Article

The effects of body-worn cameras on police-citizen encounters and police activity: evaluation of a pilot implementation in Philadelphia, PA

Elizabeth R. Groff, et al. [full author details at the end of the article]

# Springer Nature B.V. 2019

Abstract

Objectives Examine changes in officer behavior, when wearing body-worn cameras, as revealed by pedestrian stops, vehicle stops, arrests, use of force, and citizen complaints during a pilot implementation in a racially diverse jurisdiction in the Northeast region of the USA. Methods A quasi-experimental approach was used to examine the initial imple- mentation of body-worn cameras (BWCs) in one district. This provided the opportunity for a natural experiment comparing officers in the district that de- ployed cameras with officers in three similar districts where no BWCs were deployed. Propensity score matching (PSM) was used to match BWC officers with non-BWC officers. Results BWC officers had about 38.3% fewer use of force incidents than non-BWC officers with similar numbers of use of force incidents in the previous year. On average, BWC officers made 46.4% fewer pedestrian stops and 39.2% fewer arrests than non- BWC officers. Vehicle stops and citizen complaints had nonsignificant declines for BWC officers. Conclusions The reductions in proactive policing by officers are consistent with a deterrence-based view that officers respond to the increased scrutiny by curtailing interactions with citizens, which in turn, limits the potential for conflict and for police supervisors to identify behavior worthy of disciplinary action. The lack of a significant reduction in citizen complaints supports the view that the effect of BWCs on negative officer behavior is contingent upon other factors in the settings in which cameras are deployed. Future research should examine the impacts of cameras on the narrowing of police discretion because of the important implica- tions for police–community relations.

Keywords Police body-worn cameras . Use of force . Citizen complaints . Arrests .

Pedestrian stops . Vehicle stops

https://doi.org/10.1007/s11292-019-09383-0

Electronic supplementary material The online version of this article (https://doi.org/10.1007/s11292-019- 09383-0) contains supplementary material, which is available to authorized users.

Journal of Experimental Criminology (2020) 16:463–480

Published online: mber 2019Nove7

Introduction

The deployment of body-worn cameras (BWCs) began as a result of public pressure on police departments in response to several highly publicized killings of unarmed citizens (Lum et al. 2015; Lum et al. 2019; White 2014). The pace of adoption has accelerated since 2013 with the 2016 body-worn camera supplement to the LEMAS reporting full deployment in 60% of local police departments (Hyland 2018). In the rush to respond to citizen pressure to increase police transparency and accountability, police depart- ments began adopting BWCs without much evidence of their effectiveness (Lum et al. 2015; Lum et al. 2019).

This study aims to contribute to the growing evidence base on the effects of BWCs on use of force incidents, citizen complaints, and proactive police activity (pedestrian stops, vehicle stops, and arrests). It provides a unique contribution to the evidence by focusing on pilot implementation data from the city of Philadelphia, a racially diverse jurisdiction with a large unionized police agency in the Northeast region of the USA. Adding to the findings from mid-western, western, and southern regions of America will help to round out what we know and do not know about BWCs and their mechanisms of effect on these central outcomes of concern.

Study findings were consistent with the presumption that BWCs heighten the self- awareness of officers, leading to curtailed proactive activity. At the same time, an insignificant reduction in citizen complaints suggests that other mechanisms, besides mere presence of a camera during interactions, shape the effects of BWCs on police– citizen dynamics. Pre-existing police–community relations and other unique social and cultural features of implementation environments merit further study in BWC evalua- tions. As well, study findings reveal the need to more clearly understand whether police discretion is narrowing both now and into the future and whether such a narrowing, if sustained, yields desired effects on police–community relations in the long term.

Literature review

There is no doubt that many citizens and police managers expect that BWCs will alter officer behavior (Lum et al. 2015; Lum et al. 2019; White 2014). Two linked theoretical perspectives help explain the basis of this expectation: self-awareness and deterrence (see Ariel et al. 2015; Ariel et al. 2017; Farrar and Ariel 2013; Koen 2016; Rowe et al. 2018 for extensive discussions of theoretical mechanisms). When they were initially conceived, BWCs were expected to change the behavior of both citizens and police by increasing their “self-awareness” (The President's Task Force on 21st Century Policing (“Task Force”) 2015, p. 32). Theoretically, increased self-awareness would then deter the types of behavior that ignite confrontation and lead to negative outcomes such as excessive use of force and citizen complaints (Lum and Koper 2016). Since BWCs allowed both police managers and the public to view police–citizen interactions, they exposed them to scrutiny.

