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Journal of Quantitative Criminology ISSN 0748-4518 J Quant CriminolDOI 10.1007/s10940-014-9240-7
Monetary Benefits and Costs of the StopNow And Plan Program for Boys Aged6–11, Based on the Prevention of LaterOffending
David P. Farrington & ChristopherJ. Koegl
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ORI GIN AL PA PER
Monetary Benefits and Costs of the Stop Now And PlanProgram for Boys Aged 6–11, Based on the Preventionof Later Offending
David P. Farrington • Christopher J. Koegl
� Springer Science+Business Media New York 2014
AbstractObjectives To assess the monetary benefits and costs of the Stop Now And Plan-Under
12 Outreach Project (SNAP-ORP), a cognitive–behavioral skills training and self-control
program, in preventing later offending by boys.
Methods We assess the effect size of the SNAP-ORP program and convert this into a
percentage reduction in convictions. We apply this reduction to the number and types of
offenses committed by a sample of 376 boys between ages 12 and 20, taking account of co-
offending, to estimate the crimes saved by the program. Based on the cost of each type of
crime, we estimate the cost savings per boy and compare this with the cost of the SNAP-
ORP program for low, moderate and high risk boys. We also scale up from convictions to
undetected crimes.
Results Based on convictions, we estimate that between $2.05 and $3.75 are saved for
every $1 spent on the program. Scaling up to undetected offenses, between $17.33 and
$31.77 are saved for every $1 spent on the program. The benefit-to-cost ratio was greatest
for the low risk boys and smallest for the high-risk boys. However, there were indications
that the program was particularly effective for high risk boys who received intensive
treatment.
Conclusions Our benefit-to-cost ratios are underestimates. On any reasonable assump-
tions, the monetary benefits of the SNAP-ORP program greatly exceed its monetary costs.
It is desirable to invest in early prevention programs such as SNAP-ORP to reduce crime
and save money.
D. P. Farrington (&)Institute of Criminology, Sidgwick Avenue, Cambridge CB3 9DA, UKe-mail: dpf1@cam.ac.uk
C. J. KoeglOntario Correctional Institute, Ministry of Community Safety and Correctional Services, 109McLaughlin Road South, Brampton, ON L6Y 2C8, Canadae-mail: christopher.koegl@ontario.ca
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J Quant CriminolDOI 10.1007/s10940-014-9240-7
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Keywords Monetary costs � Monetary benefits � Cost of crime � Cost–benefit analysis �Cognitive–behavioral program
Introduction
The main aim of this research is to assess the monetary costs and benefits of an early
childhood intervention program for boys called Stop Now And Plan-Under 12 Outreach
Project (SNAP-ORP) in preventing later offending.
The Stop Now And Plan-Under 12 Outreach Project
The SNAP-ORP program was established in 1985 as a fully manualized mental health
program for children with identified behavior problems (Augimeri et al. 2011a, 2011b).
Children ages 6–11 are admitted to the program if they have a T-score of 70 or greater on
the delinquency subscale of the Child Behavior Checklist (CBCL; Achenbach, 1991) and/
or have had recent police contact resulting from their own misbehavior. Children can also
be admitted to the program if the program manager strongly believes that the parent-rated
CBCL score under-reports the severity and frequency of the child’s antisocial behavior
problems or if they are siblings of referred children and are showing early signs of conduct
problems. Raw scores are converted to T-scores by setting the mean = 50 and the standard
deviation (SD) = 10, so that a T-score of 70 or above identifies the 2.3 % of children who
score at least 2 SDs above the mean.
The cornerstone of the SNAP-ORP is a 12-week cognitive–behavioral self-control and
problem-solving technique called SNAP (Stop Now And Plan) that helps children and their
parents interrupt negative behavior patterns and replace them with more positive options.
Through a structured curriculum, facilitated discussion and role plays, program participants
learn to solve problems in provoking situations so that they are able to generate feasible,
personalized alternative options that lead them away from further trouble (such as
aggression or delinquency for children, or aversive parenting practices). Children and their
parents attend weekly 90-min sessions in separate groups. Other components of the SNAP-
ORP program include one-on-one family counseling, individual befriending (mentoring),
and academic tutoring for children. Initially, the same SNAP-ORP program was applied to
both boys and girls, but beginning in 1997 a separate program was used for girls (Walsh
et al. 2002). This article focuses only on the boys.
There is little direct information about the effectiveness of the SNAP-ORP program in
preventing later offending. Ideally, children ages 6–11 should be randomly assigned either
to the program or to a control group that receives no treatment, and both groups should be
followed up for at least 10 years to assess their offending. However, such long-term
follow-ups of early prevention programs are extremely rare (see later). The evaluations of
the SNAP-ORP program have focused on short-term follow-ups and outcome measures of
antisocial behavior (based on CBCL scales) rather than offending. We will report later on
the continuity between CBCL measures and later offending.
The first randomized controlled trial of the SNAP-ORP program was conducted by
Augimeri et al. (2007). Sixteen pairs of children were matched on age, gender, and severity
of delinquency at admission, and randomly assigned to SNAP-ORP or to a waiting-list
control group who received less intensive treatment. The control group received a 12-week
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nonclinical activity/recreation program consisting of arts and crafts and cooperative games,
which paralleled the SNAP-ORP program in time and duration. At the end of the 12 weeks,
the control children received the SNAP-ORP program. The effects of the SNAP-ORP
program were assessed by parent CBCL ratings before and after (i.e., 12 weeks later).
Compared with the control program, the SNAP-ORP program had significantly desirable
effects on the CBCL delinquency and aggression subscales. The standardized mean dif-
ference effect sizes were d = 1.18 for delinquency and d = 0.79 for aggression (using
T-scores).
Effect Sizes of the SNAP-ORP Program
There have been two independent evaluations of the SNAP-ORP program that have
compared treated boys with a control sample. Lipman et al. (2008), in Hamilton, Ontario,
compared 132 boys who received the program with 77 boys who were in a waiting-list
comparison group and who had before and 6 months after CBCL measures completed by
parents. The two groups were not randomly assigned. Lipman et al. reported a standardized
mean difference (d) effect size measure of 0.41 for CBCL externalizing problems and 0.38
for CBCL aggression.
Burke and Loeber (2014), in Pittsburgh, PA, compared 130 boys who received the
SNAP-ORP program with 122 boys who received ‘‘standard’’ children’s services, which
consisted of assistance from project staff and attempts to obtain wraparound services. Boys
were randomly assigned either to the SNAP-ORP program or to the standard services
group. Of the 130 children assigned to SNAP-ORP, 78 % either attended child groups and/
or their parents attended parent groups. Nevertheless, all boys were included in the ‘‘intent-
to-treat’’ analysis. Of the control boys, only 13 % had received wraparound services by the
3-month assessment point. There were two further follow-ups, at 6 and 12 months later.
Parent CBCL information was obtained at baseline (wave 1) and in all three follow-ups.
Burke and Loeber reported that, over the three assessments (waves 2–4), d = 0.29 for
CBCL aggression and d = 0.31 for externalizing problems (based on multilevel regression
analyses).
For the immediate (3-month) assessment (wave 2), we estimated d = 0.37 for both
CBCL aggression and externalizing problems (based on d = the unstandardized regression
coefficient B divided by the standard deviation of the dependent variable: Lipsey and
Wilson 2001, p. 199). For the longest (15-month) follow-up assessment (wave 4), we
estimated d = 0.25 for both CBCL aggression and externalizing problems (J.D. Burke,
personal communication, June 19 2013).
Burke and Loeber (2014) also reported that, of 149 boys who were over the age of
criminal responsibility of 10, 25 had an official record with the county juvenile probation
department after the first assessment (wave 1) and by the end of the follow-up period. This
was true of 15 of the 67 control boys (22.4 %) and 10 of the 82 treated boys (12.2 %),
corresponding to an odds ratio of 2.08. This corresponds to d = 0.40 [since d = Ln
(OR) 9 0.5513; Lipsey and Wilson 2001, p. 202]. This is the only evaluation of the effects
of the SNAP-ORP program, comparing treated and control groups, on later offending.
