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object(Timber\Post)#3711 (44) { ["ImageClass"]=> string(12) "Timber\Image" ["PostClass"]=> string(11) "Timber\Post" ["TermClass"]=> string(11) "Timber\Term" ["object_type"]=> string(4) "post" ["custom"]=> array(5) { ["_wp_attached_file"]=> string(12) "R_414STR.pdf" ["wpmf_size"]=> string(6) "296512" ["wpmf_filetype"]=> string(3) "pdf" ["wpmf_order"]=> string(1) "0" ["searchwp_content"]=> string(62413) "Corrections Realignment and Data Collection in California April 2014 Sonya Tafoya, Ryken Grattet, and Mia Bird with research support from Brandon Martin Summary The Public Safety Realignment Act of 2011 (AB 109) shifted authority for managing tens of thousands of lower-level felony offenders from the state to the counties. With a strong focus on reducing California’s high recidivism rates, the act encouraged counties to consider alternatives to incarceration and to adopt evidence- based practices. By implementing these new practices, the counties would achieve improved public safety returns on the state’s correctional investment. Yet, despite the promotion of an evidence -based approach, the act did not require or provide direct support for data collection, research, or evaluation. More than two years into realignment, some data collection efforts have been established and others are emerging, but the work of creating integrated data systems that can be used to demonstrate which correctional strategies are most effective remains largely undone. The Public Policy Institute of California is coordinating with the Board of State and Community Corrections and ele ven counties to address this need. In this report, we make a case for fully embracing the data -driven approach to corrections envisioned in AB 109. Based on our work with the eleven counties, we establish data collection priorities that will enable countie s to implement evidence- based practices. We make the following four recommendations for improving the immediate quality and availability of county data: (1) ensure that relevant data are captured, (2) link data across systems, (3) standardize definitions o f key measures, and (4) upgrade information technology systems to make the collection, extraction, and sharing of data easier. Counties can undertake some of these recommendations on their own, but other recommendations require further statewide leadership, additional technological investments, and collaboration and cooperation among justice partners. The state has made recent investments in new jail construction to increase the physical capacity for community corrections, and we suggest an analog ous investment to upgrade information technology systems and increase the capacity for evidence -based practice. Because the legislature has charged the Board of State and Community Corrections with providing leadership, coordination, and technical assistance to promote effective and evidence -based corrections practices, the responsibility for overseeing the recommended improvements in this report fit within the current role of the board. http:// www.ppic.org /main/home.asp Corrections Realignment and Data Collection in California 2 Contents Summary 2 Abbreviations 4 Introduction 5 A Vision of Data-Driven Community Corrections 6 Current State Data Collection Efforts 9 Data Priorities for Identifying Effective Strategies 11 Recommendations for an Improved Data Collection System 12 Capturing Data 12 Linking Data Across Systems 14 Standardizing Definitions and Measures 15 Upgrading Information Technology Systems 16 The State’s Role in Supporting County Improvements 17 Conclusion 19 References 20 About the Authors 21 Acknowledgments 21 A technical appendix to this paper is available on the PPIC website: www.ppic.org/content/pubs/other/414STR_appendix.pdf Abbreviations 1170(h) 1170(h) refers to the penal code designation for felony offenders convicted of non- violent, non-serious, non-sexual crimes. Under realignment, these offenders will serve their sentences in county jails rather than in state prisons. The term “1170(h)s” is often used colloquially to refer to these offenders. AB 109 Enacted into law in 2011, Assembly Bill 109 mandated the implementation of public safety realignment beginning on October 1, 2011. AB 109 shifted responsibility for certain low -level offenders from the state to the counties. AB 900 Enacted into law in 2007, Assembly Bill 900 authorized approximately $7.7 billion for prison construction and rehabilitation initiatives in order to relieve the significant overcrowding problems facing state prisons ; it also included $1.2 billion in lease revenue bond finan cing for the construction of county jails. AB 1050 Enacted into law in 2013, Assembly Bill 1050, among other mandates, required the Board of State and Community Corrections to develop standardized definitions for data collected by county probation departments and sheriff’s departments for the purpose of ev aluating the implementation of evidence -based practices and programs. AOC (California) Administrative Office of the Courts BSCC Board of State and Community Corrections CDCR California Department of Corrections and Rehabilitation CII Criminal Identification and Information number CPOC Chief Probation Officers of California CSAC California State Association of Counties DOJ (California) Department of Justice LAO Legislative Analyst’s Office PRCS Post-Release Community Supervision, as defined in realignment legislation, is the county-based supervision of offenders released from state prison, replacing the state- based parole program for a majority of released prisoners. SB 678 Enacted into law in 2009, The California Community Corrections Performance Incentive Act established a system of performance- based funding for county probation departments. SB 1022 Enacted into law in 2012, Senate Bill 1022 authorized $500 million in state lease revenue bond financing to fund local adult correctional facilit ies. http://www.ppic.org /main/home.asp Corrections Realignment and Data Collection in California 4 Introduction Public Safety Realignment (AB 109) fundamentally changed the correctional system in California, shifting responsibility for tens of thousands of lower -level felony offenders from the state correctional system to county systems. A central principle of AB 109 is that counties should have a strong hand in designing their own approach to managing offenders who are now under their purview. The statute declares that “fiscal policy and correctional practices should align to promote a just ice reinvestment strategy that fits each county,” and defines “justice reinvestment” as a “data -driven approach to reduce corrections spending and reinvest savings” using “evidence - based strategies designed to increase public safety.” 