Vocational and Educational Programs: Claire Perry April 2014

Vocational and Educational Programs:
Impacts on Recidivism
Claire Perry *
Haverford College
April 2014
This paper looks at the relationship between educational and vocational program
participation and recidivism. Using a cohort of individuals released from state prison in 1994
across five states and tracked for three years, this paper takes into consideration both rearrest and re-confinement. It finds that vocational programs in particular have significant
reductions in both re-arrest and re-confinement; results are primarily driven by programs in
Illinois and New York. This paper builds off the extensive literature on the topic by
accounting for varying levels of program completion, including different types of recidivism,
controlling for state-to-state variation, and looking at both education and vocation programs.
In addition to predicting recidivism based on program participation, this study seeks to
control for motivation of people choosing to participate in educational and vocational
programs using instrumental variable analysis and looks at time until recidivism using
proportional hazard models.
First, I would like to thank my adviser, Professor Anne Preston, whose guidance, support, and expertise have been
invaluable throughout this process. Additional thanks go to Professor Ball for instrumental help with data manipulation,
Kim Minor for logistical support, Norm Medeiros for research assistance, and Matt Durose, from the Bureau of Justice
Statistics, for help re-creating variables in the original BJS report.
*
Table of Contents
Introduction ........................................................................................................................................................................... 1
Background ............................................................................................................................................................................ 4
Literature Review ................................................................................................................................................................ 7
Data......................................................................................................................................................................................... 15
Methods ................................................................................................................................................................................ 16
Results ................................................................................................................................................................................... 27
Conclusion ........................................................................................................................................................................... 48
References ........................................................................................................................................................................... 55
Appendices .......................................................................................................................................................................... 61
Perry 1
Introduction
The continued expansion of America’s prison system has refocused debate on the cost of
incarceration, judicial fairness, and the effectiveness of programs designed to rehabilitate
incarcerated individuals and reduce recidivism. Many studies indicate that educational and
vocational programs for people in prison can reduce recidivism and thus better the lives of
formerly incarcerated individuals2 and decrease the burden on the criminal justice system (Davis et
al. 2013; Pew 2011; Wilson et al. 2000). A 2011 report by the Pew Center on the States notes that
“State corrections spending, driven almost entirely by prison expenditures, has quadrupled over
the past two decades, making it the second fastest growing area of state budgets, trailing only
Medicaid. Total state spending on corrections today is more than $50 billion a year” (Pew 2011).
This increase is due, in part, to increases in prison populations. In the period from 1987 to 2007,
the national prison population nearly tripled. Today, one in 100 Americans are behind bars in
federal prison, state prison, or local jail (Warren et al. 2008). With so many incarcerated Americans
and so much taxpayer money going toward prisons, people are asking the question: are our prisons
working?
One way to measure success of the prison system is by looking at recidivism. A commonly
accepted definition of the recidivism rate is the proportion of individuals released from prison that
are rearrested, reconvicted, or re-incarcerated within three years of their initial release (Pew
2011). If prisons are meant both to protect society from criminals and to “correct” individuals for
successful re-entry to society, then recidivism indicates a fault on both counts. Despite massive
increases in corrections spending, the recidivism rate has remained fairly constant since 1983 at
around 40% (Pew 2011; Hughes et al. 2002). This consistency means that, despite evidence of
There is discussion as to which tern is most appropriate to refer to individuals who were formerly incarcerated. Though
not without its problems, “formerly incarcerate individuals” is used throughout this paper as opposed to ex-inmate, exprisoner, ex-offender, prisoner, or returning citizen. For more information about recent legal changes in Philadelphia see
Cherri 2013.
2
Perry 2
effective programs, little has been done to alter dramatically prison recidivism. Given that it costs
$78.95 on average per day 3 to keep an individual in prison, more than 20 times the cost of a day on
probation, (Warren et al. 2009) the rate at which people return to prison presents a huge financial
cost to society (Davis et al. 2013).
One way to deal with this issue is by trying to better prepare individuals for life outside of
prison through education and vocational courses. Such programs are in high demand4,5 as they help
normalize the prison experience, teach essential skills for employment, keep people occupied and
engaged, and allow some individuals to earn “good time” in order to reduce their sentences.
Investing now saves money later and makes it more likely that people will re-enter society as
successful, tax paying citizens (Davis et al. 2013). As seen below in Table 1 the percentage of state
prisons offering different types of educational programing has increased slightly from 1995 to
2000. Yet, as seen in Table 2, the percentage of incarcerated individuals participating in these
programs has steadily decreased for all program types from 1991 to 2004.
TABLE 1: PROGRAMS OFFERED IN STATE AND FEDERAL CORRECTIONAL FACILITIES (PERCENTAGES) 6
Federal
State
1995
2000
1995
2000
With Education Programs
Adult Basic Education
Secondary/GED
Special Education
Vocational Training
96.8
85.6
92.8
32.8
68.0
91.7
89.3
90.5
54.8
85.7
86.5
73.5
78.5
33.1
52.4
90.3
79.6
82.8
39.2
55.2
College
Study Release
Life Skills/ Community Adjustment
63.2
8.8
80.0
73.8
6.0
89.3
30.5
10.9
65.4
26.4
7.6
68.3
In 2008 dollars
As part of the Serious and Violent Offender Reentry Initiative (SVORI), a Federal initiative to help States reduce
recidivism, the Urban Institute and RTI International interviewed then soon-to-be-released incarcerated people. Of their
requested reentry needs, 94% requested more education, 82% job training, and 80% post-release employment.
5 Crayton & Neusteter suggest that several states, including CA and MD reported having significant waitlists for
educational programs
6 Sources: Stephan & Karberg (2003); Stephan (1997); Crayton & Neuster (2006)
3
4
Perry 3
TABLE 2: PROGRAMS OFFERED IN STATE AND FEDERAL CORRECTIONAL FACILITIES (PERCENTAGES)7
Federal
State
Basic
1991
10.4
1997
1.9
2004
1.5
1991
5.3
1997
3.1
2004
2.1
Secondary/GED
Vocational Training
College
English as a Second Language
Life Skills/ Community Adjustment
27.3
29.4
18.9
-
23.0
31.0
12.9
5.7
-
21.1
30.8
10.1
2.3
28.6
27.3
31.2
13.9
-
23.4
32.2
9.9
1.2
-
19.2
27.0
7.3
1.0
14.3
Given that resources are clearly limited, it begs the question: which programs - vocational
training, general education, or a combination - are best at reducing recidivism of people
incarcerated in state prisons?
In this paper, I take an empirical approach to evaluate the relative effectiveness of
vocational training, educational programs, or a combination of the two in reducing recidivism to
state prison. I use probit regression analysis to study the largest national dataset on this topic,
Recidivism of Prisoners Released in 1994 (ICPSR3355) by the Bureau of Justice Statistics. This
dataset is arguably the most comprehensive study of recidivism at the individual level available in
the United States. Like some other papers on the topic I consider the difference between
educational and vocational programs, however I account for state-to-state variation, look at
different levels of participation, attempt to control for selection bias, and consider two measures of
recidivism. To deepen this work, I use a proportional hazards model to see if the rate of recidivism
overtime is impacted by their involvement in educational or vocational programs.
I begin with a brief background of recidivism and discuss the existing literature regarding
recidivism and educational and vocational programs in prisons. I provide basic information on the
7
Sources: Harlow (2003); BJS (2007); Crayton & Neuster (2006)
Perry 4
data set used in this paper, ICPSR3355, and outline the methods for analysis along with references
to specific literature using these statistical forms. Results are given and analyzed; then I conclude.
Background
Recidivism
As stated previously, the recidivism rate is the proportion of individuals released from
prison that are rearrested, reconvicted, or re-incarcerated within three years of their initial release
(Pew 2011). Recidivism is a popular metric, and while most recent studies use the previous
definition, there is still room for interpretation. For example, within that definition there are four
distinct ways to quantify recidivism, as noted by the Bureau of Justice Statistics (BJS):




Rearrests for a new offense
Reconviction for a new crime
Resentenced to prison or jail for a new crime
Resentenced to prison or jail for a new crime or for a technical violation, also known as reconfinement 8
Officially, the above are measured within a 1,096 day period after release.9 For the purpose of this
analysis, I focus on the first and last definitions as the first indicates, to some extent, return to
criminal behavior, and the last indicates reengagement with the criminal justice system either
through a new crime or a technical violation. While re-arrest is subject to many biases (one can be
arrested without actually being guilty), it is a readily available indicator and the first step in return
to the criminal justice system. Few analyses look at the difference in a new criminal conviction
versus a technical violation as it relates to educational and vocational programs. While both
indicate a violation of the law, the latter is more likely to be a function of state laws, intensity of
parole sentences, and punishments for technical violations, rather than individual motivation (Pew
8
9
For example, a technical violation may be failing a drug test or not complying with parole guidelines.
1,096 days is considered to be more accurate than simply “3 years” due to the different number of days in each month.
Perry 5
2011). For simplicity’s sake the final definition will be used as returning to prison or jail indicates a
failed attempt at “correction” and indicates the greatest cost to both society and the individual.
While recidivism is a popular metric, it should not be used in isolation when evaluating
prisons. Recidivism rates vary greatly state-to-state 10 because of different policies around parole,
technical violations, arrests, age at which one can be tried as an adult, and the likelihood of being
sent to prison after a conviction (Pew 2011). In addition to variation in state policies, Petersila
notes, “it is impossible to know whether recidivism indicators reflect more criminality on the part
of released prisoners or greater diligence on the part of the police (e.g. more arrests relative to
crimes committed)” (Petersila 2003). A low recidivism rate does not always reflect the use of
sound release, preparation, and supervision strategies (Pew 2011). For example, it could be a
symptom of over incarceration for petty crimes for which people may be less likely to recidivate. To
account for these differences, I control for state-to-state variation in my analysis by including state
dummy variables and look at both re-arrest and re-confinement for each state in the analysis.
Education and Vocational Programs
Education programs range from basic literacy classes, to GED attainment courses, to
postsecondary education. They are usually general in their aims and focus on increasing knowledge
and general skills. Vocational programs are more varied and range from job training to
interviewing skills to technical skills in a specific trade. At their core, these programs aim to
increase the employability of individuals. In both cases certificates may be awarded for successful
completion. Some studies (Harer 1995; Lawrence et al. 2002; Gaes 2008) go into even further
specificity when defining types of programs offered, but due to constraints in available data, I will
confine my focus to these two overarching program types: education and vocation.
10
See Figure 4 for the re-arrest and re-confinement rates for the states included in this analysis
Perry 6
Education and vocation programs have been integrated in the American criminal justice
system since 1790 when the Philadelphia Quakers opened the Walnut Street Jail. Their programs
focused on reform of offenders by making them penitent and by providing religious texts and
guidance (Hall 2006).11 Since then education and vocational training have been a core part of the
American penal system.
There are many arguments in favor of structured programing in prisons. To start, people
argue that employment and crime are negatively correlated (College of Criminology and Criminal
Justice, 2006 Annual Report). Thus increasing one’s ability to get a job - through training and
education - decreases the chances of committing another crime (Piehl 2000). Another argument in
favor of educational and vocational programs is that they normalize the prison experience by
rewarding achievement, helping incarcerated individuals reduce their sentences (in some cases),
and “nurturing pro-social norms supporting rule/law abiding behavior” (Harer 1995). At the most
basic level such programing has value as it keeps incarcerated individuals busy. In 1930 a Senate
Judiciary Committee noted, “It is unanimously conceded that idleness in prisons breeds disorder
and aggregates criminal tendencies. If there is any hope for reformation and rehabilitation of those
convicted it will be founded upon the acquisition be the prisoner of the requisite skill and
knowledge to pursue a useful occupation and the development of the habits of industry” (Gaes &
Saylor referring to Congressional Record Report No. 529. 71st Cong).
Finally, and perhaps the most cited of all, is that incarcerated individuals are often less
educated than the general population. “Between 1991 and 1997, the percent of inmates in state
prison without a high school diploma or GED remained the same — 40% in 1997 and 41% in 1991”
(Harlow 2003). To compare, in 1997 only 18% of the general population age 18 or older had not
finished 12th grade (Harlow 2003). This leaves incarcerated individuals even worse off when trying
to find employment post release and represents a loss of productivity for the individual and society.
11
For a more extensive history see Hall’s dissertation: Voices Behind Bars (2006)
Perry 7
As such, prison education programs provide an outlet to help correct for basic educational
deficiencies that might be further holding individuals back.
Despite many current studies indicating the success of education and vocation programs 12
(Davis et al. 2013; Pew 2011; Wilson et al. 2003) there is still disagreement about which programs
are most effective and how to fund them (Brewster & Sharp 2002; Pew 2011). Additionally, some
people still oppose educational and vocational programs, arguing that they do not prepare people
for life after prison, they are too little too late, they lack structure and resources to be effective, and
they divert resources that could go to non-incarcerated people (Strand 2012; Marc-Taylor). To
complicate matters, many studies use different definitions of recidivism or look at very different
programs. So while some find benefits in education, others point to vocation for the benefits,
though few studies find no impact of either.
As program offerings decrease (Table 2) and budgets are cut the question becomes, which
programs are the most effective at reducing recidivism? In the following section I go in to greater
detail about the existing literature first by looking at several meta-analyses written about
educational and vocational programing and recidivism, then by examining select relevant studies.
Literature Review
Meta-Analysis
The literature on the topic of prison recidivism and correctional programs is so extensive
that five meta-studies have been written to evaluate the work. Lipton, Martinson, and Wilks wrote
the first comprehensive study in 1975. It covered 231 rigorous studies between 1945 and 1967
and, among other topics, examined vocational and educational programs in prisons. It became “the
most politically important criminological study of the past half century” (Miller 1989). The authors
The Bureau of Justice Statistics includes “in-prison and post-release vocational training and work programs” in the
selected list of programs that they know to be effective in reducing recidivism (Bureau of Justice Statistics)
12
Perry 8
came to the ambiguous conclusion that some programs work, while others do not. Still, this was a
significant step from Martinson’s 1974 paper, now referred to as the “Nothing Works” paper.