A deterrence perspective assumes that people behave differently when someone is watching (Ariel et al. 2017; Lum et al. 2015). Thus, both citizens and police will change their words and their actions if they are being recorded. Early research focused on the effect of BWCs on use of force and citizen complaints, but more recent studies

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have indicated that the presence of BWCs may have wider and unforeseen impacts such as influencing the way officers use their discretion to pursue proactive stops, give citations, and make arrests (Lum et al. 2019).

Since 2015, the number of reports and research studies conducted on the topic of BWCs in policing has expanded rapidly as researchers began collaborating with agencies (Maskaly et al. 2017). Three literature reviews have been done, each including more studies than the previous. Lum et al. (2015) discussed 12 studies, and 2 years later Maskaly et al. (2017) covered 21 articles. Most recently, Lum et al. (2019) included 70 studies. Most studies examined officer behaviors (n = 32) but some also focused on citizen behaviors (n = 16) (Lum et al. 2019).

Studies examining officer behavior most frequently use the outcome measures of citizen complaints and officer reported use of force (Lum et al. 2019). Together they provide evidence that BWCs can reduce citizen complaints about officer behavior (Ariel et al. 2017; Braga et al. 2018a; Ellis et al. 2015; Goodall 2007; Goodison and Wilson 2017; Grossmith et al. 2015; Hedberg, Katz, and Choate, 2016; Jennings et al. 2014; Jennings et al. 2015; Katz et al. 2014; Mesa Police Department 2013; Peterson et al. 2018; Sutherland et al. 2017). A few found nonsignificant impacts of BWCs on complaints (Ariel et al. 2015; Toronto Police Service 2016; White et al. 2017; Yokum et al. 2017). However, why this occurs is less clear. Some researchers attribute the reduction to a change in police officer behavior (Ariel et al. 2017). Others point to the effect of being recorded on citizen’s likelihood of lodging a complaint especially if the complaint was exaggerated or unfounded (Lum et al. 2019; Wood and Groff 2018). Conversations with officers indicated that personnel were showing the recordings to potential complainants who then decide not to file a formal complaint (Lum et al. 2019; Pelfrey and Keener 2016).

Results are mixed among studies that examined officer reported use of force. Five studies found that officers used force less often when wearing cameras (Ariel et al. 2015; Braga et al. 2018; Henstock and Ariel 2017; Jennings et al. 2015; Jennings et al. 2017). One determined that the reductions in Rialto, CA, persisted over time (Sutherland et al. 2017). Eight others found no significant differences in use of force incidents between officers who wore BWCs and those who did not (Ariel 2016; Braga et al. 2018a; Edmonton Police Service 2015; Headley et al. 2017; Peterson et al. 2018; Toronto Police Service 2016; White et al. 2017; Yokum et al. 2017).

Some studies have examined the effect of BWCs on police officer behavior as it relates to arrest and citation behaviors. The increased “self-awareness” experienced by officers might also have unintended consequences. Specifically, police officers may become more concerned with their actions and their words and how they align with departmental policy, probable cause, and reasonable suspicion when wearing BWCs (Ready and Young 2015; Wood and Groff 2018). This reflects the fact that the video can be viewed by supervisors, the courts, and Internal Affairs, and most importantly, it can be used against the officer. One potential effect of the increased possibility of scrutiny is that police officers stop using discretion and instead rely on legalistic options, such as giving a citation rather than a warning or making an arrest rather than issuing a summons. Studies have shown that officers wearing BWCs tended to give more citations when they stopped someone for an ordinance violation (Braga et al. 2017), wrote more tickets when they stopped drivers for traffic offenses (Ready and Young 2015), and made more arrests (Braga et al. 2017; Katz et al. 2014; Toronto

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Police Service 2016). An RCT in Las Vegas examined mean monthly calls and found that the camera group had significantly increased officer activity in call events that resulted in citations or arrests (Braga et al. 2018b). Two studies found no significant differences on arrests between officers wearing BWCs and non-BWC officers (Grossmith et al. 2015; Wallace et al. 2018).

