An Analysis of Probation Violations and Revocations in Maine

Transcription

Technical Report
An Analysis of Probation
Violations and Revocations in Maine
Probation Entrants in 2005-2006
Submitted to the Justice Research and
Statistics Association
by
Mark Rubin, Research Associate
Muskie School of Public Service
Michael Rocque, Ph.D. Student
Northeastern University, Massachusetts
Will Ethridge, Research Assistant
Muskie School of Public Service
December 2010
Maine Statistical Analysis Center
USM Muskie School of Public Service
http://muskie.usm.maine.edu/justiceresearch
ABOUT THE UNIVERSITY OF SOUTHERN MAINE (USM) MUSKIE SCHOOL OF PUBLIC SERVICE
The USM Muskie School of Public Service educates leaders, informs public policy, and strengthens civic
life through its graduate degree programs, research institutes and public outreach activities. By making
the essential connection between research, practice, and informed public policy, the School is dedicated
to improving the lives of people of all ages, in every county in Maine and every state in the nation.
ABOUT THE MAINE STATISTICAL ANALYSIS CENTER (SAC)
The Maine Statistical Analysis Center (SAC) operates as a collaborative service of the USM Muskie School
of Public Service and the Maine Department of Corrections. The SAC is partially supported by the Bureau
of Justice Statistics and is guided by an Advisory Group of policy makers from the Maine Administrative
Office of the Courts, Maine Department of Public Safety, Maine Department of Corrections, and Maine
Criminal Justice Commission. The SAC collects, analyzes, and disseminates justice data and research
reports to criminal justice professionals, policy makers, researchers, students, advocates, and the public.
The Maine SAC website is located at: http://muskie.usm.maine.edu/justiceresearch
FUNDER
This agreement is for the performance of a portion of the work originally awarded to JRSA from the
Bureau of Justice Statistics, USDOJ, Award #2007-BJ-CX-K042, CFDA #16.734, under the direction of
Stan Orchowsky, JRSA Research Director. The opinions, findings, and conclusions expressed in this
publication are those of the authors and do not necessarily reflect the view of JRSA, the Bureau of
Justice Statistics, the Department of Justice or the Maine Department of Corrections.
Table of Contents
Section I: Introduction .......................................................................................................................1
Maine’s Sentencing Law and Probation Requirements ................................................................... 1
Split Sentences ................................................................................................................................. 3
Revocations ...................................................................................................................................... 4
Supervision of Probationers............................................................................................................. 5
Section II: Descriptive Analysis (2005-2006) ........................................................................................8
Demographic and Criminal-Legal Profiles of Maine Probationers .................................................. 8
Commitment Offense and Prior Criminal Record ............................................................................ 9
Probation Caseload and Probation Agent Characteristics ............................................................. 10
Violations: Patterns and Processes ................................................................................................ 10
Section III: Survival Analysis: Examining the Factors that influence Probation Violations .................... 11
Recidivism: All Violation Types ....................................................................................................... 4
Recidivism: Misdemeanor or Felony Analysis.................................................................................. 5
Section IV: Logistic Regression Survival Analysis: Examining the Factors that influence Probation ...... 18
Section V: Discussion ....................................................................................................................... 24
References....................................................................................................................................... 25
Section I: Introduction
In 2009, Maine was selected (with four other states1) to conduct a study of probation revocations in
Maine, and to provide data to the Justice Research and Statistics Association (JRSA) for a multi-state
study of parole/probation revocations. This study analyzes a sample of 4,725 offenders who entered
probation between January, 2005 and December, 2006 from either prison or jail, and analyzes probation
violations and revocations occurring in the sample population over a 24 month period.
Maine’s correctional system is unlike many others in the U.S. in that parole was abolished by the state
legislature in 1976. However, probation often acts as de-facto parole in Maine, as more than two thirds
of offenders enter probation from jail or prison (split sentence). Probation is a state-wide function
administered by the Maine Department of Corrections (MDOC). MDOC supervises all Maine
probationers across four probation regions. Since this study’s main objective is to examine variations in
probation violations and revocation practices, we must first understand how Maine’s unique
correctional laws and administration constrain and influence probation decision-making. The next
section of this report briefly describes those aspects of Maine’s sentencing system and probation
supervision regulations that may influence probation recidivism rates.
Maine’s Sentencing Law and Probation Requirements
In Maine, probation is a court-ordered term of community supervision with specified conditions for a
determinant period of time that cannot exceed the maximum sentence imposed by a court. Probation is
imposed on an adjudicated offender who is placed under supervision in lieu of or subsequent to
incarceration, with a requirement to comply with certain standards of conduct. The probationer is
required to abide by all conditions ordered by the court. Violation of these conditions may result in
revocation by the court and imposition of an underlying sentence which was imposed at the time the
offender was sentenced to probation. The probationer is generally required to pay the cost of
supervision to the State of Maine, and may have additional conditions requiring payment of restitution,
court costs and fines, public service, and various types of treatment.
The probationer is usually required to visit his/her supervising officer in the local field office at intervals
related to their risk of re-offending as measured by a risk/need assessment tool. If the probationer’s
assessment places him/her in the higher risk of re-offending levels, the officer will also contact the
offender at his/her home and place of employment and maintain contact with service providers and
other community members.
Maine continues to have the lowest state prison incarceration rate per capita in the nation. By the end
of 2008, Maine’s 151 inmates per 100,000 residents was the lowest rate in the country.
1
The other states are New Mexico, Oregon, Utah and Wyoming.
An Analysis of Probation Violations and Revocations in Maine Probation Entrants in 2005-2006
1
Maine’s incarceration rate was three times lower than the national average (504). Maine had fewer
total inmates (2,195) than New Hampshire (2,904), and a comparable number to Vermont (2,116).
The lower incarceration rate reflects the overall crime rate in Maine which was 31% lower than the
national average (Rubin, 2009). Between 1998 and 2007, the decline in Index crime rates in Maine
(16.7%) and New Hampshire (16.2%) was less than the decline in Vermont (22.0%) and the U.S. overall
(19.2%).
