Intensive Supervision Programs and Recidivism: How Michigan Successfully Targets High-Risk Offenders

Abstract

The United States has witnessed enormous criminal justice system growth in the past 60 years. In response to calls for reform, several jurisdictions have implemented programs that provide intensive supervision for high-risk offenders, swiftly responding to violations with sanctions. This quasi-experimental study is the first comprehensive analysis of Michigan’s Swift and Sure Sanctions Probation Program (SSSPP), an alternative-to-incarceration program. The findings indicate that SSSPP participants had lower recidivism rates compared with individuals sentenced to probation-as-usual. Policy implications and suggestions for future research are offered.

Keywords

recidivism alternative-to-incarceration high-risk offenders

Introduction

The United States has witnessed enormous growth within the criminal justice system in past 60 years and more specifically within the prison population. As noted by Pew Center on the States (2011), “Between 1973 and 2009, the nation’s prison population grew by 705%, resulting in more than one in 100 adults behind bars” (p. 5). Relatedly, 4.7 million U.S. adults were under community supervision at the end of 2013 (Durose, Cooper, & Snyder, 2014). With estimated recidivism rates ranging from 43% to 67%, calls for reform have been voiced by the public, political leaders, and others (Durose, et al., 2014; Pew Center on the States, 2011).

One solution has been the creation of a subset of alternative-to-incarceration (ATI) programs that provide intensive supervision combined with swift and certain punishments. These programs generally focus on high-risk offenders while “[D]elivering relatively modest sanctions swiftly and consistently . . .” (Hawken & Kleiman, 2009). This unique element of swift imposition of sanctions coupled with more intense levels of supervision has been found to be effective across various locales and types of offenders. Given that almost one third of probationers fail (Herberman & Bonczar, 2015), there is clearly a need for change. However, very few empirical studies have been conducted to assess these types of programs.

To this end, the current study looks to fill a void in the literature related to ATI programs. Specifically, this quasi-experimental study is the first comprehensive analysis of Michigan’s Swift and Sure Sanctions Probation Program (SSSPP), an ATI program. The research questions addressed are as follows:

Research Question 1: What factors influence successful completion of SSSPP?

Research Question 2: Is SSSPP effective in reducing recidivism among program participants as compared with probation-as-usual (PAU)?

To answer these questions, we first examined the impact of demographic, pre-program, and in-program variables on successful completion of the program among SSSPP participants. Next, utilizing propensity score matching to create a comparison group of individuals sentenced to PAU, we analyzed the influence of SSSPP participation on the likelihood of reoffending among program participants as compared with these offenders.

Literature Review

In the 1980s, concerns regarding case overload and prison overcrowding were raised in jurisdictions across the United States. In response, the development of ATI programs represented an attempt at criminal justice reform that utilized local communities and the justice system to deter individuals from engaging in criminal behavior. More specifically, ATI programs attempted to facilitate behavior change among individuals by providing intensive supervision, establishing clearly defined consequences for criminal behavior, and affording access to treatment (e.g., drug/alcohol, mental health, etc.) and other support services (e.g., education, employment, housing, etc.) (Drake, 2011; Palumbo, Musheno, & Hallett, 1994; Weissman, 2009).

Effective ATI programs often adhere to the risk and need principle model (Lowenkamp, Pealer, Smith, & Latessa, 2006). The risk principle suggests that “the intensity of treatment should be matched to the risk level of the offender” while the need principle “makes a distinction between criminogenic and noncriminogenic needs” (Bonta, Wallace-Capretta, & Rooney, 2000, p. 314). With this focus, programs can specify a target population that has the most to gain from successful program completion. Programs that identify areas of risk/need and utilize this information to match treatment modalities have been associated with reductions in recidivism (Bonta et al., 2011; Bonta et al., 2000; Lowenkamp et al., 2006). Moreover, intensive supervision programs have been shown to be the most effective for high-risk offenders, as they require intensive levels of programming and supervision in specific areas (Bonta et al., 2000; Evans, Huang, & Hser, 2011; Lowenkamp et al., 2006).

The specific supervision strategies used within ATI programs include electronic monitoring, home confinement, drug testing, and regular supervision appointments. In addition, many ATI programs developed a system of severe and certain sanctions to be imposed if the terms of program participation were violated (Weissman, 2009). This response is based upon the assumption that individuals weigh the costs (i.e., potential risks of getting caught) and benefits (i.e., potential reward/gain to result from the act) of criminal behavior before acting (Durlauf & Nagin, 2011; Hawken & Kleiman, 2009).

