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. 2023 Sep-Oct;88:102115. doi: 10.1016/j.jcrimjus.2023.102115

Criminal recidivism rates globally: A 6-year systematic review update

Denis Yukhnenko 1, Leen Farouki 1, Seena Fazel 1,⁎
PMCID: PMC10933794  PMID: 38486817

Abstract

Objectives

Previous work has shown that direct comparison of recidivism rates between jurisdictions without accounting for potential sources of their variation can be misleading. We aimed to systemically review data on recidivism rate internationally and explore sources of between-country variation.

Methods

We reviewed recidivism rates in individuals released from prison and given community sentences. We systematically searched peer-reviewed and gray literature focusing on publications since a systematic review in mid-2019. We extracted data on reoffending, reimprisonment, and re-arrests. To examine the association between index offences and recidivism rates, we calculated risk ratios. We used meta-regression to estimate the association between recidivism in released prisoners and country-level variables. We also summarised reported effects of the COVID-19 pandemic on recidivism rates.

Results

Recidivism data were found for 33 countries. Released prisoners had 2-year reconviction rates between 18% and 55%, while individuals given community sentences had rates between 10% and 47%. Recidivism rates varied based on proportions of index offences. Country-level factors like homicide, robbery, and imprisonment rates were associated with prisoner recidivism. Lower rates during COVID-19 were linked to disruptions in criminal justice processes, reduced prison populations, and fewer crime opportunities.

Conclusions

Interpreting recidivism rates requires considering individual and country-level factors. Transparent reporting of these factors is needed.

Keywords: Recidivism, Reoffending, Re-entry, Prison, Community sentence, Probation

1. Introduction

Criminal recidivism rates are often used as a key indicator of the effectiveness of criminal justice systems. In particular, they are used to decide what offender management programmes are rolled out widely (e.g., Robinson et al., 2021). Recidivism rates are routinely collected by most countries and reported by specific governmental agencies (see Federal Statistical Office, 2015; Ministry of Justice, 2023; SPAC, 2018). Recidivism is measured in different ways, and previous reviews have shown that the 2-year reconviction rate is most commonly reported outcome in individuals released from prisons (Yukhnenko, Sridhar, & Fazel, 2019) and those completing community sentences (Yukhnenko, Wolf, Blackwood, & Fazel, 2019).

Recidivism rates are primarily used to assess the performance of national and state justice systems or rehabilitation programmes over time. Governments and agencies often strive to implement policy and practice changes that lead to lower recidivism rates. However, reported recidivism rates are determined by many factors, many of which are not necessarily associated with the effectiveness of the rehabilitation and reintegration of sentenced individuals. These include the source of data, the definition of recidivism used, and the follow-up period (Andersen & Skardhamar, 2017; Yukhnenko, Sridhar, & Fazel, 2019). Recidivism rates should also be interpreted in the context of general crime rates that depend heavily on general economic and demographic factors (Anser et al., 2020). Given the complex, multicausal nature of reported recidivism rates, their use for comparison between countries and jurisdictions is often problematic. However, despite this, recidivism rates are frequently and inappropriately employed to compare the effectiveness of different approaches to criminal justice between different countries, especially by the media (BBC News, 2019; Browne, 2020). Such comparisons pose challenges due to the varying ways recidivism is operationalized, measured, and reported across different jurisdictions. Comparing recidivism rates between different countries is more likely to reflect variations in reporting practices or in other related factors than the meaningful differences in effectiveness of rehabilitation and reintegration programs for individuals released from prison or given community sentences. Understanding the factors that explain heterogeneity in between-country recidivism rates is important for policymakers, researchers and practitioners to ensure the effects of policy changes and practices are evaluated correctly. Moreover, understanding the factors contributing to reported recidivism rates across jurisdictions could assist in revising and improving how recidivism is reported, ensuring that it better captures outcomes of criminal justice agencies.

One of the factors contributing to recidivism rates that has not received adequate attention in the literature is the proportion of individuals with different index offences among the cohort of individuals released from prison or given community sentences. For instance, if most individuals released in a particular year have been sentenced for a highly recurrent offence (e.g., property crime), it would lead to an overall increase in the recidivism rate for the studiedcohort compared to a cohort where less people were sentenced for a property crime. Other potential contributors to reported recidivism rates include macroeconomic and general criminological factors within a country. Previous research has explored the relationship between crime rates, poverty, inequality, and economic growth (Anser et al., 2020; Gruszczyńska & Gruszczyński, 2023; Wolf, Gray, & Fazel, 2014). However, the connection between economic factors, specific crime rates, and recidivism rates remains unexamined. All of these factors exhibit significant variations between different countries and can also vary within the same country, which can dilute any direct effects of the criminal justice system on reported recidivism rate.

In the present study, we systematically reviewed studies on recidivism rates in individuals released from prison and those given community sentences. The primary aim was to provide a current overview of recidivism information and reporting practices. Our secondary aim was to examine possible explanations for the expected variations in the reported between-country recidivism rates. Potential explanatory factors include criminological variables in the analysed cohorts, such as differences in the proportions of index crimes, as well as country-level variables such as GDP per capita, incarceration rate, murder rate, robbery rate, and inequality as measured by Gini coefficient. Additionally, we summarized the effects of COVID-19 pandemic on reported recidivism rates.

2. Methods

This review is an update of three systematic reviews (Fazel & Wolf, 2015; Yukhnenko, Sridhar, & Fazel, 2019; Yukhnenko, Wolf, et al., 2019). We searched SAGE, MEDLINE, EMBASE, PsycINFO, PsycARTICLES, and Web of Science bibliometric databases using search terms related to criminal recidivism. The keywords included 50 countries with largest prison populations in absolute terms in 2022 (World Prison Brief, 2022) and a list of commonly reported outcomes (see Appendix A for exact search terms). Those would be the countries where successful interventions would have the greatest population impact.

