This document is a student paper that analyzes the relationship between education and crime by examining state recidivism rates. It begins by acknowledging professors and peers who provided support. It then provides an abstract that outlines the paper's goals of comparing state recidivism rates while assessing if correctional education programs impact rates. The paper includes sections on literature review, theoretical framework, empirical analysis, and conclusions. It examines previous research on the topic and develops hypotheses about the relationship between education and recidivism.

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ECONOMICS 620
Allegheny College
Meadville, Pennsylvania
16335
The Effect of Education on Crime: A Look at State Recidivism Rates
Raysean D. Glenn
April 1, 2015

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The Effect of Education on Crime: A Look at State Recidivism Rates
by
Raysean D. Glenn
Submitted to The Department of Economics
Project Advisor: Professor Russell Ormiston
Second Reader: Professor Zachary Klingensmith
Date: April 1, 2015
I hereby recognize and pledge to fulfill my responsibilities as defined in the Honor Code and to
maintain the integrity of both myself and the College as a whole.
Raysean D. Glenn

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Acknowledgements
I want to take the time to acknowledge both Professor Ormiston, and Professor
Klingensmith for helping me through this process. I also want to thank students Patrick Jones,
and Romulas Marino for keeping me on track and going above and beyond. Finally I want to
give my acknowledgements to my roommates for coping with me during this stressful time.
Without all of this support, it would have been a rough time trying to complete this project.

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Abstract
There has always been a relationship between education and crime. People who have less
education tend to find themselves in some type of incarceration system. In the United States the
main goal of the prison systems is not to punish criminals, it is to reform them so they can be
reintroduced to the public. One of the most important ways prisoners become reformed, during
their incarceration, is through correctional education programs. In order to check how released
prisoners are, the recidivism rates are usually examined. Recidivism is the process of an
individual committing another crime, being re­arrested and reconvicted, and then returned back
to prison. The purpose of this analysis is to examine and compare the recidivism rates of each
state, while seeing if participating in a correctional education program helps keep the recidivism
rates low. The major theories that will be used in this paper are the signaling theory and human
capital approach theory. The data was collected from various databases from two separate ranges
of years; the first being from 1999­2002 and the second being from 2004­2007. According to
this study, we found the number of releases to be statistically significant.

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Table of Contents
1 Introduction 1
2 Literature Review 3
2.1 Education Reduces Crime: 3 State Recidivism Study 3
2.2 On the Relation between Education and Crime 5
2.3 The Crime Reducing Effect of Education 8
2.4 Education and Crime over the Life Cycle 10
2.5 Does Education Reduce Participation in Criminal Activities? 12
2.6 Conclusion 14
3 Theoretical Framework 16
3.1 Theoretical Model 16
3.2 Concluding Remarks 22
4 Empirical Analysis 23
​4.1 Data Description 23
4.2 Data Summary 24
4.3 Model 26
4.4 Results 27
5 Conclusion 31
References 33

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1 Introduction
“He who opens a school door closes a prison one”(Hugo 1880). This was once said by
famous French poet Victor M Hugo. His work was inspired by the social and political problems
of that time era. The main concern in the eyes of the people was education and crime. In 1870,
the Elementary Education Act was passed, which set the precedent and framework of schooling
for children ages five through thirteen. When viewing the relationship between education and
crime, there are several alarming facts that suggest earning an education drastically changes the
life of individuals, even after they are released from prison. For example, students that drop out
of high school will earn $200,000 less than a high school graduate over his lifetime. And almost
a million dollars less than a college graduate. As well as ​high school dropouts commit about 75%
of crimes in the United States (11 Facts about High School Dropout Rates). ​This was very
important because high school dropouts are more likely to live below the poverty line as well as
commit more crimes. Since that period in history, the issues of education and crime have only
intensified and become more important. The crime rate has increased dramatically over the
years, so much that it has been reported that one out of every 100 American men are in prison or
on parole (Pew Study 2008). Over the last few decades the argument that less education leads to
more criminal activity from a given individual has resurfaced countless times. Each time this
argument is debated it raises the question of whether education can be used in order to combat
crime and ultimately lower crime rates within a given area.
This paper will examine and analyze different views and factors of education affecting crime
while measuring recidivism rates by state throughout the United States. Since there are different

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variations to the types of crime, it is usually broken down between four different infractions;
murder, rape, aggravated assault, and robbery. In continuation with the analysis of education on
crime, different articles were explored that deal with prison systems and high school
student/dropouts.
In examining the relationship between education and crime, more specifically in­prison
education programs, it is quite easy to come up with a few different ideas. The first one that can
be made is increasing education will lower the chance of an individual committing a crime.
Another hypothesis to consider is a person who was in prison will have a tougher time finding a
job once they are released, and eventually the individual will commit another crime. In other
words, the individual will likely fall into a cycle of recidivism. It is greatly possible for all of
these observations to materialize and show how education can be affected by crime.
Throughout this analysis there will be four other chapters presented. The first chapter
presented is the literature review. A literature review, lit review for short examines other
scholarly journal articles applicable to a specific topic. For this section there were five different
articles used. Here the articles provide a brief insight on the relationship between education and
crime, as well as contribute to the overall idea of education affecting crime. Following the
literature review, the underlying theory will be addressed. This includes 2 different theoretical
models in addition to, other theoretical reasonings presented that express more analytical ways to
look at how educations affects crime. After the theory section is completed, the data section will
be the next to be investigated. In this section, the variables, the models, and the equations will be
examined and applied. Then, the results section will be next to be discussed, followed by a
conclusion which will wrap up all ideas, and reiterate the parallel between education and crime.