Though some say Martinson later revoked this stance that there “is very little reason to hope that
we have in fact found a sure way of reducing recidivism” his paper and the subsequent meta-study
led to an era of skepticism about rehabilitation (Elliott 2010). As a result “federal- and statesponsored initiatives to address the needs of prisoners were effectively put on the defensive and in
some cases curtailed” (Davis et al. 2013).
A subsequent study, published in 2000, by Wilson, Gallegher, and MacKenzie, considered
“the recidivism outcomes of 33 independent experimental and quasi-experimental evaluations of
education, vocation, and work programs and found that program participants recidivate at a lower
rate than nonparticipants” (Wilson et al. 2000). Wilson, et al. set high standards for experimental
rigor and eliminated poorly run studies from their analysis. On the whole, these studies’ results
were heterogeneous indicating that some programs may be highly effective while others may have
no effect on future offending behavior (Wilson et al. 2000). The authors assumed a 50% baseline
recidivism rate for non-participants, and found that on average participants in some kind of
educational or vocational program recidivate at a rate of 39%. It is unclear where the 50%
assumption is from, which calls in to question the 39% figure. 13 Further, the authors note that selfselection bias and poor study design still plague many of the evaluated studies. Thus, while their
meta-analysis cannot conclusively confirm the benefits of these programs, they claim a positive
correlation between program participation and lower recidivism rates (Wilson et al. 2000). As their
analysis suggests, looking at selection bias is uncommon in recidivism studies and will thus be
included in this analysis.
Mackenzie, from the previous meta-analysis, completed a follow-up study in 2006 looking
only at programs from 1980 to 2006. She notes “research demonstrates that education programs
13
This is especially odd as research suggests the standard re-confinement rate is closer to 40% and has stayed constant
Perry 9
such as basic education, GED, postsecondary and vocational are effective in reducing later
recidivism and increasing future employment.” (Mackenzie 2006). Yet she finds huge variation in
the quality of vocational programs, which makes it difficult to draw conclusions about their
effectiveness (Mackenzie 2006). Unlike strictly educational programs, she finds that vocational
training is often paired with other reentry programs so it is difficult to separate the effects. She
notes that though the evidence indicates effective programs, more research needs to be done in
assessing specifically what makes certain programs more effective (Mackenzie 2006). In 2006 Aos,
Miller, and Drake completed another meta-study and found that recidivism was reduced by 7% for
participation in educational programs and by 9% for participation in vocational programs. Because
many individuals participated in both education and vocation programs, it was difficult to separate
the impacts of each program. The authors were, however, able to estimate that for both programs
the net benefits per participant were greater than $10,000. The exact derivation of this estimate is
complicated but is consistent with the findings in Davis et al. and with the idea that investing in
education reduces future costs to society and increases the tax base. A core facet of these studies is
the consideration of both education and vocation programs as well as participation in both program
types. While Mackenzie finds a greater correlation for education programs, Aos et al. find a greater
impact of vocational programs. Both papers suggest that the interaction between types of programs
is important in teasing out which program has an effect. Such interactions and varying levels of
participation are central to the analysis that follows in this paper.
The most rigorous and recent meta-study is the 2013 report by Davis et al. for the RAND
Corporation – a think-tank funded, in part, by the U.S. Government. Davis et al. look at 102 effects
across 58 studies and find that “between 43.3% and 51.8% of former prisoners were reincarcerated within three years of release, and two-thirds were rearrested within three years of
release” (Davis et al. 2013). Applying national recidivism data, this study finds that:
Perry 10
Correctional education would be expected to reduce three year re-arrest and reincarceration rates by 13.2 and 13.8 percentage points, respectively. According to these
estimates, eight inmates would need to receive correctional education to prevent one
additional inmate from being rearrested within three years of release, and seven inmates
would need to receive correctional education to prevent one additional inmate from
returning to prison within three years. (Davis et al. 2013 )
The authors also provide a brief cost-benefit analysis of prison education programs. They estimate
that from a budget standpoint, correctional education programs would “need to reduce the threeyear re-incarceration rate by between 1.9 and 2.6 percentage points” in order to reach the
breakeven point (Davis et al. 2013). Their research suggests “that inmates who participate in
correctional education programs have a 43 percent lower odds of returning to prison than those
who do not” (RAND Press Release) and that people participating in academic or vocational
programs had an employment rate 13% higher than those who did not participate. Specifically,
“people with vocational training were 28% more likely to be employed after release from prison
than [those] who did not receive such training.” In this way, the RAND study provides strong
support for both education and vocational programs.
These five meta-studies make it clear that there is a significant amount of research being
done on the topic of educational and vocational programing and recidivism. 14 These studies also
make clear that despite skeptical beginnings, there is some agreement that education and
vocational programs reduce recidivism. All of the studies indicate a need for more comprehensive
data and studies regarding what specific types of programming are effective. Still several questions
remain that motivate this paper:



Which programs, educational or vocational, are better at reducing recidivism?
Do these impacts still hold when accounting for selection bias – the idea that people who
choose to participate might be less likely to recede anyway?
Do the impacts of education or vocational training wear off over time?
It should be noted that some of this variation could be explained by the program and policy landscape changing over
the years. Thus one program type could have been more impactful in the past but since fallen out of favor.
14
Perry 11
Selected Studies
There are countless studies 15 that look exclusively at education programs. Some look at
one particular program and compare the “treated” group to the prison average or group of nonparticipants. For example Thorpe et al. (1984) study individuals earning secondary degrees or
certificates in prison in the state of New York. The authors find that 14% of the individuals with a
degree or certificate were returned to custody during the survey period, while 20% of the overall
population returned to confinement. The authors note that the significantly lower return rate of
this sample may “be jointly attributed to the offenders’ motivation and capabilities as well as the
impact of the program.” Thus, while the authors acknowledge the omitted variables bias in the
selection process, they do not account for it in their study.
While the Thorpe study lacks a genuine control group, Tyler et al. (2007) look at a pool of
high school dropouts in Florida who were admitted to prison around the same time; some choose to
earn their General Education Development (GED) during their time in prison and others do not. The
authors use panels of quarterly earnings and find that the effect of GED certification declines over
time. While, this paper focuses on employment, not recidivism, it indicates the potential for
decreasing returns to prison education and is one of the few studies that explicitly compares
individuals with similar educational backgrounds.
A 1994 study by Adams et al. sampled 14,411 incarcerated individuals released from prison
between 1991 and 1992 in Texas. The authors find that the more hours of participation in a
program, the lower the likelihood of recidivism, but only for those “most educationally
disadvantaged” inmates. Outside of this cohort, effects were not significant. Their findings suggest
that initial level of education can be an important factor in understanding the impacts of
educational programs. This is supported by Cho and Tyler (2013) who find that participation in
For greater detail on the variety of studies that have been used to test education and vocation programs in prisons see
https://www.ncjrs.gov/works/chapter6.htm and http://www2.ed.gov/offices/OVAE/AdultEd/OCE/19abstracts.html
15
Perry 12
adult basic education (ABE) is associated with higher post release earnings and employment rates,
especially for minorities. They do not find, however, any positive effects of participation on
reducing recidivism. Unfortunately, ICPSR3355 does not have information about prior education so
I use instrumental variable analysis in attempts to tease out these omitted variables. Cho and Tyler
(2013) also suggest that level of program completion is important, despite not being included in
many studies. ICPSR3355 does include information about the level of participation and thus this will
be taken into account in the analysis.
These types of state and program specific studies are common in the literature but do not
lend well to policy or cross-state comparison. Note that the three above studies all use different
periods to define recidivism, rather than the BJS standard of 1,096 days. A study that provides more
program comparison by Jenkins et al. (1993) compares various types of education programs in
prisons. The study looked at ABE, GED, vocational education and post-secondary students in
Maryland. The study finds a positive and significant benefit of education for students in ABE, GED,
and vocational programs, but has inclusive finding for post-secondary students due to small sample
size. The benefits include reduced recidivism, increased employability, and higher wages when
compared to similar individuals who did not participate in any programing.
There are few vocation-only studies in the literature. One particularly notable one in 1997
by Gaes and Saylor covered over 7,000 individuals in Federal Prisons over four years. It specifically
looked at UNICOR 16 workers and vocational students as compared to a matched group of
individuals with no such experience. They find UNICOR and vocational students have better
institutional and half-way house adjustment, are less likely to return to federal prison, are more
likely to be employed, and earn more money than the comparison group. This suggests that
vocational programs can have significant impacts on post release outcomes, including recidivism.
UNICOR workers are given vocational training and work on producing goods and services for the government.
Individuals are paid and usually make less than $2 an hour.
16
Perry 13
Even fewer studies compare the differences between educational and vocational outcomes.
Anderson et al. (1988) focus on both education and vocational programs. 760 individuals in Illinois
were studied for one year following their release via reports from their parole officers. The
individuals were divided into four groups while in prison: (1) vocational training, (2) vocational
and academic training, (3) no vocational training, and (4) only academic work. Vocational (1) and
vocational and academic (2) groups had higher employment and fewer arrests than the other
groups. Those who received no vocational training had the highest crime rate; the academic only
group had the lowest employment rate. Due to parole officer’s lack of knowledge regarding
employment type of the individuals, the study admits to possible data bias. Still, it provides valuable
insight and is one of few studies to look at education and vocation as well as employment and rearrest. Specifically it provides evidence that re-arrest rates can be significantly reduced for
individuals participating in vocational programs in Illinois.
A 1998 study by Mitchell Jancic finds lower recidivism rates for people earning their GED,
participating in correctional educational and vocational programs, completing high school
requirements, and completing or participating in post-secondary programs. His study looks at
seven published studies conducted in a variety of states but it is difficult to draw conclusions across
states or programs because each study measures education and vocation differently. My paper
seeks to address such cross-state discrepancies by using a centrally compiled dataset.
An Oklahoma statewide study by Brewster and Sharp (2002) looks at survival times (time
until recidivism) for individuals participating in GED programs or vocational training. They find
that completion of GED programs was strongly associated with longer survival times outside prison
and that completion of vocational-technical training was linked to shorter survival times when
compared to people participating in no programs. 17 While this study is strong and raises
Other studies of Oklahoma find a similar result for vocation training, likely because of how individuals are enrolled in
vocational programs and because often completion of such a program is a prerequisite for receiving parole.
17
Perry 14
interesting questions about vocational programs and the power of choosing to enroll, it is unable to
exclude people who die, look at people who recidivate out of state, or account for level of program
completion – all of which are accounted for in the analysis that follows.
The paper most similar to the current analysis by Zarona Ismailova (2007) uses ICPSR3355
and looks at participation in education and vocational classes as they relate to recidivism. Using
logistic regression analysis she finds that vocational training alone does not significantly impact
recidivism, while education, or some combination of the vocational and educational training, is
correlated with lower recidivism (Ismailova 2007). She controls for criminal history and several
demographic variables, but does not account for state-to-state differences or selection bias in the
analysis. Additionally, she does not consider the level of program completion.
My paper adds to the abundant existing literature by looking at a nationally representative
dataset of state prisons and specifically comparing the difference between educational and
vocational programs. Unlike other papers that do this (Ismailova 2007; Brewster and Sharp 2002;
Anderson et al. 1988) I control for state-to-state variation, look at different levels of program
completion, and consider two measures of recidivism. I attempt to control for selection bias with
instrumental variable two staged least squares analysis. Additionally, I look at the impact of
education and vocational programs on time until recidivism using a Cox Proportional Hazard
Model.
Perry 15
Data
This paper uses data from the Bureau of Justice Statistics Special Report: Recidivism of
Prisoners Released in 1994. The data is managed by the Interuniversity Consortium for Political and
Social Research (ICPSR) and is arguably the most comprehensive study of recidivism at the
individual level available in the United States. 18 In 1998 the Bureau of Justice Statistics asked 15
State Departments of Corrections 19 to release basic records and criminal information on prisoners
released in 1994. Of those, only five states recorded participation in education and vocational
programs.20 Across all 15 states 38,624 incarcerated individuals were sampled; because of the
limited data on education and vocation programs21, only 11,824 will be included in this study.
Files were collected to be representative for each state across 13 offense categories using a
stratified random sample by state in 12 of the 13 offense categories; in the case of sex-offenders
(the remaining category), all files were pulled. All individuals were then matched, based on State
and FBI identification numbers, to all appropriate Record of Arrest and Prosecution (RAP) sheets
and state criminal history repositories. Files on each incarcerated individual released in 1994 were
thus created, along with their arrest and adjudication history from their first ever arrest through
When weighted, the sample of 33,796 ex-inmates represents 272,111 people: two-thirds of all State prisoners released
with sentences greater than a year in 1994 (Langen & Levin). Still, for the purpose of this analysis weights will not be
needed as they do not produce statistically different results according to the data codebook:
Analysis done to assess the effect of non-adjustment found the effect to be negligible, both at the national level
and at the individual State level. At the national level, adjusted weights produced a re-arrest rate of 67.5%
(187,280 / 277,360 = 67.52%), virtually identical to the published rate (183,554 / 272,111 = 67.45%). Even
among those individual States that required the greatest weight adjustment – Florida and Illinois - similarly
negligible differences were found between rates produced by unadjusted weights and those from adjusted
weights. (Codebook)
19 The fifteen states included in the study are AZ, CA, FL, IL, MD, MI, MN, NJ, NY, NC, OH, OR, TX, and VI.
20 The five states that reported educational and vocational programs in 1994 are FL, IL, NY, NC, and TX.
18
As is done in the BJS analysis only individuals where analysis=1 are included. This ensures that all
individuals meet the following requirements listed in the Codebook:
- had a RAP sheet
- did not die in the 3 year follow up period
- had a sentence was greater than 1 year
- was not released via escaping, on appeal, to a warrant, for a transfer, or for administrative reasons
21
Perry 16
1,096 days (or three years) after their release in 1994. 22 Descriptive statistics can be seen in
Appendix Table 8.
A note on time period: At the time of data collection, the U.S. Congresses was passing many
“tough on crime” policies. For example, in 1993 the Violent Crime Control and Law Enforcement Act
was passed followed by the Higher Education Reauthorization Act of 1994 these acts essentially
terminated Pell grant funding for post-secondary correctional education projects. Despite the fact
that less than 5% of Pell money was directed towards individuals in prison, Congress felt it was
inappropriate to subsidize the education of “criminals” (Ubah & Robinson 2003). With Pell grants
cut, there remain four other ways individuals can get funding for higher education while in prison:
Federal Perkins Funds, private foundation grants, private funds (their own or those of family
members), and state-based education grants (Ubah & Robinson 2003). Because these acts were
passed in 1994 it is unlikely that they had an impact on the cohort of individuals in ICPSR3355. Still,
it speaks to the growing skepticism surrounding correctional education and vocation programs in
the early 1990s.
