U.S. Department of Justice
Office of Justice Programs
National Institute of Justice
National Institute of Justice
The Use and Impact of Correctional Programming for
Inmates on Pre- and Post-Release Outcomes
June 2017
Grant Duwe, Ph.D.
Minnesota Department of Corrections
This paper was prepared with support from the National Institute of Justice, Office of Justice Programs,
U.S. Department of Justice, under contract number 2010F_10097 (CSR, Incorporated). The opinions,
findings, and conclusions or recommendations expressed in this publication are those of the authors and
do not necessarily represent those of the Department of Justice.
NCJ 250476
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The Use and Impact of Correctional
Programming for Inmates on
Pre- and Post-Release Outcomes
Introduction
State and federal prisons have long provided programming to inmates during their
confinement. Institutional programming encompasses a broad array of services and
interventions, including substance abuse treatment, educational programming, and
sex offender treatment. The objective of providing prisoners with programming is to
improve their behavior, both before and after release from prison. Indeed, institutional
programming is often intended to not only enhance public safety by lowering recidivism,
but also to promote greater safety within prisons by reducing misconduct. Although U.S.
correctional systems typically offer some programming opportunities within prisons,
research suggests many prisoners do not participate in programming while incarcerated
(Lynch & Sabol, 2001).
This paper reviews the available evidence on the impact of institutional programming
on pre- and post-release outcomes for prisoners. Given the wide variety of institutional
interventions provided to inmates in state and federal prisons, this paper focuses on
programming that: (1) is known to be provided to prisoners, (2) has been evaluated, and
(3) addresses the main criminogenic needs, or dynamic risk factors, that existing research
has identified. This paper, therefore, examines the empirical evidence on educational
programming, employment programming, cognitive behavioral therapy (CBT), chemical
dependency (CD) and sex offender treatment, social support programming, mental health
interventions, domestic violence programming, and prisoner re-entry programs. In addition
to reviewing the evidence on the effects of these interventions on pre- and post-release
outcomes, this paper identifies several broad conclusions that can be drawn about the
effectiveness of institutional programming, discusses gaps in the literature, and proposes a
number of directions for future research.
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Pre- and Post-Release Outcomes
This paper reviews four pre- and post-
release outcomes: (1) prison misconduct,
(2) recidivism, (3) post-release employment,
and (4) cost avoidance.
Prison Misconduct
Commonly defined as the failure of
inmates to follow institutional rules and
regulations (Camp et al., 2003), prison
misconduct comprises behavior that ranges
from disobeying orders and possessing
contraband (e.g., alcohol, drugs, etc.)
to assaulting staff and other inmates.
Offenders typically receive sanctions for
rule infractions, including increased
incarceration time, which can exact a
monetary cost on correctional systems
(French & Gendreau, 2006).
Existing research reveals that both
individual- and institutional-level factors
are associated with prison misconduct. In
their meta-analysis, Gendreau, Goggin, and
Law (1997) found that antisocial attitudes
and behavior, a previous criminal history,
and age were the strongest individual-
level predictors of disciplinary infractions.
Reflecting the findings reported by
Gendreau and colleagues (1997) that
having antisocial companions increases the
likelihood of misconduct, several studies
have indicated that gang membership (i.e.,
identification as a member of a security
threat group) is positively associated with
rule violations (Gaes et al., 2002; Tewksbury,
Connor, & Denney, 2014). Gendreau,
Goggin, and Law (1997) also noted that
social achievement (e.g., education,
employment, marital status, etc.), early
family factors, and race had modest
associations with disciplinary infractions.
Research further indicates that misconduct
is affected by institution-level factors such
as size, location, and security level, as well
as the overall characteristics of staff and
inmates (Camp et al., 2003; Huebner, 2003;
Steiner & Woolredge, 2008).
Recidivism
Recidivism is the most common measure
of correctional program effectiveness.
Generally considered to be a return to
criminal behavior, recidivism is the main
post-release outcome reviewed in this paper.
Measures of recidivism typically include
rearrest, reconviction, resentencing to
prison for a new felony-level offense, and
a return to prison for a technical violation
revocation. Research has shown that a
majority of released prisoners recidivate,
particularly when measured as a rearrest,
within at least three years of release from
prison (Langan & Levin, 2002). In their
study of more than 400,000 offenders
released from prisons in 30 states in 2005,
Durose, Cooper, and Snyder (2014) report
that 68 percent were rearrested within three
years and 77 percent were rearrested over a
five-year follow-up period.
Durose and colleagues (2014) also found
that recidivism rates were higher for men,
non-whites, younger offenders, and those
with longer criminal histories, which is
consistent with previous research showing
that gender, race, age, and criminal history
are among the strongest “static” (i.e.,
factors that cannot change) predictors of
reoffending (Gendreau, Little, & Goggin,
1996). Research has also shown that
there are “dynamic” factors (i.e., those
that are susceptible to change) that are
generally predictive of recidivism. In their
meta-analysis, Gendreau and colleagues
(1996) reported that dynamic factors
such as criminogenic needs (e.g., attitudes
supportive of an antisocial lifestyle,
substance abuse, antisocial companions,
etc.), personal distress (e.g., anxiety,
depression, schizophrenia, etc.), and
social achievement (e.g., marital status,
level of education, employment, etc.) are
significantly associated with recidivism
risk. As discussed later in this review,
institutional programming is often geared
toward addressing such dynamic risk factors
because they are the areas in which change
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can take place. For example, delivering
substance abuse treatment to chemically
dependent offenders will presumably help
reduce their recidivism risk.
Post-Release Employment
Unlike recidivism, very little research
has focused on identifying the factors
associated with post-release employment. To
be sure, not all institutional programming
is designed to improve post-release
employment outcomes; nevertheless,
employment is often considered to be
critical in helping offenders successfully
transition from prison to the community.
Although research suggests that an offender
who finds a job is less likely to reoffend
(Skardhamar & Telle, 2012), post-release
employment is also important from a
cost-benefit perspective. After all, when
offenders are working, they are usually
paying income taxes, which helps generate
revenue for federal and state governments.
Whereas offender post-release employment
can provide a tangible monetary
benefit, research has demonstrated that
crime is very costly to society (Cohen &
Piquero, 2009). There are victim costs,
criminal justice system costs (including
police, courts, and corrections), and
lost-productivity costs associated with
individuals who are incarcerated.
Moreover, when offenders are imprisoned,
institutional misconduct represents an
additional cost because correctional staff
time is taken up with processing discipline
violations, confinement time for offenders
may be extended, and segregation sanctions
may result (Lovell & Jemelka, 1996).
Cost Avoidance
When correctional programming can
reduce misconduct, lower recidivism, and
improve post-release employment outcomes,
it can generate a monetary benefit to
society, mostly through costs avoided from
the prevention of crime. The use of cost-
benefit analyses to assess the effectiveness
of correctional programming is still in its
infancy, although research, mainly from
Washington state and Minnesota, has
provided cost-avoidance estimates for most
of the programs reviewed in this paper.
From “Nothing Works” to
“What Works”
The publication of Robert Martinson’s
“what works” study in 1974 was a pivotal
moment in the history of correctional
programming. The well-known conclusion
from this study and another he co-authored
the following year (Lipton, Martinson,
& Wilks, 1975) on the effectiveness of
programming in reducing recidivism
was that “nothing works.” The “nothing
works” conclusion helped shift the focus
from the rehabilitative ideal that had
prevailed during the 1950s and 1960s to
deterrence and “just deserts,” especially
during the 1980s and 1990s. In the wake
of the widespread attention this research
received, scholars critiqued its methods and
challenged its conclusions (Gottfredson,
1979; Palmer, 1978; Wholey, 1979). Over the
long term, however, the “nothing works”
claim was the catalyst for the emergence
of the “what works” movement within
corrections, which has shown that some
correctional interventions are effective in
reducing recidivism.
This body of research, which has come to
be known as the “what works” literature,
later gave rise to the principles of effective
correctional intervention, which hold
that programming should be matched
to an offender’s risk of reoffending,
criminogenic needs, and responsivity
issues (Gendreau, French, & Gionet, 2004).
Because correctional resources are often
scarce, the risk principle suggests that we
can get the most “bang” for our treatment
“buck” by focusing on higher risk offenders.
The risk-needs-responsivity (RNR) model
calls for offender risk to be assessed using
actuarial risk assessment tools that have
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been validated and normed (Andrews &
Bonta, 2010). The most intensive programs
— generally measured by total length and
number of hours — should be reserved
for offenders with a higher recidivism risk
(Sperber, Latessa, & Makarios, 2013).
Whereas the risk principle identifies whom
we should treat, the needs principle tells us
what areas we should treat. Criminogenic
needs are individual characteristics that
increase the risk of recidivism (Latessa
& Lowenkamp, 2005). Under the RNR
framework, one distinction among risk
factors is whether they are static or
dynamic. Although criminal history is
typically the strongest predictor of future
criminal behavior (Caudy, Durso, &
Taxman, 2013; Duwe, 2014c), it is a static
factor that cannot be changed through
intervention. Dynamic risk factors, on
the other hand, can be targeted through
intervention because changes can be made
in these factors. When offenders enter
prison, they are often undereducated,
have little or no previous work history, lack
vocational skills, have lengthy histories of
substance abuse, and are more likely to
suffer from mental illness (Petersilia, 2003).