With an estimated 6.7 million individuals under adult correctional control (Kaeble & Glaze, 2016), alternatives to incarceration are increasingly under scrutiny to provide accountability within their community and reduce recidivism. With the evolution of ATI programs, research regarding their effectiveness has been positive. Several studies have found reductions in recidivism between 10% to 30% when offender treatment is a significant component of intensive supervision programs (Bonta et al., 2000; Drake, 2011; Evans et al., 2011; Lowenkamp, Flores, Holsinger, Makarios, & Latessa, 2010; Lowenkamp et al., 2006; Paparozzi & Gendreau, 2005; Weissman, 2009). In fact, ATIs that do not incorporate treatment have little to no effect on offender recidivism (Drake, 2011; Paparozzi & Gendreau, 2005).

Overall, research has found “programs that target high-risk offenders, require them to be in the program longer, and have more referrals (particularly referrals for treatment programming) were the programs that saw the greatest decreases in recidivism” (Lowenkamp et al., 2006, p. 7). Latessa and Lowenkamp (2006) suggested that exclusively punitive methods in correctional programs are less effective in reducing recidivism than those that contain punishments and restoration. Furthermore, correctional programs failing to incorporate treatment with punitive measures could potentially increase recidivism in offenders.

ATI Programs

Although a number of ATI programs have been developed over the last 30 years, the most often cited program is Hawaii’s Opportunity Probation with Enforcement (HOPE). Established in 2004 by Judge Steven Alm, HOPE was designed to increase compliance with probation conditions and reduce recidivism among probationers. The intensive probation program focused on swiftly imposing sanctions for violations as a way to deter future transgression. Although HOPE was not expressly grounded in social science theory, the program theory embodies the principles of deterrence, rational choice, and social learning theories. Participants are encouraged to take responsibility for their actions and the resultant consequences to effect behavioral change (Hawken & Kleiman, 2009).

HOPE participants must be considered at high risk for recidivism and probation violations. One key feature of the HOPE model is that failure to comply with any probationary conditions results in the immediate filing of a probation violation, the issuance of a bench warrant, and the scheduling of a violation hearing. Violation hearings are held within 72 hr of the violation, and after a short period of incarceration (e.g., 1-3 days in the local jail), participants continue enrollment in the program (Hawken & Kleiman, 2009). This continued participation in the program is a key component of the HOPE model, which emphasizes behavior modification through the imposition of swift sanctions. A randomized controlled trial of the HOPE program found that program participants were “55% less likely to be arrested for new crimes, 72% less likely to use drugs, 61% less likely to skip supervisory appointments, and 53% less likely to have their probation revoked” than participants on standard probation (Pew Center on the States, 2010, p. 1). HOPE’s demonstrated success appealed to many within the criminal justice system attempting to address the problem of probationer noncompliance. The rise in popularity of HOPE led to several states and individual jurisdictions adopting the principles of HOPE in developing their own intensive supervision programs. What follows is a brief summary of these programs and associated program evaluation findings that exist to date.

Created in response to prison overcrowding, the Supervision With Intensive enForcemenT (SWIFT) program in Texas, developed in 2011,¹ incorporated many of the same principles of the HOPE program and adhered to the philosophy of swift and certain sanctions for every violation of community supervision (Stevens-Martin, 2014). The goal of SWIFT was to increase the number of successful probation completions and decrease the reliance on incarceration (Snell, 2007; Swift Certain & Fair, 2014).

Snell (2007) found that program participants were “much less likely to commit violations, have their probation revoked, and commit new offenses” (Snell, 2007, p. 19). Rewarding probationers by decreasing community service hours and sanctioning probationers for noncompliance with jail time were also found to predict probationer success. In addition, Stevens-Martin (2014) found that “offenders experienced a 19.72 percent reduction in technical violations and a 23.52 percent reduction in positive drug tests” (p. 77), although it should be noted that a comparison group was not included in the evaluation.

The 24/7 Sobriety program was implemented in South Dakota in 2005 as an innovative approach to reduce problem drinking. The program aims to reduce recidivism through the imposition of Swift and Sure sanctions in response to violations, intensive testing for drug/alcohol use, and intensive monitoring of participants’ behavior. Loudenburg, Drube, and Leonardson (2010) found that 24/7 participants “generally had lower recidivism rates at one, two, and three years when compared to controls” (p. 2). Relatedly, Kilmer, Nicosia, Heaton, and Midgette (2013) found a 12% reduction in repeat DUI (driving under the influence) arrests and a 9% reduction in domestic violence arrests following the adoption of the 24/7 program. The study concluded that “in community supervision settings, frequent alcohol testing with swift, certain, and modest sanctions for violations can reduce problem drinking and improve public health outcomes” (Kilmer et al., 2013, p. e37).

The Probation Accountability with Certain Enforcement (PACE) program was implemented in Anchorage, Alaska, in 2010. Carns and Martin (2011) asserted that PACE participants had fewer positive drug tests following acceptance to the program. In addition, 64% of PACE participants had no positive drug tests while enrolled and 54% had no petitions to revoke probation filed in the 3 months following their acceptance into PACE (Carns & Martin, 2011). Similar to evaluations of the HOPE program, positive drug tests, new petitions to revoke probation, and new arrests were concentrated among only a few PACE participants.