Additionally, it should be noted that in 1994 many agencies were transitioning toward
electronic documentation and more extensive use of the internet. This may explain why only five of
15 states in the study recorded information about educational and vocational program
participation to the BJS.
22
For example, the earliest arrest in the five sates of interest is in 1918.
Perry 17
Methods
PART ONE: Predicting Recidivism
Probit analysis is used to estimate the probability with which an individual recidivates
based on his/her participation in an educational or vocational program and other control variables.
Interaction terms are generated to capture the variation of the 16 different combinations of
program participation, with no participation in either as the base state. These states are mutually
exclusive – an individual can fall into only one of the 16 categories below.
TABLE 3: INTERACTION DUMMY VARIABLES FOR PARTICIPATION IN EDUCATION AND VOCATION PROGRAMS
Level of Education Participation
Level of Vocation Participation
Completed Education Program
Completed Vocation Program
Partially Completed Vocation Program
Unknown Completion of Vocation Program
Did not enroll in Vocation Program
Completed Vocation Program
Partially Completed Vocation Program
Unknown Completion of Vocation Program
Did not enroll in Vocation Program
Completed Vocation Program
Partially Completed Vocation Program
Unknown Completion of Vocation Program
Did not enroll in Vocation Program
Completed Vocation Program
Partially Completed Vocation Program
Unknown Completion of Vocation Program
Did not enroll in Vocation Program
Partially Completed Education Program
Unknown Completion of Education Program
Did not enroll in Education Program
Percent
4.88
6.83
0.06
4.93
4.38
18.44
0.08
7.29
1.15
0.07
1.57
6.42
0.91
1.38
3.25
37.72
As noted earlier, this paper looks at two very different measurements of recidivism: rearrest and re-confinement. In the five-state sample, 61.77% of the individuals are re-arrested and
43.06% return to prison or jail within 1,096 days of their release. To account for variation, certain
observable characteristics are used as controls: term served, race, sex, prior arrest history, age, and
Perry 18
criminal offense category.23 Also, a dummy variable for each state is included to help account for the
state-to-state variations in funding, policies, and crime rates – something not done in the Ismailova
paper (2007). The models are based on the following equation:
EQUATION 1: PROBIT MODEL FOR RE-ARREST AND RE-CONFINEMENT
15
4
𝑅𝑒𝑐𝑖𝑑𝑖𝑣𝑖𝑠𝑚𝑖 = 𝛽0 + ∑ 𝛽1𝑗 𝑒𝑑𝑢𝑣𝑜𝑐𝑗 + 𝛽2 𝑡𝑚𝑠𝑟𝑣 + 𝛽3 𝑛𝑜𝑛𝑤ℎ𝑖𝑡𝑒 + 𝛽4 𝑠𝑒𝑥 + 𝛽5 𝑝𝑝𝑟𝑖𝑑 + ∑ 𝛽6𝑘 𝑠𝑡𝑎𝑡𝑒𝑘
𝑗=1
12
𝑘=1
6
+ ∑ 𝛽7𝑚 𝑠𝑚𝑝𝑜𝑓𝑓𝑚 + ∑ 𝛽8𝑛 𝑎𝑔𝑒𝑟𝑎𝑛𝑔𝑒𝑛
𝑚=1
𝑛=1
Where:24

𝑅𝑒𝑐𝑖𝑑𝑖𝑣𝑖𝑠𝑚𝑖 is a dummy variable representing two measures of recidivism: re-arrest and
re-confinement within 1,096 days after release.

𝑒𝑑𝑢𝑣𝑜𝑐𝑗 represents the sixteen aforementioned interaction terms (Table 3) in which no
education and no vocation is the base state and thus excluded from the above regression.

𝑡𝑚𝑠𝑟𝑣 is the length in months of the term served for which the individual was released in
1994.

𝑛𝑜𝑛𝑤ℎ𝑖𝑡𝑒 is a dummy variable where a value of one includes black, American
Indian/Aleutian, Asian/Pacific Islander, and “other”.

𝑠𝑒𝑥 is a dummy variable for the sex of the released individual (1 = male; 0 = female).

𝑝𝑝𝑟𝑖𝑑 is a dummy variable indicating whether or not the individual had any prior sentences
before the sentence that got him/her into the study.
23
24
Full descriptions of the controls can be found in Appendix Table 7
Details on the variable interpretations can be found in Appendix Table 7
Perry 19

𝑠𝑡𝑎𝑡𝑒 represents the state in which the individual was released. These are dummy variables
for each of four states: Florida, Illinois, New York, and North Carolina. Texas is the base
state.

𝑠𝑚𝑝𝑜𝑓𝑓 are 12 dummy variables for which offense category the individual is convicted and
serving the 1994 sentence; when there are multiple, the most serious is used. The base state
crime level is homicide. A detailed description of the offense levels can be found in
Appendix 7.

𝑎𝑔𝑒𝑟𝑎𝑛𝑔𝑒 indicates one of six age groups based on the individuals’ age at release. The base
state is age for individuals aged 14-17 at release.
Coefficients ∑15
𝑗=1 𝛽1𝑗 are of primary interest. Negative and significant coefficients indicate that some
combinations of vocational and educational programs are negatively correlated with the two
dependent variables (𝑅𝑒𝑐𝑖𝑑𝑖𝑣𝑖𝑠𝑚𝑖 ) and thus that these programs may help reduce the likelihood
that someone recidivates.25 All models use robust standard errors, to correct for any problems
caused by potential heteroskedasticity.
At the State Level
Table 4 and 5 give the frequencies for each level of participation for both education and
vocation across all five states. They suggest that an aggregate level analysis might not fully
represent the underlying differences in education and vocation offerings across states. For example,
in Florida all individuals participate in either an education or vocational program, but not both. In
Texas, information is available for all individuals – there are no people recorded as “unknown” or
“questionably completed.” As such this subsection dives further into each state to see which, if any,
have programs that seem to be driving the aggregate results. The same probit models for re-arrest
25
Because correlation does not causation, this should be taken lightly
Perry 20
and re-confinement are used but are adjusted to fit each state according to how education and
vocation program participation has been recorded.
TABLE 4: PARTICIPATION IN EDUCATION PROGRAMS ACROSS STATES
Completed
Partially Questionably
Did Not
Unknown
Completed
Completed Participate Participation
Total
FL
749
1,815
0
0
0
2,564
IL
168
119
323
1,707
0
2,317
NY
464
582
454
412
554
2,466
NC
22
15
516
1,347
147
2,047
TX
473
782
0
1,175
0
2,430
11,824
TABLE 5: PARTICIPATION IN VOCATIONAL PROGRAMS ACROSS STATES
Completed
Partially
Completed
Questionably
Did Not
Unknown
Completed Participate Participation
FL
475
2,089
0
0
0
2,564
IL
13
15
108
2,181
0
2,317
NY
463
573
4
651
775
2,466
NC
26
20
473
1,400
128
2,047
TX
319
297
0
1,814
0
2,430
Total
11,824
Perry 21
PART TWO: Selection Bias
Selection bias is a problem for many studies looking at educational and vocational programs
in prisons. Because these programs are often voluntary; only those individuals who are motived to
participate will do so.26 This motivation factor could make these individual less likely to recidivate
regardless of participation. Prior education level could also influence motivation to participate as
individuals might seek education in accordance with their educational or vocational background.27
Basically, it is difficult to tell if it is the program or the person that leads to better release success
and a lower chance of recidivism. In this way the selection bias of choosing to participate in a
program could also be considered an omitted variable bias as there are omitted variables
(motivation and prior education level) that impact the choice of whether or not to participate.
One way to adjust for this is to proxy for the variables that cannot be measured with
measurable instrumental variables in a two stage least squares analysis. In the case of individual
participation in education or vocation programs, there are two unobserved omitted variables that
could impact participation in the programs: motivation and prior education level. Due to
restrictions in the data sntln (sentence length), sntln2 (sentence length squared), and nfrctns
(number of infractions) will be used to proxy for motivation and educational history. Below are the
justifications for each instrument:

If sentence length is too short 28 individuals might not see the benefit of educational or
vocational programs as program completion seems unlikely. When sentences are longer (to
an unknown point) people may feel such participation could provide necessary stimulation
and be a way to earn “good time.” Plus, individuals are more likely to be able to fully
participate in a program. One could also argue, however, that long sentences remove any
It appears that the programs in FL were not, in fact, voluntary as all individuals participated in either educational or
vocational programs. See Table 4 and Table 5.
27 Unfortunately this is not recorded in ICPSR3355
28 Recall all individuals are incarcerated for at least one year as a prerequisite for inclusion in this analysis
26
Perry 22
incentive to be prepared at release as that time is far in the future. People with very long
sentences might not see the benefit. To account for the non-linear incentives sntln2 is used.

Nfrctns is a binary variable indicating whether or not the individual was formally
disciplined for a prison-rule infraction. This can help proxy for the individual’s motivation
as individuals who are more motivated to get out of prison and do well are also more likely
to abide by rules in prison and try to earn “good time.”
Because this model is more complicated than the probit model in Part 1, four groups, rather than
16, are used for participation in education and vocational training. The four groups are described
below in Table 6 and the proportion of each group type can be seen in Figure 1.
TABLE 6: PARTICIPATION IN EDUCATION AND VOCATION PROGRAMS, VARIABLE DESCRIPTIONS
Variable Name
Education Only
Vocation Only
Both
None
Description
People who have only participated (com, quest, or part)
in education programs
People who have only participated (com, quest, or part)
in vocation programs
People who have participated (com, quest, or part) both
in education and vocation programs
People who have participated in neither an education or
vocation program; this is the base group
FIGURE 1: PERCENT PARTICIPATION IN EDUCATION AND VOCATION PROGRAMS, ALL STATES
16.63%
Education Only
Vocation Only
44.46%
Both
None
33.96%
4.95%
Perry 23
For a two stage least squares estimator, first the endogenous variables of interest (eduonly,
voconly, edu_voc) are regressed on the exogenous variables in the system plus the instruments. The
resulting coefficients are then used to predict the variables of interest which are then used in a
regression explaining recidivism.
According to Stock and Watson (2011), all instruments must be both relevant and
exogenous. For the single regressor, X, with instrument, Z, an instrument is considered relevant if
𝑐𝑜𝑟𝑟(𝑍𝑖 , 𝑋𝑖 ) ≠ 0. For the same case, the exogenous requirement is met if 𝑐𝑜𝑟𝑟(𝑍𝑖 , 𝑢𝑖 ) = 0 (Stock &
Watson 2011).
If there were only one regressor of interest simply looking at the F-statistic on the first
stage regression would be sufficient to test the weakness (or relevance) of these variables.
However, there are three variables of interest (eduonly, voconly, edu_voc ) so instead the CraggDonald Wald F-statistic is used. Baum, Schaffer, and Stillman suggest that a minimum F-value of 10
indicates strength of the instruments. Also, because this model is exactly identified (there is the
same number of regressors as instruments) the over identifying restrictions test for exogeneity is
not possible (Stock & Watson 2011), so there is no test for exogeneity.
The Instrumental Variable Two Stage Lease Squares Regression (IV 2SLS) is then compared
to a standard Ordinary Least Squares (OLS) regression using the three categories of participation
(as described in Table 6) to see the impacts of accounting for motivation and to see if these omitted
variables are driving the results. This is done separately only for those states that seem to have
successful programs as demonstrated in the previous section, as opposed to at the aggregate five
state level.
Perry 24
PART THREE: Time until Recidivism
While the previous sections examine the binary outcome of recidivating or not recidivating,
this section looks specifically at when within the 1,096 day period people recidivate. This is of
interest as it indicates the effect overtime for such programs.
A common way to predict time until recidivism is with hazard models which estimate the
likelihood of a given event (recidivism) at time 𝑡. Such models have been used since the early 1970s
in this field and much earlier in medical treatment analysis. As early as 1974 Stollmack and Harris
used hazard analysis to predict recidivism (Schmidt & Witte 1988). In the beginning, the majority of
recidivism hazard model studies used parametric models and did not consider explanatory
variables. 29 Only in 1977 did Schmidt and Witte consider different distribution models and
explanatory variables when predicting time until recidivism. Since then both parametric and
proportional hazard models 30 have been applied to recidivism. Schmitt and Witte provide an indepth analysis of different models used to predict recidivism by doing an extensive literature
review and applying a variety of hazard models to a 1978 and 1980 cohort of individuals released
from state prisons in North Carolina. Their analysis serves as a guide to understand which
proportional model is the best fit for a given dataset.
Schmidt and Witte (1988) find that including explanatory variables is helpful in predicting
recidivism, especially across cohorts. For this reason, and because this paper is concerned with the
differing effects of education and vocation programs on recidivism only models that allow for
explanatory variables will be considered; models that try to fit data based on certain distributions
will not be used. 31 Per recommendation by Schmidt and Witte in their book, Predicting Recidivism
Using Survival Models, the Cox Proportional Hazard Model will be used in this paper. Though they
Schmit & Witte cite the following; Stollmack & Harris (1974), Maltz & McCleary (1977), Maltz (1984), Maltz, McCleary,
& Pollock (1979)
30 Rhodes and Matsuba (1984)
31 Such functional forms common in this field are exponential, log-normal, log-logistic, Weibull, and LaGuerre
29
Perry 25
find there are more complicated models that more accurately predict the North Carolina cohorts.
