Advertisement

Journal of Quantitative Criminology

, Volume 12, Issue 2, pp 163–191 | Cite as

Micro-models of criminal careers: A synthesis of the criminal careers and life course approaches via semiparametric mixed poisson regression models, with empirical applications

  • Kenneth C. Land
  • Daniel S. Nagin
Article

Abstract

Much recent research and debate in criminology have centered around how to conceptualize and model longitudinal sequences of delinquent and criminal acts committed by individuals. Two approaches dominate this controversy. One originates in thecriminal careers paradigm, which emphasizes a potentialheterogeneity of offending groups in the general population—thus leading to a distinction between incidence and prevalence of criminal offending, a focus on the onset, persistence, and desistence of criminal careers, and the possibility that criminals are a distinctive group with constant high rates of offending. Another approach places criminal events within a broader context ofstudies of the life course by explicitly substituting the conceptualization of “social events” for that of “criminal careers”. With respect to analytical models, this approach emphasizes a potentialheterogeneity of offenders with respect to order of criminal events from first to second to higher orders and thus suggests an analysis of the “risks” or “hazards” of offending by order of offense. Some extant commentaries on the criminal careers and life course approaches to conceptualizing and modeling longitudinal sequences of delinquent and criminal events committed by individuals have emphasized their differences and incompatibilities. In contrast, we apply recently developed semiparametric mixed Poisson regression techniques to develop conditions under which the two conceptual/modeling approaches are formally equivalent. We also modify the semiparametric mixed Poisson regression model of criminal careers to incorporate information on order of the delinquent/criminal event and develop an empirical application. This modification demonstrates the complementarity of the criminal careers and life course approaches, even though they have somewhat different foci.

