SO
C

IA
L

SC
IE

N
C

ES

A randomized control trial evaluating the effects of
police body-worn cameras
David Yokuma,b,1,2, Anita Ravishankara,c,d,1, and Alexander Coppocke,1

a The Lab @ DC, Office of the City Administrator, Executive Office of the Mayor, Washington, DC 20004; b The Policy Lab, Brown University, Providence, RI
02912; c Executive Office of the Chief of Police, Metropolitan Police Department, Washington, DC 20024; d Public Policy and Political Science Joint PhD
Program, University of Michigan, Ann Arbor, MI 48109; and e Department of Political Science, Yale University, New Haven, CT 06511

Edited by Susan A. Murphy, Harvard University, Cambridge, MA, and approved March 21, 2019 (received for review August 28, 2018)

Police body-worn cameras (BWCs) have been widely promoted as
a technological mechanism to improve policing and the perceived
legitimacy of police and legal institutions, yet evidence of their
effectiveness is limited. To estimate the effects of BWCs, we con-
ducted a randomized controlled trial involving 2,224 Metropolitan
Police Department officers in Washington, DC. Here we show
that BWCs have very small and statistically insignificant effects
on police use of force and civilian complaints, as well as other
policing activities and judicial outcomes. These results suggest
we should recalibrate our expectations of BWCs’ ability to induce
large-scale behavioral changes in policing, particularly in contexts
similar to Washington, DC.

body-worn cameras | field experiments | policing

Police body-worn camera (BWC) programs are rapidlyspreading across the United States. In 2015, the US Depart-
ment of Justice awarded over $23 million in funding to support
the implementation of BWC programs throughout the country
(1), and a nationwide survey found that 95% of large police
departments either have already implemented or intend to
implement a BWC program (2). Much of the expansion has been
motivated by a series of high-profile, officer-involved shootings,
many of which were captured in bystander video and shared
across social media. Stakeholders such as the American Civil
Liberties Union, Campaign Zero, and Black Lives Matter have
urged the police to equip BWCs as a technological solution to
improve policing, or at least to document police practices and
civilian behavior to resolve disputes (3, 4).

The widespread support for BWCs is due, in large part, to
their anticipated effects on behavior. Both officers and civil-
ians on the street may comport themselves differently if under
the watchful lens of a camera. A wide range of research, dat-
ing back to the classic experiments at Hawthorne Works (5),
has suggested that people act differently when they believe
they are being watched, from increasing work productivity and
charitable giving (6–9) to encouraging honesty (10), promot-
ing adherence to recycling rules (11), stimulating voter turnout
(12), and reducing theft (13). Across these settings, monitoring
appears to shift behavior into alignment with socially acceptable
conduct.

In the policing context, cameras are expected to encour-
age officer adherence to departmental protocols and deter
police from engaging in unprofessional behavior or miscon-
duct, especially unjustified use of force (14). Similarly, civilians
interacting with a BWC-equipped officer may be less likely to
engage in inappropriate or combative behavior. The underlying
social or psychological mechanisms linking BWCs and behavior
could include greater self-awareness, heightened threat of being
caught, or a combination of the two. Whatever the exact mecha-
nisms, commentators sometimes allude to a so-called “civilizing
effect,” wherein BWCs are predicted to calm all parties involved
and reduce the likelihood that violence occurs (15). By captur-
ing the police–civilian interaction, the cameras are also expected
to have evidentiary value, both for internal affairs and criminal
investigations (15, 16).

The existing evidence on whether BWCs have the anticipated
effects on policing outcomes remains relatively limited (17–19).
Several observational studies have evaluated BWCs by compar-
ing the behavior of officers before and after the introduction of
BWCs into the police department (20, 21). Other studies com-
pared officers who happened to wear BWCs to those without (15,
22, 23). The causal inferences drawn in those studies depend on
strong assumptions about whether, after statistical adjustments
are made, the treatment is independent of potential outcomes.
In particular, we would need to believe that, after conditioning
on a set of pretreatment covariates, BWCs were as if randomly
assigned.