From 2007 to 2008, Maine’s state prison population grew an estimated 2.2%, continuing the growth
trend of recent years. This was the ninth fastest growth in the country, and surpassed the national
average of 0.8%. Since 2007, nearly half of all admissions to prison have been the result of prisoners
being sent back to prison for a probation revocation. Those who are returned to prison on a probation
violation are said to have had their probation revoked, either partially, meaning they will be released
back onto probation, or fully revoked, where they are to serve the remainder of their probation in
prison.
Figure 1 - Percentage of New Admissions by Year
56.0%
54.5%
54.0%
51.6%
52.0%
50.0%
50.4% 49.6%
49.5%
50.5%
48.4%
48.0%
Revocations
45.5%
46.0%
New Sentence
44.0%
42.0%
40.0%
2007
2008
2009
2010
Currently, nearly two-thirds of inmates were sentenced to state prison for a Class B or C felony crime
(61.4 %). Overall, nine % of inmates in the state prisons have been convicted of murder2, while only
0.4 % are in prison for a misdemeanor offense (Class D & E). Maine’s state prison inmates serve an
average of 7.9 years. Other than the 45 inmates in prison with a life sentence, the remainder (97.8%)
will return to the community. More than half of the prison population has, on average, a sentence of
five years or less.
2
In this table, most of the murders are in the A class category or in the Life sentence category
2
Table 1 – Prison Inmates by Crime Class and Average Sentence Length
(as of 9/28/2010)
Crime Class Frequency Percent Average Sentence Length
A
715
34.4
15.1 years
B
664
32.0
3.4 years
C
610
29.4
2.3 years
D
8
0.4
2.4 year
E
1
0.0
NA
Murder
33
1.6
44.5 years
Life
45
2.2
Life
2076
100.0
7.9 years
Total
In Maine, the increase in the county jail population has been noted by policy makers as a critical area in
need of ongoing policy adjustments and reform. County jails are populated by two distinctly different
types of inmates, those awaiting pre-trial hearings and those already convicted and sentenced.
Generally, pre-trial defendants are in jail for a short period of time, and are usually released from
custody, pending arraignment or other court hearing. Sentenced inmates generally are in the jails for a
longer period of time, and are serving a jail sentence for a criminal conviction imposed by the court.
The average in-house population of adult inmates in Maine’s county jails has nearly doubled over the
last ten years. The number of pre-trial inmates has nearly doubled (86.3%), and now represents the
majority of inmates in the county jails. The number of convicted and sentenced inmates has also
increased, but at a slower rate (17.8%).
Split Sentences
In Maine, prison sentences can be fully served while incarcerated, can be wholly suspended with
probation, or can be split, with an unsuspended portion of the sentence served in incarceration followed
by a period of probation.3
This latter form is referred to as a split sentence. Throughout the entire period of probation, the
offender is subject to having the whole suspended portion of the sentence, or any portion thereof,
ordered served in incarceration as a result of a violation of probation. In 2005, Maine’s sentencing laws
were revised to reduce the length of time an offender can be sentenced to probation. 4 Probation
lengths are based on the conviction offense:
3
4
See 17-A M.R.S.A. section 1152(2).
See 17-A M.R.S.A. section 1206(7-A)
3
Table 2 – Maine Probation Length by Class of Offense
Class
A
B
C
D
E
Years
4 years
3 years
2 years
1 year
1 year
Revocations
A revocation of probation occurs when a court orders an offender to serve the remainder of his/her
sentence in prison or jail, due to a new crime or technical (non-crime) violation. A technical violation
occurs when a probationer violates one or more conditions set by the probation officer and the judge.
Revocations for technical violations declined since 2005, falling to 17.0% in 2007. The decline in
revocations for technical violations can be attributed in part to the increased use of alternative
sanctions over the last four years. Alternative sanction refers to a punishment other than a revocation,
including a verbal warning, a graduated sanction5, amended conditions, etc. Maine’s probation officers
are far more likely in 2007 to issue a graduated sanction, or to give a warning to the probationer when
the first violation is technical in nature.
Maine’s regional probation supervisors note that there are few formal revocation guidelines.6 The most
significant guideline was issued four years ago and requires a probation officer to consult with his/her
supervisor for approval of a recommendation that exceeds 90 days of incarceration. One region offers
that it has been difficult to maintain compliance with this directive, and now makes it mandatory for all
recommendations to be documented by the officers and filed directly with the court. This formal
documentation records the risk level, provides a narrative of the criminal history of the offender, and
makes a recommendation concerning bail. As a result, the supervisors interviewed for this study believe
that probation officers do a better job of maintaining consistency with the guideline.
Although there are few mandated revocation guidelines, probation supervisors indicate that there is an
effort to limit the use of incarceration for lower risk offenders and to keep technical violators out of
prison in order to reduce prison overcrowding. Officers have a great deal of discretion to determine the
array of sanctions to apply to individual cases. Probation officers refer to a list of 17 graduated
sanctions and implement these based upon their discretion. Despite the great degree of discretion
available to officers, all recommendations are required to be documented in the Department’s
Corrections Information System (CORIS).
5
“Graduated sanction” means any of a wide range of non-prison offender accountability measures and programs, including, but
not limited to, electronic supervision tools; drug and alcohol testing or monitoring; day or evening reporting centers; restitution
centers; forfeiture of earned compliance credits; rehabilitative interventions such as substance abuse or mental health
treatment; reporting requirements to supervision officers; community service or work crews; secure or unsecure residential
treatment facilities or halfway houses; and short-term or intermittent incarceration.
6
Interviews with regional probation supervisors conducted in March, 2010.
4
Probation supervisors across the regions agree that the most common reasons for revocations are
technical in nature- most often possession or use of drugs or alcohol. Some supervisors note that there
is a perception that too many probationers are returned to prison and jail for technical violations.
Supervision of Probationers
The Level of Service Inventory- Revised (LSI-R) is used to assess the level of risk of recidivism of an
offender. The LSI-R score is comprised of 10 categories (domains): Criminal History,
Education/Employment, Finances Family/Marital, Accommodations, Leisure/Recreation, Companions,
Alcohol/Drug, Emotional/Personal, and Attitude/Orientation. The total LSI-R score can range from 0 to
54, with lower numbers indicating less likelihood of recidivating than higher numbers. Many LSI-R
domains are dynamic (can be changed) and are important for case planning and case management, as
probation officers and treatment providers work with a probationer to effect positive behavior changes.