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2 Literature Review
Every year it seems like the prison population continues to grow, while the number of
high school graduates seems to drop, which ultimately causes the recidivism rate to increase. It
appears to be a common theme throughout the United States. This phenomenon has been a
downward trend for almost 50 years. In most cases when referring to the effect of education on
crime, only statistics that are about high school students and high school dropouts get examined.
It is said that an individual that drops out of high school is 63 times more likely to end up being
incarcerated between the ages of 16 and 24 (Sum, Khatiwada, McLaughlin, & Palma, 2009).
With that stated, it is much harder for people with criminal records to obtain a job, and when
they do gain access to a job their wages are usually less than a person who did not go to prison.
2.1 Education Reduces Crime: 3 State Recidivism Study
The first article that was used is called Education Reduces Crime: 3 State Recidivism
Study​ ​(Steurer, Smith, & Tracy, 2001)​. ​This article allows its readers to view the effect of
education on crime through incarcerated offenders by looking at correctional facilities between
three different states. These states consist of Minnesota, Ohio, and Maryland. Essentially this
paper looked at prisoners that participated in education programs while being in jail, and also it
looked to see which prisoners fell under recidivism. Recidivism is the ​tendency to relapse into a

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previous condition or mode of behavior; especially relapsing into criminal behavior​. In other
words, it’s the process of committing another crime, being re­arrested, and returned back to
prison again. This reason why this study was completed is because many states were losing
money for research and struggling to keep education programs in prisons (Steurer, Smith, &
Tracy, 2001). The purpose was to find out on average how much it cost per inmate per year.
Throughout this entire paper there is virtually one research question being asked. If
education reduces crime for incarcerated offenders, or do most felons fall under recidivism.
There are various amounts of papers that examine the effect of education on crime; however this
was one of the only papers that looked at convicted felon and tried to investigate whether
education programs in prison actually were beneficial to the inmates. The paper basically
measures whether or not participating in an education program in prison would help a felon
rejoin into society and obtain a job when released from prison. Through the beginning years of
the 1990’s there were countless studies done to measure the importance and value of correctional
education. A few of these works include Eisenberg (1991) and Saylor and Gaes (1991). The
three various authors that created this document formulated eight different testable hypotheses.
Yet there are only three that are going to be emphasized. The first hypothesis is participating in a
correctional education program, felons are likely to commit less serious crimes if they do commit
another crime when released. Another hypothesis is participating in a correctional education
program increases the rate of employment, as well as increased the wage an ex­inmate can make.
A third hypothesis that may be the most important for this study is participating in a correctional
education program will reduce the rate of recidivism and reconvictions.
In this study the authors use different types of ways to collect data. One way the authors

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collect data is by study groups. Other ways include: parole officer surveys, institutional
education records, employment and wage data, and self­reported (done by ex­cons). The authors
used a cohort study in this article. A cohort study is a methodology employed in
quasi­experimental designs for nonequivalent groups where there is a belief that the treatment
group does not systematically differ from the comparison group on important variables (Steurer,
Smith, & Tracy, 2001). The sample is restricted because not all prisoners were used. Only 3170
prisoners were used in the cohort study. 1375 inmates actually engaged in the education
programs while 1795 inmates did not. There were several variables in this study. Some of the
variables used were the age of the inmates, the race of the felons, the level of literacy, as well as
the history of the inmate’s family. This study was done while the 1375 inmates were in these
education programs and carried out for three years after they were released from prison.
The results of this study say the more participants that partook in the education programs
were less likely to recidivate and be incarcerated depending on age and race. According to the
article, the younger an individual was the higher their probability was to recidivate and return
back to prison. There are six different racial groups analyzed throughout the experiment. In this
study, it is found that African Americans have the highest rate of recidivism amongst the various
ethnicities. The authors were wrong about the offenders committing less serious crimes because
on average, they committed worse (more violent) crimes than they did the first time they were
placed in prison. The last hypothesis that was answered talks about the rate of employment.
According to the results, more offenders that did not join the education program in prison,
obtained more jobs than the prisoners that did. However, the ex­prisoners that attended the
education program had an increase in wages compared to the ones that did not attend these same

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programs. In conclusion, this study shows strong support for educating offenders to allow them
to better themselves and ultimately live a wealthier life. There were also a couple of
shortcomings in this paper. The most important shortcoming of this study was that it was
criticized because it put offenders not participating in the programs at risk of recidivism,
although the inmates had the choice whether or not to attend the educational program. Also, the
data did not take into account if length in the educational program made a difference.
2.2 On the Relation between Education and Crime
This next article is out of a novel called Education, Income, and Human Behavior​ ​(Juster,
1975). This is relatively old data that was published in 1975; however it provides great
information on the relationship between crime and education, while measuring income and
human behavior. This paper examines if someone that has enough income is willing to make an
illegal action in order to gain more income. The chapter chosen to be examined out of the book is
On the Relation between Education and Crime​ ​(Ehrlich, 1975). These choices include either
making a legal decision or making an illegal decision. With these choices there are only a couple
of ways for a person to make money. The legal way consists of getting a job and earning money
in a lawful manner, the way in which money is supposed to be made in our society. The other
way the article talks about obtaining money is by committing a violation which is the illegal way
to earn money. It then continues to talk about how an offender will choose one or the other
depending on the “gain” (referring to increased income) and the “risk” (referring to an offender
being apprehended). This paper gives its readers a more detailed look at the negative properties
of education on crime, which is unique to most other papers on the same subject. This is one of

