Overview

The Marshall Project, the nonprofit investigative newsroom dedicated to the U.S. criminal justice system, has partnered with The Associated Press to compile data on the prevalence of COVID-19 infection in prisons across the country. The Associated Press is sharing this data as the most comprehensive current national source of COVID-19 outbreaks in state and federal prisons.

Lawyers, criminal justice reform advocates and families of the incarcerated have worried about what was happening in prisons across the nation as coronavirus began to take hold in the communities outside. Data collected by The Marshall Project and AP shows that hundreds of thousands of prisoners, workers, correctional officers and staff have caught the illness as prisons became the center of some of the country’s largest outbreaks. And thousands of people — most of them incarcerated — have died.

In December, as COVID-19 cases spiked across the U.S., the news organizations also shared cumulative rates of infection among prison populations, to better gauge the total effects of the pandemic on prison populations. The analysis found that by mid-December, one in five state and federal prisoners in the United States had tested positive for the coronavirus -- a rate more than four times higher than the general population.

This data, which is updated weekly, is an effort to track how those people have been affected and where the crisis has hit the hardest.

Methodology and Caveats

The data tracks the number of COVID-19 tests administered to people incarcerated in all state and federal prisons, as well as the staff in those facilities. It is collected on a weekly basis by Marshall Project and AP reporters who contact each prison agency directly and verify published figures with officials.

Each week, the reporters ask every prison agency for the total number of coronavirus tests administered to its staff members and prisoners, the cumulative number who tested positive among staff and prisoners, and the numbers of deaths for each group.

The time series data is aggregated to the system level; there is one record for each prison agency on each date of collection. Not all departments could provide data for the exact date requested, and the data indicates the date for the figures.

To estimate the rate of infection among prisoners, we collected population data for each prison system before the pandemic, roughly in mid-March, in April, June, July, August, September and October. Beginning the week of July 28, we updated all prisoner population numbers, reflecting the number of incarcerated adults in state or federal prisons. Prior to that, population figures may have included additional populations, such as prisoners housed in other facilities, which were not captured in our COVID-19 data. In states with unified prison and jail systems, we include both detainees awaiting trial and sentenced prisoners.

To estimate the rate of infection among prison employees, we collected staffing numbers for each system. Where current data was not publicly available, we acquired other numbers through our reporting, including calling agencies or from state budget documents. In six states, we were unable to find recent staffing figures: Alaska, Hawaii, Kentucky, Maryland, Montana, Utah.

To calculate the cumulative COVID-19 impact on prisoner and prison worker populations, we aggregated prisoner and staff COVID case and death data up through Dec. 15. Because population snapshots do not account for movement in and out of prisons since March, and because many systems have significantly slowed the number of new people being sent to prison, it’s difficult to estimate the total number of people who have been held in a state system since March. To be conservative, we calculated our rates of infection using the largest prisoner population snapshots we had during this time period.

As with all COVID-19 data, our understanding of the spread and impact of the virus is limited by the availability of testing. Epidemiology and public health experts say that aside from a few states that have recently begun aggressively testing in prisons, it is likely that there are more cases of COVID-19 circulating undetected in facilities. Sixteen prison systems, including the Federal Bureau of Prisons, would not release information about how many prisoners they are testing.

Corrections departments in Indiana, Kansas, Montana, North Dakota and Wisconsin report coronavirus testing and case data for juvenile facilities; West Virginia reports figures for juvenile facilities and jails. For consistency of comparison with other state prison systems, we removed those facilities from our data that had been included prior to July 28. For these states we have also removed staff data. Similarly, Pennsylvania’s coronavirus data includes testing and cases for those who have been released on parole. We removed these tests and cases for prisoners from the data prior to July 28. The staff cases remain.

About the Data

There are four tables in this data:

• covid_prison_cases.csv contains weekly time series data on tests, infections and deaths in prisons. The first dates in the table are on March 26. Any questions that a prison agency could not or would not answer are left blank.

• prison_populations.csv contains snapshots of the population of people incarcerated in each of these prison systems for whom data on COVID testing and cases are available. This varies by state and may not always be the entire number of people incarcerated in each system. In some states, it may include other populations, such as those on parole or held in state-run jails. This data is primarily for use in calculating rates of testing and infection, and we would not recommend using these numbers to compare the change in how many people are being held in each prison system.

