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Militarization and police violence: The case of the 1033 program


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Does increased militarization of law enforcement agencies (LEAs) lead to an increase in violent behavior among officers? We theorize that the receipt of military equipment increases multiple dimensions of LEA militarization (material, cultural, organizational, and operational) and that such increases lead to more violent behavior. The US Department of Defense 1033 program makes excess military equipment, including weapons and vehicles, available to local LEAs. The variation in the amount of transferred equipment allows us to probe the relationship between military transfers and police violence. We estimate a series of regressions that test the effect of 1033 transfers on three dependent variables meant to capture police violence: the number of civilian casualties; the change in the number of civilian casualties; and the number of dogs killed by police. We find a positive and statistically significant relationship between 1033 transfers and fatalities from officer-involved shootings across all models.
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Research and Politics
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I suppose it is tempting, if the only tool you have is a
hammer, to treat everything as if it were a nail.”
Abraham Maslow, The Psychology of Science:
A Reconnaissance (1966)
Soldierin’ and policin’ – they ain’t the same thing.”
Major Howard “Bunny” Colvin, The Wire
Season 3, Episode 10 (2014)
The summer of 2014 saw protracted protests to the non-
response associated with the killing of 18-year-old Michael
Brown. By the second day of protests, police officers
showed up in armored vehicles wearing camouflage, bul-
let-proof vests, and gas masks brandishing shotguns and
M4 rifles (Chokshi, 2014). That militarized response led to
a wave of criticism from observers including former mili-
tary personnel and politicians from both sides of the aisle.
In response, the federal government launched an investiga-
tion that ultimately resulted in Executive Order 13688
(EO). The EO sought to regulate the Department of
Defense 1033 program, which makes surplus military
equipment available to state, local, and tribal law enforce-
ment agencies (LEAs) at no cost. The EO banned LEAs
from acquiring certain equipment, and restricted them
from acquiring others.1 It also called for transparency and
training regarding the materials received. Some feared the
demilitarized police departments would no longer be able
to keep up with drug dealers, rioters, and terrorists. US
Representative John Ratcliffe introduced the Protecting
Lives Using Surplus Equipment Act to the House of
Representatives that would nullify all aspects of the EO.2
Militarization and police violence:
The case of the 1033 program
Casey Delehanty1, Jack Mewhirter2, Ryan Welch3
and Jason Wilks4*
Does increased militarization of law enforcement agencies (LEAs) lead to an increase in violent behavior among officers?
We theorize that the receipt of military equipment increases multiple dimensions of LEA militarization (material, cultural,
organizational, and operational) and that such increases lead to more violent behavior. The US Department of Defense
1033 program makes excess military equipment, including weapons and vehicles, available to local LEAs. The variation in
the amount of transferred equipment allows us to probe the relationship between military transfers and police violence.
We estimate a series of regressions that test the effect of 1033 transfers on three dependent variables meant to capture
police violence: the number of civilian casualties; the change in the number of civilian casualties; and the number of dogs
killed by police. We find a positive and statistically significant relationship between 1033 transfers and fatalities from
officer-involved shootings across all models.
Militarization, police, shootings
(*The names of the authors are listed in alphabetical order)
1 Department of Social Sciences, Gardner-Webb University, Boiling
Springs, NC, USA
Department of Political Science, University of Cincinnati, Cincinnati,
3 Department of Psychology, Stanford, CA, USA
Kennedy School of Government, Harvard University, Cambridge,
Corresponding author:
Casey Delehanty, Department of Social Sciences, Gardner-Webb
University, 110 South Main Street, P.O. Box 997, Boiling Springs, NC
28017, USA.
Email: Twitter: @caseydelehanty
712885RAP0010.1177/2053168017712885Research & PoliticsDelehanty et al.
Research Article
2 Research and Politics
In an interview, he said “It would be one thing if there was
some evidence that showed state and local law enforce-
ment had abuse [sic] or misused the equipment, and then
caused undue or unnecessary harm to American citizens.
That isn’t the case” (Jennings, 2016). This paper provides
the first attempt to analyze whether and to what extent
military transfers have increased the propensity by which
LEAs cause “undue or unnecessary harm.”
Drawing from Kraska (2007), we argue that increasing
LEA access to military equipment will lead to higher levels
of aggregate LEA violence. The effect occurs because the
equipment leads to a culture of militarization over four
dimensions: material; cultural; organizational; and opera-
tional. As militarization seeps into their cultures, LEAs rely
more on violence to solve problems. The mechanism mir-
rors psychology’s classic “Law of the Instrument,” whereby
access to a certain tool increases the probability that the
tool is used for problems when other tools may be more
appropriate (Maslow, 1966), including access to weapons
increasing violent responses (e.g. Anderson et al., 1998;
Berkowitz and LePage, 1967).
