Racial Equity in Montana’s Criminal Justice System: An Analysis of Court, Corrections, and Community Supervision Systems

Racial Equity in Montana’s Criminal Justice System: An Analysis of Court, Corrections, and Community Supervision Systems

Between April 2021 and February 2022, The Council of State Governments (CSG) Justice Center conducted an analysis of racial equity across Montana’s criminal justice system in partnership with Montana judicial branch stakeholders. This work identified decision-making points in Montana’s criminal justice system in which there are disparities between American Indian and White people. Key findings include American Indian people are more likely to be incarcerated for felony criminal endangerment and public order offenses relative to comparable White people; American Indian people are incarcerated for longer than similarly situated White people; and American Indian people are more likely to be revoked from probation, conditional release, and parole than comparable White people. Based on these findings, the CSG Justice Center proposed five recommendations to improve racial equity in Montana’s criminal justice system.

Sara Bastomski, Matt Herman, Alison Martin, Sara Friedman | July 2022 | The Council of State Governments Justice Center

Glossary

Key Terms

Racial disparity1 • Racial disparity refers to any situation in which different racial groups experience unequal treatment or outcomes.
• Evidence of disparity is distinct from understanding the processes that contribute to it.
Racial equity2 Racial equity means that all racial and ethnic groups have access to the resources and opportunities that they need to live full, healthy lives. The concept of equity acknowledges the role that historical and ongoing structural racism play in creating an unequal playing field.
BIPOC Acronym for Black, Indigenous, and People of Color
Systemic racism3 (sometimes referred to as structural or institutional racism) Policies, practices, and institutional norms or culture that create and perpetuate racial inequality across society.
DOC commit status4 In Montana, a judge has the option to sentence a person to “DOC commit.” The Montana Department of Corrections (MT DOC) then conducts an assessment and determines where to place the person next; options include a prison term, placement in an alternative secure facility, or community supervision.
Conditional release supervision5 • In Montana, a person sentenced to DOC commit status may then be placed on community supervision by MT DOC. When community supervision is the result of an MT DOC placement, it is termed conditional release.
• Conditional release functions day to day just like probation, but revocation decisions are made by MT DOC rather than a judge.
Alternative secure facility6 Alternative secure facilities are secure alternatives to prison; they include assessment and sanction centers, treatment centers, and prerelease centers.
Supervision noncompli- ance and compliance violations7 • In Montana, there are two types of supervision violations, as defined in 46-18-203, MCA.
• Noncompliance violations are defined as violations related to one of the following five circum- stances: (1) a new offense; (2) possession of a firearm in violation of probation conditions; (3) harassment or other prohibited behavior toward a victim or victim’s family or contacts; (4) absconding; or (5) failure to enroll in or complete a treatment program for sex or violent offenses.
• Compliance violations include a breach of supervision conditions that does not include one of the circumstances that define noncompliance violations.
Probation/conditional release revocation8 In Montana, revocation decisions for probation are made by a judge and may result in a continuation of supervision with additional conditions, or a transfer to prison or an alternative secure facility. Revocation decisions for conditional release supervision are made by MT DOC and typically result in a transfer to prison or an alternative secure facility.
Incarceration In this analysis, incarceration refers to a sentence or placement in state prison or other secure correctional facility, including alternative secure facilities.
Interstate compact status9 In this analysis, interstate compact status refers to supervision cases that originated in a state other than Montana but were transferred to Montana for supervision.

 

Methodological Terms

Relative rate index (RRI)10 • An RRI is a standardized way to compare the experiences of two groups, e.g., two racial or ethnic groups within the justice system. Typically, a BIPOC group is compared to a White group.
• An RRI greater than 1 indicates worse outcomes for the BIPOC group relative to the White group; an RRI lower than 1 indicates better outcomes for the BIPOC group.
Regression analysis11 Statistical method for examining the relationship of one variable to another Regression is helpful for making “apples to apples” comparisons between two groups.
Adjusted RRI An adjusted RRI represents any difference between groups that remains after accounting for baseline group differences. Regression analysis is used to develop an adjusted RRI.
Predicted outcome A predicted outcome is a type of output obtained from a regression analysis that describes an outcome (e.g., predicted probability of incarceration, predicted length of stay) based on the measures included in the regression analysis. For regression analysis in this report, predicted outcomes separate the differences in outcomes related to race and ethnicity from the differences that are related to other contextual or individual characteristics (e.g., judicial district, sex, age, criminal history).

Introduction

Between April 2021 and February 2022, with funding and support from the U.S. Department of Justice’s Office of Justice Programs, Bureau of Justice Assistance (BJA), The Council of State Governments (CSG) Justice Center conducted an analysis of racial equity across Montana’s criminal justice system, in partnership with Montana judicial branch stakeholders.

This project builds on previous work by CSG Justice Center staff in Montana as part of the Justice Reinvestment Initiative (JRI), which documented initial evidence of racial disparities between White and American Indian people in arrests and corrections populations.12 However, because JRI was not focused exclusively on racial equity issues, more in-depth analysis was not possible at that time.

In light of these previous findings, this new analysis aimed to investigate patterns of racial disparity across the crim­inal justice system, to the extent that they exist, and pro­vide insight into the drivers behind them in order to offer actionable recommendations to address them. Additionally, this work sought to closely document any data gaps or data quality issues so that the judicial branch can improve any racial equity-related data collection challenges within the court system. Specifically, CSG Justice Center staff’s racial equity analysis includes the following key compo­nents: (a) an in-depth analysis of sentencing and super­vision revocation decisions across racial groups and (b) a high-level analysis of existing data in the court sys­tems to identify strategies to improve future data collec­tion and monitoring related to racial disparity. This report focuses on results from component (a); an accompany­ing report, Availability of Defendants’ Race and Ethnicity Information in Montana’s Case Management System, details component (b), including the results of an assessment of court data quality.

This project focuses on American Indian-White racial disparities in Montana’s criminal justice system due to several data and methodological lim­itations (detailed in the Analytic Approach section).

However, national research shows that criminal justice disparities impact other racial and ethnic groups as well.13 CSG Justice Center staff’s recom­mendations offer strategies that Montana’s court system can adopt to make it possible to conduct a more comprehensive assessment of disparities in the future.

This report describes the results of analysis of criminal jus­tice system data for the five-year period between January 2016 and December 2020 and is organized into the fol­lowing sections:

■ A background section that reviews findings from CSG Justice Center staff’s earlier analysis of racial disparity in Montana’s criminal justice system and provides con­text from the broader research literature on racial dis­parity in the criminal justice system in the U.S.

■ An analytic approach section, which provides an over­view of the data sources and quantitative methods used in this analysis

■ A summary of results addressing the extent to which American Indian-White racial disparities exist in sentenc­ing and supervision revocation decisions

■ Data-driven policy recommendations that outline a path forward to advance racial equity in the state

 

Background

Throughout the United States, Black, Indigenous, and People of Color (BIPOC) groups face incarceration rates that are often much higher than White populations14 despite progress in reducing such disparities for Black and Hispanic groups in recent decades.15

A large body of research documents the role of historical and ongoing forms of systemic racism in creating and per­petuating these racial disparities.16 Although research on racial disparities in criminal justice has focused on Black and Hispanic people, a limited body of work indicates that American Indian people face disparities similar to those of other BIPOC groups. For example, some studies have shown that American Indian people receive harsher sen­tences than similarly situated White people.17 Several stud­ies suggest that American Indian people face disparities in supervision revocations as well.18

In Montana, American Indian people comprised 6.2 percent of the total population in 2020, the fifth largest proportion of any state.19 Previous analysis conducted by CSG Justice Center staff has shown that American Indian people are disproportionately represented in Montana’s criminal jus­tice system. Specifically, as part of JRI work conducted in 2015 and 2016, CSG Justice Center staff’s assessment of Fiscal Year (FY) 2015 arrest and corrections populations data highlighted the following key findings:20

■ Despite comprising approximately 7 percent of the state’s total population in 2015, American Indian peo­ple accounted for 19 percent of total arrests. This dis­parity in arrests was driven by failures to appear (FTA) and supervision violations.

■ American Indian people were also disproportionately represented in the corrections system, where they accounted for 17 percent of the total correctional facil­ity population.

Racial disparity refers to any situation in which different racial groups experience unequal treat­ment or outcomes.21

Evidence of disparity is distinct from understanding the processes that contribute to it.

CSG Justice Center staff’s new racial analysis reexamined key criminal justice statistics and identified similar dispar­ities. As depicted in Figure 1 below, in 2019, American Indian people were overrepresented in community super­vision and prison populations, and in arrests, relative to their representation in the general state population.

 


Figure 1: White and American Indian People as a Percentage of Total Adult and Justice-Involved Populations

figure 1

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

With this background in mind, CSG Justice Center staff’s current analysis delves further into criminal justice data to provide insight into key decision-making points and factors that are potentially associated with racial disparities.

Analytic Approach

The research literature on racial inequity uses a variety of terms to describe how to measure differences between racial groups.22 For the purposes of this analysis, CSG Justice Center staff were careful to distinguish between disparities in outcomes versus disparities in treatment. Disparities in outcomes can be observed by examining differences in group-level rates; for example, one might compare the incarceration rate of one group to another. While such disparities are important to document, they do not provide much insight into factors that may or may not be contributing to the observed disparity.

Another approach is to analyze disparities in treatment— for instance, comparing judicial sentencing decisions or supervision revocation decisions. This approach requires comparisons to be made between similarly situated, other­wise indistinguishable people from different racial groups. Analysis of disparities in treatment can provide more actionable information that criminal justice system actors can use to craft better policy and practice by identifying issues at specific decision-making points.

With these distinctions in mind, this analysis focuses stra­tegically on estimating racial disparities in treatment to empower Montana criminal justice system stakeholders with actionable information. Specifically, this project inves­tigates the extent to which racial disparities exist in incar­ceration sentencing decisions; prison length of stay; and revocations of probation, conditional release, and parole.

Importantly, due to data and sample size limitations, this analysis pertains exclusively to American Indian and White people who have been convicted of a felony offense. There are two major reasons for this. First, there was insuffi­cient race and ethnicity data in the court data system. On average, across all courts, defendants’ race and ethnic­ity information was missing in 32 percent of cases filed between 2015 and 2020. For details, see the accompa­nying report Availability of Defendants’ Race and Ethnicity Information in Montana’s Case Management System. Due to these data limitations, CSG Justice Center staff were unable to examine the pool of people who were charged with a felony but not convicted or people who were charged with misdemeanors. Instead, the analysis of sentencing decisions was conducted using Montana Department of Corrections (MT DOC) data, which track people from the point of a felony conviction.

Due to data and sample size limitations, analysis of racial equity in sentencing and supervision revoca­tions focuses exclusively on disparities between American Indian and White people who have been convicted of a felony.

Second, the statistical methods used in this analysis require a minimum sample size, and among the popula­tion of people convicted of a felony offense, only American Indian and White people met these requirements. Other important racial and ethnic disparities (e.g., impacting Black or Hispanic populations) may very well exist across Montana’s criminal justice system, and as more data become available within the Montana courts, future anal­ysis should investigate those as well.

CSG Justice Center staff used quantitative research meth­ods to conduct an analysis of racial equity and develop data-driven policy recommendations to address observed racial disparities in Montana. Data for the in-depth analy­sis detailed in this report were obtained from the MT DOC, which manages data on people in state prison, alternative secure facilities (such as treatment centers), and on com­munity supervision. Additional data on Montana’s general population were obtained from the U.S. Census Bureau. Datasets were cleaned and analyzed using standard sta­tistical methods, including relative rate index calculations and regression analysis. Through these activities, this work aimed to address the following key analysis questions:

1. Are there racial disparities in (a) sentences to incar­ceration or (b) incarceration sentence length, across offense types, after accounting for key case and defen­dant characteristics?

2. Are there racial disparities in incarceration length of stay, across offense types, after accounting for key case and defendant characteristics?

3. Are there racial disparities in decisions to revoke (a) probation/conditional release or (b) parole, across offense types, after accounting for key case and defen­dant characteristics?

 


Figure 2. Analysis Questions

figure 2

Quantitative Data Sources and Measures

MT DOC: The main dataset analyzed for this project was obtained via a data use agreement between the CSG Justice Center and MT DOC. MT DOC staff shared data from January 1, 2016, through December 31, 2020, on three groups of people: (1) people with an original sen­tence for a felony conviction with a pronouncement (i.e., sentencing) date during the study period (probation vio­lations were excluded); (2) people in custody of MT DOC, including people incarcerated in state prison as well as people in alternative secure facilities, such as treatment centers and prerelease centers; and (3) people on commu­nity supervision, including probation, conditional release, and parole. After data were cleaned and eligible cases were identified, CSG Justice Center staff constructed a series of analytic samples (for details, including a sum­mary of the entire analytic approach, see Table 1 on p. 10).

Importantly, for felony convictions, Montana’s sentenc­ing judges have a special sentencing option called “DOC commit.” A sentence to DOC commit means that MT DOC staff will complete an assessment of the sentenced per­son’s risks and needs and then make a placement deci­sion. This placement decision by MT DOC can result in incarceration or community supervision. In cases where MT DOC places a person on community supervision, the supervision is termed conditional release. In practice, conditional release is like probation—the staff, risk and needs assessment tools, and policies regarding sanc­tions and incentives are the same.23 However, conditional release is distinct from probation in one important way: decisions to revoke are made by MT DOC for people in the conditional release group, whereas a judge must make a revocation decision for people on probation.24

 

figure 3

 

U.S. Census data: CSG Justice Center staff also obtained publicly available Census data.25 Specifically, information on the number of American Indian and White Montana res­idents, ages 17 years and older, was obtained to calculate each group’s respective rates of incarceration, community supervision, and arrest (as reported in Figure 1 above).

Key measures and quantitative methods: To address analysis questions for this project, regression was employed to examine relevant outcomes—including incar­ceration sentencing and placement decisions (Analysis Questions 1a–b); length of stay (Analysis Question 2); and probation, conditional release, and parole revoca­tions (Analysis Questions 3a–b). Regression analysis is a common approach for comparing differences in out­comes between two groups, particularly when there is an interest in making an “apples to apples” comparison between those groups.