1 It is laudable that the state endorsed the use of a data -driven approach and evidence -based strategies; however, counties need additional support to meet this challenge. Realignment legislation did not dedicate funds for an evaluation of the effects of realignment subsequent to its implementation, nor did it provide counties with specific funds for assessing the success rates of their local correctional strategies. This was a missed opportunity. Through realignment, the state effectively created 58 county - level policy laborat ories. The variation across counties in correctional practices creates an ideal opportunity to identify cost -effective strategies and to disseminate these best practices across the state. Yet, without a consistent framework for data collection and evaluati on, weeding out failing strategies and identifying successful ones will be a haphazard process. Without strong evidence that adoption of another county's approach will be effective, counties will understandably be reluctant to change policies and practices that are familiar. This barrier to change is important because the long -term stability and sustainability of California’s criminal justice system depends not on the success of a few counties, but on the broad statewide adoption of successful correctional strategies that promote public safety and reduce reliance on California’s overextended prison system. Drawing on the goals laid out in AB 109, we begin our report by describing the features of a data -driven community corrections system as envisioned by th e legislation. We illustrate how counties can use data to improve the quality of the local corrections systems. More specifically, we show how the collection and use of these data will help counties to identify effective and efficient programs, match inter ventions to offender types, exchange info rmation on program successes and failures, hold service providers more accountable, and equip system leaders with an impartial basis for targeting resources. The community corrections system we describe also finds s upport among the national community of corrections professionals. Central to the vision is the idea that data and evidence should be integrated into policy decisions, that data should improve accountability, and that management and technological systems sh ould reinforce the use of data for ongoing improvements. We then turn to the current data collection efforts in California and discuss their strengths and limitations. Based on our work with policy makers, criminal justice analysts, and information techno logy staff from eleven counties, we identify the specific areas of data collection that are necessary for counties to identify effective approaches to reducing recidivism among realigned offenders. 2 We note some of the common obstacles to collecting this information, and we make four recommendations for improving the quality and availability of data, highlighting the benefits that would accrue from successfully addressing these issues. Finally, we review the state’s role in ensuring that the goals of real ignment are met. The state has invested in new jail construction to increase the physical capacity for community corrections, and we suggest an analogous investment in technical capacity and research capability. 1 California Penal Code § 3450. 2 This report is based on our experience reviewing and working with existing state -level data sources. A summary of these sources is provided in Appendix A . Our county -level findings are based on meetings with county policymakers, criminal justice analysts, and information technology staff. We conducted meetings in 11 counties participating in the BSCC -PPIC Multi-County Study. The meetings took place in the fall of 2013. http://www.ppic.org /main/home.asp Corrections Realignment and Data Collection in California 5 A Vision of Data-Driven Community Correctio ns California parole services, county probation departments, and sheriff’s offices have a variety of strategies to help an offender address the source of his or her criminal behavior, be it substance abuse, mental health problems, employability, lack of education, deficits in basic social skills, or other underlying circumstances. They also have at their disposal procedures, some newly available since the passage of AB 109, to sanction offenders when they fail to comply with the conditions of their supervis ion. Yet only a small fraction of these strategies ha s been rigorously researched and, even among the well -researched strategies, their applicability to California’s offender populations remains untested. As a result, practitioners have little basis for co nfidence that the selection of services and sanctions they make will result in the desired outcomes. Collecting data on both the characteristics of offenders and the correctional interventions those offenders receive would allow counties and the state to properly evaluate the effectiveness of specific interventions in reducing recidivism outcomes for particular offenders. To give practitioners a data -driven basis for their choices, and to thereby improve the odds that offenders will benefit from targeted i nterventions, we envision practitioners having readily available access to high -quality data on the characteristics of offenders and the underlying needs driving their criminal behavior. Based on these data, and given an adequate array of evidence- based interventions, practitioners could select a set of interventions demonstrated to be most effective among similar offenders. This approach stands in contrast to the “one- size-fits -all” solution commonly applied when information on offender characteristics and needs is not fully integrated into practice or when programming resources are scarce. In recent years, the movement to adopt evidence- based practices has expanded beyond the use of offender risk and needs data and the selection of research -based services to focus on the importance of integrating data and evidence into organizational practices (Clawson and Guevara 2011; Crime and Justice Institute 2004). This change reflects the recognition among community corrections experts that adoption of the latest evi dence -based program models without organizational support and oversight may not be sufficient to produce the desired results. Using an “integrated model” in evidence- based practices means that data are used not only to inform service and sanctioning choice s and to focus practitioners on the highest-priority outcomes, but also to provide feedback for managers about the ongoing performance of the organization, increasing transparency and the degree of accountability. Realignment legislation emphasizes the ne ed for effectiveness and efficiency in local corrections practices. If the state as a whole is going to move in this direction, then each county must not only be technologically capable of carrying out its own data -driven strategies but must also be able to contribute