Skip to main content

A randomized controlled trial of the impact of body-worn camera activation on the outcomes of individual incidents

A Correction to this article was published on 17 August 2021

This article has been updated



Evaluate the impact of body-worn cameras (BWCs) on officer-initiated activity, arrests, use of force, and complaints.


We use instrumental variable analysis to examine the impact of BWC assignment and BWC activation on the outcomes of individual incidents through a randomized controlled trial of 436 officers in the Phoenix Police Department.


Incidents involving BWC activations were associated with a lower likelihood of officer-initiated contacts and complaints, but a greater likelihood of arrests and use of force. BWC assignment alone was unrelated to arrests or complaints; however, incidents involving officers who were assigned and activated their BWC were significantly more likely to result in an arrest and less likely to result in a complaint.


Future researchers should account for BWC activation to better estimate the effects of BWCs on officer behavior. To maximize the effects of BWCs, police agencies should ensure that officers are complying with activation policies.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2
Fig. 3

Change history


  1. 1.

    Given that individual incidents could involve multiple responding officers, we additionally estimated all of our models using bootstrapped standard errors (similar to the methods used in Hedberg et al. 2017). Estimating the standard errors using sub-samples created through bootstrapping calculates the standard errors based on an empirically derived sampling distribution, as opposed to assuming independence between cases. Due to the potential for responding officers (either in isolation or combination) to influence the outcomes of individual incidents, using bootstrapped standard errors is an important robustness check. We did not identify any meaningful differences in the results in any of the models using the bootstrapped standard errors compared to traditionally estimated standard errors. For simplicity, we present the results without the bootstrapped standard errors.


  1. Ariel, B. (2017). Criminology police body cameras in large police departments. The Journal of Criminal Law and Criminology, 106(4), 729–768.

    Article  Google Scholar 

  2. Ariel, B., Farrar, W. A., & Sutherland, A. (2015). The effect of police body-worn cameras on use of force and citizen’s complaints against the police: a randomized controlled trial. Journal of Quantitative Criminology, 31(3), 509–535.

    Article  Google Scholar 

  3. Ariel, B., Sutherland, A., Henstock, D., Young, J., Drover, P., Sykes, J., 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. Journal of Experimental Criminology, 12(3), 453–463.

    Article  Google Scholar 

  4. Ariel, B., Sutherland, A., & Sherman, L. W. (2019). Preventing treatment spillover contamination in criminological field experiments: the case of body-worn police cameras. Journal of Experimental Criminology, 15(4), 569–591.

    Article  Google Scholar 

  5. Braga, A. A., Coldren, J. R., 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.

  6. Braga, A. A., Sousa, W. H., Coldren, J. R., & Rodriguez, D. (2018). The effects of body-worn cameras on police activity and police-citizen encounters: a randomized controlled trial. The Journal of Criminal Law and Criminology, 108(3), 511–538.

    Google Scholar 

  7. Braga, A. A., Barao, L. M., Zimmerman, G. M., Douglas, S., & Sheppard, K. (2019). Measuring the direct and spillover effects of body worn cameras on the civility of police–citizen encounters and police work activities. Journal of Quantitative Criminology.

  8. Cameron, A. C., & Trivedi, P. K. (2005). Microeconometrics: methods and applications. Cambridge University Press.

  9. Chin-Quee, C. J. (2018). The effects of a police body-worn camera on use of force , citizen complaints , and police productivity. In Performance. St. Thomas University.

  10. Davis, J. A., & Weber, R. P. (1985). The logic of causal order. Sage Publications.

  11. Gartin, P. R. (1995). Dealing with design failures in randomized field experiments: analytic issues regarding the evaluation of treatment effects. Journal of Research in Crime and Delinquency, 32(4), 425–445.

    Article  Google Scholar 

  12. Goodall, M. (2007). Guidance for the police use of body-worn video devices: police and crime standards directorate.

  13. Goodison, S., & Wilson, T. (2017). Citizen perceptions of body-worn cameras: a randomized controlled trial.

  14. Grossmith, L., Owens, C., Finn, W., Mann, D., Davies, T., & Baika, L. (2015). Police, camera, evidence: London’s cluster randomised controlled trial of body worn video. London: UK

    Google Scholar 

  15. Headley, A. M., Guerette, R. T., & Shariati, A. (2017). A field experiment of the impact of body-worn cameras (BWCs) on police officer behavior and perceptions. Journal of Criminal Justice, 53, 102–109.

    Article  Google Scholar 

  16. Heckman, J. (1997). Instrumental variables: a study of implicit behavioral assumptions used in making program evaluations. The Journal of Human Resources, 32(3), 441–462.

