How is community service used as a correctional tool

Although typically framed as an alternative to incarceration, probation in particular is a key driver of mass incarceration. Both probation and parole set people up to fail with long supervision terms, strict conditions, and intense surveillance.

And this happens a lot: Annually, nearly , people are shifted from community supervision to prison or jail. First, people under community supervision live under intense scrutiny, which often leads to the detection of low-level offending such as drug use or technical violations such as breaking curfew 6.

Normally, incarceration would not be appropriate for such low-level offenses; they would typically be addressed through fines, community service, drug treatment programs, or no criminal justice response at all. However, for people under community supervision, these minor offenses and technical violations can lead to incarceration. In addition to surveillance, people under community supervision must comply with numerous conditions, many of which are unrelated to the original offense and can be very costly.

In , the Robina Institute estimated that people on probation must comply with 18 to 20 requirements a day in order to remain in good standing with the probation department.

Violating any of these conditions can result in prison or jail time. While the requirements of community supervision would be burdensome for anyone, they can be especially difficult for those on probation and parole. People under community supervision have significantly higher rates of poverty, mental illness, and lower educational attainment than the general public. Furthermore, community supervision populations have much higher rates of addiction, 10 yet parole and probation policies ignore the realities of drug addiction and relapse, 11 tending to criminalize drug use rather than taking a public health approach.

Finally, like incarceration, probation and parole affect already marginalized populations in troubling ways:. Our analysis shows that, in every state, correctional systems control the daily lives of large numbers of people — and unnecessarily, in all too many cases. Prisons and jails are warehousing people struggling with substance use disorders and mental illness, who need help that correctional facilities are unsuited to provide. And probation and parole systems anticipate and respond to failure rather than success — a rational result of heavy caseloads, limited resources, and a myriad of conditions to track instead of providing individualized support.

All told, we are left with a bloated, ineffective, costly correctional system that inflicts further harm on individuals, families, and communities. Probation and parole systems, in particular, can be reformed to help people exit the criminal justice system for good and lead successful lives. Policymakers should invest in strategies to make these systems tools for decarceration rather than engines for incarceration. Parole should be used as a tool for shortening lengthy sentences, and probation solely as an alternative to incarceration.

To do so effectively, reforms will be necessary at every level, from parole offices to state legislatures: Instead of surveillance, parole systems should focus on reducing the unnecessarily high barriers that people on parole face in securing education , employment , housing , and other vital resources. In most states, there is tremendous room for improvement, both in the availability and value of parole. Probation, by design, is an important alternative to incarceration.

In cases where incarceration is the only practical alternative, the use of probation should be encouraged to minimize the broad social and economic harms of incarceration. Instead, probation should be reserved for people who are at a high risk of reoffending and who require more support and supervision.

To improve the effectiveness and efficiency of probation, states should reduce their outsized probation populations. And New York City has shown that this can be done without compromising public safety. To reduce probation populations, courts should take note from other countries and address low-level offenses or people who are low-risk with more appropriate sanctions, including warnings, fines, 18 community service, and diversion to appropriate programming, such as treatment for substance use disorders or mental health services.

These alternative sanctions would not only reduce the probation population, but also the total number of people under correctional control. In addition to restricting the use of probation to offenses serious enough to warrant correctional control, courts and probation practitioners could promote compliance and prevent future offending by:.

Only with serious reforms to both the conditions and the number of people under its control can probation be a true alternative to incarceration, rather than a system that expands correctional control and drives incarceration. This report provides another metric for understanding where your state falls within the national landscape of mass incarceration. Our state-specific breakdowns below suggest where state advocates and policymakers might start when developing proposals for meaningful justice reform.

The most effective reforms will reduce the number of people under correctional control in total, and transform broken probation and parole systems into supportive alternatives to incarceration. The non-profit, non-partisan Prison Policy Initiative was founded in to expose the broader harm of mass criminalization and spark advocacy campaigns to create a more just society.

The organization is known for its visual breakdown of mass incarceration in the U. In Boston, she continued working as a tutor in a women's prison through the Petey Greene Program. Victim compensation is a monetary compensation that may be ordered by the court as compensation for a certain loss that has been suffered by the victim. Failure to pay victim compensation as ordered by the court is regarded as a violation of a condition and the Head of Community Corrections takes the necessary steps such as informing the court of the violation and the court may impose an alternative sentence as may deem necessary at that stage.

