The Perceptual Side Effects of Government Action: The Case of
Police Militarization
Jonathan Mummolo∗
Stanford University
Dept. of Political Science
Encina Hall West, Suite 100
Stanford, California 94305-6044
[email protected]
Phone: (650)-723-1806
Fax: (650)-723-1808
September 7, 2016
Abstract
This study develops and tests a theory explaining how government actions can
cause citizens to misperceive the social world. Focusing on the case of police militarization, I use survey experiments to show that the appearance of militarized
police in a community causes individuals to perceive that violent crime is more
severe than in otherwise similar places where traditionally armed police are portrayed. This is problematic, because original data on SWAT team deployments
gathered through hundreds of public information requests show that militarized
police behavior is an unreliable predictor of violent crime rates. The experiments also show that seeing militarized police weakens support for police funding
and lowers confidence in police. Government actions can bias public perceptions
of social conditions, and unintentionally undermine the political goals of public agencies, findings with widespread implications for the study of bureaucracy,
representation and policy development.
∗
Jonathan Mummolo is a Ph.D. candidate in Stanford’s Department of Political Science. Special thanks to
Justin Grimmer, Gary Segura, Amy Lerman, Terry Moe, Shanto Iyengar, Jonathan Rodden, Dorothy Kronick, Erik
Peterson, Sean J. Westwood, and Lauren Wright for their extremely helpful guidance and insights throughout this
project. This research was funded by grants from the National Science Foundation and Stanford University, and was
approved by Stanford’s Institutional Review Board.
The ability of citizens to determine whether social conditions are improving or worsening
is central to many prominent conceptions of democratic accountability (Bartels, 2009; Ferejohn, 1986; Fiorina, 1981; Key, 1966; Healy and Malhotra, 2010; Lenz, 2013), but this task is
both costly and difficult (Downs, 1957). This paper demonstrates a fundamental and largely
overlooked method by which citizens learn about the status of social problems. Specifically,
citizens update their beliefs about social conditions by taking cues from the actions of government agents tasked with addressing those conditions. However, as the analysis below
will show, this heuristic process can mislead the public, and distort perceptions of the social
world.
I demonstrate the perceptual side effects of government action using the case of police
militarization. In recent decades, local law enforcement agencies across the country have
increasingly adopted equipment and tactics previously reserved for theaters of war (Balko,
2014a; Kraska and Kappeler, 1997; Kraska, 2007). In the wake of several high-profile incidents of police violence and the ensuing social unrest (Baker, Goodman and Mueller, 2015),
a growing awareness of police militarization has fueled normative debates on issues including the validity of the criminal justice system and the nature of state power (Lerman and
Weaver, 2014). Below, I show that militarized police tactics distort public perceptions, convincing citizens that crime is worsening, when in fact it has been trending down in major
cities nationwide since the 1990s. To show this, I first outline a theory of how the actions
of government agents closely associated with specific social problems send cues to citizens
about the status of those problems. I also enumerate the conditions under which this method
of inference is likely to mislead citizens, and bias public perceptions. I then test this theory
using original administrative data on police behavior and survey experiments conducted on
large and racially diverse samples of respondents.
In two survey experiments—one of which included a large over-sample of African American respondents—individuals read news articles about a city police chief requesting ad-
1
ditional funding from the city council, and were randomly assigned to see accompanying
images of either militarized or traditionally equipped police units. Seeing militarized police increased the perceived level of violent crime in the vignette city by between 2 and 15
percentage points. In addition, treated individuals were also more likely to oppose police
funding, and open-ended responses revealed that this was because “expensive”-looking militarized equipment gave respondents the impression that the police department was already
adequately funded.
But an analysis of administrative data from local law enforcement agencies shows that
using militarized police activity as an indicator of violent crime can be misleading. Panel
data assembled through hundreds of public information requests shows that the deployments
of militarized police teams are virtually uncorrelated with violent crime within places, and
often display imprecise, nonlinear relationships with crime across jurisdictions, making them
an unreliable heuristic. A content analysis of thousands of newspapers suggests that the
association between militarized police and serious violent crime persists because news outlets
drastically underrepresent the routine activities that make up the bulk of militarized police
behavior—the service of drug-related search warrants—in which bodily injuries and deaths
are extremely rare.
These results help us to better understand the relationship between bureaucratic behavior
and citizen beliefs. They also demonstrate a previously unexplored method by which citizens
attempt to use cognitive shortcuts to make politically relevant inferences (Kuklinski and Hurley, 1994; Iyengar and Hahn, 2009; Lau and Redlawsk, 2001; Sniderman, Brody and Tetlock,
1993). Government action represents a potentially crucial heuristic that may be widening
the gulf between perception and reality on a number of social issues, including public perceptions of crime, which have diverged sharply from actual crime rates (McCarthy, 2014).
These findings also potentially explain why a number of seemingly critical social indicators
often fail to predict incumbent performance (Hopkins and Pettingill, 2015). More broadly,
2
these results demonstrate the potential for the observable actions of government to complicate the political goals of bureaucratic agencies and incumbent politicians by producing a
new type of policy feedback (Campbell, 2003; Jones and Baumgartner, 2005; Leonard, 1986;
Pierson, 1993; Wilensky, 1976). If showing effort to combat a social problem exacerbates the
perceived level of that problem in the mass public, many public policies may undermine the
political goals of the elites who enact them, constraining their ability to implement improved
policies in the future.
Finally, this study helps to reincorporate law enforcement activities into the study of
American politics. Despite its normative importance and relevance in current U.S. politics at
both the local and national levels, police have received scant attention from political scientists
(but see Brehm and Gates (1997); Lerman and Weaver (2014); Wilson (1968)). Police,
the coercive arm of government, make up one of the most consequential and visible public
bureaucracies. Recent controversies in places like Ferguson, MO, Staten Island, NY and
Baltimore, MD have made discussions of law enforcement policy a fixture among social
activists, in congressional debate and in campaign rhetoric. Simply put, police behavior is
political behavior. By demonstrating the broad impact of police tactics on public perceptions,
this study highlights the need for ongoing research on the social and political consequences
of law enforcement policy.
A Theory of the Perceptual Side Effects of Government Action
The programs governments enact convey information to citizens, one of the many ways in
which, as Schattschneider argued, “new policies create a new politics,” (Pierson, 1993, p.
595). As Pierson (1993) wrote, “[f]or the electorate, policies may produce cues that help them
develop political identities, goals, and strategies,” (619). Much of the work on this realm has
focused on the degree to which certain “visible” policies make particular issues salient in the
minds of voters, while other “quiet” policies allow governments to operate in ways citizens
3
might object to if their profile were raised (Leonard, 1986; Pierson, 1993; Wilensky, 1976).
By focusing attention on particular issues, policies can alter the public’s priorities, which in
turn can incentivize lawmakers to craft new policy—an example of policy “feedback” (Jones
and Baumgartner, 2005). Policies can also incite political participation, mobilizing groups
of citizens who feel compelled to ensure the survival of government programs that benefit
them directly (Campbell, 2003).
But public policy may be capable of more than simply alerting citizens of the issues to
which they should allocate time and effort. More fundamentally, government action may
also shape public assessments of the severity of social problems. That is, the degree to which
a citizen perceives a social condition to be worsening or improving may itself be determined,
at least in part, by the observable actions the government takes to address that condition.
Under certain scenarios, outlined below, this method of inference may lead to fairly accurate
inferences; in others, it can bias citizens’ assessments of the state of the social world.
Government action is an appealing heuristic for citizens evaluating the status of social
problems because bureaucrats are often devoted to specific, well-known policy issues. The
Environmental Protection Agency (EPA) is tasked with curbing pollution. The Centers for
Disease Control and Prevention (CDC) is tasked with preventing the spread of disease. The
Transportation Safety Administration (TSA) is tasked with thwarting terrorist attacks on
airplanes. Further, the manner in which bureaucrats tackle these issues is often visible to
the public. When citizens observe a shift in the tactics these bureaucrats employ, my theory
holds that they will infer a change in the status of the social problem being addressed. The
core hypothesis of this theory is that when citizens observe enhanced effort on the part of a
government agency—e.g., the adoption of stricter emissions standards by the EPA, restrictive
guidelines on travel abroad by the CDC, or the use of more invasive body scanners by the
TSA—they will infer that the problems these bureaucrats are attempting to address have
become more severe. In other words, given these sorts of policy shifts, a reasonable citizen
4
might wonder: why would agencies outlay additional time and resources, or signal that
citizens need to take increased care, if there was not a greater need to do so? In this way,
citizens faced with the often costly task of making sense of the social world around them
attempt to use government bureaucrats as canaries in the mine who alert citizens, sometimes
through their mere appearance, to important changes in conditions. Crucially, this method
of inference does not require that a causal relationship exist between government action and
social conditions. If citizens can simply identify which types of government responses are
predictive of changes in conditions, they can effectively update their beliefs about the state
of the world.
In some cases, using government actions as a heuristic for the status of social ills may
be quite effective. If in 2009, for example, a citizen learned that the U.S. government had
just passed a large economic stimulus package and inferred based on that information that
the national economy was in disrepair, they would of course have been correct. The reason
the inference is valid in this case is that the government’s policy response positively covaried
with a change in conditions. That is, as an economic disaster increased in scope, so too did
the government’s effort to combat it. But when the intensity of government action deviates
from the severity of conditions, this method of inference can wildly mislead the public.
Hints of this perceptual side effects phenomenon can be seen in prior empirical work.
Previous studies have found that government-sponsored anti-corruption campaigns—which
can be launched in response to isolated political scandals—are associated with erosions in
public trust in government and increases in perceived levels of political malfeasance, which
may be overestimated (Bowler and Karp, 2004; Kumlin and Esaiasson, 2012; Zhang, 2015).
Similarly, Bonilla and Grimmer (2013) show that government-issued terror alerts increase the
public’s perceived threat of a terrorist attack. Short-term government responses to episodic
events may be capable of convincing citizens that a systematic change in conditions has
occurred. And in the event a public policy unwarranted by conditions becomes entrenched,
5
the misperceptions it causes may be severe and prolonged.
There are many reasons governments would adopt enhanced tactics even in the absence
of a pronounced need for them. Public policies are often “path dependent,” and their implementation may continue long after their impetus has faded (Pierson, 2000). Governments
may also be more likely to enforce existing policies when additional funds or equipment become available, perhaps through a transfer from federal to local bodies (Fischel, 2004), or
when political pressure to do so becomes pronounced.1 Further, the misassociation between
particular government policies and social conditions can be reinforced by strategic political communication, (e.g., a politician seeking to perpetuate a perceived threat for political
gain), or by selective reporting by profit-seeking news outlets, which overrepresent rare and
sensational events, and the government responses that follow, relative to their actual rates
of occurrence (Duwe, 2000).
This theory has several implications for democratic accountability, public opinion and
policy making, only a few of which will be explicitly tested below. If government actions
lead citizens to update their beliefs about the status of a social problem, their willingness to
fund the government agency responsible for addressing that problem may also change. The
direction of this effect, however, is not obvious. It is plausible that when citizens perceive a
worsening condition, they become more likely to support government funding to combat it.
But if they hold the government responsible for the worsening condition, they may come to
view it as illegitimate, and oppose increased funding. For example, Chanley, Rudoph and
Rahn (2000) finds that when citizens perceive crime to be worsening, they place less trust in
government and are less likely to support government initiatives. I am therefore agnostic as
1
An example of this can be found in the crackdown on illegal immigration in the mid-2000s instituted
by local governments such as Prince William County, VA. Though undocumented immigration had long
been illegal, local law enforcement agencies had not prioritized this issue before local pressure mounted to
take “back our community” from undocumented immigrants, argued to be violent and dangerous by the
new initiative’s proponents (Miroff, 2007). Local police vigorously implemented the new policy, but police
data later revealed that only 2 percent of those charged with major violent crimes in Prince William were
undocumented immigrants (Mummolo, 2009).
6
to the direction of the effect of new policy on support for government spending, and it will
be assessed empirically below.
In addition, if the appearance of enhanced tactics prompts negative assessments of a
social issue’s status, we may expect confidence in the institution responsible for addressing
that issue to erode. Consistent with this expectation, Mansbridge (1997) shows that crime
and distrust in government are positively correlated over time (Chanley, Rudoph and Rahn,
2000, p. 247). The theorized effect is also likely to be conditional on attributes of the citizen.
For the individual who witnesses a government action fairly regularly firsthand (e.g., the
deployment of a militarized police unit in their neighborhood), the observation of that tactic
may appear mundane, and fail to convey any new information or to grab attention. On the
other hand, those more likely to be negatively affected by a government action may be more
prone to losing confidence in the agency executing the new policy than those unlikely to
have their lives directly impacted by it. Trust in government may therefore be conditional
on a citizens’ perceived likelihood of being targeted by a new government program.
The opportunity for militarized policing to distort public perceptions of crime should only
arise in the presence of several necessary preconditions. First, the government’s actions must
be observable to the public. Second, the frequency and/or intensity of these actions must
be a poor predictor of the social condition they are aimed at addressing. Third, to distort
perceptions in the long term, information conveying the government’s behavior must be misrepresented in a way that prevents citizens from learning that it fails to predict conditions,
otherwise they may cease to use it as a heuristic. In what follows, I provide background
on the recent militarization of local law enforcement agencies, describe an original data set
documenting militarized police behavior, and provide empirical evidence that these preconditions are met in the context of police militarization. I then proceed to causal tests of
whether displays of militarized policing affect citizens’ perceptions of crime.
7
Police Militarization and the Distortion of Public Perceptions
In recent decades, large cities across the U.S. have seen remarkably persistent drops in
crime rates (Blumstein and Wallman, 2006; McCarthy, 2014), but as Figure 1 shows, most
Americans continue to come to the opposite conclusion. The figure displays the annual rates
of both violent and property crime in the U.S. since 2000 as reported by the FBI (left two
panels), as well as the percentage of Americans who perceive that crime in the U.S. and in
their area has increased in the past year as reported by Gallup (right two panels). What
explains this striking divergence between perception and reality?
The perceptual side effects theory outlined above suggests an unexplored culprit: the
observable actions of law enforcement agents. Over the same period that Americans have
come to increasingly believe that crime is worsening, the character and usage of police—the
government agents most directly responsible for combatting crime—has also fundamentally
transformed. Local police and sheriff’s agencies large and small have militarized their departments, i.e. adopted equipment and tactics once reserved for theaters of war and the armed
forces, typically in the form of a heavily armed SWAT team (short for “Special Weapons and
Tactics”). Militarized equipment and tactics have proliferated across local agencies in recent
decades (Kraska and Kappeler, 1997; Kraska, 2007) in part due to federal initiatives such as
the so-called “1033 Program.” This Department of Defense initiative has distributed billions
of dollars in surplus military equipment to local agencies since the 1990s—items ranging
from filing cabinets and uniforms to assault rifles, mine-resistant ambush protected vehicles
(MRAPs) and helicopters (Balko, 2014a; Wofford, 2014).
According to the federal Census of State and Local Law Enforcement Agencies (CSLLEA), the percent of local police and sheriff’s departments reporting that they performed
SWAT functions in their community grew from 25% in 2000 to 32% in 2004 to 39% in 2008,
the latest year available.2 The rate at which these units are deployed has also climbed dra2
Note: the raw number of SWAT teams reported in 2008 was slightly smaller than in 2004, but this is
8
Figure 1: The percentage of Americans who think crime is rising has trended up since 2000, even
as actual crime rates have plummeted. Sources: Gallup; FBI Uniform Crime Reports. Gallup data
unavailable in 2012.