We evaluate this proposition using county-level data on
police killings in four US states: Connecticut, Maine,
Nevada, and New Hampshire (Burghart, 2015); and the
data on 1033 program receipts (
ingtonpost/data-1033-program). Estimating a series of
regressions, we find that 1033 receipts are associated with
both an increase in the number of observed police killings
in a given year as well as the change in the number of police
killings from year to year, controlling for a battery of pos-
sible confounding variables including county wealth, racial
makeup, civilian drug use, and violent crime. Given that
establishing a causal effect between 1033 receipts is poten-
tially problematic due to concerns of endogeneity, we re-
estimate our regressions using an alternative dependent
variable independent of the process by which LEAs request
and receive military goods: the number of dogs killed by
LEAs. We find 1033 receipts are associated with an increase
in the number of civilian dogs killed by police. Combined,
our analyses provide support for the argument that 1033
receipts lead to more LEA violence.
We organize the rest of the paper as follows. First, we
provide an argument that links police militarization and
police violence. Next, we briefly introduce the reader to the
1033 program and why it is appropriate for studying the
question at hand. Next, we describe the data and empirical
strategy. Then we present the results. Finally, we conclude
with some thoughts about how the research should influ-
ence policy and can be expanded in the future.
Borrowing from Kraska (2007: 503), we define militariza-
tion as the embrace and implementation of an ideology that
stresses the use of force as the appropriate and efficacious
means to solve problems. Kraska (2007) provides four
dimensions of militarization: material; cultural; organiza-
tional; and operational. We contend these dimensions rein-
force one another so that an increase in one can lead to an
increase in others. More specifically, the military equip-
ment obtained from the 1033 program directly increases the
material dimension. With the new equipment, martial lan-
guage (cultural), martial arrangements such as elite units
(organizational), and willingness to engage in high-risk
situations (operational) increase (Balko, 2014). Military
equipment naturally increases military-style training for
said equipment. That training can increase the other dimen-
sions of militarization. One trainer’s quote illustrates well
the uptake of militarized culture: “Most of these guys just
like to play war; they get a rush out of search and destroy
missions instead of the bullshit they do normally” (Kraska,
2001, quoted in Balko, 2014: 212). But the trainees would
not have to settle for the normal “bullshit” for long. Many
LEAs began practicing SWAT raids on low-level offenders
as a way to train and then as a matter of normal policy
(Balko, 2014; Sanow, 2011). Officers running military
operations with military tools and military mindsets organ-
ized militarily will rely more on the tenets of militarization
(e.g. the use of force to solve problems) which should
increase the use of violence on average. Since 1997, LEAs
obtain much if not most of their military equipment from
the 1033 program.
1033 program and militarization
President Bill Clinton signed into law H.R. 3230 (National
Defense Authorization Act for Fiscal Year 1997). The bill
contains section 1033, which allows the Secretary of Defense
to sell or transfer excess military equipment to local LEAs.
Between 2006 and April of 2014 alone, the Department of
Defense transferred over $1.5 billion worth of equipment
including over 600 mine-resistant ambush-protected vehi-
cles, 79,288 assault rifles, 205 grenade launchers, 11,959
bayonets, 50 airplanes, 422 helicopters, and $3.6 million
worth of camouflage and other “deception equipment”
(Rezvani et al., 2014). Eighty percent of US counties received
transfers, and those transfers increased over time from 2006
to 2013 by 1414% (Radil et al., 2017). These variations
allow us to test the proposition that, all things being equal,
the receipt of higher levels of 1033 equipment will lead to
increased levels of violence from LEAs.
Ultimately, the goal of this paper is to empirically assess
the relationship between 1033 transfers and police vio-
lence. To do so, we use a unique time-series cross-sectional
dataset, drawing from several sources. The dataset consists
of county level data for four states – Connecticut, Maine,
Nevada, and New Hampshire – from 2006–2014 (n = 455).
Delehanty et al. 3
Our primary analyses use two dependent variables in
two separate models: (1) the number of civilians killed by
LEAs in a given county for a given year; and (2) the
observed change in killings in a county between a given
year and the previous year. The first most directly tests the
outcome of interest. We also regress the change in killings
from the previous year on the independent variables in
order to somewhat address endogeneity issues. That is, one
could reasonably expect LEAs with high raw levels of kill-
ing to seek more 1033 transfers. However, it should be
harder (though not impossible) for LEAs to anticipate how
their need to use violence will change from year to year. On
top of that, we control for the average number of killings in
the county in the regression using the change in killings
dependent variable.