We used Google Scholar and Google Web for subsequent targeted searches of gray literature. In addition, we scanned reference lists of included documents. If titles and abstract contained relevant keywords and did not contain any terms matching exclusion criteria, then the full-text publication was screened for appropriate data. If multiple reports were identified for the same country, we extracted the most recent data for a given outcome. If no new data for a given country were identified, we included studies from the previous reviews (Yukhnenko, Sridhar, & Fazel, 2019; Yukhnenko, Wolf, et al., 2019).

Studies for geographical regions within a country were included if national information was unavailable or dated. We followed the PRISMA guidelines (see the checklist in Supplementary material), and a corresponding flow chart is provided in Appendix B.

We included cohorts where data on reconviction, re-arrest, and re-imprisonment rates in released prisoners and/or individuals given community sentences were reported. We excluded studies that focused on recidivism in selected populations (such as young offenders or sex offenders) and intervention studies. We also excluded studies where the outcome definition was unclear or not reported. We extracted recidivism data separately for released prisoners and individuals given community sentences.

LF and DY conducted the search and independently extracted the data on country, sample selection, definitions of outcomes and rates. Included studies were accessed using the NIH Quality Assessment Tool for Before-After (Pre-Post) Studies With No Control Group (NIH, 2021). Uncertainties were checked with SF. Publications in languages other than English were translated.

To examine the association between different index offences and recidivism, we extracted relevant data from studies that reported recidivism rates for different groups of index offences and provided corresponding cohort sizes. As such data were only available for mixed cohorts of individuals (i.e., combining released prisoners and community sentences), we did not separate this analysis by sentence type. We extracted recidivism rates in individuals sentenced for the most commonly reported offence categories: violent, sexual, property, drugs, and traffic. For all included cohorts, if an individual was sentenced for several crimes, the most serious index crime was used. Prior criminal history was not taken into account. As the violent crime category had the most consistent definition, we used it as the reference category to calculate risk ratios (RRs) for other types of index offences. We pooled the RRs within the same index offence category by applying the Mantel-Haenszel method for random-effects estimation using the meta package for R (Balduzzi, Rücker, & Schwarzer, 2019).

To explore potential sources of variation in reported recidivism rates in prisoners, for a given year of their release, we extracted country-level variables for studies that reported 2-year reconviction rate, which was the most reported recidivism outcome. We additionally searched governmental reports and international agencies' website for the corresponding information. The extracted variables included incarceration rates, murder rate, robbery rate, Gini index, and GDP per capita. Our analyses were limited to countries where 2-year reconviction data, cohort sizes, and corresponding country-level variables were available. The 2-year reconviction rate was chosen as it was the most commonly reported outcome. These were based on previous studies that have shown ecological associations with crime and their reliable reporting for most countries (Wolf et al., 2014). The variables can be interpreted as proxies for the general socioeconomic situation and criminal justice in a particular country. To estimate the association of the country-level variables with the reported recidivism rate, we fitted a series of univariate meta-regression models using the weighted least squares method. The regression was implemented with statsmodels library for Python (Seabold & Perktold, 2010). To additionally examine the association between crime rates and recidivism within a single region, we extracted the rates of general, violent, and property crime for Nordic countries for the same reporting year.

3. Results

We identified 37 new publications from 33 countries and territories reporting on recidivism among released prisoners and those given community sentences that met the specified inclusion criteria. We identified 5 additional publications from Germany, Italy, Iceland, Norway, and Sweden compared with the previous review (Yukhnenko, Wolf, et al., 2019). Ten of the 50 countries with the largest prison populations had recidivism data meeting inclusion criteria (Argentina, Australia, Brazil, Canada, Chile, England and Wales, France, Germany, Italy, and South Korea). All newly identified data were published by governmental agencies, except for one source (Morgan and Morgan, 2019). The extracted data, outcome definition, and other relevant information for each individual country are presented in supplementary Vignettes. In addition, during screening, 23 studies that reported recidivism data using cross-sectional methods were identified. These did not meet inclusion criteria and excluded from the analysis (see references to these sources in Appendix C).

Recidivism data for people released from prison were available for 33 countries. A two-year reconviction was the most commonly reported outcome. In released prisoners, 2-year reconviction rates ranged from 17.6% in Norway to 54.9% in Australia (Table 1). For community-sentenced individuals, data were available for 20 countries, with a two-year reconviction being most commonly reported. The 2-year reconviction rates for community sentenced individuals ranged from 9.7% in Chile to 46.6% in Denmark (Table 2). The reimprisonments rates are presented in Appendix D.

Table 1.

Reconviction rates in individuals released from prison
Length of the follow-up period (years)
Country Year of release Cohort size 1 2 3 4 5 Publication
Europe
Nordic countries
Denmark* 2018 2710 32.0 Kristoffersen (2022)
Finland* 2018 2776 33.0 Kristoffersen (2022)
Iceland* 2018 151 21.2 Kristoffersen (2022)
Norway* 2018 4509 17.6 Kristoffersen (2022)
Sweden* 2018 7959 32.0 Kristoffersen (2022)
Sweden* 2019 NA 43.0 National Council for Crime Prevention (2022)
The United Kingdom
England and Wales 2020 48,843 38.9 Ministry of Justice (2022)
Northern Ireland 2018–2019 1309 44.9 Department of Justice (2021)
Scotland** 2018–2019 5549 43.8 Scottish Government (2021)
Other
Austria 2017 6607 13.5 24.3 30.3 34.7 Statistics Austria (2023)
Estonia 2015–2017 NA 32.0 Ahven et al. (2019)
Ireland, Republic of 2019 4026 44.6 Central Statistics Office Ireland (2022a)
Ireland, Republic of 2016 2626 62.3 Central Statistics Office Ireland (2022a)
Germany 2007 26,602 46.0 Jehle (2014)
France 2016 NA 32.9 45.4 Ministère de la Justice (2022)
Latvia 2009 NA 51.0 Kipena, Zavackis, & Nikisins (2012)
Netherlands 2017 23,302 18.8 24.6 Ministry of Justice (2017)
Switzerland 2016 1393 44.7 Federal Statistical Office (2018)
Poland 2012 30,899 16.6 28.3 35.9 39.9 40.4 Jaki (2018)