Alternatively, the potential for increased scrutiny by police management and the public may result in officers becoming less proactive. In other words, they will continue to answer calls for service but will drastically reduce the number of times they seek out interactions with citizens (Ready and Young 2015; Shjarback et al. 2017; Wallace et al. 2018). This strategy reflects the officer’s desire to minimize the risk of discipline, public scrutiny, and injury or death, from failing to decisively apply stronger language and/or force in situations (Shjarback et al. 2017). But empirical evidence on this potential effect is mixed. Lum et al. (2019) identified three studies indicating that BWC officers initiate more contacts (Headley et al., 2017; Ready and Young 2015; Wallace et al. 2018) but several other studies reported no significant effect on proactive behavior. Specifically, one study found that BWCs did not affect officer-initiated calls, arrest, response time, and time on the scene (Wallace et al. 2018). Others found no change in the number of stop and searches (Grossmith et al. 2015) or in numbers of officer-initiated calls for service (White et al. 2018). However, other studies reported that officers wearing cameras reported fewer stop-and-frisk encounters (Ready and Young 2015), and fewer “subject stops” (Peterson et al. 2018), and several others found officers made fewer arrests (Ariel 2016; McClure et al. 2017; Ready and Young 2015). Two studies found no change in the number of traffic stops (Headley et al. 2017; Peterson et al. 2018). Summarizing the group of studies that examined the effect of BWCs on proactive policing, Lum et al. (2019) note that the critical question, which is under-examined at this point, concerns the effect of cameras on the proactive behaviors that reduce crime without spawning negative community backlash.

This study adds to the mixed evidence base by using a quasi-experimental design to examine a pilot deployment of BWCs in a single district of a large police jurisdiction. Accountability measures such as citizen complaints and use of force were included as well as measures of proactive police behavior (pedestrian stops, vehicle stops, and arrests).

Methods

Study site

The study examined the effects of the initial BWC deployment in the Philadelphia Police Department (PPD). The PPD is the fourth largest police department in the USA with approximately 6300 sworn officers1 and a strong police union. The PPD was especially intriguing for two reasons. First, large departments with strong unions are less likely to deploy BWCs (Nowacki and Willits 2016). Second, the vast majority of evaluations in the USA have been of departments in western and southern regions of

1 Retrieved from the PPD home page https://www.phillypolice.com/about/index.html on 7/6/2018.

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the country. Therefore, this study adds to the American evidence base while checking the external validity of findings from those regions.

Pilot overview

This evaluation focused on the PPD’s BWC pilot project. The 1-year BWC pilot ran from May 1, 2016 to April 30, 2017. BWCs were assigned to patrol officers in a single police district in North Philadelphia with a reputation for generating a high volume of calls for service and crime. Previous studies have pointed out the importance of limited discretion in the activation of cameras when determining the effectiveness of BWCs at reducing use of force (Ariel et al. 2016a). The PPD’s body-worn cameras policy states: “BWCs will be activated prior to responding to all calls for service, during all law enforcement related encounters, and during all activities involving the general public” (Philadelphia Police Department 2018, PPD directive 4.21, p. 2). Within that context, the pilot was designed to answer three research questions:

& Does the presence of a BWC impact the number of use of force incidents? & Does the presence of a BWC impact the number of citizen complaints? & Does the presence of a BWC impact the number of proactive interactions (pedes-

trian stops, vehicle stops, and arrests) initiated by police?

Study design

The PPD provided individual-level data for all patrol officers in the BWC pilot (n = 190) as well as all patrol officers from three districts with similar socio-demographics, calls for service, and official crime levels (n = 519).2 These four districts represent the highest calls for service and crime areas of the city. Figure 1 shows these four districts, all in the same general area of North Philadelphia, geographically.

Given the above research questions, PPD provided individual-level data describing patrol officers in the pilot and comparison districts. Demographic variables included (1) sex (male or female), (2) race (black, white, other), and (3) calendar year of appoint- ment. Additionally, 1-year pre-implementation period (May 1, 2015 to April 30, 2016) and 1-year BWC pilot (treatment) period (May 1, 2016 to April 30, 2017) counts per officer for five outcomes were included: (1) pedestrian stops, (2) vehicle stops, (3) arrests, (4) complaints, and (5) use of force.