Regression analysis for each analysis question considered relevant case or contextual characteristics as well as indi­vidual characteristics, which CSG Justice Center staff con­structed using data obtained from MT DOC. Information on specific variables used for each set of analyses are summarized in Table 1 below and within each respective subsection of the Results. As with any analysis project, there were some factors that could not be accounted for due to limited data availability. For example, MT DOC’s data system does not have measures of the use of sanc­tions and rewards in community supervision that are ready for quantitative analysis.

Results of regression analysis are reported below as “relative rate indices” and “predicted” outcomes.

Importantly, where applicable, CSG Justice Center staff used regression output to obtain adjusted relative rate indices. This process was used for all analyses involving a binary (dichotomous) outcome variable; binary outcomes included incarceration sentencing and placement decisions (Analysis Questions 1a–b) and probation, conditional release, and parole revocations (Analysis Questions 3a–b), but not length of stay (Analysis Question 2).

Table 1 below summarizes key details of the analytic approach, including information on research questions, outcomes examined, case counts, data sources, and analysis conducted.

 


Table 1. Summary of Analytic Approach and Results

 

Incarceration and Incarceration Sentence Length Incarceration Length of Stay Supervision Revocations
Analysis Question 1. Are there racial disparities in (a) sentences to incarceration or (b) incarceration sentence length, across offense types, after accounting for key case and defendant characteristics? 2. Are there racial disparities in incarceration length of stay, across offense types, after accounting for key case and defendant characteristics? 3. Are there racial disparities in decisions to revoke (a) probation/conditional release or (b) parole, across offense types, after accounting for key case and defendant characteristics?
Outcome Likelihood of sentence to incarceration Incarceration sentence length Incarceration length of stay Likelihood of a probation/conditional release revocation within the first year of supervision Likelihood of a parole release revocation within the first year of supervision
Description of Cases Analyzed (2016–2020) Felony cases resulting in conviction (excluding weapons cases) Felony cases sentenced to incarceration (excluding weapons cases) Felony cases sentenced to incarceration (excluding weapons cases), in custody of MT DOC during study period, and with adequate information to estimate length of stay People on community supervision— probation or conditional release People on parole
No. of Cases N = 16,930, including 10,022 cases sentenced to DOC commit and 6,908 cases with other sentences N = 5,362 N = 3,007 N = 12,356 N = 2,115
Data Sources MT DOC prison admission data MT DOC correctional status data
Analysis Method Regression analysis, with controls for case/contextual characteristics and selected individual characteristics, including:
• Offense type (for most severe convicted offense); total convictions in case; disposition year; judicial district; if sentence included “DOC commit”
• Race; sex; age; in-state criminal history (10-year history of prior felony convictions)
• Offense type (for most severe convicted offense); total convictions in case; disposition year; judicial district; initial location (e.g., jail, supervision); sentenced prison days; secure or alternative secure facility status
• Race; sex; age; in-state criminal history (10-year history of prior felony convictions)
• Probation vs. conditional release status; reason for probation (new offense vs. prison discharge); disposition year; supervising field office
• Race; sex; age; risk and needs assessment score; prior revocations
• MT Board of Pardons and Parole decision vs. interstate compact; disposition year; supervising field office
• Race; sex; age; risk and needs assessment score

Results

Summary of Results

CSG Justice Center staff’s analysis demonstrates a largely consistent pattern of American Indian-White dispari­ties in decision-making across Montana’s criminal justice system.

Incarceration sentencing and placement:

Descriptive analysis: Most common felony offenses

■ Although there was no racial disparity observed in analysis of the most common offenses for both American Indian people and White people, over 30 percent of all felony convictions are for drug offenses.

Judicial placement decisions (non-DOC commit sentences)

■ American Indian people are 1.5 times more likely to be incarcerated for felony criminal endanger­ment and other person offenses and 1.4 times more likely to be incarcerated for felony public order offenses relative to comparable White people.

MT DOC placement decisions (DOC commit sentences)

■ American Indian people are 1.3 times more likely to be incarcerated for felony criminal endanger­ment and other person offenses and 1.2 times more likely to be incarcerated for felony public order offenses relative to comparable White people.

Incarceration sentence length:

■ There are no observed differences for incarceration sentence length.

Incarceration length of stay:

■ Once incarcerated, American Indian people remain in secure or alternative facilities for an average of 27.4 days longer than similarly situated White people.

Probation/conditional release revocations:

■ American Indian people on conditional release are 1.36 times more likely than comparable White peo­ple to have supervision revoked during the first year of supervision. Similarly, American Indian people on probation are 1.44 times more likely to have super­vision revoked during that period.

■ Supplemental analysis identified revocation dis­parities for both compliance and noncompliance violations.

Parole revocations:

■ American Indian people are 1.3 times more likely to have parole revoked relative to similarly situated White people.

Incarceration Sentencing and Placement Decisions

Prior to conducting regression analysis on incarceration decisions, CSG Justice Center staff used information from MT DOC to classify all felony offenses into high-level categories (e.g., drug offense, property offense).26 As summarized in Table 2 below, MT DOC uses a total of nine different offense categories: drug, influence, per­son, property, public order, sexual crime, vehicle, violent, and weapon offenses. CSG Justice Center staff renamed “person” offenses to “criminal endangerment and other person offenses” to reflect the high proportion of criminal endangerment convictions that comprise this category. Additionally, during the study period, only nine felony convictions were recorded for vehicle offenses. As a result, CSG Justice Center staff collapsed vehicle offenses into the influence category and developed a new category called driving while under the influence (DWI). Eight offense categories were used in analyses; however, there were too few weapons cases to analyze using regression. As such, regression results shown in Figures 5 and 6 exclude weapons offenses.

 


Table 2. Offense Categories and Most Common Offenses (2016–2020)
Among Felony Cases Resulting in a Conviction

 

Offense Category Most Common Offenses
Drug Criminal possession of dangerous drugs; criminal posses­sion with intent to distribute
DWI (Influence & Vehicle)* Driving under the influence of alcohol or drugs—fourth or subsequent offense; driving under the influence of alcohol or drugs
Criminal Endangerment and Other Person Criminal endangerment; criminal child endangerment
Property Theft; burglary
Public Order Failure to register as someone who has been convicted of a sex or violent offense; escape
Sexual Crime Sexual assault; sexual intercourse without consent
Violent Assault with a weapon; partner/family member assault
Weapon** Carrying concealed weapons; possession of deadly weapon by incarcerated person or youth in facility

* For analysis, CSG Justice Center staff collapsed influence and vehicle offenses into one category, DWI, as there were only nine felony vehicle convictions during the
study period.
** There were only 38 felony weapons convictions during the study period; due to low numbers, results were excluded in regression analysis.

 


Figure 4: Felony Convictions by Offense Type and Race

Figure 4

 

 

Most Common Felony Offenses Resulting in Conviction

In addition to categorizing all offenses, CSG Justice Center staff explored the most common felony offenses for which American Indian people and White people in the state are convicted to understand the context in which sentencing and placement decisions take place. Descriptive results, presented above in Figure 4, indicate that for both American Indian and White people, drug offenses comprise the largest proportion of all felony convictions. Notably, however, in the analysis of incarceration sentencing and placement decisions, CSG Justice Center staff did not find evidence of racial disparity in judicial or MT DOC decision-making for drug cases. In other words, the high volume of felony drug cases is a concern for both American Indian and White people in the state.

DOC Commit Status

Recall that in Montana, there are two routes by which a person may be placed in state prison after a new offense: first, a judge may decide to sentence a person to incarceration; second, a judge may decide to sentence a person to “DOC commit,” and MT DOC may then make a placement decision that involves incarceration. Because these two routes involve different decision-making processes, CSG Justice Center staff analyzed them separately. Below, results for judicial decision-making are reported first, and results for MT DOC decision-making are reported second. It is important to note that in this analysis sample, among all felony cases resulting in a conviction, the majority (about 59 percent) result in a sentence to DOC commit.

To analyze American Indian-White differences in decisions to incarcerate, CSG Justice Center staff used relative rate indices and regression analysis to examine the likelihood that a person convicted of a felony offense would be sentenced to incarceration in state prison. As noted earlier, the main analyses used regression, a statistical technique that allows for comparison after adjustment for baseline characteristics that might differ between groups. Factors that were considered for analyses of incarceration sentencing and placement decisions, as well as incarceration sentence length, are summarized in Table 3 below.

 


Table 3: Characteristics Accounted for in Analyses of Incarceration Sentencing and Placement Decisions and Incarceration Sentence Length

Case Characteristics Defendant Characteristics
Offense type (most severe convicted offense) Race
Total convictions in case Sex
Disposition year Age
Judicial district In-state criminal history (prior felony convictions)
If sentence included “DOC commit”

Judicial Decisions to Incarcerate

After accounting for key case and individual characteris­tics listed in Table 3 above, CSG Justice Center staff used regression analysis to calculate adjusted RRIs to examine American Indian-White differences in judicial sentences to incarceration for people convicted of felony offenses. Results are depicted in Figure 5 below; statistically signif­icant differences are shown in blue, and results that are not statistically significant are shown in gray.

Results shown in Figure 5 indicate that there are statistically significant American Indian-White racial disparities for two specific offense categories: criminal endangerment and other person offenses and public order offenses. After CSG Justice Center staff accounted for key characteristics that might otherwise help explain an incarceration sentence, American Indian people are 1.5 times more likely than their White counterparts to be sentenced to incarceration for criminal endangerment and other person offenses. Similarly, American Indian people are 1.4 times more likely to be sentenced to incar­ceration for public order offenses.

Importantly, in Montana, certain offenses carry mandatory prison terms, meaning judges have less discretion in their sentencing decisions for such crimes.27 CSG Justice Center staff examined these results a second time, accounting for whether an offense carries a mandatory prison term; however, results did not change substantively.

 


Figure 5: Relative Rate Indices of Probability of Incarceration for People Placed by a Judge, by Offense Type Comparing American Indian People to White People

figure 5


MT DOC Placement Decisions

CSG Justice Center staff used the same analytic approach to assess racial differences in the likelihood that a per­son convicted of a felony offense and sentenced to DOC commit would be placed in Montana State Prison or Montana Women’s Prison. Like the analysis of judicial decision-making for incarceration, CSG Justice Center staff calculated adjusted RRIs after accounting for key case and individual characteristics. Results are depicted in Figure 6 below.

Results indicate that MT DOC placement decisions exhibit American Indian-White disparities that are very similar to those seen in the analysis of judicial sentencing deci­sions. Specifically, after CSG Justice Center staff consid­ered key characteristics, American Indian people are 1.3 times more likely than their White counterparts to be incarcerated for criminal endangerment and other person offenses; similarly, American Indian people are 1.2 times more likely to be incarcerated for public order offenses.

 


Figure 6: Relative Rate Indices of Probability of Incarceration for People Sentenced to DOC Commit, by Offense Type

Comparing American Indian People to White People

Figure 6

 

Most Common Offenses Resulting in Incarceration

To understand the specific circumstances in which these incarceration disparities are occurring, CSG Justice Center staff further examined convictions for criminal endanger­ment and other person offenses, as well as convictions for public order offenses, to identify the most common specific offenses that result in incarceration in each type of case. Among all offenses that fall into the criminal endanger­ment and other person offense category, criminal endan­germent specifically is a major driver of incarceration for both American Indian and White people.

In particular, for American Indian people convicted of any crime that falls into the felony criminal endangerment or other person offense category and that results in incar­ceration (via judicial or MT DOC placement), 76 percent of such cases involve a criminal endangerment offense. Although this offense category also includes other types of crime—such as violation of a protection order or stalking— convictions for other crimes ultimately account for a much smaller proportion of all incarceration placements. At the same time, among all White people convicted of a felony criminal endangerment or other person offense, 67 per­cent of cases include a criminal endangerment offense. In sum, these findings demonstrate that any policy or practice changes intended to address racial dis­parities in criminal endangerment and other person offenses more broadly must account for disparities in criminal endangerment incarceration sentences and placements in particular.

For public order cases that result in incarceration, there is a range of specific offenses that are relevant. Escape, meaning escape from a correctional facility, is most com­mon, followed by failure to register as someone who has been convicted of a sex or violent offense, bail jumping (this is Montana’s preferred term, but in practice, it means failure to appear or FTA28 ), and tampering with/fabricating evidence. Because racial differences are occurring across a number of specific offenses, efforts to ameliorate dis­parities in public order offenses will need to address this offense class as a whole, rather than focusing on just one or two offense types.

 


Table 4: Most Common Felony Public Order Offenses (2016–2020)
For Cases Resulting in Incarceration

American Indian Defendants (N = 211) White Defendants (N = 403)
Offense Count (Pct.) Offense Count (Pct.)
Escape 92 (43.6%) Escape 140 (34.7%)
Failure to register as someone con­victed of a sex or violent offense 42 (19.9%) Failure to register as someone convicted of a sex or violent offense 60 (14.9%)
Bail jumping 35 (16.6%) Bail jumping 54 (13.4%)
Tampering with or fabricating physical evidence 10 (4.7%) Tampering with or fabricating physical evidence 31 (7.7%)

N = 614
CSG Justice Center analysis of MT DOC prison admission data.

 

Incarceration Sentence Length

CSG Justice Center staff additionally examined incarcera­tion sentence length, i.e., the length of sentence imposed, among people placed in incarceration, to assess whether there are racial disparities in this outcome. Specifically, staff used regression analysis to assess racial differences in sentence length for eight felony crime categories. After accounting for key case and individual characteristics that are summarized in Table 3 above (i.e., the same factors considered in the analysis of incarceration sentencing and placement decisions), there were no statistically sig­nificant American Indian-White differences in sen­tence length across any of the eight felony crime categories tested. Figure 7 below shows a high-level summary of this analysis, illustrating the adjusted aver­age sentence length for American Indian and White peo­ple across all crime categories.

