Education and Youth Crime:

A review of the Empirical Literature

Iryna Rud, Chris van Klaveren e.a.

TIER WORKING PAPER SERIES

TIER WP 13/06

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Education and Youth Crime: a Review of the Empirical Literature

Iryna Rud, Chris Van Klaveren, Wim Groot, Henriëtte Maassen van den Brink1

Abstract

This paper provides a systematic literature review on the relationship between education and

criminal behavior of young people, using the Technology of Skill Formation (Cunha &

Heckman, 2007) as a theoretical framework. The nature of youth crime is different than the

nature of adult crime. We analyze studies that evaluate childhood and adolescence

interventions and studies that examine the effects between education and criminal behavior of

young people. The former suggests a link between education and youth crime. The latter

shows that education reduces the probability of criminal behavior in adolescence and young

adulthood, whereas early criminal involvement is likely to have a negative impact on

educational attainment. The underlying mechanisms of these effects are generally not

identified and can combine different components, such as incapacitation effects, skill

acquisition and peer effects.

JEL-classification: I21, I28, I29, K14, K49.

Keywords: education; criminal behavior; interventions; youth; causal evidence.

1 All authors are affiliated with The Top Institute for Evidence Based Education Research, TIER, Maastricht

University, P.O. BOX 616, 6200 MD Maastricht, The Netherlands. E-mail address of the corresponding author:

i.rud@maastrichtuniversity.nl

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1. Introduction

Early school leaving and criminal behavior of young people are two important concerns in

every community as they can result in individual and public losses. School dropout is

associated with lower economic growth, youth unemployment, decreases in gross income and

with higher crime rates as well (see Psacharapoulos, 2007). Crime can also increase the

unemployment rate of the community (Calvó-Armengol & Zenou, 2003) and have a negative

impact on economic growth in the region (Detotto & Otranto, 2010). Crime generates

substantial social costs, through criminal justice system spending, security expenditures, costs

to repair damages, victimization costs and health services costs. Criminals themselves face

costs associated with criminal charges, can suffer from social stigma or social exclusion

(Hannon, 2003), and experience a decline in earnings and employment following an arrest or

imprisonment (see Lochner, 2004).

Criminal behavior in adolescence can have strong links to future negative outcomes,

among them adult crime, low academic performance and early school leaving. At the same

time, school dropout can encourage juveniles to become involved in criminal behavior. From

the one hand, lower educational attainment and criminal involvement can develop a dynamic

interrelationship. From the other hand, many mutual confounding factors can determine both

education and criminal behavior and it can be difficult to isolate a single chain of causality.

Insight in how education and youth crime is casually related may indicate possible measures

to reduce crime and educational inequality in society.

The aim of this study is to provide a comprehensive and unambiguous literature

review on the relationship between education and criminal behavior of young people. The

terms “youth”, “young people”, “adolescents” and “juveniles” are used here interchangeably.

The concept of education is used in its broad sense and, depending on the context, can refer to

educational attainment, school attendance and academic performance. Finally, youth crime

refers to any interactions with the criminal justice system as a result of criminal behavior, and

can also mean antisocial or risky behavior of juveniles such as substance abuse and

precocious sexual behavior.

We discuss how education and youth crime can relate to each other from a theoretical

and empirical perspective. The Technology of Skill Formation serves as a theoretical

framework to analyze this relationship. This theory captures all main approaches that

underline criminal behavior. Empirical studies on early childhood interventions, interventions

in early school age, and adolescence interventions provide evidence on programs that aim to

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improve education and prevent or reduce criminal behavior of young people. The empirical

literature that examines the effects between education and youth crime provides further

insights into this relationship.

The contribution of this study is twofold. First, it documents and analyses the existing

evidence on the relationship between education and criminal behavior of young people. Most

previous studies in this field have focused on examining the effects of education on criminal

behavior of adults. We note that the nature of youth crime and adult crime can be different.

Preventing crime in adolescence may have an impact that lasts throughout adulthood,

including behavioral and educational outcomes.

Causal evidence on the relationship between education and youth crime is especially

important from a policy perspective. To develop effective policies aimed at reducing school

dropout and youth crime rates, it is crucial to rely on causal evidence. Therefore, as the

second contribution, we attempt to distinguish between causal and correlational studies that

analyze the relationship between education and youth crime.

This paper is organized as follows. Section 2 discusses the nature of youth crime.

Section 3 provides a theoretical basis underlying criminal behavior in a relationship to

education. Section 4 outlines the search strategy and selection criteria of empirical studies

used in this review. Section 5 describes childhood interventions, interventions in early school

age and adolescence interventions and their effects on educational and criminal behavior

outcomes. Section 6 discusses empirical studies that examine the effects between education

and youth crime. Finally, Section 7 sums up the findings of this study and provides

suggestions for future research.

2. The nature of youth crime

The age-crime curve shows that the peak age of criminal behavior is in adolescence, between

age of 15 and 19 (Farrington, 1986; Piquero et al. 2007; Bosick, 2009). Young people

involved in criminal behavior in adolescence are usually dealt with in juvenile justice system

(see Goldson and Muncie, 2006; Loeber et al., 2013).

The vast majority of existing studies on the relationship between education and crime

do not consider differences between age groups, and have a mixed-age research population.

However, and as is shown below, youth crime can differ from adult crime in several respects,

and therefore its relationship to education might be also different.

First of all, young people appear to be involved in a greater variety of criminal

behavior, but also less serious and less sophisticated crime, compared to adults (Junger-Tas et

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al., 2010). For example, the most frequent arrests among U.S. youth are for minor crimes

against property, vandalism, drugs dealing, disorderly conduct, and obstruction of justice

(Puzzanchera et al., 2010). In European countries, group fighting, carrying weapons, drugs

dealing, shoplifting, vandalism and computer hacking predominate (Junger-Tas et al., 2009).

Secondly, young people and adults can differ in their motivation to exhibit offending

behavior. In accordance with the economic theory, adults have an economic interest to be

engaged in crime (see Becker, 1968; Lochner & Moretti, 2004; Lochner, 2011). Although

adolescents tend to report that the main motivation of their criminal involvement is gaining

economic and financial benefits, there are many other reasons of their criminal behavior, such

as, enjoyment, excitement, entertainment and pleasure (Goldson & Muncie, 2006; Farrington,

2001). Luallen (2006) considers that mischief crimes committed by juveniles “often result

from boredom rather than calculated criminal thought” (p. 88). Similarly, Scitovsky (1999)

believes that violence in school largely occurs due to feelings of boredom and a lack of

activities at school. Peer group pressure, mood swings, and lack of reflection on emotional

situations are significant factors that can stimulate offending behavior of juveniles (McCord et

al., 2001). Finally, a criminal act is frequently viewed by young people as a risk-taking

adventure that gives offenders status and particular respect within their group of peers

(Cohen, 1955).