400
3500
3000
Perceived
Local Crime
% Saying Crime Has Gotten Worse
in Past Year (Gallup)
450
% Saying Crime Has Gotten Worse
in Past Year (Gallup)
500
Perceived
National Crime
Property Crime Rate
Property Crimes per 100,000 People
(FBI)
Violent Crimes per 100,000 People
(FBI)
Violent Crime Rate
70
60
50
50
40
30
40
2000
2005
2010
Year
2015
2000
2005
2010
2015
2000
Year
2005
2010
2015
Year
2000
2005
2010
2015
Year
matically. Using surveys of law enforcement agencies, Kraska (2007) shows that, “[t]oday, an
estimated 45,000 SWAT-team deployments are conducted yearly among those departments
surveyed; in the early 1980s there was an average of about 3,000,” (6).
In other words, even as crime rates have plummeted, government agents have been increasingly sending the public signals that the threat of crime is more severe than ever before.
Some of these signals have been overt. In justifying the need for militarized equipment, law
enforcement officials often cite the threat of terrorism and the growing need to protect officers from violent criminals (Redden, 2015; Savidge, 2014). In addition, as a content analysis
of newspaper coverage will show below, these signals have been amplified by the news media, which often reports on SWAT activity in connection with extraordinary and/or violent
events, but under-represents more mundane uses of SWAT teams, such as the service of warrants to search for illegal drugs. This combination of increasingly militarized policing, and
communication flows which reinforce the association between militarized police and violent
crime, have likely led many Americans to ask a reasonable question: why would local police
officers need equipment fit for a war zone if the threat facing them did not resemble a war?
likely due to the fact that fewer agencies appear in the survey data in 2008.
9
Data on Militarized Police Behavior
While police militarization has recently received widespread public scrutiny, high-resolution
data on the behavior of militarized police units remains in short supply. As journalist Radley
Balko, author of Rise of the Warrior Cop: The Militarization of America’s Police Forces,
noted, “[c]ollecting data about how SWAT teams are used, how often they’re used, and what
results from their deployment is a critical step toward determining whether or not they’re
being used properly. The biggest barrier to performing that sort of analysis has always been
that the data just don’t exist,” (Balko, 2014b). In addition, agencies which do collect data
on SWAT team usage vary in the details they record on each incident, and also often opt to
withhold data citing concerns about privacy and threats to law enforcement investigations.
As a result, the few academic studies of the behavior of militarized police units to date
have focused mostly on data derived from surveys of law enforcement agencies (e.g. Kraska
(2007), but see ACLU (2014)).3 While this approach has provided invaluable insight into the
prevalence and usage of SWAT teams, the analyses it has generated have been aggregated
at high levels, typically resulting in statistics such as the number of SWAT deployments in a
year nationwide, which provides no purchase on the locality-specific correlates of militarized
policing.
To characterize the frequency and nature of SWAT deployments over time at the local
level, I submitted hundreds of public records requests to local law enforcement agencies across
several states. The most comprehensive, granular data on SWAT deployments obtained
from these requests came from Maryland, which in 2009 passed a law requiring that all local
agencies with SWAT teams submit annual reports to the state government detailing the
circumstances and outcomes of each SWAT deployment they conducted.4 The data cover
3
The data from (Kraska, 2007) have not been made public and a request made to the author was not
accommodated.
4
While aggregate figures from these reports have been released by the state, an independent analysis of
the raw data underlying these reports has not been conducted to my knowledge.
10
the period FY2010-2014, and include the date, zip code, reason, and outcomes of more than
8,000 deployments spanning over 40 law enforcement agencies. The law has since expired
due to a sunset provision, making these data a rare, comprehensive window into the behavior
of militarized police units at the local level.5
To avoid relying on data from a single state, I also submitted records requests to every
local agency in California known to have a SWAT team (roughly 300 police and sheriff’s
departments) asking for incident-level records on every deployment for as far back as such
data exist. Rather than cast a nationwide net, I chose California because it is a large and
diverse state with a long history of SWAT activity (e.g. the San Francisco Police Department
formed its SWAT unit in 1967). SWAT teams serving prisons, schools and federal lands were
excluded,6 and it was later determined through correspondence that 15 of the localities
on the list had no SWAT team. As data began to come in, I learned that some of these
municipalities participated in joint regional teams comprised of multiple jurisdictions, and a
single agency often warehoused data for the entire regional team. After revising the list in
light of these discoveries, a total of 271 jurisdicitons were solicited for data in summer of 2015
either directly, or indirectly through a member of a joint team. In the end, 51 agencies never
replied to the records request, and 18 rejected the request, while 189 supplied data either
directly or through a multi-jurisdictional team to which they belonged. The final response
rate was 0.7. (See Table A1 in the Appendix for a comparison between the jurisdictions
which did and did not supply data in California on key demographic variables.)
As Figure 2 shows, much of the data from California required substantial cleaning and
manual data entry before merging and analysis was possible. Due to differences in formats
and jurisdictional arrangements, several coding decisions needed to be made in order to con5
Maryland is only one of two states to have centrally collected data of this nature. The other state is
Utah, and a request to obtain data from there is still pending.
6
Though university SWAT teams were not contacted, one school is included in the analysis of SWAT
deployments and violent crime below because it is part of a joint, multi-jurisdictional team and reports
crime data to the FBI.
11
Figure 2: A portion of the Los Angeles Police Department’s SWAT log reserved for warrant service.
struct the California SWAT panel, which are detailed on p. 1 of Appendix A. For example,
since precise locations were not supplied for many deployments, the regional teams in the
California data are treated as single amalgamated jurisdictions in subsequent analyses. Despite these limitations, the California panel represents some of the most granular data on
the behavior of militarized police units available. According to FBI data, the agencies which
supplied records in California serve jurisdictions which are home to more than 20 million
residents, roughly 57% of the state’s population, and more residents than the populations of
nearly all U.S. states.
How Do Citizens Encounter Militarized Police?
For this expansion of militarized police units to affect public perceptions, it is necessary for
the activities of SWAT teams to be observable. The most obvious route to observing this
behavior is firsthand in one’s own neighborhood. To get a sense of which types of individuals
are more likely to directly observe a SWAT team, I aggregated Maryland SWAT deployments
into zip codes and paired the data with zip code-level Census information on key demograph-
12
ics.7 Figure 3 displays the relationships between the volume of deployments in a given zip
code (logged and adjusted for population size)8 and these demographic variables (n ≈ 471 zip
codes).9 As the figure shows, zip codes with higher shares of non-Hispanic Black residents,
and lower shares of non-Hispanic white residents, tend to see more SWAT team deployments
per capita. The relationship between %Hispanic in a zip code also appears slightly positive. The percent with a B.A., median household income and %Non-Hispanic Asian exhibit
inconsistent and nonlinear relationships with SWAT deployments. These results suggest
that African American residents are more likely to witness a SWAT deployment firsthand in
their neighborhoods than their counterparts, all else equal. In keeping with the expectations
outlined above, I therefore hypothesize that African Americans will be less likely to have
their perceptions of crime altered when exposed to militarized police units. However, given
the well-documented tendency of African Americans to be subjected to worse treatment by
police (Alexander, 2012; Fryer Jr., 2016; Goel, Rao and Shroff, 2016), it is also possible that
African Americans will be more likely to lose confidence in police when exposed to SWAT
teams.
Still, the chances of an individual witnessing a SWAT raid firsthand may still be relatively
low (the median number of SWAT raids per 100,000 people in 2013 in the Maryland zip code
data was roughly 30). A more probable method of encountering militarized police is in
the news media. To characterize the prevalence of coverage of SWAT teams in the news, I
conducted annual keyword searches on Access World News, a digital archive maintained by
NewsBank Inc., of thousands of newspapers across the U.S. Like many media repositories,
this database has pros and cons. It contains records from over 2,000 newspapers nationwide,
7
Zip code Tabulation Area (ZCTA)-level census data was taken from the 2013 5-year American Community
Survey (ACS). Throughout this study, ZCTA codes and US Zip Code identifiers (postal routes) are used
interchangeably, a choice which is validated on p. 11 in Appendix A.
8
Because the distribution is heavily skewed, I take the log of the number of deployments per zip code plus
one. I add one before taking the log so that the log of observations with zero deployments can be computed.
9
Due to missing data in the census files on some covariates, the number of unique zip codes used to
generate each plot ranges between 428 and 471.
13
Figure 3: Demographics of SWAT Deployments. Scatter plots displaying relationships between zip code-level demographics in Maryland and SWAT deployments per 100,000 residents
(FY2010-2014). Blue curves are LOESS fits, red lines are OLS fits.
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Log(S.W.A.T. Per 100k Residents+1)
FY2010−2014
Log(S.W.A.T. Per 100k Residents+1)
FY2010−2014
%Non−Hispanic Black
%Non−Hispanic Asian
0
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Log(S.W.A.T. Per 100k Residents+1)
FY2010−2014
% B.A. or Higher
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FY2010−2014
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Log(S.W.A.T. Per 100k Residents+1)
FY2010−2014
●
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Log(S.W.A.T. Per 100k Residents+1)
FY2010−2014
and has flexible search capabilities that allow for subsetting by date, geographic location,
content and placement within a newspaper. However, the database is periodically updated,
e.g. when new papers are added, meaning search results will vary somewhat depending
on when searches are conducted.10 Despite these drawbacks, searches on Access World
News allow for at least a rough characterization of the level and nature of news coverage of
militarized police over time.
Figure 4 displays the results of these keywords searches. As the left panel in the figure shows, the share of newspaper articles mentioning “police” and either “SWAT” or
“S.W.A.T.”—a reasonable indicator of an article pertaining to militarized police—has trended
up sharply since 2000. Further, the figure’s right panel shows that coverage of SWAT teams
have been more likely in recent years to garner front-page news placement than have articles
concerning police in general. While the share of newspaper articles covering militarized police may appear low, the fact that television news outlets heavily draw on newspapers when
deciding which topics to cover (Mondak, 1995) means that a variety of news outlets have
likely been devoting increased attention to militarized police as well.11
A final route for citizens to learn of police militarization is from local law enforcement
officials themselves, who have often gone out of their way to trumpet the acquisition of
surplus military gear and militarized police units in their communities. In many places,
militarized vehicles are regularly paraded through town, in events such as Menlo Park and
Ripon, California’s “Annual Emergency Vehicle” show, which has featured armored MRAP
vehicles and “bomb trucks,” (Kahl, 2013). Similar displays have occurred in Cobden, IL
(Hancock, 2015), Columbia, S.C. (Brooks, 2014) and Racine, WI (Sikma, 2014), (see Figure
5) to name only a few.
10
These data were collected in August of 2016.
To be clear, articles that merely mention “police” are far more prevalent in terms of raw numbers, making
up roughly 10% of all articles during this period. But the conditional probability of police articles garnering
front-page coverage is smaller than those which concern SWAT teams. For example, in the year 2000, 410 of
the 5,128 articles mentioning SWAT teams made the front page (≈ 8%), while 32,877 of the 767,594 articles
simply mentioning “police” made the front page (≈ 4%).
11
15
Figure 4: News Coverage of Militarized Police. The figure’s left panel displays the share of
U.S. newspaper articles between 2000-2015 containing the word “police” that also contain either
“SWAT” or “S.W.A.T.” The right panel displays the share of articles containing the word “police”—
as well as the share of articles containing the word “police” and either “SWAT” or “S.W.A.T.”—
which receive front-page (“A1”) coverage. Source: Access World News.
News Coverage of
Militarized Police
Front−Page Coverage
Share of Articles on Front Page
Conditional on Search Terms
Share of All Newspaper Articles
Mentioning "police" and "S.W.A.T"
0.08
0.0010
0.0009
0.0008
0.0007
Pr(Page A1 | "police"
and "SWAT" / "S.W.A.T.")
0.07
0.06
0.05
0.04
Pr(Page A1 | "police")
0.03
2000
2005
2010
2015
2000
2005
2010
2015
Year
Year
In sum, there are a variety of ways in which the increasing militarization of local law
enforcement has been observable. What inferences about crime should citizens draw from
these observations? I address this question in the following section.
Are Militarized Police a Good Heuristic for Crime Conditions?
Due to the aforementioned data availability issues, the empirical association between the
behavior of militarized police units and crime rates has rarely been assessed.12 While journalists have compiled considerable anecdotal evidence as to how and why SWAT teams are
deployed, little systematic work has been done, especially at low levels of geography and
time. In what follows, I provide what is to my knowledge the first time-series cross-sectional
assessment of the covariation between militarized police behavior and violent crime at the
local level.
12
A possible exception is Sherman and Rogan (1995), which randomized the location of drug raids on
crack houses in a single city and measured subsequent changes in crime. The study found small reductions,
but noted that the effects faded quickly.
16
Figure 5: A mine-resistant ambush-protected vehicle (MRAP) belonging to the Racine County
Sheriff’s Dept. appears in a Wisconsin parade (Sikma, 2014).
For SWAT deployments to be a useful predictor of violent crime rates for the average
citizen, the data should exhibit several features. For one, there should be a strong positive
association between the rate at which SWAT teams are deployed, and the rate at which
violent crimes occur. Note that, for prediction purposes, this relationship need not be causal.
Even a so-called “spurious” correlation, in which SWAT deployments covary with crime due
to some unobserved third factor, would suffice for heuristic purposes. Second, to be most
useful, the relationship should be linear. If it were the case, for example, that more SWAT
deployments predicted more violent crime up to a point, but that additional deployments
thereafter predicted fewer crimes, the relationship would likely be too nuanced and counterintuitive for most citizens to use effectively. Third, the predictions of crime generated by
SWAT deployments should be relatively precise.
To evaluate this relationship, I paired the SWAT deployment data from Maryland and
California with the FBI’s data on violent crimes by both agency and month (the lowest
temporal unit of crime data shared by the FBI).13 As the far left panels of Figure 6 show,
13
Since the Maryland data is a complete census of SWAT deployments during this period, I include all
agencies in the state for which FBI crime data is available, regardless of whether any SWAT deployments
occur there, while the California data includes only those agencies which supplied SWAT data in response
to public information requests. Violent crimes include total murders, manslaughters, rapes, robberies and
assaults, regardless of severity (e.g. this includes both simple and aggravated assaults).
17
agencies that report more violent crimes tend to have more SWAT deployments on average
according to the linear (OLS) approximation in both states. However, the OLS fits are
extremely imprecise in terms of their predictions.14 In the Maryland data, the root mean
squared error associated with the OLS fit is 138.24 violent crimes while the same statistic in
the California data is 341.63. To put those results in context, the mean number of violent
crimes in an agency-month is 50.00 and 149.54 in Maryland and California, respectively.