Others, such as Criminal History, are static, and cannot be changed.
Level of Supervision
Maximum Risk (LSI-R: 32+)
High Risk (LSI-R: 26-31)
Moderate Risk (LSI-R: 21-25)
Selected Probation Contact and Testing Requirements
Contacts by the Probation Officer with an offender classified as
Maximum Risk shall consist of five (5) contacts during a one (1)
month period with at least one (1) contact per week. One (1)
contact shall be in the person's home, two (2) shall be face to
face contacts with the person and the remaining two (2) may
be collateral contacts, to include at least one (1) employment
check every two (2) months. In addition to the monthly
contact in the home, when it is appropriate, officers shall
incorporate home visits into their case plans for the purpose of
monitoring identified risks.
High Risk shall consist of three (3) contacts during a one (1)
month period including (1) face to face contact- and two (2)
collateral contacts. At least one (1) face to face contact shall
be made in the home per quarter. In addition to the quarterly
home contact, when it is appropriate, officers shall incorporate
home visits in to their case plans for the purpose of monitoring
identified risks.
Moderate Risk shall consist of at least two (2) monthly contacts
by the Probation Officer, with one (1) face to face and one (1)
collateral
5
Low Risk (LSI-R: 14-20)
Low Risk shall be at least one (1) every three (3) months. The
contact may be satisfied either by an office contact in person
or by telephone.
Administrative (LSI-R: 0-13)
A person classified as administrative is not required to report,
except for the initial assessment and, if required by the
supervising Probation Officer, sixty (6) days prior to
termination of their supervision. In cases where there is a new
criminal conviction or there is a citizen or law enforcement
complaint involving an administrative case, the supervising
Probation Officer shall take appropriate action.
The probation officer responds to every violation of conditions of supervision, evaluating the nature of
the violation and the offender’s violation history in order to respond in an appropriate manner. The
responses are documented in the Corrections Information System (CORIS), a fully integrated, web based
MIS system designed to manage all aspects of MDOC data. This program has been in production since
2003 with detailed records for over 60,000 offenders.
Graduated sanctions may be applied when appropriate to address the offender’s behavior. Revocation
proceedings are initiated when graduated sanctions are deemed ineffective or considered
inappropriate.
A study conducted MDOC and the University of Southern Maine’s Muskie School of Public Service in
2005 led to two primary changes to the management of higher risk offenders by the Department. These
were: (1) lowering the LSI-R threshold scores for Moderate, High and Maximum risk offenders; and (2)
requiring a case plan for each higher risk offender. The re-calibration of Maine’s probationer risk levels
was made to better identify the higher risk probationers for increased supervision and case
management. DOC also created a new LSI-R category of “Low” risk to provide a greater ability to
manage clients according to their risk; consequently, the probation officers were able to avoid excessive
contact with offenders that were less likely to reoffend, and instead could concentrate on giving greater
attention to higher risk cases.
While probationers are supervised according to their risk levels (above), there is currently no risk
assessment tool in Maine for the purpose of recommending pretrial decisions.
Corrections System Capacity
While Maine has the lowest number of state prison inmates per 100,000 residents in the nation (151),
the state’s incarceration rate has risen 31.4% over the last ten years, and is projected to increase.
6
Despite spending a smaller percentage of its general fund dollars on correction—4.6% -- than all but
seven other states, Maine allocated $138 million on state corrections funding in 2007, a significant and
escalating cost for a rural state with low population. In 2008, Maine froze the counties’ portion of its
property taxes dedicated to jail costs to $52.5 million, with the remainder to be covered by state
government.
In 2005, the Corrections Alternatives Advisory Committee (CAAC) was established by Governor Baldacci
to improve the efficiency and effectiveness of state and county level corrections systems, and to better
manage costs. The CAAC found that the average length of stay (65 days) for pretrial defendants in a
majority of Maine jails was more than three times higher than in other states. The increasing average
length of stay for pretrial offenders in Maine jails was identified as one of the major factors contributing
to the increase in county jail population. The CAAC identified changes in the bail code and pre-trial
processes as essential elements to reducing county jail totals.
As of 9/28/2010, there are 2,076 state prison inmates in Maine. The youngest is 18 years old, and the
oldest is 79. More than half (58.7%) are under the age of 35, and 3.6% are over the age of 55 years old.
Of the 1,644 prisoners in adult facilities for whom education data is available, a majority (58.4%) have
less than a high school education, and nearly one-eighth (11.3%) have less than a 9th grade education.
Overall, 41.7% of the inmates in Maine’s prison system have a 12th grade education or higher, compared
with 89.4% in the general population.
In 2005, Maine released 2,183 offenders back into the community, an increase of 14% over the previous
year. Nearly half return to Cumberland County, primarily to the city of Portland. Though Maine has one
of the nations lowest per capita rates of incarceration, the state’s facilities were operating at overcapacity as of 20077.
7
National Governors Association for Best Practices
7
Section II: Descriptive Analysis (2005-2006)
This section describes the characteristics of the offenders who entered probation from prison or jail in
2005 and 2006. The section begins with a look at the basic demographics of the population, and then
goes on to examine the prior criminal record of the offenders, as well as their offending history. The
section concludes with a look at the differences in probation officer caseloads and practices across
probation regions.
Demographic and Criminal-Legal Profiles of Maine Probationers
Gender, Age, Race
Maine’s probationers are mostly male, white, and between the ages of 18 and 30. About 85% of
probationers in the cohort are male, and about 92% were white. Nearly 50% of offenders are age 18 to 30
when they enter probation (See Table 3).
Table 3: Demographic Characteristics of Maine Probationers, 2005 and 2006
Number
Percent
Gender
Male
4,031
85.3%
Female
694
14.7%
Race
White
Black
American Indian
Asian/P.I.