The third distinct aspect of youth crime is that adolescents are relatively more likely to

commit crime with others or in groups compared to adults (see Zimring, 1981; Greenwood,

1995; Reiss, 1988). In contrast to adult criminal associations, groups of juvenile offenders are

typically formed by territorial affiliation, and they are usually random and less stable over

time (Reiss, 1988). Therefore, social interactions at school and on the street seem to have a

great impact on behavior of young people. Juveniles are more likely to co-offend with

individuals of the same gender and the same age group compared to adults (Reiss, 1988).

Similar to adult crime, young males participate in criminal activities more often than young

females (Levitt & Lochner, 2001).

Finally, the timing of youth crime varies with the type of offence (see Gottfredson &

Soulé, 2005; Taylor-Butts, 2010). Police-reported crimes against persons typically occur

during after-school hours (Snyder & Sickmund, 1999), and more specifically, between three

and six o’clock in the afternoon (Newman et al., 2000; Taylor-Butts, 2010). However,

Gottfredson and Soule (2005) argue that violent offenses during school hours are the most

frequent. By contrast, the peak of violent crime among adults is between midnight and three

o’clock in the morning (Taylor-Butts, 2010). Furthermore, youth violence tends to decrease

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on weekends when young people are interacting less with their peers (see Jacob & Lefgren,

2003) while violent adult offences are increasing during weekends (see Falk, 1952; Briscoe &

Donnell, 2003).

The above-mentioned differences between youth crime and adult crime suggest that

the relationship of youth crime to education may also be different. Moreover, criminal

involvement of young people can influence their educational attainment, while the impact of

adult crime on educational attainment is less likely.

3. Theories underlying criminal behavior and its link to education

The theoretical literature that focuses on the underlying mechanisms of criminal behavior

distinguishes among biological, psychological, sociological (see Reid, 2011) and economic

mechanisms (Becker, 1968; Freedman, 1999). The Technology of Skill Formation of Cunha

& Heckman (2007) captures these different theoretical mechanisms. An adapted version of

their model is represented by the following equation:

));,,,,(),...,,,,(,;( 111110 ttTTttt XIegsIIegsIm .,...,1 Tt

where t represents the acquired skills on a time period t . The equation shows that the

acquired skills in period t are influenced by 1t , the acquired skills in period 1t . This

framework implies the dynamic nature of cognitive and socio-emotional development:

different skills and the same skill in different periods are strongly related. Hence, skills related

to education and skills related to criminal involvement can be in close connection, and can

influence each other over time. Skill attainment at one stage of the life cycle raises skill

attainment at later stages of the life cycle, which is referred to as self-productivity.

Furthermore, investments in skills at different stages of the life cycle can be complementary,

as later investments built upon the effectiveness of earlier investments (Cunha & Heckman,

2007).

A specific situation occurs in the first period, where the model indicates that the

acquired skills are influenced by 0 , which represents innate child characteristics. Biological

theories consider criminal behavior as the result of biological deviations. Examples of such

biological deviations are genetic predisposition to crime, brain abnormalities (e.g. brain

damage and poor brain functioning), and neurotransmitter dysfunction (see Raine, 2002).

Because biological deviations may also occur during the lifetime, we included tX , a factor

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that indicates that certain circumstances in the life time may influence the skills acquired in

that period.

Psychological and psychoanalytical theories relate criminal conduct to the emotional

and intellectual development of individuals, information processing and personality traits.

Especially, the role of early childhood experiences is emphasized (see, among others, Bartol,

2002). Innate psychological characteristics are therefore also represented by 0 , while

psychological development factors that occur over time are captured by tX .

The tI functions indicate that parents, peers and schools ( s ), as well as governments

( g ) invest in the child’s development. The size of investments varies over the T time periods

and therefore the T investment functions appear in the skills production function.

Investments depend on previous performances, such as skills and social behavior, and

captured by 1t .

As is argued by Cunha and Heckman (2007), current investments are more promising

when previous investments, 1t , were made. Also, the investments are more likely to be

made if there were successful earlier investments. We conveniently assume that investments

(acquired skills) in period t are only influenced by the investments (acquired skills) one

period earlier, while in reality it is likely that all past investments and acquired skills are of

importance.

Sociological theories underline the significant role of sociological background factors

( s ). Criminal behavior is explained from the social-organization and social-process

perspectives (see, Reid, 2011). The former assumes that criminal behavior and acquired skills

depend on variables related to the social structure (e.g. school social bonds, school climate).

The latter relates criminal behavior to social learning, labeling, and social control (see Reid,

2011).

Economic theories consider criminal acts to be the outcome of a rational decision

process in which the costs and benefits are rationally weighted (Becker, 1968; Freedman,

1999). Therefore, effort, e , is one of the factors that determines the amount of investment in a

particular period. Economic models predict that individuals compare the costs (effort) of skills

acquisition (and the benefits generated by these skills) to the costs (and benefits) of criminal

behavior.

To conclude, the Technology of Skill Formation framework enables us to consider the

relationship between education and criminal behavior of young people in a dynamic way.

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Presumably, education and criminal behavior can be related at a single point in time and their

development can be strongly intertwined over time.

4. Literature search strategy and selection criteria

In order to find evidence on the relationship between education and youth crime, we

conducted a systematic literature review. In our search, we used different combinations of

keywords which are grouped as follows: a) children, juvenile(s), adolescent(s), youth, young

people; b) crime, delinquency, offending, arrest, incarceration, antisocial, risk-taking,

violence, substance abuse, behavior, involvement, engagement, activities; c) education(al),

school(ing), academic, dropout, early-school leaving, truancy, (non-)cognitive abilities, IQ,

performance, attendance, attainment; d) early childhood, adolescence, after-school, out-of-

school-time, stimulation, intervention, experiment, program.

