Skip to main content

Thank you for visiting You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Reducing gunshot victimization in high-risk social networks through direct and spillover effects


More than 60,000 people are victimized by gun violence each year in the United States. A large share of victims cluster in bounded and identifiable social networks. Despite a growing number of violence reduction programmes that leverage networks to broaden programmatic effects, there is little evidence that reductions in victimization are achieved through spillover effects on the peers of participants. This study estimates the direct and spillover effects of a gun violence field intervention in Chicago. Using a quasi-experimental design, we test whether a desistance-based programme reduced gunshot victimization among 2,349 participants. The study uses co-arrest network data to further test spillover effects on 6,132 non-participants. Direct effects were associated with a 3.2-percentage point reduction in victimization among seeds over two years, while potential spillover was associated with a 1.5-percentage point reduction among peers. Findings suggest that peer influence and the structure of networks might be leveraged to amplify gun violence reduction efforts.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.


All prices are NET prices.

Fig. 1: Design of the intervention evaluation.
Fig. 2: The estimated effect of compliance on programme seeds (n = 2,349) and the estimated effect of compliance spillover on programme peers (n = 6,132).
Fig. 3

Data availability

The original data used in this study were provided to the corresponding author as part of a data-sharing agreement with the City of Chicago and the Chicago Police Department, and are prohibited from being shared directly. De-identified replication data generated and analysed in this study are available from the corresponding author upon request.

Code availability

All analyses were carried out in R. Code for reproducing the results of this study is available from the corresponding author upon request.


  1. 1.

    Wintemute, G. J. The epidemiology of firearm violence in the twenty-first century United States. Annu. Rev. Public Health 36, 5–19 (2015).

    PubMed  Google Scholar 

  2. 2.

    Web-based injury statistics query and reporting system. National Center for Injury Prevention and Control (2019).

  3. 3.

    Peterson, R. D. & Krivo, L. J. Divergent Social Worlds: Neighborhood Crime and the Racial-Spatial Divide (Russell Sage, 2010).

  4. 4.

    Harper, S., Lynch, J., Burris, S. & Davey Smith, G. Trends in the black-white life expectancy gap in the United States, 1983–2003. JAMA 297, 1224–1232 (2007).

    CAS  PubMed  Google Scholar 

  5. 5.

    Sharkey, P. The acute effect of local homicides on children’s cognitive performance. Proc. Natl Acad. Sci. USA 107, 11733–11738 (2010).

    CAS  PubMed  Google Scholar 

  6. 6.

    Tracy, M., Braga, A. A. & Papachristos, A. V. The transmission of gun and other weapon-involved violence within social networks. Epidemiol. Rev. 38, 70–86 (2016).

    PubMed  PubMed Central  Google Scholar 

  7. 7.

    Papachristos, A. V., Wildeman, C. & Roberto, E. Tragic, but not random: the social contagion of nonfatal gunshot injuries. Soc. Sci. Med. 125, 139–150 (2015).

    PubMed  Google Scholar 

  8. 8.

    Papachristos, A. V., Braga, A. A. & Hureau, D. Social networks and the risk of gunshot injury. J. Urban Health 89, 992–1003 (2012).

    PubMed  PubMed Central  Google Scholar 

  9. 9.

    Green, B., Thibaut, H. & Papachristos, A. V. Modeling contagion through social networks to explain and predict gunshot violence in Chicago, 2006 to 2014. JAMA Intern. Med. 177, 326–333 (2017).

    PubMed  Google Scholar 

  10. 10.

    Cooper, C., Eslinger, D. M. & Stolley, P. D. Hospital-based violence intervention programs work. J. Trauma Acute Care Surg. 61, 534–540 (2006).

    Google Scholar 

  11. 11.

    Purtle, J., Rich, J. A., Fein, J. A., James, T. & Corbin, T. J. Hospital-based violence prevention: progress and opportunities. Ann. Intern. Med. 163, 715–717 (2015).

    PubMed  Google Scholar 

  12. 12.

    Butts, J. A., Roman, C. G., Bostwick, L. & Porter, J. R. Cure violence: a public health model to reduce gun violence. Annu. Rev. Public Health 36, 39–53 (2015).

    PubMed  Google Scholar 

  13. 13.

    Whitehill, J. M., Webster, D. W., Frattaroli, S. & Parker, E. M. Interrupting violence: how the ceasefire program prevents imminent gun violence through conflict mediation. J. Urban Health 91, 84–95 (1994).

    Google Scholar 

  14. 14.

    Crandall, V. & Wong, S. L. Group Violence Reduction Strategy: Call-in Preparation and Execution (The Office of Community Oriented Policing Strategies, 2012).

  15. 15.

    Braga, A. A. & Weisburd, D. The effects of focused deterrence strategies on crime: a systematic review and meta-analysis of the empirical evidence. J. Res. Crime Delinq. 49, 323–358 (2012).