Effect Sizes of Child Skills Training Programs
These d-values, between about 0.20 and 0.40, are fairly typical of those obtained for
cognitive–behavioral skills training programs for children. Losel and Beelmann (2003)
reviewed 84 evaluations of child skills training programs with various outcomes (e.g.,
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antisocial behavior, social-cognitive skills), containing 135 comparisons between treated
and control children, and concluded that the best estimates of mean effect size were
d = 0.38 for immediate measures and d = 0.28 for follow-up measures. For children ages
7–12 specifically, d = 0.39 for immediate measures and d = 0.20 for follow-up measures.
For cognitive–behavioral programs specifically, d = 0.39 for immediate measures and
d = 0.37 for follow-up measures. For antisocial outcomes specifically, d = 0.26 for
immediate measures and d = 0.22 for follow-up measures.
A later review by Losel and Bender (2012) reported similar values (for all programs and
all outcomes) of d = 0.39 for immediate measures and d = 0.28 for follow-up measures.
For cognitive–behavioral programs and antisocial outcomes, d = 0.49 for immediate
measures and d = 0.50 for follow-up measures, but the latter estimate was based on only
seven comparisons.
There have been very few long-term follow-up estimates of the effectiveness of child
skills training in reducing later offending. Farrington and Welsh (2013) reviewed all
randomized experiments with at least 100 participants and an outcome measure of
offending, and found that there were only 11 experiments on early prevention with a
follow-up period of at least 10 years. One of these (the Seattle Social Development Project
of Hawkins et al. 2008) only had a quasi-experimental follow-up, not a follow-up of the
original randomly assigned children. Of the other 10 experiments, only two evaluated the
effects of child skills training (as well as parent training, in both cases): the Montreal
Longitudinal–Experimental Study (Boisjoli et al. 2007) and Fast Track (CPPRG 2010).
The Montreal Longitudinal–Experimental Study is the most comparable to the SNAP-
ORP program; 250 Canadian boys age 7, all rated by teachers above the 70th percentile on
disruptiveness, were randomly assigned either to the program or to an untreated control
group. Up to age 24, 33 % of control boys had a criminal record, compared with 22 % of
treated boys. Boisjoli et al. (2007) reported an odds ratio (OR) of 0.52, where values below 1
indicate an effective program, but we prefer to have values above 1 indicating an effective
program, in which case the corresponding OR = 1.92. Since d = Ln (OR) 9 0.5513, this
corresponds to d = 0.36.
The Fast Track study is less comparable, because 54 schools (rather than children) in
four US states were randomly assigned to treated or control conditions. High risk boys and
girls ages 6 to 7 (N = 891) were followed up. CPPRG (2010) reported an OR for juvenile
arrests of 0.71, which we converted to OR = 1.41 (when values above 1 indicate an
effective program) and d = 0.19.
Taking account of all these results, we consider that the best estimate for the effect size
of the SNAP-ORP program is somewhere between d = 0.20 and d = 0.40. We will use
these lower and upper estimates in our subsequent calculations.
Cost of Crime
There are many different costs of crime (Cohen 2005). There are tangible costs, such as the
value of stolen or damaged items, medical and mental health costs, victim services, lost
productivity, security measures, and criminal justice system costs (police, courts, prisons,
and noncustodial treatment). There are also intangible costs, such as the pain, suffering,
and decreased quality of life of victims. Some costs (e.g. criminal justice system) fall on
the government or taxpayer, while others fall on the victim or offender. The costs can
either be calculated in a ‘‘bottom-up’’ way, by estimating the costs within all these different
categories, or in a ‘‘top-down’’ way, for example, by asking citizens about how much they
are willing to pay to avoid becoming victims of crime (Cohen et al. 2004).
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Studies of the cost of crime to victims in the US were pioneered by Cohen (1988) and
Miller et al. (1996). They estimated intangible costs based on jury awards. The Miller et al.
study was published by the US National Institute of Justice and was particularly influential
in highlighting the enormous costs of crime. In 1993 dollars, each homicide on average
cost $2,940,000 (mostly because the cost of years of potential life lost), each rape/sexual
assault cost $87,000 (mostly because of pain, suffering, and decreased quality of life), each
robbery cost $8,000, each vehicle theft cost $3,700, and each burglary cost $1,400. The
total cost of crime in the US was estimated to be $450 billion in 1993.
The Miller et al. (1996) study stimulated other countries to estimate the cost of crime. In
a Home Office report for England and Wales, Brand and Price (2000) estimated that the
total cost of crime (including all costs, not just victim costs) was £60 billion at 1999 prices.
The average cost of a homicide was £1,100,000, of a sexual offense was £19,000, of a
wounding was £18,000, of a robbery was £4,700, and of a burglary was £2,300. Brand and
Price used a ‘‘bottom-up’’ method to estimate costs, but pain and suffering costs were
based on what citizens thought would be suitable compensation. The costs of crimes
against individuals and households were later updated by Dubourg et al. (2005) and Home
Office (2011). The first calculations of the cost of crime in Canada were published by
Zhang (2011), who estimated the total costs of crime in Canada (again including all costs,
in 2008) as $100 billion, or about $3,000 per person.
Cohen (1998) calculated the monetary value of saving a high-risk youth by estimating
the lifetime costs incurred by a typical career criminal, drug abuser, and high-school
dropout. Using ‘‘bottom-up’’ methods, he calculated that the typical high-risk youth cost
between $1.7 and $2.3 million (in 1997 dollars). Cohen and Piquero (2009) then updated
and extended this analysis, presenting results based on both ‘‘bottom-up’’ and ‘‘top-down’’
methods. They estimated that the present value of saving a 14-year-old high-risk juvenile
from a life of crime ranged from $2.6 to $5.3 million. Cohen et al. (2010b) then estimated
the crime costs incurred by youths on different criminal trajectories in the second Phila-
delphia birth cohort study, and found that the high-rate chronic offenders incurred the most
costs; 3.1 % of the sample cost $1.5 million each, females cost $750,000, African
Americans cost $1.6 million, Hispanics cost $200,000, and Whites cost $100,000, on
average (Cohen et al. 2010a).
The cost of crime has also been estimated in other longitudinal studies. In the Pittsburgh
Youth Study, based on self-reported offending, Welsh et al. (2008) calculated that 500
Pittsburgh boys cost about $100 million in total (or about $200,000 each) between ages 7
and 17 (in 2000 dollars). The 34 chronic offenders cost about $800,000 each on average. In
the Cambridge Study in Delinquent Development, based on convictions of London males,
Piquero et al. (2013) calculated that high-rate chronic offenders cost about £60,000 each
for crimes committed between ages 10 and 50 (at 2003 prices). Using the ‘‘sealing-up
factor’’ of 39 self-reported crimes per conviction reported by Farrington et al. (2006), they
would cost about £2.4 million each. Raffan Gowar and Farrington (2013) also estimated
the monetary cost of criminal careers in this study (at 2010 prices), using self-reported
offending as well as official convictions. They concluded that the total self-report cost
between ages 10 and 50 was about £50 million, or £123,000 per male. From all these
analyses, and others, it is clear that offending costs a great deal of money.
Cost–Benefit Analysis in Criminology
In cost–benefit analysis, the monetary costs of an intervention program are compared with
the monetary benefits, especially those resulting from crimes saved. It is common to
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calculate a benefit-to-cost ratio. It is important to discount future benefits to present value,
because $1 now is not the same (cannot buy the same goods) as $1 in 10 years time. The
costs of a program can include capital versus recurring costs, and average versus marginal
costs. It is common to calculate costs and benefits per program participant. A cost–benefit
analysis can only be as adequate as the evaluation on which it is based. In calculating the
effectiveness of a program, it is important to estimate what would have happened in the
absence of the program, preferably by randomly assigning children to treatment or control
groups.