They find value in the Cox model because it “allows one to estimate the effects of individual
characteristics on survival times without having to assume a particular form for the distribution
function.” (Schmidt & Witte 1988). Other studies also employ the Cox Proportional Hazard Model.32
The hazard function, conceptually is: 33
ℎ(𝑡) =
𝑛𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑖𝑛𝑑𝑖𝑣𝑖𝑑𝑢𝑎𝑙𝑠 𝑟𝑒𝑐𝑖𝑑𝑖𝑣𝑎𝑡𝑖𝑛𝑔 𝑎𝑡 𝑡𝑖𝑚𝑒 𝑡
𝑛𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑖𝑛𝑑𝑖𝑣𝑖𝑑𝑢𝑎𝑙𝑠 𝑠𝑢𝑟𝑣𝑖𝑣𝑖𝑛𝑔 𝑎𝑡 𝑡𝑖𝑚𝑒 𝑡
If an individual has a row vector of information X then the proportional hazard model assumes a
hazard rate of:
ℎ(𝑡|𝑋) = ℎ0 (𝑡)𝑒 𝑋𝛽
Where ℎ0 (𝑡) is the baseline hazard function that sets all parameters equal to 0, a purely theoretical
value. Suppose individual 𝑖 recidivates at time 𝑡𝑖 then (if there are 𝑁 total individuals, 𝑛 of whom
recidivate within the 1,096 day time period) for any time 𝑡, 𝑅(𝑡) is the set of all 𝑖 = 1,2 … 𝑁 such
that the individual 𝑖 was still at risk just prior to time 𝑡. For any observed failure time, 𝑡𝑖 , the
probability of individual 𝑖 failing is given by:
ℎ(𝑡𝑖 |𝑋𝑖 )
∑𝑗∈𝑅(𝑡𝑖) ℎ(𝑡𝑖 |𝑋𝑗 )
=
𝑒 𝑋𝑖𝛽
∑𝑗∈𝑅(𝑡𝑖) 𝑒 𝑋𝑗𝛽
To find the partial likelihood function, the above equation is multiplied over all 𝑛 failure times.
Below, 𝐿 , is the probability of any individual failing (in this case recidivating):
𝑛
𝐿 = ∏{
𝑖=1
32
33
𝑒 𝑋𝑖 𝛽
∑𝑗∈𝑅(𝑡𝑖) 𝑒 𝑋𝑗𝛽
}
Used by Rhodes and Matsuba (1984) and Sherman and Berk (1984)
The explanation following here forward draws heavily from Schmidt & Witte, 1988, pg 84
Perry 26
Looking at the hazard ratios given by the above will enable an understanding of the isolated impact
of participation in education only, vocation only, or both in reducing the likelihood of re-arrest and
re-confinement at any given time, 𝑡. Hazard rates below one indicate decreases in the rate of
recidivism for the 1,096 day period, while values greater than one indicate increases in the rate of
recidivism for the period.
Perry 27
Results
PART ONE: Predicting Recidivism
Re-arrest: At first glance there are significant and negative impacts of educational and
vocational programs on re-arrest. Figure 2, on the following page, presents the coefficients on the
variables of interest for re-arrest at the aggregate level. Longer bars indicate greater impact and
stars indicate significance level. As seen in Figure 2, eight of the fifteen participation combinations
have significant negative relationships with re-arrest at the 10% level or higher. This seems to
suggest that educational and vocational programs are helping reduce re-arrest. Still, the story is
muddled; completing both programs (educom_voccom) for example decreases the probability of rearrest by .052 and is significant at the 10% level, completing only a vocational program
(eduno_voccom) has no effect, and completing only an education program (educom_vocno) reduces
the probability of re-arrest by .044. Even more confusing is the significant and positive impact of
enrolling in and questionably completing both an education and a vocation program
(eduquest_vocquest), which suggests that such individuals are more likely to be re-arrested than
those whom have had no education or vocational programming (eduno_vocno). The largest
significant impact is for individuals who questionably complete an education program and never
participate in vocational programs (eduquest_vocno); these individuals have a decreased probability
of re-arrest by .108. Given that the probability of re-arrest in the sample is 62.6%, these reductions
are significant. Still, it is unclear from this analysis which program type has the greatest impact on
re-arrest.
Perry 28
FIGURE 2: SIGNIFICANCE OF RE-ARREST COEFFICIENTS, ALL STATES
*p<.10; **p<.05; ***p<.01
Values are reported from dprobit, thus they indicate changes in probabilities
For a full model with controls reported, see Appendix Table 1
n=10,157
Perry 29
Re-Confinement: The results for re-confinement at the aggregate level are similar to those
for re-arrest. Figure 3 presents the coefficients on the variables of interest for re-confinement at the
aggregate level. As with Figure 2, longer bars indicate greater impact and stars indicate significance.
Of the fifteen participation combinations, ten have a negative and significant impact on reconfinement. The greatest impact can be seen for individuals completing part of an education
program and questionably completing a vocational program (edupart_vocquest); for such people
the probability of re-confinement decreases by .2507, significant at the 10% level. 34 The probability
of re-confinement in the five state sample is 43.06% (lower than the 61.77% for re-arrest). Just as
for re-arrest, completing both education and vocation (educom_voccom) has significant reductions,
as does completing only educational programs (educom_vocno), but completing vocation only does
not (eduno_voccom). These data seem to suggest that there are generally significant impacts of
education and vocation programs on reducing re-confinement.
While the results for both re-arrest and re-confinement seem to indicate that education and
vocation programs reduce both re-arrest and re-confinement, it is difficult to draw specific
conclusion about which programs in particular are more effective. As noted in the Methods section
this could be due to underlying differences between the states; this is explored in the following
section.
One possible explanation for the significance of questionable and partial completion is that individuals who enroll in
programs and otherwise earn “good time” may be released early, before they can finish the programs. In this way, failing
to complete a program could be a positive signal.
34
Perry 30
FIGURE 3: SIGNIFICANCE OF RE-CONFINEMENT COEFFICIENTS, ALL STATES
*p<.10; **p<.05; ***p<.01
Values are reported from dprobit, thus they indicate changes in probabilities
For a full model with controls reported, see Appendix Table 1
n=10,157
Perry 31
At the State Level
It is possible that certain states are driving the aggregated results. This could be due to
differences in program effectiveness, recording standards (see Tables 4 and 5), or re-arrest and reconfinement rates. Figure 4 indicates the re-arrest rate and re-confinement rate by state, with the
total sample below. While the re-arrest rate is higher than the re-confinement rate in all states, the
difference ranges from less than half a percentage point (NY) to nearly 40 percentage points (FL).
This variation suggests significant differences in conditions across states and begs for deeper state
level analysis.
FIGURE 4: RECIDIVISM BY STATE
Values indicate the percent of individuals re-arrested or re-confined within 1,096 days of their release
n is the total number of individuals in the data from each state
Perry 32
The results from the probit model in Equation 1 run independently for each of the five
states can be seen for re-arrest in Appendix 2 and for re-confinement in Appendix 3. 35 Of the five
states, only Illinois and New York demonstrate programs that are significantly related to
recidivism; as such, much of the following analysis focuses heavily on these two states. In Florida,
program participation (either education or vocation) is mandatory which means there is no viable
comparison group within the state. North Carolina, as seen in Tables 4 and 5, has very low levels of
participation in both education and vocation programs. Texas, while more evenly distributed across
participation levels, has few significant results in the state level probit analysis. For these reasons,
and because Illinois and New York indicate significant relationships between education and
vocation programs and recidivism, they are the focus of the state level analysis that follows.
35
Note that because states record different levels of program completion the base condition is different for each state
Perry 33
Re-Arrest: Educational and vocational programs have a greater impact on reducing rearrest in Illinois than in New York. These impacts are demonstrated visually in Figure 5 which
depicts the coefficients on each level of participation; the longer the line the greater the impact.
In Illinois where the baseline probability of re-arrest is .7085, there is a .456 decrease in the
probability of re-arrest for individuals who complete vocation programs without participating in
education programs (eduno_voccom), and a .141 decrease in probability of re-arrest for people who
complete education programs without participating in vocation programs (educom_vocno). In
Illinois people participate only in one type of program (there are no people in both education and
vocation) though programs are not mandatory. This could reflect a specialization of the prisons
(each prison only offers education or vocation programs) or a rule that requires individuals to
participate in one program only. Whichever reason, all program combinations are highly significant
at least at the 5% level. Between the two, vocational programs seem to have a greater effect across
the board as the three vocational coefficients are larger than the three educational coefficients.
In New York, where the baseline probability of re-arrest is .6119, the impacts are smaller
but still significant. In New York, nine of the eleven participation combinations are significant, but
the story is less clear as to which program is the most effective. The largest impact can be found for
people who participate in and complete vocational programs without participating in educational
programs (eduno_voccom); they see a .304 reduction in the probability of re-arrest. This is nearly
the same as the impact for people completing educational programs and participating in but not
completing vocational programs (educom_vocpart) that see a decrease in the likelihood of re-arrest
of .296. In New York the results are smaller than for Illinois, but spread across more types of
participation.
Perry 34
FIGURE 5: SIGNIFICANCE OF RE-ARREST COEFFICIENTS, NEW YORK & ILLINOIS
*p<.10; **p<.05; ***p<.01
Values are reported from dprobit, thus they indicate changes in probabilities
For a full model with controls reported, see Appendix Table 2
Illinois: n=2,124 New York: n=1,193
Perry 35
Re-Confinement: Just as New York and Illinois drove the results for re-arrest, they also
drive the results for re-confinement, seen in Appendix 3. The coefficients on the variables of
interest are visually displayed in Figure 6; the longer the line the greater the impact.
In Illinois the impact of education and vocational programs is again larger than in New York.
Recall the baseline re-confinement rate in Illinois is 39.12%. For all types of participation in Illinois,
there is at least a .2636 decrease in the probability of re-confinement significant at the 5% level.
The largest impact can be seen for individuals who questionably complete an education program
and do not participate in vocation programs (eduquest_vocno); they have a reduction of .386 in the
likelihood of re-confinement. Because the baseline rate is 39.12% this reduction brings the
probability of re-confinement close to zero. It is less clear which program has greater impacts,
education or vocation, but it seems education is slightly greater (the lower three blue lines in Figure
6 are slightly longer than the top three blue lines). Still, the differences are small and both programs
have significant and large reductions in the likelihood of re-confinement.
In New York, where the baseline probability of re-confinement is 58.9%, the greatest impact
can be seen for individuals who complete a vocational program and part of an educational program
(edupart_voccom). For such individuals the probability of re-confinement is decreased by .2558.
Overall, both education and vocation programs seem to have negative and significant impacts on reconfinement – seven of the eleven combinations are significant at the 5% level or higher. Again, it is
not obvious which program has the overall greater impact, but it seems that vocational programs
are slightly more impactful.
Perry 36
FIGURE 6: SIGNIFICANCE OF RE-CONFINEMENT COEFFICIENTS, NEW YORK & ILLINOIS
*p<.10; **p<.05; ***p<.01
Values are reported from dprobit, thus they indicate changes in probabilities
For a full model with controls reported, see Appendix Table 3
Illinois: n=2,124 New York: n=1,193
Perry 37
PART TWO: Selection Bias
As the results from Part 1 demonstrate, the aggregate results obfuscate the deeper
programmatic variation across states. For this reason, selection bias is only considered for those
states with programs that have programs which significantly reduce recidivism: Illinois and New
York.
First, it is important to check the validity of the instruments: sentence length, sentence length
squared, and number of infractions. Instrumental variables must be both relevant and exogenous
(Stock & Watson). Recall that because there are three regressors (eduonly, voconly, edu_voc ) a
Cragg-Donald Wald F-statistic is used to test for whether or not the instruments are “relevant.”
For Illinois, eduonly and voconly are the only two regressor and sntln and sntln2 are used as
instruments. This is because the number of infractions (nfrctns) is not recorded in Illinois, and there
are no observations for edu_voc. For Illinois, the Cragg-Donald F-statistic is 2.303. For New York, all
three regressors are included and the resulting F-Statistic is .007. 36
Baum, Schaffer, and Stillman (2007) suggest that a minimum F-statistic of 10 indicates
strong instruments. Clearly then, the suggested instruments are weak. No other instruments
included in the data, however, provide a better estimate so these will be used but with great
caution. Further, since in both regressions the model is exactly identified, the over identifying
restrictions test for exogeneity is not possible (Stock & Watson 2011). Because the instruments are
weak the following results should be taken very lightly.
Note that these values are the same for re-arrest and re-confinement because values are calculated based on the
relationship between regressors and instruments without consideration to the dependent variable
36
Perry 38
Re-Arrest: Table 7 shows the coefficients of interest for re-arrest in Illinois and New York.
Columns 2 and 4 are for the two staged least squares instrumental variable regression. These
columns represent the impact of such programs when controlling for motivation. Columns 3 and 5
contain the OLS regression output not controlling for motivation. For both Illinois and New York
the instrumental variable regressions indicate that the effect of these programs is largely stemming
from motivation. The education and vocational programs are significant when included in the OLS
regression (column 3 and 5), but not significant in the instrumental variable two stage least squares
regression. This suggests that the effect from these programs is largely because those individuals
who elect to participate are more motivated then those who do not and are thus more likely to
avoid re-arrest after release. However, these large coefficients and standard errors imply that the
weak instruments have biased the magnitudes of the coefficients and their standard errors
upwards. Note that this problem is especially prominent for the New York sample where the Fstatistic is particularly low. For the OLS results, in New York, the coefficients for all three levels of
participation are similar and an F-test (F=.16, p=.8553) cannot reject the hypothesis that the
estimated effects are equal.
37
In Illinois, however, the null hypothesis that programs have equal
impact can be rejected at the 5% significance level. 38 This suggests that in Illinois vocational
programs have greater impact on re-arrest than educational programs, while in New York the
programs are more similar in their impacts on re-arrest.
The following null hypotheses are also tested: eduonly=voconly, voconly=edu_voc, and eduonly=edu_voc . None can be
rejected.
38 Because there are no observations for edu_voc in Illinois, only eduonly=voconly is tested.
37
Perry 39
Re-Confinement: Table 8 shows the coefficients of interest for re-confinement in Illinois
and New York. Columns 2 and 4 are for the two staged least squares instrumental variable
regression. These columns represent the impact of such programs when controlling for motivation.
Columns 3 and 5 contain the OLS regression output not controlling for motivation. The results for
re-confinement are near identical to those for re-arrest. The most notable difference is the negative
and significant coefficient for voconly in Illinois. This suggests that even when controlling for
motivation of people selecting to participate, there are program impacts on re-confinement. Still,
this is likely biased upward as the instruments are incredibly weak.