The Washington Intensive Supervision Program (WISP) was created in 2010 to reduce drug activity and parole violations in Seattle, Washington (Hawken & Kleiman, 2011). The structure and process of WISP are similar to that of HOPE; however, WISP’s target population is parolees not probationers. A randomized controlled trial revealed that 6.8% of WISP drug tests were positive, whereas 18.7% of control group drug tests were positive. With regard to reoffending, only one WISP participant committed a new crime within the first 6 months, while the control group of parolees had committed four new felony crimes (Hawken & Kleiman, 2011).

In July of 2012, the state of Kentucky sought to replicate HOPE through the development and implementation of the Supervision, Monitoring, Accountability, and Treatment (SMART) program. Shannon, Hulbig, Birdwhitsell, Newell, and Neal (2015) conducted quasi-experiment with a nonequivalent comparison group of individuals sentenced to PAU. The results indicate that the SMART participants had significantly fewer positive drug screens and a significantly lower rate of new charges (Shannon et al., 2015).

In sum, these programs focusing on the imposition of swift and certain sanctions have revealed success in reducing substance use, probation violations, and recidivism among program participants as compared with individuals sentenced to PAU or parole-as-usual. As noted above, a distinct feature of the HOPE model utilized by these programs was the swiftness and celerity with which the Court formally addressed transgressions of any type. Although the effectiveness of this strategy is evident, there is a relative dearth of empirical studies specifically examining these programs.

Michigan’s SSSPP

In 2011, SSSPP was initiated in Michigan within four pilot sites and, in 2012, was expanded to eight additional sites through the passage of Public Act 616 that specifically outlines several mandatory program elements for all Swift and Sure programs. These elements include (a) clearly established eligibility criteria, (b) the initial warning hearing, (c) regular probation meetings, (d) violation hearings within 72 hr, (e) possible sanctions (e.g., confinement in jail, additional reporting requirements, etc.) and remedies (e.g., counseling for mental health and/or substance abuse, increased drug/alcohol testing, etc.), (f) the need to create a sanctions grid, and (g) the need to establish criteria for deviating from established sanctions/remedies in special circumstances.

SSSPP was designed to effect change at both the individual and programmatic levels. First, at the individual level, it is believed that by participating in the Swift and Sure program participants will increase their level of compliance with probation terms and decrease their use of drug/alcohol. At the programmatic level, it is believed that by implementing SSSPP, there will be a decrease in the time between violations and the imposition of sanctions, the number of probation violations, the number of probation revocations, and the number of participants sentenced to prison. Overall, SSSPP is designed to reduce recidivism among program participants.

County officials are at liberty to structure their Swift and Sure programs as they see fit as long as the aforementioned program elements are in place. The target population for SSSPP is high-risk probationers. “High-risk” was defined by the Michigan State Court Administrative Office (SCAO) as scoring an 8 or higher on the Correctional Offender Management Profile for Alternative Sanctions (COMPAS) risk/needs assessment and having a history of probation noncompliance. This assessment is conducted by a Michigan Department of Corrections (MDOC) agent to determine eligibility for the program and participants enter the program during the initial warning hearing. During the initial warning, hearing participants are notified of the purpose of the program, specific probation conditions, expectations for participation, and the consequences for failing to comply with the stated expectations. Participants are required to meet with their supervising probation agent and Swift and Sure program staff and submit to drug testing as directed. All violations are formally addressed by the Court via sanctions hearings, which are held within 72 hr of the violation. The Court imposes a sanction, which includes confinement in jail for a specified period of time.

Research Design

The sample for the Swift and Sure participants is composed of 379 individuals in the study time period (October 1, 2011-September 30, 2013) from a total of 11 counties.² Data for each participant were obtained via a statewide electronic database and were provided to the evaluation team by SCAO. The research design included an examination of the predictors of successful discharge versus unsuccessful discharge among Swift and Sure participants in the 11 counties, as well as an analysis of recidivism among this group.

In addition, a quasi-experimental assessment of recidivism among Swift and Sure participants and a comparison group comprised PAU individuals residing in counties without a Swift and Sure program during the study time period was conducted. Specifically, data for all offenders placed on probation during the study time period were obtained from MDOC. These data included demographics, charges, legal orders, sentences, and COMPAS score categories. These data were utilized to select a comparison group of PAU for the recidivism analysis using propensity score matching, a statistical technique that allows researchers to “. . . adjust a treatment effect for measured confounders in non-randomized studies . . .” (Thoemmes, 2012, p. 1).³

Measures

Measures used in the study included demographic data, pre-program information, and in-program information for each Swift and Sure participant included in the sample. Demographic data and legal measures were included for the PAU group. In addition, recidivism data were obtained for both groups.