    Article  Google Scholar 

  17. Hedberg, E. C., Katz, C. M., & Choate, D. E. (2017). Body-worn cameras and citizen interactions with police officers: estimating plausible effects given varying compliance levels. Justice Quarterly, 34(4), 627–651.

    Article  Google Scholar 

  18. Henstock, D., & Ariel, B. (2017). Testing the effects of police body-worn cameras on use of force during arrests: a randomised controlled trial in a large British police force. European Journal of Criminology, 14(6), 720–750.

    Article  Google Scholar 

  19. Huff, J., Katz, C. M., & Webb, V. J. (2018). Understanding police officer resistance to body-worn cameras. Policing: An International Journal, 41(4), 482–495.

    Article  Google Scholar 

  20. Huff, J., Gaub, J. E., White, M. D., & Malm, A. (2020a). Impact of BWCs on Officer Activity: Directory of Outcomes.

  21. Huff, J., Katz, C. M., Webb, V. J., & Hedberg, E. C. (2020b). Attitudinal changes toward body-worn cameras: perceptions of cameras, organizational justice, and procedural justice among volunteer and mandated officers. Police Quarterly.

  22. Hughes, T. W., Campbell, B. A., & Schaefer, B. P. (2020). The influence of body-worn cameras, minority threat, and place on police activity. Journal of Community Psychology, 48(1), 68–85.

    Article  Google Scholar 

  23. Jennings, W. G., Lynch, M. D., & Fridell, L. A. (2015). Evaluating the impact of police officer body-worn cameras (BWCs) on response-to-resistance and serious external complaints: Evidence from the Orlando police department (OPD) experience utilizing a randomized controlled experiment. Journal of Criminal Justice, 43(6), 480–486.

    Article  Google Scholar 

  24. Jennings, W. G., Fridell, L. A., Lynch, M., Jetelina, K. K., & Reingle Gonzalez, J. M. (2017). A quasi-experimental evaluation of the effects of police body-worn cameras (BWCs) on response-to-resistance in a large metropolitan police department. Deviant Behavior, 38(11), 1332–1339.

    Article  Google Scholar 

  25. Katz, C. M., Choate, D. E., Ready, J. R., & Nuño, L. (2014). Evaluating the impact of officer body worn cameras in the Phoenix police department. Center for Violence Prevention & Community Safety: Arizona State University

    Google Scholar 

  26. Kyle, M. J., & White, D. R. (2017). The impact of law enforcement officer perceptions of organizational justice on their attitudes regarding body-worn cameras. Journal of Crime and Justice, 40(1), 68–83.

    Article  Google Scholar 

  27. Lawrence, D. S., & Peterson, B. E. (2019). How do body-worn cameras affect the amount and makeup of police-initiated activities? A randomized controlled trial in Milwaukee, Wisconsin. Journal of Experimental Criminology.

  28. Lawrence, D. S., McClure, D., Malm, A., Lynch, M., & La Vigne, N. (2019). Activation of body-worn cameras: variation by officer, over time, and by policing activity. Criminal Justice Review, 44(3), 339–355.

    Article  Google Scholar 

  29. Lum, C., Stoltz, M., Koper, C. S., & Scherer, J. A. (2019). The research on body-worn cameras: what we know, what we need to know. Criminology & Public Policy, 18(1), 93–118.

    Article  Google Scholar 

  30. McClure, D., La Vigne, N., Lynch, M. D., Golian, L., Lawrence, D. S., & Malm, A. (2017). How body cameras affect community members’ perceptions of police: results from a randomized controlled trial of one agency’s pilot. Urban Institute.

  31. Peterson, B. E., Yu, L., La Vigne, N., & Lawrence, D. S. (2018). The Milwaukee Police Department’s body-worn camera program: evaluation findings and key takeaways.

    Google Scholar 

  32. PPD Info Center Operations Orders. (2018).

  33. President’s Task Force on 21st Century Policing. (2015). Final Report of the President’s Task Force on 21st Century Policing. Washington, DC: Office of Community Oriented Policing Services.

  34. Ready, J. T., & Young, J. T. N. (2015). The impact of on-officer video cameras on police–citizen contacts: Findings from a controlled experiment in Mesa, AZ. Journal of Experimental Criminology, 11(3), 445–458.

    Article  Google Scholar 

  35. Rojek, J., Nix, J., Wolfe, S. E., Alpert, G. P., Burch, J., Grieco, J., & Robbins, T. (2019). Analysis of 2018 use of deadly force by the Phoenix Police Department.