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the few articles that note that education may increase an offender's productivity at self­protection
against apprehension and punishment for crime. Most papers view education as an enhancer on
crime.
Throughout this entire article there is one question that is continually brought up. This
question is whether or not education is likely to have a systematic effect on the incentive to
participate in an illegitimate activity (Ehrlich, 1975)? As stated earlier, most papers never look at
education as a negative correlation with crime; however this paper looks at education as having a
negative effect. In reviewing most papers education was commonly viewed as a positive aspect
to crime. This means that education was normally viewed as a way to reduce the severity of a
crime and the amount of crimes committed. However this article looks at education in a contrary
way. According to Ehrlich (1975), education does not just have a positive effect on crime. For
example, it refers to prison schooling as prisoners sharing stories and ideas which can make them
almost like “super­criminals.” With that being said, this article subsequently adds to the problem
of prior literatures, simply because there are no other ways to compare the data with other
articles.
This article presents six hypotheses presented within this chapter that are all relate to each
other. The first hypothesis states that an individual with a lower level of schooling and training
will tend to spend more time specializing in illegitimate activities, while those with higher
education would have less incentive to participate in such crimes. Another hypothesis used in
this chapter is offenders committing crimes are more likely to be young because lack of
schooling and legitimate training. Here young people tend to commit more crimes because it
allows them to make more money than the minimum wage law allows, according to the article.

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Finally, one of the most important hypothesis from the chapter talks about non­white workers
engaging into illegitimate activities more often than white workers. All the data used in this
study is publicly offered and it came from the Bureau of the Census. The Bureau of Census is a
division of the federal government that is responsible for taking census, and gathering
information on the population of the United States. It also contains a few different variables,
which consist of male, age, ethnicity/race, and which prison (federal or state) offenders were
placed in.
In reviewing the data it is easy to understand that this sample is based off selective
sampling. Essentially, ​this means the sample was chosen by using a sampling plan that blocks
out materials with certain characteristics, and/or selects only material with relevant
characteristics. It only uses information that is favorable when explaining the relationship
between crime and education because most prison data just looks at apprehended and convicted
prisoners. This increases the chance of biases, which in turn may understate the averages of the
data ​(Juster, 1975)​. This chapter uses ordinary least squares (OLS) with a cross­state regression,
as a type of methodology that shows rates of specific crimes against property to changes in
income ​(Ehrlich, 1975).​The results from this paper show that offenders young in age and in
federal prison were more likely to commit crimes than any other age group in a state prison. As
far as the results on non­white offenders; non­white offenders are more likely to end up
participating in non­legal activities than other white offenders.
Ultimately, this paper demonstrates which age groups of offenders are more likely to
engage in illegal activities as well as expresses that white offenders are less likely to engage in
criminal activities. Crimes against property are typically committed by the relatively young

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because they have less investment in legitimate occupations. There were no clear shortcomings
of the paper highlighted; however selective samplings can be considered as one. This is because
by selecting which variables to test for, a person may leave out an important variable which can
drastically change results and inflate the error term, thus leaving a bias or skewed result.
2.3 The Crime Reducing Effect of Education
To begin the analysis of the article, first look at the main question being addressed. That
question of if crime participation is related to an individual’s education. The article entitled, The
Crime Reducing Effect of Education, claims there are at least three different ways in which
schooling affects crime participation in socio­economic view (Machin, Marie & Vujić, 2010).
These three ways consist of income effects, time availability, and patience or risk aversion.
Income effects are understood in this article as education raises returns to legitimate work, which
ultimately increases the opportunity cost of non­legal behavior. Time availability is defined more
for an offender that is still going to school. Basically what this means is an offender may spend
more time on education which keeps them away from doing other illegal activities. If a person
drops out of school they increase the amount of time they have to commit a crime. Risk aversion
is also affected by education and can be highly correlated too. For example, this article suggest if
someone has good patience and risk aversion skills, then they are less likely to go to jail or
prison because they value their future earnings.
The topic of education on crime has been reviewed by a countless numbers of papers. In
many of the prior literatures high education rates have been shown to correlate positively with
low crime rates (Lochner & Moretti, 2003). Also, it can be concluded from prior literatures

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younger people tend to usually be the victims of higher crime rate (Osofsky, 2003). This paper
does a great job of illustrating the positive and negative characteristics of the effect of education
on crime.
This article tried to uncover if crime is significantly related to education, especially in
property values. The article uses crime measurement as a way of calculating crime Measurement
is one of the most common sources of data used. The Offenders Index Database (OID) is one
place where information was taken from. The OID contains characteristics of individual
offenders, holding criminal history data for offenders convicted of standard list offences from
1963 onwards (Machin, Marie & Vujić, 2010). Furthermore, it compares crime to education by
matching up the data. For this particular case information was collected from the Samples of
Anonymized Records (SARs). SARs are samples of individual records from the 1991 and 2001
UK Censuses between one and five percent of the population. For the article The Crime
Reducing Effect of Education,​ ​the data collected was from the United Kingdom, but SAR’s
contains data from the entire world. The other place where data is collected for this paper is the
British Crime Survey. The authors do not restrict the sample in this article. Instead they only use
data from the UK. There were not an overwhelming amount of variables included with this
article. Only age and gender were used for crime measurement. Crime measurement is a term for
explaining different ways to measure crime, and for the other samples/surveys used there were
no specific variables mentioned.
After reviewing the results of the article, it’s clear to see improving education amongst
offenders, and potential offenders should be viewed as a key policy lever that could be used in
the drive to combat crime (Machin, Marie & Vujić, 2010). This is due to an individual having