to the state’s understanding of what works. Currently, even as county agencies experiment with innovative approaches, there is no standardized means for demonstrating the effectiveness of these innovations such that counties both inform their own practices and share their findings. It is true that these problems pre- date the recent reforms, but state funding for realignment and the corresponding renewed public attention have made the shortcomings of existing data collection efforts even more pronounced. To resolve these shortcomings, we envision a system with a level of standardization that allows the state to capitalize on the experiences of various counties. With a common understanding of the data needed to identify effective strategies, count ies would be better positioned to learn from each other. http:// www.ppic.org /main/home.asp Corrections Realignment and Data Collection in California 6 The data priorities for identifying effective strategies that we describe below are not explicitly written into AB 109, but they are critical to the vision of a data-driven correctional system. Whil e AB 109 avoided mandates for the adoption of specific correctional policies and programs, it set the expectation that the system as a whole would move toward greater reliance on data. 3 Several associated developments reinforce this expectation and emphasi ze the need for greater coordination, interagency collaboration, and standardization. For example, the legislature established the Board of State and Community Corrections (BSCC) to provide “leadership, coordination, and technical assistance to promote eff ective state and local efforts and partnerships in California's adult and juvenile criminal justice system.” 4 The BSCC also has the broad duty “to collect and maintain available information and data about state and community correctional policies, practices, capacities, and needs.” 5 AB 1050, passed in 2013, further operationalized some of these functions by requiring the board “to develop definitions of specified key terms in order to facilitate consistency in local data collection, evaluation, and impleme ntation of evidence -based programs in consultation with stakeholders and experts .” The terms include but are not limited to “ recidivism, average daily population, and treatment program completion rates.” In response to the legislation, the BSCC has establi shed an Executive Steering Committee to develop definitions and a Data and Research Standing Committee to meet the data reporting requirements of AB 1050. The legislature has also mandated that the Administrative Office of the Courts (AOC) collect informa tion from the local trial courts on the implementation of realignment. 6 In recognition of the importance of data -driven correctional practices and data standardization, the attorney general launched the Division of Recidivism Reduction and Reentry in Novem ber 2013. The division is intended to “support counties and District Attorneys by partnering on best practices, such as the development of a statewide definition of recidivism, identifying grants to fund the creation and expansion of innovative anti -recidi vism programs and using technology to facilitate more effective data analysis and recidivism metrics”(California Department of Justice 2013). Data collection and evaluation are also central to some of the cost -saving, incentive -based policy initiatives th at have emerged in recent years. These initiatives are attractive because they hold the promise of furthering policy objectives while containing general fund expenditures. For example, under California’s SB 678 (the California Community Corrections Perform ance Incentives Act), the state provides counties with financial incentives to reduce probation failures that result in costly re -incarcerations in state prisons. Data on probation revocations are used to calculate the statewide savings resulting from lowe ring probation failures at the county level. The state then shares a portion of the savings with successful counties. In the first year of the program, the probation revocation rate dropped by 1.8 percentage points, saving the state $179 million dollars, a portion of which was shared with counties (AOC 2011). Social impact bonds represent another model of incentive- based funding that relies on the capacity to collect and evaluate data. Under this model, a private investor enters into an agreement with the s tate to provide and fund a social service and to deliver a mutually agreed upon and quantifiable program outcome. If the outcome is achieved, the state repays the investor with a return on the investment. For the state to benefit from social impact bonds f or corrections services, it would be necessary to expand the capacity for data collection in order to estimate returns on program investments. 3 California Penal Code § 3450. 4 California Penal Code § 6024 . 5 California Penal Code § 6027. 6 California Penal Code § 1315 5. http:// www.ppic.org /main/home.asp Corrections Realignment and Data Collection in California 7 Viewed in the context presented above, data collection and evaluation cannot be divorced from effective offender management, from ongoing system improvement, or from state cost -saving efforts. Improving the state’s capacity for data collection and evaluation must be given a higher priority because it is central to all aspects of improving corrections in California. If recognizing the centrality of data collection to the long -term success of community corrections in California is the first step toward achieving a data -driven correctional system, the next step is to examine the data collection efforts already under way and to map out a course for addressing any shortcomings. http://www.ppic.org /main/home.asp Corrections Realignment and Data Collection in California 8 Current State Data Collection Efforts Looking at the state’s current data collection efforts informs our understanding of the additional workload pressures brought on by realignment and allows us to assess the effects of realignment. Below we describe these efforts and highlight their strength s and weaknesses. We close this section by describing why enhanced data collection is critical for fully estimating the effects of realignment and for differentiating between those correctional interventions that merit replication and those that do not. Gauging Workload. The primary purpose of data collection in this area is to document the new workload pressures presented by realignment. 7 For example, the BSCC modified its Jail Profile Survey to include a variety of measures about the traffic of realigned offenders through local county jails. The Realignment Dashboard from the Chief Probation Officers of California (CPOC) is another instance of a data collection that documents workload. It provides counts of post -release community supervision (PRCS) and 1 170(h) cases, along with the movement into or out of a particular status (jail, new convictions for those under PRCS, new bookings into jail, PRCS violation cases, etc.). Yet another example of this type of data is an effort by the A OC to gather data on fe lony dispositions and petitions to revoke probation, PRCS, mandatory supervision, and parole. 