Journal of Quantitative Criminology 26(3)

Impulsivity, Offending, and the Neighborhood: Investigating the Person–Context Nexus
Gregory M. Zimmerman

The traditional trait-based approach to the study of crime has been challenged for its failure to acknowledge differences in the social environments to which individuals are exposed. Similarly, community-level explanations of crime have been criticized for failing to take into account important individual differences between criminals and non-criminals. Ultimately, a full understanding of crime requires the consideration of both individual and environmental differences, perhaps most importantly because they may interact to produce offending behavior. Yet little criminological research has examined if the effects of individual-level characteristics vary by the context in which they are embedded. The current study addresses this gap in the literature by using multivariate, multilevel item response models to examine if the influence of impulsivity on offending differs as a function of neighborhood context. Analyses using data from the Project of Human Development in Chicago Neighborhoods reveals that the effects of impulsivity are amplified in neighborhoods with higher levels of socioeconomic status and collective efficacy, and lower levels of criminogenic behavior settings and moral/legal cynicism. Implications of these findings for research and policy are discussed.

Digital Analysis of Crime Statistics: Does Crime Conform to Benford’s Law?
Matthew J. Hickman & Stephen K. Rice

Benford’s law suggests that the distribution of leading (leftmost) digits in data of an anomalous nature (i.e., without relationship) will conform to a formula of logarithmic intervals known as the Benford distribution. Forensic auditors have successfully used digital analysis vis-à-vis the Benford distribution to detect financial fraud, while government investigators have used a corollary of the distribution (focused on trailing digits) to detect scientific fraud in medical research. This study explored whether crime statistics are Benford distributed. We examined crime statistics at the National, State, and local level in order to test for conformity to the Benford distribution, and found that National- and State-level summary UCR data conform to Benford’s law. When National data were disaggregated by offense type we found varying degrees of conformity, with murder, rape, and robbery indicating less conformity than other offense types. Some tentative implications of these findings are discussed, as are areas for further research.

Violent Crime, Residential Instability and Mobility: Does the Relationship Differ in Minority Neighborhoods?
Lyndsay N. Boggess & John R. Hipp

This study examines the reciprocal relationship between violent crime and residential stability in neighborhoods. We test whether the form of stability matters by comparing two different measures of stability: a traditional index of residential stability and a novel approach focusing specifically on the stability of homeowners. We also examine whether the racial/ethnic composition of the neighborhood in which this stability occurs affects the instability—violent crime relationship. To test the simultaneous relationship between residential mobility and crime we estimate a dual multivariate latent curve model of the change in the violent crime rate and the change in the rate of home sales while controlling for neighborhood socioeconomic and demographic characteristics using data from Los Angeles between 1992 and 1997. Results indicate that the initial level of violent crime increases the trajectory of residential instability in subsequent years, whether the instability is measured as homeowner turnover specifically, or based on an index of all residents. However, the effect of instability on violent crime is only apparent when measuring instability based on an index of general residential turnover and not when including the presence of owners in this measure, or when measuring it based on homeowner turnover. We consistently find that stable highly Latino communities exhibit a protective effect against violence.

When does the Apple Fall from the Tree? Static Versus Dynamic Theories Predicting Intergenerational Transmission of Convictions
Marieke Van de Rakt, Stijn Ruiter, Nan Dirk De Graaf & Paul Nieuwbeerta

Criminal behavior of parents substantially affects the criminal behavior of children. Little is known, however, about how crime is transmitted from one generation to the next. In order to test two possible explanations against each other, we pose the question whether the timing of the criminal acts of fathers is important for children’s chances of committing crime. Static theories predict that it is the number of delinquent acts performed by fathers that is important, and that the particular timing does not affect the child’s chance of committing crime. Dynamic theories state that the timing is important, and children have a greater chance of committing crime in the period after fathers have committed delinquent acts. Results show that the total number of convictions of a father is indeed very important, but also the exact timing is key to understanding intergenerational transmission of crime. In the year a father is convicted the chance his child is also convicted increases substantially and it decays in subsequent years. This decay takes longer the more crimes father has committed. Our results show that some of the assumptions of the static theories at least need to be adjusted.

Estimating Treatment Effects and Predicting Recidivism for Community Supervision Using Survival Analysis with Instrumental Variables
William Rhodes

Criminal justice researchers often seek to predict criminal recidivism and to estimate treatment effects for community corrections programs. Although random assignment provides a desirable avenue to estimating treatment effects, often estimation must be based on observational data from operating corrections programs. Using observational data raises the risk of selection bias. In the community corrections contexts, researchers can sometimes use judges as instrumental variables. However, the use of instrumental variable estimation is complicated for nonlinear models, and when studying criminal recidivism, researchers often choose to use survival models, which are nonlinear given right-hand-censoring or competing events. This paper discusses a procedure for estimating survival models with judges as instruments. It discusses strengths and weaknesses of this approach and demonstrates some of the estimation properties with a computer simulation. Although this paper’s focus is narrow, its implications are broad. A conclusion argues that instrumental variable estimation is valuable for a broad range of topics both within and outside of criminal justice.

Journal of Quantitative Criminology, September 2010: Volume 26, Issue 3