We constructed the variables using data from the Fatal
Encounters (Burghart, 2015) database, which, drawing
from other (incomplete) datasets, public record requests,
and crowd-sourced reports, provides a more comprehen-
sive list regarding police killings for selected states. When
constructing the dataset, only data for Connecticut, Maine,
Nevada, and New Hampshire were available, thus limiting
the sample to all counties within these states.3
The explanatory variable of interest measures the total
value of military surplus goods transferred to LEAs in a
given county in the previous year (logged US$). We believe
that use of a lagged measure somewhat addresses endoge-
neity concerns since operational, organizational, and cul-
tural shifts are expected to occur sometime after the
materials arrive, and training has completed. We used the
data from the 1033 dataset released by the Washington Post
which compiles raw data regarding 1033 transfers released
by the US Department of Defense Logistics Agency in
2014. From 2006–2014, nearly 88% of the counties in our
dataset received at least one 1033-transfer: the median
county received goods valued at roughly $50,000. Note that
while our data contains information from 2006–2014, our
use of a lagged independent variable eliminates the 2006
year from our empirical analysis (n = 390).
Figure 1 illustrates the relationship between 1033 trans-
fers and counties that experienced at least one killing in
Nevada in 2013.4 No LEA killings occurred in counties that
did not receive military equipment. While suggestive, we
next move to more rigorous statistical tests with controls to
increase the credibility of our claims.
We include several variables that we expect to simultane-
ously correlate with military expenditures and police vio-
lence in order to avoid biased estimates. Based on other
research we included median household income (e.g.
Mitchell and Wood, 1998; US Census Bureau, 2016),5 pop-
ulation (e.g. Jacobs, 1998; US Census Bureau, 2016), black
population (e.g. Ross, 2015; US Census Bureau, 2016), vio-
lent crime (e.g. Federal Bureau of Investigation, 2010;
Jacobs, 1998), and civilian drug use (Balko, 2014; Substance
Abuse and Mental Health Services Administration, 2016) as
controls.6 When using the number of observed killings as
the dependent variable we also include a lagged dependent
variable to control for autocorrelation (Beck and Katz,
1995). When the change in killings is used as the dependent
variables, we include the mean number of killings in that
county over the observed time period as a control. We pro-
vide descriptive statistics for all variables in Table A1 in the
Online Appendix.
Empirical analysis
In order to test the proposed relationship between 1033
receipts and our dependent variables, we estimate two sep-
arate regressions on an unbalanced time-series cross-sec-
tion data set from the years 2006 to 2014 with standard
errors clustered on county.7 When we estimate the expected
number of killings we utilize a negative binomial regres-
sion; when we estimate the change in police killings, we
use ordinary least squares regression. Prior to estimation,
we use the multiple imputation method recommended by
King and Wittenberg (2000) to avoid potential bias intro-
duced by dropping observations that contain missing data.
The extent to which each variable is imputed is shown in
Table A2 in the Online Appendix.
The results, presented in Table 1, confirm our argument: the
receipt of more military equipment increases both the
expected number of civilians killed by police (β = 0.055; p
= 0.016) and the change in civilian deaths (β = 0.017; p =
0.082). Given the difficulty of interpreting the substantive
effect of a logged independent variable, we rely on the pre-
dicted value graph in Figures 2 and 3. As shown in Figure
2, receiving no military equipment corresponds with 0.287
expected civilian killings in a given county for a given year,
whereas receiving the maximum amount corresponds with
0.656 killings. In other words, moving from the minimum
to the maximum expenditure values, on average, increases
civilian deaths by roughly 129%. As seen in Figure 3, coun-
ties that received no military equipment can expect to kill
0.068 fewer civilians, relative to the previous year, whereas
those that received the maximum amount can expect to kill
0.188 more, holding all else constant.
Alternative dependent variable: dog
While we believe that civilian casualty dependent variables
provide the most direct test of our hypothesis, empirically
establishing a causal relationship between killings and mili-
tary transfers presents a challenge given the potential for
endogeneity. Specifically, if LEAs anticipate future conflict
with civilians (and they are correct) and thus seek more
4 Research and Politics
1033 transfers, then our estimates will be systematically
biased. To account for this, we utilize an alternative depend-
ent variable that should be independent of LEAs’
propensity to request and receive transfers: the number of
dogs killed by police in a county for each included year
(2006–2013). That is, we do not expect LEAs to consider
Figure 1. The relationship between 1033 transfers and law enforcement agency killings in Nevada counties in 2013. Map created in
ArcMap 10.4 (Esri, 2016). Darker green counties received more military equipment. Those counties with a bullseye experienced at
least one killing.