In addition, when the LOESS estimator is applied in Maryland, we see that the relationship is actually quite nonlinear.15 Moving between roughly 0 and 20 SWAT deployments per
agency-month corresponds with higher levels of violent crime, but for agency-months which
exhibit more than 30 SWAT deployments, lower levels of violent crime are predicted. This
non-intuitive nonlinearity makes it very difficult to use SWAT deployments as an accurate
predictor of violent crime in certain regions of the data. To be sure, the vast bulk of SWAT
deployments (roughly 99%) in agency-months in Maryland are less than or equal to 21, and
the vast bulk of the violent crime data in these units (98%) are less than 600, a portion of the
data for which the on-average relationship appears roughly linear and positive. But consider
the Baltimore Police Department, a jurisdiction home to roughly 10% of the state, in May
of 2010. Despite having just 5 SWAT deployments in that month, the department saw 1,781
violent crimes, far more than the OLS prediction given this level of SWAT activity: 186 violent crimes. Conversely, consider the Prince George’s County Police Department, (home to
roughly 15% of the state’s population) in March of 2012, which, despite deploying its SWAT
team 51 times, saw only 622 violent crimes, compared with the 1,753 crimes predicted by
OLS. Using SWAT deployments as a heuristic for the status of violent crime in these places
and times would have led to wildly inaccurate predictions, even for the citizen capable of
14
This analysis includes data on 131 agencies in Maryland. After combining members of joint, multijurisdictional teams into amalgamated units, the California analysis includes data on 132 agencies.
15
Due to sparseness in some regions of the data I estimate all LOESS models with a span of 1 to ease
computation. I also omit two state agencies from the Maryland data here, which account for 4.4% of
deployments in that state, in order to make the data comparable to the CA sample which consists solely of
city and county agencies.
18
intuiting the best linear approximation in the data.
Of course in this pooled estimation, we are comparing jurisdictions that differ in innumerable ways. But if we are interested in the ability of citizens to use police behavior as
a heuristic in their own communities, we should examine whether SWAT deployments predict violent crime within places. The third and fourth columns of Figure 6 display these
relationships by first residualizing the data with respect to agency fixed effects (Frisch and
Waugh, 1933; Lovell, 1963). When this is done, we see that SWAT deployments bear virtually no relationship with violent crime within places over time. In the Maryland data,
the OLS slopes are negative and indiscernible from zero. In the California data, the slopes
are slightly positive, though only statistically significant after logging the data, an operation
unlikely to be employed by the average person taking stock of their community.16 Holding
all fixed attributes of a jurisdiction constant, the appearance of a SWAT team provides a
citizen with essentially no information about the status of violent crime, on average.17
Agency jurisdictions and months are coarse units of measurement that may mask important variation. For a more granular test, I obtained incident-level data, coded by longitude
and latitude, on reported crimes from three of the largest agencies in Maryland: Baltimore
City Police, Prince George’s County Police and Montgomery County Police, which together
account for 47.6% of SWAT deployments in the state—and spatially joined these data with
zip codes in Maryland. This strategy allowed for the construction of large panels containing
the number of violent crimes and SWAT deployments in a given zip code and day between
Jan. 1, 2011 and June 30, 2014.18 In the “long” form of this panel, I include entries for
every combination of zip code and date during this period between the three jurisdictions,
16
The slopes on the OLS coefficients in the figure’s third column are -1.36 (SE=0.71) and 0.55 (SE=0.66)
in Maryland and California, respectively. The slopes on the OLS coefficients in the fourth column are -0.01
(SE=0.02) and 0.02 (SE=0.01) in Maryland and California, respectively. This last result, though statistically
significant, has a substantively small coefficient. The LOESS estimates closely track these linear fits.
17
Note: this analysis assumes that each agency’s SWAT deployments occurred within its jurisdiction. See
p. 11 in Appendix A for a validation of this assumption.
18
The data from Montgomery County begin in July of 2013, while the other two localities begin in January
of 2011.
19
20
#Violent Crimes
#Violent Crimes
0
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15
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Raw Data, CA
#S.W.A.T. Deployments
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Log(#Violent Crimes+1)
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2.5
5.0
7.5
10.0
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2.5
5.0
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#Violent Crimes
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Raw Data, MD
#Violent Crimes
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Agency F xed E ec s
Logged MD
Figure 6: Violent Crimes by SWAT Deployments, Maryland and California Agency-Months, FY2010-2014. LOESS
fits in blue, OLS fits in red. This bivariate relationship is highly imprecise and model dependent, making SWAT deployments an
unreliable predictor of violent crime.
Log #V o en C mes+1
Res dua zed
Log #V o en C mes+1
Res dua zed
imputing zeros for violent crime in zip code-dates which did not appear in the crime data.19
This approach results in a panel with over 100,000 observations and substantial power to
detect small covariances between the level of SWAT activity and the level of violent crime.
However, because the agencies which publish the incident-level data advertise it on their web
sites as potentially incomplete, I also construct “short” versions of these panels, which leave
as missing zip code-dates which do not appear in the crime data. Because only 33 observations exceeded two SWAT deployments, SWAT deployments were coded into three bins: zero
deployments, 1 deployment and ≥ 2 deployments, and were specified as separate indicator
variables during estimation.20 By coding the independent variable discretely, I avoid the
strong parametric assumption that violent crimes change linearly with SWAT deployments.
Violent crimes ranged between zero and 21 and were coded as such.21
Table 1: Zipcode-Day OLS Models of Violent Crime. Outcome ranges from 0 to 21. “Short”
panels use only observed crime data. “Long” panels impute zero violent crimes in unobserved
zipcode-days. Models (3) and (4) include zip code fixed effects.
(Intercept)
1 SWAT
≥ 2 SWAT
N
(1)
Short
1.187∗
(0.170)
0.186
(0.144)
0.429
(0.282)
64, 887
(2)
Long
0.648∗
(0.115)
0.400∗
(0.140)
0.658∗
(0.266)
117, 673
(3)
Zipcode FE, Short
0.053
(0.293)
−0.015
(0.033)
−0.020
(0.086)
64, 887
(4)
Zipcode FE, Long
0.003
(0.050)
−0.009
(0.025)
−0.013
(0.066)
117, 673
Maximum of homoscedastic and zip code-clustered robust standard errors in parentheses.
∗
indicates significance at p < 0.05
Table 1 displays the results of OLS regressions of the number of violent crimes on the
number of SWAT deployments in a zip code on a given day. The first two columns are
19
Note: In the long panels, I include all ZCTAs in the three counties that house these jurisdictions according
to a Census walk file, as well as 11 that appeared in the crime data.
20
1,878 observations took a value of one SWAT deployment and 224 took a value of ≥ 2 deployments.
21
The three agencies supplying data for this analysis used different formats. See Appendix A for details
on how violent crimes were coded and aggregated. Because the dependent variable is right-skewed, Table
A2 in the Appendix displays the results of these models after logging the outcome. Results are substantively
similar. See p. 12 in Appendix A for tests of whether lagged violent crime events predict SWAT deployments.
21
estimated without zip code fixed effects, and indicate that zip code-days which saw one or
more SWAT deployments are predicted to have at most about .7 of an additional violent
crime. However, in the “short” panel (column 1), in which we relax the strong assumption of
zero violent crimes in unobserved units, we cannot differentiate these coefficients from zero.
When zip code fixed effects are employed (columns three and four), there is no detectable
relationship between SWAT deployments and violent crime.
SWAT teams were originally conceived to handle violent emergencies (Balko, 2014a) and
the current dictionary definition of “SWAT” is, “a police or military unit specially trained
and equipped to handle unusually hazardous situations or missions,” (Merriam-Webster,
2016). So why would SWAT deployments fail to covary with violent crime? If a community
grew more violent over a given period, shouldn’t SWAT teams be deployed within that
community more frequently?
An analysis of the reasons for deployments supplied in the public records I obtained provide a plausible explanation: SWAT deployments are now a routine facet of police work. In
stark contrast to the dictionary definition, the most common use of local SWAT teams today
is to serve warrants, typically in connection with the search for illegal drugs (Balko, 2014a).
These are often pre-scheduled events. As Figure 7 shows, 91.1% of SWAT deployments in the
Maryland SWAT data occurred in conjunction with a search warrant, while the remaining
portion were for “barricade” situations (which typically involve an armed suspect or hostage,
4.9%), “other” (2.7%), arrest warrants (0.9%), and exigent circumstances (0.4%).22 Police
agencies often deem search warrants “high risk” when justifying the use of SWAT teams to
serve them. But 56.5% of search warrant deployments in Maryland occurred in connection
with Part II crimes, which are considered less serious and are typically nonviolent (e.g., assaults without a weapon or serious injury, drug abuse and fraud). 42.8% are in connection
with more serious Part I crimes. Though property was seized and forcible entry occurred in
22
Since many agencies in California did not supply the reasons for SWAT deployments, I confine this
analysis to Maryland.
22
Figure 7: Legal Authority for and Outcomes of SWAT deployments in Maryland, FY2010-2014.
Legal Authority
For Deployment
Outcomes
Property
Taken
Search
Warrant
Forcible
Entry
Arrest
Barricade
Shot
Fired
Person
Injured
Other
Officer
Injured
Arrest
Warrants
Animal
Killed
Animal
Injured
Exigent
Circumstances
Person
Killed
0
10
20
30
40
50
60
70
80
0
Percent
10
20
30
40
50
60
70
80
90
Percent
87.4% and 71.8% of these cases, respectively, arrests occurred only 64.4% of the time. Rates
of injury and death among animals, suspects and SWAT team members, as well as gunfire,
all occurred roughly 1% of the time or less during the service of search warrants. While these
incidents may have the potential for violence, and may often involve suspects with violent
histories, they do not tend to result in the bodily injury or death of any persons involved.23
In sum, there is little evidence that SWAT team behavior is a reliable predictor of violent
crime, making it a poor heuristic for the status of the social problem SWAT teams were
conceived to combat. The majority of deployments occur in connection with relatively petty
offenses (Part II crimes), and rarely result in bodily harm for anyone involved. The frequency
of SWAT deployments displays a noisy, and at times, nonlinear, relationship with violent
23
Similar statistics regarding the reasons for deployments appear in annual state reports in Maryland, e.g.
(Maryland Statistical Analysis Center, Governor’s Office of Crime Control & Prevention, 2014). Note: data
on SWAT officer injuries only available after 2010.
23
crime across police jurisdictions, and virtually no relationship within jurisdictions. For the
citizen hoping to update beliefs about crime in their own community, SWAT activity will
provide virtually no information.
The Persistence of the Association Between SWAT Teams and Violent Crime
If the behavior of militarized police is no longer an indication of violent events, why would
citizens not update their beliefs concerning the correlation between this brand of police
behavior and crime rates? There are at least two plausible explanations of why this faulty
association might persist in the mass public. The first is that law enforcement officials,
in seeking to justify the military equipment their agencies have acquired, have actively
perpetuated this association. As Redden (2015) noted, in the wake of concern over police
militarization following protests in Ferguson, MO:
Law enforcement agencies responded by stoking old fears. No community, they
argued, not even the smallest one, is safe from worst-case scenarios like mass shootings, hostage situations, or terrorist attacks. The use of this military equipment
has resulted in ‘substantial positive impact on public safety and officer safety,’
Jim Bueermann, the president of the Police Foundation, a research group, said in
a 2014 Senate hearing on police militarization. He cited hostage situations, rescue
missions, and heavy-duty shootouts where the vehicles had come in useful.
But a review of 465 applications for armored MRAP vehicles via the federal 1033 program
showed that, “in private, police justify these same programs in radically different ways,”
and that, “the single most common reason agencies requested a mine-resistant vehicle was
to combat drugs,” (Redden, 2015). Thus, even as crime rates have plummeted, and the
deployment of militarized police has become routine, police officials eager to acquire free
military-grade weapons have reinforced the narrative that militarization is a response to a
persistent threat to public safety.
24
A second likely reason this association persists is the way militarized police are covered
by the news media, which may systematically under-represents the degree to which such
units respond to less serious crimes. To assess this, I compared the rates of types of events
in the Maryland SWAT data to the rates at which key words corresponding to those events
appeared in Maryland newspaper articles during the same period. I also searched for terms
relating to violent crimes, though no clear benchmark exists for this category in the Maryland
SWAT data.24 As Table 2 shows, newspapers tend to severely misrepresent SWAT activities,
massively underreporting warrant service activities and overreporting deployments related
to violent events, such as barricade situations. Nearly half of the coverage of SWAT teams
relates to violent crimes. These mentions could have been in reference to violence which
occurred during a SWAT raid, but could also have been in reference to a suspect’s criminal
history, so it is difficult to say for certain that SWAT articles which mention violence are
portraying SWAT teams as dealing directly with violent events in these cases. Still, given the
prevalence of violent terminology in these articles, if average citizens continue to associate
SWAT teams with violent crime, they can hardly be blamed.
Table 2: Unrepresentative News Coverage of SWAT Teams. Violent situations are overreported, and warrant services are underreported, in newspaper coverage of SWAT teams. Articles
about violent crimes make up nearly half of SWAT coverage. Source: Access World News, Maryland
newspapers, FY2009-2014.
Actual Percentage of SWAT Raids
Percentage of SWAT News Coverage
barricade
4.92%
14%
search warrants
91.06%
12.22%
drug warrants
51.47%
10.44%
violent crime
46%
In addition, Table 3 displays the over-time correlations between annual rates of coverage
of various news categories and annual levels of crime (n = 15 annual pairs). Consistent with
the perceptual side effects theory, the table shows that articles mentioning militarized police
units were positively correlated with perceived levels of violent crime as measured by Gallup
surveys, while articles that simply mentioned police, or mentioned violent crimes in general,
24
See p. 18 in Appendix A for details on keyword searches.
25
were actually negatively correlated with perceived crime levels. To be sure, we should be
circumspect of drawing firm conclusions with just 15 pairs of observations in each test, but
the results do comport with the idea that exposure to militarized policing induces citizens
to overestimate crime rates—prima facia evidence for the perceptual side effects theory.
Table 3: Correlations Between Perceived Crime and News Coverage, 2000-2015. The
table displays the pairwise correlations between the share of newspaper articles in a given category
in a given year and the level of perceived crime in that year. Sources: Access World News; Gallup.
police coverage
violent crime coverage
swat coverage
perceived national crime
-0.38
-0.57
0.40
perceived local crime
-0.56
-0.73
0.30
In sum, there is ample evidence that the preconditions for police militarization to distort
perceptions of crime exist. Militarized police are observable to citizens through a variety of
channels. Their activities fail to predict crime, especially within communities over time, and
the information on SWAT activity conveyed by police officials and news outlets often overrepresents the degree to which SWAT teams handle violent events, lowering the chances that
citizens will abandon militarized police as a heuristic for crime. With these preconditions
established, I now outline the designs for survey experiments which will evaluate the causal
effect of the appearance of militarized police on perceptions of crime.
Experimental Design
To evaluate the causal effect of exposure to militarized police on citizen perceptions, I conducted two survey experiments, one via Amazon’s Mechanical Turk (N =1,566) in SeptemberOctober 2015, and one via Survey Sampling International (SSI; N =4,464) in March 2016.25
Because of the increased probability of encountering militarized police among African Americans (see Figure 3), the second sample included an oversample of 1,848 non-Hispanic African
25
See p. 3 in Appendix A for demographics of both samples.
26
Americans, in order to test whether treatment effects varied with the race of respondents.26
In both surveys, respondents were asked to read a news article concerning a police department “somewhere in the U.S” which described a city police chief requesting a modest
budget increase from the City Council.27 The article stated that the city’s police chief was
seeking additional funding in the coming fiscal year in order to hire and train new officers
because of recent promotions and retirements. The article did not give any information as
to the typical levels of crime in the community, a common omission in news coverage of law
enforcement.