The empirical literature was searched using the following electronic databases and

search engines: Elsevier, Google Scholar, Google Search, ERIC, EconLit, PsychLit, Science

Direct, SAGE’s, JSTOR, Social Science Research Network, Economic Papers, Wiley,

Springer, and Taylor & Francis. In this review, we considered peer-reviewed studies,

discussion papers, working papers, reviews and research reports that were accessible online

before October 2013. Non-published empirical studies were included because there is little

rigorous research that focuses on the effects between education and youth crime.

The content of the literature we analyzed qualitatively. There were two specific criteria

for inclusion of intervention studies. First, only interventions that have follow-ups in

adolescence (or young adulthood) were selected, and that measure effects on both educational

and criminal behavior outcomes. Second, only studies that have a randomized controlled trial

or that use a comparable control group were included. With regards to literature that examines

the effects of education on youth crime, only studies that aim to find causal effects were

included. The studies on the effects of early criminal behavior on educational outcomes are

generally rare, and therefore, we included all rigorous studies that analyze such effects.

As a result of this search and selection, in total 38 studies that evaluate 9 childhood

interventions, 3 interventions in school age and 7 adolescence interventions were identified as

relevant for this systematic literature review. With regards to studies that examine the effects

between education and youth crime, we selected and discussed 10 studies that analyze the

effects from education to youth crime and 3 studies that examine the impact of early criminal

involvement on educational outcomes.

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5. The effects of interventions on educational and criminal behavior outcomes

During the last decades, a number of social interventions have been implemented that were

aimed to develop skills and prevent problem behavior of at-risk youth, predominantly from

families with a low SES and from ethnic minority groups (see Blau and Currie, 2006; Olds et

al., 2007; Decovic et al., 2012; Durlak et al., 2011). We review a number of intervention

studies in order to establish a link between education and criminal behavior of young people.

The selected interventions and studies that evaluate them are presented below, and also in the

tables attached to this section. The tables provide information on early childhood

interventions, interventions in early school age and adolescence interventions. In left to right

order, the columns illustrate when and where an intervention occurred, target group of the

intervention, age of participants, content of the intervention, duration of the intervention,

evaluation design and the age of participants at follow-up. The last columns show the effects

of the interventions on educational outcomes (i.e. academic performance, educational

attainment) and criminal behavior outcomes.

5.1 Early childhood interventions

According to the Technology of Skill Formation theory, investments in early childhood

education provide an essential basis for further investments. A significant part of the

interventions has been aimed at children aged between 0 and 6 years old. Typically, early

childhood interventions do not only target the children, but do also target the whole system

that surrounds the child’s development. Table 1 sums up evidence on early childhood

interventions.

The Nurse-Family Partnership (NFP) and the Infant Health Development Program (IHDP)

are programs provided to low socioeconomic status families with new-born children. Both

programs include home visits during mother’s pregnancy and services for infants and toddlers

(birth to three). The IHDP also offers center-based care for children and parent group

meetings. The main aim of these programs is to reduce negative intergenerational effects (e.g.

low education, risky behavior, unemployment) from parents to children. These programs were

rigorously evaluated using randomized controlled trials and had several follows up. The

evidence shows that there is no effect of the NFP on academic performance or educational

attainment, but the program does reduce offending behavior of girls (Eckenrode et al., 2010).

The IHDP had no significant effect on criminal behavior of juveniles aged 18, the effects on

academic performance are mixed and there is no significant effect on educational attainment

(McCormick et al., 2006).

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The Jamaican Study is another infant intervention with similar content. It was targeted at

129 children aged between 9 and 24 months who came from poor disadvantaged

neighborhoods in Jamaica (Walker et al., 1990). The program provided nutrition

supplementation and psychological stimulation by weekly home visits by community health

assistants. The psychological stimulation was based on mother-child interaction. It included

play sessions with a child and the mother and sessions only for the mother to teach her how to

promote the child development through play. The results show that the intervention,

especially psychological stimulation, has a positive effect on educational outcomes at age 11-

12 and sustain through age 17-18 and 22 (Walker et al., 2005, 2011). Walker et al. (2011) find

that participants in the Jamaican program who received stimulation were less involved in

fights and in serious violent behavior at the age of 22.

The Carolina Abecederian Project (ABC) and the Carolina Approach to Responsive

Education (CARE) are early childhood interventions targeted at children aged between 6

weeks and 8 years old. The sample of children in the ABC project included developmentally

at-risk children (N=110) while children in the CARE (N = 66) were from high-risk and low-

risk families. The ABC project offered full-time center-based childcare. The CARE project

had two types of interventions: (1) center-based childcare combined with home visits, and (2)

home visits only. The results show that the ABC program has positive effects on educational

outcomes but no effect on criminal behavior on young people. CARE positively affected

academic performance and reduced marijuana use for participants of the program (Clarke and

Campbell, 1998; Campbell et al., 2002).

There are interventions that are specifically aimed at preparing children for school. The

most well-known of such interventions is the High/Scope Perry Preschool Program. 128

children from deprived African American families with low IQ scores (below 85) were

randomly assigned to a treatment and a control group. Participants of the treatment group

regularly (biweekly during four years) received home visits by the teacher until they were of

school age. The study followed 123 individuals over a longer period and showed that

preschool education had positive effects on educational attainment of females, but not for

males, and that it reduced criminal behavior of all participants (Schweinhart & Weikart, 1997;

Schweinhart et al., 2005; Heckman et al., 2010). However, the program was targeted at a very

specific group of children and these results are therefore may be not representative for

different populations.

The Chicago Child-Parent Center (CPC), the large-scale programs Head Start and the

small-scale program Syracuse Family Development Research Program (FDRP) were not

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based on randomized control trials. However, these interventions were evaluated rigorously.

For instance, in the evaluation of CPC another disadvantaged area of Chicago was used as the

control group, by applying matching techniques (Reynolds & Temple, 1995; Reynolds et al.,

2001). All three programs provide home visits for children from low socioeconomic families

with the aim to better prepare them for school. In addition, day care was provided in the

FDRP program. The studies that evaluated Head Start show positive but weak effects on

education of participants while effects on criminal behavior are mixed (Garces et al., 2002;

Deming, 2009; Currie, 2001). The CPC positively affected academic performance,

educational attainment and reduced arrest rate (Campbell et al., 2002; Barnett, 2008;

Reynolds et al., 2001). The effect of the FDRP also improved academic performance and

reduced criminal involvement (Lally et al., 1988).