Delehanty et al. 5
the number of pets they will encounter when applying for
military equipment. These data are taken from the
Puppycide Database Project (2016), a crowdsourced data-
base that provides the first nationwide database to track
police shooting of animals.8
To test the relationship between lagged transfers and
dogs killed by police, we estimate a negative binomial
regression, including the same controls as the previous
regressions (as well as a lagged dependent variable).
Results, presented in Table 2, confirm that a positive rela-
tionship exists. Holding all else constant, police that
received the highest 1033 transfers kill dogs at an order of
magnitude higher rate than those with no transfers (0.161
compared with 0.009). Such findings strengthen our confi-
dence in the claim that military transfers are related to LEA
Political scientists possess theoretical and methodological
tools to weigh into today’s debates about police violence.
This study answers the call for evidence-based policy
Table 1. Full regression results.
Variables Civilian deaths Change in civilian deaths
Expenditures (lag) 0.055**
Civilian deaths (lag) 0.073*
Civilian death (mean) −0.139*
Violent crime 0.023
Civilian drug-use −0.104
Median income −0.910*
Black population −0.040
Population 0.872**
Constant −0.387
Observations 390 390
Note: clustered standard errors in parentheses: ***p < 0.01; **p < 0.05;
*p < 0.1.
Figure 2. Expected number of killings over the range of the
explanatory variable with 90% confidence intervals. All other
variables held at their means.
Figure 3. Expected change in killings over the range of the
explanatory variable with 90% confidence intervals. All other
variables held at their means.
Table 2. Negative binomial regression results using dog
casualties as the dependent variable.
Variables Dog casualties
Expenditures (lag) 0.162*
Dog casualties (lag) 0.217*
Violent crime 0.043
Civilian drug-use 0.105
Median income −0.015
Black population 0.848
Population −0.361
Constant −8.488
Observations 389
Note: clustered standard errors in parentheses: *** p < 0.01; ** p < 0.05;
* p < 0.1.
6 Research and Politics
analysis by Representative Ratcliffe and others as they
continue to debate the merits of the 1033 program (Murtha,
2016). We acknowledge that the present analysis is rela-
tively preliminary. Due to notoriously unavailable data on
police violence against the public, we present what we
consider to be a best attempt at establishing the proposed
relationship between military transfers and violence.9
Further, while no research method offers full certainty of
a causal effect, we attempt to increase the plausibility of
the claim that 1033 transfers lead to more police violence.
We do so by measuring the transfers in the previous year,
as well as by leveraging three different dependent varia-
bles. While the first dependent variable – civilian killings
– represents the most direct measure to test the claim,
using the next two dependent variables – change in civil-
ian killings and dog killings – helped bypass endogeneity
concerns to an extent. As more social scientists take up
this sort of research, we expect replication and extension
of these results in different jurisdictions with different
As for policy, our results suggest that implementing the
EO to recall military equipment should result in less vio-
lent behavior and subsequently, fewer killings by LEAs.
Taken together with work that shows militarization actu-
ally leads to more violence against police (Carriere, 2016;
Wickes, 2015), the present study suggests demilitarization
may secure overall community safety. The EO represents
one avenue of demilitarization. However, given Kraska’s
(2007) typology, other aspects of militarization may be
targeted. For example, perhaps training can affect cultural
or operational militarization leading to less violent out-
comes. Future work should explore the relationship,
though the highly-decentralized nature of US police insti-
tutions presents serious challenges to systematic cross-
sectional study.
The scope of the present study allows us to derive expec-
tations at the organizational level. However, a focus on
micro-foundations may yield interesting insights. Our
paper cannot shed light on the effect of the military equip-
ment on an individual’s thought process in the field. Though
the quote above suggests some officers “just like to play
war,” others work to remind us “We’re just not out there
running around like Rambo” (Perez, quoted in Mendelson,
2016). Whereas our analyses shed light on average effects,
studies focusing on the individual level may offer more
nuanced understanding of how and when military equip-
ment affects certain officers.
Declaration of conflicting interest
The authors declare that there is no conflict of interest.
Support also provided by the Department of Political Science,
Florida State University and the School of Politics and Global
Studies, Arizona State University.
Supplementary material
The supplementary files are available at http://journals.sagepub.