While the text of the article remained constant across conditions, the accompanying
picture randomly varied across respondents (see p. 1 in Appendix B for images). The control condition contained an image of traditionally uniformed police officers (blue uniforms
with brimmed caps and standard sidearms), while the treatment conditions contained either
images of SWAT teams with assault rifles or dressed in armored “riot” gear (the “low” militarization conditions), or an image of a SWAT team with an armored vehicle (the “high
militarization” condition). All images have appeared in actual online news reports, and the
text of the article was based on a local report from New Jersey describing a similar budget
hearing (Zummo, 2014).28 To help ensure receipt of the treatment, the article appeared on
the screen for 30 seconds before respondents were allowed to advance in the survey. Each
of the images portrayed five male officers in the foreground, and were cropped so that other
features of the surrounding neighborhood could not be seen, thus ensuring that any average
differences in responses across conditions were due to the appearance of the officers shown
and not other aspects of the images. Following the article, individuals were asked a series of
questions that gauged perceptions of crime, support for police spending (both in the featured
26
Treatment assignment was blocked by the race of the respondent in order to increase efficiency (Gerber
and Green 2012). Indicators for blocks are included as covariates during estimation.
27
See p. 4 Appendix B for full text of the article.
28
Respondents were not initially told that the article was ficticious, but were fully debriefed at the end of
the survey.
27
city and in the U.S. generally), and confidence in police.
To determine whether these findings were robust to replication and design tweaks, several
features of the survey were altered prior to the SSI survey. Because both of the low militarization conditions produced highly similar treatment effects in the M-turk study, the SSI
survey omitted one of the militarized conditions (subfigure B1b in Appendix B) to maximize
statistical power. Both surveys measured respondent’s perceived level of crime in the vignette
city by asking them to indicate whether the city they just read about had less, about the
same, or more violent crime than a benchmark city (see p. 5 in the Appendix for complete
question wordings). However, the SSI survey also made mention of the city’s population
size in the article, (“roughly 200,000 residents,” close to the mean among departments with
SWAT teams in the Maryland data), and asked residents to assess the level of violent crime
in the featured city, “[c]ompared to a typical U.S. city of roughly the same size,” whereas the
wording in the M-Turk survey was, “[c]ompared to a typical city in the United States.” The
purpose of this change in the SSI version was to control for the possibility that respondents
were inferring population size from the appearance of the officers in the image, rather than
simply the level of militarization.29 While these changes prevent the SSI data from serving
as a pure replication of the original results, they do allow for a test of whether the original
findings are robust to various design alterations in the survey, which helps guard against
embracing findings due to arbitrary features of the survey instrument.
Results
The top panel in Figure 8 displays the treatment effects (differences in means)30 relative to
the control condition on a host of outcomes, obtained from the M-turk sample. All dependent
variables were transformed from their original three and five-point scales to range between 0
29
For additional alterations in the SSI survey, including the addition/subtraction of dependent variables,
see p. 10 in Appendix B.
30
Effects were estimated via OLS with robust (HC1) standard errors (MacKinnon and White, 1985).
28
and 1 so that treatment effects can be interpreted in percentage points. Several results are
noteworthy. First, seeing militarized police caused between an 8.9 and 15.2-point increase
in the perceived level of violent crime in the vignette city (SEs=1.9 and 1.9, respectively).
In the SSI sample, the high treatment condition again produced a detectable increase in
the perceived level of violent crime in the vignette city, albeit a more muted one of 2.2
percentage points (SE=1.1). The perception that violent crime has been worsening may also
have been increased by the SSI treatments, though those effects were not discernible from
zero. Furthermore, the stepwise pattern in the treatment effects also suggests the treatments
worked as intended, i.e., the “high dose” condition produced a larger effect than the “low
dose” condition, an indication that the level of militarization in the images, and not some
other aspect, is the feature influencing responses.
These results show strong support for the core hypothesis of the perceptual side effects
theory. The fixed effects regressions in the previous analyses showed that, holding all timeinvariant community attributes constant, the prevalence of militarized policing does not
predict violent crime. But in an analogous scenario, respondents relied on militarized police
tactics as a heuristic for crime nonetheless. When respondents saw militarized tactics on
display, they inferred that violent crime was worse than when more traditional tactics were
shown in an otherwise identical community.
The perceptual side effects theory also holds that if government action causes individuals
to perceive a worsening social condition, their support for funding the government agency
responsible for addressing that problem will also change. The M-Turk results show a clear
erosion of support for police funding, with the three treatment conditions producing between
-3.9 and -8.6-point changes in support for police spending in the vignette city (SEs=1.1 and
1.3), and the high treatment condition producing a -3.7-point change in support for police
spending in the United States (SE=1.6). The negative effect of the treatment on support for
police spending was also replicated in the SSI sample, both in the U.S. and in the vignette
29
Figure 8: Images of Militarized Police Increase Perceptions of Crime, Decrease Support
for Police Budgets. Results arranged from largest average effect within each outcome to smallest
average effect. Bars denote 95% confidence intervals.
Treatment Effects (M−Turk)
Crime High in
Vingnette City
●
Recent Changes
in National Crime
●
Police Spending
in Vingnette City
Treatment Condition:
●
SWAT with
riot gear
Police Spending
in U.S.
SWAT with
assault rifles
●
Confidence in
Police in U.S.
SWAT with
Vehicle
●
●
−10
−5
0
5
10
15
20
Difference from Control (Percentage Points)
Treatment Effects (SSI)
Crime High in
Vingnette City
Treatment Condition:
●
SWAT with
assault rifles
Crime Worsening
in Vingnette City
Confidence in Police
in U.S.
●
●
SWAT with
MRAP
●
Police Spending
in U.S.
●
Confidence in Police
in Vingnette City
●
Police Spending
in Vingnette City
●
Want More Police Patrols
in Own Neighborhood
●
−5
−4
−3
−2
−1
0
1
2
3
Difference from Control (Percentage Points)
30
4
city, with the high treatment condition producing -2 and -2.7-point effects (SEs=0.8 and 0.7)
in those categories, respectively.
Finally, there is also evidence that the treatment produced reputational damage for police,
with the high treatment condition in the SSI survey causing a -2.6-point change in confidence
in police in the vignette city (SE=0.9). While not statistically significant, the M-Turk
treatments also produced negative estimated effects in terms of confidence in police. Perhaps
most strikingly, those exposed to the high militarization treatment in the SSI survey caused
a -3.2-point change in respondents’ desire for more police patrols, not in the vignette city,
but in their own neighborhoods (SE=1.1), a powerful indication of the reputational harm
police militarization can produce.
Open-Ended Responses
Respondents were also asked two open-ended questions regrading why they felt crime was
higher/lower in the vignette city and why they supported a budget increase/decrease in the
vignette city. The responses can help shed light on the potential mechanisms behind the
causal effects reported above. An initial reading showed that crime was perceived to be
higher among treated individuals because police were presumed to be dealing with more
dangerous situations due to their clothing and equipment. For example, one respondent in
the high treatment condition in the M-Turk sample wrote, “I think [crime] is higher than
a typical neighborhood because of the picture depicting an armed vehicle and police in full
military grade riot gear.” Another in a low treatment condition wrote that crime seemed
higher than average, “[b]ecause they’re wearing swat gear and automatic rifles.” Likewise, a
treated SSI respondent wrote, “you don’t see police in the streets with military equipment
like that in the average us [sic] city.”
Respondents also cited the militarized gear in the images they saw as justification for
funding police less, reasoning that the agency in question must be well-funded already if
31
they have access to such equipment. For example, one respondent in one of the low militarization conditions in the M-Turk sample wrote that the agency’s budget should be reduced,
“[b]ecause the gear and equipment in the picture looked very expensive and up to date so
I think that they could do with less.” Another respondent in the SSI high militarization
condition wrote, “from the picture, they were buying equipment to fight a war in another
country. they are not fighting in the middle east. i think they are wasting money.”
Finally, it also appeared from the open ended responses that untreated individuals were
more likely to cite the department’s stated justification for additional funds: that it needed
to hire and train officers to make up for retirements and promotions. For example, one
respondent in the control condition wrote that police deserved more money because, “Staff
turnover is expensive.” This suggests that, had the agency in question not deployed militarized police officers, respondents would have been more receptive to the agency’s argument
to the city council.
In order to systematically test whether these lines of reasoning were more or less prevalent among treated individuals, I randomly sampled 10% of the pooled SSI and M-Turk open
ended responses (n=1,160) and developed two dictionaries: one that included words commonly used in answers that noted how militarized the officers appeared, and one containing
words common to those who cited personnel issues.31 The words in these dictionaries, as
well as the text of the open ended responses, were stemmed, made lowercase, and stripped
of punctuation. Words which did not appear more than twice in the sample were discarded.
In order to avoid overfitting, I then discarded these 1,160 responses and computed the proportion of remaining responses across all treatment conditions which contained at least one
of these words.
The results are displayed in Figure 9. The estimates in this figure are consistent with the
results of the initial reading above: mentions of words relating to police militarization in the
31
See p. 15 in Appendix B for the full contents of these dictionaries.
32
crime and budget open ends are far more common in treated conditions than in the control
condition in both the M-Turk and SSI samples.There is also evidence that responses pertaining to the department’s stated justification for its budget request—personnel—were more
common in the control condition than in treated conditions, though the clearest difference
appears between the control and high treatment conditions.
This dictionary analysis allows for an estimate of the prevalence of words deemed relevant
by the researcher. To ensure that these patterns were robust to researcher discretion, I also
conducted a fully unsupervised analysis of the content of these responses. Specifically, I constructed document term matrices for each treatment condition where each column contained
a unique word stem and each row pertained to a respondent. Each cell of these matrices contained the number of times each word was used, normalized by the total number of stemmed
words in each person’s response. Simple differences in means were then computed for each
word (column) across treatment conditions and the results were then sorted by t-statistic in
order to identify the most discriminating words across treatment conditions. Large positive
t-statistics indicate that a word is more likely to appear among treated responses, while large
negative t-statistics indicate a word more prevalent among control responses.
As Tables 4 and 5 show, treated respondents were more likely to mention words related
to militarization, such as “gear,” “arm,” “war,” “tank” and “military,” while those in the
control condition were more likely to discuss personnel issues (i.e., the police chief’s stated
argument), using words like “understaffed,” “promotion” and “promotion” more frequently
than treated respondents. When militarized police are observed, respondents are less likely
to pay heed to the stated argument of the political actor in the news article, yet another
way in which the character of government action can undermine political goals.
33
Figure 9: Open End Response Dictionary Analysis. Words relating to militarization are
much more prevalent among treated respondents. Words relating to personnel shortages are much
more prevalent among control respondents.
M−Turk
Mentions of Militarization in
Crime Rate Open End
Mentions of Militarization in
Budget Open End
Mentions of Personnel in
Budget Open End
FALSE
0.50
0.30
●
0.20
●
0.20
●
0.15
●
Proportion
0.45
Proportion
Proportion
0.25
0.15
●
0.10
●
●
●
0.40
●
0.35
0.10
0.05
●
●
●
0.05
0.30
Control
SWAT
(guns)
SWAT
(riot gear)
Armored
Vehicle.
Control
Treatment Condition
SWAT
(guns)
SWAT
(riot gear)
Armored
Vehicle
Control
Treatment Condition
SWAT
(guns)
SWAT
(riot gear)
Armored
Vehicle
Treatment Condition
SSI
Mentions of Militarization in
Crime Rate Open End
Mentions of Militarization in
Budget Open End
0.08
FALSE
0.10
0.08
Proportion
0.04
●
0.06
●
0.02
Control
●
0.28
0.26
●
0.22
0.04
Armored
Vehicle
0.30
0.24
●
SWAT
(guns)
●
●
Proportion
Proportion
●
0.34
0.32
●
0.06
Mentions of Personnel in
Budget Open End
Control
SWAT
(guns)
34
Armored
Vehicle
Control
SWAT
(guns)
Armored
Vehicle
Table 4: Top 20 Most Discriminating Words in Perceived Crime Open End. Words
relating to militarization are much more prevalent among treated respondents. Words relating to
personnel shortages are much more prevalent among control respondents.
Treatment Condition
control v. riot gear
control v. swat+guns
SSI
largest
positive
t-stats
depart, gear, photo, addit, riot,
extra, act, needed, training, fact,
world, coverag, bad, cop, protect,
requir, consist, hire, situation, tv
M-Turk
largest
positive
t-stats
gear, promotions, arm, swat, watch,
heavili, number, everywhere, type, refer,
wear, photo, led, cost, imag,
policemen, notic, militari, due, tactic
SSI
largest
negative
t-stats
human, department, strong, decis, main,
consider, offici, read, resourc, part,
experi, story, percent, good, depend,
equal, leav, mention, control, say
M-Turk
largest
negative
t-stats
addit, smaller, experienc, replac, regular,
average, changes, answer, urgent, figur,
statistics, popul, safe, rise, typical,
sound, issu, high, lower, town
control v. swat+mrap
pictur, photo, gear, local, militari,
depart, prevent, arm, dress, war,
forc, descript, compar, type, tank,
ad, heavili, year, show, full
gear, riot, pictur, swat, show,
photo, made, imag, judg, picture,
type, polic, info, forc, impli,
armor, dress, people, full, increas
militari, pictur, arm, gear, equip,
photo, heavili, show, heavi, vehicl,
equipment, type, bad, swat, made,
picture, imag, weapon, department, style
retirements, pai, one, event, strong,
community, fight, human, retire, residents,
articl, shortag, explain, main, experi,
say, crime, lack, comparison, evid
monitor, stress, face, offic, lower,
extra, issu, small, combat, regular,
determin, retirements, town, urgent, popul,
lot, stand, replac, talk, work
rates, hard, police, experienc, regular,
rise, crime, lower, urgent, articl,
addit, sound, replac, retir, cite,
work, stand, inform, issu, mention
Heterogeneous Treatment Effects
These results were generated by pooling across all respondents. However, given that militarized police units are deployed more often in communities with high concentrations of
African Americans, we might expect that the treatments impacted this group differently. To
test this, I estimated the differences in treatment effects between non-Hispanic white and
non-Hispanic African American respondents in the SSI sample. As Figure B3 in the Appendix shows, none of these differences were discernible from zero. Race may be an imprecise
proxy for experiences with law enforcement, so additional differences-in-differences were also
computed based on respondents’ answers to an item measuring perceived unfair treatment
by police. Again, no significant differences were uncovered (see Appendix Figure B4).
There are several plausible explanations for these null results. Though African Americans may be more likely to encounter militarized police on average given the geographic
distribution of SWAT deployments, such firsthand encounters may be rare. Rather, citizens
35
Table 5: Top 20 Most Discriminating Words in Budget Open End. Words relating to the
cost of police militarization are much more prevalent among treated respondents. Words relating
to personnel shortages are much more prevalent among control respondents.