Pedestrian stops captured all recorded incidents where an officer stopped and detained a person on the street using the Terry v. Ohio legal doctrine. The pedestrian stops measure includes both frisk and non-frisk stops. Vehicle stops captured all recorded incidents where an officer stopped a vehicle for a legal violation regardless of whether or not a citation was issued. Arrests captured misdemeanor or felony arrests made by an officer. Complaints consisted of the number of complaints filed against the

2 Evaluation data include only those officers who were assigned to the pilot district at the start of the BWC implementation. There was no attempt to adjust the data for reassignments to and from the BWC district. Officers who transferred into the pilot district were assigned cameras. Officers who left the pilot district lost their cameras. We have no reason to believe those two groups were not about the same size.

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officer by a citizen. All citizen complaints were included in the data regardless of seriousness. Citizens can make complaints via an online form or in-person at a District Office, the Internal Affairs Division, or to any member of the Philadelphia City Council. Use of force represented the total number of police–citizen interactions that involved the officer using force against a citizen, which ranged from striking a subject with a body appendage (e.g., hand, fist, foot, or other body appendage) up to and including lethal force.3

Although the three comparison districts were in the same section of the city and had similar demographics and calls for service/crime levels at the aggregate level, the pilot focused on the BWCs’ impact on patrol officer-level outcomes. Patrol officers could have selected into the BWC implementation district. For example, proactive officers may have transferred to the district because it is busy or inactive officers may have transferred into the district so they could “hide” from commanders who are constantly pulled in different directions. Additionally, when treatment and comparison units have

Fig. 1 Map of Philadelphia police districts, 2014–2016

3 The definitions for use of force and the guidance surrounding use of force can be found in several PPD directives pertaining to use of tactics (the 10.0 series), which are available online. All instances of force are recorded here even if they are part of an arrest. The. PPD did not provide information regarding the subject’s demographics or the subject’s level of resistance or the amount/type of force used during the incident.

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non-overlapping distributions of covariates, then treatment effects may be biased (e.g., see Sullivan and Loughran 2014). Comparisons of the BWC pilot officers (n = 190) and comparison districts officers (n = 519) shown in Table 1 reveal statistically significant differences on three demographic items (male and white/black dummies) and four pre- implementation period outcomes (pedestrian stops, arrests, complaints, and use of force incidents).

Propensity score matching (PSM) was used to identify a counterfactual comparison group with similar pre-treatment observed characteristics (Shadish et al. 2002, p. 56; Dehejia and Wahba 2002; Guo and Fraser 2015), thereby minimizing selection bias (Apel and Sweeten 2010). In effect, PSM approximates randomization and allows for the estimation of unbiased treatment effects by comparing units that have overlapping values of pre-treatment covariates (see Wooldridge 2010). PSM also has the advantage of allowing analysts to use parsimonious analytical methods common for experimental data (e.g., see footnote 8).

In the present study, a logistic regression model summarized observed characteristics into propensity scores (PS). Treatment assignment (1 = BWC pilot officer; 0 = patrol officer from another district) was predicted with an indicator capturing males vs. females, two indicators capturing black or white officers vs. all other races, a contin- uous measure of officers’ total years of service, and the five continuous measures of officers’ pre-treatment values of the outcomes (specification guided by Apel and Sweeten 2010 p. 559; Jennings et al. 2015; Wooldridge 2010, p. 910). The PSs were predicted logits. Next, nearest neighbor matching was used to match treatment (n = 190) and comparison cases (n = 190).4 PSM was performed using the matchit v.3.0.2 (Ho et al. 2006) and optmatch v.0.9 (Hansen et al. 2018) packages in R v.3.5.0.

Pre-treatment covariate balance was examined by comparing the distributions of the predictors used in the PSM logistic regression model between the treatment and comparison groups. Statistically significant differences in the predictors between the treatment and comparison groups before and after PSM were assessed using differences in proportion tests for binary variables and t tests for continuous variables. Since significant test statistics are more likely in large samples and PSM reduces sample sizes after matching by design, percent bias statistics (or standardized mean differences) were also used to assess balance (see Rosenbaum and Rubin 1985; Austin 2009).

