In 2022/23, the proportion of all adult offenders who reoffended in England and Wales was 26 percent. Since 2008/09 the reoffending rate has fluctuated between a high of 31.6 percent in 2008/09 to a low of 24 percent in 2020/21.

Statistics on the reoffending of adults under supervision of the Probation Service in England and Wales

Source: Ministry of Justice (MoJ)

Publisher: Ministry of Justice (MoJ)

Geographies: County/Unitary Authority, Police Force Area

Geographic coverage: England and Wales

Time coverage: 2007 to 2009

Type of data: Administrative data

In 2023, 47 percent of the people cleared for Penal Code offenses in Japan were repeaters. The rate of repeat offenders decreased for three consecutive years. Repeaters refer to the apprehended offenders who were previously arrested for other criminal cases, excluding traffic-related offenses.

In 2023, the number of repeat offenders arrested for stimulants-related crimes in Japan amounted to a total of 3,918. This continues a general downward trend after the highest number of the decade was reached in 2015 with 7,147 arrests.

description: This dataset reports whether an offender is re-admitted to prison or not within three years from their admission to probation. The recidivism reporting year is the fiscal year (year ending June 30) marking the end of the three year tracking period. The Department of Corrections uses recidivism as an indicator on whether strategies are reducing offenders relapse into criminal behavior. A three year time frame is used as studies have shown if an offender relapses into criminal behavior it is most likely to happen within three years.; abstract: This dataset reports whether an offender is re-admitted to prison or not within three years from their admission to probation. The recidivism reporting year is the fiscal year (year ending June 30) marking the end of the three year tracking period. The Department of Corrections uses recidivism as an indicator on whether strategies are reducing offenders relapse into criminal behavior. A three year time frame is used as studies have shown if an offender relapses into criminal behavior it is most likely to happen within three years.

Young people are those aged 10-17. The young offenders cohort is established during the period January to March and includes all those receiving a pre-court disposal (reprimand or final warning) or a first-tier or community penalty or who are released from custody. This cohort is then tracked for 12 months to determine the total number of offences committed by those in the cohort. A reoffence is counted if it occurs within the 12 month tracking period and leads to a pre-court disposal or a court conviction, even if the pre-court disposal or court conviction occurs outside the 12 month period. By knowing the total number of reoffences and the total number of young people in the cohort, the average rate of reoffending can be calculated. All offences will count even if two or more offences are grouped for sentencing purposes and result in only one pre-court disposal or court conviction.

The University of Pennsylvania, in collaboration with the Pennsylvania Board of Probation and Parole (PBPP), began developing a violent forecast model utilizing the machine learning procedure random forest. By the spring of 2013, the forecasts were provided to decision makers prior to parole interviews. The violent forecast model (VFM) measures the extent to which offenders are likely to reoffend as indicated by future arrest. The VFM is a violence classification forecast and not an individual case prediction regarding offender behavior. Purpose The purpose of this research was to evaluate the impact of introducing forecasts of "future dangerousness" into PBPP's decision making process during parole interviews. The researcher anticipated that having available a sufficiently reliable forecast, particularly within the violent category, would reduce the likelihood of a parole release. The null hypothesis tested was that there would be no difference in parole release decisions when comparing two similar groups of offenders where during one group of parole interviews the decision maker had a forecast available and the other group of interviews there was not a forecast available.

description: This dataset reports whether an offender is re-admitted to prison or not within three years from being released from prison in Iowa. The recidivism reporting year is the fiscal year (year ending June 30) marking the end of the three year tracking period. The Department of Corrections uses recidivism as an indicator on whether strategies are reducing offenders relapse into criminal behavior. A three year time frame is used as studies have shown if an offender relapses into criminal behavior it is most likely to happen within three years of being released.; abstract: This dataset reports whether an offender is re-admitted to prison or not within three years from being released from prison in Iowa. The recidivism reporting year is the fiscal year (year ending June 30) marking the end of the three year tracking period. The Department of Corrections uses recidivism as an indicator on whether strategies are reducing offenders relapse into criminal behavior. A three year time frame is used as studies have shown if an offender relapses into criminal behavior it is most likely to happen within three years of being released.

Provides information on repeat offenders such as unlicensed driving, running red lights, drunk (drug) driving, and dangerous driving exceeding the speed limit of 60 kilometers or more in Taipei City

In France in 2022, the legal recidivism and reoffending rate among those convicted of offenses was 17.5 percent. Between 2011 and 2021, the recidivism rate of offenders remained stable: between 11.3 and 14.6 percent. Nevertheless, in 2022 this figure increased significantly compared to the previous year.

NOTE TO USERS -- There may be disruption to this data set between March 19 to March 29 related to a upgrade. Please contact dsdopendata@austintexas.gov with questions.

