxin that surrounds all other dimensions of racism to produce and maintain racial health inequities. Yet, cultural racism has received relatively little attention in the public health literature. The purpose of this paper is to 1) provide public health researchers and policymakers with a clearer understanding of what cultural racism is, 2) provide an understanding of how it operates in conjunction with the other dimensions of racism to produce health inequities, and 3) offer directions for future research and interventions on cultural racism. Methods: We conducted a nonsystematic, multidisciplinary review of theory and empirical evidence that conceptualizes, measures, and documents the consequences of cultural racism for social and health inequities. Findings: Cultural racism can be defined as a culture of White supremacy, which values, protects, and normalizes Whiteness and White social and economic power. This ideological system operates at the level of our shared social consciousness and is expressed in the language, symbols, and media representations of dominant society. Cultural racism surrounds and bolsters structural, institutional, personally mediated, and internalized racism, undermining health through material, cognitive/affective, biologic, and behavioral mechanisms across the life course. Conclusions: More time, research, and funding is needed to advance measurement, elucidate mechanisms, and develop evidence-based policy interventions to reduce cultural racism and promote health equity.
Intergroup biases have been studied on an individual level for decades, but recent research has examined intergroup bias as a regional phenomenon. Aggregated responses on bias tests from individuals in geographic proximity have shown to relate to important society-level discriminatory outcomes. In the present research, we examined the pressing issue of gender inequalities in employment using this regional perspective on intergroup bias. Using large scale open-access datasets, we investigated how psychological measures of regional gender stereotypes associating men with careers and women with families (traditional gender stereotypes) related to the representation of women in the workforce and parental leave policies in 35 member countries of the Organization for Economic Co-operation and Development (OECD) across 5 continents. In countries with stronger traditional gender stereotypes, we found that women were less represented in the workforce and, specifically, in manager positions. Regional traditional gender stereotypes were inconsistently related to parental leave policies. These findings suggest that the framework of regional intergroup bias may be fruitful to explain regional differences in gender disparities.
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This data archive includes Race Implicit Association Test (IAT) scores of 2,355,303 Internet volunteers who completed educational/demonstration versions of the Race IAT at from 2002 to 2012. Data in this archive can be downloaded for all years, either separately by year or in a single file. Codebooks, indicating the variable labels and value labels, and changes of variables over years, are available for both individual-year data sets and the entire data set. Participation in the (still on-going) Race IAT “study” at the Project Implicit (PI) demonstration site includes completion of the Race IAT along with demographic questions, self-report measures of racial attitude, and various additional measures received by a portion of the participants. These data allow analyses involving changes in responding over time and interrelations among IAT and self-report measures of race attitudes, as well as the association of each of these with demographics. This archive is available at Dataset The Data described in this paper is available from the Open Science Framework: [1]
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Racial bias in the decision to shoot can be minimized if individuals have ample cognitive resources to regulate automatic reactions. However, when individuals are fatigued, cognitive control may be compromised, which can lead to greater racial bias in shoot/don't-shoot decisions. The current studies provide evidence for this hypothesis experimentally using undergraduate participants (Study 1) and in a correlational design testing police recruits (Study 2). These results shed light on the processes underlying the decision to shoot and, given the high prevalence of fatigue among police officers, may have important practical implications.
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Greenwald, Banaji, and Nosek (2015) present a reanalysis of the meta-analysis by Oswald, Mitchell, Blanton, Jaccard, and Tetlock (2013) that examined the effect sizes of Implicit Association Tests (IATs) designed to predict racial and ethnic discrimination. We discuss points of agreement and disagreement with respect to methods used to synthesize the IAT studies, and we correct an error by Greenwald et al. that obscures a key contribution of our meta-analysis. In the end, all of the meta-analyses converge on the conclusion that, across diverse methods of coding and analyzing the data, IAT scores are not good predictors of ethnic or racial discrimination, and explain, at most, small fractions of the variance in discriminatory behavior in controlled laboratory settings. The thought experiments presented by Greenwald et al. go well beyond the lab to claim systematic IAT effects in noisy real-world settings, but these hypothetical exercises depend crucially on untested and, arguably, untenable assumptions. (PsycINFO Database Record (c) 2015 APA, all rights reserved).
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Three studies examined how participants use race to disambiguate visual stimuli. Participants performed a first-person-shooter task in which Black and White targets appeared holding either a gun or an innocuous object (e.g., a wallet). In Study 1, diffusion analysis (Ratcliff, 1978) showed that participants rapidly acquired information about a gun when it appeared in the hands of a Black target, and about an innocuous object in the hands of a White target. For counterstereotypic pairings (armed Whites, unarmed Blacks), participants acquired information more slowly. In Study 2, eye tracking showed that participants relied on more ambiguous information (measured by visual angle from fovea) when responding to stereotypic targets; for counterstereotypic targets, they achieved greater clarity before responding. In Study 3, participants were briefly exposed to targets (limiting access to visual information) but had unlimited time to respond. In spite of their slow, deliberative responses, they showed racial bias. This pattern is inconsistent with control failure and suggests that stereotypes influenced identification of the object. All 3 studies show that race affects visual processing by supplementing objective information. (PsycINFO Database Record (c) 2015 APA, all rights reserved).
