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.

Prison Inmates in India

Demographics, Age, Education, Caste, Wages, Rehabilitation, Technical Info

By Rajanand Ilangovan [source]

About this dataset

This dataset provides a detailed view of prison inmates in India, including their age, caste, and educational background. It includes information on inmates from all states/union territories for the year 2019 such as the number of male and female inmates aged 16-18 years, 18-30 year old inmates and those above 50 years old. The data also covers total number of penalized prisoners sentenced to death sentence, life imprisonment or executed by the state authorities. Additionally, it provides information regarding the crimehead (type) committed by an inmate along with its grand total across different age groups. This dataset not only sheds light on India’s criminal justice system but also highlights prevelance of crimes in different states and union territories as well as providing insight into crime trends across Indian states over time

More Datasets

For more datasets, click here.

Featured Notebooks

• 🚨 Your notebook can be here! 🚨!

How to use the dataset

This dataset provides a comprehensive look at the demographics, crimes and sentences of Indian prison inmates in 2019. The data is broken down by state/union territory, year, crime head, age groups and gender.

This dataset can be used to understand the demographic composition of the prison population in India as well as the types of crimes committed. It can also be used to gain insight into any changes or trends related to sentencing patterns in India over time. Furthermore, this data can provide valuable insight into potential correlations between different demographic factors (such as gender and caste) and specific types of crimes or length of sentences handed out.

To use this dataset effectively there are a few important things to keep in mind: •State/UT - This column refers to individual states or union territories in India where prisons are located •Year – This column indicates which year(s) the data relates to •Both genders - Female columns refer only to female prisoners while male columns refers only to male prisoners •Age Groups – 16-18 years old = 21-30 years old = 31-50 years old = 50+ years old •Crime Head – A broad definition for each type of crime that inmates have been convicted for •No Capital Punishment – The total number sentenced with capital punishment No Life Imprisonment – The total number sentenced with life imprisonment No Executed– The total number executed from death sentence Grand Total–The overall totals for each category

By using this information it is possible to answer questions regarding topics such as sentencing trends, types of crimes committed by different age groups or genders and state-by-state variation amongst other potential queries

Research Ideas

• Using the age and gender information to develop targeted outreach strategies for prisons in order to reduce recidivism rates.
• Creating an AI-based predictive model to predict crime trends by analyzing crime head data from a particular region/state and correlating it with population demographics, economic activity, etc.
• Analyzing the caste of inmates across different states in India in order to understand patterns of discrimination within the criminal justice system

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: Attribution-ShareAlike 4.0 International (CC BY-SA 4.0) - You are free to: - Share - copy and redistribute the material in any medium or format for any purpose, even commercially. - Adapt - remix, transform, and build upon the material for any purpose, even commercially. - You must: - Give appropriate credit - Provide a link to the license, and indicate if changes were made. - ShareAlike - You must distribute your contributions under the same license as the original.

Columns

File: SLL_Crime_headwise_distribution_of_inmates_who_convicted.csv | Column name | Description | |:--------------------------|:---------------------------------------------------------------------------------------------------| | STATE/UT | Name of the state or union territory where the jail is located. (String) | | YEAR | Year when the inmate population data was collected. (Integer) ...

This dataset shows the number of people that are in prison by state in 2006 and 2007. These numbers are then compared to show the difference between the two years and a percentage of change is given as well. This data was brought to our attention by the Pew Charitable Trusts in their report titled, One in 100: Behind Bars in America 2008."" The main emphasis of the article emphasizes the point that in 2007 1 in every 100 Americans were in prison. To note: Many states have not completed their data verification process. Final published figures may differ slightly. The District of Columbia is not included. D.C. prisoners were transferred to federal custody in 2001

This dataset depicsts how ERO oversees the civil immigration detention of one of the most diverse and rapidly changing detained populations in the world. These noncitizens are housed within approximately 130 facilities across the nation.