Other
4,338
152
68
11
156
91.8%
3.2%
1.4%
0.2%
3.3%
Age
Age 18-30
Age 31-44
Age 45+
2,350
1,608
767
49.7%
34.0%
16.2%
4,725
Overall Total
Of the 3,354 probationers for whom education data is available, 41.5 % have less than a high school (HS)
education, and nearly one-fifth (17.1 %) have less than a 10th grade education. Of the seven
educational attainment categories, the largest share of the population completed 12th grade (39.1 %),
with an additional 14.3 % completing a GED program. Overall, 59.6 % of the offenders entering
probation from jail or prison in 2005 and 2006 have a 12th grade education or a higher level of
education, compared with 85.4 % across the state. The majority of offenders entering probation from
jail or prison in 2005 and 2006 were not married, and more than a third were not employed.
8
Table 4: Education, Marital Status, and Employment Characteristics of
Maine Probationers, 2005 and 2006
Number
Percent
Education
Elementary
256
7.7%
Some High School
1,132
33.8%
High School Graduate/GED
1,793
53.4%
Associate's Degree
107
3.2%
Bachelor's Degree
50
1.5%
Trade/Technical School
12
0.3%
Graduate Degree
5
0.1%
Marital Status
Not married
Married/Domestic Partner
3,997
728
84.6%
15.4%
Employment
Full-Time
Working (not full-time)
Not working
1,566
525
1176
47.9%
16.1%
36.0%
Commitment Offense and Prior Criminal Record
Criminal histories of probationers in the sample were quite varied. The average (mean) age at first
arrest was 20.45. More than half of the probationers entered with a felony conviction, with a violent or
drug offense as the most common offense type. As noted earlier, over three-quarters of the sample
entered probation from jail, meaning they served nine months or less of their sentence in a confined
facility.
Table 5: Commitment Offense and Prior Criminal Record Characteristics of
Maine Probationers, 2005 and 2006
Number
Percent
Age at 1st arrest
Average
20.45
NA
Offense Type
Felony
Misdemeanor
2,754
2,015
55.2%
40.4%
Presenting Offense
Violent
Property
Drugs
Sex
Other
Don't Know
1,470
1,111
1,467
279
441
224
29.4%
22.3%
29.3%
5.6%
8.8%
4.5%
Sentenced to Jail or Prison
Jail
Prison
3,862
855
77.3%
17.1%
9
Probation Caseload and Probation Agent Characteristics
While this report found no differences in the demographic make-up among probationers across the four
regions, differences (among caseloads and revocation guidelines) exist among the various regions.
Interviews conducted with probation supervisors and line-staff from all four regions indicate some of
the major distinctions in their practices and protocols.
In general, there are very few formal revocation guidelines, and as discussed previously, a central
directive was issued four years ago requiring all probation officers to gain approval for any revocation
recommendation resulting in over 90 days incarceration. Additional guidelines are far more informal.
For instance, across the regions there is a general effort to limit incarcerations for lower risk offenders
and to keep technical violators out of prison. One regional supervisor said:
“There has been an effort to look at who is being sent to the state prison system. The big
picture is implementing evidence-based principles (EBP) and making sure low risk offenders
are not needlessly put into the system. We have been educated, trained, and reminded on this.
However, I have no idea what other regions are doing. We are not being given specific
directions, more an approach to how we handle offenders, violations, and supervision. By
paying attention to these concepts, it guides our decision-making in a certain direction.”
Violations: Patterns and Processes
Violations are aggregated into the categories listed in Table 6. Technical violations include noncriminal
administrative violations experienced of the probation process. As a whole, technical violations account
for nearly half of all violations experienced on probation (48.8%). Misdemeanors constitute 35.5 % of all
violations and the majority of new criminal violations.
Table 6: Violation by Type, 2005 and 2006
Violation Category
Frequency Percent
Total Violations
Technical Violations
Criminal Violations
Felony
Misdemeanor
5059
2471
2588
792
1796
100.0%
48.8%
51.2%
15.7%
35.5%
10
Section III: Survival Analysis: Examining the Factors that Influence Probation
Violations
To examine the factors that influence probation violations we use a multivariate regression technique
called survival analysis. Survival Analysis is a multivariate method that will enable examination of the
likelihood and timing of violations. There are several different types of survival models (Allison 1995).
We use the Cox regression model, which is named for the English statistician Sir David Cox, and which
combines a proportional hazards model with partial likelihood estimation (Cox 1972). Given the nature
of our data, the fact that violations are repeatable events, that the risk of a violation is continuous
rather than experienced only in discrete time periods, and given the left truncation in our data, Cox
models are the most appropriate statistical technique. In fact, Cox models have become a standard
approach in studies of recidivism and parolee behavior (e.g., Benda, Toombs, and Peacock 2002; DeJong
1997; Langton 2006; Hepburn and Albonetti 1994).
Table7 displays descriptive information for survival time. As is shown, the average number of days to
first violation is 208 days and the average number of days to violation if the first violation was a crime
(misdemeanor or felony) is 220 days. These results are only for probationers who violated within the
study period (730 days). Note that in the dataset, several probationers recorded violations beyond the
study period. In the survival analysis that follows, the raw number of days is included but the status
variable (violation) indicates no violation if the days to violation variable is greater than 730 days.
Table 7 - Descriptive Statistics
Days to recidivism
Days to violation
Valid N (listwise)
1142
2322
1142
0
0
724
728
220.4
207.6
173.6
167.2
The results below indicate that nearly half of the probationers experienced a violation of any type (e.g.,
technical, felony, and misdemeanor). Note that these results record only violations that were “founded”
in court. That is, numerous probationers had violations in which a court did not find enough evidence for
a conviction. These violations are not included in the analysis.
The survival analysis is also conducted on a probationer’s first founded violation. Many probationers
experienced multiple (up to 12) violations during the study period. Analyses (below) take into
consideration a probationer’s first technical, misdemeanor or felony violation, regardless of prior
violation types. However, the analysis reported in this section examines the probationer’s first violation,
regardless of type. For example, if a probationer’s first violation was a felony, s/he is included in the
“recidivism” and “any violation” analyses (below). If a probationer’s first violation was a technical
violation, s/he is only included in the “any violation” analysis and not the “recidivism” analysis.
In the first set of analyses, the dependent variable is any violation during the study period. Each model
sequentially adds more covariates to determine the effect of additional variables.
