Journal of Quantitative Criminology 25(4)

Modeling the Distribution of Sentence Length Decisions Under a Guidelines System: An Application of Quantile Regression Models
Chester L. Britt

How should sentencing disparity be assessed when decisions are constrained under a sentencing guidelines system? Much of the debate over the measurement of sentence disparity under a guidelines system has focused primarily on using specific values from within the sentencing grid (e.g., minimum recommended sentence) or on using interaction terms in regression models to capture the non-additive effects of offense severity and prior record on length of sentence. In this paper, I propose an alternative method for assessing sentencing disparity that uses quantile regression models. These models offer several advantages over traditional OLS analyses (and related linear models) of sentence length, by allowing for an examination of the effects of case and offender characteristics across the full distribution of sentence lengths for a given sample of offenders. The analysis of the distribution of sentence lengths with quantile regression models allows for an examination of questions such as: Do offender characteristics, such as race or offense severity, have the same effect on sentence length for the 10% of offenders who receive the shortest sentences as they do for the 10% of offenders who receive the longest sentences? I illustrate the application and interpretation of these models using 1998 sentencing data from Pennsylvania. Key findings show that the effects of case and offender characteristics are variable across the distribution of sentence lengths, meaning that traditional linear models assuming a constant effect fail to capture important differences in how case and offender characteristics affect punishment decisions. I discuss the implications of these findings for understanding sentencing disparitites, as well as other possible applications of quantile regression models in the study of crime and the criminal justice system.

Disentangling the Crime-arrest Relationship: The Influence of Social Context
Mitchell B. Chamlin, Andrew J. Myer

Drawing on the economic and conflict perspectives of crime control, as well as insights from the tipping effect literature, the present investigation examines the extent to which the social context within which potential offenders operate tempers the macro-level, reciprocal relationship between crime and arrests. We use autoregressive integrated moving average techniques to assess the extent to which the April 2001 race-related riot in Cincinnati, Ohio conditions the reciprocal relationship between property crime and arrests for the entire city and disaggregated by police district. Consistent with a majority of prior longitudinal studies, our analyses for the entire length of the times series reveal no evidence of an association between our measures of crime and arrest, regardless of the level of spatial aggregation. In contrast to the results from our baseline models, the post-riot transfer function models indicate that there is a reciprocal association between crime and arrests that is contingent upon the social context. The implications of our findings for the further study of the reciprocal relationship between crime and arrests are discussed.

How Much Can We Trust Causal Interpretations of Fixed-Effects Estimators in the Context of Criminality?
David Bjerk

Researchers are often interested in estimating the causal effect of some treatment on individual criminality. For example, two recent relatively prominent papers have attempted to estimate the respective direct effects of marriage and gang participation on individual criminal activity. One difficulty to overcome is that the treatment is often largely the product of individual choice. This issue can cloud causal interpretations of correlations between the treatment and criminality since those choosing the treatment (e.g. marriage or gang membership) may have differed in their criminality from those who did not even in the absence of the treatment. To overcome this potential for selection bias researchers have often used various forms of individual fixed-effects estimators. While such fixed-effects estimators may be an improvement on basic cross-sectional methods, they are still quite limited when it comes to uncovering a true causal effect of the treatment on individual criminality because they may fail to account for the possibility of dynamic selection. Using data from the NSLY97, I show that such dynamic selection can potentially be quite large when it comes to criminality, and may even be exacerbated when using more advanced fixed-effects methods such as Inverse Probability of Treatment Weighting (IPTW). Therefore substantial care must be taken when it comes to interpreting the results arising from fixed-effects methods.

Detecting Specialization in Offending: Comparing Analytic Approaches
Christopher J. Sullivan, Jean Marie McGloin, James V. Ray, Michael S. Caudy

Offending specialization continues to be a subject of empirical inquiry for scholars interested in criminal careers. Early research consistently spoke to the generality of offending profiles, but more recent work has revealed somewhat mixed findings. These results have emerged alongside newly developed and applied methods that detect and describe offending specialization. To what extent these methods shape divergent conclusions and/or provide overlapping insight remains unclear, however. Therefore, the degree to which more recent inquiries are actually studying the same operational definition of specialization is unknown. In order to consider this issue further, this study utilizes four frequently applied approaches with a single data set. The study indicates when and where findings converge and also describes any unique insights provided by each method. The work concludes with a discussion surrounding the utility of applying multiple strategies in assessing specialization in criminal offending.

Hot Spots of Juvenile Crime: A Longitudinal Study of Arrest Incidents at Street Segments in Seattle, Washington
David Weisburd, Nancy A. Morris, Elizabeth R. Groff

Recent studies have shown that crime is concentrated at micro level units of geography defined as hot spots. Despite this growing evidence of the concentration of crime at place, studies to date have dealt primarily with adult crime or have failed to distinguish between adult and juvenile offenses. In this paper, we identify crime incidents in which a juvenile was arrested at street segments in Seattle, Washington, over a 14-year period, to assess the extent to which officially recorded juvenile crime is concentrated at hot spots. Using group-based trajectory analysis, we also assess the stability and variability of crime at street segments over the period of the study. Our findings suggest that officially recorded juvenile crime is strongly concentrated. Indeed, just 86 street segments in Seattle include one-third of crime incidents in which a juvenile was arrested during the study period. While we do observe variability over time in trajectories identified in the study, we also find that high rate juvenile crime street segments remain relatively stable across the 14 years examined. Finally, confirming the importance of routine activity theory in understanding the concentration of juvenile crime in hot spots, we find a strong connection between high rate trajectory groups and places likely to be a part of juvenile activity spaces. Though place-based crime prevention has not been a major focus of delinquency prevention, our work suggests that it may be an area with great promise.

Journal of Quantitative Criminology, December 2009: Volume 25, Issue 4