The author(s) shown below used Federal funding provided by the U.S. 

Department of Justice to prepare the following resource:

Document Title: Evaluating the Effects of Realignment 

Practices on Recidivism Outcomes

Author(s): Mia Bird, Ryken Grattet

Document Number: 251627

Date Received: April 2018

Award Number: 2013-R2-CX-0010

This resource has not been published by the U.S. Department of 

Justice. This resource is being made publically available through the 

Office of Justice Programs’ National Criminal Justice Reference 

Service.

Opinions or points of view expressed are those of the author(s) and 

do not necessarily reflect the official position or policies of the U.S. 

Department of Justice.
Final Summary Overview: Award #2013-R2-CX-0010

Evaluating the Effects of Realignment Practices on Recidivism Outcomes

Mia Bird (PI)

Research Fellow, Public Policy Institute of California

(415) 291-4471

bird@ppic.org

Ryken Grattet (co-PI)

Professor and Chair of Sociology, University of California, Davis

Adjunct Fellow, Public Policy Institute of California

(916) 440-1123

grattet@ppic.org

This project was supported by Award No. 2013-R2-CX-0010, awarded by the National Institute of Justice, Office 

of Justice Programs, US Department of Justice. The opinions, findings, and conclusions or recommendations 

expressed in this publication are those of the authors and do not necessarily reflect those of the Department of 

Justice.
Final Summary Overview: Award #2013-R2-CX-0010

Evaluating the Effects of Realignment Practices on Recidivism Outcomes

Mia Bird (PI) and Ryken Grattet (co-PI), Public Policy Institute of California

Project Purpose

In 2011, California enacted Public Safety Realignment, a historic policy reform that resulted in dramatic

reductions to the state prison population. Realignment shifted responsibility and authority over lower-level felons 

from the state prison and parole system to county jail and probation systems. From our current vantage point, it is 

clear that the changes brought by realignment paved the way for a series of subsequent reforms.

1 Together, these 

reforms reflect two key features of the state’s changing approach to criminal justice policy. First, the state has 

altered system incentives and rules to reduce its overall reliance on incarceration. Second, the state has increased 

the emphasis on the use of evidence-based interventions to reduce crime and criminal justice involvement.

Despite the emphasis on data-driven policy and practice, dedicated resources to support data collection, 

evaluation, or research have not been provided. In the case of realignment, this is particularly problematic given

that data capturing individuals moving through state and county criminal justice systems are kept in separate 

systems. Evaluating the effects of realignment or identifying effective recidivism-reduction interventions would 

not be possible without finding a way to collect and integrate data from these disparate systems. 

In 2013, PPIC initiated the Multi-County Study (MCS), a data collection effort aimed at producing an 

integrated dataset to address these gaps. NIJ provided support for our first phase of analytic work drawing on the 

MCS dataset. The planned work would focus on the following research questions:

(1) What was the impact of realignment on recidivism statewide?

(2) How did the effects of realignment vary across counties?

(3) How have service and sanction interventions affected recidivism outcomes? 

BSCC-PPIC MCS Data

This project was initiated by PPIC, in collaboration with the California Board of State and Community 

Corrections (BSCC). The BSCC is the state agency tasked with overseeing the implementation and monitoring 

 1 As a further step toward lowering incarceration levels, voters passed Proposition 47 in November 2014, which required that certain lower-level drug and 

property offenses be charged as misdemeanors, resulting in reductions to prison and jail populations. Prop 47 also directed state criminal justice savings 

toward behavioral health programs and services. More recently, voters passed Proposition 57 in 2016, which is likely to reduce the prison population further 

by providing time credits for participation in programs thought to reduce the likelihood of recidivism. 
the outcomes of realignment in the counties. At the start of the project, we entered into agreements with the 

California Department of Corrections and Rehabilitation (CDCR) and the California Department of Justice (DOJ) 

to provide the required individual-level data held by the state. We then identified 12 counties2 that, as a group, 

represent the demographic, economic, and geographic diversity of California. We established partnerships with 

these counties, comprising about three-fifths of the state population, to extract individual-level data capturing the 

criminal justice population moving through jail and probation systems in the post-realignment period. We worked 

with the county criminal justice agencies to construct codebooks for the data provided and, ultimately, developed 

a standardization process to allow for the integration of data across the counties. By August 2015, we completed 

the first round of county data collection and were able to send a list of individuals to the state for matching to 

criminal history and recidivism records. 