Cost–benefit analysis in criminology was surprisingly neglected until the landmark
study by Schweinhart et al. (1993). This described the evaluation of the effectiveness of the
Perry preschool intellectual enrichment program, with a follow-up to age 27. Schweinhart
et al. carried out a cost–benefit analysis and concluded that the cost of the program (in 1992
dollars) was $12,356 on average per participant, while the comparable benefits (discounted
by 3 % per year) were $108,002, so that $8.74 were saved for every $1 spent on the
program. Most of the savings (65 %) were attributable to decreased offending, and $7.16
were saved for the government or taxpayers for every $1 spent on the program. The ‘‘$7
saved for every $1 spent’’ mantra was widely publicized, and it galvanized governments in
different countries into investing in preschool education. It also encouraged criminologists
to do cost–benefit analyses of the effectiveness of interventions.
The first reviews of cost–benefit analyses in criminology were completed by Welsh and
Farrington (1999, 2000a, b; see also Farrington et al. 2001), and the first book on this topic,
bringing together most of the key researchers at that time, was published by Welsh et al.
(2001). The conclusions of these reviews were generally optimistic. For example, Welsh
and Farrington (2000b) reviewed 26 cost–benefit analyses and concluded that the monetary
benefits outweighed the monetary costs for three out of the four principal crime prevention
strategies (developmental, situational, and correctional intervention); there was insufficient
evidence regarding community interventions to draw conclusions. Later reviews of cost–
benefit analyses were completed by Swaray et al. (2005) and McDougall et al. (2008), a
later book was published by Roman et al. (2010), and later expositions of cost–benefit
analyses in criminology were published by Dossetor (2011) in Australia and McIntosh and
Li (2012) in Canada. The most recent review is by Welsh et al. (2015).
Our main focus here is specifically on cost–benefit analysis of developmental preven-
tion programs. As mentioned, this was pioneered by the Perry researchers. Their first
analysis was based on their follow-up at age 15 (Schweinhart and Weikart 1980). They
estimated that $2.48 were saved for every $1 spent, but only $0.56 was saved for taxpayers,
since most of the benefits accrued to schools and from projected lifetime earnings of
participants. At age 19 (Berrueta-Clement et al. 1984), they estimated that $3.56 were
saved per $1 spent, and now $3.00 were saved for taxpayers, because of savings to schools
and welfare and on crime (although only 9 % of the benefits were attributable to decreased
offending).
The well-publicized results at age 27 (Schweinhart et al. 1993) have already been
discussed. At age 40 (Schweinhart et al. 2005), it was estimated that $17.07 were saved for
every $1 spent, including $12.90 for taxpayers, and the crime benefits accounted for 66 %
of the total benefits. (The figure of $17.07 was later revised to $16.14.)
The well-publicized cost–benefit analyses of the Perry program in 1993 triggered
similar analyses of several other developmental prevention programs. For example, Karoly
et al. (1998) analyzed the Nurse Family Partnership program of Olds et al. (1998). They
concluded that the program cost $6,083 (at 1996 prices) but had benefits of $24,694 (in
increased employment and decreased welfare, criminal justice, and health costs), giving a
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benefit-to-cost ratio of 4.06. More recently, Kuklinski et al. (2012) concluded that between
$5 and $10 were saved for every $1 spent on the Communities That Care project.
The first cost–benefit analysis of a developmental prevention program in Canada was
carried out by Jones and Offord (1989). The Participate and Learn Skills (PALS) program
was implemented in one housing complex in Ottawa and not in a control housing complex.
The program emphasized skills development in sports, music, and art. Jones and Offord
found that the monthly average number of juveniles charged by the police was 80 % less in
the experimental site than in the control site. Overall, $2.54 were saved (on security, fire,
and police) for every $1 spent. This was an early study that only calculated benefits to
public agencies, so the total benefits were under-estimated.
Undoubtedly the most extensive cost–benefit analyses of all types of intervention
programs have been carried out by Aos and his colleagues, who carefully quantified the
monetary costs and benefits of programs in Washington State. In their latest publication
(Lee et al. 2012), they concluded that the benefit-to-cost ratio was 4.36 for multisystemic
therapy, 10.42 for functional family therapy in the community, and 4.95 for treatment
foster care. However, they have not published a benefit-to-cost ratio for cognitive–
behavioral skills training for children. Their most relevant ratios are 0.09 for the Promoting
Alternative Thinking Strategies (PATHS) program, -0.41 for Fast Track, 8.57 for
Aggression Replacement training for youth on probation, and 3.36 for community-based
mentoring for students (see www.wsipp.wa.gov/benefitcost).
Scaling Up from Convictions to Account for Undetected Crime
In assessing the number of crimes saved by an intervention program, it is important to
‘‘scale up’’ from recorded to actual offending. Only a fraction of criminal offending comes
to the attention of legal authorities. Even fewer offenses are referred to criminal court for
processing, and from this number, even fewer cases result in a criminal conviction.
Unfortunately, the extant literature comparing criminal conviction data to self-reported or
actual offending is relatively sparse. In part, the problem is definitional: self-reported
antisocial acts do not always map neatly on to criminal offense categories (e.g., drug taking
behavior versus the possession of drugs). In addition, the detection and enforcement of
crimes may differ according to age, location, and type of offense. For example, though
technically against the law, some very minor physical assaults may be viewed as normative
behavior during adolescence and, as such, may be unlikely to result in a formal criminal
prosecution. This presents a challenge for deriving accurate figures to scale up from
convictions to the ‘‘true’’ number of crimes committed.
We reviewed two studies of adolescent samples that estimated the relationship between
self-reported involvement in criminal activities and referrals or petitions to the juvenile
court. Farrington et al. (2003) estimated that adolescent males and females in Seattle
committed 30 self-reported offenses for every court referral, on average. Farrington et al.
(2007) subsequently replicated these findings with a sample of Pittsburgh adolescent males
and found that 34 crimes were self-reported for every court-petitioned offense. Both
studies found that the likelihood of going to court increased with age, but the self-reported
offending frequency increased proportionately more. This meant that, as youths got older,
they were less likely to be prosecuted for each offense. The Pittsburgh study further
explored these patterns by offense type but unfortunately the data were consolidated into
only three broad categories of property, person, and drug offenses. On the other hand, the
Seattle analysis provided offense-specific scaling up factors that could be used in the
present study (see later). The Pittsburgh and Seattle analyses were later extended into the
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adult years (Gilman et al. 2014; Theobald et al. 2014) but only for broad categories of
offenses. For estimates of adult offending, we relied on a paper by Cohen and Piquero
(2009), who presented offense multiple factors for each of 14 offense types based on prior
American research.
Assessing Costs and Benefits by Risk Level
In general, effect sizes are greater for higher risk compared to lower risk samples. Andrews
and Dowden (2006) systematically reviewed 225 studies of correctional intervention and
concluded that this was true. For interventions with young people under age 20, the
average phi correlation was 0.26 for high risk samples and 0.07 for low risk samples.
Similar conclusions were reached in systematic reviews of child skills training by Losel
and Beelmann (2003) and Losel and Bender (2012): for high risk samples and antisocial
behavior outcomes, d = 0.53 for immediate measures and d = 0.48 for follow-up mea-
sures, although the follow-up estimate was based on only eight studies.
From both a clinical and a policy perspective, it is desirable to investigate the effec-
tiveness and applicability of clinical interventions according to the risk of future offending.