The individual demographic data for Swift and Sure participants were obtained from the statewide electronic database and included age at program entry, race, sex, education level at entry, employment status at entry, and marital status at entry. The race measure originally included categories for African American, White, Hispanic/Latino, Multiracial, and Native American. However, due to small numbers in the latter groups, the variable was recoded into three categories representing White, African American, and Other. Education level at entry was recoded to create three categories: less than high school graduate, high school graduate or GED obtained, and more than high school or General Equivalency Degree (GED). The latter category includes those participants who completed some college, some technical school, graduated from college or technical school, or attended graduate school. Employment at entry was recoded into two categories to represent participants who were either unemployed or not working (including those not in the labor force) and those who were employed (either part-time or full-time). The demographic data available for the comparison group from MDOC included date of birth (used to calculate age at probation initiation), race, and sex.

The pre-program measures available for the Swift and Sure participants included the number of previous misdemeanors, the number of previous felonies, sentencing guidelines cell type, type of precipitating offense, and COMPAS score category. Type of precipitating offense and COMPAS score category were available for the comparison group. The sentencing guidelines cell type measure was recoded into include three categories: misdemeanor, intermediate, and straddle/presumptive prison. Although the misdemeanor category is not part of the sentencing guideline cell type classification, the high number of Swift and Sure participants with this classification warranted the retention of this separate category. Type of precipitating offense was created by categorizing the offense that initiated enrollment in Swift and Sure or probation for the comparison group into one of five categories: violent, property, alcohol, drug, and “other.” The final pre-program measure, COMPAS score category, provided categories of high, medium, and low.⁴

In-program measures obtained from the statewide electronic database included in the analyses were as follows: the number of misdemeanors while enrolled, the number of felonies while enrolled, the number of probation violations while enrolled, the number of drug/alcohol tests administered, the number of positive drug/alcohol tests, and the number of days enrolled in the Swift and Sure program.

Recidivism in the current study was defined as any charge after enrollment in the Swift and Sure program (Swift and Sure participants) or after the initiation date of probation (comparison group). Thus the “recidivism clock” was started immediately after Swift and Sure participants began the program and immediately after the start of probation for the comparison group.⁵ Each charging incident was classified into one of five categories: violent, property, alcohol/drug, traffic, or “other” based on the offense categories provided by the Judicial Data Warehouse (JDW).

Time to Sanction

As noted previously, a key characteristic of SSSPP is the swift imposition (e.g., within 72 hr) of official sanctions by the Court for any violation. To assess this aspect, we examined the median and average number of days recorded between each probation violation (PV) and the sanction (see Figure 1).⁶ Examining the median first, the results indicate a fairly consistent pattern across successive probation violations. However, the average number of days varies widely with a low of 1.14 days and a high of 9.95 days. It should be noted that the average is inflated considerably due to extreme values in the number of days to sanction.

Figure 1.

Median and average number of days from probation violation (PV) to sanction.

Successful Discharge Versus Unsuccessful Discharge

To assess the differences between the Swift and Sure participants who were successfully discharged and those who were unsuccessfully discharged (as of October 2, 2013), we examined demographics, pre-program, and in-program measures using bivariate analyses (chi-square test and t test). Of the 171 Swift and Sure participants discharged during the study time period, 39.8% were successfully discharged and 60.2% were unsuccessfully discharged. Table 1 presents the results of the comparisons for the demographic characteristics. Of the demographic variables, education level at entry and employment status at entry were statistically significant indicating a difference between the two groups. While 52.4% of unsuccessfully discharged participants reported an education that was less than high school, only 33.8% of successful participants fell into this category. Conversely, 42.6% of successfully discharged participants had a high school diploma or GED as compared with only 35.9% of those unsuccessfully discharged. A similar trend is revealed for those with more than a high school diploma/GED. The percentage of successful participants who were employed either part-time or full-time (32.4%) was more than twice the percentage of unsuccessful participants in this category (14.6%). A higher percentage of unsuccessful participants (85.4%) reported being unemployed/not working as compared with successful participants (67.6%).

Table 1.

Demographic Characteristics for Swift and Sure Statewide Sample by Discharge Status (as of October 2, 2013) (n = 171).

	Successfully discharged (n = 68)	Unsuccessfully discharged (n = 103)
Race
White	57.4	50.5
African American	30.9	36.9
Other	11.8	12.6
Age at entry (average)	28.6	27.0
Sex
Male	75.0	81.6
Female	25.0	18.4
Marital Status
Married	11.8	6.8
Single	79.4	86.4
Divorced/separated/widowed	8.8	6.8
Education*
Less than HS	33.8	52.4
HS diploma/General Equivalency Degree (GED)	42.6	35.9
More than HS/General Equivlency Degree (GED)	23.5	11.7
Employment*
Unemployed/not working	67.6	85.4
Employed	32.4	14.6

Note. HS = high school.

p < .05. **p < .001.