Treatment Condition
control v. riot gear
control v. swat+guns
SSI
largest
positive
t-stats
innoc, issu, wrong, plan, method,
payer, militar, militari, threat, weapon,
attract,improv, wast, gun, high,
reward, train, stress, school, city
M-Turk
largest
positive
t-stats
equip, photo, left, citi, save,
monei, combat, detail, employe, law,
fact, wast, plenti, gear, cost,
justified, threat, paid, bui, matter
SSI
largest
negative
t-stats
sound, logic, funds, departments, chief,
expect, technologi, senior, interest, adequ,
dollar, work, risk, meet, program,
make, lot, crime, bring, citi
M-Turk
largest
negative
t-stats
hire, policeman, men, reasonable, rate,
strong, vital, hires, wrong, harder,
cover, legitim, understaf, articl, request,
find, fair, promoted, depart, inflation
control v. swat+mrap
militari, law, bad, photo, gear,
show, larg, secur, pictur, adjust,
control, tank, type, services, militar,
statist, because, impress, things, wast
city, polic, retired, riot, gear,
citi, militar, valid, alloc, recent,
camera, article, larger, paid, press,
violenc, typic, cash, drug, reduc
equip, militari, pictur, gear, weapon,
spend, power, sell, grade, police,
combat, vehicl, photo, unnecessari, equipment,
article, fact, back, vehicles, matter
meet, moral, servic, fine, year,
line, simpli, hard, special, neighborhood,
everydai, cut, addit, strong, na,
due, increases, sound, living, deserv
creat, fair, issu, reflect, policeman,
men, previou, risk, base, ensur,
harder, hire, part, rais, inflat,
vital, community, sense, legitim, inflation
cities, policeman, reflect, expenses, men,
strong, warrant, understaf, older, wrong,
continu, harder, thing, lower, people,
vital, community, servic, inflation, promot
of all backgrounds may be most likely to observe militarized policing through the news media, a venue common to both those who reside in communities policed in this fashion, and
those who do not. If that is the case, homogeneous treatment effects become more plausible.
While unexpected, these results are intriguing given the renewed focus on racial disparities
in policing following a spate of high-profile incidents of violent police misconduct (Baker,
Goodman and Mueller, 2015). While the treatment of criminal suspects appears to vary
markedly depending on the race of suspects (Alexander, 2012; Fryer Jr., 2016), the reactions of individuals of different races to reports of this particular brand of policing appears
relatively homogenous, at least on these outcomes.
Discussion and Conclusion
Debates in American politics often focus on the direct effects of government programs on the
issues they are intended to address. Do unemployment benefits reduce poverty? Does Medicaid improve the health status of poor citizens? Do aggressive police tactics reduce crime?
36
Such questions deserve the intense scrutiny they receive. But as this study illustrates, a focus
on the direct effects of policy miss a potentially crucial and politically consequential set of
outcomes. Namely, under certain conditions, government policies can produce perceptual
side effects that leave citizens with a biased view of the social world.
This phenomenon has a number of implications for canonical topics in political science,
including representation, democratic accountability and policy development. The fact that
enhanced effort to combat a social problem can lead citizens to mistakenly believe that a
problem is worsening can burden policymakers and elected officials with a sort of “Catch
22,” leaving them to choose between ignoring a social problem altogether, or addressing it,
only to convince citizens that, to paraphrase Ronald Reagan, they are not “better off than
they were four years ago,” (Fiorina, 1981). From the citizen’s perspective, the story is no
less problematic. Ascertaining the state of the social world is difficult and expensive, so it
is only natural that citizens would seek cognitive shortcuts when attempting to do so. But
if the shortcuts they seize upon are misleading, the prospects of effectively holding their
elected officials accountable for performance are likely grim.
The present study explores this phenomenon in the context of police militarization, a
concerning social trend that has been understudied, largely because of data limitations.
Analyses of original data on the behavior of militarized police, alongside causal evidence
of the impact of that behavior on public perceptions, shows considerable support for the
perceptual side effects theory. The appearance of militarized police units in news reports
does not reassure the public that crime is being effectively combatted. On the contrary,
militarized police units cause individuals to perceive that violent crime is more severe. As
the observational analysis of SWAT team behavior shows, such inferences are often likely to
be faulty. Within communities, the behavior of militarized units, now a routine component
of police work in many locales, tells us virtually nothing about the status of violent crime.
Furthermore, as evidenced in the analysis of the open-ended survey responses, the display of
37
heavy weapons and equipment also inflates perceptions of agency capacity, thereby depressing
support for the funding of law enforcement agencies. Not only can public policy mislead the
public, it can undermine support for the political objectives of bureaucratic agencies.
These results shed light on several enduring puzzles in the study of crime and law enforcement, including the persistent divergence between the perceived levels of crime in the
mass public and actual crime rates. Over the same period that crime has been declining,
local law enforcement agencies have been increasingly militarizing, sending strong signals to
the public that the need to combat violent criminals is increasing. This message has been
amplified by news outlets, which misrepresent the degree to which SWAT teams respond to
violent events, and by police officials themselves, who have listed the threat of violent crime
and terrorism as justifications for the military gear they have amassed. Given these trends,
it is perhaps unsurprising that citizens have mistakenly come to believe that crime is on the
rise. These findings also help to explain why social indicators like local crime rates fail to
predict incumbent performance (Hopkins and Pettingill, 2015). If citizens take their cues
from the visible, misleading actions of government agents rather than objective indicators
of conditions, it is no wonder that they do not effectively reward or punish politicians when
conditions improve or worsen (Ferejohn, 1986; Key, 1966).
But as much as these results illuminate, they also raise new questions. Perhaps the biggest
puzzle is why elites would implement policies that threaten to undermine their political
objectives. As is often the case, the answer is likely complex and context-dependent. In some
cases, it may be in a political actor’s interest to send signals that conditions are worsening,
especially since prior work has revealed that citizens rarely hold incumbents accountable for
conditions in the beginning of their term (Bartels, 2009). Consider a newly elected mayor
intent on securing an economic stimulus package. The mayor may reason that signaling to
the public that the economy is in dire need of attention early in a new term will grease the
wheels for a legislative agenda that could pay dividends when the next election arrives.
38
Still, such explanations leave unexplained the perceptual side effects above that are clearly
unintended byproducts of government action, most notably, the opposition to government
funding that was induced by the display of enhanced tactics. Why would bureaucrats, or
the executives who command them, enact policies which produce results counter to their
own political goals? The answer may very likely be that they do not knowingly do so.
Policymakers are only human. They are not omniscient, and they operate in a world plagued
by uncertainty. In describing the “boundedly rational” policymaker, Jones and Baumgartner
(2005) write, “As human decision makers are fallible, so too are political systems,” (p.
15). Policymakers, “are goal-oriented and strategic, and they update their beliefs based
on information. But they make mistakes—not just random mistakes, but systematic and
repetitive ones,” (16). In some ways, the notion that policymakers could err in their quest
to produce self-serving or effective programs should be unsurprising to scientists familiar
with the difficulties of discerning the processes that define the social world. Policymaking
is, at its heart, a problem of causal inference, made daunting by the noise, complexity and
countervailing forces that inhabit human systems. In seeking to address a social ill, politicians
may try their best to identify the correct bureaucratic lever to pull, but they are limited both
in their ability to know ex ante whether anything will change as a result (Manski, 2013), and
to reliably detect a change when one occurs. The task of identifying any secondary impact of
policies, such as perceptual side effects, is likely even more vexing. Not only should we expect
government actions to sometimes produce unintended political byproducts, we should also
expect such byproducts to often go unnoticed, allowing the impact of mistakes in judgement
to persist and compound over time.
In the case of police militarization, some policymakers are beginning to inuit the reputational damage spurred by the routine use of SWAT teams and military-grade equipment. For
example, an internal slideshow from the Aurora, CO Police Department instructing officers
on the use of armored MRAP vehicles contains a section on “Image Awareness,” featuring
39
quotes from irate observers who liken heavily armed police to “StormTroopers” and “fascist
pigs,” (Welch, 2016). But even if awareness of public perceptions spreads among law enforcement officials, it remains unclear whether the usage of militarized police units will become
more selective, as these officials may simply prioritize other metrics when crafting policy. As
Gartner (1999) shows, the metrics embraced by bureaucrats as measures of policy success
often differ from the ones used by the American public. Similarly, one of the primary stated
justifications for the militarization of law enforcement is officer safety (Savidge, 2014), and
over the same period that police agencies have militarized, the rate at which police officers
have been killed in action, according to FBI statistics, has trended down (Balko, 2015). If
police agencies conclude that the use of heavily armored units is responsible for this trend
in officer safety, the perceptual side effects that result from their usage, even if understood,
may simply take a back seat.
Moving forward, it is critical to identify the breadth and nuance of the perceptual side
effects phenomenon, and to document the policy areas in which the phenomenon is most
pronounced, the factors that can mute these effects and the circumstances under which they
feedback into the policymaking process. Though intimately related, bureaucratic politics
and political behavior are often studied in isolation. Addressing these outstanding questions
will not only sharpen our insights into the forces that generate government action, but into
the ways in which such actions inform public opinion and political behavior in the mass
public.
40
References
ACLU. 2014. War Comes Home: The Excessive Militarization of American Policing. Technical report ACLU New York: . Accessed August 8, 2016. https://www.aclu.org/sites/default/
files/assets/jus14-warcomeshome-report-web-rel1.pdf .
Alexander, Michelle. 2012. The New Jim Crow: Mass Incarceration in the Age of Colorblindness. Thew New Press.
Baker, Al, David J. Goodman and Benjamin Mueller. 2015. “Beyond the Chokehold: The
Path to Eric Garner’s Death.” New York Times . Accessed August 8, 2016. http://www.
nytimes.com/2015/06/14/nyregion/eric-garner-police-chokehold-staten-island.html.
Balko, Radley. 2014a. Rise of the Warrior Cop: The Militarization of America’s Police
Forces. New York: Public Affairs.
Balko, Radley. 2014b. “Shedding light on the use of SWAT teams.” The Washington Post
. Accessed August 8, 2016. https://www.washingtonpost.com/news/the-watch/wp/2014/02/
17/shedding-light-on-the-use-of-swat-teams/.
Balko, Radley. 2015.
“Once again: There is no ‘war on cops.’ And those who
claim otherwise are playing a dangerous game.” The Washington Post . Accessed
August 8,
2016. https://www.washingtonpost.com/news/the-watch/wp/2015/09/10/
once-again-there-is-no-war-on-cops-and-those-who-claim-otherwise-are-playing-a-dangerous-game/.
Bartels, Larry M. 2009. Unequal Democracy: The Political Economy of the New Gilded Age.
Princeton: Princeton University Press.
Blumstein, Alfred and Joel Wallman. 2006. The Crime Drop in America. Cambridge University Press.
Bonilla, Tabitha and Justin Grimmer. 2013. “Elevated Threat Levels and Decreased Expectations: How Democracy Handles Terrorist Threats.” Poetics 41(6):650–669.
Bosman, Julie and Erik Eckholm. 2014. “Anonymity in Police Shooting Fuels Anger in
Missouri.” The New York Times . Accessed August 8, 2016. http://www.nytimes.com/
2014/08/14/us/missouri-teenager-and-officer-scuffled-before-shooting-chief-says.html? r=0.
Bowler, Shaun and Jeffrey Karp. 2004. “Politicians, Scandals Trust in Government.” Political
Behavior 26(3):271–287.
Brehm, John and Scott Gates. 1997. Working, Shirking and Sabotage. Cambridge: University
of Michigan Press.
Brooks, Drew. 2014. “N.C. law enforcement agencies get military surplus through Defense
Department’s 1033 Program.” fayobserver.com . Accessed August 8, 2016. http://tinyurl.
com/gqkrdbq.
41
Campbell, Andrea L. 2003. How Policies Make Citizens: Senior Political Activism and the
American Welfare State. New Jersey: Princeton University Press.
Chanley, Virginia, Thomas Rudoph and Wendy Rahn. 2000. “The Origins and Consequences of Public Trust in Government: A Time Series Analysis.” Public Opinion Quarterly 64(3):239–256.
Downs, Anthony. 1957. An Economic Theory of Democracy. New York: Harper.
Duwe, Grant. 2000. “Body-Count Journalism: The Presentation of Mass Murder in the
News Media.” Homicide Studies 4:364–399.
Ferejohn, John. 1986.
50(1):5–25.
“Incumbent Performance and Electoral Control.” Public Choice
Fiorina, Morris P. 1981. Retrospective Voting in American National Elections. New Haven:
Yale University Press.
Fischel, William A. 2004. “Political Economy of Public Use in Poletown: How Federal Grants
Encourage Excessive Use of Eminent Domain.” The Michigan State Law Review p. 929.
Fraser, Ronald. 2014. “Opinion: Local Police Departments are Gearing up with Military
Force.” nj.com . Accessed August 8, 2016. http://www.nj.com/opinion/index.ssf/2014/08/
opinion towns police departments are gearing up with military force.html.
Frisch, Ragnar and Frederick V. Waugh. 1933. “Partial time Regression as Compared with
Individual Trends.” Econometrica: Journal of the Econometric Society pp. 387–401.
Fryer Jr., Roland G. 2016. “An Empirical Analysis of Racial Differences in Police Use of
Force.” NBER Working Paper (No. 22399). Accessed August 8, 2016. http://www.nber.
org/papers/w22399.
Gartner, Scott Sigmund. 1999. Strategic assessment in war. New Haven: Yale University
Press.
Goel, Sharad, Justin M. Rao and Ravi Shroff. 2016. “Precinct or Prejudice? Understanding
Racial Disparities in New York City’s Stop-And-Frisk Policy.” Annals of Applied Statistics
10(1):365–294.
Hancock, Amanda. 2015. “Why does Cobden have humvees?” The Southern . Accessed August 8, 2016. http://thesouthern.com/news/local/why-does-cobden-have-humvees/
article 62ee94e3-2106-5786-a980-c448013db3f5.html.
Healy, Andrew and Neil Malhotra. 2010. “Random Events, Economic losses, and Retrospective Voting: Implications for Democratic Competence.” Quarterly Journal of Political
Science 5(2):193–208.
Hopkins, Daniel J. and Lindsay M. Pettingill. 2015. “Economic Voting in Big-City U.S.
Mayoral Elections.” Working Paper . http://papers.ssrn.com/sol3/papers.cfm?abstract id=
2567294.
42
Iyengar, Shanto and Kyu S. Hahn. 2009. “Red Media, Blue Media: Evidence of Ideological
Selectivity in Media Use.” Journal of Communication 59(1):19–39.
Jones, Bryan D. and Frank R. Baumgartner. 2005. The Politics of Attention: How Government Prioritizes problems. University of Chicago Press.
Kahl, Glenn. 2013. “Emergency vehicle parade rolls thru Ripon.” mantecabulletin.com .
Accessed August 8, 2016. http://www.mantecabulletin.com/archives/87530/.
Key, Valdimer Orlando. 1966. The Responsible Electorate. New York: Vintage Books.
Kraska, Peter B. 2007. “Militarization and Policing: Its Relevance to 21st Century Police.”
Policing pp. 1–13.
Kraska, Peter B. and Victor E. Kappeler. 1997. “Militarizing American police: The rise and
normalization of paramilitary units.” Social problems 44(1):1–15.
Kuklinski, James H. and Norman L. Hurley. 1994. “On Hearing and Interpreting Political
Messages: A Cautionary Tale of Citizen Cue-taking.” The Journal of Politics 56(3):729–
751.
Kumlin, Staffan and Peter Esaiasson. 2012. “Scandal Fatigue? Scandal Elections and Satisfaction with Democracy in Western Europe, 1977–2007.” British Journal of Political
Science 42(2):263–282.
Lau, Richard R. and David P. Redlawsk. 2001. “Advantages and Disadvantages of Cognitive
Heuristics in Political Decision Making.” American Journal of Political Science 45(4):951–
971.
Lenz, Gabriel. 2013. Follow the Leader?: How Voters Respond to Politicians’ Policies and
Performance. Chicago: University of Chicago Press.
Leonard, Herman B. 1986. Checks Unbalanced: The Quiet Side of Public Spending. New
York: Basic Books.