Notably, additional analysis conducted by CSG Justice Center staff indicates that judges tend to sentence people to incarceration in more serious felony cases (e.g., sexual or violent crimes) and sentence people to DOC commit more often for relatively less severe felony cases (e.g., DWI cases). In this way, the substantially longer adjusted aver­age sentence length for cases involving a judicial sentence to incarceration (versus a MT DOC placement decision) is not unexpected. In practice, sentenced incarceration length is not necessarily reflected in actual time served in prison, as general parole eligibility in Montana occurs at one-quarter of the sentence (for nonlife sentences).29

 


Figure 7: Predicted Length of Incarceration Sentence, by Race (2016-2020)

Figure 8

Length of Stay

Having examined differences in sentencing and place­ment, CSG Justice Center staff analyzed length of stay to understand if there are observable disparities in actual time served. For this analysis, CSG Justice Center staff again employed regression methods to account for a number of contextual and individual factors summarized in Table 5 below.

 


Table 5: Characteristics Accounted for in Analyses of Incarceration Sentencing and Placement Decisions and Incarceration Sentence Length

Case Characteristics Individual Characteristics
Offense type (most severe convicted offense) Race
Total convictions in case Sex
Disposition year Age
Judicial district In-state criminal history (prior felony convictions)
Initial location (e.g., jail, supervision)
Sentenced prison days
Secure or alternative secure facility status

 

Across all 8 felony offense types, results indicate that American Indian people are staying incarcerated for an average of 27.4 days longer than their White counterparts, across all offense categories. As shown in Figure 8 below, this disparity is apparent for sexual crimes, which have the longest overall lengths of stay, for DWIs, which corre­spond with the shortest lengths of stay, and everything in between. Although this analysis was not able to assess the role of all potential drivers of disparity—such as the MT Board of Pardons and Parole (BOPP) decision-making or availability of prison services—it is nonetheless clear that after accounting for many important factors, there is a consistent American Indian-White disparity in incarceration stays.

CSG Justice Center staff additionally examined whether this racial disparity in length of stay varied depending on where a person was incarcerated before release30—a secure facility (i.e., state prison) versus alternative secure care (alt-secure) facility. This difference is important because the BOPP makes release decisions about people in prison, whereas MT DOC leadership makes those determinations for people in an alternative facility. Results indicate that the racial disparity is worse on average for people in secure facilities: American Indian people in secure facilities serve an average of 34.6 additional days than their White coun­terparts. Among those in alt-secure facilities, American Indian people serve an average of 25.1 extra days than their White counterparts.

 


Figure 8: Predicted Length Of Incarceration Stay, by Offense Type, Race, and Placement Prior to Release

Figure 9 et 10

 

Supervision Revocations

CSG Justice Center staff additionally examined the extent to which there are American Indian-White differences in decisions to revoke supervision for people on probation, conditional release, and parole. Analysis was conducted first for people on probation and conditional release and second for people on parole, as the decision to revoke people on probation or conditional release is made by a judge or the MT DOC, respectively, while the decision to revoke people on parole is made by the BOPP.

Here, as in the analysis sections above, CSG Justice Center staff used regression methods and relative rate index cal­culations to examine racial differences in the likelihood of facing a supervision revocation within the first year of supervision. Table 6 below shows the factors accounted for in each of the revocation analyses.

 


Table 6: Characteristics Accounted for in Analyses of Supervision Revocations (2016–2020)

Probation/Conditional Release Analysis Parole Analysis
 

Context

Probation vs. CR status MT BOPP decision vs. Interstate
Reason for probation (new offense vs. prison discharge)
Disposition year
Supervising field office
 

Individual characteristics

Race, sex, age
Risk and needs assessment (MORRA/WRNA) overall score and criminal history subsection score
Prior revocations

 

Probation and Conditional Release

Regression was used to analyze the likelihood of revo­cation for people on probation and conditional release while controlling for case and individual characteristics described above. The regression results indicate that during the first year of supervision, American Indian peo­ple are 1.44 times more likely to be revoked from proba­tion compared to White people and 1.36 times more likely than White people to be revoked from conditional release. These results are presented in Figure 9 below.

CSG Justice Center staff then ran separate models com­paring two subgroups that are of particular interest to Montana stakeholders. On May 17, 2017, Montana enacted legislation that defined two types of supervision violations: compliance (relatively less serious violations related to violating conditions of supervision) and noncompliance violations (more serious violations including new offenses and four other categories of behavior—for details, see the Glossary).31 As such, CSG Justice Center staff analyzed whether there are disparities for these two types of viola­tions for probation or conditional release terms from May 2017 through 2020. Results indicate that there are racial disparities for both types of violations.

Parole

The results of the analysis of parole revocations are sim­ilar to the probation and conditional release analysis: American Indian people are revoked at higher rates than White people. Specifically, after accounting for contextual and individual factors, American Indian people are 1.3 times more likely to be revoked than White people.

 


Figure 9: Predicted Probability of Probation and Conditional Release Revocation, by Race

Figure 9

Figure 10: Predicted Probability of Parole Revocation, by Race

Figure 10

 

Policy Recommendations

The results of this analysis give the Montana judiciary more information than they have ever had on American Indian-White racial disparities in the state’s criminal justice system, particularly for people convicted of felony offenses. Informed by these results, the judiciary can understand where it should begin to target efforts to address racial disparities at critical decision-making points.

However, there are limitations in this analysis for pinpoint­ing causes of these racial disparities. To effect change, the judiciary needs to take additional steps to understand why disparities are occurring at particular decision-making points. This includes conducting qualitative research and outreach to crucial stakeholders such as judges, pros­ecutors, defense attorneys, law enforcement officers, supervision officers, and Tribal members. The following recommendations point out where the state should begin investing time and resources that will have a significant impact on racial disparities for felony convictions.

1. To address disparities in the decision to incarcer­ate American Indian people for criminal endangerment and other person offenses, the judiciary should educate judges about racial disparities driven by use of the crim­inal endangerment offense and explore the role of plea agreements in enabling these disparities.

2. To address disparities in the decision to incarcerate American Indian people for public order offenses, the judiciary should coordinate with judges, law enforcement, MT DOC, and Tribal Nations to investigate and address challenges specific to the inequitable treatment of peo­ple failing to comply with pretrial and post-conviction legal requirements, like bail jumping and failure to register.

3. To address longer lengths of stay for American Indian people, the judiciary, MT DOC, and Board of Pardons and Parole should continue their efforts to address the fac­tors contributing to this disparity.

4. To reduce unequal revocations for supervision viola­tions, MT DOC should continue to investigate and analyze how supervision revocation recommendations and deci­sions are made through a racial equity lens.

5. To impact racial disparities within the criminal justice system, the judiciary should build upon existing efforts to understand, track, and reduce bias. This includes coordinat­ing ongoing racial equity initiatives in the state, implement­ing judicial education on equity and bias, and improving the collection of race data by courts.

Recommendation 1:

To address disparities in the decision to incarcerate American Indian people for criminal endangerment and other person offenses, the judiciary should edu­cate judges about racial disparities driven by use of the criminal endangerment offense and explore the role of plea agreements in enabling these disparities.

This analysis identified statistically significant disparities in how American Indian people convicted of felony criminal endangerment and other person offenses are sentenced relative to comparable White people. These disparities were consistent whether the decision to incarcerate was made by a sentencing judge or via an MT DOC place­ment decision after a DOC commit sentence. The analy­sis revealed that for American Indian people, criminal endangerment is by far the most frequent offense in the cases in this category that leads to incarcer­ation; a criminal endangerment conviction is present in more than 75 percent of such cases.

Previous analysis by CSG Justice Center staff found that criminal endangerment32 is used far more widely in Montana than other states33 and carries a much higher penalty than in other states. Montana statute creates a felony punish­able by up to 10 years in prison, as compared to a misde­meanor punishable up to 12 to 18 months in other states.34

Montana’s laws on criminal endangerment are unusual when compared to the Model Penal Code and other states that were exam­ples for Montana in adopting the statute.

■ In Alaska, Colorado, Oregon, and Washington, reckless endangerment is a misdemeanor offense.35

■ In Arizona, endangerment is classified as a fel­ony only when there is “substantial risk of immi­nent death” and otherwise is a misdemeanor.36

Montana also defines criminal endangerment in an unusual manner, enabling a 10-year felony conviction for conduct that would constitute a misdemeanor offense elsewhere. Montana did not adopt and does not use the concept of “recklessness” in its penal law, unlike the Model Penal Code and most states.37 Anecdotally, CSG Justice Center staff heard from stakeholders that criminal endangerment is a common charge used in plea deals due to its lack of a statutory mandatory minimum sentence.

An initial way in which the judiciary can leverage CSG Justice Center staff’s analysis is to educate judges and DOC staff who make placement decisions for DOC commit sentences on the differing outcomes seen through use of the crim­inal endangerment offense. This education effort should promote careful review of how the offense is being used, particularly in plea agreements, and encourage judges to work with prosecutors and defense attorneys to promote equitable outcomes for people who plead to this offense.

 

Recommendation 2:

To address disparities in the decision to incarcerate American Indian people for public order offenses, the judiciary should coordinate with judges, law enforcement, MT DOC, and Tribal Nations to inves­tigate and address challenges specific to the ineq­uitable treatment of people failing to comply with pretrial and post-conviction legal requirements, like bail jumping and failure to register.

Results of this analysis indicated that there are American Indian-White disparities in the likelihood of facing incarceration for a public order offense. Two of the four most common public order felonies that result in incarceration for both American Indian and White people are violations of legal processes, including bail jumping (failure to appear) and failure to register as some­one convicted of a sex or violent offense.

In previous work conducted as part of JRI in 2015 and 2016, CSG Justice Center staff found that American Indian peo­ple on community supervision who live on reservations or in rural areas face challenges in accessing programs and services or meeting with supervision officers, in part due to the vast geographic distance between where they live and where services and supervision offices are located. Many justice-involved American Indian people in Montana lived on or near a reservation; for instance, about 50 per­cent of American Indian people on state supervision lived on or near a reservation.38

Though research on supervision and reentry needs in non-urban settings is limited, studies demonstrate that living in rural or reservation settings can exacerbate bar­riers to services or communication. For example, public transportation may be very limited.39 Further, about 15 percent of American Indian people living on or near a reservation have no internet at home,40 and some resi­dents of reservations do not have a mailing address that is recognized by state government.41 Such limitations are especially important when considering how to reduce nonviolent offenses that fall into the category of viola­tions of legal processes, like failure to appear in court. Research has indicated that simple communication tools, such as text reminders, can reduce failures to appear.42 The use of text reminders may be especially helpful for people without other reliable means of communication, like state-recognized mailing addresses, to alert them to upcoming court appearances, but such tools may need to be modified to fit the needs of American Indian people in Montana’s legal system.

Additionally, as part of this investigation, Montanans should consider whether certain legal processes, such as a reg­istry for people convicted of sex or violent offenses that may be overly burdensome for American Indian people and lead to a disparate racial impact, are achieving their stated policy goals. In particular, research is mixed on whether registries for people convicted of sex offenses achieve reductions in recidivism, and studies have shown adverse consequences associated with registration and noti­fication laws, such as housing problems and employment instability.43 The Montana judiciary should begin work in this area by developing strategies to educate judges on this dis­parity and the collateral consequences of convictions that require registration. With greater education on this issue, Montanans, including judges, prosecutors, defense attor­neys, Tribal leaders, and supervision staff, could collabo­rate on targeted efforts to improve compliance with these legal requirements and strategies to manage registration challenges more effectively.

For this all to work, it is important to hear from Tribal members to understand barriers that might be related to or exacerbated by living far from urban centers and hear from judges to understand the reasoning for their decision-making in sentencing for these types of offenses. With this additional understanding, the state can identify specific solutions to reduce these types of public order offenses and address unequal punishment. Along with soliciting information from Tribes on these challenges, the state should view Tribes as partners to address how barriers could be mitigated through potential community outreach and education. One initiative on which MT DOC is collaborating with some Tribes is to develop memoranda of understanding that permit supervision officers to go onto Tribal land under very specific circumstances. This could allow officers to develop stronger relationships with people on supervision and potentially mitigate challenges that may result in violations of legal processes. But this must be undertaken with a continued view of Tribes as true partners in the process, an emphasis on addressing clients’ treatment and service needs, and an understand­ing of how historical trauma may be connected to these violations of legal processes.

 

Recommendation 3:

To address longer lengths of stay for American Indian people, the judiciary, MT DOC, and Board of Pardons and Parole should continue their efforts to address the factors contributing to this disparity.

Results of this analysis indicated that once incarcerated, American Indian people face longer terms in prison and alternative secure care (alt-secure) facilities than similarly situated White people. The judiciary should work with MT DOC to implement changes designed to address this dis­parity. In recent years, MT DOC has been planning for or implementing several initiatives aimed at supporting the American Indian population in prison or on community supervision. These efforts include adopting the Integrated Correctional Program Model (ICPM), which targets multiple risk factors for people who are incarcerated and offers programming in institutions and the community. The ICPM has programs specifically designed for Aboriginal popula­tions to address their unique needs and risks. Other key initiatives include delivering cultural awareness training to MT DOC staff, planning for a career path geared toward supervision officers taking on liaison responsibilities to foster Tribal relationships in their community, and integrat­ing cultural components to supportive housing initiatives.

In 2017, during the JRI system analysis, focus groups with people in the criminal justice system and community advo­cates found that obtaining and maintaining housing was a significant barrier for people returning to the community after incarceration.44 As a result of this work, Montana established a rental voucher program for people leav­ing incarceration, which is funded at $200,000 annually. Additionally, MT DOC has a transitional assistance program funded at $400,000 annually that offers critical support for individuals leaving prison or on community supervision to help them plan for release and secure appropriate hous­ing.45 MT DOC could leverage these programs and bolster their funding to provide additional housing and reentry sup­ports specifically for American Indian people returning to rural areas, helping to reduce release delays. Analyzing whether disparities in length of stay vary by region of the state and understanding more about the number of peo­ple who get approved for rental vouchers by race could provide critical information to help direct resources.