Substantial delays at the state level postponed our intended recidivism analyses. In the interim, we 

examined the county data. Although we were focused on causal research at the start of the project, through our 

interactions with state and county agencies, we found policymakers and practitioners were operating without basic 

information about criminal justice populations and jail and probation systems. We worked to fill this knowledge 

gap with a series of papers that provided descriptive analyses focused on the demographic and criminal 

characteristics of jail, probation, and pretrial populations, as well as analysis of how these populations were 

changing in relation to statewide policy reforms and the implications of those changes for county resource needs 

and practice. NIJ was acknowledged for support of this work, and these intermediary studies are synthesized in 

the final section of this summary report.

We eventually received the previously requested data from CDCR in May 2016 and from DOJ in June 

2017. The initial dataset provided by DOJ was found to be incomplete, and we received a complete submission in 

November of 2017.3 At this stage, we have completed the analysis of the effects of realignment on recidivism and 

are working to determine how and why those effects vary across counties. We have conducted a preliminary 

assessment of how program, service, and sanction interventions vary across the counties, but have not yet 

 2 These partner counties include: Alameda, Contra Costa, Humboldt, Fresno, Kern, Los Angeles, Orange, Sacramento, San Bernardino, San Francisco, 

Shasta, and Stanislaus. 

3

The DOJ data was not accompanied by a codebook or data description file; it included 22 event types and 199 disposition types that needed to be collapsed 

into arrest and conviction outcomes. DOJ initially omitted data for a share of the requested population (a share of those with CDCR histories) so we achieved 

correspondence by reverse engineering the CDCR algorithm for classifying arrests and convictions. We submitted a follow-up request to DOJ and received a 

complete submission in November 2017.
completed the analysis of the relative effectiveness of these interventions for target subpopulations. We have also 

completed the analysis of changes in probation officer attitudes and orientations toward their roles and 

responsibilities. Here we summarize findings from our work-to-date.

Changes in Jail and Probation Populations under Realignment and Prop 47

We have been able to leverage the MCS data to provide a unique view into how county jails and 

probation caseloads are changing under California’s historic correctional reforms. Realignment reprioritized 

prisons as institutions where offenders serve time for convictions of serious, violent, and sexual offenses. Nonserious, non-violent, non-sexual drug and property offenders remain in the counties for supervision and 

incarceration. Proposition 47 prompted a second wave of reprioritization—this time concerning jail beds. 

Counties appear to be concentrating resources on more serious felony drug and property offenders, holding them 

in custody for longer periods of time. The trajectory of people held on felony drug charges is particularly clear. 

Realignment drove an increase in the number and share of individuals held or serving time in jail on felony drug 

charges. After Prop 47, people held or serving time for drug offenses decreased. However, those drug offenders 

who remain in local custody tend to stay for longer periods, suggesting that jails have prioritized more serious 

types of drug offenders. 

Changes in the nature of the population under jail custody are paralleled in changes in probation 

populations. Realignment and Prop 47 increased the seriousness of offenders under probation supervision. 

Realignment added two types of offenders—those under post-release community supervision and those under 

mandatory supervision—who were previously the responsibility of the state prison and parole system and who 

tend to be convicted of more serious offenses. In the three years following realignment, these populations 

increased as a share of all new probation cases. Following the passage of Prop 47, new misdemeanor and felony 

probation cases—the traditional purview of county probation departments—declined. As a result, the share of the 

total probation caseload under post-release community supervision (PRCS) or mandatory supervision rose relative 

to the traditional caseload types. Once under supervision, realigned offenders on PRCS or mandatory supervision 

were booked into jail more frequently than felony and misdemeanor probationers. Individuals on PRCS or 

mandatory supervision were also more likely to be booked into jail multiple times within a year after starting 
supervision, and the amount of time they stayed in jail custody was longer compared to felony and misdemeanor 

probationers. These findings document a significant shift in the role of county jails and probation departments.