11
In the demographic models, age, sex, marital status and employment all show predicted relationships
with survival. With respect to age, three categories were created (following Grattet et al., 2008):
Probationers aged 18-30, aged 31-44; and 45+. For the analyses below, the middle category (31-44) is
the reference category. Thus, probationers aged 18-30 and 45+ are compared to those aged 31-44.
The first model (table 8) with covariates includes the first and last age categories. Both are significantly
associated with the hazard of violation in a theoretically expected manner. For example, probationers
aged 18-30 have a 35% greater risk of violation than those aged 31-44. Probationers aged 45 and up
have a 38% lower risk of violation than those aged 31-44.
Table 8- Variables in the Equation
Age18 to 30
Age45 and up
B
SE
Wald
df
Sig.
Exp(B)
.297
.046
42.167
1
.000
1.346
-.483
.074
42.502
1
.000
.617
The second model includes age and sex as predictors. The age category variables change relatively little.
The sex variable (male) indicates that males have a 29% greater risk of violation than females.
Table 9 - Variables in the Equation
Age18 to 30
Age45 and up
Male
B
SE
Wald
df
Sig.
Exp(B)
.296
.046
41.758
1
.000
1.344
-.487
.074
43.306
1
.000
.614
.251
.063
16.024
1
.000
1.286
The third model includes marital status and employment. As expected, both exert a negative effect on
violation. For example, married probationers have an 18% less risk of violation than unmarried
probationers and a one unit increase in employment (from unemployed to part-time to full-time) is
associated with a 23% decrease in risk. However, there is a considerable number of probationers with
employment data missing (1425) resulting in a lower overall sample size for analysis.
12
B
SE
Wald
df
Sig.
Exp(B)
.262
.058
20.521
1
.000
1.300
-.470
.089
27.808
1
.000
.625
.338
.080
18.056
1
.000
1.402
Married
-.192
.079
5.969
1
.015
.825
Employed
-.259
.029
81.343
1
.000
.772
Age18 to 30
Age45 and up
Male
The next set of results incorporates “risk” variables. Specifically, age at first arrest and the number of
prior arrests are included along with demographic variables. As expected both age at first arrest (2%
decrease) and prior arrests (4% increase) are associated with violation risk. These are both theoretically
expected. For example, the older the probationer was at first arrest, the lower the hazard ratio of
violation. One would expect that those who begin their criminal careers at an earlier age would be more
prone to violation (Blumstein et al., 1986; Gottfredson and Hirschi, 1990; Moffitt, 1993). Interestingly
however, the incorporation of these risk/criminal history variables render sex non-statistically
significant. Thus, arguably, the effect of males on violation operates through criminal history. This is a
somewhat surprising finding. The hazard ratio for marital status also decreases, to .797 (20% decrease in
risk).
B
SE
Wald
df
Sig.
Exp(B)
.304
.076
16.110
1
.000
1.355
-.371
.112
10.899
1
.001
.690
.174
.104
2.793
1
.095
1.189
Married
-.227
.103
4.882
1
.027
.797
Employed
-.212
.036
33.915
1
.000
.809
Age at First Arrest
-.015
.005
7.869
1
.005
.985
.037
.006
41.319
1
.000
1.038
Age18 to 30
Age45 and up
Male
Prior Arrests
The next model incorporates the LSI “total risk” variable along with demographic and criminal history
variables. Interestingly, the inclusion of the LSI risk variable renders several demographic and risk
variables non-statistically significant. This is not completely unexpected, considering that the LSI
includes many of these or similar risk factors. For example, when sex is included without risk factors
(table 10), males are shown to have an increased risk of violation.
13
However, when age at first arrest, prior arrests and sex are included in the model, sex is no longer
predictive of violation. Similarly, age at first arrest and sex are not predictive of the risk of violation
when LSI is in the model (last table in violation section). However, as expected, the LSI is predictive of
violation, even including other risk factors. A one unit increase in the LSI corresponds to a 6% increase in
risk of violation.
B
SE
Wald
df
Sig.
Exp(B)
.238
.077
9.593
1
.002
1.269
-.353
.115
9.459
1
.002
.703
.216
.106
4.200
1
.040
1.242
Married
-.196
.104
3.570
1
.059
.822
Employed
-.091
.038
5.711
1
.017
.913
Age at First Arrest
-.004
.005
.541
1
.462
.996
Prior Arrests
.015
.007
4.884
1
.027
1.015
Total Risk
.057
.005
151.623
1
.000
1.059
Age18 to 30
Age45 and up
Male
Recidivism: All Violation Types
The analysis below includes age, sex, and LSI domains as predictors of violation. This analysis provides
insight into which of the components of the LSI are related to risk of recidivism. As is shown, age, sex,
education/employment, criminal history, accommodations, companions and drug/alcohol history are
significantly related to the risk of violation. Of these, age (18-30) has the largest hazard ratio, with a 95%
greater risk of violation than probationers over the age of 45. With respect to the LSI domains, the
accommodations domain is associated with the largest hazard ratio (1.13).
B
SE
Wald
df
Sig.
Exp(B)
Age18 to 30
.668
.080
70.623
1
.000
1.951
Age31 to 44
.415
.083
25.183
1
.000
1.514
Male
.143
.070
4.131
1
.042
1.154
Criminal History
.117
.012
92.521
1
.000
1.125
Education/Employment
.070
.010
54.687
1
.000
1.073
Financial
.047
.034
1.885
1
.170
1.048
14
Family/Marital
.036
.022
2.500
1
.114
1.036
Accommodations
.123
.031
16.008
1
.000
1.131
Leisure/Recreation
.043
.030
2.065
1
.151
1.044
Companions
.087
.020
18.426
1
.000
1.091
Alcohol/Drug
.049
.011
20.382
1
.000
1.050
Emotional/Personal
.018
.017
1.119
1
.290
1.018
Attitude/Orientation
.030
.019
2.425
1
.119
1.030
Recidivism: Misdemeanor or Felony Analysis
In this section, we analyze survival time to first violation conditional on whether the first violation was a
crime (e.g., misdemeanor or felony). The analysis above examined all violations regardless of type. In
this analysis, if the first violation was a technical violation, the time variable is coded as missing. This is
because it would be misleading to code the time variable as if the individual did not commit an
infraction (e.g., days to the end of the study period) when in fact they did have a violation. The other
approach would be to code days to first criminal violation, regardless of how many previous technical
violations occurred. That tactic is not taken here because it essentially implies that the person
“survived” from date of first supervision until a criminal violation, and ignores the violations that
occurred previously. We believe this is approach would be somewhat misleading.