Community service is a free service to the community which a probationer may be ordered by the court to perform for a fixed number of hours at a community service institution such as a hospital, school, old age home, nature conservation projects or any other suitable institution. Community service must be of such a nature that it is to the advantage of the broader community. Probationers may be compelled to attend specialised programmes or lectures on specific subjects. These programmes and lectures are aimed at addressing specific identified needs or problem areas of individual cases with a view to prevent further criminalisation, to foster responsibility, to prevent drug or alcohol abuse, to improve family responsibility, relationships and the acquisition of social skills.

These programmes are presented by expert personnel of the Community Corrections Offices. Where such expert personnel are not available, the Head of the Community Corrections Office may arrange for the procurement of the services of external experts. Furthermore, probationers must not commit any offence while serving the sentence of correctional supervision.

The following conditions are normally set for the offenders who are placed out on parole:. In order to ensure compliance with the set conditions as far as possible, all probationers and parolees are subject to monitoring which is executed by correctional officials, temporary correctional officials or volunteers who are under the control of the Head of Community Corrections.

Monitoring is done by means of:. Second, these data provide us with a follow-up period of eight years which gives us insight in the recidivism occurring after either of these sanctions both in the short-term as well as in the long-term. Fourth, to test the robustness of our results against bias from unobserved covariates, we apply the Rosenbaum bounds method.

Finally, to prevent interference from feed-back effects, our analyses are limited to offenders who were not previously sentenced to either community service or imprisonment.

Recorded conviction careers of all offenders sentenced in were reconstructed using abstracts from the GDF as available in Consequently, the data include the entire officially recorded criminal history, i. Footnote 7 Entries in the GDF include all criminal cases that have led to any type of judicial action. However, in this study, we only use information on those offenses that were either followed by a conviction or a prosecutorial disposition due to policy reasons, thereby excluding offenses that resulted in an acquittal or a prosecutorial disposition due to insufficient evidence.

Next to criminal career information, the GDF data contains information on other, known as confounding variables, such as sex, age, nationality, type of index offense, number of crimes in case of conviction, and severity of the offense.

The GDF also contain information on the length of community service and the length of imprisonment following a conviction. This extra information is used to control for selection effects. The overall sample size is reduced for comparison for various reasons.

First, our analysis focuses on offenders aged between 18 and 50 years. We, therefore, exclude 6, offenders aged younger than 18 and older than Feed-back effects would imply a prior sanction to affect both the chances of a subsequent community service and imprisonment as well as post-sanction recidivism.

These restrictions resulted in an analysis sample of 11, offenders, of which 7, were sentenced to community service and 3, to imprisonment. All sentences involving detention, including being placed in a reformatory school, of maximally six months were counted as imprisonment.

Footnote 9. The outcome variable in our study is the post-treatment conviction rate. All convictions after the index offense are measured as recidivism, even when these subsequent convictions took place in Since we have data up to , our follow-up period spans a maximum of eight years. When applicable, in constructing our outcome variable, we control for the duration of incapacitation by multiplying the observed number of convictions by the inverse of the proportion of the follow-up period offenders were actually free to offend.

We thereby assume that offenders would have been convicted at the same rate for the entire period if they had not been imprisoned. When comparing two types of criminal justice interventions the list of potential confounding variables is endless.

Nagin et al. Our model includes these variables and adds even more. To take case characteristics into account, we include 18 dummies representing offense types of the index offense see Table 1. A continuous variable indicating the severity of the offense based on the maximum penalty is also included in the model ranging from 0 to Next, the number of crimes in case of conviction in was added to the model.

Furthermore, we take into account the criminal history in great detail. In total, six variables are included concerning the criminal history of offenders. Distinctions are made between property crimes, violent crimes, and other crimes, and between the criminal history in the year prior to the index offense and the past ten years.

To take the demographic background into account, age divided by ten is added to our model as a continuous variable and quadratic to allow for a non-linear relationship between age and the chance of community service. Our model also includes dummy variables indicating whether the offender was male or female and whether the offender was born in The Netherlands or not.