We conclude that the common feature of the early childhood interventions is that they

are aimed at cognitive and socio-emotional development of at-risk children from the earliest

stage of life. Children are provided with similar services that involve social, mental and

psychological stimulations. It is, however, difficult to distinguish which components of these

interventions are the most effective. Even though not all early childhood interventions

reduced criminal behavior and improved educational characteristics, evidence generally

shows that investing in children at an early age, especially in at-risk children, can improve

their future educational outcomes and reduce criminal behavior.

[Table 1 is around here]

5.2 Interventions in early school age

Interventions in early school age are often aimed at improving academic performance of

students and at the regulation of relationships between students and their peers, teachers, and

parents. The selected interventions and studies that evaluate these interventions are presented

in Table 2.

The Seattle Social Development Project (SSDP) is a long term project aimed at children’s

development in public elementary schools. This program included in-service teacher

trainings, parenting classes for parents and social competence training for children. This

project targeted 808 pupils who were enrolled in fifth grade. The follows-up suggests that the

SSDP improved education while the effect on criminal behavior is mixed (Hawkings et al.,

2001, 2005).

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The Montreal Longitudinal Experimental Study (MLES) was conducted amongst at-risk

boys aged between 7 and 9 years from low socio-economic status families. The interventions

provided social skills trainings to children, their parents and teachers by professional social

workers. For example, parents received special training programs on how to better supervise

children’s behavior, train their non-cognitive skills, use non-abusive discipline strategies and

manage family crises. At the end of primary school, boys from the treatment group had a

significantly lower fighting score, were less likely to have serious behavioral and performance

problems at school compared to the boys in the non-treated groups. They were also less likely

to commit thefts and delinquent acts involving trespassing (Tremblay et al., 1992, 1996).

Another study shows that children who participated in the program were more likely to

graduate from high school, while there was no effect on the probability of having a criminal

record (Boisjoli et al., 2007).

The Los Angeles Better Educated Students for Tomorrow program (LA’s BEST) is an

after-school enrichment program that aims to provide a safe and supervised environment for

students at-risk in disadvantaged neighborhoods. It is designed for children in kindergarten

through fifth/sixth grade. LA’s BEST focuses on cognitive, non-cognitive and physical

development of children and include a number of learning and social (e.g. enrichment and

recreation) activities such as a homework assistance, tutoring services, a library services,

sports, arts and crafts and group activities. The program is not rigorously evaluated by means

of a randomized control trial, but instead the evaluation studies apply a propensity score

matching method. The evaluation results suggest that the program has an effect on

educational attainment, but not on academic performance (Huang et al., 2008, 2009). The

program also had a favorable impact on criminal behavior, however only for those who

attended the program most frequently (Goldschmidt & Huang, 2007). However, these studies

do not address the issue of selective program attendance.

We conclude that the reviewed studies suggest that well-established interventions in early

school age can contribute considerably to improving educational outcomes through

adolescence while the effects on criminal behavior are not clear. Perhaps, more such

interventions need to be evaluated in order to make a stronger conclusion with regards to its

effects on criminal behavior as well as the mechanism of these effects.

[Table 2 is around here]

5.4 Adolescence interventions

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Adolescence interventions are often aimed at preventing criminal involvement or low

educational attainment of at-risk juveniles. The selected interventions and evaluation studies

are documented below and in Table 3.

The Big Brothers Big Sisters program (BBBS) is a mentoring program for juveniles aged

between 10 and 16 who live in disadvantaged families (e.g. single-parent). The program

provided regular meetings of volunteer mentors with participants of the program. The

empirical results suggest that the program has a strong effect on academic performance

(however, only for girls) and reduces violence in school and substance abuse (Tierney et al.,

1995; Grossman & Tierney, 1998).

The Quantum Opportunity Project (QOP) provided mentoring, educational services and

financial rewards to juveniles at-risk aged 14 or 15. The program’s activities were performed

out-of-school time. Students from the treated group were promised that they would receive a

financial reward if they obtain a high school diploma (or GED) and if they continue their

education at the postsecondary level. The results of the program show that adolescents in the

treatment group graduated high school earlier and were more likely to continue postsecondary

education compared to adolescents in the control group. The intervention had a reducing

effect on criminal activity for youth in the top-half of the risk distribution and this effect

persisted up to 5 years after the program. The program, however, was not effective for youth

in the bottom-half of the risk distribution (Rodríguez-Planas, 2012).

The National Guard ChalleNGe and Job Corps are large-scale programs that provided

residence-based education and job training for young people at-risk (often school drop-outs).

From the beginning of the 1990s these programs involved thousands of participants. The

empirical results show that participants were more likely to obtain a GED certification, but

not a high school diploma (Bloom et al., 2009; Millenky et al., 2011, 2013; Schochet et al.,

2008). The favorable effect on arrest and conviction of these programs was only observed

during the intervention when young people were enrolled in residence-based education and

training and show no lasting effect on crime in the long-run (Schochet et al., 2008; Millenky

et al., 2011).

The Education Maintenance Allowance (EMA) program was designed to increase

participation, retention and achievement in post-compulsory education among young people

aged 16-18 in the U.K. by means of a weekly monetary allowance (provided to young people

or their parents). Areas with low participation in post-compulsory education and areas with

higher levels of economic deprivation were selected for this intervention. In evaluation

studies, individuals from areas that participated in the EMA program were matched to

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individuals from control areas with similar characteristics. The results show that the EMA has

a positive effect on participation in education, in particular in urban areas (Ashworth et al.,

2001, 2002; Heaver et al., 2002). The program also has a negative effect on burglary and theft

conviction rates among young people aged 16-18, while there is no effect on convictions rates

for violent crime (Feinstein & Sabates, 2005). The authors explain this effect by a direct

income effect on crime reduction due to the income support provided to youth and by

incapacitation effects of education.

“Becoming a Man” (BAM) is an intervention provided for at-risk youth from high

schools in the Chicago Public School system, which is located in low-income, racially

segregated and with high crime rates neighborhoods. The intervention included in-school

programming, after-school programming, or both, exposing young people to pro-social adults,

keeping them busy during the high-risk after-school hours, and implementing aspects of

cognitive behavioral therapy. The results of an evaluation study suggest that program

participation reduced violent-crime arrests during the program year and generated substantial

gains in schooling outcomes, leading to higher graduation rates (Heller et al., 2013).