    Google Scholar 

  36. Rushin, S., & Edwards, G. (2017). De-policing. Cornell Law Review, 102(3), 721–782.

    Google Scholar 

  37. Sousa, W. H., Coldren, J. R., Rodriguez, D., & Braga, A. A. (2016). Research on body worn cameras: meeting the challenges of police operations, program implementation, and randomized controlled trial designs. Police Quarterly, 19(3), 363–384.

    Article  Google Scholar 

  38. Stratton, M., Clissold, P., & Tuson, R. (2015). Body worn video: considering the evidence - Final report of the Edmonton Police Service body worn video pilot project.

  39. Wald, A. (1940). The fitting of straight lines if both variables are subject to error. The Annals of Mathematical Statistics, 11(4), 284–300.

    Article  Google Scholar 

  40. Wallace, D., White, M. D., Gaub, J. E., & Todak, N. (2018). Body-worn cameras as a potential source of de-policing: testing for camera-induced passivity. Criminology, 56(3), 481–509.

    Article  Google Scholar 

  41. White, M. D. (2014). Police officer body-worn cameras: assessing the evidence. Washington DC.

  42. White, M. D., Gaub, J. E., & Todak, N. (2017). Exploring the potential for body-worn cameras to reduce violence in police–citizen encounters. Policing, 12(1), paw057.

  43. White, M. D., Todak, N., & Gaub, J. E. (2018). Examining body-worn camera integration and acceptance among police officers, citizens, and external stakeholders. Criminology & Public Policy, 17(3), 1–29.

    Article  Google Scholar 

  44. White, M. D., Gaub, J. E., & Padilla, K. E. (2019a). Impacts of BWCs on use of force: directory of outcomes.

  45. White, M. D., Gaub, J. E., & Padilla, K. E. (2019b). Impact of BWCs on citizen complaints: directory of Outcomes.

  46. Whynot, C., Nykorchuk, L., Zisis, M., & Deane, S. (2016). Toronto police service body-worn cameras.

    Google Scholar 

  47. Yokum, D., Ravishankar, A., & Coppock, A. (2017). Evaluating the effects of police body-worn cameras: a randomized controlled trial. The Lab DC Working Paper.

Download references


This work was supported by the Bureau of Justice Assistance Smart Policing Initiative Grant Program under Award No. 2015-WY-BX-0004.

Author information



Corresponding author

Correspondence to Jessica Huff.

Ethics declarations

Ethical approval

All procedures performed were conducted in accordance with the ethical standards of Arizona State University (approved ASU IRB study 00005277) and with the 1964 Helsinki declaration and its later amendments.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.


Appendix 1. Using path models to estimate TOT

The purpose of this appendix is to show how path models can estimate treatment on the treated impacts equivalent to typical econometric instrumental variable regression. Our example employs two dichotomous treatment indicators, but these derivations apply to any IV model. Our exposition was kept general in order to be helpful to a broader set of readers.

To estimate the so-called “treatment on the treated” impact, researchers often employ the local average treatment effect (LATE). This impact estimate involves three key variables: the outcome Y, the randomly assigned binary treatment indicator Z, and the observed treatment behavior (X).

Given an exogenous (uncorrelated with any other factors) treatment predictor z where control and treatment conditions are randomly assigned and coded as z = {0, 1}, the instrumental variable (IV) estimate is the ratio of the mean difference in the outcome by the mean difference in the instrumented behavior variable (as noted in Cameron and Trivedi (2005) as the Wald Estimator based on Wald's (1940) paper).

$$ {\tau}_{IV}=\frac{{\overline{y}}_{z=1}-{\overline{y}}_{z=0}}{{\overline{x}}_{z=1}-{\overline{x}}_{z=0}} $$


In the parlance of two-stage-least-squares, the first stage estimates

$$ x= az+{\varepsilon}_2 $$

which produces predicted values of \( \hat{x} \), namely \( {\overline{x}}_{z=1} \) and \( {\overline{x}}_{z=0} \) where \( a={\overline{x}}_{z=1}-{\overline{x}}_{z=0} \). The second stage uses these predicted values in the model

$$ y=b\hat{x}+{\varepsilon}_1 $$

hence the name “2SLS.”

Path models

In the below, we show that the IV LATE estimate can also be achieved using path models estimated with structural equation model software. Essentially, x completely mediates the relationship between z and y (through model constraints). Path models estimate constrained covariance structures to sets of variables. We can visually represent the LATE model with the path model shown in Fig. 4.