1. Prohibited equipment includes tracked armored vehicles,
bayonets, grenade launchers, large caliber weapons and
ammunition (> 0.50 caliber). Controlled equipment (includ-
ing wheeled armored or tactical vehicles, specialized firearms
and ammunition, explosives and pyrotechnics, and riot equip-
ment) may be acquired if the law enforcement agency pro-
vides additional information, certifications, and assurances.
2. Just weeks before, US Senator Patrick Toomey introduced
a similar bill to the Senate called the “Lifesaving Gear for
Police Act” (Toomey, 2016).
3. We chose these states due to data availability. We have no
reason to believe that the data availability reflects system-
atic patterns that would affect our results. In fact, three of
the states are from the same region (New England) and have
very low crime rates. In this way, our sample represents a
hard case. Although Nevada has high crime rates, the addi-
tion of a Nevada dummy does not substantively change the
results (see Online Appendix, Table A3).
4. We chose Nevada due to the ease of seeing each county. We
have no reason to believe that Nevada is a special case. In
fact, adding a Nevada dummy does not substantively change
the results (see Online Appendix, Table A3).
5. Citations after each control list a source for theoretical justi-
fication of the variable and a source of the data.
6. We log each of the listed controls except drug use to reduce
skewness (Bland and Altman, 1997).
7. It is possible that that temporal dependencies also exist,
which could potentially affect our standard errors. To account
for this, we re-estimate all models including a dummy vari-
able for each year (using 2007 as a reference category). The
results are presented in Table A4 in the Online Appendix.
As shown, the effect of military transfers holds across all
8. This database tracks all animals killed by police. In the
county-years included in our analysis, only dog killings
were observed.
9. Data limitations also preclude us from distinguishing
between legitimate and illegitimate forms of violence.
Carnegie Corporation of New York Grant
This publication was made possible (in part) by a grant from
Carnegie Corporation of New York. The statements made and
views expressed are solely the responsibility of the author.
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... Since 1997, the 1033 program has transferred more than 4.3 billion dollars' worth of military equipment to police agencies across the United States (Bove and Gavrilova, 2017). The program is named after Section 1033, a part of the National Defense Authorization Act (H.R. 3230) that allows excess military equipment to be transferred to law enforcement officers (Delehanty, Mewhirter, Welch, and Wilks, 2017). This equipment includes military-grade vehicles, assault rifles, grenade launchers, camouflage gear, airplanes, and helicopters. ...
... Examining civilian perception and police violence as a result of the 1033 program is important to determine the benefits and downfalls of its implementation. A preliminary study by Delehanty et al. (2017) examined the relationship between the 1033 program and its effects on police violence. They found that VOLUME VIII & IX • 2021 police who received the highest 1033 transfers killed both civilians and dogs they encountered during civilian interactions at a higher rate than those who did not receive transfers. ...
... They found that VOLUME VIII & IX • 2021 police who received the highest 1033 transfers killed both civilians and dogs they encountered during civilian interactions at a higher rate than those who did not receive transfers. They noted that from the minimum and maximum ends of receiving military surplus, civilian deaths increased by about 129% (Delehanty et al., 2017). Another study examined Mexican drug trafficking agencies' responses to increased police militarization and found that militarization contributed to a deterioration of public safety and a heightened response from trafficking organizations (Flores-Macias, 2018). ...
... 35 Niskanen (1968Niskanen ( , 1971 Grandin (2019). 37 Delehanty et al. (2017); Lawson (2019). 38 Mummolo (2018). ...
Full-text available
U.S. government security along the U.S.–Mexican border has been increasingly militarized. This domestic militarization has been influenced by U.S. government military intervention abroad. Preparing for and executing foreign interventions involves investing in physical and human capital to effectively coerce and control the target population. The U.S. government’s “war on drugs” and “war on terror” created the conditions for this capital to be repurposed for domestic use in border-security efforts. While foreign policy created the conditions for border militarization, border militarization has also influenced foreign interventions. This article explores the symbiotic relationship between U.S. border militarization and foreign policy.
... There are variables exogenous to respondents themselves that are likely important for evaluations of police performance. First, policing scholars largely view militarization of the police and the public security sphere as contradictory to democratic policing, undermining public trust in law enforcement (Delehanty et al. 2017;Mummolo 2018). Second, researchers have noted that varying degrees of professionalism determines the level of trust in the police (Esparza and Ugues 2020). ...