The analysis of survival time for criminal violations mirrors that which was done for all violations. It
should be noted, however, that there are considerably more missing data in these models (which is
caused, in part, by the exclusion of technical violations—coded as missing). The first model (below)
shows a survival analysis with no covariates.
Table 14 - Recidivism Survival Analysis: No Covariates
Case Processing Summary
N
Percent
1142
24.2%
2403
50.9%
3545
75.0%
Cases with missing values
1180
25.0%
Cases with negative time
0
.0%
Censored cases before the
earliest event in a stratum
0
.0%
Total
1180
25.0%
4725
100.0%
Cases available in analysis Event
Censored
Total
Cases dropped
Total
15
Then age, sex, marital and employment status are included. The last models include demographics and
criminal history/risk factors. In the initial models, a similar pattern of results is obtained. For example,
compared to probationers aged 31-44, those aged 18-30 have a higher risk of violation and those aged
45 and up have a lower risk of violation. Sex is also a significant predictor of the risk of violation, with
males showing a 52% greater risk than females of violating with a felony or misdemeanor.
Age 18 to 30
Age 45 and up
Male
Married
B
.339
-.640
.419
-.153
SE
.066
.110
.095
.091
Wald
25.951
33.832
19.519
2.831
df
1
1
1
1
Sig.
.000
.000
.000
.092
Exp(B)
1.403
.527
1.520
.858
Interestingly, marital status does not appear to be a robust predictor of recidivism. It is not significant in
any of the models (even that which only includes age and sex). This finding is surprising, given the recent
attention paid to the importance of marriage as a predictor of desistance from crime (see Bersani et al.,
2009; Laub and Sampson, 2003; Sampson et al., 2006). Employment (table 16) is a protective factor,
reducing the risk of recidivism. Finally, age at first arrest and prior arrests are, as expected, positively
related to the hazard rate of recidivism.
Age18 to 30
Age 45 and up
Male
Married
Employed
Age at First Arrest
Prior Arrest
B
.340
-.616
.522
-.285
-.209
-.025
.042
SE
.107
.176
.173
.150
.053
.008
.008
Wald
9.990
12.228
9.091
3.619
15.769
9.521
31.687
df
1
1
1
1
1
1
1
Sig.
.002
.000
.003
.057
.000
.002
.000
Exp(B)
1.404
.540
1.686
.752
.811
.976
1.043
When the LSI total score is included in the model, age at first arrest and employment are no longer
significant.
16
Age18 to 30
Age 45 and up
Male
Married
Employed
Age at First Arrest
Prior Arrest
Total Risk
B
.239
-.562
.570
-.209
-.034
-.011
.018
.078
SE
.108
.179
.175
.151
.055
.008
.009
.007
Wald
4.856
9.869
10.552
1.913
.379
1.703
4.322
126.777
df
1
1
1
1
1
1
1
1
Sig.
.028
.002
.001
.167
.538
.192
.038
.000
Exp(B)
1.270
.570
1.768
.811
.966
.989
1.018
1.081
The LSI domains are similarly predictive of violations when examining misdemeanors and felonies. Here,
only four domains are non-significant: financial, marital, accommodations, and leisure/recreation. In
comparison to violations, the only differences are that accommodations are not significant and
emotional/personal and attitude/orientation are significant predictors of risk of violation. In general, the
story presented in table 17 suggests that the LSI domains in isolation are more related to violations that
are misdemeanor or felonies than when violations are analyzed as a whole (including technical
violations).
Age18 to 30
Age45 and up
Male
Criminal History
Financial
Family/Marital
Accommodations
Leisure/Recreation
Companions
Alcohol/Drug
Emotional/Personal
B
.334
-.551
.215
.220
.068
.019
.024
.061
.071
.102
.067
.071
.064
SE
.071
.122
.105
.018
.014
.047
.033
.047
.042
.031
.015
.024
.028
Wald
22.043
20.385
4.186
155.718
24.404
.161
.538
1.708
2.862
10.595
18.610
8.819
5.370
df
1
1
1
1
1
1
1
1
1
1
1
1
1
Sig.
.000
.000
.041
.000
.000
.688
.463
.191
.091
.001
.000
.003
.020
Exp(B)
1.397
.576
1.239
1.246
1.070
1.019
1.024
1.063
1.074
1.108
1.069
1.073
1.066
17
Section IV: Logistic Regression Survival Analysis: Examining the Factors that
Influence Probation Revocation
This section describes the results of our multivariate survival models predicting various types of
probation revocations. Arguably, the most serious outcome that a probationer can experience is
revocation of their probation status and return to jail/prison. This suggests that the probationer’s
violation upon release to the community was severe enough to warrant a new sanction and an
adjudication that the probation has “failed”. Probation revocation is also an important assessment
criterion for correctional programs and/or systems. As Grattet et al. (2008) argue, if one of the purposes
of probation is to reduce prison crowding, probation revocations are important to avoid. Finally,
Petersilia (2006) argues that revocation and return to prison based on decisions by parole/probation
agencies rather than a traditional court process may conflict with expectations of procedural justice. In
short, for a variety of reasons, it is necessary to scrutinize the causes and correlates of probation
revocation in order to gain insight into why this outcome occurs. This information can be used to help
ensure that Maine’s correctional system can identify those most at risk to experience a revocation and
to equip probationers with the tools they need to succeed.
In the analyses that follow, we present descriptive and multivariate models to shed light on the factors
related to probation revocation. In the multivariate models, we use logistic regression analyses, in which
the outcome is a binary variable, scored 1 if the probationer experienced a revocation at any time
during the study period. The predictors used include many of the same demographic, lifestyle and
offense history variables used in the survival analyses. This analysis is particularly informative because
previous research has suggested that the number of variables related to revocation is more limited than
those which predict violations (Grattet et al., 2008).