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less incentive to commit the crime with a higher education. Education has the ability to increase
income and virtually eliminate the need to commit a crime. This paper had a few potential
shortcomings. The author in this article does not include a person’s race in the paper, which is an
important trait when characterizing crime. African Americans are more likely to be convicted for
a crime than any other race/ ethnicity group. There were little findings in the paper that did not
give clear evidence of a connection between crime and education. According to this literature,
policies that subsidies schooling and human capital investment have significant potential to
reduce crime by increasing each individual’s skill level. A person that increases their skill level,
education, and productivity becomes a more valuable individual. When increasing theses three
important factors of a worker, the income of the worker should increase accordingly, which
consequently lowers the crime rate (Machin, Marie & Vujić, 2010).
2.4 Education and Crime over the Life Cycle
This next article was written by two different authors, it is entitled Education and Crime
over Life Cycles (Fella & Gallipoli 2006). In the article, it explains the life cycle of a person
going to prison. Here they calculated that the average annual cost for each prisoner imprisoned in
the United States is around $29,000. Fella & Gallipoli (2006) use property crimes which is then
calibrated to U.S data. Essentially what this article is trying to do is quantify policies to see if the
policies will reduce crime. One policy is establishing whether subsidizing high school graduation
is a quantitatively effective for reducing crime. The other policy presented in the paper is by
extending prison sentences, crime will ultimately be reduced (Fella & Gallipoli, 2006). Both of
the policies were created for the long run and did not see immediate results.

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The research question that is presented in this article is if either policy will reduce crime
and if they both do which is more effective. Lochner and Moretti (2004) is an article that is
widely used in many papers that look to see the effect of education on crime. In the article,
Education and Crime over the Life Cycle​ ​there is no testable hypothesis that is explicitly
described (Fella & Gallipoli, 2006). The variables are not clearly defined in this paper; however
it is possible to determine some of them, due to the context of the article. The variables age and
high school graduates are definitely variables in this sample. The data from this article comes
from the Department of Labor. Once again the sample does not give a definite restriction, but
there are certain groups that were excluded like college graduates. In this example, college
students were exempted from this study because college graduates arrest records are negligible.
There are a few different models and parameters created in this paper. These models
includes retirement, education, work and crime, and production technology. Also, there is a
demographic model that was created. Some of the parameters created are the direct education
costs, government, wages, and innate ability of an individual.
As far as results go, the two policies had two different effects. For the policy dealing with
an increase of prison time, there was little efficiency and no welfare gain. However there is no
surprise with the results of this first policy. Mentioned earlier, inmates cost approximately
$29,000 annually to be locked up in prison. By extending prison sentencing it costs the United
States more money to have prisoners placed in prisons for longer. For the policy dealing with
subsidizing high schools, there was a large efficiency with an even larger welfare gain. There
was a large welfare gain because there is a large return on education for graduating high school
compared to dropping out. Gaining a higher education increases wages and motivates people to

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not engage in criminal activity. Also, finishing school helps build a sense of patience,
decision­making skills, and makes a person less­likely to engage in risky behaviors (Oreopoulos
& Salvanes, 2009).
Overall in this paper, there are a few different conclusions that can be pulled. One idea is
that there was a life cycle model created that compared two alternative sets of policies: subsidies
for high school completion and increases in prison sentences. Another conclusion is under a
limited budget, the most effective policies are those targeting individuals from the poorest
backgrounds with higher transfer sizes, rather than small transfer policies targeting large sections
of the population (Fella, Gallipoli 2006). Finally the last conclusion is as long as selection is
controlled, the results will show significant effects of an education subsidy over life­cycle crime.
2.5 Does Education Reduce Participation in Criminal Activities?
Does Education Reduce Participation in Criminal Activities, begins by explaining the
different theoretical reasoning on why crime and education are related (Moretti, 2005). Here are
the reasons: First, and most importantly, schooling increases the economic returns to legitimate
work. It teaches skills that are very important in everyday life. Individuals who choose to drop
out tend to not develop some of these traits (Oreopoulos & Salvanes, 2009). Second, education
may directly increase the psychic cost of committing crime. Finally, schooling could alter
preferences in indirect ways (Moretti, 2005). Next the article shifts from the theoretical view to
more of a policy maker view.
There really is not a question being asked in the article. However, it takes the three
theoretical reasons and looks at the relationship between crime and education. Some of the prior

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literature that is mentioned in this article is the study of the 2004 Lochner and Moretti. In regards
to this article, the variables are explicit and basically stated throughout. These variables consist
of age, gender, white vs black, once again. It also includes variables like level of high school
completed, as well as property vs violent crimes. This data happens to come from all over the
place. Primarily it was pulled from the Census on Incarceration, The Uniform Crime Reports,
and the National Longitudinal Survey of Youth. One limitation of the estimates presented is that
they do not differentiate among different types of criminal offenses, which may be a way the
sample was restricted.
The results of this article are that first schooling significantly reduces criminal activity.
This is a result in majority of papers examining the relationship between crime and education.
Generally there are huge returns on education. Besides the increase in wages, punishment for
educated individuals is greatly more costly than it is for an uneducated person. Also, it is hard to
explain the unobserved characteristics of criminals and the unobserved state policies which affect
crime and schooling. Because this article also looks at the return of education as a negative
correlation, it makes it harder to overlook characteristics and policies that show this trend.
Finally, one of the most important results is estimated social externalities from reduced crime are
sizeable. A 1% increase in the high school completion rate of all men ages 20­60 would save the
United States as much as $1.4 billion per year in reduced costs from crime incurred by victims
and society at large (Moretti, 2005). By completing high school over a billion dollars can be
saved by the United States, which can help the economy to prosper.
2.6 Conclusion