As the judiciary and MT DOC work to bolster culturally responsive programs and initiatives, they should continue to speak with Tribal leaders and American Indian people to understand their perspectives on release delays and the resulting impact on individuals, along with the Board of Pardons and Parole and alternative facilities staff to under­stand their processes related to release. Further analysis could focus on the availability of in-prison programming and its potential impact on release decisions. MT DOC could also deliver training on equity and bias for staff and contractors, in addition to training it already conducts on cultural awareness, to bring attention to this disparate treatment and pursue additional training for all supervi­sion officers on cultural awareness and ways to address the needs of American Indian people on supervision.

Additionally, Montana is unusual in that it allows judges to remove parole eligibility in a sentence;46 however, it was not possible to examine whether judges apply this tool in an inequitable manner. As such, this issue should be exam­ined further to determine whether it is applied disparately and contributes to the racial disparities in length of stay.

 

Recommendation 4:

To reduce unequal revocations for supervision viola­tions, MT DOC should continue to investigate and ana­lyze how supervision revocation recommendations and decisions are made through a racial equity lens.

This analysis highlighted racial disparities in revocations— including revocations of probation and conditional release as well as parole revocations. MT DOC has been study­ing this trend and is working to identify ways in which this disparity can be addressed, including providing addi­tional training to supervision officers. A common viola­tion among people who experienced a revocation was related to drug or alcohol use. This suggests that peo­ple on supervision, particularly American Indian people, could benefit from more resources for behavioral health needs. Further, among American Indian and White people convicted of felony offenses, drug crimes were the most frequent type of offense.

Montana is not alone in having a large population of justice-involved individuals with behavioral health needs; indeed, coordination between criminal justice and behav­ioral health systems is a pressing need across the U.S. Efforts to improve behavioral health services stand to bene­fit Montana’s justice-involved population as a whole, includ­ing many American Indian people, and may help reduce the number of people incarcerated or on supervision by better meeting their behavioral health needs. During the implementation phase of Montana’s JRI, MT DOC worked to revamp their behavioral health treatment program­ming referral processes to align with A Shared Framework for Reducing Recidivism and Promoting Recovery.47 This evidence-based framework helps policymakers and practi­tioners prioritize scarce resources based on assessments of individuals’ risk of committing a future crime and their treatment and support needs. As a result of this work, there have been significant changes in how people in MT DOC’s jurisdiction enter treatment programs. It is import­ant for MT DOC to assess these new practices, including referrals and outcomes, from an equity lens to ensure their current process is effective across all racial groups. Similarly, a 2018 report highlighted that treatment courts are under-resourced relative to need in the state.48 When addressing these findings, Montanans should approach any expansion of specialty courts with an eye toward ensur­ing there is equitable access for American Indian people.

Proper use of risk and needs assessment (RNA) tools and the risk-needs-responsivity (RNR) model is especially important in targeting services to people in the criminal justice system to ensure that MT DOC is identifying and pri­oritizing people with the most urgent needs to reduce recidi­vism and supervision revocations. The Probation and Parole Division revised the Montana Incentive and Intervention Grid (MIIG) as part of JRI to provide a framework for officer responses to the behavior of people on supervision and align those responses with evidence-based practices to promote compliance on supervision. Supervision officers began using the MIIG statewide in 2019 and must exhaust appropriate responses in the MIIG before recommending a revocation. MT DOC should use data collected on the MIIG to systematically understand how supervision officers use incentives, sanctions, and referrals prior to reporting a violation and whether they are used equitably across racial groups. This effort should focus on the use of the MIIG by supervision officers to determine if modifications to either the grid or officers’ training on its use may reduce disparate treatment. Additionally, to the extent possible, MT DOC needs to be provided with necessary resources to move forward with quality assurance and local norming and validation projects for its Montana Offender Reentry and Risk Assessment/Women’s Risk and Need Assessment (MORRA/WRNA) RNA assessment tools, which it uses for people on community supervision. Quality assurance efforts on these assessment tools began during JRI. MT DOC should also confirm that these tools are appropriate (i.e., that predictive validity has been demonstrated) for American Indian people in the state.

From a racial equity perspective, it is crucial to provide programs and services in a manner that does not result in disproportionate access across the state. It is import­ant to determine where the greatest resource needs are to ensure both their fair distribution and their ability to make the greatest impact. Again, special outreach must be conducted with communities living in rural areas and on reservations, where access might otherwise be con­strained, prior to the formulation of new policies or resource allocations. MT DOC has partnered with the Montana Department of Public Health and Human Services to plan for and deliver behavioral health resources to support the needs of its population in the justice system. MT DOC should continue to pay particular attention to its behav­ioral health and reentry resources and the availability of programs and treatment that are tailored to the needs of American Indian people and accessible to them on res­ervations or in rural areas. MT DOC could also consider whether technology or transportation challenges account for some of the underlying reasons for revocations of American Indian people on supervision.

 

Recommendation 5:

To impact racial disparities within the criminal jus­tice system, the judiciary should build upon existing efforts to understand, track, and reduce bias. This includes coordinating ongoing racial equity initia­tives in the state, implementing judicial education on equity and bias, and improving the collection of race data by courts.

As a result of the systemic disparities found in this analysis and past studies, it is recommended that Montana’s judi­ciary further strengthen its commitment to study the causes of these disparities. It is also recommended that the judi­ciary continue its collective efforts to identify statewide changes, along with educational and information-sharing opportunities, that would address this disparate treat­ment, in partnership with stakeholders across the court system, including local judges, prosecutors, and defense attorneys. While specific actions to improve racial equity are important, no single policy change can address the totality of racial disparities in the criminal justice system. Advancing racial equity initiatives would ideally involve criminal justice system actors across branches to coordi­nate their responses with one another, other government agencies, and community-based partners.

Montana already has several ongoing efforts to improve outcomes for justice-involved American Indian people; to the extent possible, the judiciary should seek to share information and coordinate efforts with such programs. For example, a recent report highlighted several noteworthy pro­grams in the state,49 including the Flathead Reservation’s Tribal Defenders Office50 holistic defense program, which supports people facing criminal charges by connecting them to services like medical care or assistance with obtaining a driver’s license and has reported success in reducing recidivism among program participants.51

At the same time, there may be possible opportunities to build on existing efforts to address and share information on racial equity. For instance, while the Montana Supreme Court’s Access to Justice Commission focuses on civil legal issues,52 perhaps this body can serve as a model for coordination of criminal legal issues and develop a subcommittee focused on racial equity.

As part of these initial steps to reduce disparate treat­ment in the court system, the judiciary should provide regular training on equity and bias so judges understand the impact that implicit racial biases can have on the fair administration of justice. Improving the collection of race data by the courts is also critical to understanding the extent of disparities and their causes and tracking progress over time. Additional detailed recommendations regarding court data quality are available in the accompanying report, Availability of Defendants’ Race and Ethnicity Information in Montana’s Case Management System.

 

Conclusion

This report presented findings from an analysis of racial equity in Montana’s criminal jus­tice system between 2016 and 2020, identifying decision-making points where American Indian people fare differently than their White counterparts. Results indicate that among people convicted of certain felony offenses, American Indian people are more likely to face prison time than similarly situated White people; however, there was no observable differ­ence in sentence length imposed for those that received an incarceration sentence. Once incarcerated, evidence indicates that American Indian people serve longer sentences on average than their White counterparts. Finally, there are disparities in the decision to revoke community supervision. American Indian people are more likely than comparable White people to have probation or conditional release revoked. American Indian people are also more likely to face a revocation when on parole, relative to White people.
Taken together, the racial disparities identified in this analysis highlight a need for changes to policy and practice. CSG Justice Center staff’s recommendations provide data-driven strategies Montana can apply to begin working toward a more equitable justice system. Many of these recommendations involve the collaboration of multiple state entities to understand the fundamental reasons behind the disparities, educate their staff, and con­sider changes to their practices. As part of this collaboration, Tribal Nations and Tribal stakeholders must be engaged, and equal weight must be given to their input for new racial equity initiatives to succeed.

 

Technical Appendix

Quantitative Data Sources

CSG Justice Center staff obtained data from two main sources for this project. The first source was the Montana Judiciary, which provided court records from criminal offenses and violations, and is described in detail in the accompanying report on missing data, Availability of Defendants’ Race and Ethnicity Information in Montana’s Case Management System. The second source was the MT DOC, which provided records of prison, probation, and parole admissions and release. These two data sources were the main ones used for analysis presented in this report. CSG Justice Center staff additionally obtained U.S. Census Bureau data for select calculations.

Montana Department of Corrections

As noted earlier, due to the amount of missing race and ethnicity information in court data, the primary data for this analysis came from the MT DOC Offender Management Information System (OMIS). Staff from the Statistics and Data Quality Unit extracted and shared information about people convicted of felonies between January 1, 2016, and July 14, 2021. For all people in this cohort, we received criminal history, which in OMIS includes approximately 10 years of felony conviction information.53 There were 94,924 offenses recorded in OMIS for this cohort. CSG Justice Center staff used the offense data to construct samples for our sentencing decision and length analyses.

In addition to felony offenses, CSG Justice Center staff also received OMIS correctional status records. These data provide a timeline of individuals involved in the Montana criminal justice system and include start and end dates for when people are incarcerated in secure and alt-secure facilities or on probation, conditional release, or parole. CSG Justice Center staff used the correctional status data to construct samples for length of stay and supervision revocation analyses. CSG Justice Center staff constructed each analytic sample used in regression analysis from the MT DOC OMIS data. All analytic samples include only peo­ple identified as White or American Indian. As described in the Analytic Approach section above (see pp. 7–10), the number of people of other races or ethnicities was too small for statistical analysis.

U.S. Census Bureau

CSG Justice Center staff obtained publicly available data from the U.S. Census Bureau’s American Community Survey. Specifically, the 2019 one-year Public Use Microdata Sample54 was used to tabulate the number of non-Hispanic American Indian and White people aged 17 and older living in Montana. This information was used to calculate select descriptive statistics (specifically, sta­tistics presented in Fig. 1 on p. 6) and was not used in regression analyses.

Incarceration Sentence and Placement Decisions

Analytic Samples

Analytic Sample 1: Incarceration “In-Out” Decision
CSG Justice Center staff limited the first analytic sample to felony convictions that were pronounced (i.e., sentenced) between January 1, 2016, and December 31, 2020. Even though the data received included some convictions from earlier than 2016, only 2016–2020 data included all felony convictions in the respective calendar year. Additionally, probation violation sentences were excluded from the ana­lytic sample because it was not possible to link violation sentences to their original sentences. In sum, the first analytic sample includes only original felony sentences.

When a person had two or more felony convictions that were sentenced on the same day, all those sentences were bundled together into a single “sentencing event.” To calculate the total sentence length from a sentence event, sentence lengths across the multiple offenses were summed (as long as the sentences were not indicated to run concurrently). Additionally, if there was more than one offense type in a single sentence event (e.g., drug and property offenses), the offenses with the longest maxi­mum prison sentence were preferred in selecting the con­trolling offense type used in regression models.

An additional 1,191 cases (about 6.5 percent of the 18,215 cases identified through initial data cleaning) were excluded because they were missing values for key measures used in the regression analysis. Finally, 94 remaining cases were excluded because they were transferred to the Department of Public Health and Human Services (DPHSS). This resulted in a sample of 16,930 total cases (analytic sample 1).

Note that the felony conviction sample is composed of two distinct components: a total of 6,908 people whose placement decision (e.g., prison vs. probation) was made by a judge and a total of 10,022 people whose placement decision was made by the MT DOC (which occurs in cases where the judge sentences the person to “DOC commit” status). In the body of this report, results for two compo­nent parts are reported separately.

Analytic Sample 2: Incarceration Sentence Length
Starting with the 16,930 cases in analytic sample 1, CSG Justice Center staff further subdivided these data to develop an analytic sample for incarceration sentence length analysis. Because incarceration sentence length was relevant in only a subset of the 16,930 cases, regres­sion analysis for this second dependent variable was restricted to the 5,362 cases for which incarceration was imposed and for which comprehensive data on total sentenced prison days were available. For example, in some cases, CSG Justice Center staff could not reliably calculate total prison days for the focal case because the prison sentence was set to run consecutively to an exist­ing sentence, which CSG Justice Center staff were unable to identify due to data limitations.

Regression Analysis

Regression analysis was employed to investigate the extent to which racial disparities were present at key sentencing and placement decision-making points after accounting for case and individual characteristics.

Regression Analysis: Measures
CSG Justice Center staff constructed dependent (outcome) variables, as well as independent and control variables, using analytic samples 1 and 2.

Dependent Variables
The first dependent variable is incarceration, a binary variable indicating whether a sentence (or placement via DOC) to “straight” incarceration or a split sentence (i.e., including both incarceration and probation) was imposed in the case. As described above, there were 16,930 cases in this group (analytic sample 1: felony convictions); these are the cases used to examine likelihood of incarceration. The second dependent variable is incarceration sentence length, a measure of the number of prison days sentenced in a case. As noted above, there were 5,362 cases in this group (analytic sample 2: felony convictions resulting in incarceration).

Independent and Control Variables
The same set of independent and control variables was used to analyze both incarceration and incarceration sen­tence length outcomes. The main independent variable is the race of the person convicted of a felony crime; this is a binary variable indicating whether the person is iden­tified as American Indian or White in MT DOC records. Additionally, there were two sets of control variables con­structed to account for key case and individual character­istics, respectively.

In terms of case characteristics, the first control variable is offense category/type, which documents the most seri­ous felony conviction in the case (sometimes termed the controlling or governing offense). To categorize offenses, CSG Justice Center staff used a typology of offenses pro­vided by MT DOC to develop the following eight offense categories: influence (e.g., DWI), public order, criminal endangerment and other person, violent, sexual crime, weapon, property, and drug crimes. In cases where there were convictions for more than one offense and offenses fell into more than one crime category (e.g., one drug con­viction and one influence conviction), CSG Justice Center staff used the offense with the longest maximum prison sentence54 to categorize the case. Control variables were also constructed for total convictions in a case (this vari­able was log transformed to account for a skewed distri­bution), the case disposition year, and the corresponding judicial district.

