The co-offender as counterfactual: a quasi-experimental within-partnership approach to the examination of the relationship between race and arrest

Abstract

Objectives

Estimate the relationship between race and arrest within co-offending partnerships using a quasi-experimental framework. More specifically, this study argues that when two offenders commit an offense together (i.e., co-offend), the characteristics of the offense and victim are the same and can be removed as possible confounding variables. In this way, co-offenders can serve as counterfactual observations to one another, allowing for quasi-experimental analysis of the effects of race on arrest likelihood.

Methods

The current study restructures data from the National Incident-Based Reporting System (NIBRS) into a multi-level format wherein level-1 information on offender demographics and arrest are nested within a level-2 file containing information on co-offending partnerships, offense characteristics, and victim characteristics. By restricting the data to co-offending partnerships and examining within-partnership differences in arrest, the analysis examines racial differences in arrest given that two offenders commit the same offense together against the exact same victim.

Results

While a traditional logistic regression approach suggests that black offenders are less likely than white offenders to be arrested (OR = 0.749), the quasi-experimental analysis examining within-partnership differences suggests the opposite: black offenders are more likely than their white co-offending partners to be arrested for an offense (OR = 1.031).

Conclusions

These results have two implications. First, traditional regression analyses of the relationship between race and arrest may be subject to significant selection and omitted variable bias. Second, there is potential racial disparity in co-offender arrest: black co-offenders are more likely than their white partners to be arrested for the same violent offense.

This is a preview of subscription content, log in to check access.

Fig. 1

Notes

  1. 1.

    This research limits the sample to violent offenses for two reasons. First, violent and property offenses are theoretically different in function and form from each other, and a complete examination of all offenses in the NIBRS data is beyond the scope of a single paper. Therefore, we chose to limit the sample to the most serious offenses. Second, violent offenses are inherently interpersonal, while not all property offenses involve contact between the victim and offender. This means that there are much higher rates of offender demographic missing data for property offenses, compared to violent offenses, and these characteristics were important matching criteria in the facilitation of the analysis conducted in this research.

  2. 2.

    Because missing data is not allowable at level 2 for HLM analyses, incidents containing missing data at this level are removed from the sample (roughly 13% of incidents).

  3. 3.

    Only a very small number of offender and arrestee segments could not be matched (< .01%) because the demographic information in the offender file did not match the demographic information in the arrestee file in any way (i.e., a female was arrested when the offenders were reported as all male). These cases were removed from the final sample.

  4. 4.

    Research conducted by Addington (2015) supports the assumption that these cases do, in fact, represent the same offender. She noted that, in the early years of NIBRS data collection, there was an informal FBI practice of encouraging agencies to “overwrite and correct offender information using arrestee demographics” (p. 162). This practice is no longer encouraged, but many agencies continue the practice. These changes are not flagged in any identifiable way, and cannot be examined in the current analysis.

  5. 5.

    The current analysis elected not to also match offenders on age. While capitalizing on age would have greatly increased the number of offenders that could have been included in the analysis, there is significant variation in the amount of time between the offense data and arrest date for each incident, allowing offender age to change significantly between the offender and arrestee file. It is also markedly more difficult for victim(s)/observer(s) to accurately estimate age based on observation alone. As a result, it was determined that including age as a matching variable could significantly increase the potential for error in matching the offender and arrestee segments.

  6. 6.

    We did not perform the analysis at the individual offender level because to do so would violate the regression assumption of independence, or uncorrelated error terms, given that co-offenders are likely more similar in their outcomes than those who do not offend together.

  7. 7.

    It is worth noting that, because the sample includes all mixed race or mixed gender dyads, regardless of arrest outcome, resulting estimates might best be considered conservative. For most of these co-offending partnerships, none of the offenders are arrested (about 61% of incidents), or both of the offenders are arrested (about 27% of incidents). The estimates would likely be substantially larger if the sample was limited to only those cases wherein there is a difference in arrest outcomes (about 11% of incidents), but because that sample would not realistically represent the co-offending partnerships in the sample, and further limit the generalizability of these results, we chose to include all incidents involving mixed gender or mixed race partnerships, regardless of differences in arrest outcomes.

  8. 8.

    Sample size in multilevel modeling is determined by the total number of units at each level, so a low average number of level-one units per level-two grouping has no problematic influence on power for testing regression coefficients (Snijders 2005). Therefore, the examination of two offenders per higher-level unit is not problematic.

  9. 9.

    The NIBRS data include the use of “body parts” as a possible personal weapon. These offenses were coded as not involving a weapon because it can be assumed that all offenders have these potential weapons, and coding them as weapons would skew the data positively regarding the prevalence of weapon use (see Cunningham and Vandiver 2018 for a similar coding scheme).