The Het Alternatief (Halt) program is a Dutch afterschool program that aims to prevent

delinquent juveniles from re-offending and to improve their social behavior. The participants

of the treatment group had to perform different working and learning activities in the after-

school time, such as community work, apologize for their behavior or write an essay. The

evaluation study shows mixed results with regards to a reduction of criminal behavior

(Ferwerda et al., 2006). Nevertheless, a study that evaluates the effect of Halt on educational

outcomes suggests that Halt has a significant positive effect on educational attainment and it

is likely to reduce the probability of early school leaving (Rud et al., 2013).

Evidence on extended school days suggests a statistically significant reduction in

offending behavior of juveniles during high-risk (after-school) hours (e.g. Aizer 2004; Riggs

& Greenberg, 2004). Aizer (2004), for instance, finds that young people with adult

supervision are less likely to be truant and less often commit minor offences. This study

shows that adult supervision after school makes young people less likely to use alcohol and

drugs, participate in property and violent offences. After-school programs are likely to help

young people to solve many behavioral problems and have overall positive effects on social

skills and emotional development of young people (see Durlak at el., 2011). Finally, it is

worth mentioning the role of recreational activities as a part of educational programs that

reduce violence and aggression among high-risk youngsters. In particular, the longitudinal

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study by Caruso (2011) shows a robust negative association between participation in sport-

related programs and youth crime.

We conclude that adolescence interventions aimed at at-risk juveniles can improve their

educational outcomes. The effects on criminal behavior outcomes are rather mixed, although

incapacitation effects of the education and training programs clearly reduce participation in

criminal activities. However, the exact mechanisms of how adolescence interventions can

affect educational and behavioral outcomes are not identified, and we can speculate that the

social control, incapacitation and educational components might be the main paths for these

effects.

[Table 3 is around here]

5. Empirical studies on the relationship between education and youth crime

In this section, we further explore how education and youth crime are related. We start with

documenting the empirical results of studies that examine the effect of education on

delinquent involvement and then discuss the empirical results of studies that evaluate the

impact of early criminal behavior on educational outcomes. Table 4 and Table 5 present these

two types of studies, respectively. These tables provide the following information: name of

the study, country of the study, research population, sample size, the evaluation method of the

study, what is the input variable and the effects on criminal behavior outcomes (Table 4) or

educational outcomes (Table 5).

6.1 The effects of education on youth crime

Many studies suggested that truancy and school dropout are negatively related to the

probability of criminal behavior (e.g. Elliott, 1966; Thornberry et al., 1985; Jarjoura, 1993,

1996), however, this association can become insignificant after controlling for certain

background characteristics (e.g. Bachman et al., 1978; Krohn et al., 1995). Therefore, studies

that establish a causal relationship between education and youth crime are of particular

importance.

The empirical evidence showing that educational attainment negatively affects adult

crime is growing (e.g. Lochner & Moretti, 2004; Machin et al., 2011; Groot & Maassen van

den Brink, 2010; Meghir et al. 2010). However, little rigorous research has been conducted on

the effects between education and youth crime (see Belfield & Levin, 2009; Lochner, 2010;

15

Hjalmarsson & Lochner, 2012). This can be explained that youth crime is not always

measured accurately and that there can be many confounding factors such that isolating a

single chain of causality is difficult (Belfield & Levin, 2009).

There are several studies that focus on the incapacitation effect of education on criminal

involvement of youth. Jacob and Lefgren (2003) use teacher in-service days during the

regular school year in the U.S. as a source of exogenous variation in students’ school

attendance. On teacher in-service days students do not attend school but teachers attend,

conducting organizational tasks. The researchers discuss that these days are unlikely to be

correlated with factors that influence criminal activity. The results of their study suggest that

property crime committed by young people aged between 10 and 19 decreases with 14

percent, but incidents of violent crime increase by 28 percent on school days compared to

teacher in-service days.

Luallen (2006) expresses doubts on that teacher in-service days is an exogenous source of

variation in student absence from school, and argues that such days are usually known in

advance and therefore juveniles may plan crime on a free of school day. The researcher

replicates the study by Jacob and Lefgren (2003) using teacher strikes as an unexpected

source of school closing. For this purpose, Luallen (2006) apply data that measure criminal

activities reported at the zip code level by linking them to the corresponding school districts.

The results of this study are similar to those of Jacob and Lefgren (2003), but the magnitude

of the effects is larger. In particular, the study shows that school incapacitation reduces

property crime with approximately 29 percent, and that violent crime increases with a

percentage that lies between 32 and 37 percent. The additional tests show that these effects

only hold for urban communities. The study also suggests that the decrease in violent crime

during teacher strike-days mainly results from offenders who usually commit multiple crimes,

while the increase in property crime is driven by school absenteeism of both one-time and

repeat offenders.

Landersø et al. (2013) explores the relationship between educational incapacitation due to

early start and later graduation of compulsory education on the likelihood of criminal

behavior of adolescents. The researches use Danish register-based data and variation in school

starting age generated by administrative rules, arguing that this variation is exogenous. They

find that higher age at school start lowers the propensity of criminal behavior before age 18

and this reduction is caused by incapacitation effects of education. Their study also suggests

that this effect is not homogeneous across gender: property crime is reduced for boys while

violent crime is reduced for girls. Besides, the effect for boy with high levels of latent abilities

16

is higher. Finally, they conclude that “the effects are not caused by relative age of peers but by

one’s own school starting age” (p. 27).

A second literature focuses on the effects of increased educational attainment on criminal

behavior probabilities, even though this relationship may include different underlying

mechanisms, such as skill acquisition and educational incapacitation as well.

Åslund et al. (2012) study the impact of a large scale Swedish reform in vocational

education on criminal convictions among youth. The reform extended vocational upper

secondary education from two to three years and added more general theoretical content. The

authors argue that this reform concerned age groups where criminal activity is relatively high

and students who are overrepresented in crime statistics. The results of this study show that

increased access to prolonged and more theoretical vocational education leads to a persistent

reduction in property crime, but not significant decrease in violent crime. In particular, three-

years vocational programs lead to a reduction in property crime among students by a 1.8

percentage point, compared to no three-year vocational programs.