In addition, prior to matching on propensity scores we match by different age categories, sex, and sentence-length. For the matching by variable, we distinguish seven age-categories. Footnote 10 For the length of the sentence, we distinguish six categories, by which the classification of community service and imprisonment follows article 22b of the Criminal Code at which community service up to 60 hours can replace one month imprisonment Schuyt The first category thus includes community service up to 60 hours and prison sentences up to one month.

Subsequent categories for community service are chosen at a smaller range so that the maximum of hours is not exceeded. Footnote 11 We match by sentence length to prevent comparing offenders having preformed a minimum amount e. Table 1 presents descriptive information on the variables included in analyses. During a follow-up of eight years offenders recidivate on average 1. The mean age of those offenders sentenced to community service was Furthermore, of the offenders sentenced to community service, Also, in , at the moment of conviction for the index offense, the offenders sentenced to community sentence had on average 0.

Footnote 12 The length of community service varied from 1 to hours, with a mean length of hours, whereas the length of the imprisonment varied from one day to six months with a mean of The distribution of the length of imprisonment is skewed—half of the imprisoned had a sentence up to two months.

The number of offenders sentenced to longer imprisonment than two months is remarkably lower. This again underlines that the results of our analyses should not be generalized to the entire population of imprisoned.

They apply only to those sentenced to short-term imprisonment but see footnote 9. The objective of this paper is to compare recidivism after community service to recidivism after imprisonment. The optimal design to make such a comparison would be a randomized experiment in which offenders are randomly allocated either to community service or short-term imprisonment.

The random allocation would control extraneous influences by equating them in the two groups. However, randomized experiments are difficult to come by in a criminal justice setting. In practice therefore analysis using non-experimental observational data is more feasible. When using observational data selection processes will tend to make the community service group less crime prone compared to the imprisoned group.

After estimating the propensity scores, offenders sentenced to community service experimental group are first matched by three different variables to offenders sentenced to imprisonment control group and thereafter matched by their propensity scores. Second, after matching on propensity scores, the absolute and relative treatment effects are estimated—that is, the absolute and relative difference in recidivism after community service and after imprisonment. First, as indicated above, we match offenders from the experimental group one by one to offenders in the control group by age category, sex, and length of the sentence.

These variables were also used by Muiluvuori to control for selection into treatment. We use this strategy in addition to matching on propensity scores, since propensity score matching does not guarantee that, for example, female offenders are matched to female offenders and that matched offenders are in the same age category. Furthermore, by using this method, we are able to take the length of community service and imprisonment into account. Second, propensity score matching is used to control for selection effects.

Propensity score matching is specifically designed to achieve balance on the observed covariates between the experimental and control group Rosenbaum and Rubin , ; Haviland et al. The propensity score is the conditional probability of receiving treatment given the observed covariates. In this study, the propensity score is the conditional probability of community service at age t given observed covariates up to t versus imprisonment. The propensity score is estimated using a logistic regression model Cox and Snell Models using propensity scores are more robust concerning model misspecification compared to models based on regression techniques Drake Furthermore, results from propensity score adjustments have been shown to approximate results from randomized experiments see, for more information, Shadish et al.

Table 2 shows the results of the logistic regression estimates for the propensity score model. Most covariates significantly influence the chance of being sentenced to community service.

For example, the odds for female offenders to be sentenced to community service versus imprisonment are 1. Furthermore, Dutch-born offenders have an odds of being sentenced to community service that is five times higher than that of non-Dutch-born offenders. Younger offenders are also more likely to be sentenced to community service than older offenders. Compared to offenses in the category non-criminal law, the likelihood of being sentenced to imprisonment rather than community service is higher for most offense types: especially violent theft and arson endangering human life are significant predictors of being sentenced to imprisonment.

Based on the propensity score model, a predicted probability to being sentenced to community service—the propensity score—can be estimated for each individual offender. Figure 1 shows the distribution of estimated propensity scores for the experimental and control group. Clearly, the majority of offenders having the highest propensity scores based on the observed covariates were actually sentenced to community sentence.

Still, there is sufficient overlap—common support—between the propensity score distribution of the experimental group and that of the control group. With this level of common support, the results do not relate to only a select non-representative part of the offenders in the experimental or the control groups.

Figure 1 therefore indicates that propensity score matching is a suitable method for this data. Distribution of propensity scores of community service experimental group and imprisonment control group in full sample 11, Offenders from the experimental group are matched one by one, without replacement, to offenders from the control group with a comparable propensity score using a calliper of 0.