Asia
Malaysia 2017 NA 9.0 Wahab (2019)
Taiwan 2014 NA 27.4 42.7 51,3 56.6 Tsai and Wu (2022)
Taiwan 2015 NA 28.7 41.2 52.4 Tsai and Wu (2022)
Taiwan 2016 NA 30.9 52.0 Tsai and Wu (2022)
Taiwan 2017 NA 31.0 Tsai and Wu (2022)
Singapore 2019 10,570 19.1 SPS (2021)



Oceania
Australia 2019–2020 NA 54.9 Australian Government (2021)
New Zealand 2020–2021 NA 36.0 Department of Corrections (2022)



South America
Chile 2011 20,867 39.1 Gendarmería de Chile (2016)



North America
Canada (federal) 2011–2012 8893 17.1 27.9 35.4 43.2 Stewart, Wilton, Baglole, & Miller (2019)
Canada (Ontario) 2015–2016 NA 37.0 Government of Ontario (2021)
USA (33 states) 2012 408,300 36.8 52.9 61.5 67.0 70.8 U.S. Department of Justice (2021)
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The follow-up period for Latvia is 29 months. Data reported for cohorts aged 18 and older unless indicated otherwise. *Reported for cohorts aged 15 and older.

Table 2.

Reconviction rates in community sentenced individuals
Length of the follow-up period (years)
Country Year of release Cohort size 1 2 3 4 5 Publication
Europe
Nordic countries
Denmark* 2018 7387 45.6 Statistics Denmark (2023)
Finland* 2005 3767 25.6 Graunbøl et al. (2010)
Iceland* 2005 73 16.4 Graunbøl et al. (2010)
Norway* 2005 2839 19.8 Graunbøl et al. (2010)
Sweden* 2008 22,306 23.8 32.8 38.1 National Council for Crime Prevention (2017)
The United Kingdom
England and Wales 2020 50,136 28.0 Ministry of Justice (2022)
Northern Ireland 2018–2019 3308 21.2 Department of Justice (2021)
Scotland** 2018–2019 27,210 25.2 Scottish Government (2021)
Other
Austria 2017 10,636 11.2 21.3 27.3 31.6 Statistics Austria (2023)
Czech Republic* 2012 4233 48.1 Tomášek and Rozum (2018)
France 2004 241,999 9.1 18.1 25.2 34.2 Ministère de la Justice (2013)
Ireland, Republic of 2018 4999 28.0 Central Statistics Office Ireland (2022b)
Ireland, Republic of 2017 4909 29.0 41.0 Central Statistics Office Ireland (2022b)
Ireland, Republic of 2016 4447 31.0 43.0 48.0 Central Statistics Office Ireland (2022b)
Germany 2007 96,521 39.0 Jehle (2014)
Latvia 2009 1190 17.0 Kipena et al. (2012)
Netherlands 2017 36,095 38.8 30.3 Ministry of Justice (2017)



Oceania
Australia 2019–2020 NA 16.1 Australian Government (2021)
New Zealand 2020–2021 NA 19.0 Department of Corrections (2022)



South America
Chile 2011 36,895 9.7 Gendarmería de Chile (2016)
Brazil 2015 NA 23.9 Conselho Nacional De Justica Brazil (2020)



North America
Canada (Ontario) 2015–2016 NA 23.0 Government of Ontario (2021)
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The follow-up period for Latvia is 29 months. Data reported for cohorts aged 18 and older unless indicated otherwise. *Reported for cohorts aged 15 and older. ** Reported for cohorts aged 21 and older.

We identified seven studies that provided recidivism data in individuals stratified by index offence (Fig. 1). None of these reported index offence data separately by prison and community sentences. Compared to individuals sentenced for violent offences, individuals sentenced for property offences had a higher recidivism rate (pooled RR = 1.49, 95% CI: 1.21–1.85). Individuals sentenced for a drug offence had comparable recidivism rates with those sentenced for violence with variations between countries (pooled RR = 1.11, 95% CI: 0.83–1.47). Individuals sentenced for sexual and traffic offences had lower recidivism rates compared to those sentenced for violent offences (sexual offences: pooled RR = 0.53, 95% CI: 0.47–0.61; traffic offences: pooled RR = 0.67, 95% CI: 0.48–0.92).

Fig. 1.

Fig. 1

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Recidivism risk in individuals sentenced for different index offences compared to individuals sentenced for violent crimes.

Data were from combined cohorts of individuals (both released prisoners and community sentenced individuals). RR = relative risk; rec = proportion who recidivated.

For 11 countries, for which country-level data on the 2-year reconviction rates and cohort sizes were available, we extracted country-level variables, reflecting economic output, income inequality, and reported crime rates (Table 3). The results of the univariate meta-regressions showed that the 2-year reconviction rate had a significant positive association with the homicide rate, robbery rate, and imprisonment rate (Fig. 2, Appendix E). The Gini index showed a weak association with the 2-year reconviction rate, which was not statistically significant. Two-year reconviction rates were not associated with GDP per capita and population size. For four Nordic countries (Denmark, Finland, Sweden, and Sweden), for 2018 reporting year, we extracted the information on country-level criminal rates (Table 4). Scandinavian countries and Finland had comparable homicide rates; however, their property and drug crime rates varied substantially.

Table 3.

