Prior Record and Recidivism Risk

Abstract

An individual’s prior record can have a pronounced impact on the punishment he or she receives for a new offense, substantially increasing the likelihood and duration of an incarceration sentence. Not only does prior record contribute to mass incarceration, but prior research has consistently shown that criminal history mediates race effects and exacerbates disparities. In guidelines jurisdictions, criminal history enhancements are partially or primarily employed as proxies for risk prevention. But for the most part these scores were not developed empirically, and, to date, whether scores are valid predictors of risk has gone unexplored. This paper uses survival analysis and area under the curve analysis to examine the predictive efficacy of the Pennsylvania Prior Record Score using a sample of offenders sentenced in Pennsylvania and followed-up for 3 years after release (n = 130,758). The results show that some of the Pennsylvania PRS categories fail to accurately distinguish among offenders based on their likelihood of recidivism. Further, some of the key score components that increase the PRS (and the punishment imposed) have marginal effects on the predictive efficacy of the score, often only increasing the prediction accuracy by a single percentage point. By re-engineering the PRS categories and sub-components, this jurisdiction could recommend less punishment in some cases without any apparent increase in risk to public safety.

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Notes

  1. 1.

    Actuarial risk instruments are routinely validated. Guidelines criminal history scores, however, which are not actuarial risk instruments but which may be used for similar purposes, have not been validated.

  2. 2.

    Each offense is weighted through the assignment of a point value of 1, 2, 3, or 4. Offenders with a current OGS of 9 or higher who have 2 or more 4-point offenses are designated as REVOC. Offenders with Felony 1 and Felony 2 priors totaling 6 or more points (who are not otherwise REVOC) are designated as RFEL. For juvenile adjudications, only offenses occurring on or after the offender’s 14th birthday are counted, and only adjudications for felonies, or select misdemeanors are counted. Currently, for all juvenile offenses except for 4-point offenses (the most serious offenses), the juvenile record can lapse (or be washed out) if (1) the individual is 28 or older at the time of commission of the current offense; and (2) the individual remained crime free for the 10 years preceding their 28th birthday. The definition of crime free allows for summary violations and one misdemeanor with a statutory maximum of a year of less. The crime-free requirement went into effect with the revision to the 6th edition guidelines in 2008; prior to that, and relevant for all offenders in this study, a more generous lapsing provision automatically lapsed juvenile records (again with the exception of four-point offenses) once the offender turned 28, regardless whether a crime-free period was achieved.

  3. 3.

    Certain serious misdemeanors (Misdemeanor-1 offenses listed in section 303.7(a)(4)) receive 1 point. “Other misdemeanors” not listed in 303.7(a)(4) count less than a point each and are aggregated and scored 1 point for 2–3 misdemeanors, 2 points for 4–6 misdemeanors, and 3 points for 7 or more misdemeanors. Additional rules affecting the scoring of the PRS are found in the guidelines. See §303.8(d) on scoring former Pennsylvania offenses; §303.8(f) on scoring outs-of-state, federal, or foreign offenses; §303.8(e) on scoring misgraded convictions; 303.8(b) on scoring inchoate offenses; and §303.8(g) for information on excluded offenses, charges, and convictions.

  4. 4.

    The Commission apparently held the view that different criminogenic processes might be involved with DUI cases, and that different public policy interests may be at stake. Since DUI offending makes up a substantial portion of the state’s caseload, leaving those cases in would potentially have confounded aspects of the non-DUI risk assessment instruments that were being developed (see also Blumstein & Nakamura, 2009). The recidivism measures were thus based only on re-arrests in Pennsylvania and could not account for re-arrests occurring in other jurisdictions.

  5. 5.

    Although this presumption interjects some imprecision, the Commission found that around a third of county incarceration offenders were released upon meeting their minimum sentence, about a third were released within a month before meeting their minimum, and about a third were released within a month after meeting their minimum sentence. Thus, these county offenders do indeed appear to be released at or near their minimum sentences and the measurement error associated with the minimum release assumption appears to be roughly equally divided between slightly over-estimating time at risk for a third of offenders and slightly under estimating time at risk for another third.

  6. 6.

    The ROC analyses tested the difference in the area under the receivership operator characteristic curve between the PRS and each counterfactual PRS. ROC analysis is standard for evaluating predictive instruments. In this context, the event being predicted is recidivism. The ROC provides a statistic for comparing false positives, false negatives, true positives, and true negatives in the instrument. We know from the data which offenders went on to recidivate within the three-year follow-up and which did not. If we randomly chose one recidivist and one non-recidivist and compared their prediction scores (in this case their PRS scores) we would expect the recidivist to have a higher score than the non-recidivist. How well an instrument in fact predicts can be measured by the percentage of instances the randomly drawn recidivist would have a higher PRS than the randomly drawn non-recidivist. A perfect predictor would achieve 100% accuracy: the recidivist would always have a higher PRS than the non-recidivist. The baseline for an inutile predictor is one that does no better than chance, only correctly classifying 50% of the time (which one should achieve with a coin toss). The ROC approach allows us both to identify the accuracy of an instrument, and to compare the accuracy of two instruments, thus assessing whether a particular aspect of the PRS improved prediction.

  7. 7.

    I proceed with the ROC analysis because it is the method commonly used to gauge differences in instruments. I note however, that additional information is desirable, particularly when analyzing true actuarial risk instruments which use determined cutoffs on a scale to designate outcomes such as low and high risk (see Singh, 2013). For example, Berk, Heidari, Jabbari, Kearns, and Roth (2017) demonstrate the insight provided by descriptive statistics such as predictive values and error rates derived from 2 × 2 confusion matrices that report false positives, false negatives, true positives, and true negatives. Since the PRS does not reduce to a low or high risk identification but retains eight separate points of distinction, this analysis would require eight different confusion matrices for each five versions of the PRS analyzed. Relying on the AUC statistics provides a parsimonious commonly employed way of measuring the instruments’ relative ability to discriminate among likely risk.

  8. 8.

    As noted earlier, there is substantial disagreement among retributivists whether prior record should enhance judgments of culpability. Even for retributivists who are inclined towards an aggravation theory, important questions about the Pennsylvania PRS might be reconsidered. For instance, are individuals with 4 points always more culpable than individuals with 3 points? And are all individuals within a category equally culpable? A person could be in PRS 4 because they committed a few misdemeanors and low-level property crimes; or two drug felonies; or Second-Degree Murder or Rape.

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Acknowledgements

My thanks to Richard Frase and Julian Roberts for comments on an earlier draft. I’m also grateful to Mark Bergstrom, Leigh Tinik, and the other staff and affiliates of the Pennsylvania Commission on Sentencing for use of the data and for feedback on this project. I served as Deputy Director of the Pennsylvania Commission on Sentencing during much of the research and writing of this project; any views expressed do not necessarily represent the views of the Commission, its members, or staff.

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Appendix

Appendix

Table 5 Pennsylvania Sentencing Matrix

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Hester, R. Prior Record and Recidivism Risk. Am J Crim Just 44, 353–375 (2019). https://doi.org/10.1007/s12103-018-9460-8

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Keywords

  • Sentencing
  • Criminal history
  • Recidivism
  • Punishment theories