Table 2 displays the pre-program characteristics for all discharged Swift and Sure participants. Examining the average number of previous misdemeanors and felonies, we see that those successfully discharged had an average of 4.79 and 1.21, respectively, whereas the unsuccessful group had an average of 6.29 and 1.64, respectively. However, based on the bivariate analyses, these differences were not statistically significant nor were any of the other pre-program measures. Thus, though examining the group percentages illuminates the trends between the groups, inferences of their significant differences cannot be made.

Table 2.

Pre-Program Characteristics for Swift and Sure Statewide Sample by Discharge Status (as of October 2, 2013).

	Successfully discharged (n = 68)	Unsuccessfully discharged (n = 103)
Previous misdemeanors (average)	4.79	6.29
Previous felonies (average)	1.21	1.64
Cell type
Misdemeanor	22.1	19.4
Intermediate	45.6	39.8
Straddle	32.4	35.9
Presumptive prison	0	4.9
Precipitating offense
Violent	20.6	25.2
Property	14.7	27.2
Alcohol	13.2	5.8
Drug	25.0	23.3
Other	26.5	18.4
COMPAS general recidivism
High	72.1	79.6
Medium	17.6	15.5
Low	10.3	4.9
COMPAS violent recidivism
High	45.6	55.3
Medium	39.7	36.9
Low	14.7	7.8

Note. No variable statistically significant at p < .05. COMPAS = Correctional Offender Management Profile for Alternative Sanctions.

Lastly, Table 3 displays the in-program characteristics by discharge type with statistically significant differences between successfully discharged and unsuccessfully discharged participants. Specifically, the average number of probation violations while enrolled was higher for unsuccessfully discharged participants at 2.87 as compared with 1.04 for successful participants. Two of the three measures examining drug/alcohol tests were also statistically significant. Successful participants had an average of 64.84 drug/alcohol tests administered and unsuccessful participants had an average of only 33.18 administered. In addition, the average percentage of positive drug/alcohol tests was 1.3% for successfully discharged participants and 10.1% for unsuccessfully discharged participants. Finally, the average difference in the number of days in the program was statistically significant between the two groups with successful participants having an average of 267.8 days and unsuccessful participants having an average of 159.5 days. Although this finding is obvious, it does speak to the importance of program retention, which is further considered in the “Discussion and Conclusion” section.

Table 3.

In-Program Characteristics for the Swift and Sure Statewide Sample by Discharge Status (as of October 2, 2013).

	Successfully discharged (n = 68)	Unsuccessfully discharged (n = 103)
Misdemeanors while enrolled (average)	0.12	0.20
Felonies while enrolled (average)*	0.03	0.29
Probation violations while enrolled (average)*	1.04	2.87
Drug/alcohol test administered (average)**	64.84	33.18
At least one positive drug/alcohol tests while enrolled	54.4	66.0
Percentage of drug/alcohol tests that were positive**	1.3	10.1
Number of days in program (average)**	267.8	159.5

p < .05. **p < .001.

In order to assess the unique impact of the various characteristics presented in the bivariate analyses, logistic regression analyses were conducted to identify the variables that predict successful discharge from the Swift and Sure. Logistic regression is a statistical method that “ . . . allows one to predict the discrete outcome such as group membership from a set of variables . . .” (Tabachnick & Fidell, 2001, p. 517).

Table 4 presents the results of the logistic regression model examining the impact of the demographic characteristics while also controlling for the total number of days in the program. Of the predictors, three measures were significant: more than high school/General Equivalency Degree (GED), employment status, and number of days in the program. Using the odds ratio located in the third column, we see that participants who had more than a high school diploma/General Equivalency Degree (GED) were almost 3.5 times more likely to successfully complete the program than those with less than high school/GED. In addition, being employed increased the odds of success by 144%.

Table 4.

Logistic Regression Models Predicting Swift and Sure Successful Discharge Versus Unsuccessful Discharge With Demographic Characteristics (n = 171).

	β	SE β	e ^β
Age at entry	−0.001	.023	0.999
Sex	0.493	.447	1.64
Race (White)
Black	0.564	.434	1.76
Other	0.178	.561	1.20
Education level (less than HS/General Equivalency Degree)
HS/General Equivalency Degree	0.733	.419	2.08
More than HS/General Equivalency Degree	1.24	.530	3.46 *
Employment status (unemployed/not working)	0.891	.453	2.44 *
Marital status (single)
Divorced	0.238	.746	1.27
Married	0.834	.681	2.30
Number of days in program	0.009	.002	1.01 **
Constant	−3.38
χ²	48.51

Note. Reference group in parentheses. HS = high school.

p < .05. **p < .001.

The results of the logistic regression examining the pre-program characteristics and successful versus unsuccessful discharge are presented in Table 5. In addition to the number of days in program, two types of precipitating offenses were statistically significant. Swift and Sure participants with a violent precipitating offense have a decrease in odds of successful discharge of 74.2% compared with participants classified as an “other” type of offense. Similarly, participants with a precipitating offense categorized as a property offense had an 81.3% decrease in odds of being successfully discharged.

