EVALUATION OF THE OHIO DEPARTMENT OF
REHABILITATION AND CORRECTION AND CORPORATION
FOR SUPPORTIVE HOUSING’S PILOT PROGRAM:
INTERIM RE-ARREST ANALYSIS
Research Report
September 2010
Joshua A. Markman
Jocelyn Fontaine
John K. Roman
Carey Anne Nadeau
__________________________
Reentry Research at the
Urban Institute
In 2000, the Justice Policy Center at
the Urban Institute launched an
ongoing inquiry into prisoner reentry
research to better understand the
pathways of successful reintegration,
the social and fiscal costs of current
policies, and the impacts of
incarceration and reentry on
individuals, families, and communities.
The Institute’s research includes a
range of studies, from rigorous
program evaluations to strategic
planning partnerships with state and
local jurisdictions. For more
information, see
http://www.urban.org/justice.
Corporation for Supportive Housing
and its Returning Home Initiative
The Corporation for Supportive
Housing is a national nonprofit
organization and community
development financial institution that
helps communities create permanent
housing with services to prevent and
end homelessness. In Spring 2006,
CSH launched its Returning Home
Initiative with funding from the
Robert Wood Johnson Foundation.
The goal of the Initiative is twofold:
to establish permanent supportive
housing as an essential component of
reintegrating formerly incarcerated
persons with histories of housing
instability into their communities and
to initiate and promote local and
national policy changes to better
integrate the correctional, housing,
mental health, and human services
systems.
The views expressed are those of the authors and
should not be attributed to the Urban Institute, its
trustees, or its funders.
Introduction
In March 2007, the Ohio Department of Rehabilitation and Correction (ODRC) and the
Corporation for Supportive Housing Ohio Office (CSH) developed a permanent
supportive housing pilot program. The pilot was designed to house approximately 100
individuals returning from select prisons throughout Ohio to the Cincinnati, Cleveland,
Columbus, Dayton, and Toledo communities. The 13 institutions participating in the pilot
included the Allen, Chillicothe, Grafton, Hocking, London, Lorain, Madison, Marion,
Pickaway, and Trumbull Correctional Institutions; the Ohio Reformatory for Women;
and the Franklin and Northeastern Prerelease Centers. The pilot, funded primarily by the
ODRC, but also a part of CSH’s Returning Home Initiative, has three main goals: to
reduce recidivism; to reduce homelessness; and to decrease the costs associated with
multiple service use across the criminal justice, housing/homelessness, and mental health
service systems. The Urban Institute (UI) is evaluating the pilot to assess the impact on
recidivism and residential stability and to test whether the benefits associated with the
pilot outweigh its costs. The final report will be complete in summer 2012.
In this paper, we report the results of an interim analysis of re-arrest for both the
treatment and comparison groups, including descriptive statistics on the study sample.
We begin by outlining the pilot model, including eligibility requirements and costs.
ODRC/CSH Supportive Housing Pilot Program
Key aspects of the pilot included: (1) coordination across the correctional and health
services systems, including the ODRC, CSH, the Ohio Department of Mental Health
(ODMH), the Ohio Department of Alcohol and Drug Addiction Services (ODADAS),
and nine supportive housing providers in the community1; (2) release planning through a
reentry coordinator, case manager, or other correctional staff at each participating
prison; and (3) the provision of housing and supportive services in five cities. Individuals
were eligible for enrollment into the pilot if they were homeless at the time of arrest or
at risk of homelessness upon release and had a disability (defined broadly to include
developmental disorders, severe addiction, and behavioral problems). Enrollment into the
pilot began in March 2007 and is ongoing.2 As of June 30, 2010, the ODRC investment
into the pilot totaled more than $3.8 million, which included more than 85 supportive
housing beds across the five cities, housing more than 150 individuals released from the
13 prisons. A previous report from UI, published in March 2009, provides a more indepth discussion of the pilot and describes its first cohort of participants.3
Urban Institute Evaluation
in offending to estimate whether the pilot
improved social welfare.