The instrumental variable regression for New York suggests that though education and
vocational programs have an impact, as demonstrated by column 5 of Table 8, and this is likely
because participants are more motivated than individuals who do not participate in the programs.
As with re-arrest, the large coefficients and standard errors suggest that the weak instruments have
biased the magnitudes of the coefficients and their standard errors upwards. For the OLS
regressions testing the equality of coefficients on eduonly, voconly, and edu_voc 39 give a p-values of
.229 and .269 for Illinois and New York respectively. Thus the null hypotheses that the estimated
effects of the levels of participation are equal cannot be rejected. 40 Though all programs have
significant impacts, the F-tests suggest that in Illinois and New York there are no significant
differences in the effects of different program types on re-confinement.
Basically, for both re-arrest and re-confinement there is reason to believe that motivation is
driving the results (the exception being voconly in Illinois). These results must be taken lightly as
the instruments are weak. This analysis also suggests that the effects of different programs may not
be significant.
Recall this variable only exists for New York
The following null hypotheses are tested: eduonly=voconly=edu_voc, eduonly=voconly, voconly=edu_voc, and
eduonly=edu_voc. None can be rejected.
39
40
Perry 40
TABLE 7: INSTRUMENTAL VARIABLE ANALYSIS: RE-ARREST, ILLINOIS & NEW YORK
Illinois
Variable
eduonly
voconly
edu_voc
n
Instrumental
Variable
Estimation
New York
OLS
Regression
Coef.
-.856
(.634)
-.379
(.393)
-.184
(.023)
-.278
(.042)
--
--
2,127
Instrumental
Variable
Estimation
***
***
2,124
-2.55
(11.95)
-3.63
(20.67)
-1.89
(3.63)
OLS
Regression
Coef.
-.157
(.044)
-.126
(.060)
-.156
(.037)
1,039
***
**
***
1,039
*p<.10; **p<.05; ***p<.01
Numbers reported in parenthesis are robust standard errors
For a full model with controls reported, see Appendix Table 4
TABLE 8: INSTRUMENTAL VARIABLE ANALYSIS: RE-CONFINEMENT, ILLINOIS & NEW YORK
Illinois
Variable
eduonly
voconly
edu_voc
n
Instrumental
Variable
Estimation
-.245
(.454)
-.521
(.257)
-2,127
**
New York
OLS
Regression
Coef.
-.421
(.019)
-.389
(.029)
-2,124
*p<.10; **p<.05; ***p<.01
Numbers reported in parenthesis are robust standard errors
For a full model with controls reported, see Appendix Table 3
Instrumental
Variable
Estimation
***
***
-3.39
(14.23)
-.886
(24.64)
-1.71
(4.30)
1,039
OLS
Regression
Coef.
-.100
(.044)
-.103
(.059)
-.155
(.039)
1,039
**
*
***
Perry 41
PART THREE: Hazard Model
The Cox Proportional Hazard Model teases out the relationship of the control variables over
time when predicting the likelihood that an individual recidivates. Of individuals who are rearrested within three years, over 15% are re-arrested within their first month out of prison; within
the first year that increases to 38.95% of individuals. For re-confinement, around 5% return to
prison or jail within one month of their release; that number jumps to 42.66% within one year.
This can be seen in Figure 7A and 7B, which show the distribution of re-arrest and re-confinement,
respectively, in the 1,096 day follow-up period. Each bar represents the percent of individuals who
experience their first post-release arrest (Figure 7A) or re-confinement (Figure 7B) in a given
month after release. These figures suggest that time until recidivism is another important
dimension to study. It is possible, for example, that the impact of education and vocation programs
wear off or that they effectively delay the time until recidivism.
FIGURE 7: DAYS UNTIL FIRST RECIDIVISM, ALL STATES
Figure A: Re-Arrest
Figure B: Re-Confinement
These histograms represent the percent of individuals who experience their first post-release arrest (Figure A) or reconfinement (Figure B) within 1,096 days of their 1994 release. Because the period is three years, each of the 36 bars
represent one month. The horizontal axis indicates days until recidivism. n=10,157
Perry 42
Table 9 includes the hazard estimates on the variables of interest at the aggregate level for
both re-arrest and re-confinement. As seen in Table 9, below, all three levels of participation have
estimated hazard rates of less than one, which indicate a decrease in the likelihood of re-arrest and
re-confinement in every period.
TABLE 9: HAZARD RATIO, RE-ARREST AND RE-CONFINEMENT, ALL STATES
Variable
eduonly
voconly
edu_voc
n
Re-Arrest
Hazard Ratio
.790
(.030)
.781
(.049)
.858
(.042)
10,157
*p<.10; **p<.05; ***p<.01
For a full model with controls reported, see Appendix Table 5
Numbers reported in parenthesis are robust standard errors
***
***
***
Re-Confinement
Hazard Ratio
.656
(.039)
.726
(.065)
.745
(.051)
10,157
***
***
***
Perry 43
Re-Arrest: The results of the Cox Proportional Hazard model, in Table 9, indicate that all
three levels of program participation significantly reduce the rate at which people return to prison.
For people who participate in only education programs, holding all else constant, the rate of rearrest is reduced by 21%.41 For people only participating in vocational programs the reduction is
21.9% and for people participating in both the reduction is 14.2% - all significant at the 1% level.
This suggests that education and vocational programs decreased the rate at which individuals are
re-arrested, effectively delaying their return to the criminal justice system.
After conducting a Chi-squared test to see if there are significant differences between the
estimated effects of the three program types in the hazard analysis, the null hypothesis of equal
impact cannot be rejected at the five-state level. This indicates that all programs have similar
results. It should be noted that the difference between the coefficients on eduonly and edu_voc is
significant at the 12% level, but that no other program effects are significantly different.
One way to visualize the reductions in frequency of re-arrest is with Kaplan-Meier curves.
These curves, seen in Figure 8, demonstrate the survival functions for each type of participation. A
given point on the line indicates that 𝑥 days after release 𝑦% of individuals have not yet recidivated.
The slope of the line indicates how quickly people return to prison. The below figure suggests that
individuals with neither education nor vocation participation (𝑛𝑜𝑛𝑒) have the steepest survival
curve, meaning their likelihood of re-arrest is highest. People in both programs have an initial
drop, but level out after the first few days. Also, the below curves demonstrate that less than half of
the sample stays out of re-arrest, as seen by the ending point on all four graphs.
41
(1-hazard ratio)*100
Perry 44
FIGURE 8: RE-ARREST KAPLAN-MEIER SURVIVAL ESTIMATES BY PROGRAM TYPE, ALL STATES
Y axis is the percent “surviving” – not being re-arrested
Analysis Time, the X axis, is the number of days since release
Perry 45
Re-Confinement: The relationship between re-confinement and participation in education
or vocation program is similar to that of re-arrest, as seen in the last column of Table 13. For
individuals participating only in an educational program, holding all else constant, there is a 34.4%
reduction in the rate of re-confinement and a 27.4% reduction for people participating only in a
vocational program; both results are significant at the 1% level. Individuals participating, to some
degree, in both programs have a reduction of 25.5% in the rate of re-confinement also significant at
the 1% level. This suggests that educational and vocational programs significantly reduce the rate
at which individuals are back behind bars. This is in line with the findings in Brewster and Sharp
which suggest that education programs increase survival times, but contrary to their finding that
vocational programs reduce survival time. This analysis suggests that both programs lead to longer
survival times (a lower rate of re-confinement).
After conducting a Chi-squared test to see if there are significant differences between the
estimated effects of the three program types in the hazard models, the null hypothesis of equal
impact cannot be rejected at the five-state level. This indicates that all programs have similar
results. While the difference between eduonly and edu_voc is significant at the 10% level, no other
programs are significantly different.
Figure 9 has the four Kaplan-Meir survival estimates by program type for re-confinement.
The steepest survival curve is for people participating in neither program; it is also the one with the
lowest end point. Note that these slopes are generally less steep than the slopes for re-arrest
(Figure 8) indicating that the rate of re-confinement is slower than the rate of re-arrest. Also note
that the end points are higher for re-confinement, representing that fact that fewer people are reconfined than re-arrested. The similar slopes on the Kaplan-Meir survival estimates for eduonly,
voconly, and both confirm the findings of the Chi-squared test that suggests there is no difference
between the estimated effects of the program types on re-confinement.
Perry 46
FIGURE 9: RE-CONFINEMENT KAPLAN-MEIER SURVIVAL ESTIMATES BY PROGRAM TYPE, ALL STATES
Y axis is the percent “surviving” – not being re-confined
Analysis Time, the X axis, is the number of days since release
Perry 47
At the State Level
It is prudent to look specifically at the states that drove the analysis in Part 1: Illinois and
New York, as the results in these states may provide better hazard estimates than the aggregate
level analysis.
Re-Arrest: Table 10 has the results of the Cox Proportional Hazard model for the variables
of interest in Illinois and New York.42 The results indicate that all relevant levels of program
participation significantly reduce the rate at which people are re-arrested in both Illinois and New
York. In Illinois, people who participate in education programs, holding all else constant, have a
reduced rate of re-arrest by 50.1%.43 For people only participating in vocational programs the
reduction is 61.7%. In New York the reductions are smaller but still significant. For individuals
exclusively in education programs the reduction in the rate of re-arrest is 28.1%; nearly identical to
the 28.6% reduction for people only in vocational programs. People in both programs (edu_voc)
have a rate of re-arrest reduced by 32. 7%.
TABLE 10: HAZARD RATIO FOR RE-ARREST, ILLINOIS & NEW YORK
Variable
eduonly
voconly
Illinois
Hazard Ratio
.499
(.034)
.383
(.056)
***
***
edu_voc
--
n
2,124
New York
Hazard Ratio
.719
(.082)
.714
(.113)
.673
(.068)
***
**
***
1,193
*p<.10; **p<.05; ***p<.01
For a full model with controls reported, see Appendix Table 6
Numbers reported in parenthesis are robust standard errors
42
43
Recall that individuals in Illinois participate in only one type of programming so edu_voc has zero observations
(1-hazard ratio)*100
Perry 48
Figure 10A and 10B, on the following page, have the Kaplan-Meier curves for Illinois and
New York respectively. The figures suggest that individuals with no education or vocation
participation (𝑛𝑜𝑛𝑒) have the fastest rate of re-arrest and, on the whole, and have the highest rate
of re-arrest in the 1,096 day period (evidenced by the low ending value of the line) for both states.
In Illinois the rate of re-arrest is lowest for individuals participating only in vocational programs,
whereas in New York the hazard estimates for the education only and vocation only cohorts are
nearly identical – evidenced by the nearly identical curves in Figure 10B.
The Chi-squared test of the null hypothesis that estimated effects of eduonly=voconly in
Illinois can reject the null hypothesis with a p-value of .084. In New York, however, none of the four
possible null hypotheses 44 can be rejected indicating that there are no notable differences between
only education, only vocation, and some combination of the two. This suggests that vocational
programs in Illinois have a significantly greater impact on reducing re-arrest than do education
programs, but that the type of programming matters less in New York.
44
Null hypotheses include: eduonly=voconly=edu_voc, eduonly=voconly, eduonly=edu_voc, and voconly=edu_voc
Perry 49
FIGURE 10A: SURVIVAL MODELS FOR RE-ARREST, ILLINOIS
FIGURE 10B: SURVIVAL MODELS FOR RE-ARREST, NEW YORK
Y axis is the percent “surviving” – not being re-arrested
Analysis Time, the X axis, is the number of days since release
Perry 50
Re-Confinement: Table 11 has the results of the Cox Proportional Hazard model for the
variables of interest in Illinois and New York.45 The results indicate that four of the five relevant
levels of program participation have statistically significant reductions on the rate of reconfinement. In Illinois, people who participate in education programs, holding all else constant,
have a reduced rate of re-arrest by 91.9%.46 For people only participating in vocational programs
the reduction is 83.5%. These are by far the largest reductions observed in the analysis. In New
York the reductions are smaller and the effect for people participating in educational programs is
less significant.47 People in vocation programs only, however see a 31.8% reduction in the rate of
re-confinement, similar to people participating in both programs that see a decrease of 30%; both
significant at the 5% level or more.
TABLE 11: HAZARD RATIO FOR RE-CONFINEMENT, ILLINOIS & NEW YORK
Variable
eduonly
voconly
Illinois
Hazard Ratio
.081
(.021)
.165
(.068)
***
***
edu_voc
--
n
2,124
New York
Hazard Ratio
.855
(.118)
.682
(.132)
.700
(.087)
*p<.10; **p<.05; ***p<.01
For a full model with controls reported, see Appendix Table 6
Numbers reported in parenthesis are robust standard errors
Recall that individuals in Illinois participate in only one type of programming
(1-hazard ratio)*100
47 It is significant at the 25% level
45
46
1,193
**
***
Perry 51
In Illinois, the hazard model suggests that the greatest effect is from education program
participation, but the Chi-squared test indicates that the null hypothesis that the effect of eduonly is
equal to the effect of voconly can only be rejected at the 14% significance level. For New York the
Chi-Squared tests reveal that eduonly and edu_voc are different at the 10% level, with the addition
of a vocational program to an educational program the rate of re-confinement is reduced. Further,
the magnitude of the coefficients suggest that vocational programs have the greater effect at
reducing the rate of re-confinement for incarcerated individuals in New York when compared to
education programs but with small sample size of participants in voconly, the Chi-squared tests do
not reveal a statistically significant difference. Because of the Chi-squared results, this should be
taken lightly.
Figure 11A and 11B have the Kaplan-Meier curves for Illinois and New York respectively.
Notably, the re-confinement rates for individuals in Illinois participating in education or vocation
programs are very low – nearly horizontal lines. These curves are especially shallow when
compared to the curve for individuals with no such programming. In New York the differences
between survival curves are not as noticeable, though the rate of re-confinement for people with no
programming is steeper than for people with some kind of programming.
