Abstract
Objectives
This study examines the effect of prison versus community sanctions on recommitment to prison and compares two levels of community supervision, community control (house arrest) and probation, evaluating whether the findings are contingent on the type of matching methods used in the analysis.
Methods
Logistic regression was conducted on unmatched and matched samples. Exact, coarsened exact, and radius-matching procedures were used to create a selection on observables design. Matching variables included current offense, demographics, criminal history, supervision violations, and a rich set of Florida Sentencing Guidelines information culled from an official scoring sheet. Florida judges use this instrument to sentence offenders within the framework of the state determinate sentencing system.
Results
The results show that with exact matching, there is no effect of imprisonment on recommitment, while the other procedures suggest a specific deterrent effect of imprisonment. All four analysis methods showed that offenders under community control are more likely to reoffend than those under normal probation. Analyses between the matched and unmatched prison observations demonstrate that the matched set of prisoners is composed of offenders who have less extensive criminal records and less serious conviction offenses than unmatched offenders regardless of the matching algorithm.
Conclusions
Contrary to a prior analysis of these data, which found a criminogenic effect of prison, a null effect was found using exact matching. Comparing the matching procedures, the more precise the match the less likely there was an effect of prison. However, community control was criminogenic regardless of the matching procedure.
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Notes
These balance statistics are available from the first author.
A reviewer was concerned about the small sample sizes and whether the resulting sample can be generalized to only a small slice of the population and whether this slice is well defined. Our argument is that by circumscribing the inference to a smaller sample, we increase internal validity, and then show in Tables 5 and 6 that the generalizability of the causal inference is restricted to people with less extensive criminal histories and with a less serious conviction offense. Prior studies seem to imply that causal effects of prison apply to a much wider portion of the sample than we portray here. The results of this study should act as a caution to researchers. Depending on the jurisdiction, it may be hard to find counterfactuals for people who are imprisoned, and who have extensive criminal histories, and who have committed crimes that have a high probability of receiving a prison rather than a community supervision sentence. Indeed, judges are rational actors and are unlikely to authorize a community sanction for those people.
One of the reviewers suggested we address the downsides of using reimprisonment as an outcome variable. The reviewer was concerned that behaviors that will lead to reimprisonment for those under community supervision may not always lead to reimprisonment for those who served a prison sentence. As we mention in the body of the paper, we have no a priori reason to believe this is the case, but we acknowledge this as a thoughtful criticism.
A reviewer challenged us to discuss the potential for omitted variables that might influence the causal inferences made in this paper. In a prior version of this paper, we conducted Rosenbaum sensitivity bounds analyses (2002) showing that there was a very low probability we had omitted an influential backdoor variable. Due to space limitations, we were unable to include those results in this paper. The sensitivity results we mention are available from the first author.
A reviewer challenged us to think of the plausibility of randomization once we conditioned on the covariates. Our reading of this methodology is that the selection on observables assumption means that after conditioning on all important covariates, treatment is independent of the potential outcomes. This is not as strong an assumption as randomization. We can think of a situation where judges are confronted with two people having equivalent or near-equivalent sentencing background characteristics and who are on the threshold of prison versus probation. Judges may be uncertain of whether to sentence the person to a term of prison or probation and randomly select the choice. However, this thought experiment goes beyond what is required for the ignorable treatment assignment mechanism.
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Gaes, G.G., Bales, W.D. & Scaggs, S.J.A. The effect of imprisonment on recommitment: an analysis using exact, coarsened exact, and radius matching with the propensity score. J Exp Criminol 12, 143–158 (2016). https://doi.org/10.1007/s11292-015-9251-x
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DOI: https://doi.org/10.1007/s11292-015-9251-x