A small number of randomized controlled trials (RCTs) of
BWCs have been conducted, with mixed results. In a series of
RCTs conducted across several sites in the United Kingdom
and the United States, BWCs appeared to increase police use
of force at some sites and decrease it at others (24, 25). Cam-
eras appeared to decrease complaints in some experiments but
not others (16, 25). Further trials found no detectable treat-
ment versus control differences on measured outcomes (26). The
extant set of RCTs has typically been limited by either small sam-
ple sizes or shift-level random assignments that introduce the
potential for within-officer spillover (14, 27).

Methods
We collaborated with the Metropolitan Police Department of the Dis-
trict of Columbia (MPD) to design and implement an RCT to evaluate the
effects of BWCs citywide. Specifically, as part of MPD’s deployment of BWCs
to its police force, approximately half of all full duty patrol and station

Significance

Police departments are adopting body-worn cameras in hopes
of improving civilian–police interactions. In a large-scale field
experiment (2,224 officers of the Metropolitan Police Depart-
ment in Washington, DC), we randomly assigned officers to
receive cameras or not. We tracked subsequent police behav-
ior for a minimum of 7 mo using administrative data. Our
results indicate that cameras did not meaningfully affect
police behavior on a range of outcomes, including complaints
and use of force. We conclude that the effects of cameras are
likely smaller than many have hoped.

Author contributions: D.Y., A.R., and A.C. designed research, performed research,
analyzed data, and wrote the paper.y

The authors declare no conflict of interest.y

This article is a PNAS Direct Submission.y

Published under the PNAS license.y

Data deposition: The cleaned dataset sufficient for reproducing the difference-in-means
estimates of the treatment effects have been deposited in the Open Science Framework,
https://osf.io/p6vuh/.y
1 D.Y., A.R., and A.C. contributed equally to this work.y
2 To whom correspondence should be addressed. Email: david yokum@brown.edu.y

This article contains supporting information online at www.pnas.org/lookup/suppl/doi:10.
1073/pnas.1814773116/-/DCSupplemental.y

Published online May 7, 2019.

www.pnas.org/cgi/doi/10.1073/pnas.1814773116 PNAS | May 21, 2019 | vol. 116 | no. 21 | 10329–10332

D
o
w

n
lo

a
d
e
d
 a

t 
C

a
rn

e
g
ie

 M
e
llo

n
 U

n
iv

e
rs

ity
 o

n
 A

p
ri
l 5

, 
2
0
2
1
 

https://www.pnas.org/site/aboutpnas/licenses.xhtml
https://osf.io/p6vuh/
mailto:david_yokum@brown.edu
https://www.pnas.org/lookup/suppl/doi:10.1073/pnas.1814773116/-/DCSupplemental
https://www.pnas.org/lookup/suppl/doi:10.1073/pnas.1814773116/-/DCSupplemental
https://www.pnas.org/cgi/doi/10.1073/pnas.1814773116
http://crossmark.crossref.org/dialog/?doi=10.1073/pnas.1814773116&domain=pdf


officers were randomly assigned to wear BWCs, while the other half
remained without BWCs. With 2,224 MPD members participating in the trial,
this study is the largest randomized evaluation of BWCs conducted to date.
Our project was deemed “not human subjects research” by the Yale Univer-
sity IRB (protocol no. 2000020390), as all study activities were carried out
by MPD.

The primary outcomes of interest were documented uses of force and
civilian complaints, although we also measure a variety of additional
policing activities and judicial outcomes. All outcomes were measured
using administrative data. Before obtaining outcome data, we developed
a detailed write-up of the methodology and planned statistical analyses
(a preanalysis plan) and publicly shared it on the Open Science Framework.
The preanalysis plan is included in SI Appendix.

Our study encompassed the entire department and included geographic
coverage of the entire city. We identified eligible officers within each of the
seven police districts (as well as several specialized units) based on the fol-
lowing criteria: The officer was on active, full duty administrative status and
did not have a scheduled leave of absence during the study period, held
a rank of sergeant or below, and was assigned to patrol duties in a patrol
district or to a nonadministrative role at a police station. Eligible officers
within each district or special unit were then randomly assigned to one of
two groups: (i) no BWC (control) or (ii) with BWC (treatment). Specifically,
treatment entails assignment of an eligible participant to wear and use a
BWC in accordance with MPD policy. MPD General Order SPT-302.13 specifies
that “[m]embers, including primary, secondary, and assisting members, shall
start their BWC recordings as soon as a call is initiated via radio or commu-
nication from OUC [Office of Unified Communications] on their mobile data
computer (MDC), or at the beginning of any self-initiated police action.”
The general order enumerates the range of events for which officers are
required to activate their BWCs; this list is included in SI Appendix.