  10. 10.

    One might argue that, in order to estimate the relationship between race and arrest, one must jointly consider the level-2 and level-1 race estimate. But, according to the counterfactual framework outlined here, the primary estimate of interest is actually the level-1 race estimate, controlling for the level-2 race effect. The level-2 race estimate captures incident-level racial differences in arrest likelihood, which I argue are subject to potential omitted variable bias due to incident differences. The level-1 estimate, on the other hand, estimates differences within-partnerships, reducing this potential bias. As such, the level-1 estimate is best considered the within-incident race difference, the level-2 estimate is best considered a control measure for the impact of co-offending partner race, and the two estimates are best not considered jointly.

  11. 11.

    While the point estimate for the within-partnership difference in race is relatively small, it is worth noting that this is in part a product of the research design. That is, the intention of analyzing within-partnership differences was to reduce the portion of the observed estimate that is spurious by also accounting for unmeasured offense and victim characteristics. As such, it should be expected that the point estimates would be smaller because, theoretically, they are a closer approximation of the true effect.

References

  1. Addington, L. A. (2015). Research adventures with “kinda big” data: using NIBRS to study crime. In M. D. Maltz & S. K. Rice (Eds.), Envisioning criminology: researchers on research as a process of discovery. Switzerland: Springer International Publishing.

    Google Scholar 

  2. Akiyama, Y., & Nolan, J. (1999). Methods for understanding and analyzing NIBRS data. Journal of Quantitative Criminology, 15(2), 225–238.

    Google Scholar 

  3. Ariel, B., & Tankebe, J. (2018). Racial stratification and multiple outcomes in police stops and searches. Policing and Society, 28(5), 507–525.

    Google Scholar 

  4. Becker, S., & McCorkel, J. A. (2011). The gender of criminal opportunity: the impact of male co-offenders on women’s crime. Feminist Criminology, 6, 79–110.

    Google Scholar 

  5. Beckett, K., & Sasson, T. (2004). The politics of injustice: crime and punishment in America. Thousand Oaks: Sage.

    Google Scholar 

  6. Beckett, K., Nyrop, K., & Pfingst, L. (2006). Race, drugs, and policing: understanding disparities in drug delivery arrests. Criminology, 44, 105–137.

    Google Scholar 

  7. Black, D. (1976). The behavior of law. New York: Academic Press.

    Google Scholar 

  8. Black, D. (1980). The manners and customs of the police. New York: Academic.

    Google Scholar 

  9. Black, D., & Reiss, A. (1970). Police control of juveniles. American Sociological Review, 35, 63–77.

    Google Scholar 

  10. Bouchard, M., & Nguyen, H. (2010). Is it who you know, or how many that counts? Criminal networks and cost avoidance in a sample of young offenders. Justice Quarterly, 27, 130–158.

    Google Scholar 

  11. Bowling, B., & Phillips, C. (2007). Disproportionate and discriminatory: reviewing the evidence on police stop-and-search. Modern Law Review, 70, 936–961.

    Google Scholar 

  12. Brownfield, D., Sorenson, A. M., & Thompson, K. M. (2001). Gang membership, race, and social class: a test of the group hazard and master status hypotheses. Deviant Behavior, 22, 73–89.

    Google Scholar 

  13. Brunson, R. K. (2007). “Police don’t like black people”: African-American young men’s accumulated police experiences. Criminology & Public Policy, 6, 71–100.

    Google Scholar 

  14. Carrington, P. J. (2009). Co-offending and the development of the delinquent career. Criminology, 47, 277–315.

    Google Scholar 

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

    Google Scholar 

  16. Chesney-Lind, M. (1978). Chivalry reexamined: women and the criminal justice system. In Bowker, L.H (ed.), Women, Crime, and the Criminal Justice System. Lexington, MA: Lexington Books.

  17. Cunningham, D., & Browning, B. (2004). The emergence of worthy targets: official frames and deviance narratives within the FBI. Sociological Forum, 19, 347–369.

    Google Scholar 

  18. Cunningham, S. N., & Vandiver, D. M. (2018). Solo and multi-offenders who commit stranger kidnapping: an assessment of factors that correlate with violent events. Journal of Interpersonal Violence, 33(2): 3459-3479.

  19. D’Alessio, S., & Stolzenberg, L. (2003). Race and the probability of arrest. Social Forces, 81, 1381–1397.

    Google Scholar 

  20. Duncan, B. L. (1976). Differential social perception and attribution of intergroup violence: testing the lower limits of stereotyping of blacks. Journal of Personality and Social Psychology, 34, 590–598.