This is at the heart of the Risk-Need-Responsivity (RNR) model of correctional treatment
(Andrews and Bonta 2010) which states that more treatment should be offered to higher
risk/need individuals to offset their greater likelihood of future offending. This model,
though primarily developed for adult offenders, also applies to antisocial children since the
allocation of treatment according to patient characteristics is a well-established principle of
evidence-based practice (American Psychological Association 2005). From a service
delivery standpoint, this means that a range of service types and/or service intensities
should be offered according to the identified of risk and need. Of course, this has direct
relevance to cost–benefit analyses: higher risk cases will require more treatment, which is
typically more costly, thereby influencing the calculation (i.e., the denominator) of the
benefit-to-cost ratio.
For this study, we calculated benefit-to-cost ratios taking into account identified risk for
future offending using the Early Assessment Risk List for Boys (EARL-20B; Augimeri
et al. 2001). The EARL-20B is the most fully developed tool for assessing risk of future
offending in boys under the age of criminal liability. This instrument contains 20 risk
factors, each of which are scored 0 (not present), 1 (possibly present), or 2 (present), to
yield a total score ranging from 0 (low risk) to 40 (high risk). Prior research on the tool has
demonstrated its reliability (Augimeri et al. 2010; Enebrink et al. 2006a) and predictive
validity for both clinical and behavioral outcomes (Enebrink et al. 2006b) and criminal
offending (Koegl 2011). In the present study, we calculated effect sizes of the SNAP-ORP
program for different risk groups, based on EARL-20B total scores, to determine whether
benefit-to-cost ratios varied according to the risk status of the boys.
Method
Participants
In calculating the monetary costs associated with criminal involvement, data were drawn
from the entire population of boys (N = 379) treated in the Stop Now And Plan Under 12
Outreach Project (SNAP-ORP) between the years of 1985 and 1999 in Toronto, Canada
who were alive on their 21st birthday (N = 376; hereafter referred to as the ‘‘Toronto
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sample’’). The average age of the sample at the time of admission to the program was
9.6 years (SD = 1.4). Nearly half (48.1 %) of the boys were living with only one parent,
27.4 % were living with both parents and the remainder were living in reconstituted
homes, with a legal guardian or with a parent in a common–law relationship. More than
half (55 %) of the sample were referred to the program by the police, 11.7 % were referred
by the school, 10.9 % were referred by child protection, and the remainder were self-
referrals or were transferred to SNAP-ORP from other programs operated by the host
organization. The top five presenting problems of the boys at admission were: disobedience
(74.4 %), stealing/theft (72.3 %), aggression/assault (71.2 %), lying (64.1 %), and verbal
aggression (51.2 %). The average CBCL delinquency T-score at admission was 72.7
(SD = 9.1); 68.7 % of the participants had a T-score of 70 or greater.
Measuring Criminal Offending
Access to criminal records was granted by a youth court judge under provisions of the Youth
Criminal Justice Act 2003 (Canada) which allows records to be retrieved for research and
statistical purposes. Three separate searches were performed to generate a comprehensive
follow-up of study participants: (1) Ontario youth justice and correctional records in 1999;
(2) national records via the Canadian Police Information Centre in 2008; and (3) Ontario
youth justice and correctional records in 2009. The frequency of convictions was recorded
for each boy using offending categories that could be mapped reasonably well on to mon-
etary cost estimates. For this study, 16 mutually exclusive offense categories were used to
code 100 % of the convictions registered for the study sample. In Canada, persons can be
charged with a criminal offense from age 12. Offenses were tracked and coded for all study
participants up to age 20 inclusive, resulting in a uniform follow-up period of nine years.
This was the oldest age to which all 376 boys could be followed up.
Measuring Risk for Future Antisocial Behavior
For this study, we divided the 376 Toronto boys into three groups (low, moderate, high)
based on the distribution of their EARL-20B total scores: the lowest 25 %, the middle
50 %, and the highest 25 %. These groupings were used to approximate the distribution of
participants who typically access the SNAP-ORP program. EARL-20B scores were coded
retrospectively from the clinical case files for each of the boys in the Toronto sample as
part of initial research assessing the reliability and predictive validity of the tool (Augimeri
et al. 2010; Hrynkiw-Augimeri 2005). Low risk boys had an EARL-20B total score of 16
or less (N = 85, mean = 12.6, SD = 3.2), moderate risk boys had an EARL-20B total
score between 17 and 25 (N = 203, mean = 21.0, SD = 2.7), and high risk boys had an
EARL-20B total score of 26 or greater (N = 88, mean = 28.9, SD = 2.6). These group-
ings were subsequently used to calculate effect sizes by level of risk based on pre-post
CBCL data for the Toronto sample.
Analysis Plan
Our aim was first to get the best possible estimate of the effects on offending of the SNAP-
ORP program (see above). Second, we converted measures of effect size into percentage
reductions in the number of convictions. Third, we aimed to specify the numbers of
different types of crimes committed by SNAP-ORP boys between the ages 12 and 20
J Quant Criminol
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inclusive that led to convictions. Fourth, we calculated the number of crimes saved by the
program, taking account of co-offending. Fifth, we estimated the cost of each different type
of crime. Sixth, from knowing the percentage reduction in crimes, we estimated the cost
savings per treated boy as a consequence of committing fewer crimes. Seventh, we esti-
mated the cost of the SNAP-ORP program per boy. Eighth, we calculated a benefit-to-cost
ratio for convictions by comparing the savings from fewer crimes committed (discounted
for inflation) with the cost of the program. Ninth, we estimated the scaling up factor from
convictions to self-reported offenses. Tenth, we applied the scaling up factor to estimate
the benefit-to-cost ratio for crimes committed as opposed to convictions. Finally, we
repeated these analyses for boys in different risk categories according the EARL-20B
scores, in order to investigate whether the SNAP-ORP program was more effective and
yielded larger benefit-to-cost ratios with high risk boys compared with low risk boys.
Results
Cost per Crime
Table 1 shows the estimated cost for each of the 16 types of crime. The cost estimates were
based on figures published by Cohen and Piquero (2009) and McCollister et al. (2010). As
mentioned, Cohen and Piquero produced two estimates: one ‘‘top-down’’ based on the
public’s willingness to pay to avoid crime, and the other ‘‘bottom-up’’ based on estimating
Table 1 Cost, scaling up factor, and co-offenders per crime by type of crime
Offense type Cost per crime Proportion CJS costs Scaling up Mean numberof co-offenders
Estimate Source Percent Source Factor Source
Rape/sexual assault $246,307 MC 3.9 MC 7.9 C 1.10
Armed Robbery $179,850 C 29.4 C 11.6 C 1.86
Aggravated assault $94,336 MC 16.3 MC 7.9 C 1.72
Robbery $39,108 MC 20.0 MC 12.2 F 1.86
Simple Assault $16,350 C 45.5 C 22.9 F 1.27
Weapons offenses $5,000 E 50.0 E 7.9 C 1.37
Arson $58,767 MC 28.6 MC 7.9 C 1.72
Burglary $14,293 MC 33.5 MC 9.5 F 1.91
Motor vehicle theft $12,740 MC 44.5 MC 4.6 F 1.48
Stolen property $8,373 M 37.2 E 15.0 F 1.56
Fraud $5,213 MC 46.8 C 15.0 F 1.38
Larceny/theft $3,707 MC 37.2 MC 15.0 F 1.43
Vandalism $3,369 MC 63.0 C 24.6 F 1.51
Other $818 C 50.0 E 4.6 F 1.30
Drug offenses $1,000 E 50.0 E 100.2 F 1.51
Administration Justice $1,000 E 100.0 E 1.0 E 1.06
Source of mean number of co-offenders: calculated by Peter Carrington using data from the CanadianUniform Crime Reporting Survey (see Carrington 2014)
Cost figures are in 2012 Canadian dollars; C = Cohen and Piquero (2009); E = Estimated; F = Farringtonet al. (2003); M = McCollister et al. (2010)
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components of crime costs such as victim costs and costs of police, courts and prisons. In
order to produce a summary cost estimate from Cohen and Piquero, we averaged these two
estimates. McCollister et al. used jury compensation awards and the cost of illnesses to
estimate crime costs. Both sources included both tangible and intangible costs of crime
(see Cohen 2005). In order to generate conservative figures, we averaged estimates when
possible, and converted dollar figures to Canadian currency in 2012 dollars using an
inflation adjustment index.