This dataset lists inmates incarcerated at Cockatoo Island prison in Sydney (Australia) between 1847-1869. It offers insights into how the colonial criminal justice system operated after New South Wales’ transition from a penal colony to a ‘free’ colony when transportation ceased in 1840. It is a useful tool for genealogists tracing the lives of their criminal ancestors and for historians of crime and punishment researching nineteenth-century Australia. The dataset includes prisoners' names and aliases, their ship of arrival, place of origin, details of their colonial conviction(s) (trial place, court, offence, sentence), date(s) admitted to Cockatoo Island, and when and how they were discharged from Cockatoo Island. In some cases, it also includes prisoners' place of origin, occupation, biometric information (height, eye/hair colour, complexion, scars, tattoos), 'condition upon arrival' (convict or free), and (for convicts) details of their original conviction in Britain or Ireland. As a UNESCO World Heritage 'Convict Site' Cockatoo Island is best known as a site of secondary punishment for recidivist convicts, especially those transferred from Norfolk Island. This dataset demonstrates the diversity of the prison population: including nominally free convicts (ticket-of-leave holders), migrants from Britain, China and other Australian colonies drawn in by the gold rush, exiles from Port Phillip, Aboriginal Australians convicted during frontier warfare, colonial-born white Australians (including bushrangers), and black, Indian and American sailors visiting Sydney. Significant attention has been paid to the more than 160,000 British and Irish convicts who were transported Australia as colonists between 1787 and 1868. Much less has been said about those punished within the criminal justice system that arose in the wake of New South Wales' transition from 'penal' to 'free' colony (Finnane, 1997: x-xi). Cockatoo Island prison opened in 1839, a year before convict transportation to New South Wales ceased, and was intended to punish the most recidivist and violent of the transported convicts. This archetype has prevailed in historical discourses, and they have been described as 'criminal lunatics... [and] criminals incapable of reform' (Parker, 1977: 61); 'the most desperate and abandoned characters' (O'Carrigan, 1994: 64); and people of 'doubtful character' (NSW Government Architect's Office, 2009: 29). Yet, this was far from the truth. My analysis of 1666 prisoners arriving between 1839-52 show they were overwhelming non-violent offenders, tried for minor property crimes at lower courts. They were also far more diverse population than commonly recognised, including Indigenous Australian, Chinese and black convicts alongside majority British and Irish men (Harman, 2012). This project will make publicly available extremely detailed records relating to Cockatoo Island's prisoners to show people firsthand exactly who made up the inmate population. The digital version of the original registers will include information on convicts' criminal record, but also their job, whether they were married or had children, and even what they looked like. It will also be a name-searchable database so family historians can search for their ancestors, who may have been incarcerated on the island. As it stands, they will be able find information online about ancestors who were transported as long as they remained in the 'convict system', but they may seem to disappear as soon as they are awarded their ticket-of-leave and become 'free'. However, many former convicts, and free immigrants, to New South Wales were convicted locally, and these records can give us information about their lives within the colony. The type of data included in these registers will also allow researchers to investigate questions including: (1) were convicts more likely to offend again than free immigrants? (2) Were the children of convicts more likely to offend than others? (3) Did the influx of mostly Chinese migrants during the gold rush actually lead to a crime-wave, as reported in the press? (4) Were laws introduced between 1830 and 1853, actually effective at prosecuting bushrangers (highwaymen)? (5) Was the criminal-judicial system in Australia more rehabilitative, despite developing out of a harsher convict transportation system? Alongside the dataset, the website will include 'life-biographies' of individual convicts to show you how this dataset can be used to piece together a life-story. It also to warns against understanding a real-life person only through the records of their conviction. There many of fascinating stories to tell, including those 'John Perry' ('Black Perry') the prizewinning boxer; the love story of the 'Two Fredericks'; and Tan, the Chinese gold-digger who resisted his incarceration. In addition, there will be teaching resources for secondary school children and undergraduate university students who want to engage directly with historical materials, without having to leave their classroom. Overall, this website invites anyone with an interest in the history of crime and punishment, and any visitors to the UNESCO world heritage site 'Cockatoo Island', to try searching for a name in the database or read about a featured convict's life story. It asks them, though, to think about how and why these people's lives intersected with the state, leading to their incarceration, and how history has erased much of their lives outside of it. Data collection involved photographing a Cockatoo Island’s surviving prison registers and returns kept at the State Archives of New South Wales (call numbers: 4/4540, 4/6501, 4/6509, 6571, 4/6572, 4/6573, 4/6574, 4/6575, X819). In these volumes, clerks had listed details of incoming prisoners on the dates they arrived between April 1847 and October 1869. This prison register for the period 1839-46 (call number: 2/8285) had not survived to a good enough quality for accurate transcription and was excluded from data collection. I photographed and then transcribed these records in full into a tabular form, with minor standardisation of abbreviations and irregular spellings. Where multiple records existed for one person I combined information from two separate archival records into one line of the dataset. Where I could not verify that two people with the same name were the same person, I listed them as separate entries. Barring errors in entry at the time of record creation, the studied population represents the entire population of prisoners incarcerated at Cockatoo Island between April 1847 and October 1869 when the prison closed.