Table 1 displays the balance statistics for the PSM process. PSM improved the balance of the pre-treatment covariates between the BWC pilot officers and the comparison officers. Before PSM, there were statistically significant differences be- tween the treatment and potential comparison officers for seven out of nine pre- treatment covariates. After PSM, there were no statistically significant differences between the treatment and PSM comparison officers. Likewise, the bias statistics became smaller and less than 20% (Braga et al. 2012) for eight out of nine pre- treatment covariates. The one exception was the pre-treatment vehicle stops predictor on which the treatment and comparison officers became less similar after PSM. Since it

4 An analysis using optimal matching with a caliper of .01 and a 1:1 matching ratio was also conducted. The main benefit of optimal matching is the ability to use larger matching ratios to identify more comparison cases and increase statistical power. In the present sample, however, reasonable covariate balance could be achieved only with a 1:1 matching ratio when using optimal matching. All results presented in the manuscript were substantively similar for the analysis using optimal matching with a caliper of .01 and a 1:1 matching ratio, so the nearest neighbor matching results are presented for parsimony.

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Table 1 Propensity score matching balance statistics for nearest neighbor matching sample

Variable Sample Group n Mean SD Test % Bias

Male Full Treated 190 0.74 0.44

All others 519 0.81 0.39 * − 17.35 Matched Treated 190 0.74 0.44

Comparison 190 0.74 0.44 n.s. − 1.21 Black Full Treated 190 0.44 0.50

All others 519 0.22 0.41 *** 48.55

Matched Treated 190 0.44 0.50

Comparison 190 0.45 0.50 n.s. − 2.11 White Full Treated 190 0.41 0.49

All others 519 0.60 0.49 *** − 38.86 Matched Treated 190 0.41 0.49

Comparison 190 0.41 0.49 ns 1.06

Years of service Full Treated 190 12.91 6.72

All others 519 12.26 7.14 n.s. 9.38

Matched Treated 190 12.91 6.72

Comparison 190 13.28 7.65 n.s. − 5.19 Pre-implementation Full Treated 190 70.44 91.57

Pedestrian stops All others 519 112.80 129.89 *** − 37.70 Matched Treated 190 70.44 91.57

Comparison 190 61.59 76.33 n.s. 10.49

Pre-implementation Full Treated 190 80.95 103.26

Vehicle stops All others 519 82.71 85.58 n.s. − 1.86 Matched Treated 190 80.95 103.26

Comparison 190 64.82 80.25 n.s. 17.45

Pre-implementation Full Treated 190 18.44 22.36

Arrests All others 519 31.82 41.12 *** − 40.44 Matched Treated 190 18.44 22.36

Comparison 190 16.36 21.78 n.s. 9.42

Pre-implementation Full Treated 190 0.17 0.47

Complaints All others 519 0.43 0.85 *** − 38.00 Matched Treated 190 0.17 0.47

Comparison 190 0.19 0.51 n.s. − 4.28 Pre-implementation Full Treated 190 0.81 1.42

Use of force All others 519 1.08 1.60 * − 17.84 Matched Treated 190 0.81 1.42

Comparison 190 0.89 1.57 n.s. − 5.27

***p < 0.001; **p < 0.01; *p < 0.05

n.s. not significant

Test statistics were t tests for continuous variables and chi-square proportions tests for indicator variables

E. R. Groff et al.470

is relatively common to have problems balancing one or two covariates in applied criminal justice settings (Haberman et al. 2018; Phillips et al. 2016) and the significance test and bias statistics did not reach concerning levels, it was concluded adequate matches had been identified.

Analytic plan

ANCOVA regression models were used (for a policing example, see Ratcliffe et al. 2011). The model for each outcome included an indicator for BWC treatment vs. comparison cases and a continuous measure of the pre-treatment outcome levels. The models assessed the difference in the outcomes across groups beyond what would have been expected based on subjects’ pre-treatment scores on the outcomes. Because the outcomes were discrete counts, count regression models were appropriate (see Cameron and Trivedi 2013). Plots of the observed and predicted probabilities of different counts and likelihood ratio tests comparing negative binomial and Poisson models suggested the model that best fit the data (Long and Freese 2014). Negative binomial regression models were most appro- priate for the present data with one exception: a Poisson regression model was deemed a better fit for the complaints against police outcome. All models were estimated using Stata 14’s glm, poisson, and nbreg commands.

Results

Table 2 displays the count regression model results. Three outcomes were signif- icantly lower (p < 0.05) for the BWC group net of their pre-treatment levels. First, treatment period pedestrian stop counts were about 46.4% lower for BWC officers relative to the comparison officers net of pre-treatment pedestrian stop levels. Second, BWC officers had expected arrest counts that were about 39.2% lower relative to the comparison officers. Third, BWC officers’ expected counts of use of force incidents were roughly 38.3% lower relative to comparison officers with similar pre-treatment use of force levels.