Objective To include the factor of police malfeasance in the crisis of confidence in American police. Further, to explain the role of race, media, and contextual factors on individual perception of police performance. We argue that while the BLM movement was amplified by the deaths of Black people at the hands of police, it originates from the reality that police are continuously engaged in nefarious activities that wear down communities of color extensively. Methods Using the 2016 Collaborative Multiracial Post-Election Survey (CMPS) and data on media reported police malfeasance in 2016 collected from the CATO institute, we explore the relationship between police malfeasance, race, and evaluations of police performance. We create two sets of logit regressions, one for all CMPS respondents and second, disaggregated by race to show the effects of media reported police malfeasance on respondent’s evaluations of police performance. Results In the pooled model, we find a positive and significant correlation between poor police performance evaluations and incidences of police malfeasance. Further, substantive increases in the probability of rating police performance as poor are correlated with all respondents when disaggregated by race. We find a significant correlation among Black and White respondents, who are more likely to rate police performance as poor. Conclusion Conventional narratives around the Black Lives Matter movement seem to show that deaths at the hands of local law enforcement “created” the BLM movement. We argue that the current delegitimating of police in terms of public support is related directly to police behavior themselves. Police malfeasance increases the likelihood of negative performance evaluations, thus undermining community trust in the police.
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Latin American militaries are today in many regards inoperative and obsolete as an instrument of defence. Yet, they seek to maintain their organisational power and privileges. Governments, on the other hand, lack the adequate means to fight criminality, persisting poverty and social inequality. In an apparent win-win situation, Latin American governments have used the military as a wildcard to step in where civilian state capacity falls short, including for urban and border patrols, literacy campaigns and to collect garbage, among many other tasks. The military's manifold internal use has been defended mainly based on pragmatic reasons. We argue instead that the ostensive pareto optimality between militaries and governments has had negative effects for civil-military relations from a democratic governance point of view that takes into consideration the efficiency and effectiveness of how the state delivers basic services across different policy areas.
Little research examines the communication work that public police do following police shootings. Based on an analysis of 85 press releases, press conferences, and media interviews after police shootings in Canada spanning 2010–2020, we analyse narrative techniques used in police communications. Contributing to literature on police image management, we examine patterns in these communications, and we also identify silences and absences. We argue police press conferences and press releases after police shootings are less oriented toward misinformation or agenda-setting and more toward risk aversion. Sixty-two percent of communications in our sample used “euphemisms,” which obfuscate elements of use of force, while 31% of communications were “silent” and provided no justification for or information on the shootings. For these reasons, these communications may contribute to a sense of injustice felt by families of the victims of police shootings. Our findings may give pause to police administrators and media liaison officers who should consider what message such risk-averse communications send to families of victims, as well as to the public. In conclusion, we reflect on what these findings mean for literature on police image management.
Is militarized policing an effective way to combat insurgency? This article uses new global data on policing practices to evaluate whether states with militarized police perform better than those without them. The analysis provides no evidence that militarized police are an asset in counterinsurgency. Indeed, states with militarized units within their national or federal-level police are generally less likely to achieve favorable counterinsurgency outcomes. In explaining these findings, the article emphasizes that while militarization provides police with greater coercive capacity, it also impedes information collection and contributes to indiscriminate violence that can fuel additional dissent.
Objective: explore the role of law enforcement officers (LEOs) experiences of early adversity on work-related stress. Data/methods: LEOs were invited to participate in a data collection effort connected to a marketed LEO prevention toolkit on domestic violence (n = 247). Linear regression models were run to identify variables associated with work-related stress. Variables such as demographics, social behaviours, and other job-related factors were controlled for during analysis. Results: mean Adverse Childhood Experiences International Questionnaire (ACE-IQ) score of sample participants = 4.64; 95% of participants reported experiencing at least one ACE. ACEs are an important factor in later officer stress but is not significant when post-traumatic stress (PTS) is introduced to the regression models. ACEs, PST, and alcohol use are main explanatory variables of interest. Conclusions/implications: Out of our main variables of interest, ACEs were associated with LEO work-related stress, but the impact was blunted by PTS. PTS remained the only statistically significant variable associated with LEO work-related stress at the completion of analysis. ACEs have long been associated with development of PTS; thus, future research may explore how ACEs contribute to LEOs development of PTS.