Much of the institutional programming
provided to offenders is geared toward
addressing these criminogenic need areas.
Research has further categorized
recidivism risk factors as major, moderate,
and minor (Andrews, Bonta, & Wormith,
2006). Included among the four major
risk factors (i.e., the “big four”) are a
history of antisocial behavior, antisocial
personality pattern, antisocial cognition,
and antisocial associates. Of the big four,
a history of antisocial behavior is static,
whereas the others are dynamic needs
areas. Moderate risk factors include
family/marital, education/employment,
leisure/recreation, and substance abuse.
Major mental disorder, low IQ, and social
class are considered minor risk factors
(Andrews, Bonta, & Wormith, 2006).
The RNR model holds that because
individual characteristics can affect
responsiveness to treatment programming,
these issues should be considered when
assigning offenders to interventions
(Andrews & Bonta, 2010; Dowden &
Andrews, 1999). More specifically, the
responsivity principle indicates that
treatment delivery should be tailored to
the learning styles, abilities, and strengths
of offenders (Andrews, Bonta, & Wormith,
2006).
Program Integrity: Why It
Matters
In general, programs designed in
accordance with established principles of
effective correctional intervention that
maintain integrity upon implementation
should be more successful than those that
deviate from their original designs and
compromise evidence-based program
elements (Andrews & Dowden, 2005;
Gendreau, Goggin, & Smith, 1999;
Lowenkamp, Latessa, & Smith, 2006).
The principles of effective correctional
intervention have, over time, increasingly
been used by U.S. correctional systems
as the guiding framework for program
delivery; yet, to some extent, these
principles still represent the ideal more
than reality. Indeed, validated risk
assessment tools are not always used to
determine recidivism risk, programming
dosage is not consistently calibrated
to recidivism risk, and offenders are
sometimes assigned to interventions
regardless of their criminogenic needs or
responsivity issues.
As Latessa and colleagues (2002) point
out, many correctional programs fail to
work because they are not rooted in sound
criminological theory and, thus, exemplify
“correctional quackery.” At the same time,
however, a common reason for the failure
of programs, including those with a solid
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theoretical foundation, is that they lack
therapeutic integrity (Cullen & Gendreau,
2000). Scholars have argued that some
of the variation in effectiveness observed
among meta-analyses of correctional
programs likely stems from a lack of
program integrity (Cullen, 2002; Gendreau,
1996). Despite its importance, program
integrity has often been overlooked within
the correctional literature. The consensus
from the few existing studies on this topic,
however, is that program integrity is critical
to the success of programming.
The Correctional Program Assessment
Inventory (CPAI) and the Evidence-
Based Correctional Program Checklist
are two standardized assessments created
specifically to assess the design and
implementation of correctional programs
(Gendreau & Andrews, 1994; Latessa,
2012). Two studies have used the CPAI to
examine the relationship between program
integrity and recidivism outcomes. Using a
condensed version of the CPAI to carry out
second-hand assessments of correctional
programs based on 173 recidivism outcome
evaluations with 266 effect sizes, Nesovic
(2003) found that higher CPAI scores
were associated with larger recidivism
reduction effects. Relying on a more
complete, yet still condensed, version of the
CPAI, Lowenkamp and colleagues (2006)
analyzed data from community-based
residential programs (halfway houses) in
Ohio. Matching more than 3,000 parolees
released to halfway houses with a similar
set of parolees not released to halfway
houses, Lowenkamp and colleagues (2006)
reported that higher program integrity
was associated with larger reductions in
recidivism for halfway house residents
relative to the comparison group.
More recently, Duwe and Clark (2015)
evaluated the impact of program integrity
on recidivism outcomes for Moving On,
a cognitive-behavioral program designed
for female offenders. From the time the
program was implemented in Minnesota’s
lone female prison until 2010, Moving
On operated with relatively high fidelity;
but from 2011 to 2013, changes were
made in the program that compromised
that fidelity. The length of the class was
shortened from 12 weeks to three weeks,
class time diminished from 48 hours to 30
hours, role-playing exercises were removed,
the program went from being voluntary
to mandatory, and class sizes ballooned
from five to 10 offenders per class to more
than 40. Using three different sets of
comparisons, Duwe and Clark (2015) found
the high-fidelity program significantly
reduced reoffending, but the low-fidelity
program did not. Moreover, when directly
comparing the high-fidelity version with
the low-fidelity version, offenders who
participated in the high-fidelity version had
significantly better recidivism outcomes.
This review does not focus on “correctional
quackery” programs, such as shock-
based interventions, music therapy, or
pet therapy, which are not grounded in
sound criminological theory and have not
been subjected to much (if any) empirical
evaluation. Instead, the focus here is on
programs often provided in state and
federal prisons across the U.S. that have
not only been evaluated, but also attempt to
address one or more criminogenic needs.
Nevertheless, it bears repeating that the vast
majority of correctional program evaluation
research has generally ignored the issue of
program integrity. The empirical evidence
reviewed for this paper should therefore be
filtered through this prism of inattention
to program integrity, which suggests that
the variability in effectiveness among
correctional programs may have more to
do with program-fidelity issues than with
the design and content of the programs
themselves.
Quality of the “What Works”
Literature
Another lens through which the “what
works” literature must be viewed has
to do with the quality of the research.
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Meta-analyses rely on findings from
individual program evaluations to produce
an aggregate effect size for an intervention.
Although effort is often made to account
for the rigor with which each individual
program is evaluated, meta-analyses are
still limited, to a large extent, by the
quality of the evidence. Meta-analyses have
been published on most of the types of
programs reviewed in this paper, and these
meta-analyses have identified a number
of problems that apply to much of the
correctional program evaluation literature.
First, randomized controlled trials (RCTs),
which are widely considered to be the “gold
standard” in program evaluation research,
have seldom been used within corrections.
For example, in their first meta-analysis
of the sex offender treatment literature,
Lösel and Schmucker (2005) examined 80
comparisons (69 studies) between treated
and untreated sex offenders. Of these
comparisons, only six (7 percent) used a
randomized experimental design — most
notably, the research by Marques and
colleagues (Marques et al., 1994; Marques,
1999; Marques et al., 2005) — while seven
(9 percent) used individual matching or
statistical control in an effort to achieve
equivalence between the treatment and
comparison groups. Instead, most sex
offender treatment studies have used
either nonequivalent comparison groups
(60 percent) or research designs in which
equivalence was assumed between the
treated and untreated groups (24 percent).
Second, given the infrequency with which
random assignment or matching techniques
(e.g., propensity score matching) have been
used, selection bias has been identified
as a problem that plagues much of the
correctional program evaluation research
(Harkins & Beech, 2006; Jones, Pelissier,
& Klein-Saffran, 2006; Pellisier et al.,
2001; Rice & Harris, 2003). In evaluations
of treatment effectiveness, selection bias
refers to differences — both observable
and unobservable — between the treated
and untreated groups that make it difficult
to determine whether the observed effects
are due to the treatment itself or to the
different group compositions. Therefore,
although an evaluation may find that
recidivism rates are generally lower for
offenders who participate in treatment,
this difference may not necessarily be due
to the treatment itself but, rather, to other
differences between treated and untreated
offenders.
Other commonly identified problems
include small sample sizes, failure to
include program dropouts within the
treatment group, and the use of short
follow-up periods for recidivism (Welsh,
2002; Lösel & Schmucker, 2005). Although
it must be acknowledged that sufficient
rigor is lacking in many of the existing
correctional program evaluations, this
paper generally focuses on higher quality
evidence in reviewing the impact of
institutional programming on pre- and
post-release outcomes. This review will
emphasize the results from meta-analyses
as well as the findings from individual
evaluations that used rigorous methodology
(e.g., RCTs, regression-discontinuity,
or propensity score matching with
quasi-experimental designs) to achieve
equivalence between the treatment and
comparison groups. This paper also
includes technical reports and studies
published in peer-reviewed academic
journals.
Educational Programming
Education, like employment, is considered
to be a moderate criminogenic need
(Andrews, Bonta, & Wormith, 2006).
Compared to the general public, prisoners
are often undereducated. For example,
Duwe and Clark (2014) reported that
roughly two-fifths of offenders entering
Minnesota prisons had neither a high
school diploma nor a General Educational
Development (GED) degree. The
prevalence of educational programming in
prisons is likely due to the well-documented
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relationship between low educational
achievement and antisocial behaviors.
Several studies have linked poor academic
performance among adolescents to juvenile
delinquency and future offending, although
the direction of the causal relationship
remains unclear (e.g., Farrington, 2005;
Hagan & McCarthy, 1997; Huizinga et al.,
2000; Maguin & Loeber, 1996; Moffitt,
1993). A felony record diminishes the
likelihood of future employment (Berstein
& Houston, 2000), and many offenders have
unstable work histories (Visher, LaVigne,
& Travis, 2004). Moreover, unemployment
rates appear to directly correspond with
levels of education, and the employment
prospects for offenders are already weak,
regardless of their educational attainment.
In reviewing the impact of educational
programming on prison misconduct, the
literature has yielded mixed results. In
their meta-analysis, French and Gendreau
(2006) report that educational or
vocational programming was not associated
with a decrease in discipline infractions.
