by Emily Widra and Ta'jin Perez
One of the most important criminal legal system disparities in Montana has long been difficult to decipher: Which communities throughout the state do incarcerated people come from? Anyone who lives in or works within heavily policed and incarcerated communities intuitively knows that certain neighborhoods disproportionately experience incarceration. But data have never been available to quantify how many people from each community are imprisoned with any real precision.1
Using this redistricting data, we found that in Montana, incarcerated people come from all over the state, and unsurprisingly, the largest number of imprisoned people are from the state’s most populous cities of Billings, Great Falls, and Missoula. Surprisingly, a handful of less populous and more rural areas — like Custer County, Walkerville, and East Helena — have some of the highest imprisonment rates per 100,000 residents,3 suggesting that people all over Montana are affected by the state’s reliance on mass incarceration.
In addition to helping policy makers and advocates effectively bring reentry and diversion resources to these communities, this data has far-reaching implications. Around the country, high imprisonment rates are correlated with other community problems related to poverty, employment, education, and health. Researchers, scholars, advocates, and politicians can use the data in this report to advocate for bringing more resources to their communities.
Our analysis of the state’s adjusted redistricting data shows both that incarcerated people come from every portion of the state and that some communities bear the heaviest burden from mass incarceration. The state was able to reallocate about half of the state prison population4 to addresses outside of the facility and the distribution of those people serve as the basis of this report’s analysis.
In order to make apples-to-apples comparisons of the prevalence of incarceration between counties, cities, and other communities of different sizes, this report uses imprisonment rates expressed as a number of people in prison per 100,000 residents. For the purposes of this analysis, looking only at the numbers of people who were successfully reallocated to specific non-prison addresses in the state, Montana has an imprisonment rate of 123 per 100,000 residents.5
Most broadly, we find that incarcerated people in Montana come from all over the state: people from every single state senate district are incarcerated in Montana state prisons. While no part of Montana is immune to the consequences of the state’s reliance on mass incarceration, some communities are disproportionately impacted by imprisonment.
At the county level, we find that incarcerated people in Montana come from almost every county: 48 of the 56 counties6 are missing residents to state prison.
The state’s most populous county — Yellowstone County (Billings) — has the most residents imprisoned (304) of all Montana counties. On the other hand, the southwestern county of Silver Bow has the highest imprisonment rate in the state (284 per 100,000) and has 100 residents in state prisons. Although Silver Bow is missing fewer people to prison, the county — along with a handful of other small and mid-sized counties like Custer, Cascade, and Lewis and Clark — is missing a relatively large proportion of their population to state prisons.
Across the state, the largest cities are imprisoning the most people: Billings has 250 residents in state prison, Great Falls has 167 residents in state prison, and Missoula has 115 residents in state prisons. These three cities have imprisonment rates higher than the state average of 123 per 100,000: Billings imprisons 213 people per 100,000 city residents, Great Falls imprisons 276 people per 100,000 city residents, and Missoula imprisons 156 people per 100,000 city residents.
Not all imprisonment is concentrated in the state’s largest cities, however. A handful of small and mid-sized cities and towns with at least 1,000 residents — East Helena, Deer Lodge, Butte-Silver Bow, and Helena — have the highest imprisonment rates in the state. East Helena has 8 of its 1,952 city residents locked up in state prisons, but because the city population is relatively small, the proportion of people incarcerated is high: the city imprisonment rate is 410 per 100,000, more than 3 times higher than the statewide imprisonment rate. Butte-Silver Bow and Helena are relatively larger cities, with populations of 34,592 and 32,182 respectively, and have imprisonment rates double that of the state at 283 people in prison per 100,000 city residents in both cities.
Within cities and counties, imprisonment tends to be concentrated in a relatively small number of geographic areas and neighborhoods.
For example, the city of Billings has an imprisonment rate of 213 per 100,000 residents, but two of the 12 neighborhoods are responsible for over 38% of the city’s prison population. The South Side neighborhood is home to 3% of the city’s residents, but 20% of the city’s imprisoned population. Similarly, the East Central neighborhood is home to 13% of the city’s residents, but 18% of the city’s imprisoned population. The neighborhoods with the highest imprisonment rates — South Side (1,338 per 100,000) and North Side (876 per 100,000) — lock up people at rates more than 4 times higher than the citywide imprisonment rate.
The South Side neighborhood imprisonment rate is almost fifteen times higher than the rate in the nearby Highlands neighborhood (90 per 100,000) and more than 100 times higher than the West Shiloh neighborhood (7 per 100,000).