Anderson (2012) analyses the relationship between education and youth crime using state-

level variation in the minimum dropout age (from age 16 to 17 or 18) in the U.S. In particular,

changes in the compulsory schooling law are used in a difference-in-differences framework to

control for unobserved heterogeneity and endogeneity bias. The results show that higher

educational attainment due to a change in the compulsory schooling law reduces arrests of

young people by roughly 10 percent.

Machin et al. (2012) identify the effect of educational attainment on youth crime using the

reform in post-compulsory education system in the late 1980s and early 1990s in the U.K. as a

source of exogenous variation in educational participation of young individuals aged between

16 and 21. This reform increased the number of individuals that stayed in education. The

criminal data used for this study come from on a randomly selected sample of offenders. The

results show that a one percent increase in the proportion of males in full time education and a

one percent increase in the proportion of men staying in education after the compulsory

school leaving age reduces criminal behavior of young men by around 1.9 percent and 1.7

percent, respectively. This reduction is also present for women, although smaller in

magnitude, 1.1 percent and 1.3 percent, respectively.

Brugård and Falch (2012) exploit Norwegian data on educational characteristics and

detailed data on imprisonment for persons aged between 21 and 22 to analyse the relationship

between education and youth crime. They use exam results as an instrument for skills, and the

study track structure together with proximity to high schools as instrument for the number of

17

semesters in high school education. The results of this study suggest that an additional

semester in high school reduces the probability of imprisonment by 0.44 percentage points.

Merlo and Wolpin (2009) provide a comprehensive analysis of the dynamic interactions

among a youth’s schooling, employment and criminal behavior decisions and criminal

involvement outcomes. They use individual-level panel data reported by the Afro-American

male population aged between 13 and 22 in the U.S. To estimate youth’s decisions to engage

in schooling, employment and criminal behavior (including all possible combinations of these

three activities), they apply a multinomial discrete choice vector autoregression model. They

use the estimates to account for unobserved heterogeneity and state dependence (past choices

and outcomes). Furthermore, they simulated the effect of changing schooling status at age 16

for the same individuals and compare their criminal involvement. They conclude that not

attending school at age 16 (implying school dropout) increases the likelihood of committing

crime and being incarcerated at age 19-22 by up to 14.8 percentage points and up to 8.1

percentage points, respectively.

A third literature focuses on the effects of effects of post-secondary school attendance on

criminal behavior of young people. Cullen et al. (2006) evaluate the effects of winning a

lottery that allows high school admission in the Chicago Public Schools on educational and

criminal behavior outcomes. The study shows that although winning the lottery increases

enrollment and attendance in the high schools, there are no positive effects on other

educational outcomes. This can be explained that there is a mismatch between student ability

and school requirements (Lochner, 2010). There is, however, a negative effect on self-

reported disciplinary incidents and registered arrest rates. Self-reported arrest rates are

reduced by about 60 percent among lottery winners relative to lottery losers.

Deming (2011) examines the effect of winning a school lottery to attend a first-choice

school (middle or high school) in the U.S. on criminal activity. The results of this study

suggest that winning the lottery reduces crime that measured 7 years after random assignment,

namely, high-risk youth who won the lottery commit about 50 percent less crime compared to

youth who did not win the lottery. Besides, the reduction is crime occurs after juveniles are

graduated from their preferred school. This effect persists 4-7 years after random assignment

in both the middle and high school. This effect can be explained by the returns to investment

in schooling, higher educational attainment and increase the opportunity cost of crime for

young people who won the lottery (Deming, 2011; Lochner, 2004). The study also suggests

“evidence that the impacts of high school are more attributable to gains in school quality,

whereas the results in middle school are driven more by peer effects” (Deming, 2011, p.

18

2066). However, it is possible that juveniles (and their parents) can self-select themselves into

better schools and therefore the impact of winning the lottery can be driven by match-specific

peer effects.

To sum up, the reviewed and documented above studies reveal causal evidence on the

negative effect from education (including school attendance and educational attainment) to

youth crime. Furthermore, these studies suggest that the effect of education can be direct

(through incapacitation) and indirect (e.g. through skill acquisition). More evidence on the

driving forces of this effect would benefit the research.

[Table 4 is around here]

6.2 The effects of criminal behavior on educational outcomes

This section summarizes the empirical results of studies that focus on the effects of early

criminal involvement on educational outcomes.

Hjalmarsson (2008) analyzes the relationship between previous criminal involvement and

high school graduation by age 19 on the data from the U.S. This study uses a linear

probability model and controls for a large set of observable characteristics, and also takes into

account the state and household fixed effects. The results suggest that arrest and incarceration

at age 16 or earlier significantly reduce the probability of graduating high school by about 11

and 26 percentage points, respectively. In addition, Hjalmarsson (2008) uses techniques

proposed by Altonji et al. (2005) to assess the sensitivity of these effects. This exercise

suggests that the effect of incarceration is relatively more robust than that of arrest, and it is

likely to represent a real effect.

McGarvey et al. (2008) examine the relationship among school and neighborhood crime

and school academic outcomes using school-level data from a large urban district in U.S.

which were merged with crime data. In the instrumental variable estimation, they apply a set

of instruments such as total number of adults in the school, distance from school to the nearest

public housing, distance to public transport and other factors. The authors claim that these

factors affect school outcomes only through their correlation with crime and socioeconomic

status. Their study suggests that school violence and neighborhood violence have separate

negative effects on school outcomes. In particular, an additional violent incident in a school is

associated with a 4 percentage point decline in academic pass rate (referred in the paper as

CRCT and denotes the proportion of students meeting or exceeding standards in grades four

and six).

19

Webbink et al. (2012) analyze the relationship between criminal involvement and

educational attainment using variations within pair of twins from the Australian Twin

Register. They find that being arrested before the age of 18 results in 0.7 to 0.9 fewer years of

education and lowers the probability of finishing high school with 20 to 23 percentage points.

The study, furthermore, shows that the impact of being arrested on educational attainment is

largest for children who were arrested between the age of 13 and 15 years old. However,

although twins are genetically similar, there can be underlying factors of their different

treatment status and these factors can also influence educational outcomes differently.

These studies, in general, suggest that the reverse causality between education and youth

crime is possible. At the same time, stronger evidence on the effect from early criminal

behavior to education is needed to make a conclusion about the causal effects.

[Table 5 is around here]

Conclusion

This study provides a systematic literature review on the relationship between education and

youth crime using the Technology of Skill Formation is used as a theoretical framework. This

theory enables us to consider that education and youth crime are related in a dynamic way.