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In looking at all of these articles, it is possible to see how each one has its own unique
relationship between crime and education. There are plethora of overlapping as far as data is
concerned, but at the same time there are some distinct differences that differentiate each article.
The first article deals with a prison educational program that was created in order to see which
inmates would participate in it ​(Steurer, Smith, & Tracy, 2001). Also, it looks to see which
inmates will fall under a state of recidivism. After reviewing the results, more jobs were
achieved by the inmates who did not participate in the programs, yet the prisoners that did made
on average 30 cents more.
The next article that was reviewed focused on the relationship between education and
crime. This concentrated on the choice of making a legal or an illegal decision. It briefly
mentions education being able to be negatively correlated (super­criminals). The results show
that young individuals were more likely to commit crimes. Looking at the next article, it
examines whether or not a person’s education is related and has any effect on crime. It takes
three socio economic views that contain income wage, risk aversion, and time availability. After
viewing the results, education should be considered as a key policy in order to fight and reduce
crime rate ​(Machin, Marie & Vujić, 2010).
This next article is about education over life cycles. In this article, it is determined that
each prisoner in the United States cost about $29,000 annually. It also gives a couple of policies
that can be used in the ongoing debate of education affecting crime. Results from this article
indicate that each policy had opposite effects ​(Fella & Gallipoli, 2006)​. Finally, the last article
talks about education reducing participation in criminal activities there are a couple theoretical
reasons why crime and education are related. This article was able to determine that schooling

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significantly reduces criminal activity, and the United States can save over a billion dollars if
high school dropouts decreased by 1%.

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Chapter 3: Theoretical Framework
3 Theoretical Models
When thinking about the relationship between education and crime it is easy to come up
with multiple theoretical reasoning. The first few reasons come from a more socio­economic
background. One of the theories used in describing the relationship between crime and education
is patience/risk aversion. Education would have a strong effect on a risk averse person. This is
due to this person weighing out the future result of their choice whether to commit a crime or
not. In other words, these types of individuals think long and hard debating if the crime is worth
committing. These types of individuals weigh out all ideas and choose a path with little to no
risk. Usually most risk averse individuals tend to commit far less crimes in terms of the amount,
and seem to be highly educated. In this current situation, education may affect crime in a few
ways. First thing having a better education can do is increase risk aversion. In increasing risk
aversion, it allows for a person to gain knowledge and understand the risks from committing a
crime, which ultimately can reduce the chance of someone committing a crime. Another way
education can help reduce crimes is by increasing the level of a person’s patience. By increasing
an individual’s patience it becomes possible for this person to think if committing a crime is
worth jeopardizing their future.
In continuation with education, prison schooling also affects the willingness of a
convicted felon to engage in criminal activity once they are set free. In many prison systems,
inmates are given the opportunity to participate in educational programs. These programs help

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educate prisoners as well as help them obtain jobs after they are released. Inmates that participate
in these programs are more likely to get a job and earn higher wages than those who did not
attend these programs. Also the level of recidivism is greatly reduced as well by these same
programs. By implementing and allowing inmates into these education programs it lowers the
chance for a prisoner to return to a life of crime.​ ​The next theory that is examined in this article
is the income wage effect on the ramifications of education on crime. This theory deals directly
with the work place. It examines how education is used in determining wages a person makes
while working. The income wage effect says that the longer a person goes to school or the more
education a person obtains will subsequently cause their wages to also increase. It also increase
the opportunity cost of illegal behaviors as well.
Although at first it does not seem like it, but time availability greatly affects whether or not a
person will engage in criminal activity. When receiving an education people will have less time
to commit a crime because they limits to the hours they can operated because of school. This
keeps people from criminal life. For example, a teenager who is in school is less likely to commit

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a crime than another teenager that dropped out of school because the teenager that dropped out
has more time and more incentive to engage in criminal activity. Conclusively, it is natural for
people that have more time on their hands to be more involved in a criminal life. The graph
above is an example of the income effect and time availability. Located on the X­axis is the
measurement for time, more specifically leisure time. Leisure time is the freedom or free time
from work or duty. This looks at whether or not an individual has the time to commit a crime.
The Y­axis in this graph measures the income over time. Basically this looks at the amount of
money a person will receive if they commit a successful crime. Overall, this graph measures
whether a person has enough free time, and if the crime is lucrative enough to be committed.
Now continuing into actual theoretical models, there will be two different theories
discussed in this paper. The first deals with the Signaling theory which was originated by
Michael Spence. Signaling is a method in which one individual conveys some message to
another person, in which that person interprets it as either positive or negative. Another way to
look at signaling is a ​potential employee sends a signal about their ability level to their potential
employer (Spence, 1973). These signals can either be positive or negative depending on how the
interviewing party reacts to the information presented. For example, some signals can be a
person’s education credentials, their past experiences, or even their criminal records, etc.