Randomization was implemented using a block-randomized assignment
procedure. This approach, which uses pretreatment information to group
officers into blocks before randomly assigning a fixed number of cameras
to officers in each block, increases the statistical power of the experimen-
tal design and enforces treatment-versus-control balance on the covariates
according to which blocking occurs. We applied a two-level blocking
approach: The “major” blocks were the seven police districts and three
special units, and the minor blocks were constructed using a clustering algo-
rithm based on the background characteristics of the officers (28). Based
on the eligibility requirements noted above, our sample consisted of 2,224
MPD members, with 1,035 members assigned to the control group and 1,189
members assigned to the treatment group.

As anticipated in our preanalysis plan, some officers who were assigned
cameras did not install or use them, and some officers who were not
assigned cameras nevertheless obtained them. We estimate two compliance
measures: the number of videos uploaded to the video database by treat-
ment officers and the average length of the videos in minutes, as compared
with control officers. If officers complied with the randomization proto-
col, we would expect that officers assigned BWCs would make vastly more
videos per year, as well as have a longer average length of videos, than their
counterparts in the control group. On average, treatment officers uploaded
about 665 videos annually (compared with 14 videos uploaded among con-
trol officers). The average video recorded by a treatment officer was over
11 min long, while the average video recorded by a control officer was
just 0.8 min long. For both manipulation check measures, the treatment
assignment is both substantively and statistically significant (p < 0.001). We
conclude that compliance with the study protocol was high.

Following best practices in settings encountering two-sided noncompli-
ance, we conducted all of our analyses according to the original random
assignment (29). Our experiment thus recovers estimates of the effect of
being assigned to a BWC on a variety of outcomes (the intention-to-treat
effect).

Measurement Strategy. We assessed the effect of BWCs on four families of
outcome measures: police use of force, civilian complaints, policing activity,
and judicial outcomes.

i) Police use of force was based on officers’ self-reported use of force (in
accordance with MPD policy). It included a count of all use of force inci-
dents as well as measures of serious uses of force (as defined by MPD
policy), nonserious uses of force, and use of force incidents by the race
of the subject of force.

ii) Civilians can file complaints in two ways: with MPD itself or with the
independent Office of Police Complaints. Our measure was the total
number of complaints associated with an officer from both sources.

We also disaggregated the complaints by disposition: sustained, not
sustained, or unresolved due to insufficient facts.

iii) The policing activity category included traffic tickets and warnings
issued, reports taken from particular types of calls for service, arrests
on specific charges (e.g., disorderly conduct, traffic violations, assaults
against a police officer), and injuries sustained by officers in the line of
duty. We used these measures to evaluate the effects of BWCs on officer
discretion and activity, as well as on civilian behavior.

iv) Finally, we examined the effects of BWCs on judicial outcomes, mea-
sured by whether MPD arrest charges are prosecuted by the US
Attorney’s Office (USAO) or the Office of the Attorney General (OAG)
and the disposition of those charges. Our examination of this set of
outcomes was constrained by limitations in the available data. Namely,
we did not have access to the full datasets managed by the USAO, OAG,
and the courts. We instead had access to a subset of these data available
to MPD, which captures only the initial charges on which an individual
was arrested. A consequence is that we were unable to track court out-
comes for any changes to those initial charges. As this limitation applies
to both control and treatment groups, however, we were still able to
conduct a preliminary analysis on the evidentiary value of BWCs.

Due to logistical constraints, MPD deployed cameras on a district-by-
district basis over the course of 11 mo. Officers in two of the seven police
districts received cameras in late June 2015, with the deployment to the
remaining districts taking place from March to May 2016. By integrating
randomization directly into the BWC deployment process, we were able to
conduct this study at marginally low cost to MPD.

