Classifying Prisoner Returns

AuthorWilliam Rhodes,Gerald G. Gaes,Jeremy Luallen,Jared Edgerton
Published date01 June 2016
Date01 June 2016
DOIhttp://doi.org/10.1177/1525107116675322
Subject MatterResearch Note
Research Note
Classifying Prisoner
Returns: A Research Note
Gerald G. Gaes
1
, Jeremy Luallen
2
,
William Rhodes
2
, and Jared Edgerton
2
Abstract
Scholars oftenuse administrative correctionsdata to identify the reasons that offenders
return to prison, though such data usually obscure more complex processes underlying
the cause of a return.This article describes theprocedural nuances that make itdifficult
to record prison return paths and discusses these limitations. We focus on data ele-
ments and recording practices commonly found in administrative databases and discuss
whether and how researchers may use these data to reliably identify/classify returns.
We provide empirical demonstrations of these arguments using publicly available data
and conclude that more extensive data are often needed to accomplish this objective.
Keywords
prisons, recidivism, return, classification, revocation
A common practice in criminal justice evaluation is to measure recidivism as a return
to prison. Scholars also expand upon this definition by further decomposing the type of
return. This expansion most often employs a typology that distinguishes new court
commitments from revocation events, where revocation events themselves are further
broken down into finer groupings: (a) revocations that occur as the result of a technical
violation of the conditions of supervision or (b) revocations that result from an arrest
for a new crime. Although this taxonomy is conceptually straigh tforward, many
administrative databases do not contain a sufficient amount of information to classify
these return paths. This limits the policy insights that can be derived from studies of
prison programs, reentry services, community supervision effectiveness, or other
criminal justice interventions that use prisoner returns as the key outcome measure.
1
1
Florida State University, Tallahassee, FL, USA
2
ABT Associates, Cambridge, MA, USA
Corresponding Author:
Jeremy Luallen, ABT Associates, 55 Wheeler St., Cambridge, MA 02138, USA.
Email: jeremy_luallen@abtassoc.com
Justice Research and Policy
2016, Vol. 17(1) 48-70
ªThe Author(s) 2016
Reprints and permission:
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DOI: 10.1177/1525107116675322
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In this article, we lay out a framework for thinking through these issues in an effort
to bring clarity to the subject. Our motivation comes from our work with the National
Corrections Reporting Program (NCRP), a Bureau of Justice Statistics (B JS) data
collection of individual records of prison and community supervision admissions and
releases. These records and other sources of BJS data have been prominently featured
in the literature on reentry and community reintegr ation (e.g., Mears & Cochran,
2015; Petersilia, 2003; Petersilia et al., 2008; Travis, 2005; Travis & Visher, 2005).
Our work with these data led us into an investigation of the criminal justice processes
that affect the prison return paths and we thought it was important to document these
processes for analysts who may use NCRP or similar sources of data in the future.
We are also struck that the conventionalwisdom on returns—a very high proportion
are violations—also motivates the importance of the discussion. Much of the conven-
tional wisdom derivesfrom scholarly exegesis of prison returnsusing available national
estimates of return types; however, proper interpretation of this assertion requires that
one understand tha t a large proportion of viola tions stem from an arrest. Sc holars such
as Burke and Tonry(2006), Mears and Cochran(2015), Petersilia (2003),Travis (2005),
and Travis and Visher (2005) have relied on BJS sources that for the most part do not
distinguish revocation returns based on an arrest as opposed to a ‘‘purely’’ technical
violation.As we show later in this article, revocations often resultfrom a combination of
purely technical violations and arrests. We return to this problem later in the article.
Most important to this discussion is that accurate and reliable return classifications
have important implications for policy analysis. Knowing the type of return to prison
is an important dimension when conducting analysis of interventions intended to
reduce recidivism, whether those interventions occur prior to release or while the
person is under postrelease supervision. Any modifications to postrelease supervision
policy would be informed by accurate return classification. These types of research
questions motivated the two examples we provide later in this article in which
researchers had to gather supplemental administrative data to make sense of the
classification of prison returns (Grattet, Petersilia, & Lin, 2008; Jones, 2016). Even
the seemingly mundane task of recording time served will depend upon an analyst’s
ability to separate prison terms initiated for violations of supervision (typically much
shorter) than those resulting from new criminal actions.
Though administrative databases often contain variables meant to distinguish the
reasons for returning to prison, such measures often discount or obscure the complex
processes underlying the cause(s) of a return.
2
Simply put, databases are not designed
with the intricacies of identification in mind. Nevertheless, researchers must use these
data to inform their investigations, facing few (readily available) alternatives.
In this article, we providea detailed discussionof the procedural nuances thatmake it
difficultto reliablyrecord the prison returnpaths and by implicationmake it more difficult
to inform postprison supervision policy. Because this article is mainly intended for
researchersusing administrativedata, we focus on data elements and recording practices
commonly found in administrative databases. We discuss whether and how researchers
may use these data to reliably classify the type of return to prison. We also provide
empirical demonstrationsof these ideas using publiclyavailable data from the NCRP.
3
Gaes et al. 49

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