Country-level variables for meta-regression extracted for countries that reported 2-year reconviction rates in released prisoners

Country Year Cohort size 2-year reconviction (%) Imprisonment per 100,000 Homicide per 100,000 GDP per capita (USD) Gini index Robbery per 100,000 Population (mlllion)
Austria 2017 6607 24.3 98 0.7 47,429 27.2 24.1 8.8
Denmark 2018 4909 32.0 65 0.8 61,592 28.2 31.6 5.8
Finland 2018 2776 33.0 53 1.2 49,988 27.3 25.3 5.5
Iceland 2018 151 21.2 37 0.9 74,461 26.1 14.0 0.3
Norway 2018 4509 17.6 65 1.2 82,268 27.6 58.0 5.3
Netherlands 2017 23,302 24.6 59 0.8 48,675 28.5 47.0 17.1
Poland 2012 30,899 28.3 221 1.1 13,011 33.5 43.0 37.8
Singapore 2019 10,570 19.1 199 0.2 65,831 45.2 0.9 5.7
Chile 2011 20,867 39.1 329 3.7 14,629 46.0 535.0 17.2
Canada 2011 8893 27.9 117 1.8 52,224 33.6 86.0 38.3
USA 2012 408,300 32.1 707 4.7 51,784 40.9 113.0 331.9
Sweden 2018 7959 32.0 64 1.1 54,589 30.0 82.7 10.2
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Homicide includes murder (intentional homicide) and manslaughter (unintentional homicide). Sources for extracted indices per country are available in Vignettes.

Fig. 2.

Fig. 2

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The univariate association between country-level variables and 2-year reconviction rate.

The country-variables were extracted for 11 countries for a year of recidivism data reporting or, if data were unavailable, for the closest year: Austria (year of reporting: 2017), Denmark (2018), Sweden (2018), Finland (2018), Iceland (2018), Norway (2018), Netherlands (2017), Poland (2012), Singapore (2019), Chile (2011), Canada (2011), USA (2012). The standardised beta-coefficients were estimated with univariate weighted least squares regression, where weights were inverse variance of 2-year reconviction rates. R2 = coefficient of determination.

Table 4.

Country-level general and specific crime rates and 2-year reconviction rates in released prisoners in Scandinavian countries and Finland in 2018

Country 2-year reconviction rate (%) Crime per 100,000 Homicide per 100,000 Property crime per 100,000 Drug and alcohol crime per 100,000 Population (mlllion)
Finland 32.0 8041 1.6 3752.0 872.8 5.52
Norway 17.6 6004 1.2 1753.6 863.0 5.30
Sweden 33.0 15,157 1.1 4350.3 1041.2 102.3
Denmark 32.0 8213 1.01 5295.0 299.0 5.79
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Reconviction rates are reported according to University College of Norwegian Correctional Service (Kristoffersen, 2022). Homicide includes murder (intentional homicide) and manslaughter (unintentional homicide). Drug and alcohol related crime include drug trafficking, smuggling, selling, illegal possession, and driving under influence. Sources for extracted indices per country are available in Vignettes.

We additionally identified reports from nine countries that explored the impact of the COVID-19 pandemic and related restrictions on reported recidivism rates. All identified reports noted a decrease in recidivism during the pandemic likely caused by delay in court processing time and decreased opportunities to commit crime during lockdown.

Most identified studies were of good or fair quality as measured by the NIH Quality Assessment Tool (Appendix F). The most common problem with included studies was the absence of reported cohort sizes, which makes it not possible to reliably pool data.

4. Discussion

This systematic review synthesises criminal recidivism rates in individuals released from prison and those given community sentences from 36 studies based on around 1.4 million individuals. Only 10 out of 50 countries with the largest prison populations reported recidivism statistics. We found that 2-year reconviction was most commonly reported outcome for both populations. We examined the association between recidivism rates and different country-level determinants including the proportions with different index offences, and markers of economic output, income inequality, and general crime rates. This updated review has four main findings.

First, reported recidivism rates are generally high across most countries, with at least one in five individuals reoffending within two years. In some countries, recidivism rates exceed 40% after one year. This presents a significant societal burden in terms of public safety, healthcare, and associated costs. Recidivists are estimated to be responsible for a considerable proportion of all offences committed in any given year. In the US, repeat offending contributed to 20% of all offences (Petersilia, 2011).

Second, we identified new potential sources of variation between recidivism rates in sentenced prisoners. Previous research indicated that reported recidivism rates are sensitive to several measurement variables, including definitions, length and type of the follow-up (Andersen & Skardhamar, 2017; Yukhnenko, Sridhar, & Fazel, 2019). We found that property offences were consistently associated with the largest relative recidivism risk. Therefore, reported and detected property crimes could account for a significant portion of reported recidivism rates and a large proportion of property offenders in any cohort would increase recidivism rates.

Our results further suggest that higher imprisonment rates, robbery rates, and homicide rates within a country were associated with higher reported reconviction rates in people released from prison. Imprisonment rates may reflect the overall level of a country's criminalisation, which could increase rates of repeated crime in released individuals. Serious violent crime rates, especially homicide rates, could be used as indicators of general level of crime within a country as they have high levels of reporting and clearance (Lehti et al., 2019). In other words, the more criminogenic a society is, the higher the recidivism rates (given other factors are held constant).

The substantial contribution of property crime to overall recidivism rates may partially explain the low reported recidivism rates in Norway. All four Scandinavian countries have similar levels of serious violent crime, as indicated by similar homicide rates. We can hypothesise that the low recidivism rates in Norway, when compared to neighbouring countries, are at least partially due to low reported non-violent crime rates. This may be attributed to either true low property-related criminality, decreased reporting and detectability, or lower rates of investigation and prosecution of low-level crime.