Lerman, Amy E. and Vesla M. Weaver. 2014. Arresting Citizenship: The Democratic Consequences of American Crime Control. Chicago: University of Chicago Press.
Lovell, Michael C. 1963. “Seasonal Adjustment of Economic Time Series and Multiple
Regression Analysis.” Journal of the American Statistical Association 58(304):993–1010.
MacKinnon, James G. and Halbert White. 1985. “Some Heteroskedasticity-Consistent Covariance Matrix Estimators with Improved Finite Sample Properties.” Journal of Econometrics 29:305– 325.
Mansbridge, Jane. 1997. Social and Cultural Causes of Dissatisfaction with U.S. Government.
In Why People Don’t Trust Government, ed. Joseph S. Nye, Jr., Philip D. Zelikow and
David C. King. Cambridge, MA: Harvard University Press.
43
Manski, Charles F. 2013. Public Policy in an Uncertain World: Analysis and Decisions.
Cambridge, MA: Harvard University Press.
Maryland Statistical Analysis Center, Governor’s Office of Crime Control & Prevention.
2014. Fiscal Year 2014 SWAT Team Deployment Data Analysis. Technical report.
McCarthy, Justin. 2014. “Most Americans Still See Crime Up Over Last Year.” Gallup.com
. Accessed August 8, 2016. http://www.gallup.com/poll/179546/americans-crime-last-year.
aspx.
Merriam-Webster. 2016. “SWAT.”. Accessed August 8, 2016. http://www.merriam-webster.
com/dictionary/swat.
Miroff, Nick. 2007. “Pr. William Passes Resolution Targeting Illegal Immigration.” The
Washington Post . Accessed August 8, 2016. http://www.washingtonpost.com/wp-dyn/
content/article/2007/07/10/AR2007071002093.html.
Molotoch, Harvey. 2013. “How Much Security is Too Much?” New York Daily News .
Accessed August 8, 2016. http://www.nydailynews.com/opinion/security-article-1.1320360.
Mondak, Jeffery J. 1995. “Newspapers and political awareness.” American Journal of Political Science 39(2):513–527.
Mummolo, Jonathan. 2009. “Pr. William Arrest Data Show Illegal Residents Accused
More of Lesser Crimes.” The Washington Post . Accessed August 8, 2016. http://www.
washingtonpost.com/wp-dyn/content/article/2009/04/11/AR2009041102122.html.
Pierson, Paul. 1993. “When Effect Becomes Cause: Policy Feedback and Political Change.”
World Politics 45(4):595–628.
Pierson, Paul. 2000. “Increasing Returns, Path Dependence, and the Study of Politics.”
American Political Science Review 94(2):251–267.
Redden,
Mlly. 2015.
“Documents Reveal the Fearmongering Local
Cops Use to Score Military Gear From the Pentagon.” Mother Jones
.
Accessed August 8,
2016. http://www.motherjones.com/politics/2015/08/
new-documents-reveal-fearmongering-local-cops-use-score-military-gear-pentagon.
Savidge, Nico. 2014.
ter standoff’s peaceful
August
8,
2016.
“Police crediting controversial military
end.” Wisconsin State Journal .
vehicle afAccessed
http://host.madison.com/wsj/news/local/crime and courts/
police-crediting-controversial-military-vehicle-after-standoff-s-peaceful-end/article
dbd1e03e-4352-5554-bd81-97e2b12dcfa1.html.
Sebens, Shelby. 2013. “Oregon Fusion Center: House of spies or terror-threat prevention?”
Watchdog.org .
Accessed August 8, 2016, http://watchdog.org/86470/
spy-center-or-terrorist-threat-prevention-or-fusion-center-in-limbo/.
44
Sherman, Lawrence W. and Dennis P. Rogan. 1995. “Deterrent Effects of Police Raids on
Crack Houses: A Randomized, Controlled Experiment.” Justice Quarterly 12(3):755–781.
Sikma, Brian. 2014. “War Machines, Weapons Flood WI Law Enforcement.” mediatrackers.org . Accessed August 8, 2016. http://mediatrackers.org/wisconsin/2014/07/07/
war-machines-weapons-flood-wi-law-enforcement.
Sniderman, Paul M., Richard A. Brody and Phillip E. Tetlock. 1993. Reasoning and choice:
Explorations in political psychology. Cambridge: Cambridge University Press.
Welch, Daniel. 2016. “Clip art and controversy in Colorado police’s MRAP training materials.” muckrock.com . Accessed August 8, 2016. https://www.muckrock.com/news/archives/
2016/jul/27/colorado-1033-training/.
Wilensky, Harold. 1976. The New Corporatism, Centralization, and the Welfare State. Beverly Hills, Calif.: Sage.
Wilson, James Q. 1968. Varieties of Police Behavior. Cambridge: Harvard University Press.
Wofford, Taylor. 2014.
Program.” Newsweek .
“How America’s Police Became an Army: The 1033
Accessed August 8, 2016. http://www.newsweek.com/
how-americas-police-became-army-1033-program-264537.
Zhang, Nan. 2015. “Anti-Corruption Efforts and Public Opinion in China: Evidence from a
Natural Experiment in an Autocratic Regime.” Working Paper . http://www.mwpweb.eu/
1/189/resources/publication 2062 1.pdf .
Zummo, Mike. 2014. “Vernon Police Looking to Add Officers.” The Advertiser News .
Accessed August 8, 2016. http://www.advertisernewsnorth.com/article/20140122/NEWS01/
140129966/Vernon-police-looking-to-add-officers.
45
Appendix A: Observational SWAT Data
Details of California SWAT Sample and Coding of SWAT Data
To determine which of California’s more than 400 law enforcement agencies have SWAT
teams, I first compiled a list of agencies from the federal 2008 Census of State and Local
Law Enforcement Agencies (CSLLEA) which includes a question about whether each agency
provides SWAT services in their community. Because the 2008 survey, the most recent
available, may be out of date, I also contacted the California Association of Tactical Officers
(CATO), a professional association for members of tactical police units in the state, which
generously provided me with their current list of SWAT agencies in California.
Public Records Act (PRA) requests were sent via email either to county or city clerks,
who typically handle such requests, or in some cases directly to a police agency’s records
department. In these emails, I requested that the date of each deployment, as well as the
reason for the deployment, and if available, the location of the deployment, be provided. The
request also stated that, “If the tactical operations unit in your agency goes by another name
besides “S.W.A.T.”, (e.g. “TAC” or “PPU”), then please provide the above information for
that unit,” (referring to “Tactical Anti-Crime” and “Police Paramilitary Units”). Other
variants of units with roles analogous to those of SWAT teams include “Special Response
Units” (SRTs) and “Critical Incident Response Teams” (CIRTs). Because tactical units vary
in name, I left it up to the agency to determine whether they currently had a SWAT team
or comparable unit, and coded all responses as SWAT deployments.
The data requested in California contain a fraction of the variables contained in the
Maryland data. The reason I did not seek more detailed information is that the California
Public Records Act includes provisions allowing law enforcement agencies to deny requests
if (1) the records do not already exist and (2) the records would compromise a criminal
investigation. Asking for extremely detailed records of deployments can therefore increase
1
the chances that a request will be rejected in its entirety.
Table A1 displays mean demographic characteristics for the California agencies using place
and county-level data from the 2013 5-year American Community (ACS) and crime data from
FBI Uniform Crime Reports. City and town agencies were matched to Census place data
and county agencies were matched to Census county data. Demographic variables were put
into per capita terms using population totals provided in Census data. Violent crimes per
capita were computed using the mean agency population figures during the period FY20102014 as provided by the FBI. These are imperfect mappings to agency jurisdictions, but
nonetheless allow for a rough comparison of demographic characteristics across agencies. The
table describes: all agencies of which data was requested (column 1); those agencies which
supplied data (column 2)32 ; agencies which supplied data excluding joint, multi-jurisdictional
teams (column 3); agencies included in the subsequent analysis of crime rates (column 4);
agencies which did not supply data (column 5); and the p-values from t-tests of the differences
between pairs of columns.33 As the table shows, the sample of agencies which supplied data
are comparable to the agencies which did not in terms of population size, proportion nonHispanic White, proportion non-Hispanic Black and proportion female. The agencies for
which I have data have populations which differ modestly on the proportions non-Hispanic
Asian and Latino, are somewhat lower on median household income, are less educated and
have more violent crimes per capita. The data used for the crime analysis shares roughly
the same characteristics. Note: one agency, the Kern County Sheriff’s Office, was not
included in this tally because it required in-person review of the records, which could not be
accommodated. The San Francisco County Sheriff’s Office, the Greenbrae Police Department
and the Larkspur Police Department were also excluded from these calculations since the
FBI data did not include population data for these jurisdictions.
32
Those agencies which supplied information on deployments but did not disclose the dates of deployments
were coded as not having supplied data.
33
The violent crime data is computed during FY2010-2014, to match the Maryland data and the crime
analysis performed in the main text.
2
Several coding decisions had to be made when working with the California sample. In some
cases, agencies indicated that deployments spanned several days, but were only displayed as
a single incident. Such cases were coded as separate deployments (one for each day). In cases
where the number of days was simply specified as “multiple,” i.e., not a precise number, the
incident was coded as two deployments.
When dealing with agencies which were part of a joint, multi-jurisdictional team, special
care was taken to not double-count deployments in the event more than one member agency
supplied data. This was not an issue in the Maryland data, as agencies which work together
on SWAT operations designate a single agency to submit data to the state to avoid double
counting.
Table A1: Demographics of Agencies in California S.W.A.T. Data. Statistics are sample
means. The last two columns display p-values on the null hypotheses that columns and (2) and
(5), and (4) and (5), are equal, respectively.
Population
Nh-White
Nh-Black
Nh-Asian
Latino
Other Race
Median HH Inc. ($)
Median Age
Female
BA or Higher (%)
Violent Crimes/100k per month
No. of Agencies
(1)
all
requested
224,331
0.457
0.042
0.121
0.341
0.040
66,094
36.676
0.503
9.364
80
268
(2)
data
169,250
0.473
0.040
0.133
0.315
0.039
69,373
37.210
0.505
10.54
75
187
3
(3)
no joint
teams
150,643
0.424
0.05
0.138
0.35
0.038
67,347
35.640
0.506
9.74
82
113
(4)
in crime
panel
169,095
0.473
0.041
0.133
0.315
0.039
69,357
37.218
0.505
10.523
77
186
(5)
no data
350,815
0.420
0.045
0.095
0.4
0.041
58,567
35.451
0.497
6.664
89
81
p-val.
(2)-(5)
0.181
0.090
0.578
0.014
0.007
0.621
0.002
0.022
0.034
0.000
0.025
p-val.
(4)-(5)
0.181
0.094
0.632
0.013
0.007
0.646
0.003
0.022
0.040
0.000
0.042
Zip Code Demographics in Maryland
The plots below display variants of the zip code level demographic analysis performed in the
main text (Figure 3).
4
Figure A1: Demographics of SWAT Deployments - No State Agencies. Scatter plots
displaying relationships between zip code-level demographics in Maryland and SWAT deployments
per 100,000 residents (FY2010-2014). Blue curves are LOESS fits, red lines are OLS fits.
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Log(S.W.A.T. Per 100k Residents+1)
FY2010−2014
Log(S.W.A.T. Per 100k Residents+1)
FY2010−2014
%Non−Hispanic Black
%Non−Hispanic Asian
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Log(S.W.A.T. Per 100k Residents+1)
FY2010−2014
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Log(S.W.A.T. Per 100k Residents+1)
FY2010−2014
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8
Log(S.W.A.T. Per 100k Residents+1)
FY2010−2014
5
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25
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50
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2
4
6
8
Log(S.W.A.T. Per 100k Residents+1)
FY2010−2014
Figure A2: Demographics of SWAT Deployments - Logged Census Outcomes. Scatter
plots displaying relationships between zip code-level demographics in Maryland and SWAT deployments per 100,000 residents (FY2010-2014). Blue curves are LOESS fits, red lines are OLS
fits.
%Non−Hispanic White
●
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75
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25
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11.0
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10.5
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10.0
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4
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6
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0
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8
Log(S.W.A.T. Per 100k Residents+1)
FY2010−2014
●
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0
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1
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2
4
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6
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2
●●
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1
●
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●
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●
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●
0
● ●
8
Log(S.W.A.T. Per 100k Residents+1)
FY2010−2014
6
●
●
●
●
●
●
●
●
●
●
●
●
● ●
●
● ●
●
●
●
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●
3
●
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●
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●
●
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●
4
●
●
●
●
●
6
●
●
●
3
●●
●
4
% B.A. or Higher
●
●
●
0
●
2
Log(S.W.A.T. Per 100k Residents+1)
FY2010−2014
●
● ●
●
● ● ●
●●
0
●
●
●●●
●
● ●
●●
●
●
●
●
●
●●
●
●
●
●
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●
●
8
●
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●
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● ●
●
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●
●
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●
● ● ●
● ●● ● ●
● ●
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●
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●
●●● ● ● ●●
●
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●
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●
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●
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● ●
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●●
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●
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●
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●
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●
●
●
6
●
●
●
●
●
●
●
●
●
● ●●
●
●
●
4
%Non−Hispanic Asian
4
Log(%Non−Hispanic Asian+1)
●
●
●●
●
●
●
●
● ●●
●
●
●
● ● ●
● ● ●
●●
● ●●●
●
●
●
●
●
●
●
●
● ●
●
●●●
●
●
● ●
●●
●
●
2
Log(S.W.A.T. Per 100k Residents+1)
FY2010−2014
Log(% B.A. or Higher+1)
2
●
●
0
●
●
●
●
●
●
●
●
%Non−Hispanic Black
1
●
11.5
●
●
●
●
●
●
●
●
●
●
●
●
0
2
●
●
●
●
●
●
●
Log(S.W.A.T. Per 100k Residents+1)
FY2010−2014
3
●
●
●
● ●●●
●
●
●
●
●●
●
● ● ● ●
●
●●
●●
●
●
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●
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●
● ●●
● ●
●
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● ●
●●●●
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●
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●
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●
● ●
●
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●
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●
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●
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●
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●
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●
●●
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●
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● ● ● ●●
●
●
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●
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●
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●
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●
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● ●● ●
●
●
●●
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●
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●
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● ● ●
●
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●●
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● ●
●
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●
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●●
●
●●
●
●
●
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●
●
0
●
●
●
●
●●
●●
● ●●●
●
●
●
●
0
4
● ●●
● ●
● ●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
1
●
● ●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
Log(%Non−Hispanic Black Residents+1)
●
●
●
●
●
●
● ●
●
●
●
●
●
●
●
●
2
●
●
●
●
●
●
●
●
●
●
●
●●●
●
●
●●
●
●
●
●
●
●
●
●
●
3
●
● ●
12.0
●
●
●
●
●
● ●●
●
●
●
●
●
●
●
●
●
●
●
● ●
●
●
●
●
●
●
●
●
● ●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
● ●
●
●●
●
●
●
●
4
●
●
●
●
●
50
●
●
●
●
● ●
Median HH Income ($)
Log(Median HH Income ($)+1)
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
Log(%Hispanic/Latino+1)
Log(%Non−Hispanic White Residents+1)
100
%Hispanic/Latino
●
0
● ● ●●
●●● ●●
● ●●●●● ●
●●● ●
●
●●●
● ●● ●● ●● ●
2
4
6
●
8
Log(S.W.A.T. Per 100k Residents+1)
FY2010−2014
Robustness Checks in Agency-Month Analysis
In the FBI UCR data, some agencies submit negative estimates for the number of crimes in
a given month in order to compensate for over-reporting in prior months. This rarely occurs.