8 Workload data collection allows us to examine how frequently counties are using some of the new practices made available since realignment’s implementation. For example, some workload data track the use of alternative custody programs, split sentences, jail -only sentences, and “flash incarcerations.” 9 Because all counties collect these workload data on an ongoing basis, we can examine the variation across countie s and the change over time in the use of these practices. The BSCC, CPOC, and AOC data are also important because they give counties the information they need to document the burdens that realigned offenders are placing on various components of the local c orrections system. The weakness of these data sources stems from the fact that they are summary data rather than individual - level data. When the summary numbers rise or fall, it is difficult to determine what is driving the change. For example, if a count y experiences an uptick in bookings among offenders on PRCS , it could be attributed to a failure of policy or practice. Alternatively, the uptick could be the result of a change in the overall composition of the PRCS population in terms of risk. Summary da ta alone do not provide a basis for discerning among possible explanations. Moreover, summary measures often cannot be broken down further to reveal the forces that underlie observed differences across counties. As a result, summary data may invite inappro priate comparisons across counties. Perhaps most limiting of all, summary data do not provide a link between the services and sanctions offenders are receiving and their recidivism outcomes. Assessing Impact. Existing state -level data sources, including b oth the new summary-level data we describe above, and the ongoing individual -level data collection by the California Department of Corrections and Rehabilitation and the Department of Justice, enable researchers to begin to assess the impact of realignment on crime and recidivism (Lofstrom and Raphael 2013a; Lofstrom and Raphael 2013b). However, some 7 A ppendix A provides references, links, and a summary of the kinds of measures included in the BSCC AB 109 Jail Profile Supplement, CPOC Dashboard, and the AOC report. 8 California P enal Code § 13155 . 9 A "flash incarceration" is defined in California Penal Code § 3454 as a period of detention (1 –10 consecutive days) in county jail due to a violation of an offender's conditions of post -release community supervision . http:// www.ppic.org /main/home.asp Corrections Realignment and Data Collection in California 9 questions still cannot be fully answered using existing state-level sources. For example, it is currently not possible to analyze the recidivism patterns of 11 70(h) offenders because these offenders are either not tracked or are not identified in state- level data. Additionally, available sources do not capture the data necessary to identify effective correctional interventions. Improved data collection and analy sis is required to tell this important part of the realignment story. 10 Identifying Effective Strategies. While realignment impact studies look backward to discern the effects of this major policy shift, studies that identify effective interventions look f orward, seeking to identify practices that work so they can be adopted more broadly in the future. These data are essential to building the capacity for data- driven practices, but data collection in this area is currently the least developed of the three a reas of data collection described here. Data collection that identifies effective strategies would enable the analysis of specific correctional interventions aimed at reducing recidivism and enhancing successful reintegration into society. These data would link the recidivism outcomes of individual offenders with the services and sanctions they have received. Below we present the data priorities for a data collection effort that equips counties to identify effective strategies. 10 Some counties have collected data on realigned offenders and have produced workload reports and analyses of the effectiveness of specific interventions. Because these studies have been undertaken entirely within agencies or counties, with little attempt t o coordinate or standardize their approach with other counties, their benefit to audiences external to the county are limited. http:// www.ppic.org /main/home.asp Corrections Realignment and Data Collection in California 10 Data Priorities for Identifying Effective Strategies Evaluating the effectiveness of interventions on recidivism outcomes requires three major types of data. These types include (1) offender characteristics, including criminal histories; (2) the correctional interventions individuals experience at the county level through jail and probation systems; and (3) recidivism outcomes . Crucially, these data need to be collected at the individual level, and, once collected, they need to be linked together to provide a picture of ea ch offender as he or she moves through different agencies within the system. These three data types are described in further detail below. 11 Offender Characteristics. Keeping track of specific characteristics of offenders, such as demographic characteristic s and criminal histories, allows researchers and practitioners to identify subpopulations of interest and account for the role of offenders’ characteristics in outcomes. It is not sufficient to compare the outcomes of those who received a treatment (i.e., an intervention involving a service or sanction) with the outcomes of those who did not. In order to isolate the effect of a treatment, it is necessary to adjust for differences between the group that received the treatment and the group that did not. There are multiple ways of doing this, including using offender characteristics to match offenders between treatment and control groups, or using such data to control for differences between groups in a regression model. Collecting individual- level information about offender characteristics is essential to making appropriate “apples to apples” comparisons when assessing the effectiveness of an intervention. Interventions. Counties use a wide variety of intervention tools to reduce recidivism and maintain publi c safety. There are any number of reentry services and alternatives to incarceration . For example, job training is a reentry service commonly provided to offenders. To measure the effect of job training on recidivism outcomes, it is necessary to know which offenders were referred to the training. It is also useful to know if the referred offender entered and completed the training program. Additional details about the program, such as the duration, intensity ( e.g., dosage), and underlying approach (e.g., tr eatment model), are also helpful in making comparisons across program sites. Recidivism Outcomes. Finally, in order to assess the effects of realignment on crime and recidivism, and to identify effective practices, we need to capture the full range of rec idivism outcomes (including rearrest, reconviction, and return to prison or jail custody). 