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Imagine if an individual who dropped out of school was line segment C (h), and another
individual that graduated was line segment C (l). This graph suggest that the individual that
graduated would make more money than the individual that did not graduate from high school,
based off the amount of education received by both individuals. The Y­axis in this graph
examines how much money a person is able to make over time, while the X­axis shows the
amount of education a person may obtains over time. The returns of their education will be
much higher based on this signaling graph. This graph can be viewed as, for every additional
year of education increases the amount of money an individual can receive. ​Another way to look
at the signaling graph is by ​looking at the relationship between crime and education. Once again
the X­axis would be represented by education, which can be labeled as each additional year of
education after high school graduation. The Y­axis however, would be changed from the amount
of money received, to the probable percentage of crimes. In this model the probable percentage
of crime is measured differently than in normal Signaling graphs. It is actually being calculated
in reverse with the higher percentages of crime being located near the origin of the model. Under

26. 26
this framework the model shows high school dropouts have a higher probable percentage to
commit a crime due to their low level of education, thus resulting in the individual becoming
more likely to commit a crime. While individuals that graduated have a lower probable
percentage to commit crimes because their level of education is much higher which in turn
makes them less likely to commit any crime.
Finally the other model that will be used in this paper will be the human capital approach
theory. Human capital theory is the collection of knowledge, talents, skills, experience,
intelligence, training, judgment, and wisdom possessed by an individual in order to gain an
economic value of an employee (Investopedia). In other words, human capital theory is the
unique set of abilities an individual has that makes them a more productive worker. It is been
recognized that all labor is not equal labor, and that ​it could be invested in through education,
and training that will lead to an improvement in the quality and level of production​ (Theodore
Schultz, 1960’s).

27. 27
This next graph is a basic model of the human capital approach theory. Although it is very
similar to the basic signaling graph, there are also several important differences. To begin, the
human capital approach graph gives an alternative way of examining the signaling graph. By
referring to this graph as more realistic, means that the graph gives a more rational look into a
real life situation. This is because after so many years of education the marginal returns to
education begin to reduce. For example, the returns to education will be much higher for an
individual that completed college than it will be for a person who dropped out of school.
However, at the same time, the returns to education for an individual gaining an additional year
of education after already obtaining their doctorates degree will most likely have little effect.
Looking more specifically at the prison education, most inmates in prison systems did not finish
high school let alone graduate college, thus the reason why in prison educational programs

28. 28
would be most beneficial to prison felons. Education Located on the Y­axis is output or
productivity. Basically this represents the return on goods produced. On the X­axis it is capital or
other characteristics that makes an individual a more valuable worker. Let the dark green line
segment represent an individual that graduated from high school and let the light green segment
represent the same individual if they did not graduate high school. According to this model, the
individual that is the dark green line segment will be more successful than person that is the light
green segment due to the advancement in education by the person who graduated high school.
Although at some point the returns to the individual will be reduced and start to have a more
insignificant effect.
On the last graph technology was used in order to increase the economic value of an employee.
That same idea is used in the newer graph but in this case the prison educational programs would

29. 29
be considered as an investment into human capital. With that noted, visualize a high school
dropout that has been to prison is represented by the red line segment. Let the light green line
segment represent the same exact dropout that went to prison, however this individual
participated in one of the educational programs that were offered during their prison term. This
graph states that the individual who graduated college is a more valuable worker and a better
candidate for the jobs due to their educational level. Entering into the prison educational program
allows for the individual’s skill level to increase, thus making them a more productive worker. In
other words an individual that participates in education programs while in prison are more likely
to find a better/higher paying job.
3.2 Concluding Remarks
Throughout this section, several different theoretical models were displayed and also
explained with examples. The signaling theory and the human capital approach theory both
highlight the benefits of education in terms of its relationship to crime, even if the education is
for offenders still in prison.
Also, there were three theoretical ideas that can add to the positive impact of education
on crime. The idea of the income wage effects makes a great deal of sense ultimately because
increasing the wage of a person will likely cause the individual to stray away from criminal
activity. In essence, time availability basically falls under the same idea because it looks at the
opportunity cost. Time availability in this case, is set up between education and crime.

30. 30
Chapter 4: Empirical Analysis
4.1 Data Description
Originally, this analysis was supposed to examine the relationship between crime and
education, concentrating more directly on in prison education programs for convicted felons.
These in prison programs are significantly helpful for the prisoners in a couple different ways.
One way is it helps inmates that dropped out of college work at and ultimately obtain their GED.
Another way the program helps is by helping ex­prisoners find jobs with a higher pay rate than
ex­prisoners who did not attend the program. Unfortunately, the analysis had to be slightly
altered because of problems finding useable data.
With that stated, the analysis is still going to focus on in prison education, however the
centralized idea is now statewide recidivism rates. Throughout this paper data was collected from
various sources such as surveys and parole officers. A survey is ​a sample or partial collection of
facts, figures, or opinions taken and used to approximate a complete collection and analysis
might reveal. Most of the surveys used in collecting data come from the Census Bureau. Parole
officers are the people that directly supervise offenders. Formal offenders placed on parole must
check in with a parole officer every so often​. ​All data collected is on incarcerated officials, as
well as an investigation of a few policy changes that will conclusively reduce crime and link
prison education as positive.
The data in this analysis was collected statewide from the years of 1999­2002 as well as
the years of 2004­2007. In this model, recidivism is calculated throughout the 36 months (three
years) after an offender’s release. The year 1999 was the first year where the data was collected
to measure the recidivism rate. The next time data was collected was in 2004. The data collected