The Effects of Realignment on Recidivism

The analyses of the effects of realignment on recidivism draw on the MCS dataset.4 Data are compiled 

from probation and sheriff departments, which provide the following individual-level records: booking dates and 

charges; release dates and types; jail lengths of stay; probation entries and exits by caseload types; risk and needs 

assessments; service and sanction interventions; and jail revocations. Through the MCS, these data are linked to 

the state data sources, which provide criminal and institutional histories—allowing for the construction of both 

prior offenses, as well as statewide rearrest and reconviction outcomes—and prison and revocations. We use these 

data to construct control characteristics and recidivism outcomes. Our measure of rearrest is adjusted to include 

both formal arrests and revocations to custody that are not preceded by a formal arrest; similarly, we adjust the 

measure of reconviction to include revocations that are not preceded by a formal reconviction. These adjustments 

allow for comparison between the pre-realignment period when individuals were often returned to prison for 

revocations, and the post-realignment period when counties were incentivized to pursue formal arrests and 

convictions due to new limitations on returns to prison through revocations. This approach allows us to separate a

justice systems response to realignment from changes in underlying criminal behavior. 

We estimate the effects of realignment on recidivism for two key treatment populations, the PRCS and 

1170(h). The PRCS are individuals who would have been released from prison to state parole prior to 

realignment, but are now released to county probation supervision under realignment. The 1170(h) are formerly 

prison-eligible offenders who are now required to serve time in local jail under realignment. The treated groups 

included in this study are released between October 2011 and September 2013, and they are followed for up to 

two years. We draw on a pre-realignment comparison group composed of individuals released from prison 

between October 2009 and September 2011. We rely on a pre-post realignment design to leverage the swift 

 4 To compile these data we underwent human subjects review by PPIC’s internal Institutional Review Board (IRB) and the California State IRB. In addition, 

we received approvals from the California Department of Justice, which requires security measures and compliance with state confidentiality laws in the 

handling of Criminal Offender Record Information. Data are provided to us with identifiers, no names, only CII numbers, date of birth, race, and gender. 

These are the minimum elements needed to link data across systems and perform data quality checks. CII numbers are stripped during the analysis phase. 

Data are sent to CDCR and DOJ via secure ftp sites and are stored on highly secure IT systems at PPIC. Data are not shared with third parties. Data are never 

publicly reported if the sample size is less than 30. Further details about the measures put in place to protect the subjects of the research are described in our 

original grant proposal. These have not been modified.
passage and implementation of this natural policy experiment to identify treatment effects. Drawing on a rich set 

of individual-level characteristics, including demographics and criminal histories, we use propensity score 

matching to construct a pre-realignment control group for each of the post-realignment treatment groups. We then 

use a regression model to adjust for any remaining differences in characteristics between the treatment and control 

groups and estimate the effects of realignment on the rearrest and reconviction rates over one- and two-year

recidivism windows. 

The PRCS Group

Realignment legislation requires that for individuals to be released on PRCS rather than traditional parole, 

they must have current offenses that are non-violent and non-serious, as well as meet additional criteria. In 

addition to changing the supervision site, realignment also required that revocations for this group be served at the 

county, rather than state, level. Overall, we find that the PRCS had higher rates of recidivism when compared 

with their pre-realignment counterparts. We estimate a one-year rearrest rate 5.2 percentage points higher and a 

two-year rearrest rate 2.6 percentage points higher than their pre-realignment counterparts. The PRCS one-year 

reconviction rate was 1.8 percentage points higher and the two-year reconviction rate was 2.4 percentage points 

higher than the pre-realignment group. As shown in Table 1 below, all estimates were highly significant. 