Third, while more countries have started reporting recidivism rates in recent years, it is remains problematic to draw conclusions from them about a prison and probation system' effectiveness at rehabilitation. However, some countries, such as England and Wales, Republic of Ireland, Austria, and Australia, regularly provide detailed statistical reports on various aspects of their legal and prison systems that allow for evaluating different factors contributing to recidivism rates. These factors include reported and investigated crime, number of arrests, charges brought, court load, index sentence data, processing times, and data collection and reporting practices. This approach should be extended to other countries.

Fourth, during the COVID-19 pandemic, recidivism rates initially declined in most countries, but rebounded after the lifting of restrictions. Several factors contributed to the declines, including pandemic-related delays in reporting, processing, and data collection by reporting agencies, police, and courts. Jurisdictions also sought to reduce prison populations by deferring sentences, imposing non-custodial sentences, or reducing sentences for low-risk prisoners. Lockdowns and social distancing also limited opportunities for committing certain types of crimes, resulting in a direct reduction in recidivism events.

Overall, these findings highlight the methodological difficulties associated with recidivism reporting and comparative analysis. Even within one jurisdiction, recidivism rates are sensitive to many systemic factors associated with the police, court system, and reporting agencies. Accounting for such factors between jurisdictions requires careful and detailed analysis that needs to consider more than recidivism rates. Reporting agencies could aid in such analysis by routinely providing detailed reports using best practices (see Yukhnenko et al. (2019) for recommendations) and by creating flexible open data tools.

4.1. Strengths and limitations

This is the first study to systematically review recidivism rates in general populations of both released prisoners and individuals given community sentences. Studies included in the review were generally of high quality and were conducted using large samples. A novel aspect was investigating economic output, inequality, incarceration rates, and general crime rates as potential sources of variation for reconviction in released prisoners using between country comparisons. We were also able to quantitatively examine the relative risk associated with different types of index offences in the cohorts of sentenced individuals.

The substantial heterogeneity of the cohorts and outcome definitions did not allow for direct quantitative comparison of recidivism rates. Furthermore, the community sentenced cohorts differed from each other with regards to the nature of the supervision involved. For example, community supervision with mandated treatment is implemented in some countries. The estimated association between recidivism and country-level variables should also be interpreted with caution due to limited data availability for many countries. Consequently, findings may not necessarily apply to other jurisdictions. In addition, recidivism rates in those given community sentences should not be directly compared with released prisoners even within the same jurisdiction due to substantial differences between these two groups, such as proportion of index crimes within the cohorts and other background factors.

5. Conclusion

Recidivism rates need to be interpreted within the broader context of factors related to legal and criminal justice systems. Governmental agencies reporting these data must make efforts to regularly provide detailed and transparent background criminological data, facilitating independent analysis and pooling of results. The use of recidivism rates for international comparisons should be avoided until sufficient analysis of the underlying factors contributing to reported rates has been conducted.

Funding

This study was supported by Wellcome Trust [grant #202836/Z/16/Z to SF].

Declaration of Competing Interest

None.

Acknowledgements

We are grateful to Ragnar Kristoffersen of the University College of Norwegian Correctional Service for helpful insights on recidivism rate variation.

Footnotes

Appendix G

Supplementary data to this article can be found online at https://doi.org/10.1016/j.jcrimjus.2023.102115.

Appendix A. Terms and search conditions used for systematic search in publication databases

Search on SAGE, MEDLINE, PsychINFO, EMBASE, PsycARTICLES, and Web Science from January 1, 2019, to February 2, 2023, with no language restrictions: prisoners AND (prevalence OR rate*) AND (recidivis* OR reoffend* OR reconviction) AND (Algeria OR Argentina OR Australia OR Bangladesh OR Brazil OR Cambodia OR Canada OR Chile OR China OR Colombia OR Cuba OR Egypt OR “El Salvador” OR Ethiopia OR France OR Germany OR India OR Indonesia OR Iran OR Iraq OR Italy OR Japan OR Kazakhstan OR Kenya OR Malaysia OR Mexico OR Morocco OR (Myanmar OR Burma) OR Nigeria OR Pakistan OR Peru OR Philippines OR Poland OR “South Korea” OR Russia* OR Rwanda OR Saudi Arabia OR South Africa OR Spain OR Taiwan OR Tanzania OR Thailand OR Turkey OR Turkmenistan OR Uganda OR Ukraine OR (“United Kingdom” OR “UK” OR “England and Wales”) OR (“United States of America” OR “United States” OR USA) OR Venezuela OR Vietnam).

Appendix B. PRISMA 2020 flow diagram for updated systematic reviews which included searches of databases, registers and other sources

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Appendix C. Identified studies and reports that did not satisfy the inclusion criteria

The studies used cross-sectional data to estimate recidivism in prisoners. The most common definition – the prevalence of individuals that were sentenced for the second or more time among all sentenced individuals in a given reporting period.