We note the remaining two BWC effects were in the negative direction but did not reach statistical significance. Vehicle stops were about 20.9% lower for the BWC group after controlling for pre-treatment levels, which was almost significant (p = 0.077). Citizen complaints were about 25.6% lower for the BWC officers net of pre-treatment levels (p = 0.217).5

5 Two sets of robustness check analyses were conducted. First, an alternative analytical approach for pre-post designs with treatment and controls groups, analysis of change scores, was examined. In order to ensure the present results were robust to the analytic technique used, we conduced t tests of change scores as well as transformed change scores. Second, for relatively large samples with few predictors, one may question if multivariate regression models may be adequate for estimating unbiased treatment effects (Guo and Fraser 2015, p. 387–390). Despite using past research to guide the present study (Jennings et al. 2017), the present study’s relatively large sample permitted a robustness check using multivariate regression models for the full sample of comparison officers. Both sets of results were substantively similar to those shown in the manuscript, and are detailed in the accompanying online appendix.

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E. R. Groff et al.472

Discussion

One of the primary expectations of BWCs is that both officers and citizens will act differently when being video-recorded. In the case of officers, they would act more in accordance with their training and organizational policies (less incidents of excessive force and fewer unconstitutional actions, instances of profanity use, and general disrespect toward civilians) (Ariel et al. 2015; Ariel et al. 2017). In the case of citizens, they would act in accordance with socially acceptable behavior. Theoretically, this would reduce the likelihood that the situation would escalate because of their more agreeable and compliant actions, thereby diminishing the risk that the officer would view them as a threat to his or her safety.

The current study took advantage of the pilot deployment of BWCs to measure the impacts of the change on police behavior. One key finding is that BWC officers had about 38.3% fewer use of force incidents than non-BWC officers with similar numbers of use of force incidents in the previous year. This is consistent with some studies using experimental (Ariel et al. 2015; Braga et al. 2018b; Henstock and Ariel 2017; Jennings et al. 2015) and quasi-experimental designs (Jennings et al. 2017). Further, a follow-up study indicated the reductions in Rialto have been sustained over time (Sutherland et al. 2017). However, our finding is contrary to that of another eight studies which found no significant differences in use of force based on BWC camera presence (Ariel 2016a; Ariel et al. 2016b; Braga et al. 2018a; Edmonton Police Service 2015; Headley et al. 2017; Peterson et al. 2018; Toronto Police Service 2016; White et al. 2018; Yokum et al. 2017).

Since the literature as whole is mixed on this outcome, additional, more nuanced questions about the role(s) of BWCs in shaping officer use of force decision are merited. Although this study focused on officer-level outcomes, police in the treatment area share an operational environment in a district within a particular part of Philadel- phia. At the district level, there may be broader patterns in police–community dynam- ics, including patterns in citizen deference or, conversely resistance, that structure norms around the use of coercion to foster compliance. Granular data on types of force and levels of citizen resistance during different incident types would likely tell a more complex story, and indeed inform PSM decisions at the outset of quasi-experimental evaluations. As noted earlier, this level of granularity was not available to the research team and such data would likely require manual extraction from police files. An ethnographic study design may be fruitful in future research on BWCs to understand the relationships between BWCs, district cultures, and officer behavior. A cultural analysis, for which ethnography is best suited, would also entail an analysis of police officer styles (e.g., proclivities toward “harder” or “softer” forms of policing). A previous Philadelphia-based study of foot patrol’s effects on violent crime (Wood, et al. 2014) suggested that officer styles do vary, but the analysis did not focus on district- level norms shaping police style.

A second key finding is that the number of citizen complaints did not differ significantly between BWC officers and non-BWC officers. This is consistent with findings of no effect in six studies (Ariel et al. 2015; Edmonton Police Service, 2015; Headley et al. 2017; Toronto Police Service 2016; White et al. 2018; Yokum et al. 2017). One partial explanation for our finding of no effect is that BWCs may not affect the way police interact with civilians in situations where no force is used. In other

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words, BWCs do not affect other aspects of police–civilian interactions that might generate a complaint, such as politeness, use of profanity, and frequency of clear explanations for police actions. We join Lum et al. (2019) in calling for additional research employing systematic social observation that would reveal how interactions change with camera presence (see for example McCluskey et al. 2019; Rowe et al. 2018). In line with our prior recommendation, one could frame such a study within a cultural understanding of police–citizen dynamics within and across districts of a city. Within this line of inquiry, one question is whether the BWCs actually serve to alter the tone and tenor of police–citizen interactions, especially during routine interactions where use of force decisions usually do not come into play. In the future, granular data on citizen complaint types would help to unpack such dynamics.