However, we find that nine of the twelve studied counties had lower rates of reconviction after realignment, 

indicating the overall estimate was driven by outcomes in a few large counties. In fact, when Los Angeles 

County—which comprised nearly one-half of the MCS PRCS population and one-third of the state population—is

removed from the analysis, we estimate lower post-realignment reconviction rates. Our estimates of the effect of 

realignment on rearrest rates, however, remains similar in direction and magnitude. In general, we find substantial 

variation across counties in the effects of realignment on PRCS recidivism rates, suggesting that there may be a 

great deal to be learned about effective practice by leveraging variation in realignment strategies across counties. 

Of course, counties also differ in characteristics and these differences are likely driving some of the variation we 

observe in recidivism outcomes. 

The 1170(h) Group

Prior to realignment, individuals sentenced to more than one year in custody were generally sent to prison to 

serve those sentences. If individuals were sentenced to one year or less, they served their time in local jail. 
Realignment created a set of offenses (“1170(h)” offenses) for which individuals could no longer be sentenced to 

prison, no matter how long their incarceration term. Realignment also created a new sentencing option for 

counties—they could either sentence these offenders to serve “straight” sentences in local jail with no supervision 

following release or “split” sentences, which have shorter jail sentences combined with probation supervision 

terms following release. The 1170(h) treatment group includes felony offenders released from jail custody in the 

two years following the implementation of realignment. We examine the effects of realignment for the full 

1170(h) group, and we also conduct separate analyses for the straight-sentenced and the split-sentenced groups. 

We find no significant difference in the one-year rearrest rate, but we estimate the two-year rearrest rate was 2.3 

percentage points higher for the 1170(h) population when compared to the pre-realignment control group. In 

contrast to the PRCS, we find that reconviction rates were lower—the one-year rate was 3.8 percentage points 

lower and the two-year rate was 2.0 percentage points lower—for the 1170(h) group. These estimates were highly 

significant, as shown in Table 1. When Los Angeles County is removed from the analysis, differences in 

reconviction rates further widen; in addition, we estimate a lower one-year rearrest rate for the 1170(h) group and 

no difference in two-year rates. 

TABLE 1

Coefficient estimates of treatment effects for each post-realignment group, measures adjusted for revocations

Rearrest, 

one-year

Rearrest, 

two-year

Reconviction, 

one-year

Reconviction, 

two-year

PRCS, including all MCS counties 5.2*** 2.6*** 1.8*** 2.4***

PRCS, excluding Los Angeles county 5.4*** 2.4*** -5.7*** -2.2***

1170(h), including all MCS counties 0.1 2.3*** -3.8*** -2.0***

1170(h), excluding Los Angeles county -1.4*** -0.7 -10.1*** -7.6***

1170(h), only straight-sentenced -1.4** 0.8 -4.1*** -3.0***

1170(h), only split-sentenced 10.9*** n/a 5.2** n/a

SOURCES: Multi-County Study: demographic characteristics and criminal history data were provided by the California Department of 

Corrections and Rehabilitation (CDCR), the California Department of Justice (DOJ), and the 12 MCS county sheriff and probation 

departments.

NOTES: These estimates are for rearrest and reconviction outcomes, adjusted for changes in revocations over time. Each cell represents the 

coefficient estimate of the difference in recidivism outcomes for the referenced post-realignment treatment group when compared with the 

pre-realignment matched control group. In each case, we use a regression model to adjust for remaining, post-matching differences in the 

demographic and criminal history characteristics. Coefficient estimates should be interpreted as percentage point differences between the 

treatment and control group. ***p<.01, **p<.05, *p<.10. 
Initial Assessment of Program, Service, and Sanction Interventions