• staff_populations.csv contains a one-time, recent snapshot of the headcount of workers for each prison agency, collected as close to April 15 as possible.

• covid_prison_rates.csv contains the rates of cases and deaths for prisoners. There is one row for every state and federal prison system and an additional row with the National totals.

Queries

The Associated Press and The Marshall Project have created several queries to help you use this data:

Get your state's prison COVID data: Provides each week's data from just your state and calculates a cases-per-100000-prisoners rate, a deaths-per-100000-prisoners rate, a cases-per-100000-workers rate and a deaths-per-100000-workers rate here

Rank all systems' most recent data by cases per 100,000 prisoners here

Find what percentage of your state's total cases and deaths -- as reported by Johns Hopkins University -- occurred within the prison system here

Attribution

In stories, attribute this data to: “According to an analysis of state prison cases by The Marshall Project, a nonprofit investigative newsroom dedicated to the U.S. criminal justice system, and The Associated Press.”

Contributors

Many reporters and editors at The Marshall Project and The Associated Press contributed to this data, including: Katie Park, Tom Meagher, Weihua Li, Gabe Isman, Cary Aspinwall, Keri Blakinger, Jake Bleiberg, Andrew R. Calderón, Maurice Chammah, Andrew DeMillo, Eli Hager, Jamiles Lartey, Claudia Lauer, Nicole Lewis, Humera Lodhi, Colleen Long, Joseph Neff, Michelle Pitcher, Alysia Santo, Beth Schwartzapfel, Damini Sharma, Colleen Slevin, Christie Thompson, Abbie VanSickle, Adria Watson, Andrew Welsh-Huggins.

Questions

If you have questions about the data, please email The Marshall Project at info+covidtracker@themarshallproject.org or file a Github issue.

To learn more about AP's data journalism capabilities for publishers, corporations and financial institutions, go here or email kromano@ap.org.

A snapshot of the incarcerated population sentenced to the Indiana Department of Correction, including race, age, felony type, and most serious offense category. All data reflects December 31st of the selected year. This dataset contains the underlying data for the 'Population' tab of the 'Prison Incarceration' dashboard within the Public Safety domain.

This data collection focused on problems in the women's correctional system over a 135-year period. More specifically, it examined the origins and development of prisoner and sentencing characteristics in three states. Demographic data on female inmates cover age, race, parents' place of birth, prisoner's occupation, religion, and marital status. Other variables include correctional facilities, offenses, minimum and maximum sentences, prior commitments, method of release from prison, and presence of crime partners.

--- ### Context This dataset contains the complete detail about the Prison and various characteristics of inmates. This will help to understand better about prison system in India. ### Content 1. Details of Jail wise population of prison inmates 1. Details about the list of jails in India at the end of year 2015. 1. Jail category wise population of inmates. 1. Capacity of jails by inmate population. 1. Age group, nationality and gender wise population of inmates. 1. Religion and gender wise population of inmates. 1. Caste and gender wise population of inmates. 1. Education standards of inmates. 1. Domicile of inmates. 1. Incidence of recidivism. 1. Rehabilitation of prisoners. 1. Distribution of sentence periods of convicts in various jails by sex and age-groups. 1. Details of under trial prisoners by the type of IPC (Indian Penal Code) offences. 1. Details of convicts by the type of IPC (Indian Penal Code) offences. 1. Details of SLL (special & local law) Crime headwise distribution of inmates who convicted 1. Details of SLL (special & local law) Crime head wise distribution of inmates under trial 1. Details of educational facilities provided to prisoners. 1. Details of Jail breaks, group clashes and firing in jail (Tranquility). 1. Details of wages per day to convicts. 1. Details of Prison inmates trained under different vocational training. 1. Details of capital punishment (death sentence) and life imprisonment. 1. Details of prison inmates escaped. 1. Details of prison inmates released. 1. Details of Strength of officials 1. Details of Total Budget and Actual Expenditure during the year 2015-16. 1. Details of Budget 1. Details of Expenditure 1. Details of Expenditure on inmates 1. Details of Inmates suffering from mental ilness 1. Details of Period of detention of undertrials 1. Details of Number of women prisoners with children 1. Details of Details of inmates parole during the year 1. Details of Value of goods produced by inmates 1. Details of Number of vehicles available 1. Details of Training of Jail Officers 1. Details of Movements outside jail premises 1. Details of Details of electronic equipment used in prison ### Inspiration There are many questions about Indian prison with this dataset. Some of the interesting questions are 1. Percentage of jails over crowded. Is there any change in percentage over time? 1. How many percentage of inmates re-arrested? 1. Which state/u.t pay more wages to the inmates? 1. Which state/u.t has more capital punishment/life imprisonment inmates? 1. Inmates gender ratio per state ### Acknowledgements National Crime Records Bureau (NCRB), Govt of India has shared this [dataset](https://data.gov.in/dataset-group-name/prison-statistics) under [Govt. Open Data License - India](https://data.gov.in/government-open-data-license-india). NCRB has also shared prison data on their [website](http://ncrb.nic.in/StatPublications/PSI/PrevPublications.htm). ---