Fig. 4

Basic LATE path model

In Fig. 4, the two endogenous variables, x and y, are proposed to relate to each other through two paths. The first is that the predicted value of \( \hat{y} \) is a linear function of the predicted value of \( \hat{x} \), or \( \hat{y}=f\left(\hat{x}\right)=b\hat{x} \). The second relationship is that the residuals of y, or ε1, are correlated with the residuals of x, or ε2. This figure also includes a representation of the major reason IV models are sometimes required: there is a relationship between the predictor (which is composed of both the prediction and residual, \( x=\hat{x}+{\varepsilon}_2 \)) and the outcome residuals from the model, ε1.

An indicator of the result of random assignment, z, is exogenous by definition and thus not correlated with the observed outcome, y. However, it is a good predictor of behavior, x, and thus the third relationship in this model is \( \hat{x}=g(z)= az \).

Much of the literature on path models (e.g., Davis and Weber, 1985) note that the total impact of a chain between two variables, say z and y, are the product of the paths. Thus, the total impact of z on y in this model is the product of the first path and second path, namely \( \hat{y}=f\left(g(z)\right)= abz \) since \( \hat{y}=f\left(\hat{x}\right) \) and \( \hat{x}=g(z) \).

Two-stage-least-squares and path models

To connect 2SLS and path models, we note that another equivalent parameterization of this estimate comprises two stages of covariances, namely the ITT impact \( \mathit{\operatorname{cov}}\left(z,y\right)={\overline{y}}_{z=1}-{\overline{y}}_{z=0} \) and the covariances between behavior x and treatment assignment z, \( \mathit{\operatorname{cov}}\left(z,x\right)={\overline{x}}_{z=1}-{\overline{x}}_{z=0} \), namely

$$ {\tau}_{IV}=\frac{\mathit{\operatorname{cov}}\left(z,y\right)}{\mathit{\operatorname{cov}}\left(z,x\right)} $$

which can be rewritten as

$$ {\tau}_{IV}=\frac{f\left(g(z)\right)}{g(z)}=\frac{ab}{a}=b $$

In other words, the path, b, from x to y in Fig. 4 is the IV estimate τIV.


Table 4 provides an example of data to be analyzed such as the above discussion. Those assigned control have a value of z = 0, and those assigned treatment have a value of z = 1. The mean of x for the control observations is .2, and the mean of x for the treatment observations is .9; \( {\overline{x}}_{z=1}-{\overline{x}}_{z=0}=.7 \). The mean of y for the control observations is 57, the mean of y for the treatment observations is 53.1; \( {\overline{y}}_{z=1}-{\overline{y}}_{z=0}=-3.9 \). This can be confirmed with the regression.

Table 4 Example data

The ITT impact is thus − 3.9, and the IV impact is \( {\tau}_{IV}=\frac{{\overline{y}}_{z=1}-{\overline{y}}_{z=0}}{{\overline{x}}_{z=1}-{\overline{x}}_{z=0}}=-\frac{3.9}{.7}=-5.571429. \)

This result can be confirmed by running a model in Stata using the instrumental variable regression package (ivregress; note the coefficient for x and its standard error).

We can also fit a path model to estimate this impact, as shown in Fig 5.

Fig. 5

Path LATE model on example data

The sem procedure in Stata can be used to fit the path model (gsem can be employed for non-linear outcomes). This produces the same results as the instrumental variable regression procedure above; note the output for the coefficient of x and compare it and its standard error to the output from ivregress.

Also note that the coefficient for z predicting x is \( {\overline{x}}_{z=1}-{\overline{x}}_{z=0}=.9-.2=.7 \) as expected.

Appendix 2. Predicted probabilities based on varying levels of contamination (with 95% confidence intervals)

  Proportion of responding officers assigned to wear a BWC
  0% 33% 50% 66% 100%
Officer-initiated 11.31% 11.47% 11.56% 11.64% 11.82%
Arrest 26.42% 26.48% 26.51% 26.54% 26.60%
Use of force 0.04% 0.05% 0.05% 0.05% 0.06%
Complaint 0.01% 0.02% 0.02% 0.02% 0.02%
  1. Note: Results based on Model 1 for each outcome, holding all other covariates at their means

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Huff, J., Katz, C.M. & Hedberg, E.C. A randomized controlled trial of the impact of body-worn camera activation on the outcomes of individual incidents. J Exp Criminol (2020).

Download citation


  • Arrest
  • Body-worn cameras
  • Complaints
  • Compliance
  • Instrumental variable analysis
  • Officer-initiated activity
  • Policing
  • Use of force