The Limits of Recidivism Measuring Success After Prison (2022) / Chapter Skim
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Pages 15-65
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. Racial disparities in criminal legal system involvement among New York City overdose decedents: Implications for diversion programs.
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. Race decriminalization and criminal legal system reform.
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. The desire for greater accountability for public agencies and individuals in the criminal legal system, combined with the emergence of new arrest and court administrative data, facilitated the calculation of recidivism rates for people released from prison.
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Too often, errors can be made by those interpreting and relying on recidivism data to make policy and programmatic decisions within the criminal legal system. ANNUAL PRISON RELEASES We begin by exploring patterns of prison releases in the United States, one population for whom recidivism rates are regularly calculated, and then review Bureau of Justice Statistics (BJS)
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In the midst of the COVID-19 pandemic, the volume of releases declined but kept pace with the prior trend, whereas the decline in the volume of admissions was larger than expected by an order of magnitude. Prison recidivism rates are often measured by the proportion of individuals who left prison in a given year who are later rearrested or reincarcerated (e.g., see "Recidivism in Bureau of Justice Statistics Reports" below)
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These patterns of readmission illustrate a type of measurement error that arises in using aggregate statistics from administrative data to characterize recidivism. Prison admissions arising from technical violations of conditions of supervision may result from failure to meet conditions imposed on persons supervised in the community following release from prison (such as drug test failures, failure to show up for meetings, or failure to pay fees)
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. If pure technical violators serve less time than those admitted on technical violations stemming from new crimes, their contribution to the size of the prison population would be much smaller.
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Recidivism in Bureau of Justice Statistics Reports The BJS prisoner recidivism reports are widely cited for providing postprison recidivism statistics on large samples of persons released from state prisons. Relying on criminal history records from state and federal repositories, BJS has prepared several recidivism measures for five cohorts released from prison in 11 states in 1983 (Beck and Shipley, 1989)
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The criminal history data that BJS uses in its studies are the same records that police officers use to determine a suspect's current criminal justice status (e.g., on probation, parole, or bail) ; that judges use to make pretrial and sentencing decisions; and that corrections officials use to make inmate classification decisions (Durose, Cooper, and Snyder, 2014)
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Return to prison refers to any return to prison, including for a technical violation or following conviction for a new crime. SOURCE: Durose and Antenangeli (2021)
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. The other public order offense category is an undifferentiated category comprising conditional release violations (which include the aforementioned technical violations and arrests for new crimes reported as technical violations)
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Percentages for any violent, any property, any drug, and any public order offense do not sum to 100 because individuals may be rearrested on more than one occasion, or rearrested and charged for more than one offense type. SOURCE: Durose and Antenangeli (2021)
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that information is stored in criminal history repositories in the states that participate in the BJS cohort studies. Close inspection of rearrests indicates that while rearrests for violent crimes exceed the proportion of individuals convicted of violent offenses in the release cohort, many instances of recidivism result from other public order charges that do not necessarily align with measures of serious criminal behavior.
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As noted earlier, evidence suggests that many if not a majority of events recorded as technical violations are actually new crimes or arrests of persons on parole, where the conditions of parole lead to a technical violation for a new crime (Grattet, Petersilia, and Lin, 2008)
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They were able to attribute the difference to technical violations, such as failure to comply with parole restrictions, rather than new criminal behaviors, illustrating a type of analysis that can help illuminate the extent to which recidivism arises from the decisions made by criminal legal system actors versus new offense behaviors. Comparing outcomes across samples and locations can contribute to an understanding of what may work to help reduce recidivism events.
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. ELEMENTS OF RECIDIVISM MEASURES While several events can occur that are defined as recidivism -- rearrest, reconviction, reincarceration, technical violation, or graduated sanction -- studies vary in the criminal legal system decision point they use to measure recidivism.
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Or an evaluation of the effectiveness of in-prison programs that address criminogenic needs may study persons released from prison in different risk environments (e.g., measured by crime rates or level of police surveillance) to estimate future contacts with criminal legal system agencies.
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The BJS prison recidivism studies of individuals released from prison use event-based samples, and their reports refer to the samples as such by explicitly citing the recidivism rates of persons released from prison during a given year. However, the BJS statistics have also been misused to describe populations other than the ones from which the samples are drawn.
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. As shown in Table 2-3 below, recidivism rates for event-based release cohorts are higher than those for person-based samples.
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Recidivism Events As previously discussed, recidivism measures that are based on contact with criminal legal agencies, such as rearrest measures, consist of some
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Self-report data on offense behaviors may avoid this problem of criminal legal actors' responses, but it can introduce measurement error and bias of its own in estimating the incidence or prevalence of reoffending. Regardless of the source of data, measurement error and potential bias in estimates are major methodological challenges confronting recidivism studies.
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All classes of administrative data reflect the intersection of individual behaviors and criminal legal responses, and this source of measurement error may not be randomly distributed (see below for a detailed discussion)
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and on persons over time and place. non-criminal legal uses of criminal history data.
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Technical Probation Indication of a supervising agency's decisions, Records typically do not detail the nature of violations of supervising which when associated with other agency the events that led to a technical violation/ conditions of agencies, which data provide indications of the probability of revocation, or include the dates or severity supervision may be federal, revocation conditional upon repeated measured of the violation behavior. Few systems track state or county; behaviors (e.g., the number of failed drug tests the nature of the events.
