Prison Legitimacy and Procedural Fairness: A Multilevel Examination of Prisoners in England and Wales

Abstract

The procedural justice model has been widely used as an explanation for understanding legitimacy and compliance with the law, particularly within the context of policing. Central to this model is the importance of procedural fairness—in which the treatment of citizens and offenders by criminal justice agents can play a key role in building legitimacy and influencing compliance with legal rules and values. This paper examines the relationship between procedural fairness and legitimacy within the context of corrections. Drawing on data from a longitudinal survey of more than 3,000 prisoners across England and Wales, we identify an important link between procedural fairness and prisoner perceptions of legitimacy. We further examine variations in legitimacy in terms of individual prisoner characteristics, conditions within prison, as well as differences between prisons.

Keywords:

• prison legitimacy
• procedural justice
• multilevel

Disclosure statement

No potential conflict of interest was reported by the authors.

Notes

¹ Specifically, they suggest that their measures of legitimacy may be capturing coercive obedience, rather than obedience with prison rules because it is the right thing to do.

² The original sample includes a total of 737 prisoners serving particularly short sentences, however, these prisoners were only interviewed on arrival to prison so are not included in the current analysis. Data from one prisoner incarcerated within an immigration center were also omitted because of a lack of suitable prison-level data. This leaves an analytic sample of 3,111 prisoners.

³ The survey is comprised of a core sample of 1,435 prisoners sentenced to between 1 months and 4 years in prison, and an additional sample of 2,014 prisoners serving sentences of between 18 months and 4 years. All analyses reported here were also conducted for each subsample separately, with all effects operating in the same direction in each model. We therefore include all data simultaneously, with a control for sample type in the models.

⁴ Basic descriptive details of prisoner responses to each question can be found in Hopkins and Brunton-Smith (2014).

⁵ Items were selected based on earlier exploratory work by Liebling (2004) and were intended to represent the character of the prison regime. Data confidentiality issues mean that we only had access to an overall summary measure for each prison and not the individual survey data.

⁶ All prisoners enter prison on standard regime. Prisoners may be transferred to basic or enhanced regime based on behavior. Prisoners on basic regime generally have a lower volume of allowances and privileges. Prisoners on enhanced regime have a greater level of privilege, for example, more visits with greater flexibility over timings.

⁷ Technically, gender might be better conceived of as a prison-level attribute, as all female prisoners in England and Wales are housed separately from male offenders. However, in some instances (for example, HMP Peterborough), this is on the same prison estate as male offenders; therefore, gender is included at the individual level in our model.

⁸ Based on Copas and Marshall (1998), this details the rate at which offenders have built up convictions throughout their offending career, with higher scores given to offenders that have received more convictions in a given amount of time. This is calculated as: ln(court appearances + cautions) + 1/Career length + 10.

⁹ Adult male prisoners are given a security categorization from A to D when entering prison, based upon factors including offense committed, level of risk, length of sentence, and chances of escape, with category A being for serious offenders who pose a high risk to the public and D being the lowest risk to the public and least likely to escape.

¹⁰ Also variously referred to as hierarchical models, mixed effects models, random effects models, or nested models.

¹¹ The 10 imputed data-sets were generated using REALCOM-impute with a burn-in of 1,000 draws from the posterior conditional distribution. Every 1,000th imputation was saved to ensure that the imputed data from each saved draw were independent of the previous one.

¹² Following the methodology outlined in Brunton-Smith et al. (2014), the following auxiliary variables were included “early release from prison,” “difficult to access prison,” “high-refusal prison,” “sentenced for burglary.”

¹³ Additional models exploring whether initial treatment in prison held a different resonance for first-time prisoners or those serving longer prison sentences were also explored, however, these did not result in any clear differences.

Additional information

Notes on contributors

Ian Brunton-Smith

Ian Brunton-Smith is a senior lecturer in Criminology at the University of Surrey. His research interests cover prisons and prisoners, neighborhood effects, statistical methodology, and public opinion research.

Daniel J. McCarthy

Daniel J. McCarthy is a lecturer in Criminology at the University of Surrey. His research interests focus primarily on areas of criminological theory, juvenile justice, and policing.