Table 5.

Logistic Regression Models Predicting Swift and Sure Successful Discharge Versus Unsuccessful Discharge With Pre-Program Characteristics (n = 171).

	β	SE β	e ^β
Premisdemeanor	−0.038	.037	0.962
Prefelonies	−0.064	.089	0.938
Cell type (misdemeanor)
Intermediate	0.233	.481	1.26
Straddle/presumptive prison	−0.207	.530	0.813
Precipitating offense (other)
Violent	−1.36	.577	0.258*
Property	−1.68	.598	0.187*
Alcohol/drug	−0.363	.480	0.695
COMPAS score category index	−0.383	.325	0.682
Number of days in program	0.009	.002	1.01**
Constant	−0.380
χ²	48.70

Note. Reference group in parentheses. COMPAS = Correctional Offender Management Profile for Alternative Sanctions.

p < .05. **p < .001.

The final logistic regression model (see Table 6) examines the in-program predictors for successful or unsuccessful discharge. Among the predictors in the model, the total number of probation violations while enrolled in Swift and Sure was significant and negative. This indicates that an increase in the number of PVs while enrolled decreases a participant’s odds of successful discharge by 74.4%.

Table 6.

Logistic Regression Models Predicting Swift and Sure Successful Discharge Versus Unsuccessful Discharge With In-Program Characteristics (n = 146).

	β	SE β	e ^β
Misdemeanors while enrolled	−0.355	.503	0.701
Felonies while enrolled	−0.476	.878	0.621
Probation violations while enrolled	−1.36	.264	0.256**
Percentage of drug/alcohol tests that were positive	−0.121	.085	0.886
Number of days in program	0.015	.003	1.01**
Constant	−0.478
χ²	111.27

p < .05. **p < .001.

Recidivism Analyses

To assess the impact of Swift and Sure program participation on recidivism, we first examined whether or not an individual had any subsequent offenses.⁷ Among Swift and Sure participants, 37.7% committed at least one offense after enrolling in the program, whereas 46.7% of the comparison group members did the same after being placed on probation. Based on the bivariate analysis (chi-square test), there is a significant association between group membership and any recidivism. In other words, significantly fewer Swift and Sure participants reoffended. The results of the logistic regression presented in Table 7 reveal that Swift and Sure participants are 36% less likely to reoffend compared with the comparison group while controlling for the other variables in the model. Age at program entry was also significant and negative indicating that for each year increase in age, the odds of recidivism decrease by 3.3%. The odds of reoffending also decrease for females by 44.5% compared with males. Last, higher scores on the COMPAS scale index increased the odds for recidivism by 61%.

Table 7.

Logistic Regression Models Predicting Recidivism Among Swift and Sure Participants and the Comparison Group (n = 758).

	β	SE β	e ^β
Swift and Sure participant (comparison group)	−0.447	.155	0.640 *
Age at entry	−0.034	.008	0.967 **
Sex	−0.588	.199	0.555 *
Race (White)
Black	0.199	.170	1.22
Other	0.030	.294	1.03
Precipitating offense (other)
Violent	0.206	.253	1.23
Property	−0.024	.252	0.976
Alcohol/drug	−0.124	.217	0.883
COMPAS score category index	0.477	.134	1.61 **
Constant	−0.205
χ²	76.56

Note. Reference group in parentheses. COMPAS = Correctional Offender Management Profile for Alternative Sanctions.

p < .05. **p < .001.

In addition to any recidivism, we examined specific categories of recidivism (see Table 8). The mean difference in total recidivism as well as total misdemeanors, felonies, property, alcohol/drug, and “other” recidivism was statistically significant. Averages were consistently higher for the comparison group in these categories as compared with the SSSPP participants.

Table 8.

Mean Differences in Recidivism for All Swift and Sure Participants and Comparison Group (n = 758).

Recidivism category	Swift and Sure	Comparison group
Overall total**	.72	1.13
Total misdemeanor*	.37	0.54
Total felony*	.35	0.59
Violent	.21	0.20
Property**	.10	0.28
Alcohol/drug*	.19	0.29
Traffic	.12	0.09
Other**	.10	0.26

p < .05. **p < .001.

Table 9 presents a summary of the findings for each recidivism category. For simplicity, we have noted the measures that were statistically significant and the direction of the relationship as either negative (–) or positive (+). Observing the first row, participation in Swift and Sure or the comparison group, the findings indicate that in six out of the eight recidivism categories, Swift and Sure participants had significantly lower levels of recidivism while controlling for all other variables in the models. In addition, age at entry was significant and negative in five of the eight categories, and higher scores on the COMPAS score category index increased levels of recidivism in all categories except alcohol/drug recidivism.