11 This report is limited to the minimal data necessary to identify effective correctional strategies. We do not address the issue of relative program costs and benefits. Program evaluations based on the type of data we describe in this report are the prerequisites for cost -benefit analyses. For further information see the Bureau of Justice Assistance, Center for Program Evaluatio n and Performance Measurement at www.bja.gov/evaluation/guide/gs6.htm . http:// www.ppic.org /main/home.asp Corrections Realignment and Data Collection in California 11 Recommendations for an Improved Data Collection System While researchers and evaluators readily agree that the data types discussed above comprise the relevant research components needed to identify effective strategies, there are currently a number of obstacles to compiling such data. In this section, we identify the impediments that limit the capacity for evaluating effective strategies at the county level, and we suggest four areas for improvement. Developing a fully functional county data collection and evaluation system will require (1) capturing the necessary data, (2) linking data across systems, (3) standardizing definitions across counties, and (4) upgradin g information technology systems to capture, integrate, and extract the data. We describe the implications of these challenges both in terms of practical decision -making and in terms of research into effective correctional strategies. Capturing Data Offender Characteristics. Risk and needs assessments produce data on the characteristics of individual offenders. These data enable community corrections practitioners to gauge whether an individual will reoffend (i.e., assess the risk) and understand the facto rs that drive the offending behavior (i.e., assess the needs). These data have practical importance because limited service and supervision resources often compel corrections practitioners to focus resources on those offenders who pose the greatest risk an d to direct these offenders to interventions that address their specific criminogenic needs. A risk and needs assessment requires information drawn from an offender’s criminal history and, in some cases, from structured interviews with the offender. In addition to the direct practical guidance that these data provide to practitioners, this information is also critical to evaluators because, without these data, evaluators lack the ability to account for the role of offender characteristics. Despite the impo rtance of these data, practitioners do not always gather or draw on these assessments. Although widely used across p robation departments (AOC 2012) , their use still varies across sheriff’s departments . Broader adoption of risk and needs assessments is the first step, but even when risk and needs assessments are used, there may be impediments to data compilation. For example, assessment data are often collected using proprietary standalone software. With out full integration of these data with case management systems, many probation departments have limited capacity to use these data to influence decisions. In the case of sheriff’s departments, there is much to gain by the wider adoption and use of these t ools. For example, if sheriff’s departments collected assessment data on a consistent basis, and fully integrated these data into case management systems, then decisions regarding release plans , alternative custody placement s, and placement s in custodial p rograms could more easily be informed by an assessment of needs and prioritized by a consideration of risk . This is the standard of practice in corrections nationwide. California should support broader adoption of these tools, leveraging risk and needs dat a to enhance effectiveness in offender management and improve consistency in decisions. Interventions. Capturing data that tracks the use of services by offenders is another challenge. As with the risk and needs data, some agencies lag further behind others in the collection of use -of -service data. A few county probation departments do not currently track probation referrals to mental health programs, employment services, or other kinds of services designed to address the sources of the offender’s criminal behavior. For these counties, the first step is to initiate a tracking system. For counties that do track referrals, http:// www.ppic.org /main/home.asp Corrections Realignment and Data Collection in California 12 the data trail often stops with the referral date. In some cases, this is because the service provider does not have the capacity to colle ct additional data. This is a particularly acute problem for nonprofit community - based organizations operating on shoestring budgets. However, in other instances, partnering county agencies are not tracking the service data, or they may be tracking the dat a but not feeding the data back to probation departments. At times, the use of paper files limits the ability of providers to feed information on client progress back to the probation department. Privacy considerations may also limit data sharing for some service providers. In all cases, the result is that it is time- consuming for probation officers to confirm whether referred offenders ever entered or completed a program . Capturing and relaying program entry data back to probation officers has practical i mportance. Without such data, probation officers cannot identify offenders who require additional attention or motivation to participate in programs. Knowing how likely offenders are to succeed in specific programs also assists probation officers in decidi ng whether they should continue making referrals to the program . Moreover, creating a data feedback loop not only aid s probation decision-making and improves the quality o f supervision but also increases the accountability of service providers . At present, counties often trust that providers are using evidence- based practices. At a minimum, in order to ensure accountability, referral data, entry data, and exit data ought to be collected and shared between probation departments and service providers. Service provider data are also important for the purposes of comprehensive evaluations, particularly for creating basic measures of program performance. For example, h igh