As a result, an individual in the control group will be matched to an individual in the experimental group in such a way that the multivariate pre-treatment covariate distance is minimized. That is, the difference between the predictors in the propensity score model is minimized across the two groups. The difference between the imprisoned and the comparison group is calibrated by two types of statistical measures. One is the conventional two-sample t statistic and the other is a standardized difference statistic in percentages, suggested by Rosenbaum and Rubin Footnote According to Rosenbaum and Rubin, the characteristic between the experimental and control group is out of balance when the absolute value of the standardized difference D is greater than In this study,we matched 2, offenders from the experimental group to 2, offenders from the control group.

Offenders from the experimental group for which we were unable to find a match differ from offenders in the experimental group that we could match. The propensity scores of the unmatched offenders in the experimental group were high compared to those of offenders we were able to find a match for; these offenders were too different in terms of observed variables from offenders in the control group. Furthermore, female offenders are underrepresented in the matched sample.

Matched and unmatched offenders did not differ with regard to their age in Table 3 upper shows that the community service group and the imprisoned group differ significantly on most of the observed variables. The offenders sentenced to community service are, for example, more likely to be Dutch-born and more likely to be female than are offenders sentenced to imprisonment. Furthermore, those sentenced to community service have a less extensive criminal history and are less likely to be sentenced for violent theft or a violation of the opium act.

Table 3 lower shows that the combined method of matching by variable and matching on propensity scores was successful in creating balance on all observed covariates.

After applying the matching strategies, no statistically significant differences between the experimental and control group remain. This implies that we can be confident that differences in post-sentence convictions do not reflect already existing differences in the observed variables between the experimental and control group. After the matching process, both absolute and relative treatment effects are assessed. The absolute treatment effect refers to the absolute difference between the average recidivism rate after community service and that after imprisonment.

The difference between the community service and imprisoned group is calibrated by the conventional two-sample t statistic. When estimating the relative effects of community service on recidivism, it is taken into account that recidivism of men for example is higher than that of women and that of younger offenders is higher than older offenders, regardless whether sentenced to community service or imprisonment.

The relative treatment effect equals the absolute treatment effect divided by the average recidivism rate in the control condition, that is after imprisonment, times Consequently, the relative effect depicts the deviation of the base rate mean recidivism of the control group in percentages.

Table 4 provides the average recidivism rates after community service and imprisonment across different follow-up periods and different types of offenses. Recidivism is expressed in the yearly average number of convictions. Table 4 shows that the absolute treatment effect is negative and highly significant.

The negative sign of the treatment effect suggests that community service leads to less recidivism than imprisonment. Thus, during the five years after their first-time community service, offenders attain 1. In relative terms, community service leads to a reduction in recidivism of Similar results are shown for different types of recidivism. The relative treatment effect shows that one year after community service, recidivism for property crimes is Comparing results for the different follow-up periods shows that the negative treatment effect of community service is stable over time.

The magnitude of the treatment effect does decrease slowly as the follow-up period increases. In short, our results show that recidivism after community service is lower than that after imprisonment, for all offenses as well as for property and violent offenses separately. This effect of community service is noticeable both in the short-term as well as in the long-term. While the use of by variable and propensity score matching secures balance on the observed confounders, there is always the danger of unobserved variables compromising the validity of inferring causality from observational data.

Prior research has shown that these personal characteristics influence both the likelihood of receiving certain types of sanctions, as well as the probability of recidivism e. If these unobserved variables simultaneously affect assignment into treatment and the outcome variable, a hidden bias might be present to which matching estimators are not robust Rosenbaum To get a sense of the robustness of our results against hidden bias, we determine the extent of selection on unobserved variables needed in order to undermine our implications of the matching analyses using the Rosenbaum bounds method Rosenbaum The department utilises a number of risk assessment tools depending on the type of reoffending being considered.

This model provides a number of principles for effective intervention that should be assessed for prior to involvement in treatment programs to ensure appropriate targeting of resources.

Services must tailor the intervention to the learning style, motivation, abilities and strengths of the offender. The assessment tools that are utilised by the department identify those factors that may contribute to offending behaviour.

Examples include:. Based on the results of risk and needs assessments, an individual case plan is developed for each prisoner or offender.