The fact that the highest rates of imprisonment are concentrated in the North Side and South Side neighborhoods is no surprise, given what we know about how race and poverty impact a person’s likelihood of having interactions with the criminal legal system. Research has shown that policing tends to be concentrated in communities composed predominantly of people of color, which results in people living in these communities experiencing disproportionate arrest and incarceration rates. Billings’ population is less than 5% American Indian and Alaska Native, but the South Side population is 15% American Indian and Alaska Native and the North Side population is 11% American Indian and Alaska Native. Only 1% of Billings residents are Black, but more than 2% of South Side residents and North Side residents are Black. Similarly, about 7% of Billings residents are Hispanic or Latino, but 15% of the South Side community and 8% of the North Side community are Hispanic or Latino.7 In addition, we know that these two neighborhoods are some of the poorest areas in the city and that poor people, families, and communities are disproportionately impacted by the criminal legal system: 14% of the South Side neighborhood and 24% of the North Side neighborhood live in poverty,8 compared to less than 10% of all Billings residents living in poverty.
Using this redistricting and residence data to analyze the effects of incarceration on Native people has significant limitations and ultimately represents a sizable undercount of the actual impact of the criminal legal system on Native American and American Indian people. Native American and American Indian people are disproportionately incarcerated in Montana state prisons: in 2020, Native people were 23% of the state prison population, but less than 7% of the statewide population.9
Racial and ethnic disparities permeate all levels of the criminal legal system, and we can see evidence that disparities in policing contribute to this overrepresentation of Native people in Montana prisons. For example, in the state’s largest city — Billings — the population is 5% Native American, but Native Americans make up a startling 27% of arrests by the Billings Police Department. In Missoula, where Native Americans are less than 2% of the city population, Native Americans account for 14% of arrests by the Missoula Police Department. These findings are consistent with what data shows about Native Americans in the criminal-legal system: Native people are disproportionately arrested, jailed, and imprisoned on and off tribal land.
But because Native American and American Indian people do not all live on reservation land, the data available in this report does not accurately show Native imprisonment rates in Montana. However, even with these shortcomings, the residence data still provide some important insights into the frequency with which Native people are incarcerated in Montana. For example, despite this likely undercount, the Flathead Reservation, with 190 per 100,000 residents behind bars, has an imprisonment rate that is more than 1.5 times the statewide average.
While all communities are missing some of their members to prisons, incarceration has its broadest community impact in the places where large numbers of adults — parents, workers, voters — are locked up. The large number of adults drained from these areas seriously impacts the health and stability of the families and communities left behind.10
Across the country, researchers have connected high local incarceration rates with a host of negative outcomes for the people who live there. In a Prison Policy Initiative analysis of where incarcerated people in Maryland are from, we found that Baltimore communities with high rates of incarceration were more likely to have high unemployment rates, long average commute times, low household income, a high percentage of residents with less than a high school diploma or GED, decreased life expectancy, high rates of vacant or abandoned properties, and higher rates of children with elevated blood-lead levels, compared to neighborhoods less impacted by incarceration.
Research has revealed similar correlations11 in communities around the country:
We already have this wealth of data showing that incarceration rates correlate with a variety of barriers and negative outcomes. The data in this report build on this work by helping identify which specific neighborhoods throughout Montana are systematically disadvantaged and left behind. Montana residents can use the data in this report to examine granular local-level and state-wide correlations and choose to allocate needed resources to places hardest hit by incarceration.
These 18 data tables provided here have great potential for community advocacy and future research.
First and most obviously, these data can be used to determine the best locations for community-based programs that help prevent involvement with the criminal legal system, such as offices of neighborhood safety and mental health response teams that work independently from police departments. The data can also help guide reentry services (which are typically provided by nonprofit community organizations) to areas of Montana that need them most.
But even beyond the obvious need for reentry services and other programs to prevent criminal legal system involvement, our findings also point to geographic areas that deserve greater investment in programs and services that indirectly prevent criminal legal involvement or mitigate the harm of incarceration. After all, decades of research show that imprisonment leads to cascading collateral consequences, both for individuals and their loved ones. When large numbers of people disappear from a community, their absences are felt in countless ways. They leave behind loved ones, including children, who experience trauma, emotional distress, and financial strain. Simultaneously, the large numbers of people returning to these communities (since the vast majority of incarcerated people do return home) face a host of reentry challenges and collateral consequences of incarceration, including difficulty finding employment and a lack of housing. People impacted by the criminal legal system tend to have extremely diminished wealth accumulation. And those returning from prison and jail may carry back to their communities PTSD and other mental health issues from the trauma they’ve experienced and witnessed behind bars. Lastly, investing in core community resources to mitigate structural issues like poverty, such as housing and healthcare, will reduce vulnerabilities for criminal legal system contact.