Although Steiner and Woolredge (2008)
initially reported that participation in
education programming actually increased
misconduct, they later found that time
spent in educational or vocational
programming reduced nonviolent
misconduct (Steiner & Woolridge, 2014).
Most recently, Duwe and colleagues (2015)
found that participation in a prison bible
college significantly reduced misconduct.
Meta-analyses of research have shown
that prison education reduces recidivism,
although the effect sizes are usually modest.
Adams and colleagues’ (1994) review of
more than 90 studies of prison education
programs revealed that prison education
reduces the likelihood of recidivism,
especially for offenders with the largest
education deficits. Wilson, Gallagher,
and MacKenzie’s (2000) meta-analysis of
33 evaluations of prison-based education
programs showed modest increases in
post-release employment and reductions in
recidivism for participants. In particular,
they found that education programs
reduced recidivism by 11 percent. Aos,
Miller, and Drake (2006) found that basic
adult education programs in prison lowered
recidivism by more than 5 percent, and
prison-based vocational programs reduced
recidivism by more than 12 percent (based
on the results of three studies).
In the most recent meta-analysis, Davis and
colleagues (2013) examined the effects of
correctional education programming on
recidivism and post-release employment.
Analyzing previous studies, Davis and
colleagues reported that participation
in education programming reduced the
odds of recidivism by 43 percent and that
participating in secondary degree programs
yielded a 30 percent decrease in recidivism.
Finally, they found that participating in
education programming increased the odds
of post-release employment by 13 percent.
Since the publication of Davis and
colleagues’ (2013) meta-analysis, there have
been three separate rigorous evaluations
of prison-based educational programming.
In their study on Florida prisoners, Cho
and Tyler (2013) found that educational
programming improves post-release
employment outcomes. They did not
find, however, that it yielded a significant
decrease in recidivism. Using propensity
score matching, Kim and Clark (2013)
found that prison-based college education
programs significantly reduced recidivism
among New York prisoners. Examining
prisoners in Minnesota, Duwe and Clark
(2014) evaluated the effects of obtaining
secondary (GED or high school) and post-
secondary degrees in prison on post-release
employment and recidivism. They found
that obtaining a secondary degree in prison
increased the odds of securing post-release
employment by 59 percent but did not have
a significant effect on other employment
measures such as hourly wage, total hours
worked, and total wages earned. Moreover,
earning a secondary degree in prison did
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not have a significant effect on recidivism.
Obtaining a post-secondary degree in
prison, however, was associated with greater
number of hours worked and higher overall
wages. Furthermore, earning a post-
secondary degree significantly reduced
recidivism. Although it is important that
offenders obtain employment following
their release from prison, Duwe and
Clark (2014) argued that maintaining
employment is what appears to be critical in
reducing recidivism.
Although educational programming has
generally yielded modest effect sizes for
recidivism reduction, it has generated
relatively large cost-avoidance estimates.
Aos and Drake (2013) report a return on
investment (ROI) of $19.62 for prison-
based correctional education (basic and
post-secondary) and $13.21 for vocational
education. Duwe (2013a) reported that
every dollar spent on secondary and post-
secondary educational programming in
the Minnesota Department of Corrections
(MnDOC) generated $3.69 in cost-
avoidance benefits. Moreover, because of
the large number of offenders enrolled in
educational programming, it generated
the second-highest cost-avoidance estimate
($3.2 million) among more than a dozen
MnDOC programs evaluated.
Following is a summary of educational
programming’s effects on each of the four
outcomes examined in this paper:
Prison misconduct: The results are mixed
overall, although the evidence suggests that post-
secondary educational programming may yield
better outcomes.
Post-release employment: Both secondary and
post-secondary educational programming have
yielded positive results.
Recidivism: Although there have been
exceptions, the evidence suggests that educational
programming, especially post-secondary
education, reduces recidivism.
Cost-benefit: Existing research suggests that
educational programming produces a relatively
high return on investment.
Employment Programming
Research suggests that work is a buffer
against crime and, more narrowly,
recidivism (Skardhamar & Telle, 2012).
Individuals are less likely to commit crime
when they work more often (Uggen,
1999) and have employment that is stable
(Crutchfield & Pitchford, 1997), considered
satisfying (Uggen, 1999), and perceived as
having career potential (Huiras, Uggen, &
McMorris, 2000). As noted above, however,
offenders have criminal records and are
often undereducated, both of which make
it more difficult to find employment
following release from prison. To address
this criminogenic need, correctional
systems frequently provide prisoners
with employment programming, which
includes prison labor opportunities as well
as participation in programs such as work
release.
Although there have been exceptions
(French & Gendreau, 2006; Steiner
& Woolredge, 2008), employment
programming has generally been
found to reduce prison misconduct. As
discussed below, Saylor and Gaes (1997)
found that participation in the Federal
Bureau of Prisons’ (BOP’s) Post-Release
Employment Project (PREP) significantly
reduced misconduct. Furthermore, Gover,
Perez, and Jennings (2008) reported
that employment in prison reduced
disciplinary infractions. Similarly, Steiner
and Woolredge (2014) indicated that the
number of hours spent per week on a work
assignment was negatively associated with
both violent and nonviolent misconduct.
They also found that time spent in
educational or vocational programming
reduced nonviolent misconduct (Steiner &
Woolredge, 2014).
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In their meta-analysis of corrections-
based educational, vocational, and
work programs, Wilson, Gallagher, and
MacKenzie (2000) were able to identify only
four comparisons between offenders who
participated in a correctional work/industry
program and offenders who did not
participate in this type of programming.
Although the odds ratio for these four
contrasts was 1.48, which amounts to a
recidivism reduction of 20 percent, the
effect was not statistically significant.
Among the correctional work/industry
program evaluations analyzed by Wilson
and colleagues (2000) were studies of New
York’s Prison Industry Research Project
(PIRP) (Maguire, Flanagan, & Thornberry,
1988) and BOP’s PREP (Saylor & Gaes,
1997). In their evaluation of PIRP, Maguire
and colleagues did not find a statistically
significant difference in recidivism between
offenders who worked in prison industries
and those who did not. Unlike Maguire and
colleagues (1988), Saylor and Gaes (1997)
used propensity score matching and a Cox
proportional hazards model to control for
rival causal factors, including selection bias
and time at risk. Using a more sophisticated
and rigorous design, Saylor and Gaes
(1997) found that prison employment
significantly lowered recidivism and
increased employment.
In a more recent evaluation of a federal
prison industry program, UNICOR,
Richmond (2014) evaluated its impact on
recidivism among female prisoners. Also
relying on propensity score matching,
Redmond (2014) found that the program
did not reduce recidivism. Similarly, in
their evaluation of the Affordable Homes
Program (AHP), a prison work crew
program that trains Minnesota offenders
in the construction trade while they are
serving time in prison, Northcutt Bohmert
and Duwe (2012) report that the program
had no effect on recidivism. The results
from this study revealed, however, that
AHP participants did have significantly
higher odds of gaining employment in
a construction-related field than did
members of the comparison group but
did not have significantly higher odds of
gaining employment in “any field.”
U.S. correctional agencies have long
relied on the use of prison work release
programs, which have operated in the
U.S. since the 1920s (Turner & Petersilia,
1996). According to the most recent census
of state and federal correctional facilities,
all but one of the 50 states run a prison
work release program (Stephan, 2008).
Work release allows participants, who are
usually near the end of their prison terms,
to work in the community and return to
a correctional or community residential
facility during nonworking hours. Work
release provides offenders with a stable
residence in a controlled environment
and gives them opportunities to earn
income and accumulate savings for their
eventual release (Turner & Petersilia, 1996).
Moreover, because participants are granted
early release from prison and are typically
required to reimburse the state for part
of their confinement costs, work release
can help reduce prison overcrowding and
decrease correctional costs (Turner &
Petersilia, 1996).
Findings from existing evaluations, which
are generally outdated, suggest that work
release has, at best, a modest effect on
recidivism. Most notably, the two studies
that used a randomized experimental
design did not find that work release
reduced recidivism. For example, in
their evaluation of a Florida work release
program, Waldo and Chiricos (1977) found
that reoffending was not significantly
less among 188 work release participants
than among the 93 offenders from the
control group. Of the seven evaluations
using a quasi-experimental design, four
found that work release significantly
reduced recidivism (Drake, 2007; Duwe,
2014b; Rudoff & Esselstyn, 1973; LeClair
& Guarino-Ghezzi, 1991). Of these, the
most notable are the recent evaluations
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by Drake (2007) and Duwe (2014b). After
matching 3,913 offenders who did not
participate in Washington’s work release
program with 11,413 program participants,
Drake (2007) reported that the program
produced a statistically significant, albeit
modest, reduction in recidivism. Similarly,
in an evaluation of Minnesota’s work
release program, Duwe (2014b) found
that it significantly increased the hazard
of returning to prison for a technical
violation, although it significantly reduced,
albeit modestly, the risk of reoffending with
a new crime.
Although recidivism has been the main
outcome measure assessed in previous
work release evaluations, the three studies
that also examined employment have
yielded promising findings. Lamb and
Goertzel (1974) reported that work release
participants had higher employment rates
than did offenders in the control group.
