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.
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.
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).
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.
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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.
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).
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.
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.
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.
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.
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.
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.
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).
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.
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.
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.
Akiyama, Y., & Nolan, J. (1999). Methods for understanding and analyzing NIBRS data. Journal of Quantitative Criminology, 15(2), 225–238.
Ariel, B., & Tankebe, J. (2018). Racial stratification and multiple outcomes in police stops and searches. Policing and Society, 28(5), 507–525.
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.
Beckett, K., & Sasson, T. (2004). The politics of injustice: crime and punishment in America. Thousand Oaks: Sage.
Beckett, K., Nyrop, K., & Pfingst, L. (2006). Race, drugs, and policing: understanding disparities in drug delivery arrests. Criminology, 44, 105–137.
Black, D. (1976). The behavior of law. New York: Academic Press.
Black, D. (1980). The manners and customs of the police. New York: Academic.
Black, D., & Reiss, A. (1970). Police control of juveniles. American Sociological Review, 35, 63–77.
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.
Bowling, B., & Phillips, C. (2007). Disproportionate and discriminatory: reviewing the evidence on police stop-and-search. Modern Law Review, 70, 936–961.
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.
Brunson, R. K. (2007). “Police don’t like black people”: African-American young men’s accumulated police experiences. Criminology & Public Policy, 6, 71–100.
Carrington, P. J. (2009). Co-offending and the development of the delinquent career. Criminology, 47, 277–315.
Charette, Y., & Papachristos, A. V. (2017). The network dynamics of co-offending careers. Social Networks, 51, 3–13.
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.
Cunningham, D., & Browning, B. (2004). The emergence of worthy targets: official frames and deviance narratives within the FBI. Sociological Forum, 19, 347–369.
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.
D’Alessio, S., & Stolzenberg, L. (2003). Race and the probability of arrest. Social Forces, 81, 1381–1397.
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.
Durose, M., Smith, E., & Langan, P. (2007). Contact between police and the public (p. 2005). Washington, D.C.: Bureau of Justice Statistics.
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.
Erickson, M. L. (1971). The group context of delinquent behavior. Social Problems, 19, 114–129.
Erickson, M. L. (1973). Group violations, socioeconomic status, and official delinquency. Social Forces, 52, 41–52.
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.
Feinstein, R. (2015). A qualitative analysis of police interactions and disproportionate minority contact. Journal of Ethnicity in Criminal Justice, 13, 159–178.
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.
Feyerherm, W. (1980). The group hazard hypothesis: a reexamination. Journal of Crime and Delinquency, 17, 58–68.
Firebaugh, G. (2008). Seven rules for social research. Princeton: Princeton University Press.
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.
Gottfredson, M. R., & Hirschi, T. (1990). A general theory of crime. Palo Alto, CA: Stanford University Press.
Hindelang, M. J. (1976). With a little help from their friends: group participation in reported delinquent behavior. British Journal of Criminology, 16, 109–125.
Hindelang, M. J. (1978). Race and involvement in common law personal crimes. American Sociological Review, 43, 93–109.
Hirschi, T., & Gottfredson, M. (1983). Age and the explanation of crime. American Journal of Sociology, 89(3), 552–584.
Irwin, J. (1985). The jail: Managing the underclass in American society. Berkeley, CA: University of California Press.
Kochel, T. R., Wilson, D. B., & Mastrofski, S. D. (2011). Effect of suspect race on officers’ arrest decisions. Criminology, 49(2), 473–512.
Koons-Witt, B., & Schram, P. J. (2003). The prevalence and nature of violent offending by females. Journal of Criminal Justice, 31(4), 361–371.
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.
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).
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.
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.
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.
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.
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.
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.
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.
Lytle, D. J. (2014). The effects of suspect characteristics on arrest: a meta-analysis. Journal of Criminal Justice, 42, 589–597.
Maxfield, M. G. (1999). The National Incident-Based Reporting System: research and policy applications. Journal of Quantitative Criminology, 15, 119–149.
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.
McGloin, J. M., & Nguyen, H. (2012). It was my idea: considering the instigation of co-offending. Criminology, 50, 463–494.
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
Morgan, S. L., & Winship, C. (2007). Counterfactuals and causal inference: methods and principles for social research. Cambridge: Cambridge University Press.
Morselli, C., Tremblay, P., & McCarthy, B. (2006). Mentors and criminal achievement. Criminology, 44, 17–43.
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.
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.
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.
Piliavin, I., & Briar, S. (1964). Police encounters with juveniles. American Journal of Sociology, 70, 206–214.
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.
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.
Powell, D. D. (1990). A study of policy discretion in six southern cities. Journal of Police Science and Administration, 17, 1–7.
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.
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.
Ricksheim, E., & Chermack, S. (1993). Causes of police behavior revisited. Journal of Criminal Justice, 21, 353–382.
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.
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.
Rojek, J., Rosenfeld, R., & Decker, S. (2012). Policing race: the racial stratification of searches in police traffic stops. Criminology, 50(4), 993–1024.
Rosich, K. J. (2007). Race, ethnicity, and the criminal justice system. Washington, D.C.: American Sociological Association.
Shannon, L. (1988). Criminal career continuity, it’s social context. New York: Human Sciences Press.
Sherman, L. (1980). Causes of police behavior: the current state of quantitative research. Journal of Research in Crime and Delinquency, 17, 69–100.
Skogan, W. G., & Frydll, K. (2004). Fairness and effectiveness in policing: the evidence. Washington, D.C.: National Research Council.
Smith, D., & Visher, C. (1981). Street level justice: situational determinants of police arrest decisions. Social Problems, 29, 167–178.
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.
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.
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.
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.
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.
Tillman, R. (1987). The size of the “criminal population,” the prevalence and incidence of adult arrest. Criminology, 25, 561–579.
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.
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.
Visher, C. A. (1983). Gender, police arrest decision, and notions of chivalry. Criminology, 21(1), 5–28.
Walker, S. (1993). Taming the system: the control of discretion in criminal justice (pp. 1950–1990). New York: Oxford University Press.
Weerman, F. M. (2014). Theories of co-offending. In Encyclopedia of criminology and criminal justice.
Weitzer, R., & Tuch, S. A. (2005). Racially biased policing: determinants of citizen perceptions. Social Forces, 83(3), 1009–1030.
Whitaker, G. P. (1982). What is patrol work? Police Studies, 4, 13–22.
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.
Winship, C., & Morgan, S. L. (2006). The estimation of causal effects from observational data. Annual Review of Sociology, 25, 659–706.
Withrow, B. (2006). Racial profiling: from rhetoric to reason. Upper Saddle River: Pearson Education.
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.
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.
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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