And since we know place of origin correlates with so many other metrics of wellbeing, we can and should target these communities for support and resources beyond what we typically think of as interventions to prevent criminal legal system contact. In communities where the state or city has heavily invested in policing and incarceration (i.e. the high-incarceration neighborhoods we find in our analysis), our findings suggest that those resources would be better put toward reducing poverty and improving local health, education, and employment opportunities.
For example, we know that large numbers of children in high incarceration areas may be growing up with the trauma and lost resources that come along with having an incarcerated parent, and that these children are also more likely to experience incarceration. The information in this report can help with planning and targeting supports, resources, and programming designed to not only respond to the harms caused by incarceration, but disrupt the cycle of familial incarceration.
We invite community leaders, service providers, policymakers, and researchers to use this data to make further connections between mass incarceration and various outcomes, to better understand the impact of incarceration on their communities.
This report capitalizes on the unique opportunity presented by Montana’s ending of prison gerrymandering, which allows us to determine accurately for the first time where people incarcerated in state prisons come from. In this report’s linked datasets, we aggregate these data by a number of useful state-wide geographies such as counties, state legislative districts, congressional districts, school districts, and for some city-wide geographies such as neighborhoods in Billings, Missoula, and Great Falls.
This section of the report discusses how we processed the data, some important context and limitations on that data, and some additional context about the geographies we have chosen to include in this report and appendices. The goal of this report is not to have the final word on the geographic concentration of incarceration, but to empower researchers and advocates — both inside and outside of the field of criminal justice research — to use our dataset for their own purposes. For example, if you are an expert on a particular kind of social disadvantage and have some data organized by county, zip code, elementary school district, or other breakdown and want to add imprisonment data to your dataset, we probably have exactly what you need in a prepared appendix described below.
This report and its data are one in a series of similar reports we are releasing in the spring and summer of 2022, focusing on 13 states — California, Colorado, Connecticut, Delaware, Maryland, Montana, Nevada, New Jersey, New York, Pennsylvania, Rhode Island, Virginia, and Washington — which counted incarcerated people at home for redistricting purposes, and therefore also made this analysis possible. This report can also be seen as a template for other states because while not all states have ended prison gerrymandering, most state departments of corrections already have near-complete home residence records in an electronic format. States that have not yet ended prison gerrymandering should be encouraged to continue improving their data collection, and to share the data (under appropriate privacy protections) so that similar analyses could be performed.
Montana’s decision to end prison gerrymandering required the Montana Department of Corrections to share the home addresses of people in state prisons on Census Day 2020 with redistricting officials, so that these officials could remove imprisoned people from the redistricting populations reported by the Census for the facilities’ locations and properly credit people to their home communities. The adjusted data was then made available for state and local officials to use to draw new legislative boundaries. As a side effect, this groundbreaking dataset allows researchers to talk in detail for the first time about where incarcerated people came from.
Creating the tables in this report required several steps which were expertly performed by Peter Horton at Redistricting Data Hub:
Our analysis in this report documents the home addresses of 1,330 people in state prisons. However, the state’s total prison population was approximately 2,840 on Census day. The number of reallocated people is different from the total population of Montana residents in prison because of the completeness of the Department of Corrections records at the time of the reform decision and some of the valid policy choices made when the decision to end prison gerrymandering was made.
From the perspective of improving democracy in Montana, the state’s reallocation efforts were relatively successful in reducing both the unearned enhancement of political representation in prison-hosting areas and reduced the dilution of representation in the highest-incarceration districts. From the perspective of using that data to discuss the concentration of incarceration, some readers may want to be aware of some the reasons why our report discusses the home addresses of 1,330 people when they may be aware that the state prison system had 2,840 people on Census day:
Similarly, this report doesn’t reflect the other groups of people incarcerated from particular communities who are not reflected in these data, because18 they were:
We’ve organized the data in this report around several popular geographies, as defined by the federal government, by the state, or by individual cities, with the idea that the reader can link our data to the wealth of existing social indicator data already available from other sources.
Unfortunately, the reader may desire data for a specific geography that we have not made available — for example, their own neighborhood, as they conceive of its boundaries. Often, there was not a readily accessible and official map that we could use that defined that boundary; so where the reader has this need, we urge the reader to look for other geographies in our datasets that can be easily adapted to their needs, either one that is similar enough to their preferred geography or by aggregating several smaller geographies together to match your preferred geography.