Regarding individual characteristics, in addition to race, demographic information included a binary variable for sex (female or male); a count variable for age at the time of case sentencing; and in-state criminal history, a count variable defined as the total number of prior felony con­victions in approximately the 10 years prior, as recorded by MT DOC (this variable was log transformed to account for a skewed distribution).

 

Regression Analysis: Summary Statistics

Means and proportions for the dependent variables are shown in Tables A1–2 below; means and proportions for the independent and controls variables are summarized in Table A3–4.

 


Table A1: Incarceration Outcome Proportions for Analytic Sample 1, by Race (2016–2020)

Analytic Sample 1
N=16,930

Judicial Decision-Making
N=6,908
DOC
Decision-Making N=10,022
American Indian People
N=1,287
White People N=5,621 American Indian People
N=2,161
White People N=7,861
Prop. (SD) Prop. (SD) Prop. (SD) Prop. (SD)
Incarceration 0.21 (0.41) 0.19 (0.39) 0.55 (0.50) 0.52 (0.50)

 

 


Table A2: Incarceration Sentence Length Outcome Means for Analytic Sample 2, by Race (2016–2020)

Analytic Sample 2
N=5,362
American Indian People
N=1,206
White People
 N=4,156
Mean (SD) Mean (SD)
Incarceration Sentence Length (Days) 2,152.45 (4,976.06) 2,635.64 (7,025.98)

 

 


Table A3: Summary Statistics for Analytic Sample 1 (2016–2020)

 

Analytic Sample 1: Incarceration In-Out Decision
Judicial and DOC Groups:

N=16,930

Mean/Prop.

(SD)

Race
American Indian 0.2 0.4
White 0.8 0.4
Offense Category (Felony Cases)
Drug 0.35 0.48
DWI-Influence/Vehicle 0.1 0.1
Crim. Endanger./Other 0.16 0.16
Property 0.18 0.18
Public Order 0.08 0.08
Sexual Crime 0.03 0.03
Violent 0.09 0.09
Weapon 0 0
Age (Years) 36.5 11.59
Sex
Female 0.26 0.44
Male 0.74 0.44
Total Convictions in Case 1.26 0.68
Total Prior Felonies 0.99 1.6
DOC Commit Status
DOC Commit Sentence 0.59 0.49
Other Sentence 0.41 0.49
Disposition Year
2016 0.2 0.4
2017 0.2 0.4
2018 0.21 0.41
2019 0.21 0.41
2020 0.18 0.38
Judicial District
1st Judicial District 0.09 0.29
2nd Judicial District 0.04 0.2
3rd Judicial District 0.04 0.18
4th Judicial District 0.1 0.3
5th Judicial District 0.02 0.14
6th Judicial District 0.01 0.11
7th Judicial District 0.04 0.19
8th Judicial District 0.1 0.3
9th Judicial District 0.02 0.13
10th Judicial District 0.01 0.11
11th Judicial District 0.08 0.27
12th Judicial District 0.02 0.15
13th Judicial District 0.19 0.39
14th Judicial District 0.01 0.08
15th Judicial District 0.01 0.09
16th Judicial District 0.03 0.17
17th Judicial District 0.01 0.11
18th Judicial District 0.06 0.23
19th Judicial District 0.02 0.13
20th Judicial District 0.06 0.23
21st Judicial District 0.03 0.17
22nd Judicial District 0.03 0.16

Notes: Proportions may not add up to 1 due to rounding.

 

 


Table A4: Summary Statistics for Analytic Sample 2 (2016–2020)

 

Analytic Sample 2: Incarceration Sentence Length
N=5,362
Mean/Prop. (SD)
Race
American Indian 0.22 0.42
White 0.78 0.42
Offense Category (Felony Cases)
Drug 0.23 0.42
DWI-Influence/Vehicle 0.24 0.43
Crim. Endanger./Other 0.1 0.3
Property 0.13 0.34
Public Order 0.09 0.29
Sexual Crime 0.07 0.26
Violent 0.14 0.34
Weapon 0 0.05
Age (Years) 38.25 11.79
Sex
Female 0.17 0.37
Male 0.84 0.37
Total Convictions in Case 1.47 0.95
Total Prior Felonies 1.26 1.7
DOC Commit Status
DOC Commit Sentence 0.79 0.41
Other Sentence 0.21 0.41
Disposition Year
2016 0.21 0.41
2017 0.22 0.41
2018 0.22 0.42
2019 0.2 0.4
2020 0.15 0.36
Judicial District
1st Judicial District 0.09 0.28
2nd Judicial District 0.04 0.2
3rd Judicial District 0.05 0.22
4th Judicial District 0.09 0.29
5th Judicial District 0.02 0.15
6th Judicial District 0.01 0.09
7th Judicial District 0.02 0.15
8th Judicial District 0.08 0.28
9th Judicial District 0.01 0.12
10th Judicial District 0.01 0.12
11th Judicial District 0.07 0.26
12th Judicial District 0.02 0.13
13th Judicial District 0.23 0.42
14th Judicial District 0.01 0.08
15th Judicial District 0.01 0.08
16th Judicial District 0.04 0.18
17th Judicial District 0.01 0.1
18th Judicial District 0.05 0.21
19th Judicial District 0.02 0.14
20th Judicial District 0.06 0.23
21st Judicial District 0.04 0.19
22nd Judicial District 0.03 0.17

Notes: Proportions may not add up to 1 due to rounding.

 

Regression Analysis: Results

Regression analysis was used to examine the extent to which there are racial disparities in the decision to incar­cerate, as well as incarceration sentence length, after accounting for case and defendant characteristics. For likelihood of incarceration, a binary variable, logistic regres­sion was used. Negative binomial regression was used to analyze incarceration sentence length, the second outcome, because it is an over-dispersed count variable.

Table A5 is a summary of results from regression analy­ses examining the likelihood of incarceration. Note that the regression model examining the likelihood of incar­ceration combined judicial and MT DOC cases; that is, regressions were run for the full analytic sample includ­ing all 16,930 cases. As described below, results shown in the main body of the report, separated for judicial vs. MT DOC decisions, were estimated using predicted prob­abilities from this main regression model.

Table A6 summarizes results from regression analyses examining incarceration sentence length. In the main body of the report, adjusted results depicted in Figures 5–6 were obtained by using fully adjusted regression models to estimate predicted outcomes (i.e., the predicted prob­ability of incarceration and predicted incarceration sen­tence length). For the incarceration outcome, to calculate the adjusted relative rate index results that are depicted in Figures 5–6 (in the main body of the report), fully adjusted regression models were used to obtain predicted proba­bility of incarceration for each offense category (e.g., drug, public order offenses) by racial group and were then fur­ther grouped by the relevant decision-making authority (i.e., judicial sentence to incarceration vs. MT DOC place­ment). Relative rate indices were then calculated using those outputs.

 


Table A5: Odds Ratios from Logistic Regression Predicting Incarceration, 2016–2020

Model 1 Model 2
Odds Ratio (SE) Odds Ratio (SE)
Race
American Indian 1.15*** -0.04 1.30* -0.16
Offense Category
Drug 1.20** -0.08
DWI-Influence/Vehicle 15.23*** -1.47
Crim. Endanger./Other 1 -0.08
Public Order 1.30** -0.12
Sexual Crime 17.45*** -2.13
Violent 3.95*** -0.34
Weapon 3.30* -1.71
Race and Offense Category Interaction
American Indian*Drug 0.78 -0.11
American Indian*DWI-Influence/Vehicle 0.71 -0.15
American Indian* Crim. Endanger./Other 1.35 -0.23
American Indian*Public Order 1.24 -0.22
American Indian*Sexual Crime 1.25 -0.48
American Indian*Violent 0.88 -0.16
American Indian*Weapon 0.2 -0.23
DOC Commit Status
DOC Commit 3.18*** -0.15
Age (Years) 1.03** -0.01
Age-squared 1.00*** 0
Sex
Male 1.55*** (0.07)
Log Total Convictions 4.83*** (0.28)
Log Total Prior Felonies 3.12*** (0.11)
Disposition Year
2017 1.01 (0.06)
2018 0.88* (0.05)
2019 0.79*** (0.05)
2020 0.61*** (0.04)
Judicial District
2nd Judicial District 2.22*** (0.25)
3rd Judicial District 2.87*** (0.34)
4th Judicial District 0.93 (0.08)
5th Judicial District 1.41* (0.22)
6th Judicial District 1.26 (0.24)
7th Judicial District 0.51*** (0.07)
8th Judicial District 1.00 (0.09)
9th Judicial District 1.12 (0.18)
10th Judicial District 1.86** (0.35)
11th Judicial District 1.55*** (0.15)
12th Judicial District 0.72* (0.11)
13th Judicial District 1.17* (0.09)
14th Judicial District 1.20 (0.29)
15th Judicial District 1.23 (0.28)
16th Judicial District 1.61*** (0.21)
17th Judicial District 0.61* (0.12)
18th Judicial District 1.39** (0.14)
19th Judicial District 2.73*** (0.42)
20th Judicial District 1.21 (0.13)
21st Judicial District 2.15*** (0.28)
22nd Judicial District 1.55** (0.21)
Intercept 0.63*** (0.01) 0.03*** (0.01)
Observations 16,930 16,930
Pseudo-R2 0.000585 0.278

The omitted category for race is “White”; the omitted category for offense category is “property”; the omitted category for judicial vs. DOC decision is “judicial
decision”; the omitted category for sex is “female”; the omitted category for disposition year is “2016”; and the omitted category for county is “1st Judicial District.”
*** p<0.001, ** p<0.01, * p<0.05

 


Table A6: Incident Rate Ratios (IRRs) from Negative Binomial Regression Predicting Sentenced Incarceration Length, 2016–2020

Model 1 Model 2
Incident Rate Ratio (SE) Incident Rate Ratio (SE)
Race
American Indian 0.82** (0.06) 1.09 (0.06)
Offense Category
Drug 1.04 (0.03)
DWI-Influence/Vehicle 0.54*** (0.02)
Crim. Endanger./Other 1.06 (0.04)
Public Order 0.87*** (0.03)
Sexual Crime 2.50*** (0.17)
Violent 1.82*** (0.10)
Weapon 1.14 (0.20)
Race and Offense Category Interaction
American Indian*Drug 0.95 (0.06)
American Indian*DWI-Influence/Vehicle 1.14 (0.08)
American Indian*Crim. Endanger./Other 0.97 (0.07)
American Indian*Public Order 0.84* (0.06)
American Indian*Sexual Crime 0.99 (0.22)
American Indian*Violent 0.72** (0.07)
American Indian*Weapon 0.28** (0.14)
DOC Commit Status
DOC Commit 0.34*** (0.01)
Age (Years) 1.00 (0.01)
Age-squared 1.00 0.00
Sex
Male 1.07** (0.03)
Log Total Convictions 1.72*** (0.05)
Log Total Prior Felonies 1.17*** (0.02)
Disposition Year
2017 0.96 (0.03)
2018 0.90*** (0.03)
2019 0.98 (0.03)
2020 0.98 (0.04)
Judicial District
2nd Judicial District 0.87* (0.06)
3rd Judicial District 0.77*** (0.06)
4th Judicial District 0.91 (0.05)
5th Judicial District 0.90 (0.06)
6th Judicial District 0.84 (0.08)
7th Judicial District 0.94 (0.08)
8th Judicial District 0.90 (0.05)
9th Judicial District 0.81** (0.06)
10th Judicial District 0.88* (0.06)
11th Judicial District 0.98 (0.05)
12th Judicial District 0.95 (0.09)
13th Judicial District 0.91* (0.04)
14th Judicial District 0.84 (0.08)
15th Judicial District 0.79** (0.07)
16th Judicial District 0.91 (0.06)
17th Judicial District 0.86 (0.09)
18th Judicial District 0.89 (0.06)
19th Judicial District 1.13 (0.10)
20th Judicial District 0.85** (0.05)
21st Judicial District 0.96 (0.06)
22nd Judicial District 1.03 (0.09)
Intercept 2,635.63*** (108.98) 2,942.52*** (389.42)
Observations 5,362 5,362
Log Likelihood -47331.9 -44,399
AIC 17.66 16.57

The omitted category for race is “White”; the omitted category for offense category is “property”; the omitted category for judicial vs. DOC decision is “judicial
decision”; the omitted category for sex is “female”; the omitted category for disposition year is “2016”; and the omitted category for county is “1st Judicial District.”
*** p<0.001, ** p<0.01, * p<0.05

 

Length of Stay

Analytic Sample 3: Incarceration Length of Stay

The third analytic sample, for length of stay in Montana State Prison, Montana Women’s Prison, or an alternative secure facility, was constructed by combining the felony conviction sample described above (analytic sample 2) with correctional status data. First, all sentencing events in which the pronounced sentence included at least one day of prison were identified; second, these records were matched to the beginning of a secure or alt-secure place­ment in the correctional status data. To calculate length of stay, confinement windows were calculated and defined as consecutive secure or alt-secure placements. The total length of stay is the number of total days in each window. People who received another sentence during their con­finement window were excluded, because in those cases, it was not possible to attribute the length of stay to a sin­gle sentencing event. After initial data cleaning, a total of 3,294 cases were identified. An additional 287 cases were excluded because values for key variables used in regression analysis were missing; the final analytic sam­ple included 3,007 cases.

 

Regression Analysis

Regression analysis was employed to investigate the extent to which racial disparities were present at incarceration length of stay after accounting for contextual and individ­ual characteristics.

Regression Analysis: Measures
CSG Justice Center staff constructed the dependent (out­come) variable and independent and control variables using analytic sample 3.

Dependent Variable
The dependent variable for this analysis is incarceration length of stay, a measure of the number of consecutive prison days served as part of a sentence for a felony conviction.

Independent and Control Variables
The independent and control variables used to analyze incarceration length of stay are very similar to those used in the analysis of incarceration sentencing and placement. The main independent variable is the race of the person convicted of a felony crime; this is a binary variable indi­cating whether the person is identified as American Indian or White in MT DOC records. Additionally, there were two sets of control variables constructed to account for both key contextual and individual characteristics, respectively.

