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The Effect of Incarceration on Re-Offending: Evidence from a Natural Experiment in Pennsylvania

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Abstract

Objectives

This paper uses a sample of convicted offenders from Pennsylvania to estimate the effect of incarceration on post-release criminality.

Methods

To do so, we capitalize on a feature of the criminal justice system in Pennsylvania—the county-level randomization of cases to judges. We begin by identifying five counties in which there is substantial variation across judges in the uses of incarceration, but no evidence indicating that the randomization process had failed. The estimated effect of incarceration on rearrest is based on comparison of the rearrest rates of the caseloads of judges with different proclivities for the use of incarceration.

Results

Using judge as an instrumental variable, we estimate a series of confidence intervals for the effect of incarceration on one year, two year, five year, and ten year rearrest rates.

Conclusions

On the whole, there is little evidence in our data that incarceration impacts rearrest.

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Notes

  1. For example, sentencing guidelines routinely dictate longer prison sentences for individuals with prior convictions. Similarly, it may also be the case that prosecutors are more likely to prosecute individuals with criminal histories.

  2. A growing literature examines how perceptions of apprehension risk are updated by the experience of apprehension or not following commission of a crime. While results vary, most such studies find that the experience of apprehension results in an upward revision in the perceived risk of apprehension and that experience of apprehension avoidance is associated with a downward revision in perceived risk. See Apel (2013) for an excellent review of the sanction perceptions literature.

  3. The subjects of the Killias et al. (2000) and the Van der Werff (1979) studies both involved populations of individuals who, if imprisoned, would have received sentences of 14 days or less. Thus, their offenses were unlikely to be very serious.

  4. Balance on observables is shown in the “Results” section.

  5. Variation in the use of confinement is shown in the “Results” section. We also selected one county in which there was no variation in the use of confinement as a control.

  6. Pennsylvania’s two largest counties, Allegheny and Philadelphia, assign a subset of judges to hear specific types of cases usually involving less serious charges. Our data do not identify judges who were so assigned. As a consequence, it was not possible to test for balance and differences in judge punitiveness in these two counties. For this reason they were not candidate counties for inclusion in the analysis. From the remaining 65 counties we removed 59 because there was insufficient variation in the use of confinement to merit inclusion in the analysis.

  7. Not all sentences are reported to the Commission. (1) Philadelphia Municipal Court sentences are not reported to the Commission. These may include DUI (driving under the influence) offenses as well as other misdemeanor offenses. (2) Offenses sentenced by district magistrates are not reported to the Commission. These typically include DUI offenses or other misdemeanor offenses. (3) Murder 1 and Murder 2 offenses, which are subject to life or death mandatory sentences, do not fall under the sentencing guidelines and are not required to be reported to the Commission. The Commission encourages reporting of the Murder 1 and Murder 2 offenses; many are reported and are included in the data collection” (Pennsylvania Sentencing Commission 1999).

  8. Prior record score is a numeric variable calculated by PASC which aims to encapsulate the seriousness of the offender’s entire prior criminal history.

  9. For example, sentencing data indicated a period of confinement in state prison but no period of confinement could be located in the Department of Corrections files.

  10. Our data only measure arrests that occur in Pennsylvania. We, thus, do not measure arrests that occurred outside of Pennsylvania. While we do not have data that permits us to speak to the degree to which the offenders used in this analysis were arrested outside of Pennsylvania, there is literature which measures displacement across state lines. Langan and Levin (2002) found that 7.6 % of offenders released from confinement were rearrested out of state. Similarly, Orsagh (1992) found that roughly ten percent of offenders released from eleven state prisons in 1983 would experience an out of state arrest in the 3 years following their release. Nakamura (2010) found that slightly less than 23 % of offenders arrested in New York in 1980 were not rearrested in New York, but were arrested in another state. These studies suggest that the recidivism measures used in this analysis only modestly underestimate actual re-arrest rates.

  11. This also raises interesting questions about what exactly constitutes the treatment effect of incarceration. If one’s conceptualization of the effect of incarceration includes the aging that takes place while incarcerated, then this concern is mitigated.

  12. This is a strong assumption, but one that can be relaxed. If their exists treatment effect heterogeneity, but judges do not sort offenders into prison based on the offender’s return from prison, then this approach estimates the mean of the treatment effect distribution. See, for example, Heckman et al. (2010).

  13. Since the effect is, by assumption, additive, and if the proposed value is correct, the adjusted rearrest rate is the rate at which the incarcerated offender would have been rearrested if s/he had not been exposed to incarceration. Furthermore, the expected rate at which offenders would have been rearrested if not exposed to incarceration is balanced across judges due to randomization.

  14. Recall the even though β is fixed across i, \( r_{C,i} \) varies. Thus, this relationship only holds in expectation.

  15. To check covariate balance we use data for all 6,515 offenders sentenced in the six counties during 1999. We do so since randomization occurs at docketing and sample restrictions were made on the basis of outcomes that occur after both randomization and sentencing.

  16. All analyses were conducted in R.

  17. Detailed descriptive statistics, by judge, for each of the other five counties can be found in Appendix.

  18. Where seriousness is defined as Offense Gravity Score (OGS).

  19. Anwar and Stephens (2011), however, use a different methodological approach to estimate the dose–response function.

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Acknowledgments

This work was generously supported by National Science Foundation Grants SES-102459 and SES-0647576. We are also grateful to Mark Bergstrom for coordinating the data collection with the PSC, PA DOC, and the PA State Police.

Author information

Authors and Affiliations

  1. Carnegie Mellon University, Pittsburgh, PA, USA

    Daniel S. Nagin

  2. State of Alaska, 550 W 7th Ave, Anchorage, 99501, AK, USA

    G. Matthew Snodgrass

Authors
  1. Daniel S. Nagin
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  2. G. Matthew Snodgrass
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Corresponding author

Correspondence to Daniel S. Nagin.

Additional information

Authors listed alphabetically. Support for this research was provided by the National Science Foundation (SES1024596). The viewpoints expressed herein are those of the authors and do not reflect the views or beliefs of the State of Alaska.

Appendix

Appendix

See Tables 8, 9, 10, 11, 12.

Table 8 Descriptive statistics for convictions, by county and judge—centre county
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Table 9 Descriptive statistics for convictions, by county and judge—cumberland county
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Table 10 Descriptive statistics for convictions, by county and Judge—Dauphin county
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Table 11 Descriptive statistics for convictions, by county and judge—Erie county
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Table 12 Descriptive statistics for convictions, by county and judge—mercer county
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Nagin, D.S., Snodgrass, G.M. The Effect of Incarceration on Re-Offending: Evidence from a Natural Experiment in Pennsylvania. J Quant Criminol 29, 601–642 (2013). https://doi.org/10.1007/s10940-012-9191-9

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