As shown in Table 1, rape/sexual assault and armed robbery were the most costly
crimes, followed by aggravated assault and arson. Among the least costly crimes were
larceny/theft, vandalism, and fraud. We found no estimates of the costs of weapons
offenses, drug offenses or administration of justice (e.g., failure to appear in court, breach
of probation), so we assumed plausible, conservative estimates for the cost of these
offenses. Dollar amounts were later multiplied by the observed frequency of convictions
for boys in the Toronto Sample to generate estimates of the cost of convictions of each boy
for each of the 16 cost of crime categories.
Scaling Up Factors
We estimated scaling up factors from two sources: Farrington et al. (2003) and Cohen and
Piquero (2009). Farrington et al. analyzed court referral and self-report data from about
800 boys ages 11–17 in the Seattle Social Development Project. Scaling up factors were
estimated for each type of crime by comparing the number of self-reported offenses with
the number of court referrals. For example, for burglary, there were 71.4 self-reported
offenses and 7.5 court referrals 100 boys, yielding a scaling up factor of 9.5 (see Table 1).
Cohen and Piquero used estimates from Farrington et al. and also from studies of self-
reported offending of adult offenders by Blumstein and colleagues (1979, 1986), Cohen
(1986), Peterson and Braiker (1980), and Chaiken and Chaiken (1982).
Table 1 shows the source of the scaling up factor that we used for each offense type.
Where an estimate was available from Farrington et al., we used that. In other cases, where
an estimate was available from Cohen and Piquero, we used an average of the estimates
from the adult studies. We adopted a conservative approach in applying scaling up factors
to conviction data; where two estimates were available for an offense category, we used the
lower estimate. Using the data provided by the aforementioned studies, we mapped scaling
up factors on to the offense categories that were developed for the present study. None of
the studies calculated adjustment factors for drug offenses, weapons offenses, fraud,
possession of stolen property, or ‘‘other’’ offenses so we used similar offense categories to
estimate these figures. No adjustment was applied to the category of administration of
justice offenses because we assumed that virtually all infractions of this type would be
detected by authorities. Similar to the approach taken by Dubourg et al. (2005), and unlike
that taken by Cohen and Piquero (2009), offense multipliers were uniformly applied to
both adolescent and young adult offending up to age 20. As shown in Table 1, the scaling
up factor was very high for drug offenses, vandalism, and simple assault. It was relatively
low for motor vehicle theft, rape/sexual assault, aggravated assault, and arson.
Because undetected crimes do not result in official convictions, it is necessary to apply
cost of crime estimates to them that exclude criminal justice system (CJS) expenditures
(e.g., police, courts, corrections). To estimate these costs for each type of crime, we
divided criminal justice system costs by the total cost for each crime, relying on figures
provided by McCollister et al. (2010; Tables 3, 5) and Cohen and Piquero (2009; Table 5).
Where both sources provided CJS costs, we averaged them to arrive at the best estimate,
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Ta
ble
2S
NA
Pp
rog
ram
cost
s(i
n2
01
2C
anad
ian
do
llar
s)
SN
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com
po
nen
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ow
risk
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der
ate
risk
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hri
sk
SN
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dre
n’s
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up
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ks
$3
50
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ks
$3
50
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ks
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50
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par
ent
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up
12
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ks
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72
12
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ks
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ks
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72
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ily
scre
enin
g/S
NA
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fam
ily
sess
ion
s3
sess
ion
s$
15
06
sess
ion
s$
30
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ssio
ns
$5
00
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dsc
reen
ing/I
ndiv
idual
bef
rien
din
g1
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ion
$50
6se
ssio
ns
$300
16
sess
ions
$800
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eco
nfe
rence
/Tre
atm
ent
revie
w1.5
sess
ions
?p
rep
$2
00
3.0
sess
ion
s?
pre
p$
60
04
.0se
ssio
ns
?p
rep
$8
00
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tim
rest
ituti
on
1se
ssio
n$
50
1se
ssio
n$
50
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oo
lad
vo
cacy
/tea
cher
con
sult
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ion
s$
15
04
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s$
20
0
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mew
ork
clu
b6
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ks
$3
00
12
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$6
00
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chia
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con
sult
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sess
ion
$1
50
The
Ars
on
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ven
tion
Pro
gra
mfo
rC
hil
dre
n(T
AP
P-C
)3
sess
ion
s$
11
3
Co
nti
nu
edca
reg
rou
p(b
oy
s)1
6se
ssio
ns
$6
40
Co
nti
nu
edca
reg
rou
p(p
aren
t)8
wee
ks
$1
60
On
go
ing
case
coo
rdin
atio
n(2
5%
of
abo
ve)
$2
31
$5
56
$1
,13
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tal
$1
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2,7
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,66
9
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tco
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$5
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9$
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tal
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8,5
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g
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and it was possible to do this for about half of the cost of crime categories. For four
categories (i.e., armed robbery, simple assault, fraud, and vandalism), the percentage of
CJS costs was calculated using data exclusively from Cohen and Piquero (2009). The
remaining five categories were estimated: stolen property was assigned the same value as
larceny/theft; costs associated with administration of justice breaches were assumed to be
entirely CJS-related; and the final three categories, which accounted for only a small
proportion of offenses in the Toronto sample (i.e., arson, drug, and other offenses), were
estimated at 50 % CJS costs.
Co-offending
In converting from numbers of convictions to numbers of offenses, it is essential to
consider co-offending. This is because, for example, if 500 convictions are saved, it is not
necessarily true that 500 offenses are saved. One conviction is essentially one offender–
offense combination. If two people jointly commit one offense, there are two offender–
offense combinations, but only one offense. Therefore, in converting from numbers of
convictions to numbers of offenses, it is necessary to divide by the average number of co-
offenders. The most detailed information about co-offending in Canada was collected by
Carrington (2009). Table 1 shows the average number of offenders in 2006–2009 who
were involved in each of the 16 types of offenses between ages 12 and 20 (Carrington
2014). For example, the average Canadian burglary was committed by 1.91 offenders.
Costs of the SNAP-ORP Program
Table 2 shows the costs of the SNAP-ORP program for low, moderate, and high risk boys
in 2012 dollars, disaggregated by service component. The total cost of the SNAP-ORP
program was $1,729 for low risk cases, $4,166 for moderate risk cases, and $8,503 for high
risk cases. The total cost for an ‘‘average’’ boy was $4,641. This figure was calculated by
taking a weighted average of the low risk (25 %), moderate risk (50 %) and high risk
(25 %) program costs. As can be seen in Table 2, all three groups share common service
elements such as the SNAP-ORP children and parent groups, child and family screening
sessions, individual befriending and treatment reviews, but they differ in the intensity and
duration of some of these service elements. Moderate and high risk groups received a
variety of additional services at increasing levels of intensity, which resulted in higher
program costs relative to the low risk group. The cost of case coordination (e.g., report
writing, obtaining assessments from collateral sources) was estimated at 25 % for all three
risk groups. Indirect costs (e.g., bricks and mortar, program supplies, intake services) were
estimated at 50 % of the total of all SNAP-ORP service components including case
coordination costs. The total recurring cost of the SNAP-ORP program was about
$300,000 per year in 2012, and about 63 children are served each year, which means that
the average cost per child must be close to $5,000.
CBCL Externalizing Scores Versus Later Convictions
Externalizing scores significantly predicted both the prevalence and frequency of later
convictions in the Toronto sample. In regard to frequency, the correlation between
externalizing scores and numbers of convictions was r = 0.15 (p = 0.023, two-tailed). In
regard to prevalence, when externalizing scores predicted the convicted/non-convicted
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dichotomy, the Area Under the ROC Curve AUC = 0.60 (SE = 0.04, p = 0.018).