To address the staggered deployment process, the data collection period
varies for each police district, based on the start date of BWC deployment
in that district. All outcomes were obtained at the officer level and trans-
lated into yearly rates. These rates were calculated from the date that the
cameras were first deployed in each district. We calculate these rates before
and after the intervention based on a window of 212 d, because 212 is the
number of days between deployment and the end of the study period for
the district that was the last to receive cameras. The pretreatment and post-
treatment periods are of the same length for all districts; the pretreatment
measurements come from the same 212-d window (in the previous year) as
the posttreatment measurements, to account for seasonality in policing and
desensitization to the treatment over time.

Because all of our outcomes are unconditional event counts translated
into yearly event rates per 1,000 offices, our measurement procedure avoids
the posttreatment bias that would be associated with measuring various
conditional quantities. For example, we might want to measure the frac-
tion of an officer’s civilian interactions that include use of force, but, since
the officer–citizen interaction is posttreatment, we cannot condition on it
without the risk of bias.

Estimation Strategy. We use two estimators of the average treatment
effects: (i) difference-in-means with inverse probability weights to account
for differential probabilities of assignment by block and (ii) regression of
outcome on treatment assignment with controls for pretreatment char-
acteristics and inverse probability weights. Specifically, we control for the
pretreatment value of the outcome (e.g., past use of force), pretreatment
covariates for the officer, and indicators for each major block. Eq. 1 provides
the exact specification, as preregistered before the realization of outcomes.

YPOST = β0 + β1 Z + β2 YPRE + β3Block + β4 X + �, [1]

where Z is the treatment indicator (officer assigned camera or not); YPRE
is the pretreatment value of the outcome under study; Block is a vector of
indicator variables for an officer’s home district or special unit; X is a vec-
tor of pretreatment covariates that includes race, gender, and length of
service; and � is the error term. We estimate Eq. 1 using weighted least
squares regression with inverse probability weights, which are calculated
as the inverse of the probability of each unit being in its observed condi-
tion (29). We use HC2 robust standard errors for variance estimation (30).
We conduct our primary analysis among officers in the seven districts of
DC (n = 1,922). We exclude officers in special units from this analysis, as
policing activities and camera use patterns may differ between these units
and the district officers. We conduct this analysis at the officer level, and
report results as a yearly rate per 1,000 officers. Our analyses were con-
ducted by two independent statistical teams, to help avoid coding errors
and as a check of convergence in results.

Data Availablity. The cleaned dataset sufficient for reproducing the
difference-in-means estimates of the treatment effects will be made

10330 | www.pnas.org/cgi/doi/10.1073/pnas.1814773116 Yokum et al.

D
o
w

n
lo

a
d
e
d
 a

t 
C

a
rn

e
g
ie

 M
e
llo

n
 U

n
iv

e
rs

ity
 o

n
 A

p
ri
l 5

, 
2
0
2
1
 

https://www.pnas.org/lookup/suppl/doi:10.1073/pnas.1814773116/-/DCSupplemental
https://www.pnas.org/lookup/suppl/doi:10.1073/pnas.1814773116/-/DCSupplemental
https://www.pnas.org/cgi/doi/10.1073/pnas.1814773116


SO
C

IA
L

SC
IE

N
C

ES

Fig. 1. Average difference (with 95% confidence interval) between BWC
and non-BWC groups, per 1,000 officers over a year for police use of
force, complaints filed against officers, and arrests for disorderly conduct.
We show findings from both our difference-in-means (DIM) estimator and
ordinary least-squares (OLS) regression including pretreatment covariates.

available at the Open Science Framework at https://osf.io/p6vuh/. We are
unable to make public the raw data from which the cleaned dataset was
produced, due to privacy concerns of both officers and civilians. We are also
unable to release the officer-level covariate information that we use to esti-
mate the covariate adjusted models, as these data would uniquely identify
individual officers.

Results
Across each of the four outcome categories, our analyses con-
sistently point to a null result: The average treatment effect
estimate on all measured outcomes was very small, and no
estimate rose to statistical significance at conventional levels.
Because our study has a large enough sample size to detect small
effect sizes, these failures to reject the null are unlikely to be
due to insufficient statistical power. Fig. 1 plots the estimated
average treatment effect (as a yearly rate per 1,000 officers)
of BWCs on police use of force, civilian complaints, and offi-
cer discretion (as measured by arrests for disorderly conduct).
Our best guess is that cameras caused an increase of 74 (SE =
87) uses of force per 1,000 officers, per year. This estimate
is not statistically significantly different from zero. The effects
on complaints (57 per 1,000 officers per year, SE = 41) and
arrests for disorderly conduct (−128 per 1,000 officers per year,
SE = 277) were also nonsignificant. Effect estimates on court
appearances, judicial outcomes, domestic violence calls, and
other measures of police behavior (all null) are included in SI
Appendix.