Country Prevalence (%) Reporting period Publication Page with data
Reporting method does not fit inclusion criteria
Bahamas 46.0 2016–2019 Bergman, Seepersad, Safranoff, & Cafferata (2020) p.122
Barbados 60.0 2016–2019 Bergman et al. (2020) p.122
Fiji 2.5 2018–2019 Fiji Corrections Service (2021) p. 11
Guyana 34.0 2016–2019 Bergman et al. (2020) p.122
Suriname 46.0 2016–2019 Bergman et al. (2020) p.122
Trinidad and Tobago 49.0 2016–2019 Bergman et al. (2020) p.122
Mexico 25.9 2016 Instituto Nacional de Estadística y Geografía (2016) p.17
Colombia 20.4 2021 Corporación Excelencia en la Justicia (2023) web page
Paraguay 40.0 2019 Mecanismo Nacional de Prevención de la Tortura (2019) web page
Argentina 24.0 2020 Ministerio de Justicia y Derechos Humanos (2020) p.41
Romania 37.3 2021 Ministerul Justitiei (2021) p.5
Sri Lanka 22.6 2020 Statistics Division Prison Headquarters (2021) p.45
Ghana 22.2 2011 Antwi (2015) p.106
Brunei 53.0 N/A Mundia, Matzin, Mahalle, Hamid, and Osman (2016) p. 746
Indonesia 1.0 2006 Thailand Institute of Justice (2016) p. 3–31
India 4.7 2020 National Crime Records Ministry of Home Affairs, 2020 p.130
Papua New Guinea 11.0 2019 Morgan and Morgan (2019) p.23
No definition provided
Albania 24.0 2019 United Nations Office on Drugs and Crime (2022) p.16
Czechia 66.0 2019 United Nations Office on Drugs and Crime (2022) p.16
Thailand 24.0 2016 United Nations Office on Drugs and Crime (2022) p. 16
Unable to locate original source
USA (nationally) 66.7 N/A Antwi (2015) p.3
South Africa 47.0 N/A Antwi (2015) p.3
Malta 17.0 N/A Camilleri (2016) n/a
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Appendix D. Reimprisonment rates in individuals released from prison
Length of the follow-up period (years)
Country Year of release Cohort size 1 2 3 4 5
Europe
Other
Austria 2017 3819 46.6 Statistics Austria (2023)
Belgium 2003–2005 1175 62.3 Institut National de Criminalistique (2012)



Asia
Israel 2014 6897 10.4 19.0 26.7 33.1 39.2 Vaknin & Ben-Zvi (2021)
Israel 2015 6766 10.6 19.4 27.1 34.3 Vaknin & Ben-Zvi (2021)
Israel 2016 6549 10.4 19.6 27.9 Vaknin & Ben-Zvi (2021)
Israel 2017 6075 12.2 22.5 Vaknin & Ben-Zvi (2021)
Israel 2019 6329 12.7 Vaknin & Ben-Zvi (2021)
Japan 2017 NA 9.0 Statistics Bureau of Japan (2017)
Thailand 2016 NA 35.4 Thailand Institute of Justice (2021)
Philippines* 2020 84,133 17.5 (Bureau of Jail Management and Penology, personal communication, October 20, 2022)
South Korea 2017 30,702 24.6 Korean Government (2022)
Switzerland 2016 1393 26.1 Federal Statistical Office (2018)



Oceania
Australia 2019–2020 NA 46.0 Australian Government (2021)
New Zealand 2020–2021 NA 22.0 Department of Corrections (2022)



South America
Brazil 2015 NA 42.5 Conselho Nacional De Justica Brazil (2020)



North America
Jamaica 2014 NA 30.0 Department of Correctional Services (2017)



Reimprisonment rates in individuals given community sentences
Length of the follow-up period (years)
Country Year of release Cohort size 1 2 3 4 5 Publication
Europe
Other
Austria 2017 6652 26.2 Statistics Austria (2023)
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All data reported for cohorts aged 18 and older. *The outcome for 1 year and 9 months.

Appendix E. The association between the country level variables and the 2-year reconviction rates estimated by meta-regression

Country-level variable β-coefficient Standard error Intercept R2 t-value p-value
Imprisonments per 100,000 individuals 0.267439 0.109978 −0.038063 0.371598 2.431744 0.035345
Homicides per 100,000 individuals 0.351869 0.101627 −0.099839 0.545206 3.462368 0.006099
Robberies per 100,000 individuals 0.628735 0.217752 0.441990 0.454654 2.887383 0.016180
Gross Domestic Product per capita (in USD) −0.264347 0.282821 0.519535 0.080344 −0.934681 0.371973
Population (millions) 0.206573 0.101737 0.055140 0.291924 2.030463 0.069751
Gini index 0.500521 0.236522 0.089120 0.309306 2.116174 0.060411
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The regression was performed on the panel of 11 reports from 11 countries. The weighed least squares method was used for meta-regression with inverse variance as weights.

Appendix F. Quality assessment of included studies using the NIH Quality Assessment Tool for Before-After (Pre-Post) Studies With No Control Group

Criterion Country
Australia Austria Belgium Brazil Canada Czech Republic Chile
1. Was the study question or objective clearly stated? ✓ ✓ ✓ ✓ ✓ ✓ ✓
2. Were eligibility/selection criteria for the study population prespecified and clearly described? ✓ ✓ ✓ ✓ ✓ ✓ ✓
3. Were the participants in the study representative of those who would be eligible for the test/service/intervention in the general or clinical population of interest? ✓ ✓ ✓ ✓ ✓ ✓ ✓
4. Were all eligible participants that met the prespecified entry criteria enrolled? ✓ ✓ ✓ ✓ ✓ ✓ ✓
5. Was the sample size sufficiently large to provide confidence in the findings? Unknown ✓ ✓ ✓ ✓ ✓ Unknown
6. Was the test/service/intervention clearly described and delivered consistently across the study population? ✓ ✓ ✓ ✓ ✓ ✓ ✓
7. Were the outcome measures prespecified, clearly defined, valid, reliable, and assessed consistently across all study participants? ✓ ✓ ✓ ✓ Partially ✓ ✓
8. Were the people assessing the outcomes blinded to the participants' exposures/interventions? ✘ ✘ ✘ ✘ ✘ ✘ ✘
9. Was the loss to follow-up after baseline 20% or less? Were those lost to follow-up accounted for in the analysis? ✓ ✓ ✓ ✓ ✓ ✓ ✓
10. Did the statistical methods examine changes in outcome measures from before to after the intervention? Were statistical tests done that provided p values for the pre-to-post changes? NA NA NA NA NA NA NA
11. Were outcome measures of interest taken multiple times before the intervention and multiple times after the intervention (i.e., did they use an interrupted time-series design)? ✘ ✓ ✓ ✓ ✓ ✓ ✘
12. If the intervention was conducted at a group level (e.g., a whole hospital, a community, etc.) did the statistical analysis take into account the use of individual-level data to determine effects at the group level? Partially Partially Partially Partially Partially Partially Partially
Overall rating Fair (main problem: the cohort sizes are absent) Good Good Good Good Good Fair (main problem: the cohort sizes are absent)
Source Australian Government (2021) Statistics Austria (2023) Institut National de Criminalistique (2012) Conselho Nacional De Justica Brazil (2020) Stewart, Wilton, Baglole, & Miller (2019) Tomášek and Rozum (2018) Gendarmería de Chile (2016)