Perry 52
FIGURE 11A: SURVIVAL MODELS FOR RE-CONFINEMENT, ILLINOIS
FIGURE 11B: SURVIVAL MODELS FOR RE-CONFINEMENT, NEW YORK
Y axis is the percent “surviving” – not being re-confined
Analysis Time, the X axis, is the number of days since release
Perry 53
Conclusion
This study adds to the existing literature and suggests educational and vocational programs
are correlated with significant reductions in recidivism. I find greater influence from vocational
programs in the probit analysis (Part 1) and the hazard analysis (Part 3) than from educational
programs. That is not to say that educational programs do not have an impact on re-arrest and reconfinement, they clearly do, just that the impact of vocational programs is greater. This is contrary
to many previous studies, specifically the Ismailova paper, which uses the same dataset and finds a
greater impact from education programs.
Unlike previous studies, my analysis addresses both re-arrest and re-confinement of the
same individuals and tracks them for the BJS standard of 1,096 days after release. I find reductions
for both re-arrest and re-confinement, with greater decreases for re-confinement. This analysis also
suggests that it is important to do separate state analyses as New York and Illinois largely drive the
results for the five-state group. While state-by-state case studies are important, there is still a need
for standardized data.
There is reason to believe that motivation might be influencing the results found in Part 1,
but the weakness of the instrumental variables means these results cannot be taken with any great
certainty. Part 2 may suggest that the programs themselves are not inherently decreasing re-arrest
and re-confinement (outside of vocational programming’s impact on re-confinement). Still, this
begs the question: is selection bias bad? As Part 1 demonstrates the people who participate in
education and vocation programs have significantly reduced rates of recidivism. Perhaps these
programs allow individuals to signal their motivation, gain “good time,” and stay mentally engaged
in prison. Saylor and Gaes (1997) note that allowing incarcerated individuals the outlet for their
motivation is essential; eliminating programs because the people who elect to take them are
possibly more motivated and thus are less likely to be re-arrested or re-confined denies people the
Perry 54
chance to succeed. In addition to providing an outlet for motivation and helping to reduce
recidivism, there are also more intrinsic advantages to providing individuals with access to
education and vocational training; such as literacy, mental stimulation, decreased boredom, and
confidence building (Ubah & Robinson 2003). Because selection bias into programs is often cited as
a big issue in many studies thinking about which instruments could help proxy for motivation
would be helpful for any future follow-up studies. It should be remembered, however, that selection
bias in this case could still be a justification in favor of education and vocation programs.
In addition to adding to the literature by attempting to control for selection bias, this study
has a unique approach to hazard analysis. While many studies look at hazard analysis and
recidivism, few also look at the role of education and vocation programs in influencing survival
times. I find that both education and vocation programs increase survival times for re-arrest and reconfinement, with the larger effects for re-confinement. Between the two program types, the
greater impact tends to be for vocational programs which again points to the success of these
programs found in Part 1. This analysis suggests that such programs help to reduce the rate of rearrest and re-confinement over time and effectively delay the incidence of recidivism.
What makes the Illinois and New York programs successful? One notable finding from
this study is the variation between states in having effective programs. Illinois and New York in
particular had particularly effective programs in 1994 for reducing recidivism of individuals
through educational and vocational programs. What exactly made their programs more effective is
an area for future study and outside of the scope of this analysis. Ubah and Robinson (2003)
snuggest that Illinois provides more state funding for people in secondary education programs,
enabling more low-income incarcerated individuals to earn a secondary degree in prison. New York
was also quick to find alternative funding when Federal Pell Grants were no longer available to
incarcerated individuals (Ubah & Robinson 2003). Still, more research on these two states and the
ways in which the other states in the BJS study have updated their programs and changed
Perry 55
monitoring practices would provide a more grounded understanding of what programs work and
for whom.
Limits and Constraints: This study highlights the lack of comparable data on education
and vocation programs across states. Given that ICPSR3355 is the official BJS dataset on recidivism
it is startling that only five of 15 states had information on the education and vocational program
participation of individuals in their system. The data is nearly 20 years old, since the individuals
release in 1994 there have likely been significant policy, technology, and programmatic changes
across the states. Such poor data tracking makes it difficult to project costs, compare programs, or
make policy improvements. While there are numerous state level studies arguing for or against
vocation and education programs there is no common language or data collection. Some states
track enrollment but not level of completion, others are very detailed but only on one or two
facilities. It is also possible that individual institutions are recording this information but not
recording it to the BJS in a standard way, or at least that this was the case in 1994 and no such data
is presently available to the public. As correctional spending increases and recidivism rates stay
the same, it is time to re-evaluate what can be done to better the system. To move the already
robust literature forward, there needs to be better centrally compiled data that can be compared
year over year and across states.
As noted previously, the limited data also prevents including information regarding prior
incarceration or post release information such as education, employment, and income. Having such
information at entry would provide for better controls, while having the information for the 1,096
day follow-up period would strengthen the probit and hazard analysis. The Cox Proportional
Hazard model in particular can account for changes in the follow-up period to help better predict
the hazard rates. The inclusion of such information in future data-sets, though cumbersome to
collect, would prove incredibly helpful in creating more accurate models.
Perry 56
Should funding to correctional education and vocation programs be increased?
Despite the limits of this study, my results offer support for both educational and vocational
programs as they help to reduce re-arrest and re-confinement. While my results tend to favor
vocational programs, the benefits of both are worth noting. Given the increasing prison population
in America and the corresponding spending increases, greater efforts toward reducing recidivism
should be taken. Educational and vocational programs provide such reductions and should thus be
improved and made more available.
Perry 57
References
2006 Annual Report to the Florida Department of Education: Juvenile Justice Educational
Enhancement Program. Publication. College of Criminology and Criminal Justice, 2006.
Adams, K., Bennett, K. J., Flanagan, T. J., Marquart, J.W., Cuvelier, S. J., Fritsch, E., Gerber, J.,
Longmire, D., and Burton, V. (1994) "A Large-Scale Multidimensional Test of the Effect of
Prison Education Programs on Offenders' Behavior." Prison Journal 74:433-99.
Anderson, Dennis B., Sara L. Anderson, and Randall E. Schumacker. "Correctional Education A Way
to Stay Out: Recommendations for Illinois and a Report of the Anderson Study." Illinois
Council on Vocational Education. 1988.
Angrist, Joshua D., and Jorn-Steffen Pischke. "Ivreg2 Update." Mostly Harmless Econometrics. , 20
Feb. 2010.
Aos, Steve, Marna Miller, and Elizabeth Drake. (2006). Evidence-Based Public Policy Options to
Reduce Future Prison Construction, Criminal Justice Costs, and Crime Rates. Olympia:
Washington State Institute for Public Policy.
Batchelder, J. S., and J. M. Pippert. "Hard Time or Idle Time: Factors Affecting Inmate Choices
between Participation in Prison Work and Education Programs." The Prison Journal 82.2
(2002): 269-80.
Baum, Christopher F., Mark E. Schaffer, and Steven Stillman. "Enhanced Routines for IV/GMM
Estimation and Testing." The Stata Journal 7.4 (2007): 465-506.
Brewster, D. R., and S. F. Sharp. "Educational Programs and Recidivism in Oklahoma: Another Look."
The Prison Journal 82.3 (2002): 314-34.
Gregg, Cherri. "Once Called Ex-Cons, Philadelphia ‘Returning Citizens’ Begin Six Weeks
Of Reintegration - CBS Philly." CBS Philly., 13 Nov. 2013.
Cho, R. M., and J. H. Tyler. "Does Prison-Based Adult Basic Education Improve Post Release
Outcomes for Male Prisoners in Florida?" Crime & Delinquency 59.7 (2013): 975-1005.
Codebook: Recidivism of Prisoners Released in 1994. ICPSR 3355. U.S. Department of Justice. Bureau
of Justice Statistics. Inter-University Consortium for Political and Social Research, 25 Sept.
2002.
Coley, Richard J., and Paul E. Barton. Locked Up and Locked Out: An Educational Perspective on the
U.S. Prison Population. Rep. Policy Evaluation and Research Center: Educational Testing
Service, Feb. 2006.
Conan, Neal, Alan Johnson, Bill Seitz, and Michael Thompson. "Programs Keep Inmates From
Returning To Prison." NPR. NPR, 10 Oct. 2012.
Crayton, Anna, and Suzanne Rebecca Neusteter. "The Current State of Correctional Education." The
Urban Institute (2008): 1-29. The Urban Institute: Reentry Roundtable on Education. The
Urban Institute, May 2008.
Perry 58
Davis, Lois M., Robert Bozick, Jennifer L. Steele, Jessica Saunders and Jeremy N. V. Miles. Evaluating
the Effectiveness of Correctional Education: A Meta-Analysis of Programs That Provide
Education to Incarcerated Adults. Santa Monica, CA: RAND Corporation, 2013.
Elliott, Delbert, and Steve Aos. "A Life of Unintended Consequences." Prevention Action. N.p., 08 July
2010.
Gaes, Gerald G. "The Impact of Prison Education Programs on Post Release Outcomes”. The Urban
Institute (2008): 1-30. The Urban Institute: Reentry Roundtable on Education. The Urban
Institute, Feb. 2008.
Gaes, Gerald G., and William G. Saylor. "Training Inmates through Industrial Work Participation and
Vocational and Apprenticeship Instruction." Corrections Management Quarterly (1997): 3243. Aspen Publishers.
Gaes, Gerald G., and William G. Saylor. "Post Release Employment Project: Summary of Preliminary
Findings." Federal Bureau of Prisons, Office of Research and Evaluation. Washington D. C. June
27, 1991.
Hall, Renee, "Voices Behind Bars: Correctional Education from the Perspective of the Prisoner
Student" (2006). University of New Orleans Theses and Dissertations. Paper 384
Harlow, Caroline Wolf, Ph.D. Education and Correctional Populations. Rep. no. NCJ 195670. Bureau
of Justice Statistics: U.S. Department of Justice, Jan. 2003.
Harer, Miles D. Prison Education Program Participation and Recidivism: A Test of the Normalization
Hypothesis. Rep. Federal Bureau of Prisons Office of Research and Evaluation, May 1995.
Ho, Taiping, Katie Knutson, Susan Lockwood, and John M. Nally. "The Effect of Correctional
Education on Postrelease Employment and Recidivism: A 5-Year Follow-Up Study in the
State of Indiana." Crime & Delinquency 58.3 (2012): 380-96.
Hughes, Timothy, and Doris James Wilson. Reentry Trends in the U.S. Rep. Bureau of Justice
Statistics: U.S. Department of Justice, 25 Oct. 2002.
Ismailova, Zarona. Prison Education Program Participation and Recidivism. Thesis. Duquesne
University: McAnulty College and Graduate School of Liberal Arts, Mar. 2007. Gumberg
Library Digital Collections.
Jancic, Mitchell. "Does Correctional Education Have an Effect on Recidivism?"Journal of Correctional
Education 49.4 (1998): 152-61.
Jenkins, H. David., Jennifer Pendry, and Stephen J. Steurer. "A Post Release Follow-up of
Correctional Education Program Completers Released in 1990-1991." Maryland State
Department of Education. 1993.
Langan, Patrick A., and David J. Levin. Recidivism of Prisoners Released in 1994. Rep. no. NCJ 193427.
Washington, DC: U.S. Department of Justice, Bureau of Justice Statistics, 2002.
Lawerence, Sarah, Daniel P. Mears, Gelenn Dubin, and Jeremy Travis. The Practice and Promise of
Prison Programming. Rep. The Urban Institute, May 2002.
Perry 59
Lipton, Douglas S., Robert Martinson, and Judith Wilks. The Effectiveness of Correctional Treatment:
A Survey of Treatment Evaluation Studies. New York: Praeger, 1975. Print.
MacKenzie, Doris L. What Works in Corrections: Reducing the Criminal Activities of Offenders and
Delinquents. New York: Cambridge University Press, 2006. Print.
Maltz, Michael. D., and Richard McCleary. "The Mathematics of Behavioral Change: Recidivism and
Construct Validity." Evaluation Review 1.3 (1977): 421-38.
Maltz, Michael D., Richard McCleary, and Stephen P. Pollock. "Recidivism and Likelihood Functions:
A Reply to Stollmack." Evaluation Quarterly 3.1 (1979): 124-31.
Maltz, Michael D. "Recidivism." Academic Press, Inc. (1984)
Manglesdorf, Paul, and Dave Owens. Can They Survive? Thesis. Haverford College.
Marc-Taylor, Jon. "Pell Grants for Prisoners." PELL GRANTS FOR PRISONERS. Laird Carlson.
Miller, Jerome G. "The Debate on Rehabilitating Criminals: Is It True That Nothing Works?" The
Debate on Rehabilitating Criminals: Is It True That Nothing Works? Washington Post, Mar.
1989.
Mitchell, Michael N. Data Management Using Stata: A Practical Handbook. College Station, TX: Stata,
2010. Print.
New York State Department of Correctional Services (1989) Analysis of return rates of the inmate
college program participants. Albany, Department of Correctional Services.
Petersilia, Joan. When Prisoners Come Home: Parole and Prisoner Reentry. Oxford: Oxford UP, 2003.
Print.
Pew Center on the States. State of Recidivism: The Revolving Door of America’s Prisons. Rep.
Washington, DC: The Pew Charitable Trusts, April 2011.
Piehl, Anne Morrison. "Economic Conditions, Work, and Crime." Handbook of Crime and Punishment.
Cary, NC: Oxford University Press, 2000. 302-19. EBrary.
"Press Release: Education and Vocational Training in Prisons Reduces Recidivism, Improves Job
Outlook." RAND Corporation., 22 Aug. 2013.
Rhodes, W., and S. Matsuba. "Pretrial Release in Federal Courts: A Structural Model with Selectivity
and Qualitative Dependent Variables." Evaluation Review 8.5 (1984): 692-704.
Schmidt, Peter, and Ann D. Witte. Predicting Recidivism Using Survival Models. New York: SpringerVerlag, 1988. Print.
Schaffer, Mark E. "Stata: Data Analysis and Statistical Software." IVREG2 with Two Endogenous
Variables: CD/KP F-statvs.APF. Stata, 2 Feb. 2013.
Sherman, Lawrence W., and Richard A. Berk. "The Specific Deterrent Effects of Arrest for Domestic
Assault." American Sociological Review 49.2 (1984): 261
Sill, Mike. "Chapter 5: Cox Proportional Hazards Model."
Perry 60
Stata. Stata Survival Analysis and Epidemiological Table: Reference Manual: Release 10. College
Station, TX: StataCorp LP, 2007. Print.