Using self-report data, Witte (1977) found
that work release participants reported
higher employment rates and greater
overall earnings than did offenders in the
comparison group. Furthermore, Duwe
(2014b) indicated that work release did
not have an impact on hourly wage, but
it significantly increased the odds that
participants found work, the total hours
worked, and the total wages earned.
In contrast to employment programs that
provide services primarily in prison or
the community is Minnesota’s EMPLOY
program, which delivers services to
participants in both the institution and
the community. Duwe’s (2015b) evaluation
of the program found that approximately
60-90 days before their release from prison,
EMPLOY participants begin meeting with
a job training specialist to address issues
such as skills assessments, resumes, job
searching techniques, and interviewing
skills. During the week before a participant
is released from prison, a job development
specialist begins searching for job leads
based on the participant’s vocational skills
and calling employers who are known
to hire ex-offenders. Upon their release
from prison, a retention specialist provides
participants with a portfolio that contains
copies of their resumes, any certification
submitted to EMPLOY, job leads, and
additional resources or tools to assist them
with their job search. After this initial
meeting, the retention specialist maintains
contact with each participant during the
first year after release and continues to
provide support by helping the participant
with job leads and resume maintenance
(Duwe, 2015b). The EMPLOY evaluation
showed that the program significantly
increased employment and decreased
reoffending (Duwe, 2015b). Participants
were not only more likely than their
comparison group counterparts to find
jobs after their release from prison, but
they were also more likely to maintain their
employment, resulting in more total wages
earned.
Overall, the evidence suggests that while
the effect of prison labor on recidivism
is, at best, minimal, the impact on prison
misconduct and post-release employment
has generally been favorable. In their
cost-benefit analysis, Aos and Drake (2013)
report an ROI of $4.74 for the prison
industry. Among employment programs
that are more community-oriented, such
as work release, the findings have been
positive for employment and more mixed
for recidivism. Aos and Drake (2013) report
an ROI of $11.19 for work release and a
benefit of nearly $6,900 per participant.
In the evaluation of Minnesota’s work
release program, Duwe (2014b) reported
a cost avoidance of nearly $700 per
participant, for a total of $350,000 annually.
Furthermore, in a cost-benefit analysis
of MnDOC programming, Duwe (2013a)
reported that EMPLOY generated an ROI
of $6.45, for a total of $2.8 million in costs
avoided annually.
Following is a summary of the effects of
employment programming on each of the
four outcomes.
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Prison misconduct: Employment programming,
particularly prison labor, has generally been
found to reduce prison misconduct.
Post-release employment: Employment
programming has typically improved post-release
employment outcomes for offenders.
Recidivism: The results have varied by type
of program. Prison labor has not consistently
been found to lower recidivism and work release
produces, at best, a modest reduction. The most
promising findings have been for employment
programming that provides a continuum of
service delivery.
Cost-benefit: Existing research suggests that
employment programming produces a solid, if
unspectacular, ROI.
Cognitive Behavioral Therapy
Cognitive behavioral therapy (CBT)
programs generally address the link
between dysfunctional thought processes
and harmful behaviors through timely
reinforcement and punishment, as well as
role-playing and skill-building exercises.
These programs seek to improve decision-
making and problem-solving skills, and
to teach individuals how to manage
various forms of outside stimuli. The
programs attempt to reduce recidivism
by targeting an array of risk factors,
including general antisocial cognition and
chemical dependency. It is worth noting
that other types of programs, including
substance abuse treatment and sex offender
treatment, are often delivered within a
cognitive-behavioral framework. This
section of the paper, however, focuses
on the more general CBT programs that
address multiple criminogenic needs, but
mostly criminal thinking.
Reasoning & Rehabilitation (R&R), Moral
Reconation Therapy (MRT), and Thinking
for a Change (TFAC) are among the most
widely used and evaluated CBT programs
for offenders. While Van Voorhis et al.
(2004) found that R&R did not significantly
improve employment or recidivism
outcomes among Georgia parolees, the
program has generally proven successful
in reducing reoffending, particularly for
Canadian offenders. Evaluations of MRT
have shown that it reduces recidivism
(Ferguson & Wormith, 2012; Little,
Robinson, & Burnette, 1993), although
Armstrong (2003), using an RCT, found
that it did not have a significant effect on
recidivism among youthful jail inmates in
Maryland. While TFAC has been found
to improve recidivism outcomes, much
of its success has been with probationers
(Golden, Gatchel, & Cahill, 2006;
Lowenkamp et al., 2009). Although less
research has been conducted on Moving
On, a gender-responsive CBT designed
specifically for female offenders, results
from the two evaluations on this program
indicate that it is effective in decreasing
recidivism (Duwe & Clark, 2015; Gehring,
Van Voorhis, & Bell, 2010).
Overall, CBT programs have been found to
be successful in reducing prison misconduct
and recidivism. In their meta-analysis on
what works to reduce prison misconduct,
French and Gendreau (2006) concluded
that CBT programs are the most effective
intervention for curbing disciplinary
infractions. CBT has also been found to be
one of the more effective correctional tools
for reducing recidivism (Allen, MacKenzie,
& Hickman, 2001; Landenberger & Lipsey,
2005; Lipsey, Chapman, & Landenberger,
2001; Lipsey, Landenberger, & Wilson,
2007; Pearson et al., 2002; Wilson, Bouffard,
& MacKenzie, 2005). The results from these
meta-analyses have generally shown that
CBT programs reduce recidivism by 20
percent to 30 percent. Larger reductions
have been found for programs that
targeted higher risk offenders, had high-
quality treatment implementation, and
included anger control and interpersonal
problem solving. None of the brand-name
programs, such as TFAC, MRT, or R&R, did
significantly better or worse. Furthermore,
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the setting in which the programming was
delivered — prison or the community —
did not have a significant impact on effect
size (Landenberger & Lipsey, 2005; Lipsey,
Landenberger, & Wilson, 2007).
CBT programs have also performed well in
cost-benefit analyses. Aos and Drake (2013)
found that CBT programs for moderate-
and high-risk offenders yield a significant
ROI. The researchers reported a cost-
benefit ratio of $24.72; every dollar spent
on CBT programming yielded $24.72 in
benefits. Of the 26 interventions for adult
offenders, CBT had the third highest ROI
(Aos & Drake, 2013).
Following is a summary of the effects
of CBT programming on the outcomes
examined in this paper.
Prison misconduct: CBT programs have been
found to have the best outcomes for prison
misconduct.
Post-release employment: No evidence is
available.
Recidivism: CBT programs have produced
relatively strong results in reducing recidivism.
Cost-benefit: CBT programs have been found to
provide some of the highest ROIs for correctional
programming.
Chemical Dependency
Treatment
Among state and federal prisoners
incarcerated in 2004, Mumola and Karberg
(2006) reported that 32 percent committed
their offenses while under the influence
of drugs and 56 percent had used drugs
in the month preceding the offense.
Substance abuse has been identified as a
moderate criminogenic need, although
recent research suggests that it may be a
more important risk factor for recidivism
(Caudy, Durso, & Taxman, 2013). Given the
relatively high rate of substance abuse and
dependency among incarcerated offenders,
efforts to reduce their risk of reoffense
often include the provision of prison-based
CD treatment.
Previous evaluations of prison-based
CD treatment have concentrated mainly
on programs that use the therapeutic
community (TC) model. Originating in
England during the late 1940s, the TC
model regards CD as a symptom of an
individual’s problems rather than the
problem itself (Patenaude & Laufersweiller-
Dwyer, 2002). Viewing substance abuse as
a disorder that affects the whole person,
the TC model attempts to promote
comprehensive prosocial changes by
encouraging participants to contribute
to their own therapy and to that of others
through activities such as therapy, work,
education classes, and recreation (Klebe
& O’Keefe, 2004). Individual and group
counseling, encounter groups, peer
pressure, role models, and a system of
incentives and sanctions often comprise
the core of treatment interventions in a
TC program (Welsh, 2002). To foster a
greater sense of community, participants
are housed separately from the rest of the
prison population.
Very little research has examined the effects
of CD treatment on prison misconduct.
For example, Steiner and Woolredge
(2008) reported that participation in drug
treatment programming actually increased
misconduct.
Instead, previous evaluations have focused
on relapse and, more often, recidivism.
These studies have evaluated prison-based
TC programs for federal prisoners (Pelissier
et al., 2001) as well as for state prisoners
in California (Prendergast et al., 2004;
Wexler et al., 1999), Delaware (Inciardi et
al., 1997; Inciardi, Martin, & Butzin, 2004),
Minnesota (Duwe, 2010), New York (Wexler,
Falkin, & Lipton, 1990), Oregon (Field,
1985), Pennsylvania (Welsh, 2007) and
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Texas (Knight et al., 1997; Knight, Simpson,
& Hiller, 1999).
In general, the findings from these studies
suggest that prison-based treatment can
be effective in reducing recidivism and
relapse. Indeed, in the most recent meta-
analysis of the incarceration-based drug
treatment literature, Mitchell, Wilson, and
MacKenzie (2007) found that treatment
significantly decreased subsequent criminal
offending and drug use in their review
of 66 evaluations. The average treatment
effect sizes for recidivism and drug use were
odds ratios of 1.37 and 1.28, respectively
(Mitchell et al., 2007). In a more recent
review, Bahr, Masters, and Taylor (2012)
report that CBTs, TCs, and drug courts
were the most effective types of substance
abuse programs.
