W O R K I N G D O C U M E N T
Repeated Contacts with the
Criminal Justice System
and
Offender Outcomes
Final Report to Statistics Canada
David P. Farrington
Institute of Criminology, Cambridge University and
Darryl T. Davies
Department of Sociology and Anthropology, Carleton University
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NOTE: This report contains the views of the authors which are not necessarily those of Statistics Canada.
This report focusses on recidivism, which is defined as reoffending after a prior contact with the criminal justice system. It addresses the following questions:
1. What types of contacts with the criminal justice system have been measured and reported in studies of recidivism?
2. How has recidivism been measured?
3. Which features of criminal careers have been measured? 4. How has information about recidivism been used?
5. What are the key methodological issues arising in research on recidivism?
6. What future research on recidivism is needed to overcome previous problems and fill gaps in knowledge?
7. What are the main recommendations for policy and practice in the criminal justice system?
This report does not review non-official measures of offending, such as self-reports (e.g. Jolliffe et al., 2003). It only briefly discusses research on outcomes other than recidivism, such as problems that offenders might have in accommodation, relationships, employment, mental health, drinking and drug use (e.g. Farrington et al.,
2006). It does not review research on institutional misconduct (e.g. Blanchette & Motiuk, 2004; Kroner & Mills, 2001).
The main focus is on research conducted in Canada (especially), the United Kingdom and the United States. Special searches were conducted of publications in the last 10 years in the Canadian Journal of Criminology and Criminal Justice, the
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British Journal of Criminology, and Criminal Justice and Behavior. Similar searches were conducted of publications in the last 10 years of Public Safety and Emergency Preparedness Canada (formerly Solicitor General Canada), the Home Office in the UK, and the US Bureau of Justice Statistics. In addition, many other relevant publications were used in this review. We are particularly grateful to Jim Bonta, Karl Hanson, Britta Kyvsgaard and Patrick Langan for providing relevant reports and information.
A. Types of Contacts
In his classic book on Recidivism, Maltz (1984) lists numerous different types of contacts with the criminal justice system that have been used in recidivism studies, including arrest, reconviction, parole violation, parole revocation, probation violation, and incarceration. Similar lists have been produced by Farrington and Tarling (1985) and Lloyd et al. (1994). In this report, we will review studies that use police contacts, court contacts, corrections contacts, and probation/parole contacts.
Reconviction is the most usual measure of recidivism. For example, Tong and Farrington (2006) reviewed evaluations of the “Reasoning and Rehabilitation” program in reducing reoffending. Most comparisons between experimental and control groups were conducted in Canada (4 comparisons), the USA (9 comparisons) or the UK (12 comparisons). In all cases except one, reconviction/rearrest was measured. Revocation/violation was measured in 5 comparisons, and return to prison in 8 comparisons.
As another example, Hanson and Bussiere (1996) reviewed 61 studies of predictors of the recidivism of sex offenders. Most were conducted in the USA (30), Canada (16) or the UK (10). The most common measures of recidivism were
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reconviction (84%), rearrests (54%), self-reports (25%) and parole violations (16%). Nearly half of the studies (44%) used multiple measures of recidivism. Hanson and Morton-Bourgon (2004) reached similar conclusions in a later meta-analysis of 95 studies.
1. Police Contacts
Measures of rearrest are commonly used in research in the USA but more rarely in Canada or the UK. The major problem is that, if a person has been arrested, this does not necessarily guarantee that he or she was guilty of committing the crime. An arrested offender may not be charged or may be found not guilty. However, police contacts and charges are more direct measures of offending behaviour than are convictions. In a study of male offenders admitted for psychiatric treatment to Penetanguishene Mental Health Centre in Ontario, Harris et al. (1993) defined violent recidivism as either a new charge for a violent offence or return to the institution for violent behaviour that could have resulted in a new charge. They searched not only RCMP files but also the National Parole Service of Canada and provincial correctional and parole systems and the coroner’s office (see also Quinsey et al., 1998); in this report, RCMP records include CPIC and FPS records.
2. Court Contacts
As mentioned, many studies use reconvictions as the main measure of recidivism. For example, in a retrospective study of reconvictions of male child molesters and non-sexual criminals from Millbrook Correctional Centre in Southern Ontario, Hanson et al. (1995) studied reconvictions listed in national RCMP records.
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3. Corrections Contacts
Most definitions of recidivism based on correctional data specify return to custody as the criterion. Of course, it is not necessarily true that a person who has been returned to custody has committed a new crime. Bonta et al. (1996) carried out a retrospective study of male inmates released from federal penitentiaries in 1983-84. They defined recidivism as a custodial admission within three years following release, including new indictable offences and revocation of supervision. They obtained information from the RCMP and from the Correctional Service of Canada (CSC).