Purpose: The purpose of this study is to examine the ways in which police officers connect their own direct and secondary trauma exposure to negative collateral effects on police-public interactions. Methods: Data are drawn from in-depth, semi-structured interviews with 48 police officers from across the U.S., representing a range of professional and personal backgrounds. Data were examined using a blended inductive/ deductive coding strategy. Results: Officers’ narratives show that trauma exposure and related deteriorations in mental health are seen as an inevitable part of police work. Officers linked trauma exposure to negative cognitions about the perception of danger, the controllability of events, and the overarching nature of humanity. Finally, officers connected these negative cognitions directly to the frequency and quality of their contact with the public. Specifically, officers described (1) avoidant behaviors including physical and emotional avoidance of situations that could result in further psychological or bodily harm, and (2) approach behaviors including more defensive, hostile and enforcement-based approaches to the public. Conclusions: Findings provide support for a cognitive-behavioral framework to explain the reported effects of police trauma exposure on interactions with the public. Implications for research and practice are discussed.
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A geographically-resolved, multi-level Bayesian model is used to analyze the data presented in the U.S. Police-Shooting Database (USPSD) in order to investigate the extent of racial bias in the shooting of American civilians by police officers in recent years. In contrast to previous work that relied on the FBI's Supplemental Homicide Reports that were constructed from self-reported cases of police-involved homicide, this data set is less likely to be biased by police reporting practices. County-specific relative risk outcomes of being shot by police are estimated as a function of the interaction of: 1) whether suspects/civilians were armed or unarmed, and 2) the race/ethnicity of the suspects/civilians. The results provide evidence of a significant bias in the killing of unarmed black Americans relative to unarmed white Americans, in that the probability of being {black, unarmed, and shot by police} is about 3.49 times the probability of being {white, unarmed, and shot by police} on average. Furthermore, the results of multi-level modeling show that there exists significant heterogeneity across counties in the extent of racial bias in police shootings, with some counties showing relative risk ratios of 20 to 1 or more. Finally, analysis of police shooting data as a function of county-level predictors suggests that racial bias in police shootings is most likely to emerge in police departments in larger metropolitan counties with low median incomes and a sizable portion of black residents, especially when there is high financial inequality in that county. There is no relationship between county-level racial bias in police shootings and crime rates (even race-specific crime rates), meaning that the racial bias observed in police shootings in this data set is not explainable as a response to local-level crime rates.
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Political or threat explanations for the state's use of internal violence suggest that killings committed by the police should be greatest in stratified jurisdictions with more minorities. Additional political effects such as race of the city's mayor or reform political arrangements are examined. The level of interpersonal violence the police encounter and other problems in departmental environments should account for these killing rates as well. Tobit analyses of 170 cities show that racial inequality explains police killings. Interpersonal violence measured by the murder rate also accounts for this use of lethal force. Separate analyses of police killings of blacks show that cities with more blacks and a recent growth in the black population have higher police killing rates of blacks, but the presence of a black mayor reduces these killings. Such findings support latent and direct political explanations for the internal use of lethal force to preserve order.
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More than 30 years ago, Berkowitz and LePage (1967) published the first study demonstrating that the mere presence of a weapon increases aggressive behavior. These results have been repli- cated in several contexts by several research teams. The standard explanation of this weapons effect on aggressive behavior involves priming; identification of a weapon is believed to automatically increase the accessibility of aggression-related thoughts. Two experi- ments using a word pronunciation task tested this hypothesis. Both experiments consisted of multiple trials in which a prime stimulus (weapon or nonweapon) was followed by a target word (aggressive or nonaggressive) that was to be read as quickly as possible. The prime stimuli were words in Experiment 1 and pictures in Experiment 2. Both experiments showed that the mere identification of a weapon primes aggression-related thoughts. A process model linking weapons as primes to aggressive behavior is discussed briefly.
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We examine some issues in the estimation of time-series cross-section models, calling into question the conclusions of many published studies, particularly in the field of comparative political economy. We show that the generalized least squares approach of Parks produces standard errors that lead to extreme overconfidence, often underestimating variability by 50% or more. We also provide an alternative estimator of the standard errors that is correct when the error structures show complications found in this type of model. Monte Carlo analysis shows that these “panel-corrected standard errors” perform well. The utility of our approach is demonstrated via a reanalysis of one “social democratic corporatist” model.
Concerns about police militarization have become an important public policy issue since the aggressive police response to the 2014 protests in Ferguson, Missouri, where police officers used military-style equipment to confront protestors. This event was a stark visual reminder that many U.S. police departments have used federal programs to acquire surplus military equipment, including weapons, armored vehicles, and body armor. We explore the geographies and histories of one the most important programs, called 1033, which supplies police with military equipment under the rationale of prosecuting the War on Drugs. We show that the legal blurring of the police and the military has been ongoing for decades at the national scale but this has resulted in an uneven landscape of police militarization at the county scale. We also investigate one of the most common global arguments for why police become militarized, which is the presence of Special Weapons and Tactics-style paramilitary teams, finding little support for that claim. More geographic inquiry is needed to understand the trajectories, causes, and consequences of police militarization.