Table 9.

OLS Regression Models Predicting All Categories of Recidivism Between Swift and Sure Participants and the Comparison Group (n = 758).

	Recidivism category
	Overall total	Total misdemeanors	Total felony	Violent	Property	Alcohol/drug	Traffic	Other
Swift and Sure participant (comparison group)	–	–	–		–	–		–
Age at entry	–	–	–	–		–
Sex	–		–			–
Race (White)
Black					–
Other
Precipitating offense (other)
Violent				+
Property
Alcohol/drug
COMPAS index	+	+	+	+	+		+	+

Note. Reference group in parentheses. OLS = ordinary least squares; COMPAS = Correctional Offender Management Profile for Alternative Sanctions.

Discussion and Conclusion

There is a rich 30-year history of ATI programs being implemented within the U.S. criminal justice system. These programs focus on holding individuals accountable, addressing the issues that fuel criminal behavior, while also keeping individuals in their communities and out of prison and/or jail. HOPE and other similar programs (i.e., SSSPP) were designed with these same principles in mind and are being touted as one strategy for reducing state corrections costs. Given that HOPE was implemented over 10 years ago and the relative few number of empirical studies focusing on these programs, this research fills a much-needed gap in the extant literature.

Although PAU has often been criticized for responding to violations slowly (or not at all), SSSPP within the State of Michigan focuses on swiftly responding to violations of probation. Per Public Act 616, a goal of the program is to address violations of probation within 72 hr of their occurrence. If the Court swiftly (within 72 hr) and surely (with a prescribed jail term) responds to any violation, theoretically one would assume that, over time, there will be a reduction in probation violations. During the study time period, Swift and Sure participants committed an average of 2.27 probation violations with a median of 2.0 (data not shown). While these data were unavailable for the comparison group, the results were comparable with the findings of Shannon et al.’s (2015) evaluation of the SMART program in Kentucky. Specifically, the SMART participants committed an average of 2.3 probation violations and the control group committed an average of 1.2 violations (p. 58). The higher number of probation violations among SMART participants is not surprising given the more intense supervision as compared with PAU. Similarly, Carns and Martin (2011) reported a higher level of “petitions to revoke probation” for PACE participants after enrolling in the program compared with pre-program numbers (p. 10). Given these findings, one may assume a similar trend would have been observed between Swift and Sure participants and the comparison group.

Relatedly, the assessment of the swiftness with which Swift and Sure sanctions were imposed revealed that the median number of days between all probation violations and associated sanctions was three or less, suggesting that indeed the 72-hr timeframe is being met. However, the average number of days between violation and sanction was much higher with a maximum of almost 10 among Swift and Sure participants committing their second violation. One explanation for this finding is that some participants absconded from the program and were not arrested within the 72-hr window. In addition, program limitations may have contributed to the delay in addressing probation violations given the availability of law enforcement to arrest participants on bench warrant status. These findings should be interpreted with caution given the inconsistent collection of data regarding probation violations and the associated sanctions.

Given the high-risk nature of the target population for Swift and Sure, identifying demographic, pre-program, and in-program characteristics of successful participants will yield valuable information for program stakeholders. As noted previously, of those Swift and Sure participants discharged during the study time period, almost 40% were successfully discharged. Consistent across all models examined was the significant influence of the number of days in SSSPP. Participants with higher levels of retention (e.g., a greater number of days) were more likely to be successful in the program. Although this finding is expected, it illuminates the importance of program retention. However, one perplexing finding was that program participants were unsuccessfully discharged after accumulating three or four probation violations. This approach is clearly counterintuitive to working with this high-risk population and is in opposition to HOPE program theory (Hawken & Kleiman, 2009). Individuals discharged quickly are precisely the individuals in need of the hypothesized change SSSPP is believed to produce. As noted by Lowenkamp et al. (2006), premature discharge reduces the total number of days within the program, which has been found to have a significant and positive effect on recidivism.

An examination of the precipitating offense that led to enrollment in SSSPP revealed that participants having committed a violent or property offense were significantly less likely to complete the program compared with those participants committing an offense classified as “other.” In contrast, the number of misdemeanors and felonies committed pre-program was not predictive of successful completion. In other words, the findings suggest that it is not the accumulation of previous offenses but rather the type of previous crime committed that impacts success. This highlights the heterogeneity of Swift and Sure participants, which may influence the overall effectiveness of the program and may necessitate close scrutiny of the program’s handling of these types of offenders. Furthermore, the aforementioned practice of prematurely discharging participants may be a root cause of the higher rates of unsuccessful completion for certain categories of offenders (e.g., violent and property). This practice may also influence the findings related to the number of probation violations committed while enrolled which revealed that an increase in the number of probation violations leads to decreased odds in successful program completion. Again, premature discharge may be responsible for this finding as participants cannot amass violations and remain in the program.