Existing research highlights the importance
of aftercare, as the most promising
outcomes have been found for offenders
who complete prison-based TC programs,
especially those who participate in post-
release aftercare (Butzin, Martin, &
Inciardi, 2005; Inciardi et al., 2004; Mitchell
et al., 2007; Pearson & Lipton, 1999). In
addition, Duwe (2010) and Wexler, Falkin,
and Lipton (1990) reported that treatment
effectiveness is related to the length of time
an individual remains in treatment, but
only up to a point. As time in substance
abuse treatment increased, so did the time
until recidivism. The risk of recidivism was
greater, however, for offenders who had
been in the treatment program for a year or
more (Duwe, 2010; Wexler et al., 1990).
In their cost-benefit research on
correctional programming, Aos and
Drake (2013) reported an ROI of $14.82
for inpatient/intensive drug treatment
and $31.34 for outpatient/nonintensive,
prison-based drug treatment. In research
on Minnesota prisoners, Duwe (2013a)
found that for every dollar spent on CD
treatment, the program generated $6.32
in benefits. Moreover, with relatively high
enrollment compared to other MnDOC
programs, CD treatment produces an
estimated $22 million in costs avoided
each year, accounting for approximately
60 percent of the overall cost-avoidance
benefits produced by MnDOC
programming.
Following is a summary of the effects of CD
treatment on the four outcomes.
Prison misconduct: Very little evidence exists,
although one study found that prison-based drug
treatment increased misconduct.
Post-release employment: No evidence is
available, although CD treatment has been found
to be successful in preventing relapse.
Recidivism: Results generally show that prison-
based CD treatment is successful in reducing
recidivism, especially if the treatment provides a
continuum of care, uses a TC, and is delivered
within a cognitive-behavioral framework.
Cost-benefit: Existing research reveals relatively
strong ROI outcomes for prison-based CD
treatment, especially for outpatient/nonintensive
programs.
Sex Offender Treatment
Existing research has shown that compared
to other offenders, sex offenders are
among the least likely to reoffend (Harris
& Hanson, 2004; Langan & Levin, 2002;
Sample & Bray, 2006). Moreover, when sex
offenders recidivate, they are much more
likely to do so with a nonsexual offense
(Langan, Schmitt, & Durose, 2003).
Although sex offenders are among the least
likely to recidivate in general, they are still
more likely than other offenders to reoffend
sexually (Langan & Levin, 2002). When
sex offenders recidivate with a sex offense,
at least 75 percent victimize individuals
(both adults and children) they already
know (Greenfield, 1997; Snyder, 2000).
Common predictors of sexual recidivism
include an antisocial orientation (e.g.,
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history of rule violation), deviant sexual
interests, a history of victimizing strangers,
conflicts in intimate relationships,
emotional identification with children, and
prior noncontact sex offenses (Hanson &
Morton-Bourgon, 2004). The risk of sexual
recidivism is lower for incest offenders, first-
time sex offenders, those over the age of 50,
and those who target female children rather
than male children (Harris & Hanson,
2004).
Given the heightened interest in and
concern about sexual offending, the
deeply destructive effects of these crimes
on victims, and the fact that previous sex
offenses generally increase the risk of
sexual offending, the past several decades
have brought forth a host of legislative
efforts to control sexual offending,
particularly for offenders previously
convicted of a sex crime. To a large extent,
the guiding principle behind longer prison
sentences for sex crimes, registration
and notification, residency restrictions,
involuntary civil commitment, and lifetime
probation and parole for sex offenders is
that incidences of sexual offending can be
reduced by increasing the risks and costs
associated with committing a sex offense.
Although these legislative strategies have
been grounded in the punitive ideologies
of deterrence and just deserts, sex offender
treatment has been widely used to lower
sexual recidivism.
Since the 1960s, dozens of studies from
a number of countries have examined
whether sex offender treatment reduces
recidivism. The earliest studies drew
pessimistic conclusions about the
effectiveness of treatment. For example,
in their review of the treatment literature,
Furby, Weinrott, and Blackshaw (1989)
argued that, due to methodological
shortcomings, there was insufficient
evidence to support the notion that
treatment decreases sex offender
recidivism. Several years later, Quinsey et al.
(1993) reached a similar conclusion in their
review of existing treatment studies.
From the mid-1990s to the mid-2000s,
however, meta-analyses of the treatment
literature found, with a few notable
exceptions (Kenworthy et al., 2004; Rice &
Harris, 2003), lower sexual recidivism rates
for treated sex offenders in comparison
with untreated offenders (Alexander,
1999; Gallagher et al., 1999; Hall, 1995;
Hanson et al., 2002; Lösel & Schmucker,
2005). Among the meta-analyses that have
found a treatment effect, the rate of sexual
reoffense has been 5 percent to 10 percent
less for those who participated in treatment.
Since the publication of these meta-
analyses, there have been several rigorous
evaluations of prison-based sex offender
treatment. Examining more than 2,000
Minnesota sex offenders, Duwe and
Goldman (2009) found that participating in
treatment significantly reduced the hazard
of rearrest — by 27 percent for sexual
recidivism, 18 percent for violent recidivism,
and 12 percent for general recidivism.
Analyzing a smaller sample of sex offenders
in North Carolina’s prison system, Grady
and colleagues (2012) did not find that
treatment significantly reduced sexual
recidivism.
Most recently, Lösel and Schmucker (2015)
published an updated meta-analysis of
the sex offender treatment literature.
This time, they limited their analysis to
evaluations that used official measures of
recidivism as outcome criteria and those
that used equivalent treatment and control
groups. After restricting their focus to
more methodologically sound studies,
they reported a 3.6 percent difference in
sexual recidivism rates between treated and
untreated sex offenders, resulting in a 26
percent reduction in sexual reoffending.
The best outcomes, they concluded, were
associated with programs that delivered
cognitive-behavioral and multisystemic
treatment.
Although the literature on sex offender
treatment has not examined its effects
on misconduct or other outcomes such
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as employment, at least two studies have
estimated its cost-avoidance benefits. Aos
and Drake (2013) reported an ROI of $2.05
for prison-based sex offender treatment,
and Duwe (2013a) reported an ROI of $3.11
for prison-based sex offender treatment
in Minnesota. With annual enrollment
of approximately 200 sex offenders, the
program generates nearly $2.9 million in
cost-avoidance benefits per year.
Following is a summary of the effects of sex
offender treatment on the four outcomes:
Prison misconduct: No evidence is available.
Post-release employment: No evidence is
available.
Recidivism: The evidence indicates that, in
general, sex offender treatment significantly
lowers sexual recidivism.
Cost-benefit: Existing research suggests that
prison-based sex offender treatment provides a
moderate ROI.
Social Support Programming
Associating with antisocial peers is, as
noted earlier, one of the big four risk
factors and, thus, has been characterized
as a major criminogenic need. A prison
inmate is surrounded by peers who are
also incarcerated for antisocial, criminal
behavior. Yet, even among prisoners, one
relatively objective measure for determining
whether offenders are maintaining
antisocial relationships is their security
threat group (STG) status. Offenders who
are active STG members (i.e., an active gang
affiliation) are, in general, committed to
preserving a criminal lifestyle. Research has
shown that gang membership is not only
positively associated with prison misconduct
(Gaes et al., 2002; Tewksbury, Connor,
& Denney, 2014), but it also significantly
increases the risk of recidivism, at least for
male offenders (Duwe, 2014c).
Despite the salience of antisocial peers
as a risk factor for both misconduct and
recidivism, there are relatively few formal
institutional programs that are dedicated
to addressing this criminogenic need by
helping offenders maintain, develop, or
enhance prosocial sources of support.
Prison visitation is seldom identified as a
type of correctional program per se, but it
is arguably the most prominent source of
prosocial support for prisoners. Perhaps not
surprisingly, then, research has generally
shown that prison visitation is associated
with reduced misconduct and recidivism.
Jiang and Winfree (2006) found that visits
by children were not significantly associated
with self-reported misconduct. Relying on
administrative data to measure discipline
convictions, Siennick, Mears, and Bales
(2013) report that the odds of misconduct
were lower before a visit, but higher
afterwards. Other research has shown
that prison visitation significantly reduces
misconduct (Cochran, 2012; Tewksbury &
Connor, 2012).
The findings from studies on prisoners in
Florida (Bales & Mears, 2008; Cochran,
2014; Mears et al., 2012), Minnesota (Duwe
& Clark, 2013), and Canada (Derkzen,
Gobeil, & Gileno, 2009) suggest that
prison inmates who are visited more often
are less likely to recidivate. Although
Cochran (2014) found lower recidivism
rates for offenders who were visited early
in their incarceration, results from the
Bales and Mears (2008) and Duwe and
Clark (2013) studies suggest that visits that
occur closer to an offender’s release were
more important in reducing recidivism.
In addition, Duwe and Clark (2013) found
that recidivism decreased as the number of
individual visitors increased.