The purpose of this collection was to measure the extent to which the Prisoner Management Classification (PMC) system in Washington state improved overall operations of prison facilities and reduced safety risks to inmates and staff. Four primary issues were addressed: (1) To what extent the PMC reduces rates of assaults on staff and inmates, (2) To what extent the PMC reduces rates of other serious misconduct, (3) To what extent the PMC increases rates of inmate participation in work or vocational programs, and (4) To what extent the PMC enhances staff job satisfaction, morale, and staff performance. Information is included on outcome variables against which comparisons between the experimental and control groups can be made. For each correctional facility, figures were collected for the number of staff-inmate assaults, number of inmate-inmate assaults, number of suicides and suicide attempts, number of escapes and escape attempts, number of "serious" disciplinary incidents, number of total staff, number of inmates, number of security staff vacancies, rated capacity of the facility, number of staff transfers and reasons, and number of inmates involved in educational, vocational, and work programs. Demographic variables include date of birth, sex, and race. Additional information concerns the family structure of the inmates and conditions surrounding the inmates' lives prior to entering prison.

The Fortune Society, a private not-for-profit organization located in New York City, provides a variety of services that are intended to support former prisoners in becoming stable and productive members of society. The purpose of this evaluation was to explore the extent to which receiving supportive services at the Fortune Society improved clients' prospects for law abiding behavior. More specifically, this study examined the extent to which receipt of these services reduced recidivism and homelessness following release. The research team adopted a quasi-experimental design that compared recidivism outcomes for persons enrolled at Fortune (clients) to persons released from New York State prisons and returning to New York City and, separately, inmates released from the New York City jails, none of whom went to Fortune (non-clients). All -- clients and non-clients alike -- were released after January 1, 2000, and before November 3, 2005 (for state prisoners), and March 3, 2005 (for city jail prisoners). Information about all prisoners released during these time frames was obtained from the New York State Department of Correctional Services for state prisoners and from the New York City Department of Correction for city prisoners. The research team also obtained records from the Fortune Society for its clients and arrest and conviction information for all released prisoners from the New York State Division of Criminal Justice Services' criminal history repository. These records were matched and merged, producing a 72,408 case dataset on 57,349 released state prisoners (Part 1) and a 68,614 case dataset on 64,049 city jail prisoners (Part 2). The research team obtained data from the Fortune Society for 15,685 persons formally registered as clients between 1989 and 2006 (Part 3) and data on 416,943 activities provided to clients at the Fortune Society between September 1999 and March 2006 (Part 4). Additionally, the research team obtained 97,665 records from the New York City Department of Homeless Services of all persons who sought shelter or other homeless services during the period from January 2000 to July 2006 (Part 5). Part 6 contains 96,009 cases and catalogs matches between a New York State criminal record identifier and a Fortune Society client identifier. The New York State Prisons Releases Data (Part 1) contain a total of 124 variables on released prison inmate characteristics including demographic information, criminal history variables, indicator variables, geographic variables, and service variables. The New York City Jails Releases Data (Part 2) contain a total of 92 variables on released jail inmate characteristics including demographic information, criminal history variables, indicator variables, and geographic variables. The Fortune Society Client Data (Part 3) contain 44 variables including demographic, criminal history, needs/issues, and other variables. The Fortune Society Client Activity Data (Part 4) contain seven variables including two identifiers, end date, Fortune service unit, duration in hours, activity type, and activity. The Homelessness Events Data (Part 5) contain four variables including two identifiers, change in homeless status, and date of change. The New York State Criminal Record/Fortune Society Client Match Data (Part 6) contain four variables including three identifiers and a variable that indicates the type of match between a New York State criminal record identifier and a Fortune Society client identifier.