4. Probation/Parole Contacts
Definitions of recidivism based on probation or parole data focus on breaches of conditions (violations) or reconvictions. Of course, a probation or parole violation does not necessarily have to involve a new crime. It is difficult to know how much a breach reflects the behaviour of the offender as opposed to the supervising officer. Bonta et al.
(1997) followed up samples of aboriginal and non-aboriginal offenders on parole in Manitoba. Recidivism data came from the RCMP and from the Community and Youth Corrections Division of the province of Manitoba. They found that more of the aboriginal offenders were reconvicted or had a technical violation within three years of completing community supervision.
5. Data Sources
There are several sources of information about recidivism in Canada. Welsh and Irving (2005) have summarized federal, provincial and territorial sources, and the
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different systems for adults and youth aged 12-17. The CPIC (Canadian Police Information Centre) contains national information on convictions (e.g. Langton et al., 2007). Examples of provincial data sources are the Ontario Offender Management System (e.g. Girard & Wormith, 2004) and British Columbia Corrections Files (e.g. Catchpole & Gretton, 2003).
All data sources have strengths and weaknesses. For example, Hanson et al.
(1995) did not use criminal charges in measuring recidivism because these were not consistently noted in RCMP records. Also, they pointed out that, for offenders with only minor offences, the RCMP records may be deleted if no further crimes are recorded during a period of 5-10 years. In a retrospective survey of male violent and sex offenders seen for psychiatric assessment in Ontario in 1966-74, only 54% could be found in RCMP records in 1999 (Langevin et al., 2004). The researchers obtained information from hospital and mental health records. Since 26% of the offenders had committed crimes in other provinces, Ontario provincial records would not have measured recidivism adequately.
This system of deletion of records means that prospective studies of recidivism are usually better than retrospective studies. In prospective studies, persons defined now (e.g. convicted in 2007) are followed up into the future. In retrospective studies, persons defined in the past (e.g. convicted in 1997) are followed up to the present.
The Canadian Centre for Justice Statistics is implementing a series of person-based surveys in the various justice sectors across Canada. The Revised Uniform Crime Reporting survey (UCR2) collects incident-level data on crimes reported to police. The Integrated Criminal Court survey (ICCS) collects detail on criminal cases appearing before the courts. The Integrated Correctional Services Survey (ICSS),
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collects detailed data on persons involved in the corrections system. All of these surveys are longitudinal and will eventually permit the following of persons involved in the Canadian criminal justice system (police, courts, corrections) across all sectors and jurisdictions, and over time. A key objective is to develop linking mechanisms that will permit the statistical tracing of criminal pathways throughout the criminal justice system. The development of these surveys and linkage methodologies will permit more advanced analysis to take place..
In England and Wales, national information about reconvictions can be obtained either from the Offenders Index (derived from the courts) or from the National Identification Service/Police National Computer (derived from the police). These two data sources were compared by Friendship et al. (2001). The police data are superior in a number of ways. For example, they provide information not only about dates of convictions but also about dates of offences, they specify co-offenders, they are available sooner, and they provide more identifying information (e.g. about the race of offenders).
The United States has federal, state and local criminal justice systems. Information about convictions and other measures of recidivism can be obtained from the FBI and from state or local criminal record systems (Langan, 2004).
B. Measures of Recidivism
1. Reconvicted/Not Reconvicted
The most common measure of recidivism is the simple reconvicted/not reconvicted dichotomy. This is often used as the basis of effect sizes in meta-analyses
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(e.g. Tong & Farrington, 2006) because it is the only measure that is routinely reported in most studies. it is often incorrectly termed a “reconviction rate” (e.g. Webster et al., 2006), but a “rate” should refer to a number of offences per unit time. Unfortunately, the reconvicted/not reconvicted dichotomy is a rather insensitive measure and may reach a ceiling.
As an example, Farrington et al. (2002) evaluated the effectiveness of an intensive regime for young offenders in the UK by comparing predicted and actual reconviction percentages of experimental and control offenders. One year after release, the program appeared to be effective. Only 35% of experimentals had been reconvicted, compared with the predicted 47%. In contrast, 55% of controls had been reconvicted, compared with the predicted 56%. However, after two years, the dichotomous measure showed no effect of the program; 65% of experimentals had been reconvicted compared with the predicted 66%, while 76% of controls had been reconvicted compared with the predicted 75%.
2. Frequency of Offending
The frequency of offending is a more sensitive measure of reconviction. For example, in the Farrington et al. (2002) evaluation, experimental offenders were convicted of significantly fewer crimes than were control offenders during the two-year follow-up period (3.5 crimes compared with 5.1 crimes).