We first discussed studies that evaluate the effects of childhood and adolescence

interventions on educational and criminal behavior outcomes. We find that early-childhood

programs that focus on children from disadvantaged families tend to improve educational

outcomes and reduce criminal behavior of young people. Interventions in the early school age

and adolescence interventions show positive effects on educational outcomes while the effects

on youth crime are mixed. In general, existing evidence suggests that early interventions are

more effective than later interventions for disadvantaged children (Cunha et al. 2006).

Little is known about the mechanisms of these effects. Although many intervention

studies show that interventions can have positive effects on improving education and reducing

youth crime, these studies do not reveal whether these effects work through separate paths, or

whether this is indeed the result of an interrelationship. Therefore, we document studies that

analyze the effects of education on youth crime and studies that establish the relationship

from early criminal behavior to education.

The studies that focus on identifying the effects of education on criminal behavior of

young people find that being in school keeps juveniles from being engaged in crime, in

particular property crime. Second, not attending school increases the probability of being

20

incarcerated among juveniles. Both types of findings can be explained by incapacitation

effects of school. Third, expansion of educational attainment reduces the probability of

criminal involvement of young people. The exact mechanism of this effect is not identified,

though it is possible that higher educational attainment favorably influences emotional

development, patience, risk aversion and other skills of young people that are negatively

related to criminal behavior. Finally, being in post-compulsory education (with higher quality

education and/or better peers) has a reducing effect on criminal behavior for juveniles from

socially disadvantaged backgrounds.

Studies that focus on identifying the effect of criminal activities on educational outcomes

find that criminal behavior in adolescence provokes negative outcomes in school graduation

and school attendance. Violence in schools, moreover, results in lower educational attainment.

The reviewed studies that examine the effects of early criminal behavior on educational

attainment provide strong correlational evidence. In general, exogenous variation in youth

crime is very rare, which makes it difficult to establish causal links.

Generally, this study concludes that evidence for a relationship between education and

youth crime is convincing, although the exact dynamics of this connection are still largely

unknown. It follows that policies aimed at the combating or preventing of criminal behavior

or improving educational outcomes should take this interrelationship into account. Further

research can contribute by examining the mechanisms between education and youth crime

outcomes and by investigating what are the most effective components of childhood and

adolescence interventions for educational and social behavior outcomes. Moreover, more

evidence is needed on the effects of early criminal behavior on educational outcomes.

21

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Table 1. Summary of studies examining early childhood interventions on education and youth crime

Program Study Start &

country

Target group Age Content Evaluation design Follow-up

age

Educational outcomes Crime outcomes

Length

(years)

Random

Assign.

Sample

size

Academic

Perform.

Educat.

Attainment

NFP

Eckenrode et al.,

2010

1977;

U.S.

Low SES

families; Asian,

African

American,

Latino, White

0-2y Home visits during mother’s

pregnancy and from birth

through the child’s second

birthday

2 Yes NA 15, 19 No effect No effect Reduction in crime (for

girls)

IHDP

McCormick et al.,

2006

1985;

U.S.

Low SES; low

birth weight

newborns

0 – 36m Weekly and bi-weekly home

visits; center-based care; parent

group meetings

3 Yes T=377

C=608

18 Mixed No effect Positive but not

statistically significant

Jamaica

Study

Walker et al.,

1990; 2005; 2011

1986;

Jamaica

Low SES;

ethnic

minorities

9-24m (a) Nutritional supplement

(b) Psychological stimulation

(c) Both supplement and

stimulation

2 Yes T(a)=32

T(b)=30

T(c)=32

C(a)=33

C(b)=32

7-8, 11-12, 17-

18, 22

Positive Positive (weak) Mixed. Treatment group

that received stimulation

was less likely to be

suspended from school.

ABC Clarke &

Campbell, 1998;

Campbell et al.,

2002

1972

U.S.

At-risk children 6w/3m

– 5/8y

Full-time center-based

childcare

5 Yes T=57

C=54

12, 15 Positive Positive No effect

CARE Clarke &

Campbell, 1998;

Campbell et al.,

2002

1978;

U.S.

Children from

high-risk and

low-risk

families

6w/3m

– 5/8y

(a)Full-time center-based

childcare + home visit; (b)

home visit only

2 Yes T(a)=16

T(b)=25

C=23

12, 15, 21 Positive Reduction

in marijuana use

HighScope

Perry

Schweinhart &

Weikart, 1997;

Schweinhart et al.,

2005; Heckman et

al., 2010

1962;

U.S.

Low SES &

low IQ scores;

African

Americans

3/4 – 5y School-year part-day

Preschool program.

Home visits.

2 Yes T=58

C=65

14, 15, 19 Positive Positive (for

girls)

Positive

Chicago

CPC

Reynolds &

Temple, 1995;

Reynolds et al.,

2001

1967;

U.S.

Low SES 3/4-6/9y Preschool: Half-day

school-year

program.

School-age:

Kindergarten and

primary (to 3rd

grade) programs.

3 No

T=1,150

C=389

13, 18-20 Positive

(reading, math;

less grade

retention)

Positive (high

school

diploma), more

sizable effects

for males

Positive (less arrests and

violent arrests)

Head Start Currie, 2001;

Garces et al. 2002;

Deming, 2009

1965;

U.S.

Low SES

3 – 4y Preschool program. Nutrition

programs.

Home visits.

2 No NA 21 Positive (weak) Mixed

FDRP

Lally et al., 1988 1969-

1976;

U.S.

Low SES 6m - 5y Home visits, day care 5 No 108

families

The eighth

grade

Positive Positive

28

Table 2. Summary of studies examining interventions in early school age

Program Study Start &

country

Target group Age Content Evaluation design Follow-up

Educational outcomes Crime outcomes

Length

(years)

Random

Assign.

Sample

size

Academic

Perform.

Educat.

Attainment

SSDP Hawkins et al.,

2001 , 2005;

Durlak et al.,

2011

1985;

U.S.

White, African

Americans, Native

Americans, other

racial groups; low

SES

5th

grade

In-service teacher training,

parenting classes, social

competence training for

children

6 Yes 808

children

and

parents

Age 18, 21 Positive Positive Mixed

MLES Tremblay et al.,

1992, 1996;

Boisjoli et al.,

2007

1984;

Canada

Boys from low

SES, risk-

behavior; children

from Canadian-

born parents

6-7

years

(1st and

2nd

grade)

Preventive Treatment

Training, social skills

training for children, parents

and teachers

2 Yes T=69

C=181

Age 11, 12, 13,

21

Positive Positive on

high-school

graduation

Reduction in youth crime

(weak)

LA's BEST Goldschmidt &

Huang, 2007;

Huang et al.,

2008, 2009

1988;

U.S.