Therefore, we used effect sizes for externalizing scores as a proxy for likely effect sizes for
the prevalence and frequency of convictions.
Reductions in Crime from the SNAP-ORP Program
The effect size (d) of the SNAP-ORP program was estimated to be between 0.20 and 0.40
(see earlier). These d-values were converted into percentage changes in the prevalence of
offenders. It is known that d is approximately 2 9 r/(1-r 9 r) (Lipsey and Wilson 2001,
p. 200). Therefore, a d-value of 0.40 (our upper estimate of program effectiveness) cor-
responds to an r-value (phi coefficient) of approximately 0.20. Farrington and Loeber
(1989) showed that, in a 2 9 2 table, the r (phi) value is approximately equal to the
difference in proportions. Assuming that half of boys are in a treatment group and half are
in a control group, the percentage reduction in the prevalence of offenders corresponding to
d = 0.40 is 33 % (from 0.60 of the controls to 0.40 of the treated group who are offenders)
when the overall prevalence of offending is 50 %. We used this estimate that d = 0.40
corresponds to a 33 % reduction in offenders. Similar calculations show that d = 0.20
corresponds to a reduction in the prevalence of offenders from 0.55 to 0.45, or 18 %. We
also assume that the percentage reduction in offenses is approximately the same as the
percentage reduction in offenders. This is reasonable on the assumption that, as the
Table 3 Cost savings by offense type, upper estimate, full sample (N = 376)
Offense type Offenses per100 boys
33 %Reduction
Estimatedcost savings
Scaled upcost savings
Rape/sexual assault 5.1 1.5 $340,732 $2,599,612
Armed robbery 1.3 0.2 $37,506 $318,185
Aggravated assault 23.1 4.4 $378,024 $2,560,253
Robbery 21.5 3.8 $134,881 $1,343,630
Simple assault 74.2 19.3 $285,021 $3,686,888
Weapons offenses 21.5 5.2 $23,412 $104,185
Arson 0.8 0.2 $8,156 $48,363
Burglary 95.0 16.4 $212,116 $1,410,404
Motor vehicle theft 13.0 2.9 $33,389 $100,113
Stolen property 73.7 15.6 $118,027 $1,155,724
Fraud 8.2 2.0 $9,242 $78,079
Larceny/theft 103.2 23.8 $79,822 $782,156
Vandalism 30.6 6.7 $20,371 $198,247
Other 5.1 1.3 $958 $2,681
Drug offenses 30.1 6.6 $5,948 $300,953
Administration of justice 187.5 58.4 $52,778 $52,778
Total per 100 boys 693.9 168.2 $1,740,383 $14,742,253
Cost savings per boy $17,404 $147,423
Cost of program per boy $4,641 $4,641
Benefit to cost ratio 3.75 31.77
Savings are in 2012 Canadian dollars, discounted to take account of inflation
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underlying criminal potential decreases, both the probability of offending and the number
of offenses will decrease similarly.
Discounting Benefits
It is necessary to remove the effect of inflation when calculating benefit-to-cost ratios,
since the benefits will be overestimated as a function of the change in the purchasing power
of money over time. For the Toronto sample, convictions were counted for the 9-year
period between their 12th and 21st birthdays. Because we did not code conviction data by
age, the average age for all convictions was considered to be approximately 16.5 (i.e.,
4.5 years after their 12th birthday). Study boys were 9.6 years old, on average, when they
received the SNAP-ORP program. This means that roughly 2.5 years elapsed before any of
the ‘‘crime benefits’’ of the program could be realized. On average, there was an interval of
about seven years between the program and the ‘‘crime benefits.’’ We therefore calculated
the rate of inflation between 1992 (the average date of the program) and 1999 using the
consumer price index for Canada (Statistics Canada 2009) to be 10.6 %. This figure was
used to discount estimates of crime benefits (i.e., from fewer convictions and scaled-up
non-convictions) when calculating benefit-to-cost ratios.
Monetary Cost Savings Associated with the SNAP-ORP Program
A total of 239 boys (63.6 %) were convicted of at least one offense before their 21st
birthday. Table 3 shows the frequency of offending per 100 boys based on the entire
sample of 376 boys. There was an overall average of 694 convictions per 100 boys, or 6.9
convictions per boy. Most convictions were for administration of justice offenses, larceny/
theft, burglary, or simple assault. In comparison, arson, armed robbery, and sexual offenses
were committed relatively less frequently. None of the participants in the sample was
convicted for homicide. The third column in Table 3 lists the number of crimes per 100
boys saved by the SNAP-ORP program, assuming a 33 % reduction in convictions (the
upper estimate), and taking account of co-offending. For example, for burglary, the 33 %
reduction meant that 31.41 burglary convictions per 100 boys were saved. Taking account
of co-offending (dividing by 1.91), 16.4 burglary offenses were saved.
On average, 168 crimes were prevented per 100 boys. After applying the cost of crime
estimates in Table 1, this figure translates into total savings of $1,740,383 per 100 boys, or
$17,404 per boy. The last column in Table 3 shows the scaled up monetary savings when
the offense multiples from Table 1 are applied. When undetected crimes were factored in,
we found that that the ‘‘true’’ cost savings of the SNAP-ORP program was over eight times
higher than the estimate based on convictions, at $147,423 per boy. The savings per 100
boys totalled $14,742,253. Lastly, these per-individual estimates were divided by the cost
of the SNAP-ORP program of $4,641 to yield benefit-to-cost ratios of 3.8 for convictions
and a very high scaled up ratio of 31.8.
Table 4 presents the cost savings associated with the SNAP-ORP assuming an 18 %
reduction in offending (the lower estimate). On average, 92 crimes were prevented per 100
boys, resulting in total cost savings of $949,300 per 100 boys, or $9,493 per boy. The
scaled up savings per 100 boys totaled 8 million dollars, or $80,412 per boy. When each of
these figures was divided by the average cost of the SNAP-ORP program, the benefit-to-
cost ratios were 2.0 for convictions and 17.3 for offenses.
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Variations Over Risk Categories of Boys
We estimated effect sizes for the three risk groups from pre-post changes in CBCL
externalizing scores for the Toronto sample. For all 230 boys with both pre and post scores,
d = 0.44, which is very close to the d-values reported in the independent evaluations
discussed above. Therefore, we are justified in generalizing from these pre-post scores to d-
values that would be obtained in independent treatment–control comparisons. For the low
risk boys, the effect size was d = 0.41, corresponding to a 33 % decrease in convictions.
For the moderate risk boys, d = 0.55, corresponding to a 42 % decrease in convictions.
For the high risk boys, d = 0.32, corresponding to a 27 % decrease in convictions.
Therefore, the effect size was lowest for the high risk boys. We used these percent decrease
figures as our ‘‘upper estimates’’ when calculating the savings in crime and money for each
of the three risk groups.
In line with our upper and lower overall estimates of effect size of 0.40 and 0.20, we
divided each of the aforementioned d-values in half and used these values to obtain ‘‘lower
estimates’’ to calculate the percentage reductions in offending for each risk group. For the
low risk boys, d was assumed to be 0.20, corresponding to an 18 % decrease in convic-
tions. For the moderate risk boys, d was assumed to be 0.28, corresponding to a 25 %
decrease in convictions. For the high risk boys, d was assumed to be 0.16, corresponding to
a 15 % decrease in convictions.