Full-text available
The modal distribution of the Implicit Association Test (IAT) is commonly interpreted as showing high levels of implicit prejudice among Americans. These interpretations have fueled calls for changes in organizational and legal practices, but such applications are problematic because the IAT is scored on an arbitrary psychological metric. The present research was designed to make the IAT metric less arbitrary by determining the scores on IAT measures that are associated with observable racial or ethnic bias. By reexamining data from published studies, we found evidence that the IAT metric is “right biased,” such that individuals who are behaviorally neutral tend to have positive IAT scores. Current scoring conventions fail to take into account these dynamics and can lead to faulty inferences about the prevalence of implicit prejudice.
Two experiments used a priming paradigm to investigate the influence of racial cues on the perceptual identification of weapons. In Experiment 1, participants identified guns faster when primed with Black faces compared with White faces. In Experiment 2, participants were required to respond quickly, causing the racial bias to shift from reaction time to accuracy. Participants misidentified tools as guns more often when primed with a Black face than with a White face. L. L. Jacoby's (1991) process dissociation procedure was applied to demonstrate that racial primes influenced automatic (A) processing, but not controlled (C) processing. The response deadline reduced the C estimate but not the A estimate. The motivation to control prejudice moderated the relationship between explicit prejudice and automatic bias. Implications are discussed on applied and theoretical levels.
The verity of results about a psychological construct hinges on the validity of its measurement, making construct validation a fundamental methodology to the scientific process. We reviewed a representative sample of articles published in the Journal of Personality and Social Psychology for construct validity evidence. We report that latent variable measurement, in which responses to items are used to represent a construct, is pervasive in social and personality research. However, the field does not appear to be engaged in best practices for ongoing construct validation. We found that validity evidence of existing and author-developed scales was lacking, with coefficient alpha often being the only psychometric evidence reported. We provide a discussion of why the construct validation framework is important for social and personality researchers and recommendations for improving practice. 3
Rationale: Research suggests that, among Whites, racial bias predicts negative ingroup health outcomes. However, little is known about whether racial bias predicts ingroup health outcomes among minority populations. Objective: The aim of the current research was to understand whether racial bias predicts negative ingroup health outcomes for Blacks. Method: We compiled racial bias responses from 250,665 Blacks and 1,391,632 Whites to generate county-level estimates of Blacks' and Whites' implicit and explicit biases towards each other. We then examined the degree to which these biases predicted ingroup death rate from circulatory-related diseases. Results: In counties where Blacks harbored more implicit bias towards Whites, Blacks died at a higher rate. Additionally, consistent with previous research, in counties where Whites harbored more explicit bias towards Blacks, Whites died at a higher rate. These links between racial bias and ingroup death rate were independent of county-level socio-demographic characteristics, and racial biases from the outgroup in the same county. Conclusion: Findings indicate that racial bias is related to negative ingroup health outcomes for both Blacks and Whites, though this relationship is driven by implicit bias for Blacks, and explicit bias for Whites.
Perceptions of racial bias have been linked to poorer circulatory health among Blacks compared with Whites. However, little is known about whether Whites’ actual racial bias contributes to this racial disparity in health. We compiled racial-bias data from 1,391,632 Whites and examined whether racial bias in a given county predicted Black-White disparities in circulatory-disease risk (access to health care, diagnosis of a circulatory disease; Study 1) and circulatory-disease-related death rate (Study 2) in the same county. Results revealed that in counties where Whites reported greater racial bias, Blacks (but not Whites) reported decreased access to health care (Study 1). Furthermore, in counties where Whites reported greater racial bias, both Blacks and Whites showed increased death rates due to circulatory diseases, but this relationship was stronger for Blacks than for Whites (Study 2). These results indicate that racial disparities in risk of circulatory disease and in circulatory-disease-related death rate are more pronounced in communities where Whites harbor more explicit racial bias.
Prejudice, stereotyping and discrimination: Theoretical and empirical overview This chapter has two main objectives: to review influential ideas and findings in the literature and to outline the organization and content of the volume. The first part of the chapter lays a conceptual and empirical foundation for other chapters in the volume. Specifically, the chapter defines and distinguishes the key concepts of prejudice, stereotypes, and discrimination, highlighting how bias can occur at individual, institutional, and cultural levels. We also review different theoretical perspectives on these phenomena, including individual differences, social cognition, functional relations between groups, and identity concerns. We offer a broad overview of the field, charting how this area has developed over previous decades and identify emerging trends and future directions. The second part of the chapter focuses specifically on the coverage of the area in the present volume. It explains the organization of the book and presents a brief ...