In terms of contextual characteristics, the first control variable is offense category/type, which documents the most serious felony conviction in the case (sometimes termed the controlling or governing offense). To catego­rize offenses, CSG Justice Center staff used a typology of offenses provided by MT DOC to develop the follow­ing eight offense categories: influence (e.g., DWI), public order, criminal endangerment and other person, violent, sexual crime, weapon, property, and drug crimes. In cases where there were convictions for more than one offense and offenses fell into more than one crime category (e.g., one drug conviction and one influence conviction), CSG Justice Center staff used the offense with the longest max­imum prison sentence to categorize the case.55 Control variables were also constructed for total convictions in a case; the case disposition year; and sentenced prison days, a count variable. Additionally, an indicator variable was constructed for the initial location, which refers to the location or facility type where the person was placed initially post-sentencing. Possible locations included an alternative secure facility, jail, prison, and community supervision. Finally, a control for secure versus alternative secure status was constructed. To determine this status, CSG Justice Center staff examined the final correctional status that was recorded prior to release. Although peo­ple can be moved between secure and alternative secure facilities during their incarceration term, the last correc­tional status is a good indicator of where a person spent most of their time incarcerated. Specifically, among peo­ple whose final correctional status is a secure facility, on average about 87 percent of their term was served in prison. Among those whose final correctional status is an alternative secure facility, about 90 percent of individ­uals spent no days in prison; that is, in the vast majority of cases, their entire term was served in an alternative secure facility.

Regarding individual characteristics, in addition to race, demographic information included a binary variable for sex (female or male); a count variable for age at the time of case sentencing; and in-state criminal history, a count variable defined as the total number of prior felony con­victions in approximately the 10 years prior, as recorded by MT DOC.

Regression Analysis: Summary Statistics

Means and proportions for the dependent variables are shown in Table A7 below; means and proportions for the independent and control variables are summarized in Table A8 below.

 


Table A7: Incarceration Length of Stay Outcome Means for Analytic Sample 3, by Race (2016–2020)

Analytic Sample 3
N=3,007
American Indian People
N=630
White People N=2,377
Mean (SD) Mean (SD)
Incarceration Sentence Length (Days) 457.25 (299.06) 406.37 (279.36)

 

 


Table A8: Summary Statistics for Analytic Sample 3 (2016–2020)

Analytic Sample 3 Incarceration Length of Stay N=3,007
Mean/Prop. (SD)
Race
American Indian 0.21 0.41
White 0.79 0.41
Offense Category
Drug 0.23 0.42
DWI-Influence/Vehicle 0.34 0.47
Crim. Endanger./Other 0.09 0.29
Public Order 0.11 0.32
Sexual Crime 0.05 0.22
Violent 0.04 0.2
Weapon 0.12 0.32
Age (Years) 39.32 11.92
Sex
Female 0.18 0.39
Male 0.82 0.39
Total Convictions 1.44 0.9
Total Prior Felonies 0.92 1.42
Disposition Year
2016 0.25 0.44
2017 0.25 0.43
2018 0.23 0.42
2019 0.18 0.38
2020 0.1 0.29
Sentenced Prison Days 1,349.78 1,583.81
Top Offense Description
Persistent Felony Offender 0.01 0.08
Initial Location
Alternative Secure Facility 0.46 0.5
Community Supervision 0.02 0.14
Jail 0.41 0.49
Prison 0.11 0.32
Other 0 0.06
Final Location/Facility Type
Secure 0.21 0.4
Alternative-secure 0.8 0.4

Proportions may not add up to 1 due to rounding.

 

Regression Analysis: Results

Regression analysis was used to examine the extent to which there are racial disparities in incarceration length of stay, accounting for contextual and defendant characteristics. Negative binomial regression was used to analyze prison length of stay because it is an over-dispersed count variable. Results from regression analysis are presented in Table A9 below.

 


Table A9: Incident Rate Ratios (IRRs) from Negative Binomial Regression Predicting Incarceration Length of Stay, 2016–2020

Model 1 Model 2
Incident Rate Ratio (SE) Incident Rate Ratio (SE)
Race
American Indian 1.13*** (0.03) 1.07** (0.02)
Offense Category
Drug 0.99 (0.03)
Influence/MV 0.66*** (0.02)
Crim. Endanger./Other 1.03 (0.04)
Public Order 0.96 (0.05)
Sexual Crime 1.27*** (0.07)
Violent 1.14*** (0.04)
Weapon 1.11 (0.25)
Age (Years) 0.99** 0.00
Age-squared 1.00* 0.00
Sex
Male 1.01 (0.02)
Total Convictions 1.02* (0.01)
Total Prior Felonies 1.02* (0.01)
Disposition Year
2017 0.95* (0.02)
2018 0.96 (0.03)
2019 0.86*** (0.03)
2020 0.68*** (0.02)
Sentenced Prison Days 1.00 0.00
Top Offense Description
Persistent Felony Offender 1.53*** (0.17)
Previous Location
Comm. supervision 0.15*** (0.01)
Jail 1.10*** (0.02)
Prison 1.39*** (0.05)
Other 1.07 (0.17)
Facility Status
Secure 1.38*** (0.03)
Intercept 406.37*** (5.50) 500.26*** (55.17)
Observations 3,007 3,007
Log Likelihood -20679.01 -19791.66
AIC 13.76 13.19

 

The omitted category for race is “White”; the omitted category for offense category is “property”; the omitted category for sex is “female”; the omitted category for
disposition year is “2016”; the omitted category for top offense description is “offense does not include persistent felony offender indicator”; the omitted category for
previous location is “alternative secure facility”; and the omitted category for facility status is “alternative secure facility.”
*** p<0.001, ** p<0.01, * p<0.05

 

Revocations of Probation and Conditional Release

Analytic Sample 4: Probation and Conditional Release

The analytic sample for American Indian and White peo­ple on probation and conditional release was constructed from the correctional status data, yielding a total of 14,774 cases. To construct the sample, CSG Justice Center staff started with all people whose correctional status of pro­bation or conditional release started between January 1, 2016, and June 30, 2020. If an individual had more than one probation or conditional release start during that period, a single start was selected at random. People who were supervised out of state were excluded. After more data processing, an additional 2,418 cases were excluded because they were missing values for key variables used in regression analysis; the data most frequently missing were risk assessment data. This yields an analytic sam­ple of 12,356 cases.

Regression Analysis

Regression analysis was employed to investigate the extent to which racial disparities were present in the likelihood of supervision revocation after accounting for contextual as well as individual characteristics.

Regression Analysis: Measures
CSG Justice Center staff constructed a dependent (out­come) variable, as well as independent and control vari­ables, using analytic sample 4.

Dependent Variable
The dependent variable for this analysis is supervision revocation, a binary measure indicating whether supervi­sion was revoked during the first year of the probation or conditional release term. Note that analysis of supervision revocations over two- and three-year periods, respectively, did not yield substantively different results.

Independent and Control Variables
The main independent variable is the race of the person convicted of a felony crime; like the other analyses doc­umented in this report, this is a binary variable indicat­ing whether the person is identified as American Indian or White in MT DOC records. Additionally, there were two sets of control variables constructed to account for both key contextual and individual characteristics, respectively.

In terms of contextual characteristics, the first control variable is probation versus conditional release status, a binary variable. Additionally, control variables were con­structed for the reason for probation/conditional release supervision, a series of dummy variables indicating the reason for supervision, including a new offense; a revoca­tion of prior status; prison discharge (i.e., as part of a split sentence that included prison time followed by probation); or behavioral health program completion. Finally, a control was developed for disposition year of the case resulting in probation/conditional release, as well as a control for supervision agency, a series of dummy variables indicat­ing which of the 23 states probation and parole field offices were involved with each supervision case.

In terms of individual characteristics, in addition to race, demographic information included a binary variable for sex (female or male) and a count variable for age at the time of case sentencing. A control for prior revocations was included, coded as count variable for the total number of prior supervision revocations. Finally, a control for over­all risk and need score was included, using RNA informa­tion (the Montana Offender Reentry and Risk Assessment, or MORRA, for men, and the Women’s Risk and Needs Assessment, or WRNA, for women). Note that scores for the MORRA versus WRNA have different ranges and dis­tributions; to account for this, CSG Justice Center staff standardized the scores and additionally included an inter­action between sex and the standardized overall score. This interaction term allows the effect of RNA variables to vary by sex.

Regression Analysis: Summary Statistics

Means and proportions for the dependent variables are shown in Table A10 below; means and proportions for the independent and control variables are summarized in Table A11.

 


Table A10: Probation/Conditional Release Revocation Outcome Proportions for Analytic Sample 4, by Race (2016–2020)

Analytic Sample 4
N=12,356
American Indian People
N=2,397
White People
N=9,959
Mean (SD) Mean (SD)
Probation/Conditional Release Revocation (1st year of supervision) 0.14 (0.35) 0.09 (0.28)

 

 


Table A11: Summary Statistics for Analytic Sample 4 (2016–2020)

Analytic Sample 4:
N=12,356
Mean/Prop. (SD)
Race
American Indian 0.19 0.40
White 0.81 0.40
Probation vs. Conditional Release Status
Conditional Release 0.24 0.43
Probation 0.76 0.43
Sex
Female 0.29 0.45
Male 0.71 0.45
Std. RNA Total Score 0.00 0.99
Age (Years) 36.61 11.76
Reason for Supervision
New Offense 0.83 0.38
Prior Status Revoked 0.01 0.07
Prison Discharge 0.13 0.33
BH Program Completion 0.04 0.19
Disposition Year
2016 0.20 0.40
2017 0.22 0.41
2018 0.22 0.42
2019 0.24 0.43
2020 0.12 0.32
Number of Prior Revocations 0.21 0.48
Supervision Field Office
Anaconda 0.02 0.15
Billings 0.22 0.41
Bozeman 0.06 0.24
Butte 0.06 0.23
Cut Bank 0.01 0.09
Dillon 0.01 0.10
Glasgow 0.01 0.12
Glendive 0.02 0.12
Great Falls 0.11 0.32
Hamilton 0.03 0.17
Hardin 0.02 0.14
Havre 0.03 0.17
Helena 0.09 0.28
Kalispell 0.07 0.25
Lewistown 0.01 0.12
Libby 0.02 0.13
Livingston 0.01 0.10
Miles City 0.02 0.14
Missoula 0.11 0.31
Polson 0.04 0.20
Shelby 0.01 0.10
Sidney 0.02 0.15
Thompson Falls 0.01 0.09

Proportions may not add up to 1 due to rounding.

Regression Analysis: Results

Regression analysis was used to examine the extent to which there are racial disparities in the likelihood of fac­ing a probation or conditional release revocation during the first year of supervision after accounting for contex­tual and defendant characteristics. Logistic regression was used since the outcome is binary.

CSG Justice Center staff used a series of logistic regres­sion models to examine this outcome. First, a model pre­dicting the likelihood of any supervision revocation during the first year was run, and after accounting for key contex­tual and individual characteristics, evidence of an American Indian-White disparity was identified. These results are presented in Table A12 below.

CSG Justice Center staff then ran separate models com­paring two subgroups that are of particular interest to Montana stakeholders. Specifically, in May 2017, Montana enacted legislation that defined two types of supervision violations: compliance (relatively less serious violations related to violating conditions of supervision) and non­compliance violations (more serious violations including new offenses and four other categories of behavior—for details, see the Glossary).56

Although MT DOC did not track compliance and noncom­pliance violations in their data system until 2021, CSG Justice Center staff were able to develop a proxy for these differences after consulting MT DOC staff.57 Cases in which a revocation resulted in an incarceration term greater than 9 months were coded as noncompliance violations (more serious), and cases in which a revocation resulted in an incarceration term of nine months or less were coded as compliance violations (relatively less serious).

Using this information, CSG Justice Center staff analyzed whether there are disparities for these two types of vio­lations for probation or conditional release terms that started on May 17, 2017, through 2020. Results indicate that there are racial disparities for both types of violations.

Sensitivity Tests
Finally, CSG Justice Center staff ran several sensitivity tests. A bivariate logistic regression examining the rela­tionship between race and likelihood of a revocation was run on the full MT DOC sample (i.e., before dropping cases due to missing data, such as risk assessment information); a statistically significant American Indian-White disparity was observed in this group. Additionally, regression mod­els were run examining the likelihood of revocation over two- and three-year periods; results were very similar to those in the main analysis. The results of these tests indi­cate that the evidence of racial disparity in revocations (as presented in Table A12 below) is a robust finding, i.e., it is not sensitive to decisions to analyze specific subgroups of the supervision population.