Table 4 Cost savings by offense type, lower estimate, full sample (N = 376)
Offense type Offenses per100 boys
18 %Reduction
Estimatedcost savings
Scaled upcost savings
Rape/sexual assault 5.1 0.8 $185,854 $1,417,970
Armed robbery 1.3 0.1 $20,458 $173,556
Aggravated assault 23.1 2.4 $206,195 $1,396,502
Robbery 21.5 2.1 $73,571 $732,889
Simple assault 74.2 10.5 $155,466 $2,011,030
Weapons offenses 21.5 2.8 $12,770 $56,828
Arson 0.8 0.1 $4,448 $26,380
Burglary 95.0 9.0 $115,699 $769,311
Motor vehicle theft 13.0 1.6 $18,212 $54,607
Stolen property 73.7 8.5 $64,379 $630,395
Fraud 8.2 1.1 $5,041 $42,589
Larceny/theft 103.2 13.0 $43,540 $426,631
Vandalism 30.6 3.6 $11,111 $108,135
Other 5.1 0.7 $522 $1,462
Drug offenses 30.1 3.6 $3,244 $164,156
Administration of justice 187.5 31.8 $28,788 $28,788
Total per 100 boys 693.9 91.8 $949,300 $8,041,229
Cost savings per boy $9,493 $80,412
Cost of program per boy $4,641 $4,641
Benefit to cost ratio 2.05 17.33
Savings are in 2012 Canadian dollars, discounted to take account of inflation
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Ta
ble
5S
um
mar
yo
fre
sult
s
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imat
ean
dgro
up
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lcr
imes
per
10
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oy
s%
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uct
ion
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mes
sav
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er1
00
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ys
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ings
per
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ased
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tio
ns
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efit
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led
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ing
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9
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der
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(N=
20
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Table 5 shows the results by risk group. For low risk boys, assuming a 33 % reduction
in convictions, the number of convictions decreased from 505 to 382 per 100 boys, cor-
responding to cost savings of $11,452 per boy, on average. The lower estimate of an 18 %
reduction in convictions resulted in cost savings of $6,246 per boy, on average. When these
savings were compared to the program cost per low risk boy of $1,729, the benefit-to-cost
ratios for convictions were 6.6 (upper) and 3.6 (lower), respectively. When these were
scaled up, the benefit-to-cost ratios were estimated at 56 and 31, respectively. For moderate
risk boys, assuming reductions in convictions of 42 and 25 % yielded decreases in con-
victions of 192 and 114 per 100 boys, respectively, corresponding to cost savings of
$22,016 and $13,105 per boy. When these estimates were compared to the program cost
per moderate risk boy of $4,166, the benefit-to-cost ratios for convictions were 5.3 and 3.1.
Scaling up these figures, the benefit-to-cost ratios increased to 45 and 27. For high risk
boys, assuming reductions of 27 and 15 % in convictions, the number of convictions
decreased by 207 and 115 per 100 boys respectively, corresponding to cost savings of
$19,101 and $10,612 per boy, on average. When these estimates were compared to the
program cost per high risk boy of $8,503, the benefit-to-cost ratios for convictions were 2.2
and 1.2. Scaling up these estimates to account for undetected offenses, the benefit-to-cost
ratios were estimated at 19 and 11.
It might be considered strange that the cost savings per moderate risk boy were greater
than the cost savings per high risk boy, because high risk boys committed more offenses
and, even though the effect size for high risk boys was lower, their absolute reduction in
the number of offenses was greater than for moderate risk boys. The main reason why more
money was saved for moderate risk boys was because they committed a higher proportion
of (the more costly) violent offenses than did the high risk boys. Table 6 shows that 24 %
of the offenses of the moderate risk boys were violent, compared with 18 % of the offenses
of the high risk boys. In order to calculate the mean crime cost per boy (the mean cost of
each crime by each boy), the percentage distribution of offenses was multiplied by the cost
per crime (shown in Table 1). Table 6 shows that the mean crime cost per boy was $13,885
for the moderate risk boys, compared with $11,584 for the high risk boys and $11,281 for
the low risk boys. It also shows that the greater crime cost per boy for the moderate risk
boys was mainly driven by the cost of violent crimes.
Discussion
Major Conclusions
The monetary benefits of the SNAP-ORP program greatly exceed its monetary costs. This
held true for estimates based on self-reported offending and for those based on convictions
only. Table 5 summarizes the main results of these analyses. Overall, based on convictions
only, it shows that the SNAP-ORP program produced savings ranging between 92 and 168
crimes per 100 boys, amounting to monetary savings of $9,493 to $17,404 per boy, on
average. When this was compared to the cost of the program per boy of $4,641, the benefit-
to-cost ratio ranged between 2.0 and 3.8. After scaling up from convictions to offenses, the
benefit-to-cost ratio became substantially larger, ranging between 17.3 and 31.8.
The SNAP-ORP program had a higher benefit-to-cost ratio than PATHS and Fast Track
for a number of reasons. First, these programs were not very comparable to the SNAP-ORP
program, because they targeted school classes. The SNAP-ORP program treated children
in small groups (of 7, typically) in a community center. Second, the SNAP-ORP program
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was more intensive, including individual befriending, family counseling, and academic
tutoring. Third, the external evaluation sites were trained by the program developers, which
likely yielded a SNAP-ORP replication with high fidelity.
The cost of the program was lowest for the low risk boys ($1,729), followed by the
moderate risk boys ($4,166), and greatest for the high risk boys ($8,503). The total savings
per boy were greatest for the moderate risk boys ($13,105 to $22,016 for convictions,
$111,011 to $186,498 for all crimes) even though the number of crimes saved per 100 boys
was lower for moderate risk boys than for high risk boys. This was because, as mentioned
above, the average cost of a crime committed by a moderate risk boy was greater than the
average cost of a crime committed by a high risk boy. The benefit-to-cost ratios were
always greatest for the low risk boys, followed by the moderate risk boys, and lowest for
the high risk boys. However, it must be remembered that all the SNAP-ORP boys are very
high risk compared to the whole population.
The lower effect of the program with the highest risk boys seems to conflict with prior
research (reviewed above) showing that effect sizes are generally greater for high risk
samples. Further analyses, however, revealed that the effect size was greatest for high risk
boys who received intensive treatment (including individual befriending or mentoring). In
the pre-post analysis, the d value was 0.67 for high risk boys who received individual
befriending, compared with 0.15 for those who did not. The d value was 0.61 for moderate
risk boys who received individual befriending, compared with 0.52 for those who did not.
(Too few low risk boys received individual befriending to carry out this analysis for them.)
Table 6 Percentage distribution of crimes and mean crime cost per boy
Offense type Percentage distribution of offenses Mean crime cost per boy
Low (%) Mod (%) High (%) Low Mod High
Rape/sexual assault 0.7 1.0 0.3 $1,722 $2,566 $793
Armed robbery 0.2 0.2 0.1 $419 $432 $193
Aggravated assault 2.1 3.8 3.2 $1,979 $3,628 $3,037
Robbery 1.9 3.7 2.9 $729 $1,441 $1,133
Simple assault 10.7 11.8 9.2 $1,753 $1,926 $1,509
Weapons offenses 3.0 3.7 2.4 $152 $184 $118
Total violence 18.6 24.3 18.1 $6,755 $10,178 $6,782
Arson 0.0 0.2 0.1 $0 $94 $63
Burglary 15.4 11.0 16.5 $2,199 $1,569 $2,362
Motor vehicle theft 2.6 1.8 1.6 $327 $235 $205
Stolen property 10.5 8.2 13.9 $878 $684 $1,168
Fraud 3.7 0.9 0.4 $194 $46 $22
Larceny/theft 13.3 15.9 14.3 $493 $588 $529
Vandalism 3.3 4.8 4.4 $110 $162 $148
Total property 48.7 42.7 51.3 $4,201 $3,378 $4,497
Drug offenses 4.3 4.9 3.6 $42 $49 $36
Administration of justice 28.0 27.4 26.1 $280 $274 $262
Other 0.5 0.7 0.9 $4 $6 $7
Total other 32.7 33.0 30.6 $325 $329 $305
Grand total 100.0 100.0 100.0 $11,281 $13,885 $11,584
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The effectiveness of the SNAP-ORP program has theoretical implications (see Piquero
et al., 2009, 2010). The 12-week cognitive–behavioral SNAP program for children is based
on the hypothesis that impulsiveness and low self-control are important causes of
offending. This hypothesis is a feature of many criminological theories. Koegl et al. (2008)
found that, as the number of SNAP sessions increased, the effect sizes increased, in terms
of convictions and CBCL delinquency and aggression scores. Therefore, there seemed to
be a dose–response relationship between the intensity of the cognitive–behavioral treat-
ment and the reduction in antisocial behavior. The 12-week parenting program is based on
the hypothesis that inconsistent parenting is an important cause of offending, which is also
an assumption in many criminological theories. In any multi-modal program, it is difficult
to know what are the active ingredients. However, the effectiveness of the SNAP-ORP
program is concordant with criminological theories that include these individual and
family influences on offending.