The 2019 Census of State and Federal Adult Correctional Facilities (CCF) was the ninth enumeration of state institutions and the sixth enumeration of federal institutions sponsored by the Bureau of Justice Statistics and its predecessors. Earlier censuses were completed in 1979 (ICPSR 7852), 1984 (ICPSR 8444), 1990 (ICPSR 9908), 1995 (ICPSR 6953), 2000 (ICPSR 4021), 2005 (ICPSR 24642), and 2012 (ICPSR 37294). The 2019 CCF consisted of two data collection instruments - one for confinement facilities and one for community-based facilities. For each facility, information was provided on facility operator; sex of prisoners authorized to be housed by facility; facility functions; percentage of prisoners authorized to leave the facility; one-day counts of prisoners by sex, race/ethnicity, special populations, and holding authority; number of walkaways occurring over a one-year period; and educational and other special programs offered to prisoners. Additional information was collected from confinement facilities, including physical security level; housing for special populations; capacity; court orders for specific conditions; one-day count of correctional staff by payroll status and sex; one-day count of security staff by sex and race/ethnicity; assaults and incidents caused by prisoners; number of escapes occurring over a one-year period; and work assignments available to prisoners. Late in the data collection to avoid complete nonresponse from facilities, BJS offered the option of providing critical data elements from the two data collection instruments. These elements included facility operator; sex of prisoners authorized to be housed by facility; facility functions; percentage of prisoners authorized to leave the facility; one-day counts of prisoners by sex, and holding authority. Physical security level was an additional critical data element for confinement facilities. The census counted prisoners held in the facilities, a custody count. Some prisoners who are held in the custody of one jurisdiction may be under the authority of a different jurisdiction. The custody count is distinct from a count of prisoners under a correctional authority's jurisdiction, which includes all prisoners over whom a correctional authority exercises control, regardless of where the prisoner is housed. A jurisdictional count is more inclusive than a prison custody count and includes state and federal prisoners housed in local jails or other non-correctional facilities.

The Ministry of the Solicitor General annually releases data on the segregation, restrictive confinement, and deaths in custody of inmates in Ontario’s adult correctional system. Data Source: Offender Tracking Information System (OTIS) Segregation is defined in Ontario Regulation 778 as any type of custody where an inmate is in highly restricted conditions for 22 to 24 hours or does not receive a minimum of two hours of meaningful social interaction each day, excluding circumstances of an unscheduled lockdown. A record is created each time an inmate meets the conditions of segregation and closed when the inmate no longer meets those conditions. A break in a segregation placement is defined as occurring when an individual is out of segregation conditions for 24 or more continuous hours. The Ministry of the Solicitor General defines restrictive confinement as any type of confinement that is more restrictive than the general population but less restrictive than segregation. As a result, the ministry is reporting on any case within the fiscal year reporting period where an individual was held in a unit regularly scheduled to be locked down for 17 hours or more per day. This timeframe is considered more restrictive than that of the general population based on an assessment of provincewide lockdown times. Regularly scheduled lockdowns are daily routine times where movement out of a cell is restricted, such as during meal times and overnight. The Ministry of the Solicitor General is committed to providing greater transparency by releasing data on all custodial-related deaths that occurred within the calendar year reporting period. The datasets in this category include information on gender, race, age, religion or spiritual affiliation, and alerts for mental health concerns and suicide risk. To simplify the provision of data, several data tables include information on both individuals in segregation conditions and individuals in restrictive confinement. Due to the differences in the way that the data on segregation conditions and restrictive confinement have been collected, and the differences in the definitions of these concepts, these numbers should not be compared to each other. Some individuals may have both placements in restrictive confinement and segregation conditions, within the reporting period. Therefore, these numbers should not be added together when calculating proportions out of the total. Please refer to https://www.ontario.ca/page/jahn-settlement-data-inmates-ontario for additional information on the data release, including written overviews of the data and disclosure on data collection methods.

