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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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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

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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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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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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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