31. 31
shows recidivism rates throughout the years.
4.2 Data Summary
Throughout this analysis, only eleven variables were used. The first variable used
in the regression deals with each state. In the regression this variable is named state. This
variable just looks at each state in the United States. This is an important variable for this
regression because it sorts all the data by state, which eventually makes it easier to understand, as
well as give a better depiction of how each state’s prison data differs.
The next few variables used are the most important variables used throughout the entire
regression. These variables consist of the release of prisoners by state from 1999­2002 and
2004­2007. This is important to the analysis because it looks at how many inmates were
released by each state through each cycle of years. The next few variables that are used is the
recidivism rates by state from 1999­2002 and 2004­2007. These two variables are the most
significant in the analysis, and are the focal point for the paper. Recidivism rates are looked at
when talking about prisons, in order to see how effective the prison system actually is. Also,
these rates show whether or not the prison system “corrects” and individual rather than “punish”
them, since the goal of prisons is to stop criminals from reverting back to criminal ways. These
are the single handedly the most valuable variables used throughout the analysis.
Some other variables used in this analysis that played major roles are the average cost of
a single inmate and the average salary of correctional officers. These variables are significant to
this study because they allow a connection to be made on how much a given state spends on their
prison systems as a whole. The average cost of an inmate may affect whether or not a state’s

32. 32
recidivism rate increases or decreases. The intuition is that if the average cost of an inmate is too
high, the state’s courts system may be reluctant to send former prisoners back to prison. The
average cost of a correctional officer is a variable that can be closely related to recidivism rates.
If a correctional officer’s salary is based on the amount of convicts in prison, then it would be
beneficial to the officer to have more inmates in prison, thus having a positive relationship
between the average salary of correctional officer and recidivism rates.
In the tables below, the summary statistics are displayed, which further explain the
variables in the regression.
Variable Obs Mean Std. Dev. Min Max
rea~99­02 33 14262.61 22855.26 906 126456
rec~99­02 33 .4039091 .1004339 .241 .658
lnrelease 33 8.938314 1.101559 6.809039 11.74765
These last variables only appear in the second multiple regression analysis. The first few
variables describe the type of inmate through gender. The next variables deal with where
prisoners housed. With that stated, the variable male describes the amount of male prisoners by
state, in the year 2007. While the female variable describes the amount of female prisoners in
2007 per state. These two variables were used in order to see whether the gender of a prisoner
impacts the recidivism rate for each state. Moving along to the next set of variables, prisoners
location by state can be an important factor when trying to determine if it affects the recidivism
rates. These variables focus on the location of prisons, however they differ in whether they are

33. 33
private or local. A state that has a higher population may place more of its offenders into a
private facility because there are more private facilities in highly populated areas. The same idea
can be used when examining local jails. A state that has a low population may place more
offenders into local jails because the amount of correctional facilities are limited.
Variable Obs Mean Std. Dev. Min Max
rec~20042007 40 .39845 .0884374 .228 .612
lnreleases 40 8.902897 1.186799 6.558198 11.68004
lncostinm 40 10.28316 .3971481 9.588983 11.00337
lnmale 40 9.660881 1.402451 4.913316 12.68448
lnfemale 40 7.185519 .9412415 5.141664 9.514068
lnavgsal 40 10.4855 .2272754 10.12583 11.03859
prisonershpri 40 8.3825 11.6406 0 42.1
prisonershlj 40 6.7125 9.807433 0 45.5
4.3 Model
As stated earlier the original purpose of the analysis was to examine in­prison education
systems to see if they affect recidivism rates throughout the United States. However because of
complications in data retrieval, this paper now investigates different factors that may cause
statewide recidivism rates to vary.
Recidivism99­02= β​0​ +β lnreleases+ ԑ

34. 34
In this paper there will be different models ran in order to see how recidivism rates
change from state to state. For the first regression, a simple regression was used with recidivism
from 1999­2002 as the dependent variable and the release of prisoners as the independent
variable. A simple regression is the most basic regression that describes the relationship between
the independent variable and the dependent variable using a straight linear line. It is estimated
that as the number releases increase, so will the rate of recidivism. This is because as more
individuals are released, more individuals will be placed back into prison. Whether it is from
committing a new crime or violating parole.
Recidivism04­07= β​0​ +β​1​lnreleases+ β​2​lnreleases + β​3​lncostinm + β​4​lnmale+
β​5​lnfemale + β​6​lnavgsal + β​7​prisonershousedpr +
β​8​prisonershousedinlj
The second model that will be used in this analysis is a multiple regression. A multiple
regression is an extension of a single regression. Unlike a single regression, a multiple regression
looks at two or more independent variables in order to predict the relationship between the
variables. In this second model my dependent variable is recidivism rate from 2004­2007. My
independent variables are the release of prisoners from 2004 to 2007, the average cost of a single
inmate, male prisoners by state in 2007, female prisoners by state in 2007, the average salary of a
correctional officer, prisoners housed in a private facilities in 2007, and prisoners housed in local
jails in 2007. It is estimated that the variables release of prisoners from 2004 to 2007, the average
cost of a single inmate, and male prisoners by state in 2007 to be positive. While the other
variables are expected have negative relationships with the recidivism rates.