The methodology used in this article shows how it is possible to carry out a cost–benefit
analysis by linking disparate items of information. We first showed how it was possible to
convert effect size measures into percentage reductions in the number of convictions.
Then, using the actual numbers and types of convictions of SNAP-ORP boys, we estimated
how many convictions of each type were saved, taking account of co-offending. Then,
based on the cost of each type of conviction, we estimated the cost savings per treated boy
as a consequence of fewer convictions. We then calculated a benefit-to-cost ratio by
comparing these savings (discounted for inflation) with the cost of the program. Finally, we
scaled up from convictions to self-reported crimes and also estimated the effects of the
program for boys in different risk categories. Compared with previous cost–benefit anal-
yses in criminology, our work is distinctive in taking account of co-offending and in
scaling up from official records to self-reported crimes.
Limitations
Our benefit-to-cost ratios are underestimates for a number of reasons. First, the benefits
were only estimated for reductions in crime, not for reductions in other costs such as
health, education, or welfare. Basically, we followed Cohen and Piquero (2009) in cal-
culating the number of crimes saved and then multiplying these by cost of crime estimates.
Second, the crime benefits were only estimated between ages 12 and 20, not for the rest of
people’s lives. Third, our estimates of effect size from the Lipman et al. (2008) and Burke
and Loeber (2014) evaluations are underestimates because their control groups received
some treatment and because these were new implementations of the SNAP-ORP program
that, according to the program developers, had not yet reached full treatment impact and
integrity.
We have treated our SNAP-ORP sample as though it was a control group, but in fact all
study participants received the SNAP-ORP treatment. However, the treatment from 1985
to 1996 was less intensive than it is now. It then only lasted 12 weeks, but from 1997 the
continued care model extending up to eight months was introduced. Also, the gender
specific programs were introduced in 1996, and the EARL-20B was introduced in 1998, so
that interventions were based on levels of risk and need. If we had regarded our sample as a
treated group, the savings would have been even greater.
As mentioned, we estimated the costs of crimes by averaging the ‘‘top-down’’ and
‘‘bottom-up’’ figures. If we had only used the more conservative ‘‘bottom-up’’ figures, the
benefit-to-cost ratios of 2.0–3.8 would have decreased to 1.6–3.0, and the scaled up figures
would have decreased from 17.3–31.8 to 13.9–25.5. Therefore, even with these more
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conservative figures, which we believe are under-estimates, the monetary benefits of the
SNAP-ORP program exceeded its monetary costs.
It might be argued that our scaled up benefit-to-cost ratios are too large because
undetected crimes are less serious and less costly than crimes leading to convictions. We
therefore decided to investigate the effects on our results of assuming that undetected
crimes cost only half as much, on average, compared to crimes leading to convictions
(excluding criminal justice costs). When we did this for the entire group of participants, the
scaled up benefit-to-cost ratios decreased from 17.3–31.8 (Table 5) to 9.7–17.8. Therefore,
even with this assumption, the monetary benefits still greatly outweighed the monetary
costs of the program.
We assume that the SNAP-ORP program has equal effects on all types of crimes. We
further assume that the scaling up factors and cost estimates from US research are
applicable to our Canadian sample and that they are applicable to all ages from 12 to 20
and to all risk groups. We have assumed lower and upper limits for the effectiveness of the
SNAP-ORP program, but ideally all estimates should have confidence intervals. It would
also be highly desirable to evaluate the SNAP-ORP program in a randomized trial with a
long-term follow-up and repeated measures of self-reported and official offending.
Scaling up factors and the monetary costs of different types of crimes need to be
estimated in Canadian research. It is true that the US spends more per capita than Canada
on criminal justice (policing, prosecution, courts, and corrections). According to Ky-
ckelhahn (2011), the US spent $755 per US resident in 2007, while Canada spent $478 per
capita in 2012 (in 2002 dollars: see Story and Yalkin 2013). These numbers correspond to
$866 for the US versus $578 for Canada in 2012 Canadian dollars; the Canadian and US
dollars were generally of equal value in 2012. However, justice costs, especially for young
people, are known to be very high in Ontario. Based on the Cohen and Piquero (2009) and
McCollister et al. (2010) estimates, our sample accrued $63,168 in victim costs and
$24,516 in criminal justice costs per person, on average, between ages 12 and 20 (in 2012
Canadian dollars). Therefore, 28 % of the total costs were criminal justice costs. However,
based on the average costs of correctional dispositions, Koegl (2011) calculated that the
average SNAP-ORP boy cost $59,096 in 2008 Canadian dollars (or $62,314 in 2012
Canadian dollars) between ages 12 and 20. These costs do not include police, prosecution,
and court costs. Therefore, perhaps the Ontario criminal justice costs are not lower than US
criminal justice costs.
As mentioned, we assumed the same scaling up factors for all risk groups. It might be
argued, however, that scaling up factors should be higher for high risk boys and lower for
low risk boys. As an exercise, we reduced scaling up factors by 25 % for low risk boys and
increased them by 25 % for high risk boys. With these assumptions, the total savings per
boy were slightly higher for high risk boys ($111,283–$200,490) than for moderate risk
boys ($111,011–$186,498). However, the scaled up benefit-to-cost ratios were still much
higher for moderate risk boys (26.65–44.77) than for high risk boys (13.10–23.58).
Policy Implications
Donohue and Siegelman (1998) raised the question of whether spending on prisons in the
United States should be reduced and the savings used to fund early prevention programs
targeted on children most at risk of criminal behavior. They estimated that a 50 % increase
in incarceration in 15 years would produce a 5–15 % decrease in crime. Alternatively, if
the same amount of money was invested instead in early prevention programs for high-risk
youth, there would be 7–26 % decrease in crime, which in general would be greater.
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Furthermore, early prevention programs can lead to a considerable decrease even in the
most serious crimes such as homicide. Loeber and Farrington (2011) estimated that, if the
Olds, Perry and Multisystemic Therapy programs were implemented nationwide in the US,
this would save one third of all murders (about 4,200 per year) and $5 billion per year of
the cost of incarcerating violent criminals.
It might be objected that there is more public support for imprisonment than for early
intervention. Welsh and Farrington (2011) reviewed the evidence on this argument and
concluded that it was not true. For example, Nagin et al. (2006) found that households were
willing to pay an average of $126 in additional taxes for nurse home visiting programs to
prevent delinquency compared to only $81 for longer sentences. Cohen et al. (2006) and
Cullen et al. (2007) discovered that the public overwhelmingly supported the increased
spending of tax dollars on youth prevention programs rather than on building more prisons.
Based on our analyses and the results of many other research projects, the main policy
implication is that governments should invest in early prevention programs such as SNAP-
ORP in order to reduce crime and save money.
Acknowledgments We are very grateful to Leena Augimeri and Margaret Walsh for providing us withcost estimates for the SNAP-ORP program and for providing some financial support for this research.Nevertheless, we conducted this research independently of the program developers and we have no financialor other stake in the SNAP-ORP program. We are also very grateful to Jeffrey Burke and Peter Carringtonfor carrying out special analyses for us.
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