Journal of Quantitative Criminology

, Volume 14, Issue 3, pp 283–305 | Cite as

Incorporating Co-offending in Sentencing Models: An Analysis of Fines Imposed on Antitrust Offenders

  • Elin J. Waring


Analyses of sentencing (and other criminal justice processes such as the decision to prosecute, plea bargaining, and contact with the police) often use the isolated individual as the unit of analysis. However, the criminal justice system often processes either offenses or court cases rather than persons. If court cases always involved one individual, this would have little impact. However, offenses involving co-offending—two or more persons acting together—comprise a substantial proportion of criminal activity (Reiss, 1980, 1986). Depending on the prevalence of co-offending, it may be very likely that two or more individuals involved in the same case will be selected as members of the same sample of criminal justice or criminological data. Unless it can be shown that both the individual-level variables of co-offenders and their error terms are mutually independent, analyses based on methods such as ordinary least-squares multiple regression would violate the underlying assumptions of such models. However, alternatives to linear models assuming either type of independence are available. Among the most useful of these are mixed models, specifically those assuming compound symmetry. This is illustrated with an analysis of fines imposed on criminally convicted antitrust offenders. These models may yield results which are substantially different than those from models which ignore co-offending. In a model of fines imposed on antitrust offenders, models which ignore co-offending generally overstate both estimates and statistical significance of offense-level variables and understate those of offender-level variables.

co-offending sentencing mixed models antitrust fines 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. Bryk, A., and Raudenbush, S. (1992). Hierarchical Linear Models: Applications and Data Analysis Methods, Advanced Quantitative Techniques in the Social Sciences, Sage, Newbury Park, CA.Google Scholar
  2. Doreian, P. (1992 [1989]). Models of network effects on social actors. In Freeman, L. C., White, D., and Romney, A. K. (eds.), Research Methods in Social Network Analysis, Transaction, New Brunswick, NJ, pp. 295-318.Google Scholar
  3. Greenberg, D. (1991). Modeling criminal careers. Criminology 29(1): 17-46.Google Scholar
  4. Kennedy, P. (1992). A Guide to Econometrics, 3rd ed., MIT Press, Cambridge, MA.Google Scholar
  5. Kreft, I. G. G., DeLeeuw, J., and van der Leeden, R. (1994). Review of five multilevel analysis programs: BMDP-5V, GENMOD, HLM, ML3, VARCL. Am. Stat. 4(48): 324-335.Google Scholar
  6. Littell, R., Milliken, G., Stroup, W., and Wolfinger, R. (1996). SAS System for Mixed Models, SAS Institute, Cary, NC.Google Scholar
  7. Nardulli, P., Eisenstein, J., and Fleming, R. (undated). Comparing Case Processing in Nine Courts, 1979-1980 Codebook, Interuniversity Consortium for Political and Social Research, Ann Arbor, MI.Google Scholar
  8. Neter, J., William Wasserman, W., and Kutner, M. H. (1989). Applied Linear Regression Models, 2nd ed., Richard D. Irwin, Boston.Google Scholar
  9. Reiss, A. J., Jr. (1980). Understanding changes in crime rates. In Fienberg, S., and Reiss, A. J. (eds.), Indicators of Crime and Justice: Quantitative Studies, U.S. Department of Justice, Bureau of Justice Statistics, Washington, DC.Google Scholar
  10. Reiss, A. J., Jr. (1986). Co-offending influences on criminal careers. In Blumstein, A., Cohen, J., Roth, J., and Visher, C. (eds.), Criminal Careers and Career Criminals, National Academy Press, Washington, DC.Google Scholar
  11. Reiss, A. J., Jr. (1988). Co-offending and criminal careers. In Tonry, M., and Morris, N. (eds.), Crime and Justice: A Review of Research, University of Chicago Press, Chicago.Google Scholar
  12. Reiss, A. J., and Farrington, D. (1991). Advancing knowledge about co-offending: Results from a prospective longitudinal survey of London males. J. Crim. Law Criminol. 82: 360-395.Google Scholar
  13. Rothman, M. (1982). The Criminaloid Revisited, University Microfilms International, Ann Arbor, MI.Google Scholar
  14. Sarnecki, J. (1986). Delinquent Networks, National Council for Crime Prevention, Stockholm.Google Scholar
  15. Searle, S., Casella, G., and McCulloch, C. (1992). Variance Components, John Wiley and Sons, New York.Google Scholar
  16. Waring, E. (1993). Co-offending in White Collar Crime: A Network Approach, University Microfilms International, Ann Arbor, MI.Google Scholar
  17. Wasserman, S., and Faust, K. (1994). Social Network Analysis, Cambridge University Press, New York.Google Scholar
  18. Weisburd, D., Wheeler, S., Waring, E., and Bode, N. (1991). Crimes of the Middle Classes, Yale University Press, New Haven, CT.Google Scholar
  19. Wheeler, S., Weisburd, D., and Bode, N. (1988). Study of Convicted Federal White-Collar Crime Defendants, National Archives of Criminal Justice Data, Interuniversity Consortium for Political and Social Research, Ann Arbor, MI.Google Scholar

Copyright information

© Plenum Publishing Corporation 1998

Authors and Affiliations

  • Elin J. Waring
    • 1
    • 2
  1. 1.Department of Sociology and Social WorkLehman CollegeUSA
  2. 2.Department of Sociology, Graduate SchoolCity University of New YorkBrony

Personalised recommendations