Several studies have examined whether
some offender-visitor relationships are
more beneficial than others in reducing
recidivism. The results of two Florida
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studies suggest that visits from spouses
or significant others were associated with
better recidivism outcomes (Bales & Mears,
2008; Mears et al., 2012). In both studies,
offender-visitor relationships comprised
seven categories: parent, spouse, significant
other, child, relative, friend, and other. In
their study on visitation with Minnesota
prisoners, Duwe and Clark (2013) analyzed
the effects of visitor type on recidivism in
greater detail by examining 16 offender-
visitor relationship categories. They found
that visits from siblings, in-laws, fathers,
clergy, and, to a lesser extent, mentors were
the most beneficial in reducing the risk of
recidivism.
Despite the generally positive outcomes
associated with prison visitation, the
literature indicates that many prison
inmates are not visited at all. For example,
the rate of unvisited offenders in previous
studies varied from a low of 39 percent
(Duwe & Clark, 2013) to a high of 58
percent (Bales & Mears, 2008). In an effort
to better understand why some inmates are
visited more often in prison, Tewksbury and
Connor (2012) analyzed visitation among
a sample of 585 male prisoners. Offenders
who were white, younger, more educated,
and admitted to prison on a new sentence
received significantly more visits. Inmates
who were identified as gang members
or had longer criminal histories and
disciplinary records in prison received fewer
visits (Tewksbury & Connor, 2012).
Faith-based programming can offer
prosocial support. One of the main
components of the InnerChange Freedom
Initiative (IFI), a faith-based program run
by Prison Fellowship Ministries, involves
providing participants with volunteer
mentors from the community. Mentors are
expected to meet with IFI participants on
a weekly basis during the last six months
of their incarceration and continue to
meet with them following their release.
Johnson and Larson (2003) reported
that participation in an IFI program that
originated in a Texas correctional facility in
1997 did not significantly lower recidivism
for all participants. However, in a more
recent evaluation of an IFI program in
Minnesota’s prison system, Duwe and King
(2013) found that program participation
significantly reduced reoffending. As they
explain, the beneficial recidivism outcomes
for program participants may have been
due, in part, to the continuum of mentoring
support that some offenders received in
both the institution and the community.
Results from a study by Camp and
colleagues (2008) further suggest that
faith-based programs can also improve
inmate behavior within the institution. In
their evaluation of BOP’s Life Connections
Program, the researchers found that
participation significantly decreased
more serious forms of misconduct but
participation had no impact on minor
infractions.
Research on Circles of Support and
Accountability (CoSA), a sex offender
re-entry program, offers additional
evidence that providing offenders,
especially those who are higher risk, with
prosocial support is effective in reducing
recidivism. Designed as an intervention to
be used for high-risk sex offenders, CoSA
involves surrounding a “core member”—
the sex offender participant — with a small
group (four to six) of community volunteers
who provide offenders with support
and help them to remain accountable
during the transition from prison to
the community. An evaluation of CoSA
involving sex offenders from Canada, where
the program originated, showed that CoSA
significantly lowered recidivism, including
sexual reoffending (Wilson, Cortoni, &
McWhinnie, 2009). Similarly, using an
RCT to evaluate the CoSA program in
Minnesota, Duwe (2013b) reported that it
significantly decreased multiple measures
of recidivism.
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To date, researchers have not examined
whether prison visitation produces
cost-avoidance benefits. However, in a
follow-up evaluation of the IFI program
in Minnesota, Duwe and Johnson (2013)
showed that the program yielded nearly
$8,300 in costs avoided per participant.
With an average enrollment of at least
90 offenders each year, IFI produces
approximately $750,000 annually in
cost-avoidance benefits. Moreover, in his
evaluation of Minnesota Circles of Support
and Accountability (MnCoSA), Duwe
(2013b) found that every dollar spent on
MnCoSA returned $1.82 in benefits over
a three-year period. The program also
generates nearly $94,000 in cost-avoidance
benefits (Duwe, 2013a).
Following is a summary of social support
programming’s effects on each of the four
outcomes.
Prison misconduct: Existing research has
generally found that prison visitation decreases
misconduct.
Post-release employment: No evidence is
available.
Recidivism: The evidence indicates that social
support programming is successful in lowering
offender recidivism.
Cost-benefit: Little evidence currently exists,
although a few evaluations of programs that
provide social support suggest that they deliver a
solid ROI.
Mental Health Programming
Compared to the general population,
prisoners have relatively high rates of
mental illness (Fazel & Danesh, 2002). In a
study that reported the results of interviews
with more than 20,000 offenders across
the United States, James and Glaze (2006)
found that nearly two-thirds of jail inmates
and more than half of state and federal
prisoners reported having a mental health
problem. The researchers also found that
offenders with mental illness — who were
more likely to be female, white, and young
— experienced higher rates of institutional
misconduct, homelessness, substance abuse,
and previous physical or sexual abuse.
In addition to demonstrating that
individuals with major mental disorders
have an elevated risk for violence, especially
if they misuse substances (Silver, 2006),
research has shown that mental illness is
associated with higher recidivism rates
(Eno Louden & Skeem, 2011; Messina
et al., 2004; Porporino & Motiuk, 1995).
Although Andrews and colleagues (2006)
acknowledge that major mental illness is a
risk factor for recidivism, they emphasize
that it has only a modest, indirect impact
on reoffending. They argue that any effect
of mental illness on recidivism likely
reflects the impact of substance abuse (one
of the “central eight” risk factors) along
with antisocial cognition and antisocial
personality pattern (two of the big four).
Several recent studies have not only
confirmed that mental illness is a weak
predictor of recidivism, but also that the
same risk factors (i.e., the central eight)
apply to all offenders, regardless of whether
they have a mental disorder (Bonta, Blais, &
Wilson, 2014; Hall et al., 2012).
As Skeem, Manchak, and Peterson (2011)
point out, mental health interventions that
have proven successful in improving clinical
outcomes such as reduced hospitalizations
have not been especially effective in
lowering recidivism when they have been
adapted to correctional populations.
Indeed, evidence indicates that programs
focused on linking offenders to mental
health services have not yielded positive
recidivism outcomes overall, primarily
because these interventions assume that
reoffending is caused by untreated mental
illness (Barrenger & Draine, 2012; Duwe,
2015a; Lurigio, 2011).
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Despite mental health interventions’ relative
lack of success in reducing recidivism, some
correctional programs have yielded positive
outcomes. In their evaluation of a modified
therapeutic community (MTC) program,
Sacks and colleagues (2004) compared
offenders released from prison with
co-occurring substance abuse and mental
disorders who participated in an MTC
program to participants from a traditional
mental health program. They not only found
that MTC participants had significantly
lower reincarceration rates, but also that
the best outcomes were observed for
completers of the in-prison MTC program
who participated in the community-based
aftercare portion of the program following
their release from prison. Skeem and
colleagues (2011) noted that the MTC
program evaluated by Sacks and colleagues
(2004) was the only program reviewed that
targeted criminal thinking in addition to
symptoms of mental illness.
Much like the MTC program evaluated
by Sacks and colleagues, Washington
state’s Dangerous Mentally Ill Offender
(DMIO) program, now called the Offender
Reentry Community Safety Program,
focused on providing offenders with
mental disorders with a continuum of care
from the institution to the community.
The legislatively mandated program
provides interagency collaboration and
state-funded mental health and substance
abuse treatment, housing, and other
support services. Following designation
as a DMIO, which typically occurs six
months before release, an offender is
immediately assigned a treatment provider
by the Department of Social and Health
Services. In the final 90-120 days before
release, DMIO program participants
receive pre-engagement services and special
treatment and transition planning. For
up to five years after their release from
prison, DMIO participants receive services
(based on their assessed needs) that may
include mental health and substance abuse
treatment, housing and medical assistance,
training, and other support services (Lovell,
Gagliardi, & Phipps, 2005).
In the initial evaluation of the program,
Lovell and colleagues (2005) found that
program participants were more likely
to receive prerelease community mental
health services, obtain steady service in the
first year after release, and be served more
rapidly and in higher proportions than was
the comparison group. Similarly, a more
recent evaluation found that the program
reduced felony recidivism by 42 percent
and violent felony recidivism by 36 percent
(Mayfield, 2009). Furthermore, results of
a cost-benefit analysis indicate that the
benefit per participant is nearly $25,000
and generates $1.75 in benefits for every
dollar spent on the program (Aos & Drake,
2013).
Following is a summary of the impact of
mental health programming on the four
outcomes:
Prison misconduct: No evidence is available.
Post-release employment: No evidence is
available.
Recidivism: Existing research has found that
mental health interventions do not reduce
recidivism when the programming targets mental
health symptoms. There is, however, some
evidence indicating that these interventions
can lower reoffending if they also target known
criminogenic needs.
Cost-benefit: Little evidence exists, although
evaluations of Washington’s DMIO program
have yielded strong ROI outcomes.
Domestic Violence
Programming
Family criminality (i.e., parents or siblings
in trouble with the law), rearing practices
(e.g., conflict, abuse, lack of supervision and
affection), and structure (e.g., separation
from parents, broken home, foster parents)
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have been found to be significant, static
predictors of recidivism (Gendreau,
Little, & Goggin, 1996). Gendreau and
colleagues further note that interpersonal
conflict, which includes family discord
and conflict with significant others, is a
dynamic predictor of reoffending. Given
the association between family conflict
and recidivism, family/marital conflict
is, as noted earlier, a moderate risk factor
(Andrews, Bonta, & Wormith, 2006). To
address this criminogenic need, corrections
agencies frequently provide offenders with
programming designed to reduce domestic
violence (DV) recidivism.