    Google Scholar 

  21. Durose, M., Smith, E., & Langan, P. (2007). Contact between police and the public (p. 2005). Washington, D.C.: Bureau of Justice Statistics.

    Google Scholar 

  22. Eberhardt, J. L., Goff, P. A., Purdie, V. J., & Davis, P. G. (2004). Seeing Black: race, crime, and visual processing. Journal of Personality and Social Psychology, 87(6), 876–893.

    Google Scholar 

  23. Erickson, M. L. (1971). The group context of delinquent behavior. Social Problems, 19, 114–129.

    Google Scholar 

  24. Erickson, M. L. (1973). Group violations, socioeconomic status, and official delinquency. Social Forces, 52, 41–52.

    Google Scholar 

  25. Fagan, J., & Davies, G. (2000). Street stops and broken windows: Terry, race, and disorder in new York City. Fordham Urban Law Journal, 28, 457–504.

    Google Scholar 

  26. Feinstein, R. (2015). A qualitative analysis of police interactions and disproportionate minority contact. Journal of Ethnicity in Criminal Justice, 13, 159–178.

    Google Scholar 

  27. Felson, R., & Lantz, B. (2016). Are victims of intimate partner violence and sexual assault less likely to cooperate with police than victims of other crimes? Aggressive Behavior, 42(1), 97–108.

    Google Scholar 

  28. Feyerherm, W. (1980). The group hazard hypothesis: a reexamination. Journal of Crime and Delinquency, 17, 58–68.

    Google Scholar 

  29. Firebaugh, G. (2008). Seven rules for social research. Princeton: Princeton University Press.

    Google Scholar 

  30. Gase, L. N., Glenn, B. A., Gomez, L. M., Kuo, T., Inkelas, M., & Ponce, N. A. (2016). Understanding racial and ethnic disparities in arrest: the role of individual, home, school, and community characteristics. Race and Social Problems, 8, 296–312.

    Google Scholar 

  31. Gottfredson, M. R., & Hirschi, T. (1990). A general theory of crime. Palo Alto, CA: Stanford University Press.

  32. Hindelang, M. J. (1976). With a little help from their friends: group participation in reported delinquent behavior. British Journal of Criminology, 16, 109–125.

    Google Scholar 

  33. Hindelang, M. J. (1978). Race and involvement in common law personal crimes. American Sociological Review, 43, 93–109.

    Google Scholar 

  34. Hirschi, T., & Gottfredson, M. (1983). Age and the explanation of crime. American Journal of Sociology, 89(3), 552–584.

    Google Scholar 

  35. Irwin, J. (1985). The jail: Managing the underclass in American society. Berkeley, CA: University of California Press.

  36. Kochel, T. R., Wilson, D. B., & Mastrofski, S. D. (2011). Effect of suspect race on officers’ arrest decisions. Criminology, 49(2), 473–512.

    Google Scholar 

  37. Koons-Witt, B., & Schram, P. J. (2003). The prevalence and nature of violent offending by females. Journal of Criminal Justice, 31(4), 361–371.

    Google Scholar 

  38. Lantz, B. (2017). The consequences of crime in company: an analysis of co-offending group violence and arrest patterns in the NIBRS data, 2003–2012. Doctoral dissertation: The Pennsylvania State University.

  39. Lantz, B. (2018). The consequences of crime in company: co-offending, victim-offender relationship, and quality of violence. Journal of Interpersonal Violence ​(online publication ahead of print).

  40. Lantz, B., & Hutchison, R. (2015). Co-offender ties and the criminal career: the relationship between co-offender group structure and the individual offender. Journal of Research in Crime and Delinquency, 52(5), 658–690.

    Google Scholar 

  41. Lantz, B., & Kim, J. (2018). Hate crimes hurt more, but so do co-offenders: separating the influence of co-offending and bias on hate motivated injury. Criminal Justice and Behavior, 46(3), 437–456.

    Google Scholar 

  42. Lantz, B., & Ruback, R. B. (2017). The relationship between co-offending, age, and experience using a sample of adult burglary offenders. Journal of Developmental and Life-Course Criminology, 3(1), 76–97.

    Google Scholar 

  43. Lantz, B., Gladfelter, A., & Ruback, R. B. (2019). Stereotypical hate crimes and criminal justice processing: a multi-dataset comparison of bias crime arrest patterns by offender and victim race. Justice Quarterly, 36(2): 193-224.

  44. Loughran, T. A., Paternoster, R., Piquero, A. R., & Fagan, J. (2012). “A good man always knows his limitations”: the role of overconfidence in criminal offending. Journal of Research in Crime and Delinquency, 50, 327–358.