A third key finding is thatwearing aBWC reduced proactive policing behavior compared with similar officers over the same period. Pedestrian stops and arrests by BWC officers decreased significantly at the same time as numbers for non-BWC officers increased. On average, BWC officers made 46.4% fewer pedestrian stops and 39.2% fewer arrests than non-BWC officers. Vehicle stops also decreased but the difference was only marginally significant. Over the same period, non-BWC officers made more pedestrian stops, vehicle stops, and arrests. Related to pedestrian stops, two studies also found a decrease among BWCofficers (Peterson et al. 2018; Ready and Young 2015) but only one found an increase among non-BWC officers (Ready and Young 2015). In other studies, BWC officers increased the number of pedestrian stops (Headley et al., 2017; Ready and Young 2015; Wallace et al. 2018). Related to arrests, our finding that BWC officers made fewer arrests was consistent with some previous work (Ariel 2016; McClure et al. 2017; Ready and Young 2015) but not with others which found increased arrests when officers wore BWCs (Braga et al. 2018; 2018b; Katz et al. 2014) and two other studies that found no effect (Grossmith et al. 2015; White et al. 2018). The reduction in use of force for BWC officers may be related to the reductions in proactive actions by BWC officers since it is those types of proactive interactions that more often lead to use of force incidents. The question remains whether this downward trend in proactive policing will hold over time. The temporality of any changes in officer behavior is also an important line of inquiry, because temporary changes in officer behavior may not mark a sustained transformation in police culture.

Given the high stakes of a police officer’s livelihood, it would not be surprising if officers chose to minimize the number of citizen contacts and thus the potential for getting disciplined. Wallace and colleagues have suggested cameras can generate “camera induced passivity” (Wallace et al. 2018, p. 3), which they define as a specific form of “depolicing” under which officers react to increased surveillance of their actions by reducing proactive policing, which in turn reduces the potential they might be disciplined. These results were consistent with the deterrence-based view that officers respond to the increased scrutiny by police management and the public by becoming less proactive. Depolicing, avoidance of proactive police work in response to negative publicity, is also a component of the “Ferguson effect” (MacDonald 2016; Wolfe and Nix 2016). Shjarback et al. (2017) found evidence that most depolicing occurred in predominantly minority communities. Our findings indicate that BWCs decrease proactive policing activity beyond the effect of being in a minority commu- nity. At the same time, these findings are in stark contrast to reports that some officers with BWCs feel their discretion not to arrest is limited, which results in increased arrests (Rowe et al. 2018).

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Wallace et al. (2018) also have identified characteristics of how BWCs are deployed in an agency that they hypothesize will mitigate the risk of camera-induced passivity. Philadelphia’s camera adoption process addressed all but one of the process-related risk factors, yet proactive measures of police activity still declined. Specifically, the PPD took a systematic, measured process to the topic of BWC use, spending almost 2 years on a pilot project in one district. A BWC policy was in place before the first camera was deployed and has been changed several times in response to lessons learned. Line officers were asked to evaluate each potential camera model, the policy, and the use of BWCs more generally (Wood and Groff 2019). The Police Union was informed of the pilot but did not participate in the working group. In short, the process was unlikely to have been the source of the reductions in proactive behavior measured here.

The context of the study, Philadelphia, is important. This was the first district to get BWCs in Philadelphia, so there was a great deal of concern about what additional changes cameras would bring. The Police Union did not actively oppose the BWC pilot program, although they were concerned. Some PPD management attributed lack of protest to the slow, systematic adoption of BWCs by the PPD which provided time for officers to get used to the idea. Unrelated to the BWC program, officers perceived that the 2011 Bailey Agreement6 had already significantly reduced the amount of discretion they could exercise (Weisburd et al. 2015). One fear was that BWCs would reduce discretion even further. Because police officers are employees, and BWCs essentially represent a major workplace change, there was considerable concern among officers about keeping their job because when wearing a camera, all their activity is automat- ically collected for potential review by management.