At this stage, we have examined the effects of one new sanctioning tool—the split sentence—on recidivism 

outcomes. We find that the realignment treatment group of 1170(h) offenders receiving sentences split between a 

jail term and a probation supervision term had higher rearrest rates but lower reconviction rates when comparted 

to a control group of pre-realignment prison releases; however, their 1170(h) counterparts who received only a jail 

term with no supervision, had universally lower recidivism outcomes under realignment (see Table 1). This 

finding, together with our finding that the PRCS had higher rates of recidivism, suggest we need to carefully 

examine the likely complex relationship between supervision and recidivism. The MCS dataset includes a wide 

range—from education programs to behavioral therapy to parenting classes—of reentry program and service 

interventions. The use of these interventions, as well as the availability of data capturing them—varies across 

counties and over time. Table 2 summarizes the prevalence of these programs and services across counties and the 

extent to which counties have been able to provide data on these interventions.

TABLE 2

Summary of program and service interventions data by high-level category and county

Education

Behavioral/

Life Skills

Cognitive

Behavior

Employ- ment

General

Reentry

Health Housing

Substance

Abuse

Alameda Received Pending NA Received Pending Received Received Received

Contra Costa Received Pending Pending Pending Pending Pending Pending Pending

Fresno Received Received Received Received Received Pending Pending Pending

Humboldt Received Received Received Pending NA Received NA Pending

Kern Received Pending Pending Received Pending NA Pending Pending

Los Angeles Received Pending Received Received Pending Pending Pending Pending

Orange Received Received Received Received Received Received Received Received

Sacramento Received Pending NA Pending Pending Pending NA Pending

San Bernardino Pending Pending Pending Pending Pending Pending NA Pending

San Francisco Pending Pending Received Pending Received NA Received Pending

Shasta Received Pending Pending Pending Pending NA NA Pending

Stanislaus Received Received Received Received NA Received NA Received

SOURCES: Multi-County Study programs and services data. 

NOTES: “NA” is used to indicate that the county does not have a program or service that fits the column category.
Changes in Probation Officer Attitudes and Orientations

Our original NIJ proposal included a short survey that was to be fielded on probation officers in 

the 12 counties and was designed to gauge the importance of social work and law enforcement aspects of 

probation work. During the course of the study, we learned of the BSCC survey, which posed many more 

questions on both the social work and enforcement aspects of work, contained a wide range of 

respondents covering both probation officers and their supervisors, used a large statewide sample, and 

was undertaken at two points in time (2002, 2014). Compared to our planned survey, the BSCC survey 

provides a stronger empirical basis to investigate the nature of probation work, and to assess how it has 

changed over time. 

We use the survey data to explore the concept of “hybrid” supervision practices, in which social 

work and law enforcement aspects are balanced. We want to know whether the survey shows evidence of 

increasing hybridity in California probation and whether supervisors and front-line probation officers 

differ in the degree to which they embrace the practice of blending social work and law enforcement 

activities. Prior research suggests the hybrid model has emerged as a dominant paradigm in probation 

nationally and has been shown to be more effective than a heavy emphasis on enforcement alone. We find 

evidence that probation supervisors have shifted more toward a hybrid model, but that probation officers 

report less change. Supervisors’ ratings of the importance of many of the enforcement aspects of 

probation work have remained similar or increased between 2002 and 2014. Meanwhile, supervisors’ 

ratings of the importance of the social work aspects have mostly increased over time. On the other hand, 

front-line probation officers report they are doing less of several kinds of enforcement and the same or 

more of others. Probation officers also mostly report they are doing social work tasks at roughly the same 

frequency in 2014 as they were in 2002. These differences across years and across levels points to a 

potential mismatch between supervisors and probation officers in terms of whether the social work 

dimension of probation work is becoming more central, as it appears to be for supervisors, or whether it 

has remained largely the same, as it appears to be for probation officers. 
In addition, the 2014 survey introduced a range of new questions pertaining to case management, 

data collection, and research. We find further evidence of a mismatch between probation officers and 

supervisors. In particular, supervisors are more likely to report higher frequencies of those newer aspects 

of the job than probation officers. Although there are differences between probation officers and 

supervisors revealed by these data, it is important not to overstate them. With the exception of using data 

to measure program outcomes, both probation officers and supervisors report doing these new tasks at the 

same or greater frequency than probation officers report doing both social work and enforcement tasks. 