City of Austin Open Data Terms of Use https://data.austintexas.gov/stories/s/ranj-cccq

The location and activity of "repeat offender properties" with respect to city code violations. To learn more about the Repeat Offender Program, see City's website: http://www.austintexas.gov/department/repeat-offender-program

NOTE TO USERS -- There may be disruption to this data set between March 19 to March 29 related to a upgrade. Please contact dsdopendata@austintexas.gov with questions. City of Austin Open Data Terms of Use https://data.austintexas.gov/stories/s/ranj-cccq The location, violation type, and description of deficiencies of properties registered in the Repeat Offender Property program. To learn more about the Repeat Offender Program, see City's website: http://www.austintexas.gov/department/repeat-offender-program

Increase the percentage of offenders re-entering the workforce with vocational training or certifications from 3.76% in 2013 to 6% by 2017.

In 2023, around 32.8 percent of about 27.6 thousand drug offenders in South Korea reoffended. The recidivism rate has been relatively stable over the past five years. Reoffending was least common among drug crimes related to narcotics.

NOTE TO USERS -- There may be disruption to this data set between March 19 to March 29 related to a upgrade. Please contact dsdopendata@austintexas.gov with questions.

City of Austin Open Data Terms of Use https://data.austintexas.gov/stories/s/ranj-cccq

This is a slimmed down view of Repeat Offender Registrations for the purposes of tabular display.

Link to complete dataset: https://data.austintexas.gov/City-Government/Repeat-Offender-Registrations/86z9-i27i

Using data from RECIDIVISM OF PRISONERS RELEASED IN 1994: UNITED STATES, this study developed an analytical approach to utilize detailed dated criminal arrest history information in order to investigate whether, and to what extent, incarceration is able to deter offenders from future offending. This data collection consists of the syntax for a SAS macro used to estimate individual specific offending micro-trajectories, project counterfactual trajectories, and to assess the actual post-release offending patterns against the backdrop of these counterfactuals. The arrest records of individuals were clustered in chronological order and were truncated after the first post-release re-arrest event. The key independent variables used in estimating the pre-release criminal history accumulation process included the arrest number, the age at first arrest, whether or not the individual was confined as a result of the previous arrest event, and a measure of the number of years taken to reach each arrest event cumulated through the last arrest event. The same set of basic variables were used to model first re-arrest after release (recidivism).

Number of convictions recorded against Prolific and other Priority Offenders.

In 2020, the recidivism rate of juvenile offenders from the 2017 cohort was at 12.2 percent. This was a decrease of almost two percent of the recidivism rate of the 2016 cohort. The recidivism rate of juvenile offenders in Singapore are measured in a three-year period.

CJA23 - One year Re-offenders under age 25 classified by re-offending offence and custodial indicator. Published by Central Statistics Office. Available under the license Creative Commons Attribution 4.0 (CC-BY-4.0).One year Re-offenders under age 25 classified by re-offending offence and custodial indicator...

The Serious and Violent Offender Reentry Initiative (SVORI) funded agencies to develop programs to improve criminal justice, employment, education, health, and housing outcomes for released prisoners. SVORI was a goal-oriented initiative that specified outcomes that should be achieved by programs that were developed locally. The original Multi-site Evaluation of SVORI funded under NIJ grant 2004-RE-CX-0002 included a quasi-experimental impact evaluation to determine the effectiveness of programming. Specifically, the purpose of the impact evaluation was to determine whether individuals who participated in enhanced reentry programming, as measured by their enrollment in SVORI programs, had improved post-release outcomes than comparable individuals who did not participate in SVORI programming. Impact evaluation data collection for both SVORI and non-SVORI participants consisted of four waves of in-person, computer-assisted interviews and oral swab drug tests conducted in conjunction with two of the follow-up interviews. The research team collected data on a total of 2,391 individuals including 1,697 adult males (Part 1), 357 adult females (Part 2), and 337 juvenile males (Part 3). As part of the impact evaluation, experienced RTI field interviewers conducted pre-release interviews with offenders approximately 30 days before release from prison and a series of follow-up interviews at 3, 9, and 15 months post-release. These data provided information on criminal history and recidivism occurring by December 31, 2007. The Adult Males Data (Part 1), Adult Females Data (Part 2), and the Juvenile Males Data (Part 3) each contain the same 5,566 variables from the 3 waves of offender interviews, 10 drug test lab results variables, and 3 weight variables. (Note: Some interview questions were only asked of adults, and other questions were only asked of juveniles.) Offender interview variables include demographics, housing, employment, education, military experience, family background, peer relationships, program operations and services, physical and mental health, substance abuse, crime and delinquency, and attitudes toward those topics. Under NIJ Grant 2009-IJ-CX-0010, the original Multi-site Evaluation of SVORI data were updated in order to examine the questions of, "What works, for whom, and for how long?" This included follow-up interview questions of those previously (and currently still) incarcerated. New variables derived from data collected under the original SVORI impact evaluation between 2004 and 2007 were also added to Part 3. Part one included an additional 100 variables, part two an additional 102 variables and part 3 an additional 99 variables.