Social Scientists rarely take full advantage of the information available in their statistical results. As a consequence, they miss opportunities to present quantities that are of greatest substantive interest for their research and express the appropriate degree of certainty about these quantities. In this article, we offer an approach, built on the technique of statistical simulation, to extract the currently overlooked information from any statistical method and to interpret and present it in a reader-friendly manner. Using this technique requires some expertise, which we try to provide herein, but its application should make the results of quantitative articles more informative and transparent. To illustrate our recommendations, we replicate the results of several published works, showing in each case how the authors' own conclusions can be expressed more sharply and informatively, and, without changing any data or statistical assumptions, how our approach reveals important new information about the research questions at hand. We also offer very easy-to-use Clarify software that implements our suggestions.
Despite the transition from authoritarian rule to a democratically elected government in 1985, there remains in Brazil a persistent gap between the formal principles and the actual practices of democracy. The gap is particularly manifest in the daily contacts between citizens and representatives of state authority, especially regarding the treatment of Afro-Brazilians. Analyses of the “regulated” and “relational” character of citizenship in Brazil, as well as observations about the attitudinal dispositions of the members of the criminal justice system, suggest that Afro-Brazilians are likely to benefit from fewer protections compared to whites and are more likely to suffer discrimination at the hands of the police. Analyses of the 1988 National Household Survey (PNAD-88) support both hypotheses: Net of statistical controls for key socioeconomic indicators, Afro-Brazilians are more likely than whites to be the victim of assault, and they are more likely to be assaulted by the police. The findings show how the perceptions of class, color, and criminality produce differential protections and treatments inconsistent with the attributes of universal citizenship. Our analysis points more generally to the formidable institutional and cultural challenges that confront the attempt to fully consolidate a democratic regime in Brazil.
When we use transformed data in analyses,1 this affects the final estimates that we obtain. Figure 1 shows some serum triglyceride measurements, which have a skewed distribution. A logarithmic transformation is often useful for data which have positive skewness like this, and here the approximation to a normal distribution is greatly improved. For the untransformed data the mean is 0.51 mmol/l and the standard deviation 0.22 mmol/l. The mean of the log10 transformed data is -0.33 and the standard deviation is 0.17. If we take the mean on the transformed scale and back transform by taking the antilog, we get 10-0.33=0.47 mmol/l. We call the value estimated in this way the geometric mean. The geometric mean will be less than the mean of the raw data.View larger version:In a new windowDownload as PowerPoint SlideFig 1 Serum triglyceride and log10 serum triglyceride concentrations in cord blood for 282 babies, with best fitting normal distributionWhen triglyceride is measured in mmol/l the log of a single observation is the log of a measurement in mmol/l. The average of n such transformed measurements is also the log of a number in mmol/l, so the antilog is back in the original units, mmol/l.The antilog of the standard deviation, however, is not measured in mmol/l. Calculation of the standard deviation of the log transformed data requires taking the difference between each log observation and the log geometric mean. The difference between the log of two numbers is the log of their ratio.2 As a ratio is a dimensionless pure number, the units in which serum triglyceride was measured would not matter; the standard deviation on the log scale would be the same. As a result, we cannot transform the standard deviation back to the original scale.If we want to use the standard deviation or standard error it is easiest to do all calculations on the transformed scale and transform back, if necessary, at the end. For example, the 95% confidence interval for the mean on the log scale is -0.35 to -0.31. To get back to the original scale we antilog the confidence limits on the log scale to give a 95% confidence interval for the geometric mean on the natural scale (0.47) of 0.45 to 0.49 mmol/l. For comparison, the 95% confidence interval for the arithmetic mean using the raw, untransformed data is 0.48 to 0.54 mmol/l. These limits are wider than those for the geometric mean. This is because with highly skewed data the extreme observations have a large influence on the arithmetic mean, making it more prone to sampling error. Lessening this influence is one advantage of using transformed data.If we use another transformation, such as the reciprocal or the square root,1 the same principle applies. We carry out all calculations on the transformed scale and transform back once we have calculated the confidence interval. This works for the sample mean and its confidence interval. Things become more complicated if we look at the difference between two means. We shall look at this in another Statistics Note.References1.↵Bland JM, Altman DG. Transforming data. BMJ 1996; 312: 770.OpenUrlFREE Full Text2.↵Bland JM, Altman DG. Logarithms. BMJ 1996; 312: 700.OpenUrlFREE Full Text