Journal of Quantitative Criminology 25(3)

Assessing the Impact of First-Time Imprisonment on Offenders’ Subsequent Criminal Career Development: A Matched Samples Comparison
Paul Nieuwbeerta, Daniel S. Nagin and Arjan A. J. Blokland

Using data from the Netherlands-based Criminal Career and Life-course Study the effect of first-time imprisonment between age 18–38 on the conviction rates in the 3 years immediately following the year of the imprisonment was examined. Unadjusted comparisons of those imprisoned and those not imprisoned will be biased because imprisonment is not meted out randomly. Selection processes will tend to make the imprisoned group disproportionately crime prone compared to the not imprisoned group. In this study group-based trajectory modeling was combined with risk set matching to balance a variety of measurable indicators of criminal propensity. Findings indicate that first-time imprisonment is associated with an increase in criminal activity in the 3 years following release. The effect of imprisonment is similar across offence types.

Measuring Long Term Individual Trajectories of Offending Using Multiple Methods
Shawn D. Bushway, Gary Sweeten and Paul Nieuwbeerta

Criminal career researchers and developmental criminologists have identified describing individual trajectories of offending over time as a key research question. In response, recently various statistical methods have been developed and used to describe individual offending patterns over the life-course. Two approaches that are prominent in the current literature are standard growth curve modeling (GCM) and group-based trajectory models (GTM). The goal of this paper is to explore ways in which these different models with different sets of assumptions, do in fact lead to different outcomes on individual trajectories. Using a particularly rich dataset, the criminal career and life-course study, we estimate a unique trajectory for each individual in the sample using the GCM and GTM. We also estimate separate trajectories for each individual directly using the long time series. We then compare these three separate trajectories for each individual. We find that the average trajectories obtained from the different approaches match each other. However, for any given individual, these approaches tell very different stories. For example, each method identifies a rather different set of individuals as desistors. This comparison highlights the strengths and weaknesses of each approach, and more broadly, it reveals the uncertainty involved with measuring long term patterns of change in latent propensity to commit crimes.

Crime is the Problem: Homicide, Acquisitive Crime, and Economic Conditions
Richard Rosenfeld

A question that emerges from recent research on the relationship between economic conditions and street crimes committed for monetary gain concerns the effect of changing economic conditions on violent crime. I propose that the economy stimulates violent crime indirectly through its effect on acquisitive crime. This hypothesis is evaluated in fixed-effects panel models of change in acquisitive crime and homicide rates between 1970 and 2006. The analysis indicates that collective perceptions of economic conditions have a significant effect on an index of acquisitive crime and an indirect effect, through acquisitive crime, on homicide. Consistent with this result, the effect of collective economic perceptions is stronger for felony than argument-related homicides. A promising focus for future research is the role of underground markets in the production of both property and violent crime.

Do US City Crime Rates Follow a National Trend? The Influence of Nationwide Conditions on Local Crime Patterns
David McDowall and Colin Loftin

This study considers the degree to which the crime rates of US cities follow a uniform national trend. A nationwide trend has consequences for theories that explain aggregate changes in crime, but how closely subnational units hold to a common time path has received almost no research attention. Using annual panel data, the current study presents analyses that attempt to measure the correspondence between city-level and national-level crime rates. The results of each analysis are consistent with a clear single pattern that operates across the nation’s major urban areas. This supports the idea that a meaningful national trend exists, and it suggests the desirability of continuing efforts to explain it.

Measuring and Modeling Repeat and Near-Repeat Burglary Effects
M. B. Short, M. R. D’Orsogna, P. J. Brantingham and G. E. Tita

We develop a mathematical framework aimed at analyzing repeat and near-repeat effects in crime data. Parsing burglary data from Long Beach, CA according to different counting methods, we determine the probability distribution functions for the time interval t between repeat offenses. We then compare these observed distributions to theoretically derived distributions in which the repeat effects are due solely to persistent risk heterogeneity. We find that risk heterogeneity alone cannot explain the observed distributions, while a form of event dependence (boosts) can. Using this information, we model repeat victimization as a series of random events, the likelihood of which changes each time an offense occurs. We are able to estimate typical time scales for repeat burglary events in Long Beach by fitting our data to this model. Computer simulations of this model using these observed parameters agree with the empirical data.

Journal of Quantitative Criminology, September 2009: Volume 25, Issue 3