Emerging from the tradition of econometric models of deterrence and crime, this study attempts to improve estimates of how crime rates are affected by the apprehension and punishment of persons charged with criminal activity. These data are contained in two files: Part 1, State Data, consists of a panel of observations from each of the 50 states and contains information on crime rates, clearance rates, length of time served, probability of imprisonment, socioeconomic factors such as unemployment rates, population levels, and income levels, and state and local expenditures for police protection. Part 2, SMSA Data, consists of a panel of 77 SMSAs and contains information on crime rates, clearance rates, length of time served, probability of imprisonment, socioeconomic factors such as employment rates, population levels, and income levels, and taxation and expenditure information.

The purpose of the study was to quantify the effect of the embrace of DNA technology on offender behavior. In particular, researchers examined whether an offender's knowledge that their DNA profile was entered into a database deterred them from offending in the future and if probative effects resulted from DNA sampling. The researchers coded information using criminal history records and data from Florida's DNA database, both of which are maintained by the Florida Department of Law Enforcement (FDLE), and also utilized court docket information acquired through the Florida Department of Corrections (FDOC) to create a dataset of 156,702 cases involving offenders released from the FDOC in the state of Florida between January 1996 and December 2004. The data contain a total of 50 variables. Major categories of variables include demographic variables regarding the offender, descriptive variables relating to the initial crime committed by the offender, and time-specific variables regarding cases of recidivism.

These data were gathered to investigate the usefulness of statistical methods, particularly multiple-regression analysis, in predicting repeat criminal activity subsequent to an individual's release from prison. The data collection consists of follow-up information, collected in 1988, on a sample of males released from California prisons. The follow-up study identified criminal activity subsequent to individuals' release from prison through 1988. Predictor variables include age, prior periods of arrest, history of drug use, seriousness of original offense, and number of arrests for nuisance, person, property, and fraud offenses.

The purpose of this data collection was to measure the validity or accuracy of four recidivism prediction instruments: the INSLAW, RAND, SFS81, and CGR scales. These scales estimate the probability that criminals will commit subsequent crimes quickly, that individuals will commit crime frequently, that inmates who are eligible for release on parole will commit subsequent crimes, and that defendants awaiting trial will commit crimes while on pretrial arrest or detention. The investigators used longitudinal data from five existing independent studies to assess the validity of the four predictive measures in question. The first data file was originally collected by the Vera Institute of Justice in New York City and was derived from an experimental evaluation of a jobs training program called the Alternative Youth Employment Strategies Project implemented in Albuquerque, New Mexico, Miami, Florida, and New York City, New York. The second file contains data from a RAND Corporation study, EFFECTS OF PRISON VERSUS PROBATION IN CALIFORNIA, 1980-1982 (ICPSR 8700), from offenders in Alameda and Los Angeles counties, California. Parts 3 through 5 pertain to serious juvenile offenders who were incarcerated during the 1960s and 1970s in three institutions of the California Youth Authority. A portion of the original data for these parts was taken from EARLY IDENTIFICATION OF THE CHRONIC OFFENDER, [1978-1980: CALIFORNIA] (ICPSR 8226). All files present demographic and socioeconomic variables such as birth information, race and ethnicity, education background, work and military experience, and criminal history, including involvement in criminal activities, drug addiction, and incarceration episodes. From the variables in each data file, standard variables across all data files were constructed. Constructed variables included those on background (such as drug use, arrest, conviction, employment, and education history), which were used to construct the four predictive scales, and follow-up variables concerning arrest and incarceration history. Scores on the four predictive scales were estimated.

recidivism rates for persons released from state prisons with specific demographic, criminal history, and sentence attributes.

This study was undertaken to examine the ways in which different felony sanctions impact the future behavior of felony offenders. The study sought to determine whether the following made a difference in subsequent criminal behavior: (1) sentences of confinement, (2) the length of sentence (both the sentence imposed and that which was actually served), and (3) sentences of probation combined with jail ("split" sentences), or combined with fines, restitution, or other alternative sanctions. Data were collected from questionnaires completed by 18 judges of the Essex County, New Jersey, courts and by probation staff. Follow-up data were collected from official records provided by probation, jail, prison, and parole case files. Follow-up data were also collected from the following official records: (1) the New Jersey Offender-Based Transaction System Computerized Criminal History, (2) the New Jersey Department of Corrections Offender-Based Correctional Information System, (3) the United States Department of Justice Interstate Identification Index, (4) the National Crime Information Center Wanted Persons File, (5) the New Jersey PROMIS/GAVEL Prosecutors Case Tracking System, and (6) administrative record files of the New Jersey Department of Corrections. Variables in the data file include the most serious offense charge, most serious offense of conviction, dimension of conviction, offense type (person, property, social order, fraud, or drug offense), number of prior probations, number of probation revocations, number of prior jail and prison terms, mitigating and aggravating factors affecting the sentence, type of sentence, special conditions of probation, fines and restitutions imposed, minimum and maximum incarceration terms (in months), history of drug offenses, type of drugs used, probation and parole violations, total number of prior arrests and prior convictions, and longest arrest-free period after first arrest. The type of post-sentence offense, dimension, disposition charge, sentence, and date of arrest are provided for arresting events and charge episodes 1 through 108 for any offender. For up to 43 arrest events (for any offender), the date of lockup and date of exit from confinement are provided. The file also includes recommendations made by prosecutors and probation officers, and judges' ratings (on a scale of one to nine) with respect to the likelihood of an offender committing future property crimes, crimes against persons, or any crime. Judges also rated the arrest record length, conviction record length, and social stability of each offender. Retribution points, incapacitation points, and specific deterrence points assigned by the judges complete the file. Demographic variables include the race and sex of each convicted offender, and the age of the offender at first conviction.

The purpose of this study was to estimate the deterrent effect of capital punishment by employing a methodology that accounted for model uncertainty by integrating various studies into a single coherent analysis. First, this study replicated the results from two previous studies, Dezhbakhsh, Rubin and Shepherd (2003) and Donohue and Wolfers (2005), that draw on the same data. Second, the researchers implemented model averaging methods using standard frequentist estimators to take a weighted average of the findings across all possible models that could explain the effect of the difference in crime rates under alternate laws. Each model's effect was weighted based on its ability to explain the data. Variables used in this study included deterrence variables as well as various demographic and economic control variables.

This study examined the public's preferences regarding sentencing and parole of criminal offenders. It also investigated the public's willingness to pay for particular crime prevention and control strategies and tested new methods for gathering this kind of information from the public. This involved asking the public to respond to a series of crime vignettes that involved constrained choice. The study consisted of a telephone survey of 1,300 adult respondents conducted in 2000 in the United States. Following a review by a panel of experts and extensive pretesting, the final instrument was programmed for computer-assisted telephone interviews (CATI). The questionnaire specifically focused on: (1) the attitudes of the public on issues such as the number of police on the street, civil rights of minority groups, and the legal rights of people accused of serious crimes, (2) the randomized evaluation of preferred sentencing alternatives for eight different crime scenarios, (3) making parole decisions in a constrained choice setting by assuming that there is only enough space for one of two offenders, (4) the underlying factors that motivate the public's parole decisions, and (5) respondents' willingness to pay for various crime prevention strategies.