This study assessed the effects of male inmate religiosity on post-release community adjustment and investigated the circumstances under which these effects were most likely to take place. The researcher carried out this study by adding Federal Bureau of Investigation criminal history information to an existing database (Clear et al.) that studied the relationship between an inmate's religiousness and his adjustment to the correctional setting. Four types of information were used in this study. The first three types were obtained by the original research team and included an inmate values and religiousness instrument, a pre-release questionnaire, and a three-month post-release follow-up phone survey. The fourth type of information, official criminal history reports, was later added to the original dataset by the principal investigator for this study. The prisoner values survey collected information on what the respondent would do if a friend sold drugs from the cell or if inmates of his race attacked others. Respondents were also asked if they thought God was revealed in the scriptures, if they shared their faith with others, and if they took active part in religious services. Information collected from the pre-release questionnaire included whether the respondent attended group therapy, religious groups with whom he would live, types of treatment programs he would participate in after prison, employment plans, how often he would go to church, whether he would be angry more in prison or in the free world, and whether he would be more afraid of being attacked in prison or in the free world. Each inmate also described his criminal history and indicated whether he thought he was able to do things as well as most others, whether he was satisfied with himself on the whole or felt that he was a failure, whether religion was talked about in the home, how often he attended religious services, whether he had friends who were religious while growing up, whether he had friends who were religious while in prison, and how often he participated in religious inmate counseling, religious services, in-prison religious seminars, and community service projects. The three-month post-release follow-up phone survey collected information on whether the respondent was involved with a church group, if the respondent was working for pay, if the respondent and his household received public assistance, if he attended religious services since his release, with whom the respondent was living, and types of treatment programs attended. Official post-release criminal records include information on the offenses the respondent was arrested and incarcerated for, prior arrests and incarcerations, rearrests, outcomes of offenses of rearrests, follow-up period to first rearrest, prison adjustment indicator, self-esteem indicator, time served, and measurements of the respondent's level of religious belief and personal identity. Demographic variables include respondent's faith, race, marital status, education, age at first arrest and incarceration, and age at incarceration for rearrest.

The data contain records of sentenced offenders in the custody of the Bureau of Prisons (BOP) at year-end of fiscal year 2018. The data include commitments of United States District Court, violators of conditions of release (e.g., parole, probation, or supervised release violators), offenders convicted in other courts (e.g., military or District of Columbia courts), and persons admitted to prison as material witnesses or for purposes of treatment, examination, or transfer to another authority. These data include variables that describe the offender, such as age, race, citizenship, as well as variables that describe the sentences and expected prison terms. The data file contains original variables from the Bureau of Prisons' SENTRY database as well as additional analysis variables. Variables containing identifying information (e.g., name, Social Security Number) were either removed, coarsened, or blanked in order to protect the identities of individuals. These data are part of a series designed by Abt and the Bureau of Justice Statistics. Data and documentation were prepared by Abt.

The purpose of this study was to develop and validate a reliable and accurate method for measuring the effectiveness of offender classification systems to improve the management of correctional facilities. In the early 1980s, the state of Hawaii began classifying its prisoners with a newly developed Federal Bureau of Prisons classification instrument. This study was designed to develop a method to evaluate this form. Two prediction models were used. The first, initial classification, used the sum of four variables to arrive at a security score, which was taken to be predictive of violence. The second, reclassification, used the sum of seven different variables to obtain a custody total, which was then used as a major determinant of reclassification. Two groups of inmates were used: inmates who had committed infractions and inmates with no reported infractions. Research variables include (a) initial classification: offense (severity), expected length of incarceration (sentence), type of prior commitments, and history of violence, and (b) reclassification: percentage of time served, involvement with drugs/alcohol, mental/psychological stability, most serious disciplinary report, frequency of disciplinary reports, responsibility that the inmate demonstrated, and family/community ties. In addition, the collection supplies information on race and sex of inmates, sentence limitation, history of escapes or attempts, previous infractions, entry, reclassification, and termination dates (month and year), and custody level. There are demographic variables for sex and race. The unit of observation is the inmate.