Due to the absence of research on the
effects of DV programming on prison
misconduct or post-release employment,
there is no evidence as to whether this
intervention affects either outcome. A fairly
large number of evaluations have assessed
DV’s relationship with recidivism and,
similar to the literature on mental health
interventions, the results have not been
favorable. Several relatively recent, rigorous
evaluations, such as those by Gordon and
Moriarity (2003), Labriola, Rempel, and
Davis (2008), and Haggard and colleagues
(2015) found that DV programs had no
impact on recidivism. In a meta-analysis
of 22 evaluations, Babcock, Green, and
Robie (2004) report that DV interventions,
including the commonly used Duluth
model and those delivered within a CBT
framework, did not reduce reoffending.
Furthermore, in a more recent meta-
analysis that focused on more rigorous
evaluations, Miller, Drake, and Nafziger
(2013) also concluded that DV programs
failed to lower recidivism. In their cost-
benefit analysis of adult correctional
programs, Aos and Drake (2013) report that
DV programming actually costs, rather than
saves, taxpayer dollars. For every dollar
spent on DV interventions, these programs
cost taxpayers, on average, an additional
$4.41; in fact, DV programs had the worst
ROI of the more than two dozen types of
interventions evaluated.
Why have DV programs been ineffective
at reducing recidivism? In a recent study,
Radatz and Wright (2015) argue that this
failure is largely due to a lack of adherence
to the principles of effective correctional
intervention. Although some DV programs
use a cognitive-behavioral approach, Radatz
and Wright (2015) suggest that, in general,
these programs may not be adequately
aligned with the risk, need, and responsivity
principles. In particular, feminism-based
programs such as the Duluth model
emphasize altering patriarchal attitudes.
Instead, as Radatz and Wright contend,
DV programs should focus more on
addressing known criminogenic needs such
as antisocial attitudes, substance abuse, and
social support.
Following is a summary of DV
programming’s effects on the four outcome
measures:
Prison misconduct: No evidence is available.
Post-release employment: No evidence is
available.
Recidivism: Existing research has found that DV
interventions do not reduce recidivism.
Cost-benefit: Little evidence currently exists,
although Washington state found that DV
programs actually cost, rather than save,
taxpayer dollars.
Prisoner Re-Entry Programs
Since the turn of the 21st century, prisoner
re-entry has attracted a great deal of
interest for a few key reasons. First, despite
the modest downturn in the imprisonment
rate over the last ten years, the prison
population boom over the previous several
decades had led to a rise in the volume of
offenders released from prison. Second, as
illustrated by the large recidivism studies
conducted by the Bureau of Justice Statistics
(Durose, Cooper, & Snyder, 2014; Langan
& Levin, 2002), the available evidence
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suggests that released prisoners tend to
have relatively low success rates.
In response to concern about growing
numbers of released prisoners with
seemingly high recidivism rates, the
federal government has sponsored
several major initiatives that have led to
the implementation of community-level
prisoner re-entry projects across the
country. In 2001, the Serious and Violent
Offender Reentry Initiative (SVORI)
provided $100 million in funding to 69
grantees at 89 U.S. sites. Five years later,
the Prisoner Reentry Initiative provided
funding to support re-entry programs in
more than 30 states and, most recently, the
Second Chance Act has generated several
rounds of federal funding to aid local and
state agencies in the creation and operation
of offender re-entry projects.
The prisoner re-entry concept has been
broadly applied to any program that
attempts to reduce recidivism for offenders
released from prison. In general, however,
programs given the “prisoner re-entry”
label tend to focus on improving the
delivery of services and programming
across multiple areas such as housing,
education, employment, and substance
abuse treatment.
Among the published outcome evaluations
of offender re-entry programs, the findings
about these programs’ ability to reduce
recidivism have been mixed. Results
from evaluations of programs in Indiana
(McGarrell, Hipple, & Banks, 2003),
Maryland (Roman et al. , 2007), Minnesota
(Minnesota Department of Corrections,
2006; 2011), New York (Wilson & Davis,
2006; McDonald, Dyous, & Carlson, 2008)
and Pennsylvania (Smith & Suttle, 2008)
indicate that none of these programs
produced a statistically significant reduction
in reoffending. Some explanations offered
for the inability of these re-entry programs
to lower recidivism include program
design problems (Smith & Suttle, 2008;
Wilson & Davis, 2006), low dosage or short
program duration (McGarrell et al., 2003;
Smith & Suttle, 2008; Wilson & Davis,
2006), lack of administrative oversight
(Smith & Suttle, 2008), poor program
implementation (Minnesota Department of
Corrections, 2006; Wilson & Davis, 2006),
and the absence of a community aftercare
component (Wilson & Davis, 2006).
Results from outcome evaluations of
programs in California (Zhang, Roberts,
& Callanan, 2006), Massachusetts (Braga,
Piehl, & Hureau, 2009), Minnesota (Clark,
2014; Duwe, 2012, 2014a), New York (Jacobs
& Western, 2007) and Nebraska (Sample
& Spohn, 2008) suggest that they lowered
recidivism. Five of these programs focused
on improving employment outcomes for
participants (Braga, Piehl, & Hureau,
2009; Clark, 2014; Duwe, 2012; Jacobs &
Western, 2007; Zhang, Roberts, & Callanan,
2006), and three targeted substance abuse
(Jacobs & Western, 2007; Sample & Spohn,
2008; Zhang et al., 2006) and transitional
housing (Clark, 2014; Duwe, 2012; Zhang
et al., 2006). Two programs provided life
skills programming (Clark, 2014; Sample &
Spohn, 2008), and one delivered mentoring
services (Clark, 2014). Braga and colleagues
(2009) and Duwe (2012) also cited
interagency collaboration and increased
social support as important reasons why
these programs lowered recidivism.
As discussed above, millions of dollars
in state and federal funding have been
dedicated to the establishment of offender
re-entry programs over the past 10 to
15 years. Only a handful of evaluations,
however, have examined whether these
programs are cost effective. In their cost-
benefit analysis of the Maryland Reentry
Partnership Initiative (REP), Roman and
colleagues (2007) reported that for every
dollar spent on REP, the program produced
about $3 in benefits over an average
follow-up period of three years. The overall
benefits of the program amounted to more
than $7 million, or $21,500 per participant.
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Sample and Spohn (2008) stated that
Nebraska’s SVORI site generated more
than $10,000 in savings per participant
over a 12-month follow-up period, although
it is worth emphasizing that the authors
did not account for program operating
costs (i.e., SVORI funding) in their
analysis. In their cost-benefit analysis of
16 SVORI sites, Cowell, Lattimore, and
Roman (2010) found that the SVORI
group did not produce net benefits
relative to the comparison group over an
average follow-up period of nine months.
In cost-benefit research on Minnesota’s
prisoner re-entry programs, Duwe (2013a)
reported that two of the three projects
did not produce cost-avoidance benefits.
The Minnesota Comprehensive Offender
Reentry Plan program, however, yielded a
return of $1.80 for every dollar spent on the
program and generated $4,300 in benefits
per participant for a total of $600,000
annually (Duwe, 2013a).
Following is a summary of the effects of
prisoner re-entry programming on the four
outcomes:
Prison misconduct: No evidence is available.
Post-release employment: Some evidence
indicates that prisoner re-entry programs can
improve post-release outcome measures.
Recidivism: The results are mixed. Because
of the relative absence of program evaluations
that measure service delivery for offenders in
the treatment and comparison/control groups,
determining what distinguishes a successful
prisoner re-entry program from an unsuccessful
one is difficult.
Cost-benefit: With one exception, studies have
found that prisoner re-entry programs can deliver
a positive return on investment.
Discussion
Several broad conclusions can be drawn
about the effectiveness of institutional
programming. First, the evidence presented
herein indicates that CBT programs
have proven to be the most effective in
reducing prison misconduct. Moreover,
these programs, including substance abuse
treatment and sex offender treatment,
have consistently demonstrated success
in decreasing recidivism. CBT programs
also tend to yield an impressive ROI. CBT
programs had one of the highest ROIs
in Aos and Drake’s (2013) cost-benefit
analyses. CD treatment in Minnesota, which
is delivered within a cognitive-behavioral
framework, accounted for approximately
60 percent of the overall cost-avoidance
benefits produced by MnDOC programs
(Duwe, 2013a).
Second, social support interventions
have also shown success in decreasing
misconduct, reducing recidivism, and
producing cost-avoidance benefits but
have arguably been underused in U.S.
correctional systems. Programming
that increases prosocial sources of
support warrants greater attention as a
correctional intervention, not only because
of its demonstrated efficacy in reducing
recidivism, but also because of its potential
cost effectiveness. Compared with other
correctional programs, interventions
that focus primarily on increasing social
support for offenders are generally less
costly to operate. For example, programs
such as CoSA and IFI have relatively
low operational costs because they rely
heavily on volunteers from the community.
Similarly, efforts to promote greater
visitation in correctional facilities (e.g.,
revising institutional policies to make
them more visitor friendly, implementing
video visitation, etc.) are relatively low-
cost strategies that could yield significant
public safety benefits. As a result of
recent research findings, visitation has
increasingly been recognized as a way to
reduce recidivism but is not yet widely
considered to be a “correctional program.”