We also want to caution subsequent users of this data that some geographies change frequently and others change rarely, so they should note the vintage of the maps we used to produce each table. For example, county boundaries change very rarely, and when they do, it is often in extremely small ways. On the other hand, legislative districts may change frequently and significantly, so depending on your goals some specific tables may be more or less applicable for your future use.
Finally, readers should note that occasionally the incarcerated numbers in our tables for some geographies will not sum precisely to the total 1,330 home addresses used in this report. That discrepancy arises because of how census blocks — the basic building block of legislative districts — nest or fail to nest within geographies drawn by agencies other than the Census Bureau.
Criminal legal system data is often poorly tracked, meaning researchers must cobble together information from different sources. But by using complete data from state redistricting committees, this report (and a series of other state reports that the Prison Policy Initiative developed with state partners) are uniquely comprehensive and up-to-date. The series includes two previous reports on Maryland (published in 2015, in collaboration with the Justice Policy Institute) and New York (published in 2020, in collaboration with VOCAL-NY), and our newest reports on California, Colorado, Connecticut, Delaware, Maryland, Nevada, New Jersey, New York, Pennsylvania, Virginia, and Washington.
While these reports are the first to use redistricting data to provide detailed, local-level data on where incarcerated people come from statewide, other organizations have previously published reports that focused on individual cities or that provided data across fewer types of geographic areas. For example, the Justice Mapping Center had a project that showed residence data for people admitted to or released from state prisons in a given year for almost two dozen states. That project made those states’ annual admission and release data available at the zip code and census tract levels, most recently mapping 2008-2010 data. Separately, it also mapped the residences of people admitted to state prisons from New York City down to the block level using 2009 data.
Another resource (particularly helpful for states that are not included in our series of reports) is Vera Institute for Justice’s Incarceration Trends project, which maps prison incarceration rates for 40 states at the county level, based on county of commitment (meaning where individuals were convicted and committed to serve a sentence, which is often but not necessarily where they lived). ↩
American Indian and Alaska Native areas (AIANAs) are geographies defined by the Census Bureau and across the country, these areas include reservations and trust lands, tribal jurisdiction statistical areas, Alaska Native Regional Corporations, Alaska Native village statistical areas, and tribal designated statistical areas. In Montana, there are American Indian reservations and trust lands. ↩
Imprisonment rates per 100,000 are a useful tool for comparison between different geographic regions with varying population sizes. For example, using a rate per 100,000 allows us to compare the frequency of imprisonment between the most populous Montana counties like Yellowstone County — with over 164,000 residents — to the smaller, less populated counties, like any one of the 52 Montana counties with less than 100,000 residents. ↩
As we discuss in the methodology, not everyone in state prison should or could be reallocated to a home address. By design, people incarcerated in Montana who are residents of other states could not be reallocated to a home address in the state. In addition, Montana’s Department of Corrections was not, prior to the state’s request for this data, “consistently record[ing] home addresses upon intake.” ↩
As explained in the methodology, this report’s imprisonment rate is based on the number of people in state prisons who were reallocated to individual communities as part of the state’s law ending prison gerrymandering. This number is necessary for making apples-to-apples comparisons of imprisonment between specific communities and the state as a whole. For the purposes of comparing incarceration in Montana with that of other states, other more common metrics would be more useful. For these other uses, we would recommend using other numbers for the statewide incarceration rate, likely either the 362 per 100,000 published by the Bureau of Justice Statistics in Prisoners in 2020 for the number of people in state prison per 100,000 residents, or our more holistic number of 789 per 100,000 residents used in States of Incarceration: The Global Context 2021 that includes people in state prisons, federal prisons, local jails, youth confinement, and all other forms of incarceration. ↩
The eight counties that had no county residents imprisoned and successfully reallocated back to that county during the 2020 redistricting cycle were some of the least populous counties in the state: Phillips, Sheridan, Wheatland, Daniels, Carter, Golden Valley, Treasure, and Petroleum. This is likely to be a temporary anomaly, however, as the Department of Corrections reported that in fiscal year 2019, only one Montana county — Garfield — did not send a single person to state prison. ↩
The South Side neighborhood contains all of Census Tract 3 and very small pieces of Census Tracts 9.01 and 9.02 in Yellowstone County. The North Side neighborhood contains all of Census Tract 2 and very small pieces of Census tracts 4.02 and 7.06 in Yellowstone County. For the demographic data presented here on the South Side and North Side neighborhoods, we utilized tract-level data for Census Tract 3 and 4 available from the 2020 Census Demographic Data Map Viewer. ↩