This project sought to investigate a possible relationship between sentencing guidelines and family structure in the United States. The research team developed three research modules that employed a variety of data sources and approaches to understand family destabilization and community distress, which cannot be observed directly. These three research modules were used to discover causal relationships between male withdrawal from productive spheres of the economy and resulting changes in the community and families. The research modules approached the issue of sentencing guidelines and family structure by studying: (1) the flow of inmates into prison (Module A), (2) the role of and issues related to sentencing reform (Module B), and family disruption in a single state (Module C). Module A utilized the Uniform Crime Reporting (UCR) Program data for 1984 and 1993 (Parts 1 and 2), the 1984 and 1993 National Correctional Reporting Program (NCRP) data (Parts 3-6), the Urban Institute's 1980 and 1990 Underclass Database (UDB) (Part 7), the 1985 and 1994 National Longitudinal Survey on Youth (NLSY) (Parts 8 and 9), and county population, social, and economic data from the Current Population Survey, County Business Patterns, and United States Vital Statistics (Parts 10-12). The focus of this module was the relationship between family instability, as measured by female-headed families, and three societal characteristics, namely underclass measures in county of residence, individual characteristics, and flows of inmates. Module B examined the effects of statewide incarceration and sentencing changes on marriage markets and family structure. Module B utilized data from the Current Population Survey for 1985 and 1994 (Part 12) and the United States Statistical Abstracts (Part 13), as well as state-level data (Parts 14 and 15) to measure the Darity-Myers sex ratio and expected welfare income. The relationship between these two factors and family structure, sentencing guidelines, and minimum sentences for drug-related crimes was then measured. Module C used data collected from inmates entering the Minnesota prison system in 1997 and 1998 (Part 16), information from the 1990 Census (Part 17), and the Minnesota Crime Survey (Part 18) to assess any connections between incarceration and family structure. Module C focused on a single state with sentencing guidelines with the goal of understanding how sentencing reforms and the impacts of the local community factors affect inmate family structure. The researchers wanted to know if the aspects of locations that lose marriageable males to prison were more important than individual inmate characteristics with respect to the probability that someone will be imprisoned and leave behind dependent children. Variables in Parts 1 and 2 document arrests by race for arson, assault, auto theft, burglary, drugs, homicide, larceny, manslaughter, rape, robbery, sexual assault, and weapons. Variables in Parts 3 and 4 document prison admissions, while variables in Parts 5 and 6 document prison releases. Variables in Part 7 include the number of households on public assistance, education and income levels of residents by race, labor force participation by race, unemployment by race, percentage of population of different races, poverty rate by race, men in the military by race, and marriage pool by race. Variables in Parts 8 and 9 include age, county, education, employment status, family income, marital status, race, residence type, sex, and state. Part 10 provides county population data. Part 11 contains two different state identifiers. Variables in Part 12 describe mortality data and welfare data. Part 13 contains data from the United States Statistical Abstracts, including welfare and poverty variables. Variables in Parts 14 and 15 include number of children, age, education, family type, gender, head of household, marital status, race, religion, and state. Variables in Part 16 cover admission date, admission type, age, county, education, language, length of sentence, marital status, military status, sentence, sex, state, and ZIP code. Part 17 contains demographic data by Minnesota ZIP code, such as age categories, race, divorces, number of children, home ownership, and unemployment. Part 18 includes Minnesota crime data as well as some demographic variables, such as race, education, and poverty ratio.

This collection provides annual data on jail populations across the nation and examines the "spillover" effect on local jails resulting from the dramatic growth in federal and state prison populations. These data permit an assessment of the demands placed on correctional resources and provide a comprehensive picture of the adult correctional system and changes that occur within the system. Information is available on the number of inmates by sex, race, adult or juvenile status, reason being held, and cause of death. Also added in the 1992 survey were variables on citizenship, population movement, and total number of inmate deaths for inmates originally confined to the facility in question who died either at that facility or elsewhere. Also, the 1992 version included a more complete survey of jail programs and a supplemental questionnaire (CJ-5S), which dealt with AIDS-related questions. In addition, information was collected for the first time on drug testing, programs that treated or educated inmates, boot camps, work release, and alternatives to incarceration such as electronic monitoring, house arrest, community service, and weekend or day reporting.

This dataset tracks annual two or more races student percentage from 2011 to 2020 for Taycheedah Correctional Institution vs. Wisconsin and Wisconsin Department Of Corrections School District

Abstract (en): This survey of inmates in five California prisons was conducted by the RAND Corporation with a grant from the National Institute of Justice. Researchers distributed an anonymous self-administered questionnaire to groups of 10-20 inmates at a time. Using the self-report technique, the survey obtained detailed information about the crimes committed by these prisoners prior to their incarceration. Variables were calculated to examine the characteristics of repeatedly arrested or convicted offenders (recidivists) as well as offenders reporting the greatest number of serious crimes (habitual criminals). The variables include crimes committed leading to incarceration, rates of criminal activity, and social-psychological scales for analyzing motivations to commit crimes, as well as self-reports of age, race, education, marital status, employment, income, and drug use. ICPSR data undergo a confidentiality review and are altered when necessary to limit the risk of disclosure. ICPSR also routinely creates ready-to-go data files along with setups in the major statistical software formats as well as standard codebooks to accompany the data. In addition to these procedures, ICPSR performed the following processing steps for this data collection: Checked for undocumented or out-of-range codes.. Male prisoners who were incarcerated in five California state adult correctional institutions in July and August of 1976. 2005-11-04 On 2005-03-14 new files were added to one or more datasets. These files included additional setup files as well as one or more of the following: SAS program, SAS transport, SPSS portable, and Stata system files. The metadata record was revised 2005-11-04 to reflect these additions. Funding insitution(s): United States Department of Justice. Office of Justice Programs. National Institute of Justice (83-IJ-CX-0006).