    Google Scholar 

  16. 16.

    Braga, A. A., Papachristos, A. V. & Hureau, D. The effects of hot spots policing on crime: an updated systematic review and meta-analysis. Justice Q. 31, 633–663 (2014).

    Google Scholar 

  17. 17.

    Braga, A. A., Weisburd, D. & Turchan, B. Focused deterrence strategies and crime control: an updated systematic review and meta-analysis of the empirical evidence. Criminol. Public Policy 17, 205–250 (2018).

    Google Scholar 

  18. 18.

    Heckman, J. J. The scientific model of causality. Sociol. Methodol. 35, 1–97 (2006).

    Google Scholar 

  19. 19.

    Durlauf, S. N., Navarro, S. & Rivers, D. A. Understanding aggregate crime regressions. J. Econ. 158, 306–317 (2010).

    Google Scholar 

  20. 20.

    Gravel, J. & Tita, G. E. With great methods comes great responsibilities. Criminol. Public Policy 14, 559–572 (2015).

    Google Scholar 

  21. 21.

    Valente, T. W. Network interventions. Science 337, 49–53 (2012).

    CAS  PubMed  Google Scholar 

  22. 22.

    Proestakis, A. et al. Network interventions for changing physical activity behaviour in preadolescents. Nat. Hum. Behav. 2, 778–787 (2018).

    PubMed  PubMed Central  Google Scholar 

  23. 23.

    Kennedy, D. M., Braga, A. A. & Piehl, A. M. The (un)known yniverse: mapping gangs and gang violence in Boston. in Crime Mapping and Crime Prevention (ed. Weisburd, D. & McEwan, T.) 219–262 (Criminal Justice Press, 1997).

  24. 24.

    Papachristos, A. V. & Kirk, D. S. Changing the street dynamic: evaluating Chicago’s group violence reduction strategy. Criminol. Public Policy 14, 525–558 (2015).

    Google Scholar 

  25. 25.

    National Network for Safe Communities. Group Violence Intervention: An Implementation Guide (US Department of Justice, Office of Community Oriented Policing Services, 2016).

  26. 26.

    Broockman, D. & Kalla, J. Durably reducing transphobia: a field experiment on door-to-door canvassing. Science 352, 220–224 (2016).

    CAS  PubMed  Google Scholar 

  27. 27.

    Clarke, R. V. & Weisburd, D. Diffusion of crime control benefits: observations on the reverse of displacement. Crime Prev. Stud. 2, 165–184 (1994).

    Google Scholar 

  28. 28.

    Kennedy, D. M., Piehl, A. M. & Braga, A. A. Youth violence in Boston: gun markets, serious youth offenders, and a use-reduction strategy. Law Contemp. Probl. 59, 147–196 (1996).

    Google Scholar 

  29. 29.

    Braga, A. A. & Weisburd, D. Focused deterrence and the prevention of violent gun injuries: practice, theoretical principles, and scientific evidence. Annu. Rev. Public Health 36, 55–68 (2015).

    PubMed  Google Scholar 

  30. 30.

    Charette, Y. & Papachristos, A. V. The network dynamics of co-offending careers. Soc. Netw. 51, 3–13 (2017).

    Google Scholar 

  31. 31.

    Paluck, E. L., Shepherd, H. & Aronow, P. M. Changing climates of conflict: a social network experiment in 56 schools. Proc. Natl Acad. Sci. USA 113, 566–571 (2016).

    CAS  PubMed  Google Scholar 

  32. 32.

    Aronow, P. M. & Samii, C. Estimating average causal effects under general interference, with application to a social network experiment. Ann. Appl. Stat. 11, 1912–1947 (2017).

    Google Scholar 

  33. 33.

    Hill, J. L. Bayesian nonparametric modeling for causal inference. J. Comput. Graph. Stat. 20, 217–240 (2011).

    Google Scholar 

  34. 34.

    Chipman, H. A., George, E. I. & McCulloch, R. E. Bart: Bayesian additive regression trees. Ann. Appl. Stat. 4, 266–298 (2010).

    Google Scholar 

  35. 35.

    Green, D. P. & Kern, H. L. Modeling heterogenous treatment effects in survey experiments with Bayesian additive regression trees. Public Opin. Q. 76, 491–511 (2012).

    Google Scholar 

  36. 36.

    Hainmueller, J. Entropy balancing for causal effects: a multivariate reweighting method to produce balanced samples in observational studies. Polit. Anal. 20, 25–46 (2012).

    Google Scholar 

  37. 37.

    Palinkas, L. A. et al. Influence network linkages across implementation strategy conditions in a randomized controlled trial of two strategies for scaling up evidence-based practices in public youth-serving systems. Implement. Sci. 8, 133 (2013).

    PubMed  PubMed Central  Google Scholar 

  38. 38.