If that were to change, perhaps the RNR
framework might be applied to visitation,
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whereby efforts would be made to provide
or locate social support programming for
high-risk offenders who have a high “need”
for antisocial peers.
Third, education and employment
programs have, on the whole, produced
favorable outcomes for post-release
employment and cost avoidance. The results
for prison misconduct and recidivism are
more modest and inconsistent, although
still generally positive. When we look more
closely at the different types of educational
programming provided to prisoners,
more recent evidence suggests that post-
secondary educational programming
generates better outcomes in the areas
of prison misconduct and recidivism.
Similarly, although the evidence is far
from conclusive, research on employment
interventions suggests that a continuum of
employment programming may yield better
outcomes than that delivered exclusively in
prison or in the community.
Fourth, evidence indicates that it may
be unreasonable to expect interventions
designed to treat mental illness to reduce
prison misconduct or recidivism. Rather,
programs that address criminogenic needs
and deliver a continuum of care have shown
promise in producing favorable outcomes
for offenders with mental disorders.
Similarly, unless DV interventions begin
to consistently deliver programming that
targets known criminogenic needs (e.g.,
criminal thinking, substance abuse, and
antisocial peers), this type of programming
may continue to yield disappointing
results. This is not to say that mental health
interventions should not attempt to treat
the symptoms of offenders with mental
disorders or that DV programs should
not also address patriarchal attitudes, but
simply that programs should align with the
principles of effective intervention.
Finally, despite mixed results overall,
prisoner re-entry programs have shown
an ability to reduce recidivism, improve
employment, and yield cost-avoidance
benefits. Prisoner re-entry programs can
work, but much of what distinguishes
an effective re-entry program from an
ineffective one remains unknown. Many
of these programs attempt to provide an
array of services that address multiple
criminogenic needs. Because very few
evaluations have measured service delivery
for this treatment group relative to the
control or comparison group, it is unclear
whether the inconsistent results are due to
a failure by some programs to give more,
or better, services to participants in the
re-entry program.
Conclusion
Several areas warrant additional research
to further advance our understanding
of institutional programming and its
implications for correctional systems in the
U.S. First, the “what works” literature has,
by and large, used recidivism as the lone
metric to determine program performance.
As suggested by this review, other metrics,
including prison misconduct, intermediate
outcome measures such as employment
or abstinence from illicit substances, and
cost avoidance should also be used. The
use of multiple metrics provides a more
complete picture of program performance.
Educational programming is a case in
point: Were we to rely on recidivism as
the only performance metric, we would
conclude that educational programming
is modestly effective. When we consider
other metrics, such as employment and
cost avoidance, educational programming
appears to be a more effective intervention
that generally yields an impressive ROI.
Similarly, the use of multiple program
performance metrics should include
cost-benefit analyses on a more consistent
basis. To be sure, measures such as prison
misconduct, employment, and recidivism
are critical, but cost avoidance subsumes
all of these metrics and, thus, provides,
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arguably, a more complete assessment of
effectiveness. To illustrate, we would assume
that a program that has been found to be
successful in reducing recidivism is one
that works. Would that assessment change if
the costs to operate the program exceeded
the cost-avoidance benefits it generated
through reduced recidivism? Again, we
might consider the program to be highly
successful if we relied only on recidivism
as a measure of success. When we take cost
avoidance into account, we begin to see that
the program may ultimately be a burden,
rather than a boon, to taxpayers.
An increased emphasis on the cost
effectiveness of a program would not only
mean that more correctional program
evaluations would assess cost avoidance, but
also that a growing number of cost-benefit
analyses would likely, over time, lead to
insights about how to deliver correctional
programming more efficiently. For
example, whether a business is profitable
(and to what degree) often depends on
its economy of scale. In terms of cost
avoidance, is the same true for correctional
programs? Moreover, although violent
crime is, fortunately, less common than,
say, drug or property offenses, it is also
much costlier to society. Are interventions
that focus on reducing violent recidivism
more likely to yield a better ROI? If both
economy of scale and type of recidivism
reduced matter, what are the implications
for programs that are smaller in scale?
Second, as indicated by the review of the
literature, employment, substance abuse
treatment, social support, and mental
health programs are more likely to produce
positive outcomes when they provide a
continuum of care (or service delivery)
from prison to the community. Granted,
an intervention must address criminogenic
need(s), and it should be delivered with
therapeutic integrity. But the “what works”
evidence suggests that continuity of care
may be another critical component. For
example, among the programs reviewed
that targeted offenders’ education and
employment needs, Minnesota’s EMPLOY
program — the only such program that
provided a continuum of service delivery
from the institution to the community
— yielded some of the better recidivism,
employment, and cost-avoidance outcomes.
Similarly, in addition to adhering to the
principles of effective intervention, the
MTC program and Washington’s DMIO
program provided services in both the
institution and the community. Future
research should attempt to further clarify
the extent to which a continuum of
service delivery is associated with positive
outcomes, particularly for the types
of programming in which little or no
research on continuity of care exists (e.g.,
employment, CBT, sex offender treatment,
DV, etc.).
Third, as this review has illustrated, the
“what works” movement has produced a
large body of evidence on what has been
effective with offenders, particularly in
reducing recidivism. In shorter supply,
however, is evidence that reveals why
programs succeed or fail. The paucity of
empirical research on the link between
program integrity and recidivism is a
testament to this gap. Closely connected
to the scarcity of research on program
integrity and recidivism is the virtual
absence of studies that attempt to identify
the best policies and procedures for
implementing evidence-based practices.
In short, while we know a lot about “what
works” with prisoners, we know very little
about making “what works” work. Future
research should attempt to identify the
most effective methods for implementing
research findings within an applied
correctional context.
Fourth, amid the growing consensus
that some correctional programs work,
a more specific question has arisen:
What works best for whom, and under
what circumstances? As noted earlier,
research has shown that increasing the
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dosage and length of treatment (up to a
point) generally yields better recidivism
outcomes, particularly for offenders at
greatest risk of recidivism (Duwe, 2010;
Lowenkamp, Latessa, & Holsinger, 2006;
Sperber, Latessa, & Makarios, 2013; Wexler
et al., 1990). Yet, some have argued that
the sequencing of programming is also
important to maximizing the benefits of
effective interventions. That is, depending
on risk, need, and responsivity factors, the
order or timing of offenders’ participation
in programming may help improve
recidivism outcomes. For example, Mailloux
and colleagues (2003, p. 182) suggest that,
“it may be useful for offenders to complete
a program such as cognitive skills (which
introduces basic elements associated with
cognitive-behavioral therapy as well as
concrete suggestions as to how to apply
these principles to everyday situations)
prior to completing more intensive
therapeutic programs.” Similarly, it may be
more beneficial for prisoners to participate
in an intervention toward the end of their
confinement period, as opposed to the
beginning of their incarceration. Future
research should address whether the timing
and sequencing of programming matter.
Finally, the “what works” literature consists
of either individual program evaluations
or meta-analyses of multiple evaluations
for a specific type of intervention (e.g.,
CBT, sex offender treatment, substance
abuse treatment, etc.). Existing research,
however, has not examined the aggregate
effectiveness of programming within an
entire prison system, either at the state or
federal level. Moreover, very few, if any,
studies have recently documented the
extent to which prisoners are involved
in programming while incarcerated. As
a result of the scarcity of system-wide
research on the effectiveness of institutional
programming, future studies should
address a number of important questions.
For example, what percentage of prisoners
participate in an intervention? To what
extent does the provision of programming
affect system-wide recidivism rates? Similar
to the dosage issue noted above, does
providing offenders with access to multiple
interventions yield better outcomes? On
the other hand, what is the impact of
depriving inmates of programming? That
is, does “warehousing” prisoners (i.e.,
idle offenders who do not participate in
any programming) affect recidivism? By
focusing on broader, system-level questions
such as these, future research may be able
to shed light on whether programming can
influence overall recidivism rates and, if so,
the level of programming resources needed
to significantly drive down the rate at which
prisoners reoffend.
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About the Author
Grant Duwe is the Director of Research and Evaluation for the Minnesota Department
of Corrections, where he evaluates correctional programs, develops risk assessment
instruments, and forecasts the state’s prison population. He holds a Ph.D. in Criminology
and Criminal Justice from Florida State University.
Dr. Duwe is the author of the book
Mass Murder in the United States: A History
(McFarland
and Company, Inc.), and he is a co-author (along with Michael Hallett, Joshua Hays, Byron
Johnson, and Sung Joon Jang) of the recently published book,
The Angola Prison Seminary:
Effects of Faith-Based Ministry on Identity Transformation, Desistance, and Rehabilitation
(Routledge). He has written more than 50 articles that have been published in peer-
reviewed academic journals such as
Criminology, Criminology & Public Policy, Criminal
Justice and Behavior,
and
Corrections: Policy, Practice and Research
.
Currently a visiting fellow with the Bureau of Justice Statistics and a nonresident senior
fellow with Baylor University’s Institute for Studies of Religion, Dr. Duwe is co-principal
investigator for a project involving the development of a sexual recidivism risk assessment
instrument for juvenile sex offenders.
The Use and Impact of Correctional Programming for Inmates on Pre- and Post-Release Outcomes
39