Poverty rate from the Census Bureau’s American Community Survey 2020 Table S170 5-year-estimates for Census Tract 3 (South Side neighborhood) and Tract 2 (North Side neighborhood). ↩
In July 2022, the Council of State Governments Justice Center published a report on the racial disparities in Montana correctional systems between white people and Native American and American Indian people. The findings of this report include that Native American and American Indian people are more likely to be sentenced to incarceration, have longer prison stays, and have their conditional release revoked. In 2018, the ACLU of Montana reported that Native American and American Indian people in Montana accounted for 20% of the men’s state prison population and 34% of the women’s state prison population. ↩
These impacts of incarceration on families and communities include higher rates of disease and infant mortality, housing instability, and financial burdens related to having an incarcerated loved one. For more detailed information on how incarceration impacts families and communities, see On life support: Public health in the age of mass incarceration from the Vera Institute of Justice. ↩
These various correlative findings are once again in line with previous research on health disparities across communities, which have been linked to neighborhood factors such as income inequality, exposure to violence, and environmental hazards that disproportionately affect communities of color. Public health experts consider community-level factors such as these — including incarceration — “social determinants of health.” To counteract these problems, they suggest taking a broad approach, addressing the “upstream” economic and social disparities through policy reforms, as well as by increasing access to services and supports, such as improving access to clinical health care. ↩
We also know that people who have been incarcerated have a shorter life expectancy than people who have not. ↩
There are many additional studies linking incarceration rates and high community rates of STIs, including gonorrhea and chlamydia in North Carolina. ↩
Asthma prevalence has been used as a tool to measure population health in both sociological and public health research because it is easily correlated with environmental factors, like air quality and triggers (i.e. second hand smoke, mold, dust, cockroaches, dust mites), access to appropriate healthcare, and healthcare literacy. See the American Lung Association’s Public Policy Position for a literature review of the relevant public health research. ↩
Again, this finding is consistent with previous research on the relationship between education and imprisonment rates. We previously reported that the high school educations of over half of all formerly incarcerated people were cut short. This is in line with earlier studies showing that people in prison have markedly lower educational attainment, literacy, and numeracy than the general public, and are more likely to have learning disabilities. We also know there are relationships between parental incarceration and educational performance. ↩
In Montana’s final report on reallocation of people in prisons, the state reported that the records that were unable to reallocated to home addresses included 1,389 records without addresses, 28 records with addresses for jails or similar facilities, 20 records noting transience or homelessness, and 12 addresses that were P.O. Boxes. ↩
The Montana final report also specified that 56 records were for addresses outside of Montana and were therefore not reallocated. ↩
This list of groups of people who could not be counted at home is yet another set of reasons why the U.S. Census Bureau is the ideal agency to end prison gerrymandering: they are the only party with the ability to provide a complete solution and they can do this work far more efficiently than the states can. ↩
We would like to thank the Redistricting Data Hub, particularly Peter Horton, for providing valuable technical expertise and the key data in the appendix tables. Redistricting Data Hub’s assistance processing the redistricting data and connecting us with other demographic data enabled us to produce and distribute these reports faster and more affordably than would otherwise have been possible.
We would also like to thank the Indigenous communities of Montana and tribal nation governments. Special thanks to those who testified to the Montana Districting and Apportionment Committee calling for prison gerrymandering reform. Additionally, we would like to thank the Indigenous elected officials across Montana for continuing to inspire leadership in their home communities.
The non-profit, non-partisan Prison Policy Initiative produces cutting-edge research that exposes the broader harm of mass criminalization and sparks advocacy campaigns that create a more just society. In 2002, the organization launched the national movement against prison gerrymandering with the publication of Importing Constituents: Prisoners and Political Clout in New York. This report demonstrated how using Census Bureau counts of incarcerated people as residents of the prison location dilutes the votes of state residents who do not live next to prisons, in violation of the state constitutional definition of residence. Since then, Montana is one of over a dozen states that have used Prison Policy Initiative’s research to end prison gerrymandering.
Western Native Voice is a non-profit, non-partisan organization working to increase Native American participation and engagement in voting and self-determination. Western Native Voice works to nurture and empower new native leaders and impact policies affecting Native Americans through community organizing, education, leadership, and advocacy. Western Native Voice is engaged on all seven Montana Indian reservations and engaged in major Montana urban centers because approximately half of Montana's native population lives off the reservation.