In the Maryland data, 0.37% of agency-months contain negative values. In the California
data, 0.37% of agency-months contain negative values. Below, Figure A3 displays the results
of the agency-month analysis with these values dropped, which look nearly identical to Figure
6 in the main text.
In addition, while the California analysis excluded agencies in the state which did not
supply SWAT data, it included imputed zeros for agencies which did supply data during
months where no deployments were reported. To guard against the inclusion of “false zeros,” estimates produced after omitting imputed zeros are displayed below, as are estimates
produced after dropping amalgamated, multi-jurisdictional teams. See Figures A4 and A5.
Again, the results are unaffected by these omissions.
7
8
#Violent Crimes
#Violent Crimes
0
2000
4000
0
500
1000
1500
●
●
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15
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Raw Data, CA
#S.W.A.T. Deployments
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Log(#Violent Crimes+1)
Log(#Violent Crimes+1)
0.0
2.5
5.0
7.5
10.0
0.0
2.5
5.0
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Logged, MD
#Violent Crimes
Residualized
Raw Data, MD
#Violent Crimes
Residualized
−1000
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Agency Fixed Effects
MD
−4
−2
0
−7 5
−5 0
−2 5
00
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Log #S W A T +1
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−1
1
Log #S W A T +1
Res dua zed
0
2
Agency F xed E ec s
Logged CA
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Agency F xed E ec s
Logged MD
Figure A3: Violent Crimes by SWAT Deployments, Maryland and California Agency-Months, FY2010-2014 - No
Negative Crime Values. LOESS fits in blue, OLS fits in red. This analysis omits agency-months for which the FBI data
contained negative values for violent crime counts.
Log #V o en C mes+1
Res dua zed
Log #V o en C mes+1
Res dua zed
9
#Violent Crimes
#Violent Crimes
0
2000
4000
0
500
1000
1500
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30
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15
●
#S.W.A.T. Deployments
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Raw Data, CA
#S.W.A.T. Deployments
10
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50
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Log(#Violent Crimes+1)
Log(#Violent Crimes+1)
2.5
5.0
7.5
0.0
2.5
5.0
7.5
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2.0
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2.5
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Log(#S.W.A.T.+1)
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Logged, CA
3.0
●●
4
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Logged, MD
#Violent Crimes
Residualized
Raw Data, MD
#Violent Crimes
Residualized
−5
0
5
10
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10
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1000
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20
#S.W.A.T. Deployments
Residualized
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Agency Fixed Effects
CA
−1000
●
−1000
−500
0
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Agency Fixed Effects
MD
−1
0
1
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−7.5
−5.0
−2.5
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Log(#S.W.A.T.+1)
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Figure A4: Violent Crimes by SWAT Deployments, Maryland and California Agency-Months, 2009-2014, No
Imputed Zeros. This analysis omits agency-months from the California data in which zero SWAT deployments are known to
have occurred, since some of those zeros may be due to incomplete records.
Log(#Violent Crimes+1)
Residualized
Log(#Violent Crimes+1) Residualized
10
#Violent Crimes
#Violent Crimes
0
2000
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15
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#S.W.A.T. Deployments
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Raw Data, CA
#S.W.A.T. Deployments
10
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Log(#Violent Crimes+1)
Log(#Violent Crimes+1)
0.0
2.5
5.0
7.5
10.0
0.0
2.5
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Logged, MD
#Violent Crimes
Residualized
Raw Data, MD
#Violent Crimes
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−5
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CA
−1000
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−500
0
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MD
−7.5
−5.0
−2.5
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Agency F xed E ec s
Logged CA
●
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Agency Fixed Effects
Logged, MD
Figure A5: Violent Crimes by SWAT Deployments, Maryland and California Agency-Months, 2009-2014, No
Joint Teams. This analysis omits agency-months from the California data belonging to joint, multi-jurisdictional teams, since
the geographic matching of deployments and violent crime data is imprecise.
Log(#Violent Crimes+1)
Residualized
Log #V o en C mes+1 Res dua zed
The agency-month analysis also assumes that each agency’s SWAT deployments occurred
in its jurisdiction. To validate this assumption, I purchased a crosswalk file from ZipCodeDownload, a commercial vendor, that cross-referenced zip codes (postal codes) with
cities and counties, and merged these data with the Maryland SWAT data (see https:
//zipcodedownload.com/Products/Product/Z5Commercial/Standard/Overview).
I found that
among city agencies, 93% of deployments occurred in a zip code associated with the same
city. The comparable figure for county agencies and counties was 100%. The county-level result is likely an overestimate, as county sheriff’s offices sometimes do not patrol incorporated
areas within county borders (this varies by institutional arrangement).
Validation of ZCTA / Zip code Coding
A ZCTA/zip code crosswalk file published online by the UDS Mapper project shows that
when post offices and other large volume customers are excluded, the two units take the same
five-digit code in 98.8% of cases in the U.S (see http://www.udsmapper.org/zcta-crosswalk.cfm).
While imperfect, the use of ZCTA data provides a viable method for pairing zip code-level
data with census variables. In the Maryland data, 54 zip codes could not be matched to
ZCTAs. These zip codes account for only 1.6% of the SWAT deployments in that state.
Another 3 zip codes were matched to ZCTAs in neighboring states are included in the
calculations used to produce Figure 3.
Robustness Checks for Zip code-Day SWAT Analysis
The crime data obtained from Baltimore, Prince George’s County and Montgomery County
Maryland were not uniformly coded, so it was necessary to classify violent crimes in the data.
The FBI classifies murder, rape, robbery and assault as violent crimes. In the Baltimore
data, crimes took on 15 distinct categorical values. I coded any crime event containing the
FBI terms, as well as the word “shooting,” as a violent crime. In the Prince George’s County,
11
where crimes were coded into 21 categories, I used the same technique. The crime categories
in the Montgomery County data took over 300 distinct values. I hand coded these as violent
or not based on the FBI definitions, and also counted terms like “simple assault,” “bomb
threat” and “explosive device.” In addition, the Montgomery County web site noted that
single rows in the data could represent multiple offenses, but did not specify which rows, so
they are assumed to be single incidents by necessity.
Below, Table A2 displays the results after logging the dependent variable.
Table A2: Zipcode-Day OLS Models of Violent Crime, Logged DV. Outcome ranges from
0 to 21. “Short” panels use only observed crime data. “Long” panels impute zero violent crimes in
unobserved zipcode-days.
(Intercept)
1 SWAT
≥ 2 SWAT
N
Short
0.541∗
(0.062)
0.062
(0.049)
0.126
(0.092)
64, 887
Long
0.295∗
(0.045)
0.165∗
(0.050)
0.244∗
(0.089)
117, 673
Zipcode FE, Short
0.036
(0.099)
−0.008
(0.010)
−0.025
(0.029)
64, 887
Zipcode FE, Long
0.002
(0.017)
−0.005
(0.008)
−0.019
(0.021)
117, 673
Maximum of homoscedastic and zip code-clustered robust standard errors in parentheses.
∗
indicates significance at p < 0.05
Does Lagged Violent Crime Predict SWAT Deployments?
Here, I test for the possibility that SWAT teams are deployed in response to recent, rather
than comtemporaenous, crime events. Unlike, the models presented in the main text, I
specify the number of SWAT deployments in a zip code-day (which ranges from 0 to 4) as
the dependent variable, and the number of violent crimes in a zip code-day as the independent
variable, as well as one-day and seven-day lagged versions of the independent variable. In
the first lagged specification, the crime counts enter the model discretely, to avoid linearity
assumptions, with the highest “bin” denoting ≥ 10 violent crimes due to sparseness in the
data above that value. The results appear in Table A3. The same model was estimated
12
without binning the independent variable, allowing the number of crimes to enter linearly
with all values counted. Observations with ≥ 10 violent crimes are still grouped. The results
appear in A4. Results from analogous models with one-week lags appear in Tables A5 andA6.
In general, these results lead to the same substantive conclusions as reported in the main
text.
13
Table A3: Zipcode-Day OLS Models of Violent Crime - One Day Lag. “Short” panels use
only observed crime data. “Long” panels impute zero violent crimes in unobserved zipcode-days.
Crimes are lagged by one day.
(Intercept)
1 Crime
2 Crimes
3 Crimes
4 Crimes
5 Crimes
6 Crimes
7 Crimes
8 Crimes
9 Crimes
≥10 Crimes
Lagged 1 Crime
Lagged 2 Crimes
Lagged 3 Crimes
Lagged 4 Crimes
Lagged 5 Crimes
Lagged 6 Crimes
Lagged 7 Crimes
Lagged 8 Crimes
Lagged 9 Crimes
Lagged ≥10 Crimes
N
Short
0.032∗
(0.004)
−0.002
(0.002)
0.003
(0.004)
0.008
(0.005)
0.004
(0.005)
0.000
(0.007)
0.019
(0.017)
0.005
(0.009)
0.033∗
(0.015)
0.023
(0.023)
0.017
(0.016)
0.009∗
(0.004)
0.005
(0.004)
0.003
(0.005)
0.003
(0.008)
0.011
(0.006)
0.006
(0.007)
0.000
(0.012)
0.011
(0.015)
0.008
(0.015)
−0.001
(0.013)
64, 808
Long
0.020∗
(0.003)
0.009∗
(0.003)
0.012∗
(0.004)
0.017∗
(0.005)
0.013∗
(0.004)
0.009
(0.007)
0.028
(0.017)
0.014
(0.008)
0.042∗
(0.015)
0.032
(0.022)
0.025
(0.016)
0.014∗
(0.004)
0.010∗
(0.004)
0.007
(0.004)
0.007
(0.008)
0.015∗
(0.006)
0.010
(0.006)
0.004
(0.012)
0.014
(0.015)
0.012
(0.015)
0.003
(0.011)
117, 548
Zipcode FE, Short
0.000
(0.048)
−0.005∗
(0.002)
−0.000
(0.003)
0.002
(0.004)
−0.005
(0.005)
−0.012∗
(0.006)
0.004
(0.013)
−0.011
(0.009)
0.017
(0.012)
0.004
(0.023)
−0.002
(0.017)
0.001
(0.003)
−0.002
(0.004)
−0.007
(0.005)
−0.010
(0.006)
−0.005
(0.006)
−0.012
(0.007)
−0.020
(0.012)
−0.009
(0.015)
−0.015
(0.015)
−0.024
(0.013)
64, 808
Maximum of homoscedastic and zip code-clustered robust standard errors in parentheses.
∗
indicates significance at p < 0.05
14
Zipcode FE, Long
0.000
(0.009)
−0.004∗
(0.002)
0.000
(0.004)
0.003
(0.004)
−0.004
(0.004)
−0.011∗
(0.005)
0.005
(0.013)
−0.010
(0.009)
0.018
(0.012)
0.005
(0.023)
−0.002
(0.017)
0.000
(0.002)
−0.002
(0.003)
−0.006
(0.004)
−0.010
(0.006)
−0.005
(0.005)
−0.013∗
(0.006)
−0.020
(0.011)
−0.010
(0.015)
−0.015
(0.014)
−0.024∗
(0.011)
117, 548
Table A4: Zipcode-Day OLS Models of Violent Crime - One Day Lag. “Short” panels use
only observed crime data. “Long” panels impute zero violent crimes in unobserved zipcode-days.
Crimes are lagged by one day.
(Intercept)
No. Violent Crimes
Lagged No. Violent Crimes
N
Short
0.033∗
(0.005)
0.002
(0.001)
0.001
(0.001)
64, 808
Long
0.022∗
(0.003)
0.004∗
(0.001)
0.002∗
(0.001)
117, 548
Zipcode FE, Short
0.000
(0.048)
−0.000
(0.001)
−0.002∗
(0.001)
64, 808
Maximum of homoscedastic and zip code-clustered robust standard errors in parentheses.
∗
indicates significance at p < 0.05
15
Zipcode FE, Long
0.000
(0.009)
−0.000
(0.001)
−0.002∗
(0.001)
117, 548
Table A5: Zipcode-Day OLS Models of Violent Crime - One Week Lag. “Short” panels use
only observed crime data. “Long” panels impute zero violent crimes in unobserved zip code-days.
Crimes are lagged by one week.
(Intercept)
1 Crime
2 Crimes
3 Crimes
4 Crimes
5 Crimes
6 Crimes
7 Crimes
8 Crimes
9 Crimes
≥10 Crimes
Lagged 1 Crime
Lagged 2 Crimes
Lagged 3 Crimes
Lagged 4 Crimes
Lagged 5 Crimes
Lagged 6 Crimes
Lagged 7 Crimes
Lagged 8 Crimes
Lagged 9 Crimes
Lagged ≥10 Crimes
N
Short
0.033∗
(0.005)
−0.002
(0.002)
0.002
(0.004)
0.007
(0.005)
0.003
(0.005)
−0.001
(0.007)
0.018
(0.016)
0.003
(0.009)
0.032∗
(0.014)
0.022
(0.023)
0.016
(0.016)
0.003
(0.003)
0.003
(0.005)
0.011
(0.006)
0.004
(0.006)
0.007
(0.007)
0.007
(0.012)
0.011
(0.010)
0.013
(0.015)
−0.007
(0.014)
0.005
(0.014)
64, 402
Long
0.020∗
(0.003)
0.008∗
(0.003)
0.011∗
(0.004)
0.016∗
(0.005)
0.011∗
(0.004)
0.007
(0.007)
0.026
(0.016)
0.011
(0.009)
0.040∗
(0.014)
0.030
(0.023)
0.024
(0.016)
0.011∗
(0.003)
0.009∗
(0.004)
0.016∗
(0.005)
0.009
(0.006)
0.012
(0.007)
0.012
(0.012)
0.016
(0.010)
0.018
(0.015)
−0.002
(0.012)
0.010
(0.013)
116, 798
Zipcode FE, Short
0.000
(0.049)
−0.005∗
(0.002)
−0.000
(0.004)
0.002
(0.004)
−0.005
(0.005)
−0.012∗
(0.006)
0.004
(0.013)
−0.012
(0.010)
0.017
(0.011)
0.004
(0.023)
−0.002
(0.017)
−0.004
(0.002)
−0.002
(0.004)
0.003
(0.005)
−0.006
(0.005)
−0.008
(0.007)
−0.009
(0.009)
−0.007
(0.009)
−0.004
(0.014)
−0.028
(0.015)
−0.016
(0.016)
64, 402
Maximum of homoscedastic and zip code-clustered robust standard errors in parentheses.
∗
indicates significance at p < 0.05
16
Zipcode FE, Long
0.000
(0.009)
−0.005∗
(0.002)
0.000
(0.004)
0.003
(0.004)
−0.004
(0.004)
−0.011∗
(0.005)
0.005
(0.013)
−0.012
(0.010)
0.018
(0.011)
0.005
(0.023)
−0.002
(0.017)
−0.002
(0.002)
−0.001
(0.004)
0.004
(0.005)
−0.006
(0.004)
−0.007
(0.007)
−0.008
(0.009)
−0.006
(0.009)
−0.004
(0.014)
−0.027∗
(0.012)
−0.015
(0.016)
116, 798
Table A6: Zipcode-Day OLS Models of Violent Crime - One Week Lag. “Short” panels use
only observed crime data. “Long” panels impute zero violent crimes in unobserved zip code-days.