Discussion
We consider here a few possible explanations for our null find-
ings. First and most obviously, it is possible the null finding needs
no explanation: The devices, in fact, have no effect on behavior.
Perhaps neither the officer nor civilian involved in an interaction
are actually aware of or affected by the camera, either due to
attention being diverted elsewhere or desensitization over time
to the presence of the cameras.

Second, Washington, DC may be different from other places
in important ways. Perhaps BWCs have no effect in the nation’s
capital, but they do in other municipalities. We are sympa-
thetic to this possibility, but we also note that, as BWCs
were randomly assigned within each of the seven police dis-
tricts, we conducted the equivalent of seven mini-experiments.
Despite substantial district-to-district heterogeneity in baseline
outcomes, we observe small, insignificant effects in all seven
districts.

A third explanation for the null findings considers the possi-
bility that other factors are masking the true effect of the BWCs:
The cameras do affect the measured outcomes, but these effects
are being hidden by interference across units, or spillovers from
treated to control officers. Approximately one-third of calls were
responded to by control officers only, one-third by treatment

officers only, and the last third by a mix of treatment and control
officers. This distribution of calls indicates that control officers
were frequently performing their duties without cameras nearby.
As a check of whether the introduction of cameras affected
both treatment and control officers, we examined time trends
for documented uses of force and civilian complaints before and
after cameras were deployed (analysis presented in SI Appendix).
We observed no differences in precamera versus postcamera
outcomes for either group.

Finally, the true effect of BWCs may be masked by the
widespread presence of nonpolice cameras (e.g., civilians’ cell
phones). Civilians regularly record encounters with MPD mem-
bers with their own cameras, and closed caption television
(CCTV) is widespread. Perhaps the BWCs do not change behav-
ior at the margin, simply because there is no more room to have
an effect. To explore this possibility (we note that this analysis
was not preregistered), we examined the effect of treatment on
use of force at night, when exposure to nonpolice cameras is
lower. We also found no effect of cameras on this alternative
dependent variable.

Other researchers have suggested that BWCs may fail to
affect results because of nonadherence: Officers, for a variety
of reasons, may not use their assigned cameras according to
departmental policy (15, 22, 26). Officers may fail to activate the
camera, for example. We have no indication that nonadherence
was a widespread problem in our experiment. For 98% of the
days in 2016, MPD averaged at least one video (and often many
more) per call for service associated with a treatment officer.
Further, even for the 2% of days in 2016 in which the number
of videos uploaded was less than the number of incidents for
which we would expect them, the difference is minimal, with 96%
average adherence based on our measure. That said, effects may
depend on the level of discretion officers are given to activate the
cameras, although evaluation of that possibility will have to await
further experiments.

We acknowledge that BWCs may have had effects that are
not measurable with administrative data. For example, it may be
the case that there were uses of force that were previously going
unreported, and those have now dropped with the introduction
of BWCs. However, because our data do not capture unreported
uses of force, we are unable to detect this kind of change. As
a matter of speculation, however, we find it implausible that we
would measure very small effects on reported outcomes but that
the true average effect on unreported outcomes is large.

In summary, we measured the average effects of BWCs on
documented uses of force and civilian complaints as well as a
variety of additional policing activities and judicial outcomes.
Our sample size was unusually large, enhancing our ability to
detect differences, should they exist. In addition, our compar-
ison groups were constructed from an individual-level officer
randomization scheme, which avoids several problems of infer-
ence present in other methodologies used to date. We are
unable to detect any statistically significant effects. As such,
our experiment suggests that we should recalibrate our expec-
tations of BWCs as a technological solution to many policing
difficulties.