With respect to the full sample, table 19 shows that nearly half of the probationers experienced a
revocation during the study period (n=2,345). However, individuals could incur more than one
revocation. Summing the number of revocations, the data show that there were 4,691 revocations total.
Thus, on average, each probationer that experienced a revocation had on average two revocations.
Table 19- Descriptive Statistics
Revocation
Valid N (listwise)
N Minimum Maximum
4725
.00
1.00
4725
Mean Std. Deviation
.4963
.50004
18
Table 20 - Number of Revocations
Valid
.00
1.00
2.00
3.00
4.00
5.00
6.00
7.00
8.00
9.00
Total
Frequency
2380
1050
685
328
178
66
27
6
4
1
4725
Percent Valid Percent
50.4
50.4
22.2
22.2
14.5
14.5
6.9
6.9
3.8
3.8
1.4
1.4
.6
.6
.1
.1
.1
.1
.0
.0
100.0
100.0
Cumulative
Percent
50.4
72.6
87.1
94.0
97.8
99.2
99.8
99.9
100.0
100.0
The multivariate results predicting revocation follow the analyses predicting time to violation. That is,
we focus here on the first revocation for simplicity sake. Alternative strategies could examine the
revocation as the unit of analysis and employ a multilevel or random effects model; however here we
simply aim to provide a profile of individual level characteristics related to whether or not a person
experienced a revocation. Thus, we employ multivariate logistic regression analysis. Our predictor
variables are theoretically driven (see Grattet et al., 2008), and include demographic and risk factors.
The models include additional independent variables in a stepwise manner such that model 1 in table
20includes only one predictor (age). As can be seen, age is related to revocation in a theoretically
expected manner, with those aged 18-30 having increased odds of revocation and those over the age of
45 having lower odds of revocation as compared to those aged 31-44. As expected, males, those
employed and marital status significantly predict revocations. For example, males have 51% greater
odds of experiencing a revocation than females (in the model that includes only sex and age as
independent variables – Table 22).
19
Table 21 - Age Predicting Revocation
Variables in the Equation
Age18 to 30
Age 45 and
up
Constant
B
.452
S.E.
.065
Wald
48.151
df
1
Sig.
.000
Exp(B)
1.572
-.683
.093
53.955
1
.000
.505
-.135
.050
7.243
1
.007
.874
Table 22 - Age and Sex Predicting Revocation
Age 18 to 30
Age 45 and
up
Male
Constant
B
.451
S.E.
.065
Wald
47.670
df
1
Sig.
.000
Exp(B)
1.570
-.695
.093
55.621
1
.000
.499
.411
-.483
.085
.088
23.529
30.203
1
1
.000
.000
1.508
.617
Table 23 - Age, Sex, and Marital Status Predicting Revocation
Age18 to 30
Age 45 and
up
Male
Married
Constant
B
.420
S.E.
.066
Wald
40.061
df
1
Sig.
.000
Exp(B)
1.522
-.689
.093
54.522
1
.000
.502
.403
-.223
-.428
.085
.085
.090
22.560
6.865
22.403
1
1
1
.000
.009
.000
1.496
.800
.652
20
Table 24 - Age, Sex, Marital Status and Employment Status Predicting Revocation
Age 18 to 30
Age 45 and up
Male
Married
Employed
Constant
B
.383
-.675
.571
-.266
-.366
-.271
S.E.
.081
.113
.106
.103
.041
.116
Wald
22.443
35.791
28.991
6.638
80.320
5.484
df
1
1
1
1
1
1
Sig.
.000
.000
.000
.010
.000
.019
Exp(B)
1.467
.509
1.771
.767
.693
.763
The next set of results incorporates age at first arrest and the number of prior arrests along with
demographic variables. Again, the older an offender is at their age of first arrest decreases the likelihood
of a revocation (2% decrease). Also, for each prior arrest, the risk of being revoked increases the odds
by 9%. Again, the incorporation of these risk/criminal history variables have an effect on other
variables: being married is rendered non-statistically significant.
Table 25 - Demographics and Offense History Predicting Revocation
Age18 to 30
Age 45 and up
Male
Married
Employed
Age at First Arrest
Prior Arrest
Constant
B
.469
-.666
.393
-.273
-.336
-.015
.088
-.120
S.E.
.112
.152
.146
.141
.055
.007
.013
.243
Wald
17.511
19.214
7.308
3.767
37.508
4.541
44.581
.244
df
1
1
1
1
1
1
1
1
Sig.
.000
.000
.007
.052
.000
.033
.000
.621
Exp(B)
1.599
.514
1.482
.761
.714
.985
1.092
.887
21
The next model incorporates the LSI “total risk” variable along with demographic and criminal history
variables. Again, age at first arrest and being married are not predictive of the risk of revocation when
LSI (total risk) is in the model. A one unit increase in the LSI corresponds to a 9% increase in risk of
revocation.
Table 26 - Demographics, Offense History and LSI Predicting Revocation
B
.389
-.652
.505
-.188
-.185
-.004
.043
.089
-2.208
Age18to30
Age45andup
Male
Married
Employed
Age at First Arrest
Prior Arrest
Total Risk
Constant
S.E.
.119
.159
.154
.149
.059
.008
.013
.008
.314
Wald
10.779
16.792
10.717
1.592
9.809
.277
10.201
132.029
49.592
df
1
1
1
1
1
1
1
1
1
Sig.
.001
.000
.001
.207
.002
.598
.001
.000
.000
Exp(B)
1.476
.521
1.657
.828
.831
.996
1.044
1.093
.110
The analysis below includes LSI domains as predictors of any revocation. This analysis provides insight
into which of the components of the LSI are related to risk of revocation. As is shown, criminal history,
education/employment, accommodations, companions and drug/alcohol history are significantly related
to the risk of violation. Of these, the criminal history domain is associated with the largest odds ratio
(1.22).