3. Time to Failure
Another common measure of recidivism is the time to the first offence. For example, in the Farrington et al. (2002) study, the average time between release and
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reoffending was 228 days for experimentals and 177 days for controls, a significant difference. However, the problem with this measure is that it is only available for persons who reoffend.
Survival analysis is often used in analyzing data on time to failure. For example, Day (1998) followed up children aged 6-12 who were referred for conduct problems to a mental health centre in Toronto. He searched provincial court records, noting that he had to get a court order to obtain youth court data. He plotted the probability of surviving (not being a recidivist) up to each age and hazard probabilities (of being a recidivist) at each age, and used Cox regression models to examine the relationship between predictor variables and survival time. Survival analysis is particularly useful when the follow-up time is not standard for all persons (Friendship et al., 2002).
4. Types of Offences
More sensitive measures of recidivism can be obtained by investigating the variety, seriousness and cost of offences committed. For example, in their evaluation study Farrington et al. (2002) estimated that the crimes of the average experimental offender cost UK society £7,423 during the two-year follow-up period, compared with the £9,903 cost of the crimes of the average control offender. These figures had been adjusted to take account of pre-existing differences between the two groups.
5. Defining Success
It should not necessarily be assumed that a person who commits a crime during a follow-up period is a “failure”. There are many possible definitions of “success” that do not require the complete cessation of offending. For example, in comparing
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offending after an intervention with offending before, a decrease in the frequency, variety, seriousness or cost of offending may indicate that the intervention was successful.
It would be desirable to measure indicators of life success (e.g. in accommodation, employment, relationships, substance use, mental health: see Farrington et al., 2006) as well as measures of recidivism. These could be measured in the CCJS microdata surveys. If a person’s life success in many areas is improving, an intervention could have been successful on some criteria, even if the person continues to offend.
6. Routine Measures of Recidivism
No government in Canada (federal or provincial) publishes recidivism data on a regular basis. One of the first efforts to collect such data was a study by Bonta et al.
(2003) conducted for the Solicitor General Portfolio Corrections Statistics Committee. They investigated all releases from federal penitentiaries in 1994-95 and samples of offenders released in 1995-96 and 1996-97, and studied new convictions within two years of release. The percentage reconvicted varied from 41% to 44%, and male and Aboriginal offenders were more likely to be reconvicted than female and non-Aboriginal offenders. The researchers pointed out that there was a 6 to 12 month delay in recording new offences in RCMP criminal history records, and they corrected for deaths of offenders.
One aim of this research was to develop a common definition of recidivism and a method of measuring year-to-year trends. It was hoped that yearly recidivism data would be routinely published in the Corrections and Conditional Release Statistical
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Overview. The 2006 version of this publication (Public Safety and Emergency Preparedness Canada, 2006) provides information about parole revocations, offending after statutory release, and convictions of offenders under community supervision.
One of the first large-scale studies of recidivism in Canada was conducted by Campbell (1993) for the CCJS. Just over one-third (35%) of a cohort of offenders were reconvicted within two years, according to RCMP FPS records. In the first publication based on the ICSS, Johnson (2005) followed up persons under supervision in Saskatchewan; 22% were returned to corrections within one year of release, 34% within two years, and 41% within three years.
In the UK, the Home Office routinely publishes data on reconvictions within two years after sentences. For example, Barclay and Tavares (1999) reported that, after adjustment for age and number of previous convictions, the probability of reconviction of males was similar (about 56%) for prison and community penalties. Males were reconvicted more than females, young people were reconvicted more than older people, and the probability of reconviction increased with the number of previous convictions (from 26% of those with none to 76% of those with more than 10).
The US Bureau of Justice Statistics occasionally publishes information about recidivism of offenders within three years after release. The first major study of recidivism of prisoners released in 11 states was published by Beck and Shipley (1989), who found that 63% were rearrested and 47% were reconvicted within three years. In a later study, Langan and Levin (2002) reported on the recidivism of 272,000 prisoners released in 15 states, using FBI and state data. They found that 68% were rearrested, 47% were reconvicted, and 25% were returned to prison with a new sentence. In addition, Langan et al. (2003) provided detailed information about the recidivism of sex
11 offenders released from prison.
The problem of linking police, court and corrections records is acute in many countries. This is less of a problem in Scandinavian countries such as Denmark and Sweden, where each person has a personal identification number (including the date of birth) which is used everywhere, including police, courts and corrections. The Danish Prison and Probation Service publishes yearly statistics on recidivism, but in Danish. Similarly, the Swedish Prison and Probation Service publishes yearly statistics on recidivism,. but in Swedish. Dr. Britta Kyvsgaard (Chief of Research at the Danish Ministry of Justice) informs us that Table 7.1 of the Swedish statistics shows recidivism percentages one, two and three years after release from prison.