attrition from referral to entry—the first step in accessing services— undermines the chances that the program will positively affect offender recidivism outcomes. If those referred to a program fail to show up, otherwise effective programs will appear to underperform in research studies . Similarly , data on who receives particular sanctions in response to non -compliant behavior is frequently scant . The lack of data on sanctions creates problems for probation officers and managers alike. For example, having access to dates of non -compliance as well as sanctioning dates is important because existing research shows that the swiftness and certainty of sanctions are essential to their effectiveness (Durlauf and Nagin 2011). In addition, capturing this kind of data facilitates good practices because the information is necessary for carry ing out graduated sanctioning. For example, probation officers should know if offender s have been given multiple referrals to a day reporting center before a flash incarceration is imposed . It is also important for managers to track the patterns of use for particular sanctions to assess whether they are applied swiftly, consistently , and appropriately across the agency . It is clear that capturing data in these areas facilitates research and evaluation. However, it is equally important that the captured data give corrections professionals the ability to monitor key aspects of their own work. To the extent that practitioners do not receive feedback from the service providers they rely on to alter the behavior of offenders under their supervision, they not onl y operate in the absence of potentially critical information, but they also allow service providers to avoid accountability for their work with offenders. Capturing these currently missing data elements is essential to supporting better outcomes for offend ers. These data enable better case management, promote accountability among service providers, and form the basis of high- quality evaluations. http://www.ppic.org /main/home.asp Corrections Realignment and Data Collection in California 13 Linking Data Across Systems The capacity to link and share data across agencies within local correctional systems is similarly in need of improvement. In many counties, officials have reported that realignment has intensified the need for collaboration among community corrections age ncies, in addition to increasing county agencies’ interactions with municipal police departments, city governments, and state agencies. This, in turn, has stimulated the desire to share data across different departments and levels of government. Data shari ng among agencies has long been a challenge for law enforcement agencies (Ball 2010; Ball and Weisberg 2010). Even though the Department of Justice has initiated some work in this area, there remains more work to be done. Criminal I dentification and I nformation Number. D ata must be collected on individual offenders as they move between different parts of the criminal justice system . To forge these data links, common identifying information on offenders must be used consistently across all parts of the syst em. Counties possess the Criminal Identification and Inf ormation (CI&I) number, an identification number assigned to every person arrested. The CI&I number is the ideal bridge between many different parts of the system. Probation departments and s heriff’s departments often have their own uni que identifiers that can usually be linked to CI&I numbers and then used to link to other sources. However, sometimes when risk and needs assessment data are collected using standalone software, offender names rather than identification numbers may be stored in the system. This makes it difficult to link these data to probation or s heriff’s departments, which primarily use local identification number s or the CI& I. Because data have historically been s iloed in different parts of the criminal justice system , some counties have developed data -sharing arrangements to link these data. Those counties that have not shared data across agencies in the past have noted that realignment has led to greater appreciation of the import ance of data linking and sharing. Integration of County and State Data. While data- sharing efforts are progressing among county agencies, the state currently lacks a system for data integration of state -level data with data held at the county level. The C alifornia Department of Corrections and Rehabilitation and the Department of Justice hold substantial criminal justice data. Historically, these two agencies have shared data in order to produce a comprehensive picture of the criminal histories, institutio nal experiences, and recidivism outcomes of the felony offender population held in and released from state prison. Since the passage of realignment, however, a large and increasing portion of the felony population will never reach state prison. Those popul ations are “off the radar” of state tracking systems and their information is not available to be shared for either law enforcement or research purposes. The result is that some counties cannot link data on their populations to state -level records to compu te recidivism measures. Efforts to close this gap are under way, but are not yet operational. Validation of Risk and Needs Assessments. Beyond the previous discussion on the adoption and integration of data from risk and needs assessments into routine pra ctice, validation of these instruments is also necessary. Developing the capacity to link risk assessments to recidivism outcomes on an ongoing basis is the minimum necessary step to determining whether a risk assessment method is performing adequately or requires improvement. Risk assessments that lack predictive validity may nonetheless provide a structure for decision -making and ensure equity in treatment; however, if they fail to adequately forecast who will reoffend, their usefulness as a mechanism for reducing recidivism and maintaining public safety remains open to question. Moreover, if risk assessments are not used or if they fail to identify the highest -risk groups, probation resources may be misdirected. For example, counties without access to hig h-quality risk information may rely more heavily on higher- cost incarceration strategies in cases where lower -cost alternatives may be adequate. http://www.ppic.org /main/home.asp Corrections Realignment and Data Collection in California 14 Standardizing Definitions and Measures A third area for improvement, one that is sometimes contentious, is the effort to standardize definitions and outcome measures. Standardizing the terminology used across counties and across agencies would allow the state to capitalize on the knowledge gained by individual counties as they put a variety of approaches into prac tice. As indicated above, the legislature mandated