To draw the study sample, UI identified a
prospective sample of prisoners released from
the target prisons. Individuals receiving
permanent supportive housing upon release
(treatment group) were compared with a
contemporaneous
cohort
of
released
prisoners (comparison group) who were
eligible for the pilot but did not receive
housing due to limited resources. The
evaluation team requested signed, affirmative
research consents from every individual
referred to the pilot and matched information
with data from CSH and the providers to
determine whether an individual was housed
upon release. Data were collected from
multiple sources. Programmatic data were
collected from the ODRC, CSH, ODMH,
ODADAS, and supportive housing providers.
Cost data were collected from the ODRC,
CSH, ODMH, ODADAS, and supportive
housing providers. UI researchers conducted
semi-structured interviews with the ODRC,
CSH, and supportive housing staff. Finally,
electronic and hardcopy official records were
collected
from
the
aforementioned
government agencies.
Findings from the process, impact, and cost
evaluations are forthcoming. This interim
report offers an initial look at the recidivism
outcomes of the individuals in our study. For
this report, recidivism is defined as a new
arrest. This measure of recidivism (rather than
re-incarceration) was preferred since most of
the individuals in our sample have not been in
the community long enough to experience
both a new arrest and a new conviction. It
should be noted, however, that while re-arrest
is a common indicator of program impact, the
pilot is focused mainly on reducing reincarceration and the costs associated with
returns to prison. We also note that the use of
re-arrest to measure recidivism is less precise
with this target population since people with a
mental illness may come to greater attention
of police and result in increased arrests for
crimes such as loitering and other public
disturbances.4 Thus, re-incarceration will be
the main measure of recidivism studied in the
final report.
The arrest data were developed by the ODRC
from law enforcement data sources in the
Ohio Law Enforcement Gateway (OHLEG)
search engine as well as county clerk of court
websites. Our analyses estimate whether there
were differences in the incidence and
prevalence of re-arrest and the time to the
first re-arrest between the treatment and
comparison group.
When complete in 2012, the three
components of the evaluation will have
different, but complementary, objectives. The
process evaluation will describe the logic and
performance of the pilot and determine
whether it achieved short-term objectives,
including increased access to permanent
supportive housing; increased access to other
supportive services, such as mental health and
addiction services for participants; and
successful provider in-reach to the ODRC
prisons. The impact evaluation will test
whether the pilot reduced recidivism and
increased residential stability. The evaluation
will compare the treatment and the
comparison groups: rates of re-arrest, number
of re-arrests, rates of re-incarceration, changes
in types of new offenses, and number of
shelter stays. Finally, using data from the
impact evaluation analyses, the cost-benefit
analyses will compare the costs of service use
for all individuals—both treatment and
comparison—and the benefits from reductions
Sample
Enrollment into the study, which began in
October 2007 and ended November 2009,
was not a condition of enrollment in the pilot
program. Therefore, there were fewer
individuals in the study’s treatment group than
were actually housed by the pilot. When study
enrollment ended in November 2009, 240
individuals had consented to participate. Arrest
data from the ODRC was requested in July
2010 for all 240 consented participants. Data
were received for 233 participants, split nearly
evenly between the treatment (119) and
comparison (114) group.5
2
treatment group. Precisely half of the full
sample was white although there were slightly
more non-whites in the treatment group (61
percent). The median age of study participants
was 43 years, and almost half of the sample
was between 41 and 50 years old.
Study Limitations
The main challenge in this research is that the
data used in this analysis were very limited.
The only available data for this interim report
are three items describing the study’s
demographics (gender, race/ethnicity, and age)
and four measures of prison experiences
(length of stay, release type, security level, and
risk level). As shown in table 1, the two
samples were quite different on these limited
indicators, notably race/ethnicity and length of
stay. Thus, we have limited information about
whether the treatment and comparison groups
are, in fact, similar. If these dissimilarities relate
to recidivism risk, then we will not be able to
determine from these limited data whether
differences in re-arrest were due to underlying
differences in risk or due to differential receipt
of services. To address this issue, we ran
several different models to adjust for
differences in risk. However, with these few
variables, we cannot rule out the possibility
that differences in the samples—rather than
real differences in program effects—explain
the results.
We tested for differences in the means of key
demographic characteristics: gender, race/
ethnicity, age at enrollment into the study, and
the number of street days between release
date and the date of data collection for the
outcome measures. The treatment group was
significantly more likely to be non-white and
have more street days than those in the
comparison group (p < 0.01). No statistically
significant differences between the two groups
were observed on age at enrollment or gender
(table 1).
Length of Stay and Release
Across the 233 individuals in the sample, the
average length of stay for their most recent
incarceration was almost 48 months, with a
maximum stay of 343 months (about 28 years).
The median length of stay was much less—16
months—due to people who had with long
prison stays. Although the average length of
stay was longer for the comparison group (56
months) than the treatment group (40
months), the difference in means was not
statistically significant.
Data Analysis—Univariate and
Bivariate Analyses
Methods
To assess whether the treatment and
comparison group were comparable using the
data available, we conducted univariate and
bivariate analyses on each of the variables in
our model. The univariate model estimated the
mean and standard deviation for each of the
variables, including the outcome measures. The
bivariate analyses—independent sample ttests—tested whether the difference between
the treatment and comparison group means
was different from zero. For instance, if the
mean for the treatment group is greater than
the comparison group mean, this tests
whether that difference could have occurred
by chance.
There were differences in the type of release
for the 233 study participants. Most were
released by expiration of stated terms (EST—
48 percent), were subject to post-release
control (PRC—31 percent) or were paroled
(PAR—19 percent). Those with an EST
release—equivalent to sentence expiration—
were released without supervision. Those with
a PRC release completed their sentence but a
judge required post-release supervision.
Individuals who were released PAR (paroled),
completed their minimum prison sentence but
remained under the supervision of the ODRC
for a designated period. We found no
differences between the treatment and
comparison groups on post-release supervision
levels (table 1).
Demographics
The 233 participants were comprised of 180
men (77 percent) and 53 women (23 percent).
About half of both the women (49 percent)
and the men (53 percent) were in the
3
greater security risk than those in the
comparison group.
Security and Risk
We included two measures of security level in
our data analysis—one measure for the
individual’s security level while incarcerated
prior to their most recent release and another
measure level for their security level one year
prior to their most recent release. Security
level classifications ranged from one to five.
Level one is the lowest security level in the
ODRC and those inmates with a 1A
classification are permitted to work in camps,
on work crews, and on unsupervised work
detail outside prison. Other classifications
include 1B, 2, 3, 4A, and 4B, with the riskiest
inmates being classified as level 5 (super-max).
In addition to security classification, which is
based on current conviction and assessments
of conduct while incarcerated, the ODRC also
classifies inmates’ risk of re-incarceration. The
scale ranges from negative one (basic risk) to
eight (intensive risk). Efforts are made to
target programming toward the riskiest
inmates. Most of the sample was classified as
“basic risk” and about a quarter had the lowest
risk level. Less than 5 percent of the
participants in the study were considered by
the ODRC as “intensive risk”—or having a risk
score of seven or eight. While the treatment
groups’ security levels were higher on average
than the comparison groups’, we found no
statistically significant differences (table 1).
Every individual in the sample was in security
levels 1A to 3. Nearly everyone in our sample
was either classified as 1B (42 percent) or 2
(47 percent). We found that the average
current level for the treatment group was
significantly higher than the average current
security level for the comparison group
individuals. This suggests that the treatment
group was considered by the ODRC to be a
In summary, there were differences observed
between the treatment and the comparison
group on key indicators such as race/ethnicity,
days on the street, and current and previous
security level. If those differences are not
controlled for in the analysis, they will tend to
Table 1.
Sample Demographics
Age at Enrollment (years)
White
Male
Length of Stay (months)
Street Days
Release Type (dummy
coding)
- Expiration of
Stated Term
- Post-Release
Control
- Parole
Security Level
Risk Level
Treatment Cases
(n = 119)
Mean
S.D.
42.29
8.55
0.40***
0.49
0.76
0.43
40.03
66.28
716.90***
307.8
All Cases
(n = 233)
Mean
S.D.
42.39
9.32
0.50
0.50
0.77
0.42
47.75
78.22
578.63
310.36
Comparison Cases
(n = 114)
Mean
S.D.
42.51
10.09
0.61***
0.49
0.78
0.42
55.74
88.49
434.3***
240.2
0.48
0.50
0.50
0.50
0.46
0.50
0.31
0.19
2.54
3.36
0.46
0.40
0.69
1.87
0.32
0.16
2.64**
3.51
0.47
0.37
0.69
1.85
0.30
0.23
2.43**
3.21
0.46
0.42
0.69
1.88
Source: Urban Institute analysis of data from the ODRC.
Note: The independent samples t-test tests whether the difference in the means of the treatment group and the
comparison group is significantly different from zero. .
Significance testing: * p < 0.10, ** p < 0.05, *** p < 0.01.
4
Among those in the comparison group who
were re-arrested, 47 percent were for felony
offenses and 53 percent were for
misdemeanor offenses. Of the individuals who
were re-arrested, the median number was 1
re-arrest, and the maximum number was 15.
Half of those who were re-arrested were rearrested only once, 24 percent were rearrested twice, 12 percent were re-arrested
three times, and the remaining 13 percent
were re-arrested four or more times (figure
1). Of those 84 individuals who were rearrested, the number of days to their first rearrest ranged from 2 days to 952 days
(approximately two and a half years), with the
median number of days to re-arrest of 151
days. The sample enrollment spanned more
than two years; therefore, some of the sample
was in the community longer than others with
more time to be re-arrested. Of those who
were re-arrested, the majority (85 percent)
were re-arrested in less than one year (figure
2).
bias the results to the extent they also predict
recidivism. In addition to risk measures, the
treatment group spent significantly more time
on the street, which exposed the group to
more opportunities to be arrested. Therefore,
multivariate analyses discussed below included
control variables to assess whether these
differences are related to re-arrest outcomes.
However, if important predictors of risk were
omitted from this analysis, as they surely were,
then even those statistical controls may not be
sufficient to balance the samples and allow an
apples-to-apples comparison.
Re-arrest Outcomes
Of the 233 individuals in the sample, 84 were
re-arrested during the study period (36
percent of the study sample). Of the 84 who
were re-arrested, 57 percent were in the
treatment group and 43 percent in the
comparison group. Among those in the
treatment group who were re-arrested, 50
percent were for felony offenses and 50
percent were for misdemeanor offenses.
Figure 1.
Description of Sample Outcomes – Number of Re-arrests
70%
60%
50%
40%
30%
20%
10%
0%
0
1
2
As reported by the ODRC
N: 233
5
3
4 and up
for a large number of zeros (zero-inflated
negative binomial regression) and tested the
goodness of fit for models using the Akaike
Information Criteria (AIC). Based on the AIC,
the negative binomial regression was selected.
Finally, when modeling the “time to re-arrest,”
we used a Cox proportional-hazards model.
Typically referred to as a survival analysis, this
analysis models the time (in days) to the first
new re-arrest.
Table 2 shows the univariate and bivariate
statistics for the outcomes: any re-arrest,
number of re-arrests, and time to re-arrest.
The mean number of treatment group rearrests was significantly more than the number
of comparison group re-arrests. No
statistically significant differences between the
treatment and comparison group were
observed when comparing the prevalence of
re-arrest or time to re-arrest.
For each of the three outcome measures, we
specified five models of the effect of pilot
participation on re-arrest. This was done
iteratively to adjust for variables that may
independently explain re-arrest. First, we
regressed each re-arrest outcome on a binary
indicator of receiving the treatment or not
(e.g., whether the individual was in the
treatment or comparison group). To test
whether those differences were due to
differences
in
group
attributes
and
experiences, the second model includes
demographic measures, street days, length of
stay, and release variables. The third model
adds the security and risk variables. The fourth
Data Analysis—Multivariate
Statistics
Methods
In the multivariate analyses, a different
regression model was specified for each of the
three outcomes. When the measure was “any
re-arrest,” a logistic regression tested whether
there were differences in the mean likelihood
of re-arrest between the treatment and
comparison group. When the measure was the
“number of re-arrests,” we used a negative
binomial regression to test whether the
number of re-arrests varied by group. Note
that we also specified count models to adjust
Figure 2.
Description of Sample Outcomes – Days to First Re-arrest
25%
20%
15%
10%
5%
0%
160
61120
121180
181240
241300
301365
As reported by the ODRC
N: 84
Note: Of the 233 individuals, 149 were not re-arrested and 1 re-arrest was missing a date.
6
365
and up
after either adding covariates to the model or
replacing the covariates with the propensity
weight. This process tests whether our control
variables predict a different group assignment
than we actually observe in the data. The
difference between predicted and actual
assignment is not statistically significant
suggesting that the treatment and comparison
groups are comparable based on the data
observed. Second, we used the AIC to choose
among competing models, as previously
mentioned, when the number of re-arrests
was the outcome measure. The results from
the AIC test show that the negative binomial
regression is the best fit for the distribution in
the data. However, these corrections will not
control for unobserved bias if important
predictors are missing from our models.
model adds an additional variable, the square
of the risk score6, to test whether those at
most serious risk of re-arrest explain
differences in outcomes.
Unlike the previous four models that adjust for
the relative risk of re-arrest by controlling for
underlying differences in the groups, the fifth
model simply adds a weight to the bivariate
models to adjust for group differences. We
calculate this weight as a propensity score that
is the conditional probability of being assigned
to the treatment group based on the
demographic, detention, release, security, and
risk variables as well as days on the street. The
intuition behind the propensity score weight is
that there may be variables that predict
whether an individual is accepted into the
treatment group that are also related to the
likelihood of re-arrest. So, if the treatment
group is at higher risk of re-arrest, this
approach will re-weight the samples to adjust
for those differences. These scores are
calculated as the inverse probability of
treatment weights (IPTW), which is the
inverse of the probability score. The IPTWs
are weighted based on the entire sample.
Results
For each outcome, we tested five model
specifications on each of the three measures:
any re-arrest, number of re-arrests, and time
to first re-arrest (tables A.1-A.3 in Appendix
A). Of the five models specified, we report on
the fifth model, which utilizes propensity
weights (table 3). We prefer the propensity
weighted models as they efficiently use all of
the information contained in the data to
reduce selection bias.
Diagnostics
To test whether these models effectively
balance the samples and allow for a fair
comparison, we compared observed group
assignment to the predicted group assignment
Any re-arrest was modeled using logistic
Table 2.
Univariate and Bivariate Sample Outcomes by Recidivism Measure
Any Re-arrest
Number of
Re-arrests
Time to First
Re-Arrest (days)
All Cases
(n = 233)
Mean
S.D.
0.36
0.48
Independent
Samples T-Tests
Comparison Cases
Treatment Cases
(n = 114)
(n = 119)
Mean
S.D.
Mean
S.D.
0.40
0.31
0.32
0.47
0.84
1.80
1.10**
2.23
0.56**
1.15
214.49
190.23
223.60
209.00
202.40
164.00
Source: Urban Institute analysis of data from the ODRC.
Note: The independent samples t-test tests whether the difference in the means of the treatment group and the
comparison group is significantly different from zero. The sample size for Time to First Re-Arrest is as follows: N = 84;
Treatment = 48; Comparison = 36.
Significance testing: * p < 0.10, ** p < 0.05, *** p < 0.01.
7
the treatment group compared with the
comparison group in the model that uses the
propensity score weights. Finally, we find that
the general direction of the results suggest
more time before first re-arrest in the
treatment group than we find in the
comparison group. While this is the general
direction, the results are not statistically
significant. Thus, our findings are somewhat
mixed, suggesting the treatment group was rearrested more—and more often—but took
longer to do so.
regression. The coefficient estimate 0.510
suggests that the treatment group is rearrested more than we observe in the
comparison group. Our model of the number
of re-arrests estimates a coefficient 0.660
which suggested that the number of re-arrests
is significantly higher in the treatment group
than in the comparison group. Finally, we
modeled a Cox Proportional Hazard model,
which estimated a negative coefficient (-0.098).
This suggested that the time to re-arrest is
longer for the treatment group as compared
to the comparison group.
Despite these analyses, however, the findings
may be an artifact of limited data. We could
not observe many important facts about the
individuals in the sample, so it is difficult to
assess whether the differences were due to
program impacts or underlying differences in
the risk of re-arrest. For example, we note
that the individuals targeted by the pilot had at
least one disability and were homeless at the
time of arrest or at risk of homelessness upon
release. While data were not available to
control for these variables, the presence of a
disability or risk of homelessness may be
associated with a greater risk of re-arrest. If
this is true, then our samples were not
comparable along unobserved measures that
may explain the treatment groups’ outcomes.
Additional variables not included in the analysis
might reveal disparities in the group
assignment such as mental health, disability,
and homelessness. Exploration of the
relationships among these variables and others,
and group assignment, may reveal a selection
bias that, once accounted for, changes the
estimated program impact. We anticipate that
we will have these data available for the final
report.
It is important to note, however, that the
results of the propensity models differ in
magnitude from the models with covariates
(but are quite similar to the bivariate analyses).
That is, the direction and size of the effect
when the treatment and comparison groups
are compared with no controls is very similar
to the direction and size of the effect using the
models with propensity scores. However, the
propensity score and bivariate models show
bigger (negative) effects of treatment than is
observed in the models with covariates. While
all but one of the models show consistent
results (e.g., that the introduction of different
groups of controls does not change the general
finding), the covariate models consistently
show non-significant effects. The choice to
highlight the propensity weights is based on
statistical tests (not shown but available from
the authors) that suggest that the control
variables and the group assignment variable
significantly covary. Thus, the coefficient on
any single variable, including the group
assignment variable, is unreliable. This problem
is not present in the propensity weighted
models.
Conclusions
Another potential explanation for the higher
number of re-arrests observed in the
treatment group are disparities in supervision
intensity. Individuals in supportive housing are
likely to be monitored more closely by
community corrections officials and housing
and service providers than are individuals who
are not in supportive housing. This seems
sensible since individuals in the pilot were
housed in central city locations that may lead
to heightened contact with case managers,
housing specialists, and other staff who might
The results of these analyses suggested that
those who received permanent supportive
housing were more likely to be re-arrested
and to have more re-arrests than those who
did not receive housing, although this
relationship may be spurious, as discussed
below. We observe higher odds of re-arrest
for those in the treatment group in the model
that uses the propensity score weights.
Similarly, we observe more re-arrests among
8
readily report criminal activity. If treatment
group members were more closely supervised,
differences in re-arrest may have been due to
closer observation and certainty of detection
rather than differences in offending behavior.
sample has been recruited fully, the evaluation
team is collecting additional data for the
evaluation. Additional data will include baseline
and follow-up data from the housing/service
providers on the experiences of those who are
housed, semi-structured interviews with the
housing/service providers, and administrative
data from the ODMH, the ODADAS, and the
local shelters in the five cities where the pilot
is based. Using all of these data to inform the
process, impact, and cost evaluation, the final
analyses will be reported in summer 2012.
Time to first re-arrest (and the number and
likelihood of re-arrest) may also be an artifact
of our limited data. We observe that the
treatment group had significantly more street
days than did the comparison group—about
nine months more street days. Thus, the
individuals in the treatment group were
exposed to more days where they were at risk
of re-arrest than individuals in the comparison
group.
1 The nine housing/service providers that have
been associated with the pilot include: Amethyst, Inc
(Columbus); Community Housing Network (Columbus);
EDEN, Inc. (Cleveland); Miami Valley Housing
Opportunity (Dayton); Mental Health Services
(Cleveland); Neighborhood Properties, Inc. (Toledo);
Volunteers of America—Northwest Ohio (Toledo);
Volunteers of America—Ohio River Valley (Cincinnati);
and YMCA of Central Ohio (Columbus). EDEN and
Mental Health Services worked in a partnership, where
EDEN managed the housing component of the pilot and
Mental Health Services managed the services component.
2 Though enrollment into the pilot is still ongoing, the housing program has continued in the same
prisons as a full program of the ODRC.
3 See J. Fontaine, C.A. Nadeau, C. Roman, and
J. Roman, “Evaluation of the Ohio Department of
Rehabilitation and Correction and Corporation for
Supportive Housing’s Pilot Program, Interim Report:
October 2007 – September 2008” (Washington, DC:
Urban Institute, 2009).
4 Council of State Governments, “Criminal
Justice Mental Health Consensus Project” (New York:
Council of State Governments, 2002); P. Hirschfield, T.
Maschi, H. R. White, L. G. Traub, and R. Loeber, “Mental
Health and Juvenile Arrests: Criminality, Criminalization,
or Compassion?” Criminology 44 (2006): 593–630.
5 Data from the ODRC were not available for
seven individuals, including five who are still incarcerated,
one whose record was not in the ODRC files, and
another one who had no data.
6
Squaring the risk term creates bigger
differences between each level of risk, especially for
those with very high levels. For example, risk values of 1,
2, 3, and 4 would be 1, 4, 9, and 16 in the squared term.
This increases the variance to allow us to test whether
those with the most serious risk levels had different
outcomes than for those with other risk levels.
However, we did not have any detention
information for individuals after they enrolled
in the study. Individuals who were re-arrested
could have been detained before trial or after
conviction. This is important to note; since, it
means we were unable to adjust our time on
the street estimate for these detention stays.
Once this additional information is obtained
and included in the analysis, the time on the
street we observed in the data could be
reduced for both the treatment and
comparison groups. Changes to this variable
would affect all of the recidivism measures
reported in this document.
The final evaluation will use data from various
sources to create a more complete picture of
the sample’s histories, characteristics, and
outcomes. The evaluation will assess other key
variables to gain a better understanding of
whether permanent supportive housing
reduces recidivism. In addition, re-arrest will
not be the only recidivism measure
considered; we will also explore differences
between the treatment and comparison groups
on re-incarceration and offending types. The
final report will test the pilot’s goals on
reducing re-incarceration, an analysis the time
limitations prevented for this interim report.
Next Steps
While the analyses presented here were based
solely on data from the ODRC, the full
evaluation relies on multiple methods to assess
the process, impacts, and costs and benefits
associated with the pilot. Now that the study
9
Appendix A
Table A.1.
Multivariate Outcomes – Logistic Regression of Any Arrest
Treatment
(1)
0.382
(0.275)
Age at Enrollment (years)
-
White
-
Male
-
Length of Stay (months)
-
Street Days x 30
-
(2)
-0.122
(0.146)
-0.0277
(0.017)
-0.4036
(0.300)
0.917**
(0.378)
-0.008**
(0.003)
0.012
(0.03)
(3)
0.2175
(0.3422)
-0.0241
(0.0188)
-0.2471
(0.3125)
1.0595***
(0.3954)
-0.00652*
(0.00336)
0.008
(0.016)
(4)
0.2601
(0.3481)
-0.0256
(0.0195)
-0.1808
(0.3179)
1.0154**
(0.397)
-0.00625*
(0.0034)
0.007
(0.017)
-0.695
(0.507)
-0.771
(0.504)
-0.3585
(0.5264)
-0.5549
(0.5203)
-0.2168
(0.35)
0.2981***
(0.0913)
-0.4653
(0.5342)
-0.6908
(0.5317)
-0.1462
(0.3525)
1.3419***
(0.4043)
-0.1379***
(0.0512)
283.6
232
(5)
0.510**
(0.242)
-
Release Type
- Expiration of Stated Term
-
- Post-Release Control
-
Security Level
-
-
Risk Level
-
-
Risk Quadratic
-
-
-
306.7
233
300.5
232
289.3
232
AIC
N
434.7
232
Source: Urban Institute analysis of data from the ODRC.