35. 35
4.4 Results
Simple Regression
rec~19992002 Coef. Std. Err. T Significant
lnrelease .0204329 .0170838 1.20 N
Multiple Regression
reci~20042007 Coef. Std. Err. T Significant
lnreleases .0845807 .022442 3.77 Y
lncostinm .0206295 .0443441 0.47 N
lnmale .0199397 .0063034 3.16 Y
lnfemale ­.1174484 .0250288 ­4.69 Y
lnavgsal .0272918 .0918271 0.30 N
prisonersho~i .0003425 .0009655 0.35 N
prisonersho~s .0014847 .0013035 1.14 N
After running and completing each regression, it was discovered that there are only three
variables that are statistically significant at the 90% or 95% confidence interval. The variable
number of releases is one of the variables that ended up being significant with a t­stat of 3.77 at
the 95% confidence level. The next variable that was significant was the amount of male
prisoners from 2007. The t­stat for this variable 3.16 which makes it significant at 95%
confidence level. Finally, the last variable to be significant at 95% confidence interval was the
number of females in 2007. Although there was no significance in this study, there are other

36. 36
important aspects that can be used from the study.
To begin, most of the independent variables were logged because of the large range of
numbers. Using the log of all the variables allows the data to be transformed so it is more
readable and useable. It essentially allows for the model and data to be simplified. Also, logging
the data helped eliminate any outliers from the regression. The simple regression is a level­log
model with the log on the number of releases. A level­log model is a model that has one logged
variable and one normal variable. Essentially this model is saying for every one percent increase
in number of releases is associated with a 20.4% increase in recidivism rates from 1999 to 2002.
In continuation, the multiple regression also has several independent variables logged.
This creates a combination between level­level and level­log models. A level­ level model is
when both variables are normal. In this model, another release variable is used however it is for
the years 2004 to 2007. When referring to the number of releases, it says for every additional
person released from prison is associated with an 84.5% increase. Another variable that stuck out
was the number of women in prison. This was also logged which makes this a level­log model.
This variable state for a one unit increase in females is associated with an 11.7% decrease in
recidivism rates.
lncostin
m
lnavgsa
l
Lncosti
n
1.000
lnavgsal 0.7736 1.000

37. 37
Throughout this analysis there were two variables that were closely related. These
variables were the average cost of a single inmate and the average salary of a correctional
officer. Because this comparison was made, a pair wise correlation was ran to see the connection
between the variable. According to the regression, the two variables are highly correlated at
0.7736. Along with the pair wise correlation, a scatter plot was also created. This was done to
see the linear correlation between the two variables.
Looking deeper into the recidivism rates, it is possible for this rate to be broken down.
All 50 states were unable to be used in this analysis because there was not data calculated for all
of them. The recidivism rates are measured between two different year ranges. These year ranges
are from 1999 to 2002 and the years 2004 to 2007. Here each state’s released criminals are
examined and separated into three different categories. The first category is the percentage of
offenders that went back to prison for committing a new/ different crime than first time. For
example, Alabama in 2004 had 17% of its offenders go back to prison for committing a different
crime than they did the first time. The second category measures the amount of offenders who
were placed back into prison due to violations of parole. The third category looks at the amount

38. 38
of offenders who did not return back into prison, within the recidivistic time frame. In going
through the data, in 1999 North Carolina had the most offenders placed back into jail for
committing a new crime, but in 2004 Arkansas was the state with the most offenders place back
into jail. In 1999 in prison education was most successful in Oklahoma because 76 percent of its
offenders did not return to prison. However in 2004, Oregon with 77 percent, had the least
amount of offenders return to prison.
Overall after running each regression, the results did not come out as expected. There
were no statistically significant variables, however there was other information that was still
useful.
Chapter 5: Conclusions
The original plan for this analysis was to research in­education programs to see the
relationship between education and crime. More specifically looking at the recidivism rates of
the inmates for the years 1999 to 2002 and 2004 to 2007. However, the main idea of this thesis
was altered in order to better accommodate the data collected. In continuation, the idea changed
to looking at recidivism rates throughout the same years as the original plan, except now it would
be examined by state. Before completing any results, it could be assumed that a convicted felon

39. 39
who participated in a correctional education program would have an easier time finding a job
with higher wages than another inmate who did not participate in an education program. It also
could be assumed that a state that spend more money on their prison systems can offer more
programs than a state that spends less on its prison system. After reviewing prior literatures,
using economic theories, and running an empirical analysis on the collected data, it is safe to say
there is a positive relationship between a state’s recidivism rates and the average cost of a single
inmate, although it was not statistically significant. Between the two separate ranges of years the
recidivism rates have increased, but this is due to the increase in the prison population.
Besides the recidivism rates, the average cost of an inmate is the most important variable
in this regression. As stated earlier, it is not statically significant however, when viewed with the
variable average salary of a correctional officer, it is easier to isolate the cost per inmate variable
to be program related. In other words when having both variables together it is easier to see
which state spends money for educational programs for its prisoners.
Looking at it through a real world view, this paper was supposed to show and explain the
relationship between education and crime in correctional facilities. The results from this analysis
appears to be highly accurate. After viewing this data it would be possible to see how a state
spends money on its prison system, by reviewing the average salary of a correctional officer and
the cost of a single inmate by state. With results like these, it makes it possible for states to
monitor the amount of money invested, which can ultimately makes it easier to set up and
regulate educational programs in prison.
Like in all economic papers, there were a few empirical weaknesses that were
encountered during this paper. For starters, the variables male and female prisoners in 2007

40. 40
could have been combined because they are slightly redundant in this regression when having the
variable number of releases. If this was done instead, ​it would isolate gender distribution and not
serve as a proxy for size of the prison system in a state, since the variable number of release
captures the size already. Another weakness of this paper is that there were a few missing
variables. One of these variables is the crime rate by state for the years 2002 and 2007.
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About the author

42. 42
My name is Raysean Glenn and I am from New Castle, Pennsylvania. I am a
graduating senior here at Allegheny College majoring in economics and minoring in
Spanish. In my time at Allegheny College, I participated on the Allegheny Gators
football team where I lettered for four years. My main focus after graduating is to find a
job in a bank, so I can wear and shirt and tie to work every day.