that the Board of State and Community Corrections address this issue, and this work is commencing at the BSCC. In spite of these developments, however, there is still widespread disagreement concerning the definition of key measures. Recidivism. Perhaps the topic that generates the greatest degree of contention is the measurement of recidivism. Recidivism rates can vary widely according to whether arrests, convictions, or returns to custody are used to det ermine whether an individual has recidivated. Recidivism rates will also vary with the length of the follow -up period under consideration. Re- arrests, re-convictions, and returns to custody each reveal something different about the intersection between off ender behaviors and local justice system practices. Hence, the key to creating a comprehensive system for evaluating recidivism is for counties and the state to collect data at each step along the path. Using this “building block” approach will allow maxim al flexibility for calculating a full array of recidivism measures. Because t he state, the CPOC , or law enforcement groups may favor one definition over others, it should be routine to collect data that facilitate the construction of multiple measures over different observational periods. Intervention. Attempts to standardize definitions in other areas can be equally problematic. In particular, what counts as an “ intervention” can be the source of considerable ambiguity. If programs are not directly design ed to reduce recidivism, are they truly interventions? For example, individuals might be assigned to an alternative custody program in which jail inmates are released from custody to work on roadside litter collection crews. Is the central goal of this pro gram to reduce recidivism, or is it a mechanism for relieving pressure on the jail population? Is it fair to assess such a program for its effect on recidivism when recidivism reduction is not the goal of the program? Program components matter as well. Fo r example, the components of a “life skills” class may vary widely from place to place, but these classes may be grouped together and judged as one form of treatment. Thus, ”apples to apples” comparisons are not just a concern when comparing offenders, but also when comparing treatment programs. In order to appropriately judge the effectiveness of particular interventions, we not only need to know the characteristics of the participants, but we also need to ensure that interventions are defined and implemen ted consistently within and across counties. Whereas some established correctional interventions benefit from a great deal of definitional consensus (e.g., cognitive behavioral therapy), more loosely defined interventions, along with local innovations supp orted under realignment, present challenges to collecting standardized data. Service providers in several counties have raised concerns that high -quality programs may be lumped together with lower- quality programs of the same type, resulting in findings th at underestimate the effectiveness of their programs. Settling on common definitions for intervention types will be challenging, but will ultimately benefit the state by enabling researchers to generalize findings from one context to the next. http://www.ppic.org /main/home.asp Corrections Realignment and Data Collection in California 15 Upgrading Information Technology Systems Many county information technology (IT) systems will require improvements to enable the kind of data collection, data linkage, and data extraction we have described. Counties may face one or more of the following technical challenges: (1) they may be using programming languages that are no longer supported or operating on systems that were built by companies that h ave gone out of business; (2) they may be using systems that were purchased “off the shelf,” and hence reliant on vendors and additional funds for system upgrades; or (3) they may be using locally developed systems that may not be integrated across agencies. For counties with outdated programming languages or systems from vendors who have gone out of business, f inding staff who are capable of making program changes can be very difficult. With these systems, seemingly simple tasks such as extracting data and producing a list of specific offender subpopulations may require substantial effort. Overall data storage capac ity can also be a problem with aging information system s. For example, the systems may allow a limited number of data elements to be captured, and thus adding a new field might require removing an existing one. If it is not possible to make adequate progra m changes, counties with outdated systems will eventually need to invest in newer systems. In counties with newer, locally developed systems, technologically altering the system for new purposes may be relatively easy . However, to execute these upgrades, a gency administrators must agree on the alterations, define the alterations clearly for programmers, devote resources for staff programming time, and , in some cases, provide additional training for end users . Similarly, in counties with “off -the -shelf” syst ems, upgrades may be technically straightforward, but they may require expensive contracting arrangements with vendors and close project oversight. In cases where counties are in the process of converting old IT systems or adding new components, they shoul d be encouraged to fully and systematically upgrade their systems so as to be able to capture and exchange relevant data. In particular, the adoption of new electronic case management systems in probation departments presents an opportunity for capturing c ritically important individual-level data on service referrals, participation, and outcomes. http://www.ppic.org /main/home.asp Corrections Realignment and Data Collection in California 16 The State’s Role in Supporting County Improvements In the preceding section, we made four recommendations for improving the quality and availability of corrections data: (1) capture the relevant data, (2) link data across systems, (3) standardize definitions of key measures, and (4) upgrade information technology systems to make the collection, sharing, and extraction of data easier and more effective. Here we foc us on the state role in supporting these county improvements, and provide examples of how these efforts could be funded. Given its legislative mandate, the BSCC could assume responsibility for coordinating the implementation of these improvements. In response to prison overcrowding, and to meet the increased need for county jails after the implementation of realignment, the state passed AB 900 and SB 1022 to fund the expansion and enhancement of local correctional facilities. The BSCC administered the proc ess of allocating these funds. We recommend a similar state investment to enhance the technological capacity