All official recidivism measures, of course, underestimate the true rate of reoffending. By comparing crimes reported by victims with numbers of persons convicted (and adjusting for co-offending), it is possible to estimate the number of crimes committed per conviction. This is actually the number of offender-offence combinations (one offender committing one offence) per offender-conviction combination (one offender receiving one conviction). For England and Wales in 1999, there were an estimated 136 burglaries per conviction for burglary, and an estimated 173 robberies per conviction for robbery (Farrington & Jolliffe, 2004). For the USA in 1996, there were an estimated 63 burglaries per conviction for burglary, and an estimated 42 robberies per conviction for robbery (Langan, 2004). Comparable figures have not been estimated for Canada.
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The problem of measuring and studying recidivism could be regarded as a subtopic within the general field of criminal career research (Piquero et al., 2003, 2007). A criminal career is defined as the longitudinal sequence of crimes committed by an individual offender. It has a beginning (onset), an end (desistance) and a career length in between (duration). Only a certain proportion of each birth cohort (prevalence) commits offences and has a criminal career. During their careers, offenders commit crimes at a certain rate (frequency) while they are at risk of offending in the community (e.g. not incarcerated, emigrated, or incapacitated by illness). For offenders who commit several offences, it is possible to investigate how much they specialize in certain types of crimes and to what extent the seriousness of their offending escalates over time.
An early onset of offending typically predicts a long criminal career and the commission of many offences. A small fraction of each birth cohort (e.g. 5% of males) typically commits at least half of all offences. In the criminal career literature, these are often termed “chronic offenders”, although Moffitt’s (1993) theory distinguishes between “life-course-persistent” and “adolescence-limited” offenders. Most crimes up to the teenage years are committed with others, whereas most crimes from age 20 onwards are committed alone. Some persistent offenders are “recruiters” who constantly commit crimes with less experienced offenders, thus dragging more and more people into the net of offending. The reasons given for offending up to the teenage years are quite variable, including both utilitarian or rational reasons and more emotional reasons (e.g. for excitement or enjoyment, because the person got angry). In contrast, from age 20 onwards, utilitarian motives become increasingly dominant.
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desirable to identify chronic offenders (and recruiters) at an early stage and devise special programs for them (Blumstein et al., 1985). Whether offences are committed alone or in groups influences the probability of recidivism (Reiss & Farrington, 1991) and the effectiveness of sanctions. For example, if one member of a group is incarcerated, this will not necessarily prevent offending by the group. Also, the versatility of offenders means that it does not make much sense to have specific programs for violent offenders. Since criminal career research shows that violent offenders are essentially frequent offenders (Farrington, 1991), programs to prevent violent offending could target frequent or chronic offenders.
In regard to recidivism, the most salient criminal career features are career duration (both in terms of number of crimes and length of time) and desistance. It is important to propose and test developmental theories that predict the future course of criminal careers, including recidivism (see e.g. Farrington, 2005).
1. Career Duration
Very few studies have examined either the residual career length or the residual number of offences in criminal careers. These topics have important policy implications. In particular, it is desirable for judges to take account of residual career length in setting the length of prison sentences, because it is futile to incarcerate people after they would have stopped offending anyway. Using UK data, Kazemian and Farrington (2006) estimated residual career length and residual number of offences in criminal careers and investigated to what extent these quantities could be predicted at an early stage, based on age, conviction number, time since the previous conviction, and age of onset of offending. They found that risk scores significantly predicted both residual career
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Carrington et al. (2005) studied court careers of Canadians born in six provinces. They reported that the average length of completed court careers was 20 months. However, their research extended only up to age 22, and they considered careers to be completed if there were no recorded crimes after age 20. A longer follow-up would, of course, yield longer average lengths of careers.
To the extent that career durations are long, short follow-up periods in recidivism studies may be inadequate. For example, in a follow-up study of sex offenders referred for psychiatric assessment or treatment in Ontario, Langevin et al. (2004) found that the average criminal career lasted nearly 20 years.
2. Desistance
In many ways, desistance is the opposite of recidivism. A great deal is known about desistance (see e.g. Kazemian, 2007). The measured termination of offending can be distinguished from more gradual decreases in the underlying potential for offending. A key empirical issue is how to determine that a person has really stopped offending for good. There could be intermittency in criminal careers, with a person ceasing to offend in the early 20s but then restarting in the late 20s after a series of life events such as losing a job, getting divorced and starting heavy drinking. Barnett et al.
