Does providing inmates with education improve postrelease outcomes? A meta-analysis of correctional education programs in the United States

Abstract

Objectives

Our study addresses the question: Does providing inmates with education while incarcerated reduce their chances of recidivism and improve their postrelease employment prospects?

Methods

We aggregated 37 years of research (1980–2017) on correctional education and applied meta-analytic techniques. As the basis for our meta-analysis, we identified a total of 57 studies that used recidivism as an outcome and 21 studies that used employment as an outcome. We then applied random-effects regression across the effect sizes abstracted from each of these studies.

Findings

When focusing on studies with the highest caliber research designs, we found that inmates participating in correctional education programs were 28% less likely to recidivate when compared with inmates who did not participate in correctional education programs. However, we found that inmates receiving correctional education were as likely to obtain postrelease employment as inmates not receiving correctional education.

Conclusion

Our meta-analysis demonstrates the value in providing inmates with educational opportunities while they serve their sentences if the goal of the program is to reduce recidivism.

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Notes

  1. 1.

    Duguid (1982) provides limited discussion of sociopolitical development; we do not include this last component in our discussion here.

  2. 2.

    Despite the empirical rigor of this study, it is excluded from our own meta-analysis as our focus is solely on correctional education programs administered in the USA.

  3. 3.

    Databases included the Education Resources Information Center (ERIC), Education Abstracts Criminal Justice Abstracts, National Criminal Justice Reference Service Abstracts, Academic Search Elite, EconLit, Sociological Abstracts, Google Scholar, and the Rutgers Library of Criminal Justice Grey Literature Database.

  4. 4.

    Online repositories included the Vera Institute of Justice, Urban Institute, Washington State Institute for Public Policy, American Institutes for Research, Mathematica Policy Research, John Jay College of Criminal Justice Re-entry Institute, Justice Policy Institute, Center for Law and Social Policy (CLASP), Juvenile Justice Educational Enhancement Program (JJEEP), RTI International, and Manpower Demonstration Research Corporation (MDRC).

  5. 5.

    In cases where multiple versions of the same paper were identified, such as when a conference presentation later becomes a peer-reviewed article, we used the most recent version of the study. In a few cases, there were multiple studies by the same author(s) that used variations of the same sample. In those cases, we chose the version of the study that had the broadest sample (e.g., all prisoners released between 1990 and 1995 rather than all prisoners released between 1990 and 1992).

  6. 6.

    Intent-to-treat analysis most closely reflects a practical program implementation scenario because it incorporates noncompliance and protocol deviations, which are common features of many prisoner rehabilitation programs. Additionally, intent-to-treat analysis maintains the initial balance of inmate characteristics generated from the original assignment to treatment or control in cases where there is assignment (Gupta 2011).

  7. 7.

    Our aggregation of multiple types of recidivism and time periods is based on the assumption that the estimated effect of correctional education is not contingent on the measurement strategy or specification used by the researcher. We tested this assumption by sampling studies that reported the effects of correctional education on recidivism using consistent definitions and time periods, and estimated our models on these limited subsamples with consistent metrics. We found that the effect of correctional education did not differ across the definition of recidivism (e.g., reincarceration, rearrest, parole failure) or time period used (e.g., 6 months since release from prison, 1 year since release from prison, 10 years since release from prison). This gives us confidence that the findings from our meta-analysis are robust and apply to a range of postrelease settings, circumstances, and outcomes. It is worth noting that the previous meta-analyses faced similar limitations due to variation in metrics reported by the study authors; our aggregation approach is in line with how the previous meta-analyses empirically dealt with this limitation. Without this aggregation approach, it would be impossible to apply meta-analytic methods to the study of correctional education due to the heterogeneity in measurement approaches.

  8. 8.

    It is not possible to discern the total number of studies that include female inmates in their samples due to inconsistencies in reporting. For a more detailed discussion of women’s participation in correctional education, see Rose (2004).

  9. 9.

    Random-effects models were also the estimation method used in three major meta-analyses published to date (Wilson et al. 2000; MacKenzie 2006; Aos et al. 2006).

  10. 10.

    We computed robust standard errors for meta-regression using the ROBUMETA command available in Stata (Hedberg 2011). This was necessary only for our analysis of recidivism, as there was not sufficient nesting in the pool of eligible studies of employment to permit this computation. The results were not contingent on the method for estimating the standard errors; tests of significance reflect unadjusted standard errors.

  11. 11.

    Note that the last row, which includes the pooled effect size for levels 2, 3, 4, and 5, is the same as the pooled effect size for the total sample because they both are based on all 57 studies.

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Funding

This research was conducted with support from a grant from the U.S. Department of Justice’s Bureau of Justice Assistance (2010-RQ-BX-0001). All analyses and interpretations are the authors alone.

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Correspondence to Robert Bozick.

Appendices

Appendix 1

Table 6 Studies included in the meta-analysis of recidivism

Appendix 2

Table 7 Studies included in the meta-analysis of employment

Appendix 3

Forest plots

In this appendix, we present two forest plots: one for the recidivism analysis and one for the employment analysis. Each row in the plot corresponds to an effect size, labeled on the left with the corresponding first author of the study and the year of publication. Studies with multiple effect sizes are listed multiple times with a capital letter to differentiate among them. The black box represents the effect size estimate for the study, and the “whiskers” extend to the range of 95% confidence intervals. The box and whiskers for each effect size are plotted in relation to the dashed line down the center of the graph, which indicates an odds ratio of 1. Effect sizes to the right of this line indicate that the treatment group had a higher odds of recidivating (or being employed), and effect sizes to the left of this line indicate that the comparison group had a higher odds of recidivating (or being employed). If the whiskers for the corresponding box do not cross this dashed line, then the study yielded a significant difference between the treatment and comparison group for that particular effect size at the conventional level of p < 0.05. Conversely, if the whiskers for the corresponding box cross this dashed line, then there is no significant difference detected between the treatment and the comparison group for that particular effect size at the conventional level of p < 0.05.

Fig. 1
figure1

Recidivism analysis forest plot

Fig. 2
figure2

Employment analysis forest plot

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Bozick, R., Steele, J., Davis, L. et al. Does providing inmates with education improve postrelease outcomes? A meta-analysis of correctional education programs in the United States. J Exp Criminol 14, 389–428 (2018). https://doi.org/10.1007/s11292-018-9334-6

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Keywords

  • Correctional education
  • Recidivism
  • Meta-analysis