The 1922 Prison System Enquiry Committee Report said that: 'In order to judge our Prison system rightly it is necessary to know what kind of people become prisoners... How many go to prison? For what length of sentence?' These questions persist, and are especially relevant for today's prison crisis. This project will assess nearly 100 years of historical data to explore, for the first time, how prison numbers were largely dictated by the repeat incarceration of recidivist's offenders with short sentences. It questions how the prison authorities attempted to manage increasing numbers of offenders by using early release schemes (licenses) in the nineteenth and twentieth centuries (licenses have only recently become available, generous access granted by The National Archives). This project will explore whether short sentences contributed to repeat offending, and whether early release schemes accelerated or inhibited recidivism. It investigates the financial costs of imprisonment to the country (and the human costs to those imprisoned) and does this over a significant period of time (allowing an examination of how repeated incarcerations affected the whole of an offender's criminal career). It concludes by asking what lessons can be learnt for today's debates about sentencing offenders and managing the prison population? Data was derived from the following sources: PCOM 3 (1853-1887, 1902-08, 1912-42) – these files contained 45,000 licenses and also the registers of license holders. They listed the prisoner’s name, sentence, where/when convicted, dates and conditions of the current license; previous convictions, age, previous occupation, when and from where the prisoner was released; and most had photographs of the prisoner. The National Archives granted us access to these records pre public release (they are now available on Find-My-Past and Ancestry). Criminal Registers 1853-1892 (contained offenders tried for indictable crimes, whether they were found guilty, details of the offence, and sentence imposed). Where possible we traced these offences in the Quarter Sessions Calendars in order to trace the antecedent criminal history. From these main sources, we were then able to trace prisoners released on license using a wide variety of other extant sources. These sources provided us with a considerable amount of additional information on offenders who were released on license: Census returns from 1841-1911 censuses (which gave details of the residence, family status, and occupation, of each person we will be searching for). Online Birth, Marriage and Death indices (which detailed if and when our offender was married, and had children; and, of course, when they died). Military records (mainly referring to World War One; these included service records - which in turn included disciplinary breaches - medal indices and pensions details. Metropolitan Police records including Habitual Criminal Registers (MEPO 6) which contain details of criminals as defined by sections 5-8 of the Prevention of Crimes Act 1871. From the sources above we constructed approximately 650 life grids. These were divided into an early (1853-55 n=62), middle (1871-73 n=201), and late (1885-1887 n=184) tranche, for 356 men and 288 women. Each life-grid charted offending/life histories for each offender. Studies funded by Leverhulme Trust (F/00130/H)) and ESRC (RES-062-23-0416) used life grids and `whole-life’ research methods and the method is now well-tested. The life-grid data was then entered into excel and SPSS in order to produce quantifiable data on - the progress of their criminal careers, their periods of incarceration, their employment careers, life events such as marriage, death of parents, and other significant life events. We had over two hundred thousand fields of data at the conclusion of our data collection/analysis. By analysing each of the life grids we were able to see the relationships and connections between life events and offending post-imprisonment (both short and long periods of custody, whilst on licence, and after license had expired.