    Google Scholar 

  45. Lundman, R. J., & Kaufman, R. L. (2003). Driving while black: effects of race, ethnicity, and gender on citizen self-reports of traffic stops and police actions. Criminology, 41, 195–220.

    Google Scholar 

  46. Lundman, R. J., Sykes, R. E., & Clark, J. P. (1978). Police control of juveniles: a replication. Journal of Research in Crime and Delinquency, 15(1), 74–91.

    Google Scholar 

  47. Lytle, D. J. (2014). The effects of suspect characteristics on arrest: a meta-analysis. Journal of Criminal Justice, 42, 589–597.

    Google Scholar 

  48. Maxfield, M. G. (1999). The National Incident-Based Reporting System: research and policy applications. Journal of Quantitative Criminology, 15, 119–149.

    Google Scholar 

  49. McDowall, D., Loftin, C., & Wiersema, B. (1996). Using quasi-experiments to evaluate firearm laws: comment on Britt et al.’s, reassessment of the D.C. Gun Law. Law and Society Review, 30(2), 381–392.

    Google Scholar 

  50. McGloin, J. M., & Nguyen, H. (2012). It was my idea: considering the instigation of co-offending. Criminology, 50, 463–494.

    Google Scholar 

  51. McGloin, J. M., & Nguyen, H. (2013). The importance of studying co-offending networks for criminological theory and policy. In Morselli, C. (ed.), Crime and Networks, 13–27. Abingdon, UK: Routledge

  52. Morgan, S. L., & Winship, C. (2007). Counterfactuals and causal inference: methods and principles for social research. Cambridge: Cambridge University Press.

    Google Scholar 

  53. Morselli, C., Tremblay, P., & McCarthy, B. (2006). Mentors and criminal achievement. Criminology, 44, 17–43.

    Google Scholar 

  54. Ousey, G., & Lee, M. (2008). Racial disparity in formal social control: an investigation of alternative explanations of racial inequality. Journal of Research in Crime and Delinquency, 45, 322–355.

    Google Scholar 

  55. Payne, B. K. (2001). Prejudice and perception: the role of automatic and controlled processes in misperceiving a weapon. Journal of Personality and Social Psychology, 81, 181–192.

    Google Scholar 

  56. Petrocelli, M., Piquero, A. R., & Smith, M. R. (2003). Conflict theory and racial profiling: an empirical analysis of police traffic stop data. Journal of Criminal Justice, 31, 1–11.

    Google Scholar 

  57. Piliavin, I., & Briar, S. (1964). Police encounters with juveniles. American Journal of Sociology, 70, 206–214.

    Google Scholar 

  58. Pollock, W., Oliver, W., & Menard, S. (2012). Measuring the problem: a national examination of disproportionate police contact in the United States. Criminal Justice Review, 37(2), 153–173.

    Google Scholar 

  59. Pope, C. E., & Snyder, H. E. (2003). Race as a factor in juvenile arrests. Washington, DC: US Department of Justice, Office of Justice Programs, Officer of Juvenile Justice and Delinquency Prevention. 

  60. Powell, D. D. (1990). A study of policy discretion in six southern cities. Journal of Police Science and Administration, 17, 1–7.

    Google Scholar 

  61. Ramirez, D., McDevitt, J., & Farrell, A. (2000). A resource guide on racial profiling data collection: promising practices and lessons learned. Washington, D.C: Department of Justice, National Institute of Justice.

    Google Scholar 

  62. Reiss, A. J., Jr., & Farrington, D. P. (1991). Advancing knowledge about co-offending: results from a prospective longitudinal survey of London males. The Journal of Criminal Law and Criminology, 82, 360–395.

    Google Scholar 

  63. Ricksheim, E., & Chermack, S. (1993). Causes of police behavior revisited. Journal of Criminal Justice, 21, 353–382.

    Google Scholar 

  64. Roberts, A. (2009). Contributions of the National Incident-Based Reporting System to the substantive knowledge in criminology: a review of research. Sociology Compass, 3(3), 433–458.

    Google Scholar 

  65. Roberts, A., & Lyons, C. J. (2009). Victim-offender racial dyads and clearance of lethal and nonlethal assault. Journal of Research in Crime and Delinquency, 46, 301–326.

    Google Scholar 

  66. Rojek, J., Rosenfeld, R., & Decker, S. (2012). Policing race: the racial stratification of searches in police traffic stops. Criminology, 50(4), 993–1024.

    Google Scholar 

  67. Rosich, K. J. (2007). Race, ethnicity, and the criminal justice system. Washington, D.C.: American Sociological Association.