(1989) showed that the probability that termination was final increased with the time interval since the previous offence. Also, there could be desistance from some types of crimes but not others.
Much is known about life events that encourage desistance. These include getting married (e.g. Farrington & West, 1995), obtaining a steady job (Farrington et al.,
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1986), and moving to a low-crime area (Osborn, 1980). Therefore, desistance might be encouraged by programs that help offenders to settle down with a steady job and a steady partner. Information about protective factors that foster desistance is important in informing interventions after the onset of criminal careers. Information about the desistance process after release from prison could indicate which offenders need particular types of supervision or support.
A final issue is when (if ever) an ex-offender becomes indistinguishable from a non-offender in the probability of future offending (Kurlychek et al., 2006). As mentioned, this probability decreases with time since the previous offence. To the extent that ex-offenders are indistinguishable from non-offenders, ex-offenders should not suffer discrimination (e.g. in employment).
D. Uses of Recidivism Data
The main use of recidivism data is in evaluating the effectiveness of sentences or programs in reducing reoffending (e.g. Davies, 2002; McWhinnie & Andrews, 1997). In addition, there is a great deal of research on risk assessment to predict recidivism, which might assist criminal justice decision-makers in sentencing or release decisions. Recidivism data could also assist in forecasting the need for future resources such as the number of prison cells (e.g. Cooke & Michie, 1998). Ideally, a model of the Canadian criminal justice system should be developed specifying the flow of offenders through each stage (see Cassidy, 1985). It would then be possible to forecast the effects of changes in recidivism at any stage.
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1. Evaluating Effectiveness
In order to evaluate the effectiveness of any program, it is essential to compare offenders who receive the intervention with comparable offenders who do not receive the intervention. The best method of ensuring comparable groups and dealing with threats to internal validity (see e.g. Farrington, 2003) is to assign people at random to experimental or control conditions. There have been many reviews of the effectiveness of intervention programs (e.g. Dowden & Andrews, 2000; Latimer, 2001).
Few evaluations conducted outside the USA have used randomized experiments (Farrington & Welsh, 2005, 2006). For example, in their meta-analysis of the “Reasoning and Rehabilitation” (R & R) program, Tong and Farrington (2006) found that most North American evaluations used random assignment but that most British evaluations were retrospective quasi-experimental evaluations based on comparisons of predicted and actual reconviction percentages.
Two Canadian evaluations of R & R used random assignment. However, the first, by Ross et al. (1988) included too few offenders (45) to achieve the benefits of random assignment in equating groups beforehand on all possible extraneous variables that might influence recidivism. The second study, by Robinson (1995), randomly assigned about 4,000 federal offenders either to R & R (about 3,500) or to a waiting list control group (about 500). The evaluation focussed on 1,573 R & R offenders and 369 control offenders who had been released for at least one year. Slightly more controls were reconvicted than program participants (24% compared with 21%) but the difference was not statistically significant. Other large-scale Canadian randomized experiments have been carried out by Byles and Maurice (1979) in Hamilton, Annis (1979) in Ontario, Tremblay et al. (1996) in Montreal, and Leschied and Cunningham
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Quasi-experimental evaluations are more common. For example, in Newfoundland, Bonta et al. (2000) compared probationers with electronic monitoring, probationers without electronic monitoring, and released inmates. Reconvictions within one year were ascertained from RCMP and provincial records. The three groups did not differ significantly in LSI-R scores, which predicted recidivism. Nor did they differ in the percentage reconvicted, although a retrospective analysis suggested that high risk offenders with electronic monitoring did better than prison inmates.
Few evaluations of correctional effectiveness have included post-release interviews with offenders. Such interviews are desirable to assess self-reports of offending and life success in areas such as accommodation, relationships, employment, substance use and mental health (Farrington et al., 2006). In turn, all this follow-up information is useful in analyses of the financial costs and benefits of different sentences and programs (Welsh et al., 2001). It is, of course, difficult to trace and obtain cooperation from ex-offenders, but successful methods of tracing and obtaining cooperation have been developed (Farrington et al., 1990).
Interviews are also needed to test theories of change and hypotheses about mediating processes between interventions and outcomes. For example, a decrease in offending could be caused by changes in individual deterrence (where the propensity to offend is unchanged but the person is deterred by increases in the subjective probability and/or severity of sanctions) or by rehabilitation (where the person’s underlying propensity to offend decreases). It is not possible to distinguish between these two hypotheses using only criminal career information in official records (although other “official” data, such as reports by probation or parole staff, could be useful).