ACKNOWLEDGMENTS. We thank Katherine Barnes, JD, PhD, Donald Green,
PhD, Bill Egar, PhD, Jennifer Doleac, PhD, and Donald Braman, JD, PhD,
and reviewers from The Lab @ DC and its partners for valuable feedback.
We thank the many individuals who participated in briefings and shared
their thoughtful insights and opinions with us. This study would not have
been possible without the Metropolitan Police Department of the District of
Columbia. They welcomed our research team and were committed to under-
standing, as rigorously as possible, the impacts of the BWC program. Special
thanks go to Chief Cathy Lanier (ret.), Chief Peter Newsham, Matthew
Bromeland, Commander Ralph Ennis, Heidi Fieselmann, Derek Meeks, and
all the sworn members who dutifully adapted to a new, complicated pro-
gram and participated in the study. We also thank the Executive Office
of the Mayor, especially Mayor Muriel Bowser, City Administrator Rashad
Young, Deputy Mayor for Public Safety and Justice Kevin Donahue, and

Yokum et al. PNAS | May 21, 2019 | vol. 116 | no. 21 | 10331

D
o
w

n
lo

a
d
e
d
 a

t 
C

a
rn

e
g
ie

 M
e
llo

n
 U

n
iv

e
rs

ity
 o

n
 A

p
ri
l 5

, 
2
0
2
1
 

https://www.pnas.org/lookup/suppl/doi:10.1073/pnas.1814773116/-/DCSupplemental
https://www.pnas.org/lookup/suppl/doi:10.1073/pnas.1814773116/-/DCSupplemental
https://www.pnas.org/lookup/suppl/doi:10.1073/pnas.1814773116/-/DCSupplemental


Chief Performance Officer Jennifer Reed, for dedicating their support, time,
and resources to advancing evidence-based governance and policy in the
District. Thanks to Objectively for layout and web design. We thank the

Laura and John Arnold Foundation for generous financial support. The
research and views expressed in this report are those of The Lab @ DC and
do not necessarily represent the views of the foundation.

1. Department of Justice (2015) Justice Department awards over $23 million in fund-
ing for body worn camera pilot program to support law enforcement agencies in 32
states. Available at https://www.justice.gov/opa/pr/justice-department-awards-over-
23-million-funding-body-worn-camera-pilot-program-support-law. Accessed October
20, 2017.

2. Major Cities Chiefs and Major County Sheriffs (2015) Survey of technology needs–
Body worn cameras (Major Cities Chiefs Major County Sheriffs, Alexandria, VA).

3. Stanley J (2013) Police body-mounted cameras: With right policies in place, a win for
all (American Civil Liberties Union, New York).

4. Campaign Zero (2018) Solutions. Available at https://www.joincampaignzero.org/
solutions#solutionsoverview. Accessed October 10, 2017.

5. Hart C (1943) The Hawthorne experiments. Can J Econ Polit Sci 9:150–163.
6. Izawa MR, French MD, Hedge A (2011) Shining new light on the Hawthorne

illumination experiments. Hum Factors 53:528–547.
7. McCambridge J, Witton J, Elbourne DR (2014) Systematic review of the Hawthorne

effect: New concepts are needed to study research participation effects. J Clin
Epidemiol 67:267–277.

8. Ekström M (2012) Do watching eyes affect charitable giving? Evidence from a field
experiment. Exp Econ 15:530–546.

9. Haley KJ, Fessler DM (2005) Nobody’s watching?: Subtle cues affect generosity in an
anonymous economic game. Evol Hum Behav 26:245–256.

10. Bateson M, Nettle D, Roberts G (2006) Cues of being watched enhance cooperation
in a real-world setting. Biol Lett 2:412–414.

11. Francey D, Bergmüller R (2012) Images of eyes enhance investments in a real-life
public good. PLoS One 7:e37397.

12. Gerber AS, Green DP, Larimer CW (2008) Social pressure and voter turnout: Evidence
from a large-scale field experiment. Am Polit Sci Rev 102:33–48.

13. Nettle D, Nott K, Bateson M (2012) ‘Cycle thieves, we are watching you’: Impact of a
simple signage intervention against bicycle theft. PLoS One 7:e51738.

14. Ariel B, Farrar WA, Sutherland A (2014) The effect of police body-worn cameras on
use of force and citizens’ complaints against the police: A randomized controlled trial.
J Quant Criminol 31:509–535.