We use these findings to demonstrate that the hybrid model has come to encompass not just traditional 

social work and enforcement tasks, but also ascendant professional ideas about risks-based supervision, 

case management, and data and research. An article from these findings will be submitted to a journal in 

December. 

Scholarly Products

Our goal in undertaking this work was to generate research to inform policy and practice in 

California, as well as in other states wishing to pursue criminal justice reform based on California’s 

experience. We have sought out opportunities to publish PPIC reports, reaching a broad network of 

policymakers and practitioners in California; presented our work at national conferences; and published in 

scholarly journals. 

Published Peer-reviewed Policy Research Reports

• Grattet, Ryken, Sonya Tafoya, Mia Bird, and Viet Nguyen. 2016. California’s County Jails in the 

Era of Reform. Public Policy Institute of California

• Tafoya, Sonya, Mia Bird, Viet Nguyen, and Ryken Grattet. 2017. Pretrial Release in California. 

Public Policy Institute of California.

• Nguyen, Viet, Ryken Grattet, and Mia Bird. 2017. California Probation in the Era of Reform. 

Public Policy Institute of California.

• Bird, Mia, Ryken Grattet, and Viet Nguyen. 2017. Realignment and Recidivism in California. 

Public Policy Institute of California. 

Published and In progress Peer-review Journal Articles

• Grattet, Ryken, Mia Bird, Viet Nguyen, and Sonya Tafoya. 2017. “California Jails under 

Realignment and Proposition 47.” California Journal of Politics and Policy 9(3):1-15 [DOI:

10.5070/P2cjpp9336924]
• Nguyen, Viet, Ryken Grattet, and Mia Bird. Forthcoming. “Changing Characteristics of California 

Probation Populations: Revelation of New Data” APPA Perspectives.

• Nguyen, Viet, Ryken Grattet, Mia Bird, and Justin Goss. In progress. “Probation’s Changing Role 

in California: Challenges and Opportunities for Hybrid Supervision.”

• Bird, Mia, Ryken Grattet, and Viet Nguyen. In preparation. California’s Great Experiment: How 

has public safety realignment affected recidivism? 

Implications for Criminal Justice Policy and Practice

To date, our research has portrayed the changes in the local correctional populations across two 

major reforms—2011’s Public Safety Realignment and 2014’s Proposition 47—and across probation 

systems and county jails. Moreover, through the survey data we have compiled, we have been able to 

explore the way the nature of probation work has changed. And, finally, we have provided an in-depth 

analysis of how realignment has affected recidivism and are in the preliminary stages of identifying 

effective program, service, and sanction interventions.

Realignment changed major features of the correctional system by lessening deterrence and 

incapacitation and aiming to improve rehabilitation. The results we see here are likely reflective of the 

impacts of these countervailing changes. The strongest conclusion from this work is that, in the first years 

under realignment, recidivism outcomes have varied substantially across realignment treatment groups 

and counties, with some offenders achieving much better outcomes under realignment and others faring 

worse in comparison to their pre-realignment counterparts. However, analysis of the first two years of 

realignment is insufficient to draw policy conclusions because many counties were unprepared to take on 

the challenges of implementing evidence-based interventions with more serious offender groups. Given 

that context, our findings show some promise that improvements can be made over time, particularly if 

we are able to leverage the diversity of county approaches to identify and disseminate effective practices. 

Our work on changes in jail and probation populations has demonstrated that the state and 

counties have prioritized correctional resources for more serious offenders under Realignment and Prop 

47. This change has reduced overall incarceration levels and criminal justice contact, but has also 

increased the need for guidance on evidence-based practices at the local level. The MCS serves as an 
important data resource to allow the research team to work with counties to identify the most effective 

practices for target populations. This work is ongoing and comprises the next steps for the project.