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Data collected by Statistics Canada could be used in quasi-experimental evaluations of the effectiveness of sentences and programs. Comparable control groups could be established using risk scores or propensity matching, based on analyses of factors that predict the future course of criminal careers. Alternatively, there could be statistical control of influencing variables using general linear modelling techniques. For example, negative binominal regression analysis could be used where the outcome is a (skewed) count of the number of offences committed. The intervention variable (e.g. received or did not receive a program) should be added last in the equation to estimate its effect on offending.
It is conceivable that recidivism data might be used as performance indicators for police, courts and corrections (McWhinnie & Andrews, 1997). However, it would be important to control for many extraneous influences on recidivism, for example by comparing actual and expected recidivism percentages. Performance indicators for police, courts and corrections, set by the Home Office, have been used in England and Wales for many years. For example, in 1996 the provision of programs designed to reduce recidivism was established as Key Performance Indicator No. 7 for the prison service (McGuire, 2001).
2. Risk Factors for Recidivism
There have been many studies of risk factors for recidivism, but little is known about protective factors. We will focus on Canadian research. Bonta et al. (1996) investigated the Statistical Information on Recidivism or SIR instrument, developed by Nuffield (1982) for use in parole decisions with federal inmates. They carried out a retrospective prediction study of male inmates released from federal penitentiaries in
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1983-84. Recidivism was defined as a custodial admission within three years following release. The following factors predicted recidivism: current offence (burglary), young age at admission, previous imprisonment, previous parole breach, young age at first conviction, previous violence, and short time at risk since previous offence. These are typical items included in static prediction scales. Static items are those which do not change over time, whereas dynamic items can change over time.
Bonta et al. (1997) investigated predictors for probationers in Manitoba, using the Manitoba Risk-Needs Scale. These included frequent address changes, unemployment, alcohol/drugs, age, gender, prior convictions, marital status, financial circumstances and peer influence. Hanson et al. (1995) studied predictors for male sex offenders in an Ontario correctional institution. The strongest predictors of violent and sexual recidivism (reconviction) included young age, number of prior convictions, and number of current convictions. Harris et al. (1993) investigated predictors for men who were assessed or treated in a psychiatric institution in Ontario. The strongest predictors of violent recidivism included the Psychopathy Checklist (PCL), separation from parents under age 16, never married, elementary school maladjustment, failure on prior conditional release, young age at index offence, history of alcohol abuse, and DSM-III personality disorder.
There have been several meta-analyses of risk factors for recidivism. Gendreau
et al. (1996) carried out a meta-analysis of the predictors of recidivism among adult offenders, based on 131 studies. The strongest predictors included young age, criminal history, child-rearing, antisocial personality, delinquent peers, and criminogenic needs. Hanson and Morton-Bourgon (2004) completed a meta-analysis of the predictors of recidivism among sex offenders, based on 95 studies. The strongest predictors of
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sexual recidivism included deviant sexual interests, antisocial personality disorder, impulsiveness, pro-criminal attitudes, childhood criminality, prior criminal history, non-compliance with supervision, conflicts in intimate relationships and low sex knowledge.
Cottle et al. (2001) published a meta-analysis of predictors of recidivism (rearrest) in juveniles, based on 23 studies. The best predictors were (in order) young age at first commitment, young age at first contact with the law, non-severe pathology, family problems, conduct problems, poor use of leisure time, delinquent peers, length of first incarceration, number of out-of-home placements, number of prior commitments, type of crime, low achievement, substance abuse, low IQ, special education, history of abuse, male gender, single parent, minority race, and low socio-economic status.
The early research on predictors of recidivism was used as the basis for the development of risk assessment instruments such as the PCL-R (Hare, 1991), the LSI-R (Andrews & Bonta, 1995), the VLSI-RAG and SOLSI-RAG (Quinsey et al. 1998), and the Static-99 and Static-2002 (Hanson & Thornton, 1999, 2003). Most recent Canadian research on the prediction of recidivism has studied or compared one or more of these instruments in the prediction of general recidivism (e.g. Catchpole & Gretton, 2003; Simourd, 2004), violent recidivism (e.g. Gendreau et al., 2002; Girard & Wormith, 2004; Glover et al., 2002) or recidivism of sex offenders (e.g. Barbaree et al., 2001; Langton et al., 2007).
Most studies of risk factors for recidivism have been based on information that is available in records. However, Zamble and Quinsey (1997) carried out a retrospective study of the psychological predictors of recidivism (defined as a new offence within a year of previous release) by interviewing men who were newly returned to prison.
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and this has been discussed by Bonta (2002). Hilton et al. (2005) investigated ways of improving the communication of risk assessment information to forensic practitioners.