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For example, one person may think of or characterize a burglary as a robbery even though the event was breaking and entering and did not involve use of force to take property. Self-report surveys that allow respondents to self-define criminal events or to affirm behaviors that fall into broad categories introduce measurement error into the classification of events.
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But most recidivism studies do not use self-report data because of their cost and the need for skilled staff to conduct the interviews.
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SOURCE: See: https://www.nationalacademies.org/event/07-27-2021/evaluating-success among-people-released-from-prison-meeting-2-public-information-gathering-session-1. Administrative Data Most recidivism studies use data from official records of arrests, convictions, and incarceration that are drawn from the operational databases of criminal legal agencies.
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Arrest records are supplemented by information provided by prosecutors' of fices, courts, and correctional agencies, each of which contributes data to complete the CHRI by submitting information about subsequent dispositions of charges. A completed criminal history record contains information about arrest charges, including their dispositions, sentences, and custody dates.
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Failure to distinguish between a parole revocation that occurs because of a new offense and a technical violation may lead to an overestimate of criminal behavior if violations of supervision such as drug test failures are included as criminal behaviors. While criminal history records can provide researchers with the most comprehensive and accessible source of data on recidivism as measured by criminal legal agencies, a number of reliability concerns are associated with administrative data.
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State requirements on the non-felonious arrests to report to the criminal history repositories differ, so the scope of what is included in nonfelonious arrest records also varies. Dispositions of arrests are incomplete.
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Court dispositions are reported to the states' criminal history repositories, although complete disposition data may not exist in the repositories. Court records include information about each charge in a case when a defendant is charged with multiple offenses.
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. Alternatively, if the data from state criminal history repositories are used, the jail incarceration information would be more likely to be included, but the reason for the incarceration (e.g., a technical violation)
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who evaluated technical violations in California and concluded that 65 percent involved behaviors alleged to violate the California Penal Code, and that about 10 percent of these were serious penal code violations. The BJS Annual Survey on Probation and Annual Survey on Parole reports that 6.5 percent of individuals exiting probation are incarcerated without a new sentence and 4.9 percent are incarcerated with a new sentence.
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Measurement Error Earlier we described, in general terms, the sources of measurement error in both administrative and self-report data that measure recidivism events. All recidivism measures derived from administrative data reflect decisions by criminal legal system actors to take action and to record the actions taken in specific ways, as dictated by their roles in the criminal legal system and administrative records systems.
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recommend thoughtful consideration of the extent and form of noise in dependent variables and how the noise may bias inferences. A more complete understanding of the nature of measurement error can improve the use of administrative data for measuring recidivism events.
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; • designing studies that take into account differences among or within agencies in responses to offenses and how this affects the recording of recidivism events; • further work on self-report and administrative data to better understand, in particular, how relatively minor offenses find their way into administrative data; and • other research designs that improve our understanding of the effects of measurement error on recidivism estimates. The sources and types of measurement error in recidivism measures require greater attention by researchers.
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To the committee's knowledge, no such efforts are underway to document sentencing decisions. Within states, the state criminal history record repositories collect and integrate records of arrests and prosecutions.
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, but they still have a long way to go.8 In the interim, the absence of uniform standards for criminal legal events results in measurement error, the full extent of which is unknown, that hinders comparisons within and across jurisdictions in the rearrest, reconviction, and reincarceration of persons released from prison. RECIDIVISM AS BINARY: LIMITATIONS Reporting recidivism in a binary way -- sorting people into those who are and those who are not rearrested, reconvicted, or reincarcerated during the period of time being measured -- gives an incomplete picture of a person's post-release experiences.
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For example, NIBRS data report on 52 offenses in 23 categories. Criminal history records contain even more detailed offense information.
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Self-report data may over- or under-state criminal behavior and are typically costly to collect. Administrative data and their associated measures reflect some combination of individual behavior and criminal legal system actors' responses and decisions.
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Because there are many recidivism events that can be measured, the general term "recidi vism" needs to be accompanied by explicit reference to the recidivism events under study (recidivism as rearrest, as reconviction, etc.)
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5. Improvements are warranted in administrative data and criminal history records to enable them to focus on distinguishing events, such as pure technical violations vs.
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the same way are best avoided. Researchers are best served by drawing on recent reports on modernizing crime statistics for ideas about taxonomies and classification of the many types of criminal legal system actions so that greater emphasis can be placed on offense-specific measures to assess the impact of a policy or program on criminal behavior.
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. The parolee–parole officer relationship as a mediator of criminal justice outcomes.
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, Beyond Recidivism: New Approaches to Research on Prisoner Reentry and Reintegration, 13–38. NYU Press.
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. Multistate Criminal History Patterns of Prisoners Released in 30 States.
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Committee on Evaluating Success Among People Released from Prison Meeting #2: Public Information Gathering Session. Washington, DC: National Academies of Sciences, Engineering, and Medicine.
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Committee on Evaluating Success Among People Released from Prison Meeting #2: Public Information Gathering Session. Washington, DC: National Academies of Sciences, Engineering, and Medicine.
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. Presentation to Committee on Evaluating Success Among People Released from Prison Meeting #2: Public Information Gathering Session.
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Journal of Criminal Justice 36, 416–425. Miller, J., Caplan, J.M, and Ostermann, M
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. Using State Criminal History Records for Research and Evaluation.
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