This is an Annotation for Transparent Inquiry (ATI) data project. The annotated article can be viewed on the publisher's website here. Project Summary Scholarship on human rights diplomacy (HRD)—efforts by government officials to engage publicly and privately with their foreign counterparts—often focuses on actions taken to “name and shame” target countries, because private diplomatic activities are unobservable. To understand how HRD works in practice, we explore a campaign coordinated by the US government to free twenty female political prisoners. We compare release rates of the featured women to two comparable groups: a longer list of women considered by the State Department for the campaign; and other women imprisoned simultaneously in countries targeted by the campaign. Both approaches suggest that the campaign was highly effective. We consider two possible mechanisms through which expressive public HRD works: by imposing reputational costs and by mobilizing foreign actors. However, in-depth interviews with US officials and an analysis of media coverage find little evidence of these mechanisms. Instead, we argue that public pressure resolved deadlock within the foreign policy bureaucracy, enabling private diplomacy and specific inducements to secure the release of political prisoners. Entrepreneurial bureaucrats leveraged the spotlight on human rights abuses to overcome competing equities that prevent government-led coercive diplomacy on these issues. Our research highlights the importance of understanding the intersection of public and private diplomacy before drawing inferences about the effectiveness of HRD. Data Generation We generated four sources of data for this project: 1. A dataset of political prisoners from 13 countries based on Amnesty International Urgent Action reports between 2000 and 2015. 2. Arrest and release information for a dataset of female political prisoners 3. A dataset on media attention based on both news articles from LexisNexis and online search trends from Google Trends 4. Interviews conducted with U.S. government officials and other human rights advocates involved in the #Freethe20 campaign to free political prisoners launched in September 2015 We used two sources of data for each of our two research questions. Our first research question was: Did the #Freethe20 campaign have an impact on the release rate of political prisoners? In an ideal world, we would have a comprehensive set of female political prisoners to compare with #Freethe20 prisoners. However, as we explain in the manuscript, in countries with more dire human rights situations, arrests often go unreported. In some cases, the sheer volume of political prisoners makes chronicling information about them challenging, if not impossible. Therefore, in order to construct a comparable set of cases, one strategy we used was to collect information from Amnesty International’s “Urgent Action” campaigns. To our knowledge, Amnesty International has the most comprehensive, publicly available list of contemporary political prisoners globally. Their records are public and searchable, which allowed us to construct a population of political prisoners from the countries targeted by the #Freethe20 campaign. We began our data collection with a base set of Urgent Actions metadata generated by Judith Kelley and Dan Nielson via webscraping from the Amnesty International website. Using a list of URLs that linked to each Urgent Action Report, we coded the name and sex of individuals featured in each Urgent Action Report from 2000 through September 2015 (the start of the #Freethe20 campaign) in the 13 countries featured in the campaign (Azerbaijan, Burma, China, Egypt, Ethiopia, Eritrea, Iran, North Korea, Russia, Syria, Uzbekistan, Venezuela, and Vietnam). Instructions about how we coded this information and sample documents are available in the QDR repository (QDR: MyrickWeinstein_codebook_urgentaction.pdf). After compiling a base dataset of individuals featured in Urgent Action reports, we identified the women in the dataset (~17% of entries) and conducted additional research about (1) whether these women could be classified as political prisoners, and (2) whether and when these women were released from prison, detention, or house arrest. Here, we relied on both follow-up reporting from Amnesty International as well as a variety of online news sources. We deposited the coding instructions for this process (MyrickWeinstein_codebook_releaseinfo.pdf) and also include documentation on additional online news sources that we used to make a judgment on a particular case. Our second question was: How and under what conditions did #Freethe20 affect the release rate of female political prisoners? To answer this question, we look at strategies of both public pressure and private, coercive diplomacy. For the former, we collected data on media attention and online search trends. We searched for newspapers and news articles that featured...