This dataset tracks annual two or more races student percentage from 2012 to 2022 for Kettle Moraine Correctional Institution vs. Wisconsin and Wisconsin Department Of Corrections School District

Adult correctional services, custodial and community supervision, average counts of adults in provincial and territorial programs, five years of data.

These data are part of NACJD's Fast Track Release and are distributed as they were received from the data depositor. The files have been zipped by NACJD for release, but not checked or processed except for the removal of direct identifiers. Users should refer to the accompanying readme file for a brief description of the files available with this collection and consult the investigator(s) if further information is needed. This study addresses changes to state correctional systems and policies in response to correctional spending limits brought on by the worsening economic climate beginning in late 2007. These changes include institutional changes, such as closing prisons and reducing staffing, "back-end" strategies, such as reductions in sentence lengths and reduced parolee supervision, and "front-end" measures, such as funding trade-offs between other governmental and social services. A survey of the 50 state correctional administrators addressed fiscal stress, including size and characteristics of the prison population, prison crowding, prison expenditures, institutional safety, staff morale, public safety and other justice spending. Additionally, six states were selected for in depth case studies, which included interviews with facility personnel and site visits by research staff in order to thoroughly understand the challenges faced and the resulting decisions made. Additionally, each state's demographic, correctional spending, and overall financial information was collected from census and other publicly available reports. Information on the overall health and safety of the inmates was examined through an econometric comparison of funding levels and statistics as to prisoner mortality, crime and incarceration rates.

The 1999 Census of Jails is the seventh in a series of data collection efforts aimed at studying the nation's locally administered jails. Previous censuses were conducted in 1970, 1972, 1978, 1983, 1988, and 1993. The 1999 census enumerated 3,365 locally administered confinement facilities that held inmates beyond arraignment and were staffed by municipal or county employees. Among these were 47 privately operated jails under contract for local governments and 42 regional jails that were operated for two or more jail authorities. In addition, the census identified 11 facilities maintained by the Federal Bureau of Prisons that functioned as jails. The nationwide total of the number of jails in operation on June 30, 1999, was 3,376. For purposes of this data collection, a local jail was defined as a locally operated adult detention facility that receives individuals pending arraignment and holds them awaiting trial, conviction, or sentencing, readmits probation, parole, and bail-bond violators and absconders, temporarily detains juveniles pending transfer to juvenile authorities, holds mentally ill persons pending their movement to appropriate health facilities, holds individuals for the military, for protective custody, for contempt, and for the courts as witnesses, releases convicted inmates to the community upon completion of sentence, transfers inmates to federal, state, or other authorities, houses inmates for federal, state, or other authorities because of crowding of their facilities, relinquishes custody of temporary detainees to juvenile and medical authorities, operates community-based programs with day-reporting, home detention, electronic monitoring, or other types of supervision, and holds inmates sentenced to short terms. Variables include information on jail population by legal status, age and sex of prisoners, maximum sentence, admissions and releases, available services and programs, structure and capacity, facility age and use of space, expenditure, employment, staff information, and health issues, which include statistics on drugs, AIDS, and tuberculosis.

The prisoner dataset extracts from the Prison National Offender Management Information System (p-NOMIS) and the Offender Assessment System (OASys), operational databases used in prisons for the management of offenders.

Tables: NOMIS - One record per prisoner per custodial journey (i.e. each separate instance that a person was in prison during the period, but combining connected spells in custody, remand and recalls) providing details around their personal characteristics, main offence, sentence length, and release information.

OASys - One record per prisoner per assessment (prisoners may have more than one assessment per custodial journey). The OASys data only includes OASys records that have been linked to an offender in the NOMIS dataset.

Safety in Custody - One record per prisoner per incident