Stock, James H., and Mark W. Watson. "Chapter 12: Instrumental Variables Regression."
Introduction to Econometrics. 3rd ed. Boston: Pearson/Addison Wesley, 2011. 419-68. Print.
Stollmack, Stephen, and Carl M. Harris. "Failure-Rate Analysis Applied to Recidivism
Data." Operations Research 22.6 (1974): 1192-205.
Strand, Paul. "Punishing Taxpayers: US Prison System Run Amok?" Christian Broadcasting Network.
1 Mar. 2012.
Thorpe, Thomas, Donald MacDonald, and Gerald Bala. "Follow-Up Study of Offenders Who Earn
College Degrees While Incarcerated in New York State." Journal of Correctional Education
35.3 (1984): 86-89.
Tyler, John H. and Jeffrey R. Kling (2007) Prison-based education and reentry into the mainstream
labor market. In Shawn Bushway, Michael A. Stall, and David F. Weiman (Eds.), Barriers to
Reentry: The Labor Market for Released prisoners in Post-Industrial America, New York:
Russel Sage Foundation, 227-256.
Ubah, Charles B. A., and Robert L. Robinson Jr. "A Grounded Look at the Debate Over Prison-Based
Education: Optimistic Theory Versus Pessimistic Worldview." The Prison Journal 83.2
(2003): 115-29.
Visher, Christy A., Ph.D., and Pamela K. Lattimore, Ph.D. "Major Study Examines Prisoners and Their
Reentry Needs." National Institute of Justice Journal 258 (n.d.): 30-33. National Institute of
Justice. National Institute of Justice, Oct. 2007.
Warren, Jennifer, Susan Urahn, Richard Jerome, Jake Horowitz, and Joe Gavrilovich. One in 31: The
Long Reach of American Corrections. Rep. Pew Center on the States: The Pew Charitable
Trusts, Mar. 2009.
Warren, Jennifer, Susan Grange, Lori Grange, and Tim Lynch. One in 100: Behind Bars in America
2008. Rep. Pew Center on the States: The Pew Charitable Trusts, Feb. 2008.
"What Have We Learned From Evaluations of Reentry Programs?" BJA Center for Program
Evaluation and Performance Measurement. Bureau of Justice Assistance, Office of Justice
Programs, U.S. Department of Justice,.
Wilson, David B., Catherine A. Gallagher, and Doris L. MacKenzie. "A Meta-Analysis of CorrectionsBased Education, Vocation, and Work Programs for Adult Offenders." Journal of Research in
Crime and Delinquency 37.4 (2000): 347-68. SAGE Social Science Collections.
Perry 61
Appendices
Appendix Table 1: Re-Arrest & Re-Confinement, All States
Variable
educom_voccom
edupart_voccom
eduquest_voccom
eduno_voccom
educom_vocpart
edupart_vocpart
eduquest_vocpart
eduno_vocpart
educom_vocquest
edupart_vocquest
eduquest_vocquest
eduno_vocquest
educom_vocno
edupart_vocno
eduquest_vocno
tmsrv
nonwhite
sex
pprid
FL
IL
NY
NC
Re-Arrest
dF/dx
-.052
*
(029)
-.076
***
(.030)
-.011
(.048)
-.074
(.059)
-.068
**
(.029)
-.080
(.025)
-.022
***
(.064)
-.041
(.045)
.191
(.172)
-.211
(.169)
.079
**
(.038)
-.086
***
(.031)
-.044
*
(.026)
-.050
**
(.022)
-.108
***
(.023)
-.0002
(.0001)
.120
***
(.011)
.099
***
(.021)
.120
***
(.011)
.273
***
(.017)
.172
***
(.014)
.095
***
(.017)
-.054
***
(.019)
Re-Confined
dF/dx
-.079
***
(.027)
-.141
***
(.026)
.028
(.053)
-.054
(.053)
-.117
***
(.026)
-.118
***
(.023)
-.074
(.058)
-.096
**
(.042)
.078
(.224)
-.251
*
(.110)
.009
(.041)
-.105
***
(.026)
-.131
***
(.022)
-.106
***
(.020)
-.174
***
(.017)
.0003
(.0001)
.097
***
(.011)
.070
***
(.020)
.120
***
(.011)
.032
(.023)
-.030
*
(.017)
.218
***
(.020)
.019
(.019)
Perry 62
SMPOFF2
SMPOFF3
SMPOFF4
SMPOFF5
SMPOFF6
SMPOFF7
SMPOFF8
SMPOFF9
SMPOFF10
SMPOFF11
SMPOFF12
SMPOFF13
age rage 2
age rage 3
age rage 4
age rage 5
age rage 6
age rage 7
.100
(.029)
.184
(.026)
.184
(.026)
.232
(.023)
.251
(.012)
.219
(.028)
.204
(.026)
.172
(.028)
.173
(.037)
.201
(.028)
.196
(.026)
.216
(.025)
-.169
(.085)
-.240
(.085)
-.286
(.083)
-.347
(.080)
-.414
(.073)
-.485
(.065)
*p<.10; **p<.05; ***p<.01
Values are reported from dprobit, thus they indicate changes in probabilities
Numbers reported in parenthesis are robust standard errors
n=10,157
***
***
***
***
***
***
***
***
***
***
***
***
**
***
***
***
***
***
.117
(.034)
.209
(.037)
.174
(.038)
.242
(.036)
.269
(.036)
.209
(.046)
.149
(.041)
.148
(.036)
.192
(.052)
.149
(.036)
.238
(.039)
.215
(.040)
-.033
(.071)
-.089
(.068)
-.111
(.068)
-.149
(.065)
-.200
(.058)
-.263
(.051)
***
***
***
***
**
***
***
***
***
***
***
***
**
***
***
Perry 63
Appendix Table 2: Re-Arrest by State
Variable
Florida
Illinois
New York
North Carolina
Texas
Probability of re-arrest
72.8%
70.9%
61.2%
56.1%
50.5%
educom_voccom
-.049
(.045)
edupart_voccom
eduquest_voccom
eduno_voccom
educom_vocpart
edupart_vocpart
-.456
(.145)
-.033
(.035)
-.057
(.029)
***
**
eduquest_vocpart
eduno_vocpart
-.327
(.159)
**
-.149
(.063)
-.222
(.089)
-.132
(.067)
-.304
(.119)
-.296
(.069)
-.205
(.058)
-.115
(.085)
-.086
(.079)
**
**
**
**
eduquest_vocquest
educom_vocno
edupart_vocno
eduquest_vocno
tmsrv
nonwhite
-.0003
(.0002)
.081
(.019)
***
***
***
***
***
***
-.164
(.079)
-.168
(.063)
-.198
(.079)
.0003
(.0004)
.125
(.032)
.034
(.058)
.036
(.078)
.026
(.078)
***
edupart_vocquest
-.296
(.054)
-.141
(.045)
-.194
(.052)
-.259
(.035)
.0001
(.0003)
.236
(.023)
.182
(.125)
.233
(.125)
***
educom_vocquest
eduno_vocquest
.010
(.048)
-.106
(.044)
.039
(.080)
**
***
***
***
.122
(.173)
-.025
(.175)
.165
(.248)
-.142
(.175)
.118
(.047)
.015
(.038)
-.103
(.144)
.047
(.184)
.041
(.035)
.0009
(.001)
.113
(.027)
**
**
**
.031
(.037)
.018
(.028)
***
-.0007
(.0003)
.057
(.022)
*
***
Perry 64
sex
pprid
SMPOFF2
SMPOFF3
SMPOFF4
SMPOFF5
SMPOFF6
SMPOFF7
SMPOFF8
SMPOFF9
SMPOFF10
SMPOFF11
SMPOFF12
SMPOFF13
age rage 2
age rage 3
age rage 4
age rage 5
age rage 6
age rage 7
.098
(.042)
.085
(.019)
.141
(.045)
.172
(.033)
.185
(.031)
.169
(.034)
.229
(.023)
.171
(.038)
.210
(.027)
.187
(.033)
.158
(.049)
.195
(.037)
.209
(.026)
.206
(.027)
-.004
(.180)
-.080
(.194)
-.129
(.202)
-.173
(.208)
-.283
(.219)
-.359
(.215)
***
***
***
***
***
***
***
***
***
***
**
***
***
***
*
.067
(.049)
.142
(.022)
.118
(.053)
.119
(.051)
.158
(.043)
.163
(.043)
.210
(.034)
.191
(.039)
.181
(.043)
.109
(.053)
.130
(.067)
.148
(.050)
.168
(.042)
.118
(.053)
-.975
(.003)
-.956
(.004)
-.948
(.004)
-.916
(.006)
-.862
(.007)
-.879
(.007)
***
**
**
***
***
***
***
***
*
**
***
*
***
***
***
***
***
***
.093
(.066)
.142
(.031)
.037
(.090)
.289
(.057)
.085
(.097)
.294
(.057)
.279
(.061)
.134
(.116)
.134
(.116)
.226
(.074)
-.063
(.165)
.106
(.103)
.134
(.099)
.265
(.067)
-.933
(.0070
-.956
(.005)
-.974
(.003)
-.915
(.008)
-.838
(.011)
-.826
(.010)
***
***
***
***
*
***
***
***
***
***
***
***
.111
(.042)
.159
(.025)
.140
(.080)
.290
(.063)
.252
(.070)
.365
(.051)
.327
(.058)
.338
(.054)
.279
(.066)
.243
(.073)
.304
(.070)
.291
(.065)
.291
(.063)
.341
(.052)
-.207
(.105)
-.258
(.103)
-.275
(.102)
-.333
(.096)
-.382
(.086)
-.447
(.073)
***
***
*
***
***
***
***
***
***
***
***
***
***
***
**
**
***
***
***
***
.095
(.046)
.043
(.022)
.012
(.062)
.058
(.071)
.170
(.066)
.184
(.064)
.197
(.064)
.211
(.079)
.177
(.072)
.110
(.065)
.146
(.093)
.175
(.072)
.069
(.075)
.148
(.072)
-.325
(.184)
-.351
(.179)
-.385
(.174)
-.419
(.156)
-.458
(.121)
-.496
(.110)
**
**
**
***
***
**
**
**
**
**
*
*
**
***
***
Perry 65
Appendix Table 3: Re-Confinement by State
Variable
Probability of re-confinement
educom_voccom
Florida
34.55%
.080
(.048)
Illinois
39.1%
New York
58.9%
*
edupart_voccom
eduquest_voccom
eduno_voccom
educom_vocpart
edupart_vocpart
-.264
(.067)
**
-.006
(.038)
.003
(.034)
eduquest_vocpart
eduno_vocpart
-.317
(.032)
***
-.179
(.061)
-.256
(.085)
-.059
(.070)
-.109
(.127)
-.214
(.069)
-.213
(.055)
-.100
(.080)
-.147
(.075)
***
.218
(.133)
.332
(.126)
***
edupart_vocquest
eduquest_vocquest
educom_vocno
edupart_vocno
eduquest_vocno
tmsrv
nonwhite
-.0007
(.0003)
.112
(.021)
**
**
-.324
(.217)
-.327
(.020)
-.304
(.022)
-.386
(.016)
.0003
(.0004)
.109
(.024)
***
***
***
***
***
-.163
(.072)
-.133
(.060)
-.047
(.080)
.004
(.0005)
.067
(.032)
**
***
**
**
**
***
**
Texas
37.2%
-.074
(.043)
-.051
(.042)
***
educom_vocquest
eduno_vocquest
North
Carolina
47.9%
-.046
(.179)
.073
(.178)
.023
(.262)
-.180
(.163)
.140
(.049)
.059
(.038)
.096
(.141)
.012
(.198)
.041
(.036)
-.001
(.001)
.159
(.026)
-.023
(.077)
.017
(.055)
.022
(.041)
***
.062
(.079)
***
.011
(.035)
.013
(.027)
***
-.0004
(.0003)
.028
(.021)
Perry 66
sex
pprid
SMPOFF2
SMPOFF3
SMPOFF4
SMPOFF5
SMPOFF6
SMPOFF7
SMPOFF8
SMPOFF9
SMPOFF10
SMPOFF11
SMPOFF12
SMPOFF13
age rage 2
age rage 3
age rage 4
age rage 5
age rage 6
age rage 7
.075
(.036)
.134
(.022)
.096
(.066)
.171
(.080)
.150
(.080)
.175
(.078)
.244
(.077)
.171
(.098)
.100
(.085)
.122
(.074)
.206
(.113)
.157
(.123)
.252
(.078)
.280
(.082)
-.053
(.152)
-.114
(.143)
-.126
(.141)
-.174
(.129)
-.175
(.122)
-.273
(.091)
**
**
**
**
**
***
*
***
***
**
-.048
(.055)
.064
(.028)
.162
(.080)
.224
(.092)
.203
(.095)
.277
(.090)
.318
(.088)
.241
(.108)
.115
(.107)
.149
(.088)
.215
(.127)
.061
(.113)
.260
(.095)
.071
(.100)
.989
(.002)
.955
(.004)
.944
(.005)
.891
(.007)
.804
(.010)
.818
(.009)
**
**
**
**
***
***
**
*
*
***
***
***
***
***
***
***
-.010
(.063)
.113
(.037)
.251
(.098)
.382
(.043)
.296
(.072)
.386
(.046)
.404
(.040)
.316
(.068)
.332
(.059)
.353
(.065)
.179
(.132)
.270
(.079)
.308
(.066)
.390
(.031)
-.932
(.007)
-.955
(.005)
-.955
(.005)
-.907
(.008)
-.824
(.010)
-.810
(.012)
***
**
***
***
***
***
***
***
***
**
***
***
***
***
***
***
***
***
.151
(.039)
.162
(.025)
.014
(.090)
.235
(.087)
.201
(.090)
.350
(.073)
.314
(.077)
.301
(.090)
.200
(.092)
.176
(.087)
.282
(.010)
.212
(.097)
.212
(.093)
.333
(.077)
-.067
(.100)
-.117
(.099)
-.113
(.099)
-.138
(.098)
-.233
(.090)
-.319
(.077)
***
***
***
**
***
***
***
**
**
**
**
**
***
**
***
.068
(.042)
.077
(.021)
.108
(.061)
.081
(.062)
.115
(.073)
.053
(.070)
.107
(.071)
.066
(.091)
.055
(.077)
.030
(.065)
.072
(.099)
.050
(.079)
.122
(.077)
.017
(.076)
-.012
(.161)
-.073
(.154)
-.080
(.154)
-.107
(.149)
-.177
(.131)
-.183
(.131)
***
*
Perry 67
Appendix Table 4: Selection Bias, All States
Re-Arrest
Variable
eduonly
voconly
edu_voc
tmsrv
nonwhite
sex
pprid
FL
IL
NY
NC
SMPOFF2
SMPOFF3
SMPOFF4
SMPOFF5
SMPOFF6
SMPOFF7
SMPOFF8
SMPOFF9
Instrumental
Variable
Estimation
-3.867
(2.31)
-1.551
(4.931)
.830
(2.322)
-.001
(.001)
.092
(.034)
-.099
(.135)
.113
(.048)
-1.620
(2.036)
.212
(.662)
-.619
(.404)
-.392
(.800)
.020
(.166)
.047
(.160)
.166
(.118)
.239
(.147)
.062
(.216)
.003
(.214)
.072
(.218)
.093
(.117)
*
***
**
Re-Confinement
OLS
Regression
Coef.