The overarching goal of SSSPP and similar programs is to reduce recidivism among program participants and, thus, reduce the overall costs to the criminal justice system and taxpayers. So, does participation in SSSPP reduce recidivism? Based upon the analyses, the answer is yes. Among SSSPP participants, only 37.7% committed an offense postenrollment, whereas 46.7% of the comparison group did so after being placed on probation. In addition, the odds of any recidivism among Swift and Sure participants were 36% lower than for the comparison group members while controlling for other common risk factors known to influence recidivism (i.e., age, sex, race, COMPAS score, etc.). SSSPP participants were also found to have significantly less reoffending when examining specific categories of recidivism. More specifically, total recidivism, total misdemeanors, total felonies, property, alcohol/drug, and “other” reoffending were all lower for program participants even when controlling for demographic and risk factors. Thus, consistent with the findings of similar ATI programs (Loudenburg et al., 2010; Shannon et al., 2015; Snell, 2007; Pew Center on the States, 2010), SSSPP is indeed effective in reducing recidivism among participants. Clearly, the swift imposition of sanctions that is the crux of SSSPP and similar programs may indeed lead offenders to consider the costs and benefits of committing subsequent violations or criminal offenses. Jail time for what may be considered minor transgressions otherwise such as curfew violations, missed appointments, and so on, deter an individual from committing these acts. As noted by Maxwell and Gray (2000), when offenders are aware of the certainty of punishments, they are more likely to be successful in ATI programs and comply with supervisory conditions as well as abstain from criminal activity. Similarly, Nagin and Pogarsky’s (2006) assessment of criminal decision making revealed that the certainty of consequences had a greater impact on deterrence than did severity.

A number of limitations should be considered when interpreting the results of the current study. First, there were a relatively small number of Swift and Sure participants enrolled and discharged during the study time period. Having a small sample size restricts the analyses that can be used to answer evaluation questions while also reducing the precision of estimates within the analyses performed. Small sample size therefore also reduces the power of tests to identify program effects. Second, the availability of program-related activities for Swift and Sure participants was limited. For example, no data were available regarding the number of case management and/or probation appointments. Similarly, the data collection across the 11 sites was inconsistent for the measures included in the study. For example, some sites recorded activities such as probation violations and the associated sanctions consistently whereas other sites did not. This hindered the ability to adequately assess whether programs were meeting the 72 hr to sanction hearing requirement. Fourth, as noted previously, limited data were available on the comparison group. Data such as drug/alcohol testing, probation violations, and probation appointments would be advantageous to fully understand the degree to which Swift and Sure is different from PAU. Last, it is important to note that utilizing official crime data as a measure of recidivism only includes offenses known to law enforcement. Thus, these data likely provide an underestimate of the level of criminal activity.

Given the current fiscal climate and the call for greater accountability within the criminal justice system, programs targeting high-risk/high-need offenders are both necessary and viable alternatives to incarceration. Our research is especially timely given Governor Rick Snyder’s call to reduce MDOC costs and adopt data-driven programs, practices, and policies.⁸ The Justice Center of the Counsel of State Governments

recommended that Michigan incorporate swift and certain principles in community supervision and set clear parameters around the length of confinement as a response to probation violations. Short and certain periods of detention ordered in response to probation violations are equally or more effective than long probation revocations at much lower cost. (Snyder, 2015, p. 10)

In a similar vein, the Citizens Alliance on Prisons and Public Spending (2015) asserted that “Just as we adopted strategies that seemed appropriate in the 1980s and 1990s, we can respond to the changes of the last 35 years and adopt new strategies in 2015” (p. 83). The time is ripe for SSSPP to be embraced by politicians, criminal justice practitioners, and the general public both in Michigan and elsewhere. Researchers should also work to amass a body of literature examining the effectiveness of these programs in meeting the stated goals.

Footnotes

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This research was funded by the Michigan Supreme Court State Court Administrative Office [contract # SCAO-2014-076].

Notes

Author Biographies

Kristen E. DeVall, PhD, is an associate professor in the Department of Sociology and Criminology, University of North Carolina Wilmington. Her research focus is programming in substance abuse, mental health, probation, and reentry.

Christina Lanier, PhD, is an associate professor in the Department of Sociology and Criminology, University of North Carolina Wilmington. Her current research examines the effectiveness of offender reentry, substance abuse, and mental health programs.

David J. Hartmann, PhD, is professor and chair of the Department of Sociology, Western Michigan University. His teaching and research areas include urban sociology, statistics, survey design, philosophy of science, substance abuse, and criminology.

Sarah Hupp Williamson is a PhD student in the sociology program at North Carolina State University. She has research interests in Eastern European human trafficking and U.S. alternatives to incarceration.

LaQuana N. Askew is a research associate for the Correctional Management Institute, Sam Houston State University. Her research interests include prisoner reentry, sexual offender rehabilitation and treatment, and cognitive behavioral therapy.

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