Table 27 - LSI DOMAINS Predicting Revocation
Criminal History
ducation/Employment
Financial
Family/Marital
Accommodations
Leisure/Recreation
Companions
Alcohol/Drug
Emotional/Personal
Constant
B
.199
.142
-.020
.021
.189
.045
.174
.037
.025
-.010
-2.067
S.E.
.018
.015
.051
.035
.052
.045
.031
.016
.026
.031
.105
Wald
120.302
92.307
.146
.365
13.179
1.010
31.212
5.014
.980
.111
390.363
df
1
1
1
1
1
1
1
1
1
1
1
Sig.
.000
.000
.703
.546
.000
.315
.000
.025
.322
.739
.000
Exp(B)
1.220
1.152
.981
1.022
1.208
1.046
1.190
1.037
1.026
.990
.127
22
Table 28- Technical Violation Revocation Logistic Regression of LSI Domains
Criminal History
Financial
Family/Marital
Accommodations
Leisure/Recreation
Companions
Alcohol/Drug
Emotional/Personal
Constant
B
.080
.095
.052
.045
.155
.031
.133
.079
.004
.030
-2.697
S.E.
.020
.016
.057
.038
.052
.050
.034
.018
.028
.032
.122
Wald
15.945
36.666
.847
1.394
8.799
.372
15.347
18.785
.018
.833
487.012
df
1
1
1
1
1
1
1
1
1
1
1
Sig.
.000
.000
.357
.238
.003
.542
.000
.000
.894
.361
.000
Exp(B)
1.083
1.100
1.054
1.046
1.168
1.031
1.142
1.082
1.004
1.030
.067
Finally, the LSI domains are similarly predictive of revocations following a misdemeanor or felony. In
comparison to all revocation, only three variables are significant predictors of risk of revocation for a
new crime: criminal history, education/employment and companions. Once again, the criminal history
domain is associated with the largest odds ratio (1.19) of being revoked.
Table 29 - Criminal Violations Revocation Logistic Regression of LSI Domains
Criminal History
Financial
Family/Marital
Accommodations
Leisure/Recreation
Companions
Alcohol/Drug
Emotional/Personal
Constant
B
.173
.068
-.047
-.020
.018
.057
.078
-.029
.025
-.058
-2.234
S.E.
.020
.016
.056
.038
.053
.049
.034
.018
.028
.033
.114
Wald
75.478
19.112
.704
.269
.119
1.356
5.374
2.571
.788
3.130
385.525
df
1
1
1
1
1
1
1
1
1
1
1
Sig.
.000
.000
.401
.604
.730
.244
.020
.109
.375
.077
.000
Exp(B)
1.188
1.071
.954
.980
1.019
1.059
1.081
.972
1.025
.944
.107
23
Section V: Discussion
This study and technical report builds upon our knowledge about Maine’s probation system by helping
to identify specific factors that influence probation violations and revocations. By analyzing the cohort
of offenders who entered probation between January, 2005 and December, 2006, this study helps
uncover important differences across regions and specific offender characteristics that influence
recidivism outcomes.
The study found that Maine has few formal revocation guidelines, and that it has been difficult to
maintain consistency across regions. Probation supervisors across the regions agree that the most
common reasons for revocations are technical in nature- most often possession or use of drugs or
alcohol. Previous SAC analyses found that recidivism rates have not changed significantly across recent
probation entry cohorts over the last five years, but that lower risk offenders have had better outcomes
than their counterparts as new policies were implemented, such as ‘banking’ Administrative cases and
supervised Low risk probationers far less intensively than in the past. Probation supervisors indicated
that there is an effort to limit the use of incarceration for lower risk offenders and to keep technical
violators out of prison in order to reduce prison overcrowding.
The study also found that violations were influenced by age of the offender, risk defined by Maine’s risk
assessment tool and lack of employment. Marital status did not appear to be a robust predictor of a
violation for a new crime. It is not significant in any of the models (even that which only includes age and
sex). Gender was also not a predictor after risk and elements of criminal history (age at first arrest and
priors) were included in our regression model.
Finally, binary logistic regression identified a number of variables as having a significant effect on
revocation outcomes. These included static factors such as gender, probationer age and number of
prior arrests, and the LSI-R domains of criminal history, education/employment and companions.
This report is an initial inquiry of offenders entering probation from prison and jail in Maine. This project
may benefit state policy makers by providing a clearer picture of violations and revocations in Maine
that to this point has not yet been fully explored. Through this project, MDOC’s capacity to identify
systemic, state-wide revocation trends, and create a more robust and ongoing data analysis of
revocations and the offenders who commit them has increased substantially. This study also began to
shed light on the relationship of regional probation practice to revocations, which may help policy
makers and probation administrators answer critical questions as to whether probation officers from
certain regions obtain better results. As the Maine SAC continues its collaborative work with MDOC,
future research on Maine probationers will continue to examine policy and management strategies that
may have an influence on recidivism outcomes.
24
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Quantitative Criminology 10: 159-179.
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25
Credits
AUTHORS
Mark Rubin, Research Associate, USM Muskie School of Public Service
Michael Rocque, Ph.D. Student, Northeastern University
William Ethridge, Graduate Research Assistant, USM Muskie School of Public Service
EDITORS
Carmen Dorsey, Director, Justice Policy Program, USM Muskie School of Public Service
DESIGN AND LAYOUT
Sheri Moulton, Project Assistant, USM Muskie School of Public Service
__________________________________________________________
All authors are on staff or affiliated with the Maine Statistical Analysis Center
(SAC) and the USM Muskie School of Public Service.
______________________________________________________
ACKNOWLEDGEMENTS
We wish to convey special thanks to the Maine Department of Corrections for the
generous provision of data and their time and assistance with this report. Special
acknowledgment of Chris Coughlan, Bud Doughty, Scott Landry, Denise Lord, Lisa Nash
and Chris Oberg.
26
USM Muskie School of
of Public Service
This report is available on the
Maine Statistical Analysis Center Website at:
http://muskie.usm.maine.edu/justiceresearch
or by calling: (207) 780-5843
University of Southern Maine
P.O. Box 9300
Portland, Maine 04104-9300
www.muskie.usm.maine.edu
A member of the University of Maine System

An Analysis of Probation Violations and Revocations in Maine

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