    Christakis, N. A. & Fowler, J. H. Social network sensors for early detection of contagious outbreaks. PLoS One 5, e12948 (2010).

    PubMed  PubMed Central  Google Scholar 

  39. 39.

    Centola, D. The spread of behavior in an online social network experiment. Science 329, 1194–1197 (2010).

    CAS  PubMed  Google Scholar 

  40. 40.

    Kim, D. A. et al. Social network targeting to maximise population behavior change: a cluster randomised controlled trial. Lancet 386, 145–153 (2015).

    PubMed  PubMed Central  Google Scholar 

  41. 41.

    Shalizi, C. R. & Thomas, A. C. Homophily and contagion are generically confounded in observational social network studies. Sociol. Methods Res. 40, 211–239 (2011).

    PubMed  PubMed Central  Google Scholar 

  42. 42.

    Legewie, J. & Fagan, J. Aggressive policing and the educational performance of minority youth. Am. Sociol. Rev. 84, 220–247 (2019).

    Google Scholar 

  43. 43.

    Valente, T. W. Social network thresholds in the diffusion of innovations. Soc. Netw. 18, 69–89 (1996).

    Google Scholar 

  44. 44.

    Rogers, E. M. Diffusion of preventative innovations. Addict. Behav. 27, 989–993 (2002).

    PubMed  Google Scholar 

  45. 45.

    Forastiere, L., Airoldi, E. M. & Mealli, F. Identification and estimation of treatment and interference effects in observational studies on networks. Preprint at arXiv (2018).

  46. 46.

    Hamilton, B., Rosenfeld, R. & Levin, A. Opting out of treatment: self-selection bias in a randomized controlled study of a focused deterrence notification meeting. J. Exp. Criminol. 14, 1–17 (2018).

    Google Scholar 

  47. 47.

    Rosenbaum, P. R. Choice as an alternative to control in observational studies. Stat. Sci. 14, 259–304 (1999).

    Google Scholar 

  48. 48.

    Rubin, D. B. Bayesian inference for causal effects: the role of randomization. Ann. Stat. 6, 34–58 (1978).

    Google Scholar 

  49. 49.

    Pearl, J. Causal inference in statistics: an overview. Stat. Surv. 3, 96–146 (2009).

    Google Scholar 

  50. 50.

    Steinman, K. J. & Zimmerman, M. A. Episodic and persistent gun-carrying among urban African-American adolescents. J. Adolesc. Health 35, 356–364 (2003).

    Google Scholar 

  51. 51.

    Cook, P. J. & Laub, J. H. After the epidemic: recent trends in youth violence in the united states. Crime Justice 29, 1–37 (2002).

    Google Scholar 

  52. 52.

    Peterson, R. D. & Krivo, L. J. Macrostructural analyses of race, ethnicity, and violent crime: recent lessons and new directions for research. Annu. Rev. Sociol. 31, 331–356 (2005).

    Google Scholar 

  53. 53.

    Jones-Webb, R. & Wall, M. Neighborhood racial/ethnic concentration, social disadvantage, and homicide risk: an ecological analysis of 10 U.S. cities. J. Urban Health 8, 662–676 (2008).

    Google Scholar 

  54. 54.

    Papachristos, A. V., Braga, A. A., Piza, E. & Grossman, L. S. The company you keep? The spillover effects of gang membership on individual gunshot victimization in a co-offending network. Criminology 53, 624–649 (2015).

    Google Scholar 

  55. 55.

    Fewell, Z., Davey Smith, G. & Sterne, J. A. C. The impact of residual and unmeasured confounding in epidemiologic studies: a simulation study. Am. J. Epidemiol. 166, 646–655 (2007).

    PubMed  Google Scholar 

  56. 56.

    Groenwold, R. H. H. et al. Sensitivity analysis for the effects of multiple unmeasured confounders. Ann. Epidemiol. 26, 601–605 (2016).

    Google Scholar 

Download references


This research was supported by a CAREER award (No. SES-1151449) from the Sociology and Law and Social Science Programs at the National Science Foundation. We thank Y. Charette and D. Kirk for providing valuable feedback. The funder had no role in the conceptualization, design, data collection, analysis, decision to publish or preparation of the manuscript.

Author information




G.W. and A.V.P. designed research. A.V.P. obtained data. G.W. performed research and analysed data. G.W. and A.V.P. wrote the paper.

Corresponding author

Correspondence to Andrew V. Papachristos.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Peer review information: Primary Handling Editor: Aisha Bradshaw.

Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information


Supplementary Notes, Supplementary Methods, Supplementary Results, Supplementary Figures 1–8, Supplementary Tables 1 and 2, and Supplementary References.

Reporting Summary

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Wood, G., Papachristos, A.V. Reducing gunshot victimization in high-risk social networks through direct and spillover effects. Nat Hum Behav 3, 1164–1170 (2019).

Download citation


Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing