Crime & Delinquency
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Original Research Article
Just as Good as the Real
Thing? The Effects of
Prison Video Visitation
and Susan McNeeley2
While research has consistently shown that in-person prison visitation
is associated with reduced recidivism, much less is known about the
effects of video visits. This study compares recidivism outcomes
between 885 inmates who had at least one video visit and a matched
comparison group of 885 who did not receive a virtual visit. Video
visits reduced two measures of recidivism (general and felony
reconvictions) but did not have a significant effect on the other two
measures (violent reconviction and technical violation revocations). As
the number of video visits increased, so did the size of the recidivism
reduction, at least for general and felony reoffending. Despite the
generally favorable impact on recidivism, video visitation was used
sparingly by Minnesota’s prison population.
video visitation, social support, prison, recidivism
1Research Director, Minnesota Department of Corrections, St. Paul, MN, USA
2Senior Research Analyst, Minnesota Department of Corrections, St. Paul, MN, USA
Grant Duwe, Research Director, Minnesota Department of Corrections,
1450 Energy Park Drive, Suite 200, St. Paul, MN 55108-5219, USA.
943168CADXXX10.1177/0011128720943168Crime & DelinquencyDuwe and McNeeley
2 Crime & Delinquency 00(0)
Existing research suggests prison visitation is an underutilized resource that
yields beneficial outcomes for those in prison. Indeed, visits improve mental
health issues such as depression and anxiety and reduce misbehavior while
incarcerated (De Claire & Dixon, 2015; Siennick et al., 2013; Wooldredge,
1997). Many studies indicate that recidivism is lower among inmates who
receive visits while in prison (Bales & Mears, 2008; Cochran, 2014; Derkzen
et al., 2009; Duwe & Clark, 2013; McNeeley & Duwe, 2019; Mears et al.,
2012), decreasing reoffending by an estimated 26% (Mitchell et al., 2016).
But research also suggests that many prisoners do not receive visits while
incarcerated (Cochran et al., 2016), which has been attributed to factors such
as poor conditions in visitation areas and the inconveniences associated with
travel to the facility (Arditti, 2003; Austin & Hardyman, 2004; Casey-
Acevedo & Bakken, 2001; Christian, 2005; Clark & Duwe, 2017; Cochran
et al., 2016; Farrell, 2004; Fuller, 1993; McNeeley & Duwe, 2019; Sturges,
2002; Tewksbury & DeMichele, 2005).
To increase prison visitation, the Minnesota Department of Corrections
(MnDOC) began offering remote video visitation—in which visitors are able
to schedule and hold 30-minute calls with prisoners from a remote location—
in November 2015. Like in-person visitation, video visitation may allow pris-
oners to maintain social ties in the community while avoiding many of the
barriers discussed above. Video visitation is also believed to improve opera-
tions within facilities because it reduces time and costs associated with pro-
cessing visitors, monitoring visits, and moving inmates from place to place;
prevents the introduction of contraband into the facility; and increases staff
and inmate safety (Boudin et al., 2014; Brown et al., 2014). Despite the
potential benefits, there has been little research on video visits. In fact, we are
aware of no studies that have examined the relationship between video visita-
tion and recidivism.
The present study fills this gap in the literature by testing whether persons
who received video visits were less likely to recidivate than those who did not,
while accounting for traditional, in-person visits. Examining people released
from Minnesota prisons between 2016 and 2018, we compared recidivism
outcomes among 885 who had at least one video visit with a matched com-
parison group of 885 who did not receive any video visits. In doing so, we test
the assumption that video visits provide similar benefits as in-person visits
while avoiding some of the barriers that reduce visitation. This study not only
extends the literature on prison visitation and the importance of social support
for successful reentry into the community, but also informs correctional policy
and practice regarding the use of video visitation.
Duwe and McNeeley 3
Prior Research on Prison Visitation
There are several reasons visitation could be expected to affect recidivism.
Consistent with social bond theory (Hirschi, 1969), prison visitation may
reduce reoffending by allowing offenders to maintain personal connections
with others. Indeed, studies show that visits are influential in improving
inmates’ relationships with friends and family members, and that this
improves reentry outcomes (Brunton-Smith & McCarthy, 2017; Liu et al.,
2014). Consistent with social support theory (Cullen, 1994), visitation may
improve outcomes because visitors can help navigate the challenges that
released prisoners face upon returning to the community (see Maruna &
Toch, 2005; Martí & Cid, 2015). However, not all visitors are expected to
provide the same level of benefits: Meyers et al. (2017) found that offenders
with supportive visitors—those with stronger relationships with the offender,
a desire for more visits, who gave and asked for advice during visits, and who
had fewer arguments during visits—expected to receive greater social sup-
port in achieving their goals after release from prison. In line with this idea,
prisoners who receive more visits while incarcerated are more likely to secure
post-release employment (Brunton-Smith & McCarthy, 2017; Liu et al.,
2014). Further, these challenges can create strain (see Agnew, 1992), and
close connections with others may help inmates cope with strain in prosocial
ways (Colvin et al., 2002; Cullen et al., 1999). Finally, it is important for
desistance that prisoners experience a change in identity (Maruna, 2001;
Paternoster & Iovanni, 1989). Visitation may facilitate this process by
strengthening relationships with prosocial peers who model conventional,
non-criminal behavior and attitudes.
Consistent with these theories, many studies indicate that recidivism is
lower among inmates who receive visits while in prison (Bales & Mears,
2008; Cochran, 2014; Duwe & Clark, 2013; Mears et al., 2012). According
to a recent meta-analysis of 16 studies, visitation is associated with a 26%
decrease in recidivism (Mitchell et al., 2016). Bales and Mears (2008)
found that any visitation, more frequent visits, and visits that occurred
close to the release date reduced the risk of recidivism. Notably, a study
of Minnesota prisoners released between 2003 and 2007 showed that sev-
eral types of visitation (any visitation, the number of visits, the monthly
rate of visits, and recent visits) were associated with lower risk for mul-
tiple types of recidivism (Duwe & Clark, 2013). The effect of visitation
on recidivism has been observed even when accounting for social bonds
with friends and family that preceded the offenders’ stay in prison (Mears
et al., 2012; but see Atkin-Plunk & Armstrong, 2018). In addition, the
relationship between offenders and their visitors matters. For example,
4 Crime & Delinquency 00(0)
Bales and Mears (2008) found visits from spouses had the strongest pro-
tective effect, while Duwe and Clark (2013) found that visits from fathers,
siblings, in-laws, and clergy were most beneficial, while visits from ex-
spouses increased recidivism.
Few prisoners receive visits, however, and studies reveal the unvisited rate
ranges from a low of 39% (Duwe & Clark, 2013) to a high of 74% (Cochran
et al., 2016). The literature identifies several barriers to visitation. Policies
regarding visitation may be restrictive, reducing one’s ability to actually visit
and making visitors feel humiliated and degraded (Arditti, 2003; Austin &
Hardyman, 2004; Comfort, 2003; Farrell, 2004). The setting of the visitation
area is often an inhospitable and stressful environment, discouraging friends
and family members from visiting frequently (Sturges, 2002). Because most
prisons are located in rural areas far from the urban areas where offenders
lived, family members and friends often have to travel a great distance, mak-
ing visits difficult and therefore rare (Casey-Acevedo & Bakken, 2001;
Schirmer et al., 2009; Tewksbury & DeMichele, 2005). Quantitative studies
confirm that distance between the facility and the likely location of visitors
reduces the frequency of visitation (Clark & Duwe, 2017; Cochran et al.,
2017; McNeeley & Duwe, 2019). Relatedly, there is often a financial burden
associated with visitation, as visitors frequently incur costs due to travel
requirements, including transportation and, in some cases, lodging (Christian,
2005; Fuller, 1993).
The MnDOC began offering remote video visitation at all facilities in
November 2015. Like in-person visitors, video visitors must be on the pris-
oner’s visiting list. To be placed on a visiting list, individuals must submit an
application and undergo a background check. Visitors may participate in a
video visit from any location that has a computer1 with a camera and micro-
phone and a high-speed internet connection. Prisoners participate in the visit
at a kiosk located in their living unit, and they must have an account with the
vendor in order to receive visits.
Video visits must be scheduled in advance, and the kiosk schedule and
availability varies by facility and living unit. The cost of each video visit,
which can last up to 30 minutes, is $9.95. According to MnDOC policy, there
is a maximum number of in-person visiting hours allowed per month, which
varies by security level and ranges from 16 to 36 hours per month. But
MnDOC policy does not restrict the number of video visits an inmate can
receive, and video visits do not count toward the maximum in-person visiting
hours per month.
Duwe and McNeeley 5
Prior Research on Video Visiting
Much of the research on video visitation has focused on how prisoners and
visitors respond to this type of visit. Many inmates are grateful for video
visits; they feel that the correctional setting is harsh and don’t want their
loved ones—especially their children—to experience that setting (Hilliman,
2006). In addition, prisoners and visitors believe video visits still help them
maintain ties with their families (Murdoch & King, 2019; Tartaro & Levy,
2017a, 2017b). Furthermore, many visitors appreciate the convenience pro-
vided by remote video visitation (Tartaro & Levy, 2017b) and appreciate
being able to avoid the unpleasant institutional setting (Sitren et al., 2020).
Adults who escort children to visits tend to prefer this type of visit for chil-
dren (Tartaro & Levy, 2017a). At the same time, a majority of prisoners and
visitors report a preference for in-person visitation; they feel it results in
higher-quality visits and that the intimacy provided by face-to-face visits
allows for stronger maintenance of social ties (Murdoch & King, 2019;
Tartaro & Levy, 2017b). While many barriers that reduce visitation are
avoided, there are still flaws such as technological issues and the costs of the
visits (Murdoch & King, 2019). In a recent study, Sitren et al. (2020) found
that off-site video visitors considered technological issues to be a major
source of frustration, with 30% of participants experiencing problems that
caused visits to be cut short.
A couple of studies (Boudin et al., 2014; Brown et al., 2014; O’Very,
2016) suggest that video visitation also provides benefits for correctional
staff and administration: First, when visits take place remotely rather than
inside the facility, staff workload is reduced because employees are not
required to register and monitor on-site visitors or move inmates from place
to place for the visit. Second, remote video visitation is also believed to pre-
vent the introduction of contraband into the facility. Third, video visitation
reduces the risk of fights and assaults that may take place in the visiting
room, improving safety for offenders and staff.
Despite the potential benefits of video visitation, little research has studied
its effects on prisoner outcomes. Hilliman (2006) conducted a mixed-meth-
ods study of 671 women incarcerated in two prisons in Florida, linking par-
ticipation in video visitation to institutional misconduct. While there were
observed benefits of video visitation such as improved self-esteem and
improved relationships with children and other family members, the results
showed no significant effect of video visitation on institutional misconduct.
Murdoch and King (2019) reported that inmates believed the promise of
video visits from family and friends would motivate them to follow the rules
while incarcerated, and about one-third said video visits made them want to
6 Crime & Delinquency 00(0)
improve their behavior after release. However, there have been no studies
examining the relationship between video visitation and recidivism.
Data and Method
We used a retrospective quasi-experimental design to determine whether
video visits had an impact on recidivism. The population for this study con-
sisted of all releases from Minnesota prisons between 2016 and 2018. Of the
20,868 releases from prison, there were 885 inmates (4%) who received at
least one video visit while incarcerated. The comparison group pool for this
study consists of the 19,983 prisoners who did not receive a video visit. As
discussed later, we used propensity score matching (PSM) to individually
match the 885 who received video visits with 885 inmates from the larger
comparison group pool (N = 19,983).
In this study, we defined recidivism as a (1) reconviction for any offense,
(2) reconviction for a felony offense, (3) reconviction for a violent offense,
and (4) revocation for a technical violation. In doing so, we are able to
determine whether video visitation not only has an effect on general recidi-
vism, but also more serious reoffending such as felony and violent recidi-
vism. While the first three variables strictly measure new criminal offenses,
technical violation revocations (the fourth measure) represent a broader
measure of rule-breaking behavior. People released from prison can have
their supervision revoked for violating the conditions of their supervised
release. Because these violations can include activity that may not be crimi-
nal in nature (e.g., use of alcohol, failing a community-based treatment
program, failure to maintain agent contact, failure to follow curfew, etc.),
technical violation revocations do not necessarily measure reoffending.
Yet, technical violation revocations are costly, which is why it is important
to include it as a recidivism measure.
We collected recidivism data through December 31, 2019. We obtained
electronic data on convictions from the Minnesota Bureau of Criminal
Apprehension and data on revocations from the Correctional Operations
Management System (COMS) database maintained by the MnDOC. The
main limitation with using these data is that they measure only convictions
and incarcerations that took place in Minnesota. Because the prisoners in this
study were released between January 2016 and December 2018, the follow-
up time for recidivism ranged from 2 to 5 years with an average of 3.5 years.
Prior research suggests the majority of offenders who recidivate do so within
Duwe and McNeeley 7
the first 1 to 2 years after release from prison (Durose et al., 2014; Hunt &
Dumville, 2016; Langan & Levin, 2002).
To analyze the effects of video visitation on recidivism, we used Cox
regression, which is a type of survival analysis. Cox regression uses “time”
and “status” variables to estimate the impact of the independent variables on
recidivism. By using these time-dependent data, Cox regression can deter-
mine whether and when offenders recidivate. For our analyses presented
later, the “time” variable measures the amount of time from the date of release
until the date of first reconviction, technical violation revocation, or December
31, 2019, for those who did not recidivate. The “status” variable, on the other
hand, measures whether a prisoner recidivated (reconviction or technical vio-
lation revocation) during the period in which he or she was at risk to recidi-
vate. We estimated Cox regression models for each of the four recidivism
measures mentioned above.
To accurately measure the total amount of time offenders were actually at
risk to reoffend (i.e., “street time”), we needed to account for supervised
release revocations in the recidivism analyses. More specifically, for the three
recidivism variables that strictly measure new criminal offenses (general
reconviction, felony reconviction, and violent reconviction), we deducted the
amount of time spent in prison for technical violation revocations from the
total at-risk period. For these analyses, if we failed to deduct the time spent in
prison as a supervised release violator, the length of the at-risk periods for
these persons would appear to be longer than they actually were. Therefore,
we achieved a more accurate measure of “street time” by subtracting the
amount of time a person spent in prison as a supervised release violator from
his or her at-risk period, but only if it preceded a reconviction or if the person
did not have a reconviction prior to January 1, 2020. Moreover, in our Cox
regression models for the three reconviction measures, we included a control
variable that counted the number of supervised release revocations prior to
reconviction or January 1, 2020 for those who were not reconvicted.
Our main variable of interest was whether inmates received video visits. Data
on video visits were obtained from JPay, the vendor that provided video visits
to MnDOC prisoners. We created two measures for video visits. The first
variable was a dichotomous measure for whether inmates received any video
visits, while the second variable measured the total number of video visits
To isolate the effects of video visits on recidivism, we included a relatively
large number of variables in our propensity score and Cox regression models
8 Crime & Delinquency 00(0)
that might have affected our outcome measure (recidivism) and/or whether
inmates received a video visit. For the propensity score model, a logistic
regression model we estimated in which the dichotomous video visit measure
was the dependent variable, the covariates consisted of variables that may
have an influence on receiving a video visit, recidivism, or both. Some of our
covariates only had an impact on recidivism because they are postprison,
community-based measures that do not temporally precede video visitation.
Although excluded from the propensity score models, we included these
measures in our Cox regression models.
In Table 1, we describe the covariates in the propensity score model and
show their effects on whether inmates received video visits. Most of the vari-
ables in Table 1 are demographic, criminal history, and prison-based mea-
sures that were included on the first two versions of the Minnesota Screening
Tool Assessing Recidivism Risk (MnSTARR), a recidivism risk assessment
that has proven to perform well in predicting recidivism for Minnesota pris-
oners (Duwe, 2014; Duwe & Rocque, 2017; Duwe & Rocque, 2019). The
area under the curve (AUC) for this model was 0.833, which suggests it was
accurate in predicting which individuals were more or less likely to receive
Perhaps not surprisingly, the strongest predictor of whether someone
received a video visit is whether they had an in-person visit in prison. More
specifically, inmates who received in-person visits were nearly six times
more likely to receive a video visit (see Table 1). Neither gender nor any of
the criminal history measures had a significant effect on receiving video
visits. Inmates were significantly more likely to have video visits when they
were younger, non-white, married, had greater involvement in a security
threat group (STG), had a secondary degree, were participating in the CIP
program (i.e., a correctional boot camp run by the MnDOC), and had a longer
length of stay in prison. Conversely, the odds of video visitation were signifi-
cantly lower for those who entered prison as a supervised release violator,
had a history of suicidal tendencies, or were in prison for either a violent or a
Propensity Score Matching
After estimating the logistic regression model predicting video visitation,
we used the propensity scores derived from this model to match the 885
who received video visits with those who did not. Propensity score match-
ing (PSM) is a method that estimates the conditional probability of selec-
tion to a particular treatment or group given a vector of observed covariates
(Rosenbaum & Rubin, 1985). In matching offenders who received video
Table 1. Logistic Regression Model for Video Visit Selection.
Predictors Predictor description Odds ratio Standard error
In-person visits Total number of visits during current prison term 5.729** 0.091
Males Male = 1; Female = 0 0.936 0.137
Age at release Age (in years) at date of release from prison 0.967** 0.005
White Non-Hispanic White = 1; Non-White = 0 0.692** 0.080
Married Married = 1; unmarried = 0 1.680** 0.109
Total Supervision failures Number of prior revocations while under correctional supervision 0.941 0.034
Total convictions Number of total criminal convictions, excluding index conviction(s) 0.996 0.007
Felony convictions Number of felony convictions, excluding index conviction(s) 1.004 0.016
Violent convictions Number of total violent convictions, excluding index conviction(s) 0.998 0.037
Drug convictions Number of total drug convictions, excluding index conviction(s) 1.026 0.027
Assault convictions Number of assault convictions, excluding index conviction(s) 0.992 0.044
VOFP convictions Number of violation of order for protection convictions, excluding index conviction(s) 1.060 0.040
Disorderly conduct Number of disorderly conduct convictions, excluding index conviction(s) 1.000 0.043
Obstruction convictions Number of obstruction convictions, excluding index conviction(s) 1.010 0.051
Release violator Release violator = 1; other = 0 0.488** 0.139
Probation violator Probation violator = 1; other = 0 0.851 0.105
Non-sex violent offense Non-sex violent offense = 1; other offense = 0 0.645** 0.121
Sex offense Sex offense = 1; non-sex offense = 0 0.253** 0.217
Drug offense Drug offense = 1; non-drug offense = 0 0.982 0.117
Property offense Property offense = 1; non-property offense = 0 0.591** 0.152
DWI offense Felony DWI offense = 1; non-Felony DWI offense = 0 1.136 0.157
Suicidal history History of suicidal tendencies 0.761* 0.114
Prison discipline Number of discipline convictions in prison during current term 0.998 0.003
STG Member of security threat group (STG) 1.074** 0.024
Secondary degree Secondary degree or higher = 1; less than secondary degree = 0 1.437** 0.105
CD treatment Entered chemical dependency (CD) treatment in prison = 1; other = 0 1.157 0.084
CIP Entered Challenge Incarceration Program (CIP) = 1; other = 0 3.100** 0.102
LOS Number of months between prison admission and release dates 1.007** 0.001
Constant Challenge Incarceration Program (CIP) = 1; non-CIP = 0 0.054 0.242
**p < .01.
*p < .05.
10 Crime & Delinquency 00(0)
visits with those who did not on the conditional probability of receiving
video visits, PSM reduces selection bias by helping create a counterfac-
tual estimate of what would have happened to the video visit offenders
had they not received these visits. An advantage with using PSM is that it
can simultaneously “balance” multiple covariates on the basis of a single
Still, there are some limitations with PSM that are important to point out.
First, because propensity scores are based on observed covariates, PSM can-
not control for “hidden bias” from unmeasured variables that are associated
with both the assignment to treatment and the outcome variable. Second,
unless there is sufficient overlap among propensity scores between the treat-
ment and comparison groups, the matching process will yield incomplete or
inexact matches (Shadish et al., 2002). Finally, PSM tends to work best with
larger sample sizes (Rubin, 1997). We attempted to address these limitations,
to the extent possible, by using a sizable number of theoretically-relevant
covariates (29) in the propensity score model on a large sample (N = 20,868).
Matching Prisoners on Video Visits
After obtaining propensity scores for the 20,868 prisoners, we used a “greedy”
matching procedure that utilized a without replacement method to match
those who received video visits with those who did not. Inmates with at least
one video visit were matched to comparison group prisoners who had the
closest propensity score (i.e., “nearest neighbor”) within a caliper (i.e., range
of propensity scores) of 0.01. Using this narrow caliper, we found matches
for all 885 video visit inmates. Table 2 presents the covariate and propensity
score means for both groups prior to matching (“unmatched”) and after
In addition to providing a more traditional test of statistical significance
(“t test p value”) in Table 2, we present a measure (“Bias”) developed by
Rosenbaum and Rubin (1985) that quantifies the amount of bias between the
treatment and comparison samples (i.e., standardized mean difference
between samples), where Xt and St
2 represent the sample
Bias X -
mean and variance for the treated offenders and Xc and Sc
2 represent the sam-
ple mean and variance for the untreated offenders. If the value of this statistic
exceeds 20, the covariate is considered to be unbalanced (Rosenbaum &
Duwe and McNeeley 11
Due to the large sample size we used, most of the differences in covariates
between video visit inmates and the comparison group pool for the unmatched
sample were statistically significant at the .05 level for the t tests (see Table 2).
In addition, 11 of the covariates, including the propensity score, were imbal-
anced insofar as they had bias values greater than 20. But in the matched
sample, we achieved covariate balance given that none of the covariates had
bias values greater than 20. Further, none of the t tests for the matched sample
were statistically significant at the .05 level.
In Table 3, we present recidivism rates over a 2-year follow-up period for the
885 who received video visits, the 885 in the comparison group, and the
19,983 in the comparison group pool. The results show that recidivism rates,
at least for the three reconviction measures, were higher for the releases in the
comparison group pool. As shown earlier in Table 2, however, the inmates in
the video visit and matched comparison groups were more likely to be mar-
ried, receive in-person visits and participate in programming, which are pro-
tective factors associated with less recidivism. When we compare the video
visit inmates with those in the matched comparison group, the 2-year rates
were lower for those who received video visits for all three reconviction mea-
sures. The technical violation revocation rate, on the other hand, was similar
for both groups.
Although these findings suggest video visits may have an impact on recidi-
vism, especially for the three measures of reoffending, the observed recidi-
vism differences between the video visit inmates and those in the comparison
group may be due to other factors we could not control for through PSM. In
particular, the presence and type of post-release supervision can influence
recidivism outcomes (Duwe, 2014; Duwe & McNeeley, 2020), and we did not
include any post-release supervision measures in the propensity score model
because they could not affect whether inmates received video visitation.
But in our Cox regression models, which are shown in Table 4, the follow-
up period for recidivism ranged from a minimum of 2 years to a maximum of
5 years. In addition, we included several dichotomous measures related to
post-release supervision. More specifically, these models contain covariates
that measure whether inmates were released to intensive supervision, were
discharged at the time of release (i.e., released from prison to no correctional
supervision because they had completed their sentence), or were assigned to
work release. In the Cox regression model that estimates the effects of video
visits on technical violation revocations, we removed from our analyses the
18 inmates who were discharged from prison and, thus, could not have had
12 Crime & Delinquency 00(0)
Table 2. Propensity Score Matching and Covariate Balance for Video Visits.
Propensity score Unmatched 0.13 0.04 85.59 0.00
Matched 0.13 0.13 0.08 −99.91% 0.97
In-person visits Unmatched 0.79 0.31 91.53 0.00
Matched 0.79 0.80 2.00 −97.81% 0.73
Males Unmatched 0.91 0.90 2.85 0.35
Matched 0.91 0.91 0.00 −100.00% 0.80
Age at release Unmatched 34.12 36.08 17.28 0.00
Matched 34.12 34.27 1.39 −91.95% 0.46
White Unmatched 0.48 0.50 3.27 0.07
Matched 0.48 0.49 1.63 −50.00% 0.42
Married Unmatched 0.14 0.09 12.35 0.00
Matched 0.14 0.14 0.00 −100.00% 0.95
Unmatched 0.96 1.60 39.34 0.00
Matched 0.96 0.94 1.35 −96.57% 0.53
Total convictions Unmatched 12.26 13.63 12.31 0.00
Matched 12.26 12.23 0.28 −97.69% 0.88
Felony convictions Unmatched 4.50 4.67 4.28 0.24
Matched 4.50 4.48 0.53 −87.58% 0.75
Violent convictions Unmatched 1.58 1.86 11.49 0.00
Matched 1.58 1.59 0.42 −96.35% 0.72
Drug convictions Unmatched 1.45 1.19 12.44 0.00
Matched 1.45 1.43 0.93 −92.49% 0.62
Assault convictions Unmatched 0.97 1.17 10.70 0.00
Matched 0.97 0.97 0.00 −100.00% 0.95
VOFP convictions Unmatched 0.34 0.40 5.01 0.02
Matched 0.34 0.35 0.84 −83.24% 0.54
Unmatched 0.45 0.55 8.65 0.01
Matched 0.45 0.44 0.92 −89.31% 0.72
Unmatched 0.34 0.41 7.24 0.03
Matched 0.34 0.33 1.08 −85.14% 0.68
New court commit Unmatched 0.74 0.49 44.33 0.00
Matched 0.74 0.74 0.00 −100.00% 0.75
Release violator Unmatched 0.09 0.31 50.94 0.00
Matched 0.09 0.09 0.00 −100.00% 0.74
Probation violator Unmatched 0.17 0.21 8.44 0.03
Matched 0.17 0.17 0.00 −100.00% 0.90
Unmatched 0.25 0.29 7.40 0.03
Matched 0.25 0.25 0.00 −100.00% 0.74
Duwe and McNeeley 13
Sex offense Unmatched 0.03 0.10 24.83 0.00
Matched 0.03 0.03 0.00 −100.00% 0.99
Drug offense Unmatched 0.33 0.26 12.41 0.00
Matched 0.33 0.33 0.00 −100.00% 0.58
Property offense Unmatched 0.08 0.15 18.61 0.00
Matched 0.08 0.08 0.00 −100.00% 0.99
Unmatched 0.11 0.07 11.18 0.00
Matched 0.11 0.12 2.61 −76.63% 0.19
Other offense Unmatched 0.19 0.13 13.14 0.00
Matched 0.19 0.19 0.00 −100.00% 0.99
Suicidal history Unmatched 0.12 0.21 20.84 0.00
Matched 0.12 0.13 2.50 −88.00% 0.46
Prison discipline Unmatched 4.60 2.95 11.77 0.00
Matched 4.60 4.40 1.33 −88.72% 0.51
Security threat group Unmatched 0.69 0.54 8.12 0.00
Matched 0.69 0.66 1.58 −80.57% 0.44
Secondary degree Unmatched 0.86 0.75 23.64 0.00
Matched 0.86 0.86 0.00 −100.00% 0.95
Unmatched 0.42 0.27 25.69 0.00
Matched 0.42 0.41 1.66 −93.55% 0.44
Unmatched 0.26 0.06 42.79 0.00
Matched 0.26 0.25 1.87 −95.63% 0.79
Length of stay Unmatched 29.74 15.82 30.18 0.00
Matched 29.74 30.17 0.78 −97.41% 0.67
Total VV = 885; Total Comparison Group Pool = 19,983; Matched VV = 885; Matched
Comparison = 885.
Notes: VV = Video Visit; VOFP = violation of order for protection.
Variables are bolded if the bias value exceeds 20 and/or the t test value < .05.
Table 2. (continued)
their supervision revoked. Moreover, as noted earlier, we included a covariate
that measured the number of times a person returned to prison for a technical
violation revocation in the models using the three reconviction measures.
And we also included in our models the propensity score, which can be con-
ceptualized as a single covariate that approximates adjusting for all of the
covariates in the propensity score estimation model since it captures the dis-
tribution of these covariates (Austin, 2017).
To determine model fit, we tested the assumption that the hazards are pro-
portional and for nonlinearity in the relationships between the log hazard and
14 Crime & Delinquency 00(0)
covariates. Our inspection of the residuals revealed that all of the Cox regres-
sion models adequately fit the data. The results in Table 4 indicate that, con-
trolling for the effects of the other independent variables in the statistical
model, receiving at least one video visit significantly reduced the hazard ratio
for two of the recidivism measures (general and felony reconviction). In par-
ticular, video visits decreased the hazard by 22% for general reconviction and
21% for felony reconviction. Video visits did not have a significant effect on
either violent reconvictions or technical violation revocations.
We also estimated Cox regression models that analyzed the effects of the
number of video visits on recidivism. Similar to our binary measure for video
visits, the results were statistically significant for general and felony recon-
viction but failed to reach statistical significance for violent reconviction or
technical violation revocations. As the number of video visits increased, so
did the size of the reduction in recidivism, at least for general and felony
reconvictions. In particular, for every additional video visit, the hazard of
recidivism decreased by 3.1% for general reconviction and 3.6% for felony
reconviction (Table 5).
To further isolate the impact of video visits on recidivism, we also con-
ducted analyses on the 364 inmates in our sample of 1,770 who did not
receive an in-person visit while in prison. Of the 364, 184 received a video
visit while the remaining 180 did not. The hazard ratios for video visits were
generally in the expected direction for all eight Cox regression models and
were similar to those presented in Table 4. Due in part to the smaller sample
size, however, only one was statistically significant at the .05 level. More
specifically, of those without an in-person visit, receiving a video visit sig-
nificantly reduced the hazard of general reconviction by 31%.
While video visits did not have a significant effect on all of the recidivism
measures, the results still showed a reduction for general and felony
Table 3. Two-Year Recidivism Rates for Video Visit and Comparison Group
Recidivism Video visit Comparison Comparison group pool
General reconviction 24.6% 31.4% 38.6%
Felony reconviction 15.3% 19.3% 24.9%
Violent reconviction 5.9% 6.2% 10.5%
Technical violation revocation 24.6% 24.1% 25.3%
N 885 885 19,983
Table 4. Impact of Video Visits on Recidivism.
Reconviction Felony Violent TVR
HR SE HR SE HR SE HR SE
Any video visit 0.785** 0.086 0.793* 0.109 0.965 0.171 1.078 0.097
Propensity score 0.029** 0.485 0.063** 0.603 0.016** 1.057 2.207 0.462
Work release 0.867 0.259 0.505** 0.196 0.667 0.297 1.244 0.135
ISR 0.709 0.954 1.093 0.153 1.332 0.227 2.765** 0.116
Discharge 1.448 0.359 0.998 0.506 2.007 0.590
Number of TVR’s 0.818** 0.073 0.892 0.196 1.094 0.119
Number of video visits 0.969* 0.012 0.964* 0.016 0.994 0.019 1.007 0.097
Propensity score 0.034** 0.486 0.032** 0.605 0.016** 1.061 2.121 0.464
Work release 0.861 0.238 0.500* 0.196 0.665 0.297 1.240 0.135
ISR 0.733 0.957 1.090 0.153 1.332 0.226 2.759** 0.115
Discharge 1.413 0.335 0.999 0.506 2.014 0.590
Number of TVR’s 0.820** 0.074 0.843 0.089 1.095 0.119
N 1,770 1,770 1,770 1,752
Notes: HR = hazard ratio; SE = Standard Error; ISR = intensive supervised release; TVR = technical violation revocation.
**p < .01.
*p < .05.
Table 5. Impact of Video Visits on Recidivism for Inmates without In-Person Visits.
Reconviction Felony Violent TVR
HR SE HR SE HR SE HR SE
Any video visit 0.695** 0.172 0.728 0.209 0.668 0.340 1.097 0.207
Propensity score 0.000** 4.181 0.000** 5.074 0.000** 9.271 1560.862 0.462
Work release 0.875 0.262 0.407* 0.425 0.716 0.614 1.373 0.319
ISR 0.953 0.283 1.106 0.323 2.798* 0.424 4.401** 0.258
Discharge 2.069 0.520 1.075 0.725 1.721 1.034
Number of TVR’s 0.873 0.143 0.977 0.163 0.991 0.228
Number of video visits 0.949 0.012 0.946 0.036 0.924 0.068 1.036 0.020
Propensity score 0.000** 4.141 0.000** 5.032 0.000 9.159 1197.307 3.960
Work release 0.864 0.262 0.397* 0.425 0.689 0.615 1.415 0.320
ISR 0.995 0.281 1.132 0.321 2.915* 0.421 4.446** 0.256
Discharge 2.040 0.519 1.162 0.729 1.931 1.043
Number of TVR’s 0.882 0.144 0.988 0.163 0.999 0.228
N 364 364 364 357
Notes: HR = hazard ratio; SE = Standard Error; ISR = intensive supervised release; TVR = technical violation revocation.
**p < .01.
*p < .05.
Duwe and McNeeley 17
reconvictions. Consistent with prior research on in-person visits, the findings
also indicated that as the number of video visits increased, so did the magni-
tude of the decrease for general and felony reconvictions. Further, among
inmates who did not have an in-person visit, receiving a video visit was asso-
ciated with a reduction in general recidivism. This study thus offers some
support for the notion that video visits can be just as effective as in-person
visits in reducing recidivism.
These results should not be interpreted to mean that video visitation should
replace in-person visits. Indeed, the limited use of virtual visits by the
MnDOC, which we discuss in more detail below, suggests that eliminating
face-to-face visits would not be a prudent strategy. Instead, given that the
findings from this study and prior research suggest that both types of visita-
tion are associated with less recidivism, correctional agencies should attempt
to simultaneously maximize the use of both in-person and video visits.
Although the results from our study are encouraging, there are several
limitations worth highlighting. First, because we examined video visita-
tion in one state’s prison system, the findings may not be generalizable to
other correctional systems. Second, prior research has shown that the pris-
oner-visitor relationship has an influence on whether visitation reduces
recidivism and, if so, to what extent (Bales & Mears, 2008; Duwe & Clark,
2013). Because the video visit data we used did not identify the relation-
ship between inmates and visitors, we were unable to examine whether
video visits from some people were more beneficial than others. Third,
despite using procedures to control for observable selection bias and fac-
tors that influence reoffending, it is possible the people who received
video visits had greater access to unmeasured social and economic
resources that may have contributed to better recidivism outcomes. Finally,
the results indicated that only 4% of Minnesota prisoners released between
2016 and 2018 used video visitation, and the relatively small number of
inmates who only received video visits hampered our ability to fully assess
the relationship between video visitation and recidivism.
When the MnDOC introduced video visitation in late 2015, one of the
goals of this initiative was to expand the accessibility of visitation. After all,
research has not only shown that visitation is associated with less recidivism
(Mitchell et al., 2016), but also that visitation is less likely to happen when
potential visitors have to travel greater physical distances (Clark & Duwe,
2017). Therefore, it was believed that video visitation could be a key resource,
especially for unvisited inmates who were separated by longer distances from
their potential visitors. As the findings clearly showed, however, video visita-
tion was not used much by the Minnesota prison population. And, when it
was used, it was mostly by inmates who were already receiving in-person
18 Crime & Delinquency 00(0)
visits. Only 184 prisoners (less than 1% of all releases from 2016 to 2018)
received a video visit without an in-person visit.
Why was video visitation used so sparingly? Conducting qualitative
research with prisoners and visitors, which was beyond the scope of this
study, would help determine why video visitation was underutilized.
Nevertheless, anecdotal evidence from MnDOC staff suggests a number of
problems might have been responsible for its infrequent use. First, techno-
logical difficulties were relatively commonplace, resulting in what may have
been a poor user experience. Second, the vendor’s software, which was not
compatible with most smartphones and tablets, essentially required visitors
to use laptop computers, which may have been a barrier for some potential
visitors. Third, even though a video visit would generally be less costly than
an in-person visit for many, the cost (about $10 for a 30-minute visit) may
still be too much to bear for some potential visitors. Just as prior research has
shown that barriers to in-person visitation tend to be felt more acutely when
potential visitors live in areas affected by concentrated disadvantage (Clark
& Duwe, 2017), the same may be true for video visitation.
The MnDOC will be using a different vendor for video visitation services,
which may (or may not) address some of the difficulties users might have
experienced. To substantially expand the use of video visitation, however, it
may be necessary for the MnDOC to explore whether strategies for subsidiz-
ing part of the cost would have much of an impact. For example, to lessen the
effects of concentrated disadvantage, a subsidy could be made available for
lower-income families. The MnDOC could also forge partnerships with com-
munity agencies to provide the families of prisoner with the technology
needed for video visits. Another strategy could involve applying a subsidy
specifically to higher-risk inmates who are less likely to be visited, which
would be consistent with the risk-needs-responsivity model that is used by
many correctional agencies in the U.S. Likewise, in an effort to promote
desistance, video visit credits could be given to those who refrain from mis-
conduct over a period of time. Or, in recognition of the public safety benefits,
Minnesota’s legislature could provide a broad subsidy that decreases the
costs of visits in general. Regardless of which cost-reduction strategy is used,
the MnDOC will need to more closely review its visitation policies and prac-
tices to ensure greater use of this resource in the future.
The findings provide additional evidence that social support, even if it is
delivered virtually, can help people make a successful transition from prison
to the community. In a similar vein, the results may bode well for the use of
technologies, such as tablets, to deliver virtual programming to incarcerated
populations. Research has shown that many prisoners do not participate in
programming while they are confined (Duwe & Clark, 2017; U.S. Department
Duwe and McNeeley 19
of Justice, 2019), and the shortage of programming is often tied to a lack of
resources, staff, and physical space. Because the staff and physical space
requirements for tablets are relatively minimal by comparison, this mode of
program delivery may be worth considering by correctional systems that
struggle to provide enough programming to those in their custody.
In general, virtual programming is an area that warrants greater explora-
tion and study. Likewise, while this study represents one of the first evalu-
ations of video visitation, there is, of course, much that remains to be
learned. For example, as suggested by the Minnesota experience, research
should attempt to identify the conditions that make video visitation more or
less likely by interviewing or surveying prisoners and those who might visit
them. Moreover, given that video visits did not significantly reduce the
hazard of violent reconvictions and technical violation revocations, future
studies should examine whether these findings are generalizable to other
correctional populations. Research should also examine whether video vis-
its have a positive impact on inmate misconduct. If so, then increasing
access to video visitation could help improve the safety of correctional
institutions for both inmates and staff. And, given the differential recidi-
vism reduction effects observed across prisoner-visitor relationships for in-
person visits, future studies should attempt to determine whether the same
holds true for virtual visits.
Declaration of Conflicting Interests
The author(s) declared no potential conflicts of interest with respect to the research,
authorship, and/or publication of this article.
The author(s) received no financial support for the research, authorship, and/or publi-
cation of this article.
Grant Duwe https://orcid.org/0000-0003-0632-560X
Susan McNeeley https://orcid.org/0000-0001-8923-6973
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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. His recent work has been
published in Corrections: Policy, Practice and Research, Journal of Experimental
Criminology, Journal of Offender Rehabilitation, Law and Human Behavior, and The
Susan McNeeley is a research analyst with the Minnesota Department of
Corrections. In addition to corrections, her research has focused on criminological
theory and victimology. Her recent work can be found in Journal of Experimental
Criminology, Crime and Delinquency, Journal of Criminal Justice, and Criminal
Justice and Behavior.