E. Methodological Issues
1. Measuring Recidivism
It is essential to obtain dates of offences rather than dates of convictions or dates of other types of official processing (e.g. return to prison). The time lags between dates of offences and dates of convictions can produce misleading conclusions about recidivism. For example, some prisoners could be convicted after release for offences that they committed before imprisonment. It is also essential to measure dates of entering and leaving custody, in order to know when offenders are at risk of offending in the community. Offences can be committed during custodial sentences, either in the institution or during short leaves or furloughs.
A key issue is: What should be the length of the follow-up period? Most routinely-published government statistics on recidivism in the UK and USA use a two-year or three-two-year follow-up period, and measure dates of convictions rather than dates of offences. Therefore, a person who committed an offence 18 months after release and then waited 12 months to be convicted would count as an unconvicted person in a two-year follow-up. Longer follow-up periods may be needed, especially in light of information about the length of criminal careers (e.g. Langevin et al., 2004). However, most persons who are going to be reconvicted are reconvicted within three years (McWhinnie & Andrews, 1997).
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recidivism. This depends on the definition of recidivism and on the objectives of measuring recidivism. For example, if the main objective is to measure the effectiveness of imprisonment, the follow-up period could start on the day of release from prison and measure reconviction or return to custody. If the main objective is to measure the effectiveness of sentences, the follow-up period could start on the day of conviction and measure reconvictions. However, this leads to the question of whether offenses committed during periods of imprisonment (e.g. in custody or during a home leave) should be counted or not.
It is essential to measure time at risk of offending in the jurisdiction being studied. It is important to know when people are not at risk because of death, emigration or other incapacitating factors (e.g. confined to bed or to a wheelchair). Offenders have disproportionally high rates of mortality and morbidity (e.g. Tremblay & Pare, 2003). It is also important to equate the follow-up period for all offenders. Variable follow-up periods in retrospective prediction studies (e.g. Langton et al., 2007; Quinsey et al.,
1998) can cause analytic problems. In addition, local variations in police and court policies can influence recidivism and ideally should be taken into account.
The definition of an offence can be quite problematic. If three offenders attack two victims, two offences are committed but six convictions could occur. Several offences could lead to several convictions on one sentencing occasion or on several sentencing occasions. One event could lead to several offences (e.g. burglary and going equipped to steal). Plea bargaining is a problem in many countries. It is desirable to ensure that the number of offences that are counted approximates the number of incidents, as much as possible. This is important in measuring the frequency of offending.
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2. Issues in Prediction Research
Issues arising in selecting and combining predictors into a risk assessment instrument, and in measuring predictive efficiency, have been reviewed by Farrington and Tarling (1985).
The simplest method of combining predictors is to give each dichotomous risk factor a weight of 1 point according to whether risk is present or absent. Following the work of Burgess (1928), this is termed the Burgess method. a slightly more complicated method, devised by Nuttall et al. (1977) and used in Canada by Nuffield (1982), gives 1 point to each category for every 5% difference between its recidivism percentage and the overall recidivism percentage. More complicated weighting techniques are based on regression analyses or hierarchical splitting techniques (see e.g. Silver et al., 2000). These more complex techniques increase predictive efficiency in construction samples but not usually in validation samples (e.g. Farrington, 1985; Nuffield, 1982; Simon, 1971). Therefore, the simple Burgess method seems to be the most satisfactory method of combining predictors into a risk assessment instrument, especially in light of the need for practitioners to have a simple method to use in practice.
A variety of measures of predictive efficiency have been used, including the phi correlation (e.g. Gendreau et al., 2002), the point biserial correlation (e.g. Hemphill & Hare, 2004), and Relative Improvement Over Chance or RIOC (e.g. Loza & Loza-Fanous, 2003). Unfortunately, all of these are problematic. For example, the maximum possible value of phi can be much less than 1 (e.g. Farrington & Loeber, 1989). For a comparison between N ordered categories and a dichotomous outcome variable such as reconviction, the best measure of predictive efficiency is the area under the ROC
24
curve (Mossman, 1994). Several Canadian studies have used this index (e.g. Bonta et al., 1996; Langton et al., 2007; Quinsey et al., 1998).
3. Issues in Correctional Effectiveness Research
A key issue is when the follow-up period should begin. For example, in comparing the effectiveness of prison and probation, should the follow-up period begin on the date of sentence? But then, offenders in prison will not be at risk of recidivism for a certain time. Or should the period begin on the date of sentence for probationers and on the date of release for prisoners? But then, prisoners will be older than probationers at the start of their follow-up period, and age is an important predictor of recidivism. Or should the period begin on the date of completion of prison and probation sentences? But then, what about those who offend while on probation, who might possibly be imprisoned as a result? The least bad solution is probably to begin the follow-up period on the date of sentence.