    Google Scholar 

  68. Shannon, L. (1988). Criminal career continuity, it’s social context. New York: Human Sciences Press.

    Google Scholar 

  69. Sherman, L. (1980). Causes of police behavior: the current state of quantitative research. Journal of Research in Crime and Delinquency, 17, 69–100.

    Google Scholar 

  70. Skogan, W. G., & Frydll, K. (2004). Fairness and effectiveness in policing: the evidence. Washington, D.C.: National Research Council.

    Google Scholar 

  71. Smith, D., & Visher, C. (1981). Street level justice: situational determinants of police arrest decisions. Social Problems, 29, 167–178.

    Google Scholar 

  72. Smith, D., Visher, C. A., & Davidson, L. (1984). Equity and discretionary justice: the influence of race on police arrest decisions. The Journal of Criminal Law and Criminology, 75, 234–249.

    Google Scholar 

  73. Snijders, T. A. B. (2005). Power and sample size in multilevel models. In B. S. Everitt & D. C. Howell (Eds.), Encylopedia of Statistics in Behavioral Science. Chichester: Wiley.

    Google Scholar 

  74. Sommers, S. R., & Marotta, S. A. (2014). Racial disparities in legal outcomes: on policing, charging decisions, and criminal trial proceedings. Policy Insights From the Behavioral and Brain Sciences, 1, 103–111.

    Google Scholar 

  75. Steffensmeier, D., Ulmer, J., & Kramer, J. (1998). The interaction of race, gender, and age in criminal sentencing: the punishment cost of being young, black, and male. Criminology, 36(4), 763–798.

    Google Scholar 

  76. Sunshine, J., & Tyler, T. (2003). The role of procedural justice and legitimacy in shaping public support for policing. Law and Society Review, 37(3), 513–547.

    Google Scholar 

  77. Tillman, R. (1987). The size of the “criminal population,” the prevalence and incidence of adult arrest. Criminology, 25, 561–579.

    Google Scholar 

  78. Tillyer, M. S., & Tillyer, R. (2015). Maybe I should do this alone: a comparison of solo and co-offending robbery outcomes. Justice Quarterly, 32, 1064–1088.

    Google Scholar 

  79. van Mastrigt, S. B., & Farrington, D. P. (2009). Co-offending, age, gender, and crime type: implications for criminal justice policy. British Journal of Criminology, 49, 552–573.

    Google Scholar 

  80. Visher, C. A. (1983). Gender, police arrest decision, and notions of chivalry. Criminology, 21(1), 5–28.

    Google Scholar 

  81. Walker, S. (1993). Taming the system: the control of discretion in criminal justice (pp. 1950–1990). New York: Oxford University Press.

    Google Scholar 

  82. Weerman, F. M. (2014). Theories of co-offending. In Encyclopedia of criminology and criminal justice.

    Google Scholar 

  83. Weitzer, R., & Tuch, S. A. (2005). Racially biased policing: determinants of citizen perceptions. Social Forces, 83(3), 1009–1030.

    Google Scholar 

  84. Whitaker, G. P. (1982). What is patrol work? Police Studies, 4, 13–22.

    Google Scholar 

  85. White, K. M. (2015). The salience of skin tone: effects on the exercise of police enforcement authority. Ethnic and Racial Studies, 38(6), 993–1010.

    Google Scholar 

  86. Winship, C., & Morgan, S. L. (2006). The estimation of causal effects from observational data. Annual Review of Sociology, 25, 659–706.

    Google Scholar 

  87. Withrow, B. (2006). Racial profiling: from rhetoric to reason. Upper Saddle River: Pearson Education.

    Google Scholar 

Download references

Acknowledgments

The authors wish to thank Barry Ruback, Wayne Osgood, Holly Nguyen, Jeremy Staff, and Scott Gest for comments on this and earlier versions of this research.

Funding

This research was completed in part with funding from the Bureau of Justice Statistics (2015-R2-CX-K032). The views presented represent those of the author and do not necessarily represent those of the Bureau of Justice Statistics.

Author information

Affiliations

Authors

Corresponding author

Correspondence to Brendan Lantz.

Additional information

Publisher’s note

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

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Lantz, B., Wenger, M.R. The co-offender as counterfactual: a quasi-experimental within-partnership approach to the examination of the relationship between race and arrest. J Exp Criminol 16, 183–206 (2020). https://doi.org/10.1007/s11292-019-09362-5

Download citation

Keywords

  • Race
  • Arrest
  • Co-offending
  • Counterfactual
  • Quasi-experiment
  • NIBRS