Key Words

criminal careers studies of the life course Poisson models hazards models 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Allison, P. D. (1982). Discrete-time methods for the analysis of event histories.Sociol. Methodol. 1982: 61–98.Google Scholar
  2. Allison, P. D. (1984).Event History Analysis, Sage, Beverly Hills, CA.Google Scholar
  3. Aptech Systems (1992).GAUSS 3.0 Systems and Applications Manual, Aptech Systems, Inc., Maple Valley, WA.Google Scholar
  4. Avi-Itzhak, B., and Shinnar, R. (1973). Quantitative models in crime control.J. Crim. Just. 1: 185–217.Google Scholar
  5. Barnett, A., Blumstein, A., and Farrington, D. P. (1987). Probabilistic models of youthful criminal careers.Criminology 25: 83–108.CrossRefGoogle Scholar
  6. Barnett, A., Blumstein, A., and Farrington, D. P. (1989). A prospective test of a criminal career model.Criminology 27: 373–385.CrossRefGoogle Scholar
  7. Blumstein, A., and Cohen, J. (1979) Estimation of individual crime rates from arrest records.J. Crim. Law Criminol. 70: 561–585.Google Scholar
  8. Blumstein, A., Cohen, J., Roth, J. A., and Visher, C. A. (eds.) (1986).Criminal Justice and “Career Criminals”, 2 vols, National Academy Press, Washington, DC.Google Scholar
  9. Blumstein, A., Cohen, J., and Farrington, D. P. (1988a). Criminal career research: Its value for criminology.Criminology 26: 1–36.Google Scholar
  10. Blumstein, A., Cohen, J., and Farrington, D. P. (1988b) Longitudinal and criminal career research: Further clarifications.Criminology 26: 57–74.Google Scholar
  11. Cox, D. R. (1972). Regression models and life tables (with discussion).J. Roy. Stat. Soc. Ser. B 34: 187–220.Google Scholar
  12. Elder, G. H., Jr. (1985)Life Course Dynamics, Cornell University Press, Ithaca, NY.Google Scholar
  13. Farrington, D. P. (1986). Age and crime. In Tonry, M., and Morris, N. (eds.),Crime and Justice: An Annual Review of Research, Vol. 7, University of Chicago Press, Chicago.Google Scholar
  14. Farrington, D. P., Loeber, R., Elliott, D. S., Hawkins, J. D., Kandel, D. B., Klein, M. W., McCord, J., Rowe, D. C., and Tremblay, R. E. (1990). Advancing knowledge about the onset of delinquency and crime In Lahey B., and Kazdin, A. E. (eds.)Advances in Clinical Child Psychology, Vol. 13, Plenum, New York.Google Scholar
  15. Glueck, S., and Glueck, E. (1937).Later Criminal Careers, Commonwealth Fund New York.Google Scholar
  16. Glueck, S., and Glueck, E. (1940).Juvenile Delinquents Grown Up, Commonwealth Fund, New York.Google Scholar
  17. Gottfredson, M., and Hirschi, T. (1988). Science, public policy, and the career paradigm.Criminology 26: 37–56.CrossRefGoogle Scholar
  18. Gottfredson, M., and Hirschi, T. (1990).A General Theory of Crime, Stanford University Press, Stanford, CA.Google Scholar
  19. Gourieroux, C., Monfort, A., and Trognon, A. (1984a). Pseudo maximum likelihood methods: Theory.Econometrica 52: 681–700.Google Scholar
  20. Gourieroux, C., Monfort, A., and Trognon, A. (1984b). Pseudo maximum likelihood methods: Applications to Poisson models.Econometrica 52: 701–720.Google Scholar
  21. Greenberg, D. F. (1991). Modeling criminal careers.Criminology 29: 17–46.CrossRefGoogle Scholar
  22. Greene, W. H. (1992).LIMDEP: User's Manual and Reference Guide, Econometric Software, Inc., New York.Google Scholar
  23. Hagan, J., and Palloni, A. (1988). Crimes as social events in the life course: Reconceiving a criminal controversy.Criminology 26: 87–100.CrossRefGoogle Scholar
  24. Hausman, J., Hall, B. H., and Griliches, Z. (1984). Econometric models for count data with an application to the patents-r&d relationship.Econometrica 52: 909–938.Google Scholar
  25. Heckman, J. J. (1984a). A method for minimizing the impact of distributional assumptions in econometric models for duration data.Econometrica 52: 271–320.Google Scholar
  26. Heckman, J. J. (1984b). Econometric duration analysis.J. Econometr. 24: 63–132.Google Scholar
  27. Heckman, J. J., and Singer, B. (1982). Population heterogeneity in demographic models. In Land, K. C., and Rogers, A. (eds.),Multidimensional Mathematical Demography, Academic Press, New York, pp. 567–599.Google Scholar
  28. Kalbfleisch, J., and Prentice, R. (1980).The Statistical Analysis of Failure Time Data, Wiley, New York.Google Scholar
  29. Laird, N., and Olivier, D. (1981). Covariance analysis of censored survival data using loglinear analysis techniques.J. Am. Stat. Assoc. 76: 231–240.Google Scholar
  30. Land, K. C. (1992). Models of criminal careers: Some suggestions for moving beyond the current debate.Criminology 30: 149–155.CrossRefGoogle Scholar
  31. Land, K. C., McCall, P. L., and Nagin, D. S. (1996). A comparison of Poisson, negative binomial, and semiparametric mixed Poisson regression models, with empirical applications to criminal careers data.Sociol. Methods. Res. 24: 387–442.Google Scholar
  32. Laub, J. H., and Sampson, R. J. (1991). The Sutherland-Glueck debate: On the sociology of criminological knowledge.Am. J. Sociol. 96: 1402–1440.CrossRefGoogle Scholar
  33. Manton, K. G., and Stallard, E. (1981). Methods for the analysis of mortality risks across heterogeneous small population: Examination of space-time gradients in cancer mortality in North Carolina Counties 1970–75.Demography 18: 217–230.Google Scholar
  34. Moffitt, T. E. (1993). Adolescent-limited and life-course persistent antisocial behavior: A developmental taxonomy.Psychol. Rev. 100: 674–701.Google Scholar
  35. McCullagh, P., and Nelder, J. A. (1989).Generalized Linear Models, 2nd ed., Chapman and Hall, New York.Google Scholar
  36. Nagin, D. S., and Farrington, D. P. (1992a). The onset and persistence of offending.Criminology 30: 501–523.CrossRefGoogle Scholar
  37. Nagin, D. S., and Farrington, D. P. (1992b). The stability of criminal potential from childhood to adulthood.Criminology 30: 235–260.CrossRefGoogle Scholar
  38. Nagin, D. S., and Land, K. C. (1993). Age, criminal careers, and population heterogeneity: Specification and estimation of a nonparametric, mixed Poisson model.Criminology 31: 327–362.CrossRefGoogle Scholar
  39. Nagin, D. S., and Paternoster, R. (1991). On the relationship of past to future delinquency.Criminology 29: 163–189.Google Scholar
  40. Nagin, D. S., Farrington, D. P., and Moffitt, T. E. (1995). Life course trajectories of different types of offenders.Criminology 33: 111–137.CrossRefGoogle Scholar
  41. Polakowski, M. (1990).Criminality, Social Control, and Deviance in a Life-Course Analysis, Unpublished Ph.D. dissertation, University of Wisconsin-Madison.Google Scholar
  42. Polakowski, M. (1994). Social and self control, life-course events, and crime: A hazard analysis of criminal convictions, Unpublished manuscript, School of Public Administration and Policy, University of Arizona, Tuscon, AZ 85721. Presented at the annual meeting of the American Society of Criminology, Miami, FL.Google Scholar
  43. Rowe, D. C., Osgood, D. W., and Nicewander, W. A. (1990). A latent trait approach to unifying criminal careers.Criminology 28: 237–270.CrossRefGoogle Scholar
  44. Tontodonato, P. (1988). Explaining rate changes in delinquent arrest transitions using event history analysis.Criminology, 26: 439–460.CrossRefGoogle Scholar
  45. Tracy, P. E., Wolfgang, M. E., and Figlio, R. M. (1990).Delinquency in Two Birth Cohorts, Plenum, New York.Google Scholar
  46. Trussell, J., and Hammerslough, C. (1983). A hazards-model analysis of the covariates of infant and child mortality in Sri Lanka.Demography 20: 1–26.Google Scholar
  47. Tuma, N. B., and Hannan, M. T. (1984).Social Dynamics: Models and Methods, Academic Press, New York.Google Scholar
  48. West, D. J., and Farrington, D. P. (1973).Who Becomes Delinquent? Heinemann, London.Google Scholar
  49. West, D. J., and Farrington, D. P. (1977).The Delinquent Way of Life, Heinemann, London.Google Scholar
  50. Yamaguchi, K. (1991).Event History Analysis, Sage, Newbury Park, CA.Google Scholar

Copyright information

© Plenum Publishing Corporation 1996

Authors and Affiliations

  • Kenneth C. Land
    • 1
  • Daniel S. Nagin
    • 2
  1. 1.Department of SociologyDuke UniversityDurham
  2. 2.Heinz School of Public Policy and ManagementCarnegie-Mellon UniversityPittsburgh

Personalised recommendations