 


Table A12: Odds Ratios from Logistic Regression Predicting Likelihood of Probation/Conditional Release Revocation for Compliance or Noncompliance Violation, 2016–2020

Model 1 Model 2
Odds Ratios (SE) Odds Ratios (SE)
Race
American Indian 1.80*** (0.12) 1.53*** (0.13)
Probation vs. Conditional Release Status
Probation 0.35*** (0.03)
Sex
Male 1.75*** (0.17)
Std. RNA Total Score 1.87*** (0.14)
Male * Std. RNA Total Score Interaction 1.13 (0.10)
Age (Years) 1.00 (0.02)
Age-squared 1.00 0.00
Reason for Supervision
Prior Status Revoked 0.74 (0.44)
Prison Discharge 1.03 (0.12)
BH Program Completion 0.47* (0.17)
Disposition Year
2017 1.03 (0.11)
2018 1.20 (0.12)
2019 1.21 (0.12)
2020 0.53*** (0.08)
Number of Prior Revocations 1.33*** (0.09)
Supervision Field Office
Billings 1.08 (0.27)
Bozeman 0.75 (0.22)
Butte 1.37 (0.37)
Cut Bank 0.74 (0.32)
Dillon 0.30 (0.23)
Glasgow 0.54 (0.24)
Glendive 0.90 (0.35)
Great Falls 1.39 (0.35)
Hamilton 1.27 (0.39)
Hardin 0.84 (0.30)
Havre 0.66 (0.21)
Helena 1.22 (0.32)
Kalispell 0.94 (0.26)
Lewistown 1.03 (0.42)
Libby 1.00 (0.36)
Livingston 1.27 (0.53)
Miles City 1.04 (0.36)
Missoula 1.07 (0.28)
Polson 0.76 (0.23)
Shelby 1.85 (0.73)
Sidney 0.35* (0.17)
Thompson Falls 0.34 (0.23)
Intercept 0.09*** 0.00 0.13*** (0.06)
Observations 12,356 12,356
Pseudo-R2 0.00881 0.165

The omitted category for race is “White”; the omitted category for probation vs. conditional release status is “conditional release”; the omitted category form sex is “female”; the omitted category for reason for supervision is “new offense”; the omitted category for disposition year is “2016”; and the omitted category for supervision agency is “Anaconda.”
*** p<0.001, ** p<0.01, * p<0.05

 

Revocations of Parole

Analytic Sample 5: Parole

Like the analytic sample for people on probation and con­ditional release, the analytic sample for American Indian and White people on parole is constructed from the cor­rectional status data; a total of 2,908 cases were iden­tified. To construct the sample, CSG Justice Center staff started with all people whose correctional status of parole started between January 1, 2016, and June 30, 2020. If an individual had more than one parole start during this period, a single start was selected at random. People who were supervised out of state were excluded. After more data processing, an additional 747 cases were excluded because they were missing values for key variables used in regression analysis; the data most frequently missing were risk assessment data. Finally, an additional 46 cases were excluded from four supervision agencies because there was no variation in revocation outcomes for supervi­sion field offices (including Dillon, Glasgow, Lewistown, and Libby), such that it was not possible to include them in the model. The final analytic sample consists of 2,115 cases.

Regression Analysis

Regression analysis was employed to investigate the extent to which racial disparities were present in the likelihood of parole revocation after accounting for contextual and individual characteristics.

Regression Analysis: Measures
CSG Justice Center staff constructed a dependent (out­come) variable, as well as independent and control vari­ables, using analytic sample 5.

Dependent Variable
The dependent variable for this analysis is parole revocation, a binary measure indicating whether parole was revoked during the first year of a parole term. Note that analysis of parole revocations over two- and three-year periods, respectively, did not yield substantively different results.

Independent and Control Variables
The main independent variable is the race of the person convicted of a felony crime; like the analyses reported above, this is a binary variable indicating whether the per­son is identified as American Indian or White in MT DOC records. Additionally, there were two sets of control vari­ables constructed to account for both key contextual and individual characteristics, respectively.

In terms of contextual characteristics, the first control variable is a binary variable indicating whether the deci­sion regarding parole eligibility was made by MT BOPP or came about by interstate compact (meaning there was an out-of-state conviction and Montana arranged with the other state to have the person serve parole in Montana). Additionally, a control was developed for disposition year of the case resulting in parole. A control was also developed for supervision agency—a series of dummy variables indi­cating which state parole field office was involved in the case (as noted above, 4 of the 23 supervision agencies, including Dillon, Glasgow, Lewistown, and Libby, had too few cases to analyze and were not included in the model, so a total of 19 agencies were part of the analysis).

In terms of individual characteristics, in addition to race, demographic information included a binary variable for sex (female or male) and a count variable for age at the time of case sentencing. Finally, a control for overall risk and need score was included, using RNA information (the Montana Offender Reentry and Risk Assessment, or MORRA, for men, and the Women’s Risk and Needs Assessment, or WRNA, for women). Note that scores for the MORRA versus WRNA have different ranges and distributions; to account for this, CSG Justice Center staff standardized the scores and additionally included an interaction between sex and the standardized overall score. This interaction term allows the effect of RNA variables to vary by sex.

Regression Analysis: Summary Statistics
Means and proportions for the dependent variables are shown in Table A13 below; means and proportions for the independent and control variables are summarized in Table A14.

 


Table A13: Parole Revocation Outcome Proportions for Analytic Sample 5, by Race (2016–2020)

Analytic Sample 5
N=2,115
American Indian People
N=487
White People
N=1,628
Prop. (SD) Prop. (SD)
Parole Revocation 0.21 (0.40) 0.13 (0.33)

 

 


Table A14: Summary Statistics for Analytic Sample 5 (2016–2020)

Analytic Sample 5
Mean/Prop. (SD)
Race
American Indian 0.23 0.42
White 0.77 0.42
Parole Decision
Interstate Compact 0.08 0.27
MT BOPP 0.92 0.27
Sex
Female
Male 0.86 0.35
Std. RNA Total Score 0.00 1.00
Age (Years) 40.40 11.41
Disposition Year
2016 0.15 0.36
2017 0.19 0.39
2018 0.26 0.44
2019 0.25 0.43
2020 0.15 0.36
Supervision Agency
Anaconda 0.02 0.14
Billings 0.31 0.46
Bozeman 0.03 0.17
Butte 0.09 0.28
Cut Bank 0.01 0.09
Dillon 0.00 0.00
Glasgow 0.00 0.00
Glendive 0.01 0.10
Great Falls 0.11 0.31
Hamilton 0.03 0.17
Hardin 0.01 0.11
Havre 0.02 0.14
Helena 0.11 0.32
Kalispell 0.06 0.24
Lewistown 0.00 0.00
Libby 0.00 0.00
Livingston 0.01 0.07
Miles City 0.01 0.10
Missoula 0.12 0.33
Polson 0.03 0.18
Shelby 0.01 0.08
Sidney 0.01 0.09
Thompson Falls 0.01 0.09

Proportions may not add up to 1 due to rounding. Four supervision agencies, including Dillon, Glasgow, Lewistown, and Libby, had too few cases to analyze and were not included in the model.

 

Regression Analysis: Results

Regression analysis was used to examine the extent to which there are racial disparities in the likelihood of facing a parole revocation during the first year of supervision after accounting for contextual and defendant characteristics. Logistic regression was used since the outcome is binary. Results from regression analysis are presented in Table A15 below.

 


Table A15: Odds Ratios from Logistic Regression Predicting Likelihood of Parole Revocation, 2016–2020

Model 1 Model 2
Odds Ratios (SE) Odds Ratios (SE)
Race
American Indian 1.79*** (0.24) 1.46* (0.22)
Parole Decision
MT BOPP 8.34*** (4.99)
Sex
Male 1.72* (0.40)
Std. RNA Total Score 1.53* (0.32)
Male * Std. RNA Total Score Interaction 1.12 (0.25)
Age (Years) 1.06 (0.05)
Age-squared 1.00 0.00
Disposition Year
2017 0.96 (0.22)
2018 1.07 (0.23)
2019 1.34 (0.28)
2020 0.67 (0.17)
Supervision Agency
Billings 0.66 (0.35)
Bozeman 0.71 (0.48)
Butte 0.69 (0.38)
Cut Bank 0.24 (0.22)
Glendive 0.76 (0.70)
Great Falls 0.77 (0.42)
Hamilton 1.02 (0.64)
Hardin 0.40 (0.33)
Havre 0.58 (0.39)
Helena 0.59 (0.33)
Kalispell 0.53 (0.31)
Livingston 0.75 (0.91)
Miles City 1.65 (1.29)
Missoula 0.87 (0.46)
Polson 0.78 (0.49)
Shelby 0.64 (0.62)
Sidney 0.51 (0.59)
Thompson Falls 0.37 (0.45)
Intercept 0.14*** (0.01) 0.01*** (0.01)
Observations 2,115 2,115
Pseudo-R2 0.0103 0.115 0.010

The omitted category for race is “White”; the omitted category for parole revocation decision is “interstate”; the omitted category for sex is “female”; the omitted category for disposition year is “2016”; and the omitted category for supervision agency is “Anaconda.” Four supervision agencies, including Dillon, Glasgow, Lewistown, and Libby, had too few cases to analyze and were not included in the model.
*** p<0.001, ** p<0.01, * p<0.05


Endnotes

1. Rowena Fong, Ruth McRoy, and Alan Dettlaff, “Disproportionality and Disparities,” Encyclopedia of Social Work, June 2, 2014; accessed June 15, 2020, https://oxfordre.com/socialwork/view/10.1093/acrefore/9780199975839.001.0001/acrefore-9780199975839-e-899.

2. “Equity vs. Equality and Other Racial Justice Definitions,” Annie E. Casey Foundation, 2021, accessed May 28, 2021, https://www.aecf.org/blog/racial-justice-definitions/.

3. Matthew Clair and Jeffrey S. Denis, “Sociology of Racism” in The International Encyclopedia of the Social and Behavioral Sciences, 2nd edition, Volume 19, ed. James D. Wright (Elsevier Ltd., 2015), 857–863.

4. Montana Department of Corrections, Montana Department of Corrections Adult Male System (Helena, MT: Montana Department of Corrections, 2007); Meeting between The Council of State Governments and Montana Departments of Corrections Probation and Parole Division, November 29, 2021.

5. Ibid.

6. “State Owned and Contracted Correctional Facilities,” Montana Department of Corrections, 2021, accessed November 15, 2021, https://cor.mt.gov/Facilities/Facilities.

7. See 46-23-1001, MCA, as amended by Sec. 11, Ch. 392, L. 2017; Matthew R. Durose, Alexia D. Cooper, and Howard N. Synder, Recidivism of Prisoners Release in 30 State in 2005: Patterns from 2005 to 2010, (Washington, DC: Bureau of Justice Statistics), 2014. Note Montana’s distinction between com­pliance and noncompliance violations makes it difficult to compare precisely to what other states often call technical violations (i.e., violations resulting from a breach of conditions of probation rather than an arrest or conviction for a new criminal offense). While a precise comparison is not possible, Montana’s com­pliance violations are relatively less serious than noncompliance violations.

8. Montana Department of Corrections, Montana Department of Corrections Adult Male System (Helena, MT: Montana Department of Corrections, 2007); Meeting between The Council of State Governments and Montana Departments of Corrections Probation and Parole Division, November 29, 2021.

9. Montana Department of Corrections, MT Department of Corrections Glossary (Helena, MT: Montana Department of Corrections, 2021).

10. Development Services Group, Inc., Disproportionate Minority Contact (Washington, DC: Office of Juvenile Justice and Delinquency Prevention, 2014), https://www.ojjdp.gov/mpg/litreviews/Disproportionate_Minority_Contact.pdf.

11. Roxy Peck, Chris Olsen,and Jay L.Devore, Introduction to Statistics and Data Analysis, 5th Edition (Boston: Cengage Learning, 2016).

12. William J. Sabol, Thaddeus L. Johnson, and Alexander Caccavale, Trends in Correctional Control by Race and Sex (Washington, DC: Council on Criminal Justice, 2019).

13. Ibid.

14. E. Ann Carson, Prisoners in 2019 (Washington, DC: Bureau of Justice Statistics, 2020); “Since you asked: What data exists about Native American people in the criminal justice system?” The Prison Policy Initiative, 2020, accessed August 20, 2021, https://www.prisonpolicy.org/blog/2020/04/22/native/. Analysis conducted in 2010 by the Prison Policy Initiative showed that American Indian people are incarcerated at over twice the rate of White people on average in the U.S., but that in some states, American Indian people are incarcerated at seven times the rate of White people.

15. Sabol, Johnson, and Caccavale, Trends in Correctional Control by Race and Sex.

16. Jeremy Travis, Bruce Western, and F. Stevens Redburn, The Growth of Incarceration in the United States: Exploring Causes and Consequences (Washington, DC: National Research Council, 2014). For an overview of this topic, see Susan Nembhard and Lily Robin, Racial and Ethnic Disparities throughout the Criminal Legal System: A Result of Racist Policies and Discretionary Practices (Washington, DC: Urban Institute, 2021). For a dis­cussion of the recent literature on this topic, see “There’s Overwhelming Evidence that the Criminal Justice System is Racist” editorial, Washington Post, June 10, 2020, https://www.washingtonpost.com/graphics/2020/opinions/systemic-racism-police-evidence-criminal-justice-system/.

17. Alexander Alvarez and Ronet D. Bachman, “American Indians and sentenc­ing disparity: An Arizona test,” Journal of Criminal Justice 24, no. 6 (1996): 549–561; Keith A. Wilmot and Miriam A. Delone, “Sentencing of Native Americans: A Multistage Analysis Under the Minnesota Sentencing Guidelines,” Journal of Ethnicity in Criminal Justice 8, no. 3 (2010): 151–180; Travis W. Franklin, “Sentencing Native Americans in US Federal Courts: An Examination of Disparity,” Justice Quarterly 30, no. 2 (2013): 310–339.

18. Kevin F. Steinmetz and Jamilya O. Anderson, “A Probation Profanation: Race, Ethnicity, and Probation in a Midwestern Sample,” Race and Justice 6, no. 4 (2015): 1–25; Kelly Roberts Freeman et al., Reducing Probation Revocations in Pima County, Arizona (Washington, DC: Urban Institute, 2021).

19. “Race and Ethnicity in the United States: 2010 Census and 2020 Census,” U.S Census Bureau, https://www.census.gov/library/visualizations/interactive/race-and-ethnicity-in-the-united-state-2010-and-2020-census.html.

20. The Council of State Governments Justice Center, Justice Reinvestment in Montana: Report to the Montana Commission on Sentencing.

21. Development Services Group, Inc. Disproportionate Minority Contact (Washington, D.C.: Office of Juvenile Justice and Delinquency Prevention, 2014). Accessed May 3, 2021, https://www.ojjdp.gov/mpg/litreviews/Disproportionate_Minority_Contact.pdf

22. “Race and Ethnicity in the United States: 2010 Census and 2020 Census,” U.S Census Bureau, https://www.census.gov/library/visualizations/interactive/race-and-ethnicity-in-the-united-state-2010-and-2020-census.html.

23. Meeting between The Council of State Governments and Montana Departments of Corrections Probation and Parole Division, November 29, 2021.