Note. Each column reports selected coefficients from a logistic regression. The coefficient on treatment is
the expected change in the recidivism measure any arrest from being in the treatment group as
opposed to the comparison group. Positive values would indicate that the treatment group is
recidivating more than the comparison group, while negative values indicate that the comparison group
is recidivating more than the treatment group.
Significance testing: * p < 0.10, ** p < 0.05, *** p < 0.01
Table A.2.
Multivariate Outcomes – Negative Binomial Regression of Number of Re-Arrests
Treatment
(1)
0.673***
(0.257)
Age at Enrollment (years)
-
White
-
Male
-
Length of Stay (months)
-
Street Days x 30
-
(2)
0.316
(0.290)
-0.0342*
(0.017)
-0.115
(0.264)
0.6765*
(0.3304)
-0.004
(0.003)
0.027*
(0.015)
(3)
0.320
(0.292)
-0.026
(0.017)
0.068
(0.270)
0.695**
(0.3263)
-0.004
(0.003)
0.024
(0.015)
(4)
0.396
(0.282)
-0.029*
(0.017)
0.145
(0.262)
0.7758**
(0.3153)
-0.003
(0.003)
0.018
(0.015)
0.383
(0.469)
0.2276
(0.468)
0.677
(0.464)
0.380
(0.458)
-0.322
(0.286)
0.231***
(0.082)
0.496
(0.450)
0.109
(0.448)
-0.217
(0.276)
1.636***
(0.358)
-0.1839***
(0.045)
535.6
232
(5)
0.660***
(0.235)
-
Release Type
- Expiration of Stated Term
-
- Post-Release Control
-
Security Level
-
-
Risk Level
-
-
Risk Quadratic
-
-
-
565.1
233
559.2
232
551.1
232
AIC
N
569.4
232
Source: Urban Institute analysis of data from the ODRC.
Note. Each column reports selected coefficients from a negative binomial regression. The coefficient on
treatment is the expected change in the recidivism measure number of re-arrests from being in the
treatment group as opposed to the comparison group. Positive values would indicate that the treatment
group is recidivating more than the comparison group, while negative values indicate that the comparison
group is recidivating more than the treatment group.
Significance testing: * p < 0.10, ** p < 0.05, *** p < 0.01
Table A.3.
Multivariate Outcomes – Cox Proportional-Hazards Regression of Time to Re-Arrest
Treatment
(1)
-0.121
(0.223)
Age at Enrollment (years)
-
White
-
Male
-
Length of Stay (months)
-
(2)
-0.184
(0.267)
0.0235
(0.017)
0.2974
(0.241)
0.101
(0.343)
-0.003
(0.003)
(3)
-0.3306
(0.2828)
0.0306*
(0.0177)
0.2653
(0.240)
0.191
(0.350)
-0.004
(0.003)
(4)
-0.3839
(0.2843)
0.0358**
(0.0183)
0.2946
(0.2424)
0.156
(0.351)
-0.003
(0.003)
0.142
(0.381)
0.232
(0.375)
0.271
(0.385)
0.344
(0.383)
-0.636**
(0.278)
0.078
(0.084)
0.377
(0.391)
0.530
(0.411)
-0.739**
(0.291)
-0.475
(0.395)
0.067
(0.047)
568.6
82
(5)
-0.098
(0.201)
-
Release Type
- Expiration of Stated Term
-
- Post-Release Control
-
Security Level
-
-
Risk Level
-
-
Risk Quadratic
-
-
-
584.7
84
572.9
82
568.6
82
AIC
N
782.8
84
Source: Urban Institute analysis of data from the ODRC.
Note. Each column reports selected coefficients from a Cox proportional-hazards regression. The
coefficient on treatment is the expected change in the recidivism measure time to first re-arrest from being
in the treatment group as opposed to the comparison group. Positive values would indicate that the
treatment group is recidivating more than the comparison group, while negative values indicate that the
comparison group is recidivating more than the treatment group.
Significance testing: * p < 0.10, ** p < 0.05, *** p < 0.01