The United Nations International Crime Prevention and Criminal Justice Branch began the Surveys of Crime Trends and Operations of Criminal Justice Systems (formerly known as the World Crime Surveys) in 1978. The goal of the data collection effort was to conduct a more focused inquiry into the incidence of crime worldwide. To date, there have been five quinquennial surveys, covering the years 1970-1975, 1975-1980, 1980-1986, 1986-1990, and 1990-1994, respectively. Starting with the 1980 data, the waves overlap by one year to allow for reliability and validity checks of the data. For this data collection, the original United Nations data were restructured into a standard contemporary file structure, with each file consisting of all data for one year. Naming conventions were standardized, and each country and each variable was given a unique identifying number. Crime variables include counts of recorded crime for homicide, assault, rape, robbery, theft, burglary, fraud, embezzlement, drug trafficking, drug possession, bribery, and corruption. There are also counts of suspects, persons prosecuted, persons convicted, and prison admissions by crime, gender, and adult or juvenile status. Other variables include the population of the country and largest city, budgets and salaries for police, courts, and prisons, and types of sanctions, including imprisonment, corporal punishment, deprivation of liberty, control of freedom, warning, fine, and community sentence. The countries participating in the survey and the variables available vary by year.

In 2011, a historic Supreme Court decision mandated that the state of California substantially reduce its prison population to alleviate overcrowding, which was deemed so severe as to preclude the provision of adequate healthcare. To comply, California passed the Public Safety Realignment Act (Assembly Bill [AB] 109), representing the largest ever court-ordered reduction of a prison population in U.S. history. AB109 was successful in reducing the state prison population; however, although the policy was precipitated by inadequate healthcare in state prisons, no studies have examined its effects on prisoner health. As other states grapple with overcrowded prisons and look to California’s experience with this landmark policy, understanding how it may have impacted prisoner health is critical. We sought to evaluate the effects of AB109 on prison mortality and assess the extent to which policy-induced changes in the age distribution of prisoners may have contributed to these effects. To do so, we used prison mortality data from the Bureau of Justice Statistics and the California Deaths in Custody reporting program and prison population data from the National Corrections Reporting Program to examine changes in overall prison mortality, the age distribution of prisoners, and age-adjusted prison mortality in California relative to other states before and after the implementation of AB109. Following AB109, California prisons experienced an increase in overall mortality relative to other states that attenuated within three years. Over the same period, California experienced a greater upward shift in the age distribution of its prisoners relative to other states, suggesting that the state’s increase in overall mortality may have been driven by this change in age distribution. Indeed, when accounting for this differential change in age distribution, mortality among California prisoners exhibited a greater reduction relative to other states in the third year after implementation. As other states seek to reduce their prison populations to address overcrowding, assessments of California’s experience with AB109 should consider this potential improvement in age-adjusted mortality.

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.

This dataset shows the comparison between the amount of spending that was spent on higher education and corrections by each state in the United States from 1987 to 2007. This data was brought to our attention by the Pew Charitable Trusts in their report titled, "One in 100: Behind Bars in America 2008." The main emphasis of the article emphasizes the point that in 2007 1 in every 100 Americans were in prison. To note: Many states have not completed their data verification process. Final published figures may differ slightly. The District of Columbia is not included. D.C. prisoners were transferred to federal custody in 2001

This dataset shows the percentage of State Employees that work in Corrections by state in the year 2006. This data was brought to our attention by the Pew Charitable Trusts in their report titled, One in 100: Behind Bars in America 2008. The main emphasis of the article emphasizes the point that in 2007 1 in every 100 Americans were in prison. To note: The District of Columbia is not included. D.C. prisoners were transferred to federal custody in 2001

This dataset shows the amount of money that each state spent on their Corrections program both in percentage of the Overall amount of money spent in the State and as a total amount of money. This data was brought to our attention by the Pew Charitable Trusts in their report titled, One in 100: Behind Bars in America 2008. The main emphasis of the article emphasizes the point that in 2007 1 in every 100 Americans were in prison. To note: The District of Columbia is not included. D.C. prisoners were transferred to federal custody in 2001.