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Criminal Specialization Revisited: A Simultaneous Quantile Regression Approach

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Abstract

Whether criminals are specialized or versatile in their offending is a long-standing research area that has been recently revitalized by a paradigm that recognizes that both specialization and versatility characterize offending careers. Based on data from an enriched sample of 500 adult habitual criminals, the current study introduces a measure of relative specialization—the offense specialization coefficient—and a novel analytical technique called simultaneous quantile regression to further the study of specialization. Although offenders committed a mix of offenses, there was considerable and at times pronounced evidence of specialization. Age, sex, and arrest onset had differential predictive validity of specialization for eight crimes at the 75th and 95th quantiles. Implications and suggestions for future research are offered.

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Notes

  1. There might be important cross-cultural differences between juvenile sex offenders and other serious nonsex juvenile offenders. For instance, a similar study using data from juvenile offenders in The Netherlands found multiple differences between juvenile sex offenders and nonsex offenders in terms of problem behaviors, personality traits, and demographic characteristics (van Wijk et al. 2005b). Lussier et al. (2005a) also found evidence of specialization among convicted child molesters using data from offenders in Canada.

  2. The substantive basis that offenders specialize in violence has been questioned. Britt (1994) indicated that only 20 violent specialists existed in the Danish cohort of nearly 29,000 males (Brennan et al. 1989). Similarly, Piquero (2000) found no evidence of violence specialization based on data from the Philadelphia Collaborative Perinatal Project (also see, Piquero and Buka 2002; Piquero et al. 2007).

  3. Although it is outside the purview of mainstream criminological research on specialization, there is ample evidence of specialization or compulsive, repetitive antisocial behavior in psychiatry (American Psychiatric Association 2000). These include what is effectively clinical specialization in arson (pyromania), assault (intermittent explosive disorder), larceny (kleptomania), and even extraordinarily specific forms of crime, such as assaultive eye injury and enucleation (Bukhanovsky et al. 1999).

  4. The use of both official and self-reported data is important because prior research found that specialization is evident from self-reports but not official records based on analyses of data from the Dunedin Multidisciplinary Health and Development Study (Lynam et al. 2004).

  5. One reason for such divergent findings on specialization is that diverse methods and measures have been used. As noted by Farrington (1986, p. 225), there is no standard measure of specialization. Prior studies have used a host of measures (e.g., the diversity index, forward specialization coefficient, and transition matrices) and analytical approaches (e.g., multinomial logit regression, marginal logit modeling, Tobit regression with Cragg specification, probit regression, truncated regression, and log linear models) (Armstrong and Britt 2004; Britt 1996; Deane et al. 2005; Paternoster et al. 1998; Sullivan et al. 2006) yet no definitive measure or analytical technique has emerged (Sullivan et al. 2009).

  6. It could be argued that the OSC is biased by the total number of offenses and since the current study utilized offenders with a minimum of 30 arrests, there was greater opportunity to specialize. We view the large arrest criterion as a strength compared to offenders with fewer arrests. For instance, claiming theft-specialization for an offender with five career arrests (two of which are for theft) is less compelling than evidence of specialization among an offender with 30 arrests. Also, the eight crimes of interest were selected for two reasons, one substantive and one statistical. First, they represent a cross-section of violent, property, white-collar, traffic, and public-order offenses which maximizes experimental variance. Second, there was inadequate statistical power (e.g., convergence was not achieved) to execute simultaneous quantile regression models for other offenses, such as murder, rape, arson, kidnapping, receiving stolen property, sex offenses, and embezzlement.

  7. Although commonly used in econometric research, quantile regression models are beginning to be used in criminology. Recently, Britt (2009) used quantile regression models to demonstrate that case- and offender-specific variables have differential predictive validity across sentencing quantiles based on sentencing data from Pennsylvania.

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DeLisi, M., Beaver, K.M., Wright, K.A. et al. Criminal Specialization Revisited: A Simultaneous Quantile Regression Approach. Am J Crim Just 36, 73–92 (2011). https://doi.org/10.1007/s12103-010-9083-1

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