Criterion Country
Denmark Estonia Finland France Ireland, Republic of
1. Was the study question or objective clearly stated? ✓ ✓ ✓ ✓ ✓ ✓
2. Were eligibility/selection criteria for the study population prespecified and clearly described? ✓ ✓ ✓ ✓ ✓ ✓
3. Were the participants in the study representative of those who would be eligible for the test/service/intervention in the general or clinical population of interest? ✓ ✓ ✓ ✓ ✓ ✓
4. Were all eligible participants that met the prespecified entry criteria enrolled? ✓ ✓ ✓ ✓ ✓ ✓
5. Was the sample size sufficiently large to provide confidence in the findings? ✓ ✓ Unknown ✓ Unknown ✓
6. Was the test/service/intervention clearly described and delivered consistently across the study population? ✓ ✓ ✓ ✓ ✓ ✓
7. Were the outcome measures prespecified, clearly defined, valid, reliable, and assessed consistently across all study participants? ✓ ✓ ✓ ✓ ✓ ✓
8. Were the people assessing the outcomes blinded to the participants' exposures/interventions? ✘ ✘ ✘ ✘ ✘ ✘
9. Was the loss to follow-up after baseline 20% or less? Were those lost to follow-up accounted for in the analysis? ✓ ✓ ✓ ✓ ✓ ✓
10. Did the statistical methods examine changes in outcome measures from before to after the intervention? Were statistical tests done that provided p values for the pre-to-post changes? NA NA NA NA NA NA
11. Were outcome measures of interest taken multiple times before the intervention and multiple times after the intervention (i.e., did they use an interrupted time-series design)? ✘ ✓ ✘ ✘ ✓ ✓
12. If the intervention was conducted at a group level (e.g., a whole hospital, a community, etc.) did the statistical analysis take into account the use of individual-level data to determine effects at the group level? ✘ Partially ✘ ✘ ✘ Partially
Overall rating Fair (main problem: few details about methods and data source) Good Fair (main problems: no cohort sizes, no detailed breakdown of the cohort) Fair (main problem: few details about methods and data source) Fair (main problem: no cohort sizes) Good
Source Kristoffersen (2022) Statistics Denmark (2023) Ahven et al. (2019) Kristoffersen (2022) Ministère de la Justice (2022) Central Statistics Office Ireland, 2022a, Central Statistics Office Ireland, 2022b



Criterion Country
Iceland Israel Jamaica Japan Korea, Republic of Malaysia
1. Was the study question or objective clearly stated? ✓ ✓ ✓ ✘ ✘ ✘
2. Were eligibility/selection criteria for the study population prespecified and clearly described? ✓ ✓ ✓ ✓ ✓ ✓
3. Were the participants in the study representative of those who would be eligible for the test/service/intervention in the general or clinical population of interest? ✓ ✓ ✓ ✓ ✓ ✓
4. Were all eligible participants that met the prespecified entry criteria enrolled? ✓ ✓ ✓ Unknown Unknown Unknown
5. Was the sample size sufficiently large to provide confidence in the findings? ✓ ✓ ✓ ✓ ✓ ✓
6. Was the test/service/intervention clearly described and delivered consistently across the study population? ✓ ✓ ✓ ✓ ✓ ✓
7. Were the outcome measures prespecified, clearly defined, valid, reliable, and assessed consistently across all study participants? ✓ ✓ ✓ ✓ ✓ ✓
8. Were the people assessing the outcomes blinded to the participants' exposures/interventions? ✘ ✘ ✘ ✘ ✘ ✘
9. Was the loss to follow-up after baseline 20% or less? Were those lost to follow-up accounted for in the analysis? ✓ ✓ ✓ ✓ ✓ ✓
10. Did the statistical methods examine changes in outcome measures from before to after the intervention? Were statistical tests done that provided p values for the pre-to-post changes? NA NA NA NA NA NA
11. Were outcome measures of interest taken multiple times before the intervention and multiple times after the intervention (i.e., did they use an interrupted time-series design)? ✘ ✓ ✘ ✘ ✓ ✓
12. If the intervention was conducted at a group level (e.g., a whole hospital, a community, etc.) did the statistical analysis take into account the use of individual-level data to determine effects at the group level? ✘ Partially ✘ ✘ ✘ ✘
Overall rating Fair (main problem: few details about methods and data source) Good Fair (main problems: no subgroup analyses, lack of data) Poor (excerpt from conference proceedings, few details) Fair (main problems: no subgroup analyses, lack of data) Fair (main problems: no subgroup analyses, lack of data)
Source Kristoffersen (2022) Vaknin & Ben-Zvi (2021) Department of Correctional Services (2017) Morgan and Morgan (2019) Korean Government (2022) Wahab (2019)