24. Ibid.

25. American Community Survey Public Use Microdata Sample, 2019, U.S. Census Bureau.

26. Montana Department of Corrections, MDOC Offense Table Information (Helena, MT: Montana Department of Corrections, n.d.); “Data and Statistics: Offense Counts Dashboard,” Montana Department of Corrections, 2022, accessed January 3, 2022, https://dataportal.mt.gov/t/COR/views/DOCPublicDashboard/OffenseCountDashboard?%3AshowAppBanner=false&%3Adisplay_count=n&%3AshowVizHome=n&%3Aorigin=viz_share_link&%3AisGuestRedi­rectFromVizportal=y&%3Aembed=y.

27. See 46-18-205 and 46-18-222, MCA.

28. Meeting between The Council of State Governments and Montana Departments of Corrections Probation and Parole Division, January 13, 2022.

29. “Getting Ready for Parole,” Montana Board of Pardons and Parole, 2021, accessed January 3, 2022, https://bopp.mt.gov/GeneralInfo.

30. To determine a person’s secure versus alternative secure status, CSG Justice Center staff examined the final correctional status that was recorded prior to release. Although people can be moved between secure and alterna­tive secure facilities during their incarceration term, the last correctional status is a good indicator of where a person spent most of their time incarcerated. Specifically, among people whose final correctional status is a secure facility, on average about 87 percent of their term was served in prison. Among those whose final correctional status is an alternative secure facility, about 90 per­cent of individuals spent no days in prison; that is, in the vast majority of cases, their entire term was served in an alternative secure facility.

31. See 46-23-1001, MCA, as amended by Sec. 11, Ch. 392, L. 2017.

45-5-207 Criminal endangerment — penalty. (1) A person who knowingly engages in conduct that creates a substantial risk of death or serious bodily injury to another commits the offense of criminal endangerment. This conduct includes but is not limited to knowingly placing in a tree, log, or any other wood any steel, iron, ceramic, or other substance for the purpose of damaging a saw or other wood harvesting, processing, or manufacturing equipment. (2) A high blood alcohol concentration alone is not sufficient to support a criminal endangerment charge. (3) A person convicted of the offense of criminal endan­germent shall be fined an amount not to exceed $50,000 or imprisoned in the state prison for a term not to exceed 10 years, or both. (4) As used in this sec­tion, “alcohol concentration” has the meaning provided in 61-8-1001.32. M.C.A. §45-5-207 was enacted in 1987 and amended in 1989 to explicitly criminalize tree spiking. Through the Justice Reinvestment process, it was fur­ther amended in 2017 to exclude its use as an additional charge in DWI cases supported only by high blood alcohol concentration.

33. Carl Reynolds, Montana Statute Memo to Sentencing Commission (New York: CSG Justice Center, 2016). Specifically, in 2015, criminal endanger­ment was the third most frequent felony conviction as reported by the MT DOC, with 1,684 cases out of 14,308 total cases over 5 years, about 12 percent of the total. This is an extraordinary volume compared to other states—in 2015, CSG Justice Center staff compared these results to conviction data in North Dakota and Massachusetts and found that the comparable offense (reckless conduct), a misdemeanor, represented less than 1 percent of the total convic­tion volume. Reckless conduct is not a UCR-reported crime because it is low volume in most states, so arrest comparisons are not available.

The offense can result in a 10-year felony conviction but provides prosecu­tion and defense a recourse to plead downward from assaultive and sexually assaultive offenses. 34. In the Montana House Judiciary Committee in 1987, proponents of H.B. 301 provided statutory examples from Alaska, Arizona, Colorado, Oregon, and Washington. All those states use “recklessness” and attach top-level misde­meanor punishment of 12 or 18 months, while Montana created a “knowing” version of the offense as a 10-year felony (MCA 45-5-207) and a “negligent” version as a 1-year misdemeanor (MCA 45-5-208). Note: Montana also uses a culpable mental state that is ostensibly higher—“knowing” conduct versus “reckless” conduct elsewhere. Montana did not adopt and does not use the concept of “recklessness” in its penal law, unlike the Model Penal Code and most states. The Montana definition of “negligently” merges the Model Penal Code concept of recklessness, or conscious disregard of a risk, with negli­gence or disregard of a risk that should have been known. From the enactment of MCA 45-5-207 forward, there has been confusion and blurring of the requi­site culpability for criminal endangerment.

Sec. 11.41.250. Reckless endangerment.

(a) A person commits the crime of reckless endangerment if the person recklessly engages in conduct which creates a substantial risk of serious physical injury to another person.

(b) Reckless endangerment is a class A misdemeanor [1 year].

Colorado: in 2022 the offense will be upgraded to a Class 2 misdemeanor, 364 days

18-3-208. Reckless endangerment

A person who recklessly engages in conduct which creates a substantial risk of serious bodily injury to another person commits reckless endanger­ment, which is a class 3 misdemeanor [6 months].

Oregon: Misdemeanor only

163.195 Recklessly endangering another person

(1) A person commits the crime of recklessly endangering another person if the person recklessly engages in conduct which creates a substantial risk of serious physical injury to another person.

(2) Recklessly endangering another person is a Class A misdemeanor [1 year]. 35. Alaska: Misdemeanor only

13-1201. Endangerment; classification

A. A person commits endangerment by recklessly endangering another person with a substantial risk of imminent death or physical injury.

B. Endangerment involving a substantial risk of imminent death is a class 6 felony [18 months]. In all other cases, it is a class 1 misdemeanor [6 months]. 36. Arizona: Misdemeanor or felony depending on dangerousness of conduct

37. Jeff Essman, A Primer on the Element of Mental State in the Montana Criminal Code of 1973, 37 Mont. L. Rev. (1976), fn. 31, citing MONT. CRIM. CODE ANN. § 94-2-101(32) comment at 52.

38. “Criminal Justice Reinvestment in Montana: Improving Outcomes for American Indians,” Montana Budget and Policy Center, 2018, accessed December 1, 2021 https://montanabudget.org/report/criminal-justice-rein­vestment-in-montana-improving-outcomes-for-american-indians); Decennial Census P.L. 94-171 Redistricting Data, 2020, Table P1, U.S. Census Bureau. Census data indicate that among the general population, 58 percent of American Indian people in Montana lived on one of the 7 federally recognized Indian reservations in the state.

39. Alison R. Colby, Sorry, This Bus Is Not in Service: Public Transportation Accessibility as a Source of Recidivism Risk in Rural Areas (Proquest Dissertations, 2013).

40. Brian Howard and Traci Morris, Tribal Technology Assessment: The State of Internet Service on Tribal Lands (Phoenix, AZ: American Indian Policy Institute at Arizona State University, 2019).

41. “For Some Native Americans, No Home Address Might Mean No Voting,” Pew Charitable Trusts, 2019, accessed December 1, 2021 https://www. pewtrusts.org/en/research-and-analysis/blogs/stateline/2019/10/04/ for-some-native-americans-no-home-address-might-mean-no-voting.

42. Alissa Fishbane, Aurelie Ouss, and Anuj K. Shah, “Behavioral Nudges Reduce Failure to Appear for Court,” Science 370, no. 6517 (2020)..

43. Geneva Adkins, David Huff, and Paul Stageberg, The Iowa Sex Offender Registry and Recidivism (Des Moines, IA: Iowa Department of Human Rights, 2000); Jill S. Levenson and Leo P. Cotter, “The Effect of Megan’s Law on Sex Offender Reintegration” Journal of Contemporary Criminal Justice 21, no. 1 (2005): 49–66; Richard Tewksbury, “Collateral consequences of sex offender registration,” Journal of Contemporary Criminal Justice 21, no. 1 (2005): 67–82; Richard G. Zevitz and Mary Ann Farkas, “Sex Offender Community Notification: Managing High-Risk Criminals or Exacting Further Vengeance?” Behavioral Sciences and the Law 18, no. 2–3 (2000): 375–391; Kristen M. Zgoba, Wesley G. Jennings, and Laura M. Salerno, “Megan’s Law 20 Years Later: An Empirical Analysis and Policy Review,” Criminal Justice and Behavior 45, no. 7 (2018): 1028–1046.

44. The Council of State Governments Justice Center, Justice Reinvestment in Montana: Report to the Montana Commission on Sentencing (New York: The Council of State Governments, 2017).

45. “Transitional Assistance, Rental Vouchers,” Montana Department of Corrections, accessed March 9, 2022, https://cor.mt.gov/ ProgramsandServices/TransitionalAssistanceandRentalVoucherInformation.

46. Alexis Lee Watts et al., Profile in Parole Release and Revocation: Examining the Legal Framework in the United States (Minneapolis, MN: Robina Institute of Criminal Law and Criminal Justice at the University of Minnesota, 2019).

47. Fred Osher et al., Adults with Behavioral Health Needs under Correctional Supervisions: A Shared Framework for Reducing Recidivism and Promoting Recovery (New York: The Council of State Governments Justice Center, 2012).

48. Juliette R. Mackin and Shannon M. Carey, Bringing Treatment Courts to Scale in Montana (Portland, OR: NPC Research, 2018), http://mthcf.org/ wp-content/uploads/Treatment-Courts-Report_12.21.18-FINAL.pdf.

49. “Criminal Justice Reinvestment in Montana: Improving Outcomes for American Indians,” Montana Budget and Policy Center, 2018, accessed December 1, 2021 https://montanabudget.org/report/criminal-justice-rein­vestment-in-montana-improving-outcomes-for-american-indians).

50. “Tribal Defenders Office,” Confederated Salish and Kootenai Tribes of the Flathead Reservation, 2021, accessed December 1, 2021, https://csktribes. org/judicial/tribal-defenders.

51. “Criminal Justice Reinvestment in Montana: Improving Outcomes for American Indians.”

52. “Access to Justice Commission,” Montana Judicial Branch, 2021, accessed December 1, 2022, https://courts.mt.gov/Courts/boards/a2j/#:~:­text=The%20Access%20to%20Justice%20Commission%20is%20proposing%20 a%20bill%20to,with%20their%20civil%20legal%20problems.

53. Email communication between The Council of State Governments Justice Center and Montana Department of Corrections Records Department, October 26, 2021.

54. Montana Supreme Court, Montana Supreme Court Bond Schedule (Helena, MT: Montana Supreme Court, 2022).

55. Ibid.

56. See 46-23-1001, MCA, as amended by Sec. 11, Ch. 392, L. 2017.

57. Meeting between The Council of State Governments and Montana Departments of Corrections Probation and Parole Division, January 13, 2022.

Project Credits

Writing and Research: Sara Bastomski, PhD*; Matt Herman, MS*; Alison Martin, PhD; and Sara Friedman, MPA, CSG Justice Center

Advising: Angela Gunter, MPA; Carl Reynolds, JD/MPA; Elizabeth K. Lyon; and Jessica Saunders, PhD, CSG Justice Center

Editing: Leslie Griffin, CSG Justice Center

Public Affairs: Brenna Callahan, CSG Justice Center

Design: Michael Bierman

Web Development: Catherine Allary, CSG Justice Center

*Equal contributors

Acknowledgments

 

bja logo
This project was supported by Grant No. 2019-ZB-BX-K002 awarded by the Bureau of Justice Assistance. The Bureau of Justice Assistance is a component of the Department of Justice’s Office of Justice Programs, which also includes the Bureau of Justice Statistics, the National Institute of Justice, the Office of Juvenile Justice and Delinquency Prevention, the Office for Victims of Crime, and the SMART Office. Points of view or opinions in this document are those of the authors and do not necessarily represent the offi­cial position or policies of the U.S. Department of Justice.

Data analyzed for this report were provided by the Montana Office of Court Administration and the Montana Department of Corrections. The findings and conclusions documented in this report are those of the authors and The Council of State Governments (CSG) Justice Center and do not rep­resent the views of Montana state government staff or associated stakeholders. The authors thank staff of the Montana Office of Court Administration and the Montana Department of Corrections for their support of this effort.

About the authors


Image for:
Sara Bastomski
Research Manager, Research
Dr. Sara Bastomski conducts research and evaluations on public safety policy. She has over 10 years of experience studying the causes and consequences of crime victimization and using data to inform policy in the criminal justice and victim service fields.
...
Her research employs advanced quantitative methods, such as statistical modeling and quasi-experimental approaches, as well as qualitative methods. Sara’s work has emphasized researcher-practitioner partnerships to ensure that project findings are meaningful to diverse stakeholder audiences. She previously conducted research at the Urban Institute, RAND Corporation, and for community organizations, and was an adjunct faculty member in sociology at the George Washington University. She holds a PhD in sociology from Yale University.
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    Matt Herman
    Data Scientist II, Research
    Matt Herman performs data collection, manipulation, and visualization for several CSG Justice Center projects. Before joining the CSG Justice Center, Matt was a deputy director in the Office of Research and Analytics at the NYC Administration for Children's Services, where
    ...
    he created automated and reproducible workflows to generate outcome measures and performance indicators for investigations of child abuse and neglect. He collaborated with managers of frontline child protection staff to interpret this data in order to create actionable quality improvement plans and assess the impact of these plans. Matt received a BA in American studies from Columbia University and an MS in applied social research from Hunter College.
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    Alison Martin
    Deputy Program Director, State Initiatives
    Alison Martin provides technical assistance and content expertise to states implementing Justice Reinvestment legislation. Prior to joining the CSG Justice Center, Alison worked as a postdoctoral research associate at the National Institute of Justice, where she was the lead investigator
    ...
    on projects related to the criminal justice response to crimes against American Indian and Alaska Native women. Alison also has experience conducting research on corrections, including topics such as prison violence, reentry, recidivism, and restorative justice. She earned her BS in public policy and administration from James Madison University and her MS and PhD in justice, law, and society from American University.
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  • Image for:
    Sara Friedman
    Program Director, State Initiatives
    Sara Friedman leads project management for states implementing Justice Reinvestment legislation, helping states adopt and measure research-driven corrections and community supervision policies and practices that reduce recidivism. Prior to joining the CSG Justice Center, Sara was the director of Resource
    ...
    Development for the Center for Community Alternatives, a nonprofit organization that provides alternative-to-incarceration programs. In this role, she focused on grant writing, program development, and building organizational capacity through data collection and program evaluation. Sara earned her BA from the University of Pennsylvania and her MPA from the New York University Wagner School of Public Service.
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