Another problem is how to deal with those who complete or fail to complete a program of treatment. Completers invariably have lower recidivism rates than dropouts. For example, in the evaluation of “Reasoning and Rehabilitation” by Robinson (1995), the completers had a significantly lower recidivism percentage than the controls, but the program participants (completers plus dropouts) were not significantly lower than the controls. It could be argued that the recidivism analysis should be based on completers, because only the completers have truly received the intervention. However, differences in recidivism between completers and dropouts might reflect the prior non-comparability of these groups, as documented by Wormith and Olver (2002) in Saskatoon. The least bad solution is probably to carry out an “intent-to-treat” analysis
25
and analyze all those initially assigned to the treatment.
There are techniques for adjusting results to take account of the fact that less than 100% of the treatment group received the treatment, or of treatment cross-overs (e.g. Angrist, 2006). An alternative strategy is to start with matched pairs of offenders and randomly assign one member of each pair to the treatment. If the “treated” person does not complete the treatment, both members of the pair would be deleted from the analysis. This would preserve the initial equivalence of experimental and control conditions.
F. Conclusions
1. Future Research Needs
Methodological research is needed in Canada on the comparison of different data sources to measure recidivism. This could address the question: to what extent are conclusions about correctional effectiveness or about risk assessment affected by the use of different operational definitions of recidivism? It is also important to measure the effect of variations in local police and court policies on conclusions about recidivism. It is also desirable to compare self-reported delinquency with official records in a survey to estimate the “scaling-up” factors from official records to self-reports. For example, for juveniles, there were 30 self-reported crimes per court referral in Seattle (Farrington et al., 2003) and 80 self-reported crimes per court petition in Pittsburgh (Farrington et al., 2007).
It is important to interview offenders in follow-up studies to measure self-reported offending and indicators of life success. These measures are needed in order to carry
26
out adequate cost-benefit analyses of sentencing and interventions. It is also desirable to carry out more research in Canada on desistance, residual career length, and the residual number of offences committed in criminal careers. Kazemian et al. (2007) addressed these questions in a Montreal longitudinal study. More criminal career research is needed in Canada.
More meta-analyses of predictors of recidivism (as opposed to risk assessment instruments) are needed. More prospective studies of the prediction of recidivism are needed. More attempts are needed in primary research to study protective factors as well as risk factors, and to develop dynamic instruments that can be used to measure improvements in risk during prison sentences. More Canadian research is needed on non-Caucasian and female samples (e.g. Blanchette & Motiuk, 2004). And more randomized experiments are needed to assess the effectiveness of correctional treatment.
2. Policy Implications
Attempts should be made to reach agreement on operational definitions of recidivism for use by Canadian justice agencies, including reconviction, revocation and return to prison. Ideally, reconviction data should be routinely collected and regularly published, for example during three years after prison as opposed to probation sentences. Information is needed on the reconvicted/not reconvicted dichotomy, on the number of crimes committed per year, on the time period between sentencing or release and reconviction, and on types of offences committed. Information is also needed on the financial costs of different types of crimes. It is undesirable to delete information about offenders from records. It is important to collect data on exact dates
27
of release and of offences and on death and emigration to make it possible to determine the time at risk of offending. It would also be desirable to collect data on each offender (e.g. age, gender, number of prior offences, type of current offence) that would permit the calculation of a risk score, and to adjust recidivism data for this risk score. It might then be possible to use routinely published official statistics in evaluating the effectiveness of sentences or as key performance indicators.
The microdata survey strategy at the CCJS is potentially an extremely valuable source of information about recidivism. Assuming that problems of data linkage can be overcome, the longitudinal, multi-sectoral case record data could be used for many purposes. As a minimum, we suggest that the following types of information should be routinely published:
1. Percentage convicted for offences committed within three years of sentencing and within three years of release. This indicator has the great merit of being simple and easily understandable by a wide audience. It is the most direct measure of reoffending.
2. Percentage convicted for different types of offences.
3. Actual percentage convicted compared with expected percentage convicted based on characteristics (risk factors) and prior criminal histories of offenders. For example, the greater recidivism of Aboriginal offenders may possibly be explained away by their greater number and intensity of risk factors.
4. Average cost of offences committed within three years at risk. This could be compared with expected cost and with the cost of criminal justice programs. Of course, adjustments should be made for changes in the value of money over time.
28
It would also be highly desirable to use the ICCS to collect information about criminal careers, especially the residual length of careers and the residual number of offences committed after different sentences and different programs. While prospective longitudinal studies including repeated face-to-face interviews are ideally needed, the ICCS represents an efficient way of obtaining relatively quick and cheap large-scale information about these vitally important topics.
29
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