The Path of Least Desistance: Inmate Compliance and Recidivism

Abstract

Scholars have speculated that inmate behavior may provide a signal about the probability of desistance. One such signal may be the successful avoidance of prison infractions or the cessation of them during the course of incarceration. Drawing on studies of prison socialization, recidivism, and desistance, we assess whether patterns of inmate misconduct throughout the course of incarceration provide insight into the likelihood of a successful transition back into society. Specifically, using data on a cohort of state prisoners, this study examines whether, after controlling for potential confounders, inmate misconduct trajectories predict recidivism. The analyses indicate both that unique misconduct trajectories can be identified and that these trajectories predict the probability of recidivism and desistance net of factors associated with recidivism. Results of the study lend support to scholarship on desistance and signaling, which emphasizes the salience of in-prison experiences for understanding reentry and, in particular, reoffending.

Keywords:

• recidivism
• desistance
• inmate misconduct
• prison experiences
• signaling

Acknowledgments

We thank Joseph Schwartz and William Bales for their valuable assistance and guidance with the trajectory modeling analyses and data file, respectively. We also thank Elisa Toman and the three anonymous reviewers for providing feedback on the paper, along with the Florida Department of Corrections for permission to use their data. The views expressed here are those of the authors and do not reflect those of the Department of Corrections.

Notes

¹ Prior studies that have compared results when using formal records of misconduct vs. self-reported inmate misconduct have identified similar findings (Simon, 1993; Steiner & Wooldredge, 2014; Reisig & Mesko, 2009).

² In ancillary analyses, we conducted the same multivariate analyses using a 4-group specification; the results mirrored those presented below, but necessarily provided no information about the fifth group.

³ One of the anonymous reviewers suggested that we assess the robustness of these results by analyzing misconduct trajectories by type of misconduct. We conducted a series of ancillary analyses to address this question, which involved differentiating between violent and non-violent misconduct. The analyses could not support identification of violent-only misconduct trajectories; there were too few misconduct events in any given time period, which led to unstable estimation. We were, however, able to analyze non-violent misconduct trajectories and results of the trajectory modeling and subsequent multivariate regression models predicting recidivism were substantively similar to those presented here. We thank the reviewer for the suggestion.

Additional information

Notes on contributors

Joshua C. Cochran

Joshua C. Cochran, PhD, is an Assistant Professor at the University of Cincinnati's School of Criminal Justice, email ([email protected]). His research interests include criminological theory, imprisonment, prisoner reentry, and sentencing. His work has appeared in Criminology, the Journal of Quantitative Criminology, Justice Quarterly, the Journal of Research in Crime and Delinquency and, with Daniel P. Mears, Prisoner Reentry in the Era of Mass Incarceration (Sage).

Daniel P. Mears

Daniel P. Mears, PhD, is the Mark C. Stafford Professor of Criminology at Florida State University’s College of Criminology and Criminal Justice, 634 West Call Street, Tallahassee, FL 32306-1127, phone [phone]), fax [phone]), e-mail ([email protected]). He conducts basic and applied research, and his work has appeared in leading crime and policy journals and American Criminal Justice Policy (Cambridge University Press), which received the Academy of Criminal Justice Sciences Outstanding Book Award, and, with Joshua C. Cochran, Prisoner Reentry in the Era of Mass Incarceration (Sage).