-.045
(.013)
-.053
(.021)
-.015
(.015)
-.0001
(.0002)
.110
(.009)
.094
(.018)
.119
(.009)
.235
(.017)
.170
(.014)
.062
(.014)
-.045
(.015)
.091
(.027)
.186
(.030)
.194
(.031)
.264
(.030)
.263
(.030)
.267
(.038)
.211
(.033)
.182
(.043)
***
***
***
***
***
***
***
***
***
***
***
***
***
***
***
***
Instrumental
Variable
Estimation
-5.798
(3.37)
-2.313
(7.206)
1.288
(3.391)
-.001
(.001)
.070
(.050)
-.220
(.195)
.111
(.071)
-2.839
(2.97)
.064
(.965)
-.835
(.588)
-.490
(1.163)
-.012
(.242)
-.047
(.235)
.113
(.175)
.189
(.217)
-.060
(.319)
-.201
(.315)
-.078
(.321)
-.005
(.173)
OLS
Regression
Coef.
*
-.118
(.013)
-.074
(.020)
-.061
(.015)
.0005
(.0001)
.096
(.009)
.069
(.018)
.119
(.010)
-.010
(.018)
-.020
(.015)
.183
(.014)
. 013
(.015)
.097
(.023)
.162
(.028)
.153
(.029)
.227
(.028)
.238
(.028)
.190
(.038)
.130
(.032)
.128
(.026)
***
***
***
***
***
***
***
***
***
***
***
***
***
***
***
***
Perry 68
SMPOFF10
SMPOFF11
SMPOFF12
SMPOFF13
group 2
group 3
group 4
group 5
group 6
group 7
_cons
.229
(.214)
.083
(.166)
-.005
(.194)
.238
(.156)
.165
(.270)
.108
(.257)
.100
(.266)
.080
(.275)
-.086
(.246)
-.062
(.307)
1.424
(.778)
*
.222
(.036)
.222
(.036)
.211
(.032)
.228
(.033)
-.135
(.049)
-.204
(.050)
-.241
(.050)
-.288
(.050)
-.361
(.051)
-.447
(.051)
.432
(.059)
*p<.10; **p<.05; ***p<.01
Numbers reported in parenthesis are robust standard errors
***
***
***
***
***
***
***
***
***
***
***
.240
(.318)
-.078
(.245)
-.100
(.286)
.201
(.230)
.379
(.374)
.335
(.374)
.361
(.387)
.371
(.401)
.174
(.358)
.266
(.447)
1.733
(1.130)
.167
(.044)
.125
(.034)
.220
(.031)
.182
(.031)
-.054
(.068)
-.116
(.068)
-.132
(.068)
-.167
.069
-.224
(.069)
-.299
(.069)
.263
(.074)
***
***
***
***
*
**
***
***
***
***
Perry 69
Appendix Table 5: Cox Proportional Model Hazard, All States
Variable
eduonly
voconly
edu_voc
tmsrv
nonwhite
sex
pprid
FL
IL
NY
NC
SMPOFF2
SMPOFF3
SMPOFF4
SMPOFF5
SMPOFF6
SMPOFF7
SMPOFF8
SMPOFF9
SMPOFF10
SMPOFF11
Re-Arrest
Hazard Ratio
.790
(.030)
.781
(.049)
.858
(.042)
.999
(.0005)
1.344
(.036)
1.335
(.071)
1.396
(.038)
2.335
(.121)
1.679
(.069)
1.391
(.069)
.876
(.039)
1.416
(.138)
1.950
(.204)
1.857
(.197)
2.193
(.227)
2.452
(.255)
2.205
(.273)
2.039
(2.039)
1.746
(.177)
1.746
(.237)
1.912
***
***
***
**
***
***
***
***
***
***
***
***
***
***
***
***
***
***
***
***
***
ReConfinement
Hazard Ratio
.656
(.039)
.726
(.065)
.745
(.051)
.999
(.0008)
1.535
(.068)
1.134
(.089)
1.607
(.068)
1.129
(.094)
1.156
(.077)
2.802
(.195)
1.389
(.089)
1.529
(.304)
2.780
(.570)
2.389
(.495)
3.473
(.705)
3.896
(.791)
2.991
(.684)
2.440
(.520)
2.500
(.502)
2.076
(.517)
2.463
***
***
***
***
***
**
***
***
**
***
***
***
***
***
***
***
***
***
Perry 70
SMPOFF12
SMPOFF13
age group 2
age group 3
age group 4
age group 5
age group 6
age group 7
n
(.237)
1.989
(.218)
2.042
(.226)
.594
(.093)
.496
(.078)
.437
(.069)
.386
(.061)
.313
(.051)
.233
(.038)
10,157
***
***
***
***
***
***
***
***
(.573)
2.903
(.641)
3.012
(.641)
.876
(.210)
.775
(.187)
.637
(.154)
.627
(.153)
.499
(.125)
.325
(.084)
10,157
***
***
*
*
***
***
Perry 71
Appendix Table 6: Cox Proportional Hazard Model, Illinois & New York
Illinois
Variable
eduonly
voconly
edu_voc
tmsrv
nonwhite
sex
pprid
SMPOFF2
SMPOFF3
SMPOFF4
SMPOFF5
SMPOFF6
SMPOFF7
SMPOFF8
SMPOFF9
SMPOFF10
SMPOFF11
SMPOFF12
SMPOFF13
age group 2
Re-Arrest
Hazard
Ratio
.499
(.034)
.383
(.056)
-.999
(.001)
1.946
(.120)
1.218
(.153)
1.542
(.096)
1.392
(.290)
1.762
(.391)
1.924
(.435)
2.138
(.475)
2.529
(.565)
2.558
(.669)
2.094
(.523)
1.554
(.337)
1.496
(.423)
1.719
(.492)
1.939
(.459)
1.622
(.386)
.133
***
***
***
***
***
***
***
***
***
***
**
*
***
**
**
New York
ReConfinement
Hazard Ratio
.081
(.021)
.165
(.068)
--
***
***
.999
(.002)
2.389
***
(.293)
1.121
(.254)
1.373
***
(.141)
1.166
(.493)
1832
(.796)
1.384
(.622)
1.943
(.849)
2.534
(1.101)
1.489
(.764)
1.645
(.779)
1.889
(.809)
1.717
(.878)
.588
(.424)
1.715
(.795)
.968
(.474)
see note
Re-Arrest
Hazard
Ratio
.719
(.082)
.714
(.112)
.673
(.068)
1.000
(.001)
1.35
(.111)
1.299
(.215)
1.528
(.122)
.941
(.284)
1.185
(.706)
1.165
(.385)
2.616
(.840)
2.485
(.812)
1.508
(.627)
1.401
(.476)
1.685
(.541)
.781
(.363)
1.261
(477)
1.466
(.525)
2.089
(.726)
.256
ReConfinement
Hazard Ratio
***
**
***
***
***
**
***
***
*
**
**
.855
(.118)
.682
(.132)
.700
(.087)
1.001
(.002)
1.358
(.139)
.961
(.177)
1.676
(.162)
1.693
(.872)
4.810
(2.549)
2.013
(1.095)
5.338
(2.829)
5.937
(3.169)
3.491
(2.119)
3.105
(1.694)
3.398
(1.804)
.846
(.654)
2.076
(1.25)
3.016
(1.701)
4.915
(2.708)
.119
**
***
***
***
***
***
***
**
**
**
**
***
***
Perry 72
age group 3
age group 4
age group 5
age group 6
age group 7
n
(.134)
.098
(.099)
.089
(.090)
.073
(.074)
.065
(.0669)
.045
(.045)
2,124
**
**
***
***
***
.901
(.105)
.744
(.099)
.732
(.119)
.616
(.123)
.459
(.112)
2,127
**
**
***
(.152)
.204
(.121)
.161
(.096)
.154
(.093)
.128
(.078)
.088
(.055)
1,193
***
***
***
***
***
(.072)
.110
(.067)
.075
(.045)
.085
(.052)
.064
(.040)
.060
(.038)
***
***
***
***
***
1,193
*p<.10; **p<.05; ***p<.01
Numbers reported in parenthesis are robust standard errors
For IL re-confinement age group 1 and 2 were combined to make the base state as there were too few observations in group 1, I believe this is
what causes the increase in 3 observations.
Perry 73
Appendix Table 7: Description of Variables
The following descriptions are adapted from the ICPSR3355 Codebook.
Variable
Long Name of
Variable
Description
Value & Meaning in Analysis
Dependent Variables
REARRD
Number of Rearrests
Dummy variable indicating
whether the individual was
rearrested at any time within
3 years following release
1= re-arrested
0= not re-arrested
RCNFITV
Re-confined –
Including Technical
Violations
Dummy variable indicating
whether the individual was
resentenced to prison or jail
for any reason (new sentence
or technical violation) within
the 3 years following release
1= re-confined
0= not re-confined
. = not applicable
Indicates whether or not the
individual took part in an
education program while
serving the sentence that
ended in 1994
Indicates whether or not the
individual took part in a
vocation program while
serving the sentence that
ended in 1994
1= participated & completed
2= participated, not completed
3= participated, unknown completion
4= did not participate
. = unknown
Is the total maximum
sentence length (in moths) for
all offenses. If sentences are
concurrent this records the
longest sentence. This is used
to create SNTLN2, the square
of sentence length.
Indicates whether or not the
.=not applicable
Variables of Interest
48
EDUCAT
Education Courses
VOCAT
Vocational Courses
1= participated & completed
2= participated, not completed
3= participated, unknown completion
4= did not participate
. = unknown
Instrumental Variables
SNTLN
Sentence Length
NFRCTNS
Number of
1= has a record
The above variables were used to generate dummies for each possible outcome. The dummies were then interacted to create 16 new
variables. The dummies are listed in Table 3 and are mutually exclusive. The base state in the interactions was eduno_vocno, meaning no
participation in education or vocational programs.
48
Perry 74
Infractions
individual was formally
disciplines for a prison-rule
infraction.
0= does not have a record
.=unknown
Time served (in months)
before release
Sex of released individual
integers >12
Control Variables
TMSRV
SEX
Time Served for
1994 Imprisonment
Sex
PPRID
Prior Sentences to
Prison
SMPOFF13
Sample Offense 13
Categories
RLAGE
Age Range
STATE
State Releasing
Prisoner in 1994
RACE
Race
1= male
0= female
. = unknown
Dummy variable indicating
1= prior sentences to prison
whether, prior to the sentence 0= no prior sentence to prison
. = not applicable
that got him/her into the
study, he/she had previously
received a prison sentence for
another offense
The offense (13 levels) for
1= homicide
which the sampled individual 2= rape/sexual assault
was in prison
3= robbery
4= agg assult
5= burglary
6= larceny –mvt
7= ffe (fraud, forgery, or
embezzlement)
8= drug possession
9= drug trafficking
10= weapons
11= dui
12= other public order
13= other
Age of prisoner in months at
1= 14 to 17 years old
2= 18 to 24 years old
the date of release in 1994
3= 25 to 29 years old
broken into seven distinct
4= 30 to 34 years old
groups.
5= 35 to 39 years old
6= 40 to 44 years old
7= 45 years and older
9= unknown
State from which the
1= alabama
2= alaska
individual was released in
…
1994. There are 15 states
54= puerto rico
included in the study but
variables for all 55 states and 55= virgin islands
territories
Race of released individual.
1= White
This variable is used to create 2= Black
3= American Indian/Aleutian
a dummy for NONWHITE
where people who identify as 4= Asian/Pacific Islander
5= Other
Black, American
. = unknown
Perry 75
Indian/Aleutian,
Asian/Pacific Islander, or
Other are coded as
“nonwhite”
Date Variables
A0##MO
Month of Arrest
Month arrested in cycle ##
A0##DA
A0##YR
J0##MO
Day of Arrest
Year of Arrest
Month of
Adjudication
Day of arrest in cycle ##
Year of arrest in cycle ##
Month of Adjudication in
cycle ##
J0##DA
Day of Adjudication
J0##YR
Year of Adjudication
J0##PJP1
Type of Sentence
Day of Adjudication in cycle
##
Year of Adjudication in cycle
##
Prison, jail or probation for
first adjudicated offense cycle
1= January
2= February
…
12=December
integers 1-31
integers 1918 - 1998
1= January
2= February
…
12=December
integers 1-31
integers 1936 - 1999
1= prison
2= jail
3= probation-fine-other
4= confined, type unknown
5= convicted, sentence blank
6= not convicted
8= not applicable
9= unknown
Perry 76
Appendix Table 8: Descriptive Statistics
The below provides key descriptive statistics for the five-state sample.
Number of
Observations
Mean
Standard
Deviation
Min
Max
Sex (where male=1)
11,824
.9361
.3445
0
1
Non-white
11,352
.5347
.4988
0
1
Prior Arrest History
11,824
.4075
.4914
0
1
Term Served (in months)
11,815
27.946
33.6417
.03
436.43
Sentence Length (in months)
11,824
85.954
85.3665
0
1,800
Number of Infractions
9,153
.4156
.4929
0
1