Crimes are lagged by one week.
(Intercept)
No. Violent Crimes
Lagged No. Violent Crimes
N
Short
0.033∗
(0.005)
0.002
(0.001)
0.001
(0.001)
64, 402
Long
0.022∗
(0.003)
0.004∗
(0.001)
0.003∗
(0.001)
116, 798
Zipcode FE, Short
0.000
(0.049)
−0.000
(0.001)
−0.001
(0.001)
64, 402
Maximum of homoscedastic and zip code-clustered robust standard errors in parentheses.
∗
indicates significance at p < 0.05
17
Zipcode FE, Long
0.000
(0.009)
−0.000
(0.001)
−0.001
(0.001)
116, 798
Details on Newspaper Analysis
Keyword searches used to compute Table 2 occurred within articles containing the words
“police” and “SWAT” or “S.W.A.T.” during FY2010-2014, roughly between 60 and 100
articles per year. Articles were coded as being about barricade situations if they also contained any of the words “barricade,” “hostage” or “surrender,” which are very common terms
in coverage of barricade scenarios. Articles were coded as being about search warrants if
they contained the words “search” and “warrant.” Articles were coded as being about drug
warrants if they contained the words “warrant” and “drug.” Articles were coded as being
about violent crimes if they contained the word “police” and either “S.W.A.T.” or “SWAT”,
as well as any one of the following terms: “hostage,” “weapon,” “murder,” “homicide,”
“bomb,” “terror,” “assault,” “rape” or “rob.” In the SWAT data, events are coded as drug
warrants if they were to serve search warrants in connection with Part II crimes, since a
Maryland state report analyzing these data indicated that “most” deployments related to
Part II crimes, “would be activated to recover and seize illegal drugs and other contraband
items from the offender,” (Maryland Statistical Analysis Center, Governor’s Office of Crime
Control & Prevention, 2014, p. 6).
Keyword searches used to compute Table 3 were coded as being about violent crimes if
they contained the word “police” and either “hostage,” “weapon,” “murder,” “homicide,”
“bomb,” “terror,” “assault,” “rape” or “rob.”
18
Appendix B: Survey Experiment
Visual Manipulations
The following images, obtained from online news reports, were randomly assigned to respondents. The control image is of Boston police officers and appeared in Molotoch (2013).
The original source was listed as “Jessica Rinaldi/Reuters.” The “riot gear” image portrays
an image of police in Portland during a protest and appeared in Sebens (2013). The same
image was located on depositphotos.com, a stock photo warehouse, where a high-resolution
version was purchased. The photo credit is listed there as “jpldesigns.” The “assault rifles”
image accompanied Fraser (2014) and portrays officers deployed in Ferguson, MO. The photo
was credited to the Associated Press. The “armored vehicle” image also portrays officers
deployed in Ferguson, MO and accompanied Bosman and Eckholm (2014). The image was
credited to “Whitney Curtis for The New York Times.”
1
Figure B1: Experimental Manipulation
(a) Traditional Police (Control)
(b) Militarized Police - Riot Gear
(c) Militarized Police - Assault Rifles
(d) Militarized Police - Armored Vehicle
2
Sample Demographics
Table B1: Mean Values on Demographic Traits. Note: N computed as number of respondents
who answered violent crime question about vingnette city. Due to non-response, these proportions
may vary depending on the dependent variable being analyzed.
male
age
nhwhite
nhblack
latino
other race
Has B.A.
N
M-turk
0.52
34.25
0.77
0.07
0.08
0.09
0.50
1,566
SSI
0.47
41.74
0.45
0.41
0.08
0.05
0.39
4,464
Balance on Observables
The OLS models below predict treatment assignment in the SSI and M-Turk surveys as a
function of demographic items. The F statistic and accompanying p-value at the bottom
of each model correspond to the null hypothesis that all coefficients are jointly zero, which
should be true if balance was achieved through random assignment. As the table shows, no
p-value on any F -statistic allows us to reject this null (i.e., p > .05), indicating that the
randomization was successful.
3
Table B2: OLS Models Predicting Treatment Assignment
(Intercept)
male
age
nhwhite
nhblack
latino
has B.A.
N
F -stat
p-value on F -stat
SSI, Control SSI, Treatment 1 SSI, Treatment 2 Turk, Control Turk, Treatment 1 Turk, Treatment 2 Turk, Treatment 3
0.27∗
0.33∗
0.40∗
0.23∗
0.35∗
0.14∗
0.28∗
(0.04)
(0.04)
(0.04)
(0.05)
(0.05)
(0.05)
(0.05)
0.02
−0.03
0.01
0.01
−0.03
0.00
0.02
(0.01)
(0.01)
(0.01)
(0.02)
(0.02)
(0.02)
(0.02)
0.00
0.00
−0.00
0.00
−0.00∗
0.00∗
−0.00
(0.00)
(0.00)
(0.00)
(0.00)
(0.00)
(0.00)
(0.00)
0.07∗
−0.02
−0.05
−0.00
−0.05
0.06
−0.01
(0.03)
(0.03)
(0.03)
(0.04)
(0.04)
(0.04)
(0.04)
0.07∗
−0.01
−0.06
−0.08
0.01
0.04
0.03
(0.03)
(0.03)
(0.03)
(0.06)
(0.06)
(0.06)
(0.06)
0.01
0.04
−0.06
−0.01
−0.07
0.09
−0.01
(0.04)
(0.04)
(0.04)
(0.05)
(0.05)
(0.06)
(0.06)
−0.01
−0.01
0.02
0.01
0.02
−0.02
−0.00
(0.01)
(0.01)
(0.01)
(0.02)
(0.02)
(0.02)
(0.02)
4, 702
4, 702
4, 702
1, 563
1, 563
1, 563
1, 563
1.996
1.612
1.255
0.568
1.644
1.644
1.602
0.063
0.139
0.275
0.756
0.131
0.131
0.143
Standard errors in parentheses
∗
indicates significance at p < 0.05
Text of Experimental News Article, M-turk Sample
The article provided to respondents was based on an online news article from The Advertiser
News, a small local newspaper in New Jersey (Zummo, 2014).
City police ask for budget increase
Feb. 23, 2015
(Image Manipulation Here)
Above: Five city police officers stand guard during a local protest.
The chief of the city police department requested a 7 percent budget increase for
his agency at Thursday’s City Council meeting, arguing that additional officers
are needed in light of recent personnel changes.
According to the police chief, retirements and promotions in the police department
4
made it necessary to hire additional officers in 2014. This led to an increase in
costs associated with officer training and implementation.
The council is expected to vote on a final budget some time next month.
Question Wording for Dependent Variables, M-turk sample
Crime in vignette city:
“Compared to a typical city in the United States, would you say the city in the news article you just read has less violent crime, the same amount of violent crime, or more violent
crime?” [Less violent crime; A typical amount of violent crime; More violent crime]34
Police budget in vignette city:
“Thinking about the article you just read, do you think the City Council should decrease
the police department’s budget, keep funding the same, or increase funding?” [Significantly
decrease budget; Decrease budget; Keep budget the same; Increase budget; Significantly
increase budget]
Violent crime open end:
Please briefly explain why you think the rate of violent crime in the city discussed with the
news article was lower, about the same, or higher than a typical neighborhood in the U.S.
You may answer in one sentence or less.” [Text box]
Budget open end:
“Please briefly explain why you think the police budget in the city discussed in the news
article should be increased, kept the same, or decreased. You may answer in one sentence
34
The response option “A typical amount of violent crime” was changed to “The same amount of violent
crime” in the SSI version of this survey in order to more closely match the wording of the question.
5
or less.” [Text box]
Crime worsening in United States: “Over the last ten years, would you say that the
number of violent crimes per person in the United States has fallen, remained about the
same, or risen?” [Fallen; Remained about the same; Risen]
Police budgets in U.S. “Please indicate whether you would like to see more or less
government spending on the police and law enforcement in the United States.” [Spend much
less; Spend less, Spend the same as now, Spend more, Spend much more]
Support for Three-Strikes Law: “As you may know, several states have recently
passed laws commonly known as “three strikes and you’re out” laws. These laws say that
anyone with two serious felony convictions on their record who is convicted of a third serious
crime must be given a sentence of life in prison. Do you support or oppose ‘three strikes
and you’re out’ laws?” [Strongly oppose, Oppose, Neither support nor oppose; Somewhat
support; Strongly support]35
Confidence in Institutions in U.S.
“Now you will read a list of institutions in American society. Please tell me how much
confidence you, yourself, have in each one – a great deal, quite a lot, some, or very little?”
[Institutions listed: “The police”; “Congress”; “The criminal justice system”; “The presidency”; “The U.S. Supreme Court”]
35
Note, the response option here read “Oppose” instead of “Somewhat oppose” due to an error.
6
Text of Experimental News Article, SSI Sample
City police ask for budget increase
Feb. 26, 2016
(Image Manipulation Here)
Above: Five city police officers stand guard during a local protest.
The chief of the city police department requested a 7 percent budget increase for
his agency at Thursday’s City Council meeting, arguing that additional funds are
necessary in light of recent personnel changes.
According to the police chief, retirements and promotions last year mean the
department will need to hire additional officers to serve the city, which has roughly
200,000 residents. The additional funds will be used for training, equipment and
salaries, the chief said.
The council is expected to vote on a final budget later this year.
Question Wording for Dependent Variables, SSI sample
Crime in vignette city:
“Compared to a typical U.S. city of roughly the same size, would you say the city you just
read about has less violent crime, the same amount of violent crime, or more violent crime?”
[Less violent crime; The same amount of violent crime; More violent crime]
Worsening crime in vignette city:
“In the past year, would you say that the amount of violent crime in the city you just read
about has decreased, stayed about the same, or increased?” [Decreased; Stayed about the
7
same; Increased]
Police budget in vignette city:
“Thinking about the article you just read, do you think the City Council should decrease the
police department’s budget, keep the budget the same, or increase the budget?” [Significantly
decrease budget; Decrease budget; Keep budget the same; Increase budget; Significantly increase budget]
Confidence in police in vignette city:
“Please tell us how much confidence you, yourself, have in the police department in the city
you just read about – a great deal, quite a lot, some, or very little?” [Very little; Some; Quite
a lot; A great deal]
Crime in vignette city open end:
“Please briefly explain why you think the amount of violent crime in the city you just read
about was lower, about the same, or higher than a typical U.S. city of roughly the same
size?” [Text box]
Worsening violent crime open end:
“Please briefly explain why you think the amount of violent crime in the city you just read
about has decreased, stayed about the same, or increased in the past year.” [Text box]
Budget open end: “Please briefly explain why you think the police budget in the city
you just read about should be decreased, kept the same, or increased.” [Text box]
Police budgets in U.S.
8
“In your opinion, should local governments in the United States be spending less, about the
same, or more on police agencies?” [Spend much less; Spend less; Spend the same as now;
Spend more; Spend much more]
Confidence in police in U.S.:
“Please tell us how much confidence you, yourself, have in police in the United States – a
great deal, quite a lot, some, or very little?” [Very little; Some; Quite a lot; A great deal]
Police patrols in respondent neighborhood:
“In general, would you be in favor of police patrolling your neighborhood less often, more
often, or would you rather the amount of patrols remain the same?” [Fewer patrols; Remain
the same; More patrols]
Perceived discrimination:
“Have you ever been treated unfairly by police?” [Yes; No; I don’t know]36
Changes between the M-Turk and SSI surveys
The M-turk survey included several dependent variables pertaining to trust in government
institutions, support for punitive policy and perceived changes in crime in the U.S. that were
not repeated in the SSI survey. The estimated treatment effects on these omitted outcomes
are displayed in Figure B2. In addition, the SSI survey added items gauging perceived
changes in crime within the vignette city, perceived fairness in treatment by police, and
support for police patrols in respondents’ own neighborhoods. The SSI survey also included
an attention screen at the start of the survey which screened out respondents who were
36
Responses of “I don’t know” were coded as missing. This question was asked in the final block of the
survey.
9
unable to correctly use a sliding bar scale, a measurement tool used by other researchers
who were sharing space on the survey instrument. The police experiment appeared first in
the survey before the other researchers’ content. In the M-Turk survey, the prompts for the
open-ended questions stated, “you may answer in one sentence or less.” This was omitted
in the SSI survey in order to encourage more complete responses. In addition, the font size
of the text of the mock news article was made larger in the SSI survey than in the M-turk
survey to make it easier to read.
Demographic items were measured at the start of the SSI survey, but were asked at the
end of the M-Turk survey, in order to accommodate another experiment that was included
in the same module by other researchers. The SSI survey also fully randomized the order
of all questions measuring dependent variables, while the M-turk survey listed questions
pertaining to the vignette city, including perceived crime in that city, immediately following
the mock news article.
10
Figure B2: Treatment Effects on Additional Dependent Variables in M-Turk Survey.
These dependent variables were not included on the SSI survey. Bars denote 95% confidence
intervals.
M−Turk
Treatment Condition:
SWAT with riot gear
Trust in Criminal
Justice System
SWAT with assault rifles
●
SWAT with assault rifles
and Armored Vehicle
●
Trust in
Congress
●
Trust in
President
●
Trust in
Supreme Court
●
−6
−5
−4 −3 −2 −1 0
1
2
3
Effect Relative to Control Condition
(Normalized 0−1 Scale)
11
4
5
Heterogeneous Treatment Effects
12
Figure B3: Difference in Treatment Effects between African American and white Respondents. Positive estimates indicate larger effects for African American respondents relative to
white respondents. Bars denote 95% confidence intervals.
SSI
Crime Worsening in
Vingnette City
●
Crime High in
Vingnette City
●
Confidence in Police
in U.S.
●
Police Spending
in U.S.
●
Confidence in Police
in Vingnette City
●
Want More Police Patrols
in Own Neighborhood
Treatment Condition:
●
SWAT with assault rifles
Police Spending
in Vingnette City
●
−8
SWAT with assault rifles
and Armored Vehicle
−6
−4
−2
●
0 1 2 3 4 5 6 7
Percentage Points
13
Figure B4: Difference in Treatment Effects between those reporting being treated unfairly by police and those who did not. Positive estimates indicate larger effects for those
treated unfairly by police. Bars denote 95% confidence intervals.
SSI
Crime Worsening in
Vingnette City
●
Crime High in
Vingnette City
●
Confidence in Police
in U.S.
●
Treatment Condition:
Police Spending
in U.S.
SWAT with assault rifles
●
●
Confidence in Police
in Vingnette City
SWAT with assault rifles
and Armored Vehicle
●
Want More Police Patrols
in Own Neighborhood
●
Police Spending
in Vingnette City
●
−10
−8
−6
−4
−2
0 1 2 3 4 5 6
Percentage Points
14
Details of Analysis of Open Ended Responses
Note: An open-ended response was also asked about the reasoning behind the perceived
changes in crime in the vignette city in the SSI survey, but these responses were not included
in the text analysis since that outcome was not asked in the M-Turk survey.
The full contents of the dictionaries used to produce Figure 9 in the main text, which
included corrected versions of words misspelled by respondents, are as follows:
Militarization Dictionary:
battl gear suit war militar gun arm guard riot equip power militari armi tank milit cloth
armor uniform apparel combat swat outfit dress technic rifl artilleri
Personnel Dictionary:
personnel retir promot number hire turnov manpow pentsion salari retire newbi train
wage staff retrain presenc replac retain recruit understaf new pension
15