PROBABILISTIC MODELS OF YOUTHFUL CRIMINAL CAREERS

Authors


  • Arnold Barnett is Professor of Operations Research at MIT's Sloan School of Management. His research specialty is applied probabilistic modeling, with much of his work stimulated by two prime passions of his life: fear of flying and fear of crime.

  • Alfred Blumstein is J. Erik Jonsson Professor of Urban Systems and Operations Research and Dean of the School of Urban and Public Affairs at Carnegie Mellon University. He has chaired the National Academy of Sciences Committee on Research on Law Enforcement and the Administration of Justice and has chaired the committee's panels on Research on Deterrent and Incapacitative Effects, on Sentencing Research, and on Criminal Careers.

  • David P. Farrington is a lecturer in criminology at Cambridge University, England. His major research interest is in the longitudinal study of delinquency and crime, and he is director of the Cambridge Study in Delinquent Development.

Abstract

This paper focuses on the characterization of the criminal careers of youthful offenders. It was found that these criminal careers could be modeled with parameters rejecting constant individual rates of offending and constant probability of career termination; population heterogeneity could be adequately represented by two distinct groups—designated here as “frequents” and occasionals.” These parameters were estimated for the multiple offenders in a London cohort studied from their first convictions until age 25. In that cohort, the frequents were estimated to have an annual conviction rate of 1.14 convictions per year (constant with age) and a probability of career termination of .10 following each conviction; the occasionals had an annual conviction rate of .41 and termination probability of .33 following each conviction; the frequents were estimated to comprise 43% of the population, and the occasionals the others 57%. While this parsimonious model structure was adequate for the London cohort, it must still be tested with other offender populations.

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