Low SES, low

academic

performance;

disadvantaged

neighborhoods.

80% Hispanic;

12% African

Americans, 8%

others.

Kinderg

arten -

5/6th

grade

After-school program,

learning and social activities

provided by well-trained

staff

5-6 No N=28,000

per year

NA No effect Positive No effect

29

Table 3. Summary of studies examining adolescence interventions

Program Study Start &

country

Target group Age Content Evaluation design Follow-up

Educational outcomes Crime outcomes

Length

(years)

Random

Assign.

Sample

size

Academic

Perform.

Educat.

Attainment

BBBS Tierney et al.,

1995; Grossman

& Tierney, 1998

1992;

U.S.

Low SES, single-

parent households;

African American,

Hispanic, biracial,

Native Americans

10-16 Mentoring programs, meeting

with volunteer mentors

1 Yes T=487

C=472

18 months after

the random

assignment

Positive (for

girls)

Reduction in usage of

substances. Reduced

violence in school.

QOP Rodriguez-Planas,

2012

1995;

U.S.

High-risk juveniles;

Afro Americans,

Hispanic

14-15 Mentoring, educational

services, financial rewards

5 Yes T=580

C=489

Age 19, 21 Positive (weak) Positive (weak) Reduction in criminal

activity for youth in the

top-half of the risk

distribution

ChalleNGe Bloom et al.,

2009; Millenky et

al., 2011;

Millenky et al.,

2013

1993;

U.S.

High school

dropouts,

unemployed. 80%

participants are male.

16-18 Participants live at the program

site (often on a military base).

Education and training

1 Yes N=75,000 During and

shortly after the

program

Positive. GED

or high school

diploma

No effect. Only less

arrested during the

program.

Job Corps Schochet et al.,

2008

1994;

U.S.

Disadvantaged

youth, low SES

16-21 Help in finding work, training 1 Yes N=15,400 During and

shortly after the

program

Positive Mixed. Only during the

program.

EMA Ashworth et al.,

2001, 2002;

Heaver et al.,

2002; Feinstein &

Sabates, 2005

1999,

U.K.

Areas with high

economic

deprivation, low

SES, females and

males

16-18 A weekly money allowance

paid either to young people or

their parents.

2 No N=11,169 One year after

the first

interview

Positive Reduction in youth crime

rates for burglary and

theft (violent crime is not

affected).

BAM Heller et al., 2013 2009;

U.S.

Disadvantaged males 7th-

10th

grade

Interaction with pro-social

adults, after-school

programming, cognitive

behavioral therapy

1 Yes N=2,740 One year after

the random

assignment

Positive Reduction in violent-

crime arrests during

the program year

Halt Ferwerda et al.,

2006; Rud et al.,

2013

2003-

2004;

the

Netherla

nds

Juvenile offenders;

White and other

racial groups

12-18 Community work, learning

assignments

1 Yes T=465

C=480

1-5 years after

the program

Positive Mixed

30

Table 4. Summary of studies examining the effect of education on youth crime

Study Country Source (crime

data)

Research population Sample Evaluation method Input variable Criminal behaviour outcomes

Violent crime Property crime Jacob & Lefgren, 2003

U.S. Official records Males & female aged

10-19

38,339 Neg. Binom. Reg. School attendance Increase Reduction

Luallen, 2006 U.S. Official records Males & females in

“school age”

401,864 Neg. Binom. Reg. School attendance Increase Reduction

Landersø et al., 2013

Denmark Official records Males & females

aged up to 18

98,929

2SLS School attendance Reduction for girls Reduction for boys

Aslund et al., 2012

Sweden Official records Males and females aged 16 and above

116,787 2SLS Educational attainment No significant effect Reduction

Anderson, 2012 U.S. Official

records; self-

report

Males aged 13 to 18 Above 16,000 law enforcement agencies

Dif-in-dif Educational attainment Reduction in the probability of arrest rates for individuals aged 16-18

Machin et al., 2012

U.K. Official records Males & females aged 16-21

125 2SLS Educational attainment Reduction in the probability of criminal behaviour of individuals aged between 16 and 21

Høiseth Brugard & Falch

Norway Official records Males & females aged 16-22

55102 OLS, 2SLS Educational attainment Reduction in the probability of imprisonment

Merlo & Wolpin, 2009

U.S.

Self-reports Afro-Americans; males aged 13 to 22

9,000 Multinomial discrete choice vector autoregression model

Not attending school at age 16

(school dropout)

Increase the probability of committing crime and being incarcerated at age 19-22

Cullen et al., 2006

U.S. Self-report, official records

Afro-Americans, White, Hispanic in 8th grade

14,434 OLS, Probit model for the intention-to-treat

Winning a lottery and high school

enrolment/attendance

Reduction in the self-reported disciplinary incidents and registered arrest rates.

Deming, 2011 U.S. Official records Afro-Americans, White, aged 13-21

10,664 in high school; 11,570 in middle school

OLS, including lottery fixed effects

Wining a lottery for enrolment in

a preferred (middle/high) school

Reduction in crime rates

Table 5. Summary of studies examining the effect of risky behavior on educational outcomes

Study Country Source (crime

data)

Research population Sample Evaluation method Input variable Educational outcomes

Hjalmarsson,

2008

U.S.

Self-reports 27% Afro-

Americans; 21%

Hispanics; rest –

Whites

7,417 Linear probit Arrest, charge, incarceration and

conviction in age of 16 (or

before)

Court intervention in 16 or before

Reduction in the probability of high school graduation.

McGarvey,

Smith, &

Walker, 2007

U.S. Official records Students in schools

in Atlanta, U.S.

61 elementary

schools and 13

middle schools

OLS and 2SLS

Violent incidents in schools and

in the neighbourhood

In-school violent crime is associated with lower academic pass rates.

Webbink et al.,

2012

Australia Retrospective

self-reports

Twins aged 24 to 36 6,267 OLS, fixed effects

on the twins level

Arrest before age of 18

Reduction in the probability of completing senior high school.

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