15. Katz CM, Kurtenbach M, Choate DE, White MD (2015) Phoenix, Arizona, smart polic-
ing initiative. Evaluating the impact of police officer body-worn cameras (US Dep
Justice, Washington, DC), Technical Report 250190.

16. Braga A, Coldren JR Jr, Sousa W, Rodriguez D, Alper O (2017) The benefits of body-
worn cameras: New findings from a randomized controlled trial at the Las Vegas
metropolitan police (Cent Naval Analyses, Arlington, VA), Technical Report 251416.

17. Miller L, Toliver J (2014) Implementing a body-worn camera program: Recommenda-
tions and lessons learned (Police Executive Res Forum) (Office of Community Oriented
Policing Services, Washington, DC), Technical Report 029644.

18. Cubitt TI, Lesic R, Myers GL, Corry R (2017) Body-worn video: A systematic review of
literature. Aust N Z J Criminol 50:379–396.

19. Lum CM, Koper CS, Merola LM, Scherer A, Reioux A (2015) Existing and ongoing body
worn camera research: Knowledge gaps and opportunities (George Mason University,
Fairfax, VA).

20. Ellis T, Jenkins C, Smith P (2015) Evaluation of the introduction of personal issue
body worn video cameras (Operation Hyperion) on the Isle of Wight: Final report
to Hampshire Constabulary (University of Portsmouth, Portsmouth, UK), Technical
Report 9781861376541.

21. Gaub JE, Choate DE, Todak N, Katz CM, White MD (2016) Officer perceptions of body-
worn cameras before and after deployment: A study of three departments. Police Q
19:275–302.

22. Hedberg E, Katz CM, Choate DE (2017) Body-worn cameras and citizen interactions
with police officers: Estimating plausible effects given varying compliance levels.
Justice Q 34:627–651.

23. Consulting O (2011) Body worn video projects in Paisley and Aberdeen, self
evaluation (ODS Consulting, Glasgow, UK).

24. Ariel B, et al. (2016) Wearing body cameras increases assaults against officers and
does not reduce police use of force: Results from a global multi-site experiment. Eur
J Criminol 13:744–755.

25. Ariel B, et al. (2017) “Contagious accountability”: A global multisite randomized con-
trolled trial on the effect of police body-worn cameras on citizens’ complaints against
the police. Crim Justice Behav 44:293–316.

26. Ariel B, et al. (2016) Report: Increases in police use of force in the presence of body-
worn cameras are driven by officer discretion: A protocol-based subgroup analysis of
ten randomized experiments. J Exp Criminol 12:453–463.

27. Grossmith L, et al. (2015) Police, camera, evidence: London’s cluster randomised
controlled trial of body worn video (College Policing, London).

28. Moore RT (2016) blockTools: Blocking, Assignment, and Diagnosing Interference
in Randomized Experiments. R Package Version 0.6-3. Available at https://cran.r-
project.org/web/packages/blockTools/index.html. Accessed October 10, 2017.

29. Gerber AS, Green DP (2012) Field Experiments: Design, Analysis, and Interpretation
(WW Norton, New York).

30. Samii C, Aronow PM (2012) On equivalencies between design-based and regression-
based variance estimators for randomized experiments. Stat Probab Lett 82:365–370.

10332 | www.pnas.org/cgi/doi/10.1073/pnas.1814773116 Yokum et al.

D
o
w

n
lo

a
d
e
d
 a

t 
C

a
rn

e
g
ie

 M
e
llo

n
 U

n
iv

e
rs

ity
 o

n
 A

p
ri
l 5

, 
2
0
2
1
 

https://www.justice.gov/opa/pr/justice-department-awards-over-23-million-funding-body-worn-camera-pilot-program-support-law
https://www.justice.gov/opa/pr/justice-department-awards-over-23-million-funding-body-worn-camera-pilot-program-support-law
https://www.joincampaignzero.org/solutions#solutionsoverview
https://www.joincampaignzero.org/solutions#solutionsoverview
https://cran.r-project.org/web/packages/blockTools/index.html
https://cran.r-project.org/web/packages/blockTools/index.html
https://www.pnas.org/cgi/doi/10.1073/pnas.1814773116