Criterion Country
Netherlands New Zealand Norway Poland the Philippines
1. Was the study question or objective clearly stated? ✓ ✓ ✓ ✓ ✓
2. Were eligibility/selection criteria for the study population prespecified and clearly described? ✓ ✓ ✓ ✓ ✓
3. Were the participants in the study representative of those who would be eligible for the test/service/intervention in the general or clinical population of interest? ✓ ✓ ✓ ✓ ✓
4. Were all eligible participants that met the prespecified entry criteria enrolled? ✓ ✓ ✓ ✓ ✓
5. Was the sample size sufficiently large to provide confidence in the findings? ✓ Unknown ✓ ✓ ✓
6. Was the test/service/intervention clearly described and delivered consistently across the study population? ✓ ✓ ✓ ✓ ✓
7. Were the outcome measures prespecified, clearly defined, valid, reliable, and assessed consistently across all study participants? ✓ ✓ ✓ ✓ ✓
8. Were the people assessing the outcomes blinded to the participants' exposures/interventions? ✘ ✘ ✘ ✘ ✘
9. Was the loss to follow-up after baseline 20% or less? Were those lost to follow-up accounted for in the analysis? ✓ ✓ ✓ ✓ ✓
10. Did the statistical methods examine changes in outcome measures from before to after the intervention? Were statistical tests done that provided p values for the pre-to-post changes? NA NA NA NA NA
11. Were outcome measures of interest taken multiple times before the intervention and multiple times after the intervention (i.e., did they use an interrupted time-series design)? ✓ ✘ ✘ ✓ ✘
12. If the intervention was conducted at a group level (e.g., a whole hospital, a community, etc.) did the statistical analysis take into account the use of individual-level data to determine effects at the group level? Partially Partially ✘ Partially ✘
Overall rating Good Fair (main problem: the cohort sizes are absent) Fair (main problem: few details about methods and data source) Good Fair (main problems: lack of data)
Source Ministry of Justice (2017) Department of Corrections (2022) Kristoffersen (2022) Jaki (2018) Not published. Obtained from Bureau of Criminal Justice and Penology of the Philippines



Criterion Country
Sweden Switzerland Taiwan Thailand UK: England and Wales
1. Was the study question or objective clearly stated? ✓ ✓ ✓ ✓ ✘ ✓
2. Were eligibility/selection criteria for the study population prespecified and clearly described? ✓ ✓ ✓ ✓ ✓ ✓
3. Were the participants in the study representative of those who would be eligible for the test/service/intervention in the general or clinical population of interest? ✓ ✓ ✓ ✓ ✓ ✓
4. Were all eligible participants that met the prespecified entry criteria enrolled? ✓ ✓ ✓ ✓ Unknown ✓
5. Was the sample size sufficiently large to provide confidence in the findings? ✓ ✓ ✓ Unknown Unknown ✓
6. Was the test/service/intervention clearly described and delivered consistently across the study population? ✓ ✓ ✓ ✓ ✓ ✓
7. Were the outcome measures prespecified, clearly defined, valid, reliable, and assessed consistently across all study participants? ✓ ✓ ✓ ✓ ✓ ✓
8. Were the people assessing the outcomes blinded to the participants' exposures/interventions? ✘ ✘ ✘ ✘ ✘ ✘
9. Was the loss to follow-up after baseline 20% or less? Were those lost to follow-up accounted for in the analysis? ✓ ✓ ✓ ✓ ✓ ✓
10. Did the statistical methods examine changes in outcome measures from before to after the intervention? Were statistical tests done that provided p values for the pre-to-post changes? NA NA NA NA NA NA
11. Were outcome measures of interest taken multiple times before the intervention and multiple times after the intervention (i.e., did they use an interrupted time-series design)? ✘ ✓ ✘ ✘ ✘ ✘
12. If the intervention was conducted at a group level (e.g., a whole hospital, a community, etc.) did the statistical analysis take into account the use of individual-level data to determine effects at the group level? ✘ Partially Partially ✘ ✘ Partially
Overall rating Fair (main problem: few details about methods, data source) Good Good Fair (main problem: the cohort sizes are absent) Poor (no supporting data provided) Good
Source Kristoffersen (2022) National Council for Crime Prevention (2022) Federal Statistical Office (2018) Tsai and Wu (2022) Thailand Institute of Justice (2021) Ministry of Justice (2022)



Criterion Country
UK: Northern Ireland UK: Scotland USA (34 states)
1. Was the study question or objective clearly stated? ✓ ✓ ✓
2. Were eligibility/selection criteria for the study population prespecified and clearly described? ✓ ✓ ✓
3. Were the participants in the study representative of those who would be eligible for the test/service/intervention in the general or clinical population of interest? ✓ ✓ ✓
4. Were all eligible participants that met the prespecified entry criteria enrolled? ✓ ✓ ✓
5. Was the sample size sufficiently large to provide confidence in the findings? ✓ ✓ ✓
6. Was the test/service/intervention clearly described and delivered consistently across the study population? ✓ ✓ ✓
7. Were the outcome measures prespecified, clearly defined, valid, reliable, and assessed consistently across all study participants? ✓ ✓ ✓
8. Were the people assessing the outcomes blinded to the participants' exposures/interventions? ✘ ✘ ✘
9. Was the loss to follow-up after baseline 20% or less? Were those lost to follow-up accounted for in the analysis? ✓ ✓ ✓
10. Did the statistical methods examine changes in outcome measures from before to after the intervention? Were statistical tests done that provided p values for the pre-to-post changes? NA NA NA
11. Were outcome measures of interest taken multiple times before the intervention and multiple times after the intervention (i.e., did they use an interrupted time-series design)? ✘ ✘ ✓
12. If the intervention was conducted at a group level (e.g., a whole hospital, a community, etc.) did the statistical analysis take into account the use of individual-level data to determine effects at the group level? Partially Partially Partially
Overall rating Good Good Good
Source Department of Justice (2021) Scottish Government (2021) Bureau of Justice Statistics (2021)
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Appendix G. Supplementary data

Supplementary material 1

Appendices

mmc1.docx (31.8KB, docx)
Supplementary material 2

information vignettes for recidivism rates for individual countries

mmc2.pdf (1.7MB, pdf)

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplementary material 1

Appendices

mmc1.docx (31.8KB, docx)
Supplementary material 2

information vignettes for recidivism rates for individual countries

mmc2.pdf (1.7MB, pdf)

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