District-level socio-demographic characteristics were similar across both the treat- ment and comparison units. All were predominantly African American and race did not vary much across the districts. The finding also was unlikely to have been affected by differences in opportunities to witness illegal behavior. All four districts were extremely high crime; therefore, the number of opportunities for proactive work was high and consistent across districts. They also had similar housing styles and therefore popula- tion density, as well as the mix of commercial and residential land use, was typical of large, northeastern cities. However, this study left unexplored whether the treatment and comparison districts diverged along the lines of culture, histories of police– community relations, and other potentially relevant historic events that shape how a community may see the police and their legitimacy. As suggested above, a place-based or district-based approach is needed, especially in large urban cities like Philadelphia, to evaluate BWC’s impacts on police behavior. As part of such an effort, establishing which placed-based characteristics and cultural conditions play roles in moderating BWC’s effects on police and citizen behaviors would be a needed conceptual contribution.

Future research should also examine the impacts of cameras on the narrowing of police discretion because of the important implications for police–community relations. According to Shjarback et al. (2017), depolicing may actually have positive benefits. It

6 In 2010, a lawsuit was brought against the City of Philadelphia alleging the Philadelphia Police Department engaged in stopping, frisking, searching, and detaining persons without the necessary reasonable suspicion or probable cause. The City did not admit intentional wrongdoing but did sign the Bailey Agreement which prohibited ten different types of stops. However, this affected the behavior of all police in the city, not just those included in this study.

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is plausible that the reduction in pedestrian stops, vehicle stops, and arrests could translate into fewer citizens who are likely to have felt they were harassed by police (i.e., unjustly detained and then cited) (Gau and Brunson 2010). There is considerable evidence that unfavorable attitudes toward the police among African Americans have been related to adverse experiences with police whether personal or experienced by someone else in their racial group (Brunson 2007). Related to this study, fewer interactions would partially explain the finding of fewer use of force incidents for BWC officers compared with non-BWC officers since they had fewer chances for an interaction to escalate.

There are several limitations to our study. First, we used the strongest design possible, but it still had limitations. We were not able to randomize police officers. One advantage of PSM is that it helps address selection bias and strengthen internal validity by achieving balance between the treatment and comparison group across a large number of observed covariates. We had access to only a limited number of pre- treatment covariates, and there may have been other important confounders that were not balanced via PSM, thereby producing biased treatment effect estimates. Second, we were not able to obtain information regarding the extent to which cameras were actually used or compliance with the directive on when to use cameras (Hedberg et al. 2017). To the extent that BWC officers wore the cameras but did not turn them on, levels of officer and/or citizen self-awareness were not increased and thus there was no reason for a behavior change to occur that would reduce citizen complaints (Ariel et al. 2016a). Third, the outcome measures used were from official data and thus they are subject to the well-known limitations of official data. When possible, future studies that use a randomized design should verify compliance with policy, and collect data clearly describing the exercise of discretion by police officers will make the greatest contri- bution to the body of knowledge. Finally, because Philadelphia is one of the largest cities in America, the results may only apply to other very large American cities.

In sum, this study provides an important contribution to the evidence base of how BWCs affect police officer behavior. The results caution against assuming that BWCs will automatically reduce citizen complaints and provide additional evidence that one reaction to BWCs is for officers to reduce the amount of proactive policing activity they undertake. Additional research is needed to gain a more nuanced understanding of what is happening as police and citizens interact in particular situations situated within places.

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Elizabeth Groff is a Professor in the Criminal Justice department at Temple University. She is an applied researcher who has held positions in practice, policy and academia. Her research interests include: place-based criminology; crime prevention and policing. She is a fellow of the Academy of Experimental Criminology.

Cory Haberman is an Assistant Professor in the School of Criminal Justice at the University of Cincinnati. His research focuses on crime and place and police effectiveness.

Jennifer Wood is a Professor of Criminal Justice at Temple University. She is the North American Regional Editor of Policing and Society: An International Journal of Research and Policy. Her areas of study are public and private forms of policing and the complex relationships between law enforcement and public health.

Affiliations

Elizabeth R. Groff1 & Cory Haberman2 & Jennifer D. Wood1

* Elizabeth R. Groff [email protected]

Cory Haberman [email protected]

Jennifer D. Wood [email protected]

1 Temple University, 1115 W. Polett Walk, Gladfelter Hall, Philadelphia, PA 19122, USA

2 School of Criminal Justice, University of Cincinnati, 660H Teachers-Dyer, Cincinnati, OH 45221- 0389, USA

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