for data -driven strategies and evidence -based practices at the county level. Unlike the long -term operational costs associated with expanded jail c apacity, these targeted IT changes would be short -term efforts to support counties in enhancing their data infrastructure. And, unlike the unsuccessful attempt to develop a statewide system for court case management, these technological improvements would be “grassroots” in nature. Given a well - designed and standardized set of project requirements, participating counties could not only improve their internal capacity but could also contribute a standardized set of data elements to a state -level research dat abase. This state -level coordination would maximize the opportunity for counties to share findings and allow the state to evaluate the statewide effects of realignment on recidivism and public safety outcomes. A voluntary and competitive grant program woul d allow the state to provide guidance to counties as they design their data infrastructure improvements, ensuring that new data collection systems meet minimum standards and reporting requirements. In addition to state funding, federal funds might also be identified to support these efforts. For example, the federal Edward Byrne Memorial Justice Assistance Grant (JAG) program includes “Planning, evaluation, and technological improvements” as one of the seven major JAG funding categories. In recent years, ho wever, only 8 to 12 percent of JAG funds nationwide have gone into this area. 12 In the past, California has opted to use these funds primarily to support the Marijuana Suppression Program, the Campaign Against Marijuana Planting program, and multi -community crime task forces. The fact that evaluation and technological improvements are presented as funding areas in competition with programs and direct services, rather than as integral components of the corrections system, may explain why this category has rec eived such a small share of JAG funding. The state could take a more active role in directing a share of each JAG award for county planning, evaluation, and technological improvements. With this integrated approach, the state could maintain flexibility in prioritizing specific program areas, while still expanding county capac ity for data-driven practices. Adopting this latter approach would also be consistent with national trends. For example, a recent report by New York University’s Brennan Center for Jus tice has recommended that when allocating JAG funds, the 12 Program areas for JAG funding include (1) law enforcement; (2) prosecution, court, and defense; (3) prevention and educati on programs; (4) corrections and community corrections; (5) drug treatment and enforcement; (6) crime victim and witness initiatives; and (7) planning, evaluation, and technology improvement. http://www.ppic.org /main/home.asp Corrections Realignment and Data Collection in California 17 U.S. Department of Justice should not only adopt “success-oriented” performance measures but should also encourage recipients to use funds to implement the data collection systems necessary to gather the information and to construct the proposed measures. The report also recommends that the U.S. Department of Justice should “provide as much technical assistance and training as possible to recipients,” noting that “this would make reporting on performa nce far easier” (Chettiar et al. 2013). Last , although this report focuses on policy issues related to AB 109, the data priorities we present and the recommendations we make will be applicable to future criminal justice practice reforms. The legislature may wish to revise future corrections funding models to reward counties for adopting practices that save the state money while maintaining public safety. If so, we recommend that the state begin investing in improvements that enable counties to track offenders, intervention strategies, and recidivism outcomes. http://www.ppic.org /main/home.asp Corrections Realignment and Data Collection in California 18 Conclusion Enacted in response to a series of federal court rulings and occurring in the midst of a state budget crisis, AB 109 represents a significant shift in responsibilities from the state to the counties. It has been characterized as “t he biggest penal experiment in modern history” (Santos 2013). This experiment has led to the adoption of a wide range of county strategies and has created a unique opportunity to make significant advances in reducing recidivism, increasing public safety, a nd conserving public resources. As it stands, the state has announced its commitment to these goals and its intent to meet them by relying more heavily on evidence -based practices, but many challenges remain before counties can evaluate their progress towa rd achieving these goals. The central problem is that community corrections practitioners lack the necessary information to make the best service and sanctioning decisions. Local- level policymakers lack objective data on program performance that could be used to direct course adjustments, and officials at the state level do not know whether the funds they have provided to counties are yielding the outcomes envisioned under realignment. Acquisition of the data necessary to identify effective practices is a goal that is within reach. However, it will require counties to make improvements in four areas: capturing data, linking data across systems, standardizing definitions, and upgrading technology to facilitate extraction of data for multiple purposes. Addre ssing these obstacles will require leadership and a directed use of available resources. But, if counties can make these adjustments, there will be significant benefits, including an improved ability to identify the most effective strategies and target res ources toward those correctional interventions, an expanded base of evidence to support difficult policy choices, and an increased ability to share successful interventions. For the state as a whole, increasing the capacity for data -driven practices at the county level will result in a more efficient, effective, and sustainable corrections system. It will also enable the state to better track the overall results of realignment and to more easily implement incentive- based funding in the future. http://www.ppic.org /main/home.asp Corrections Realignment and Data Collection in California 19 References Administrative Office of the Courts. 2011. SB 678 Year 1 Report: Implementation of the California Community Corrections Performance Incentive Act . Available at www.courts.ca.gov/documents/SB678 -Year-1-Report -FINAL.pdf . Administrative Office of the Courts. 2012. SB 678 Year 2 Report: Implementation of the California Community Corrections Performance Incentives Act. Available at www.courts.ca.gov/documents/SB678 -Year-2-report.pdf . Ball, W. David. 2010. “ E Pluribus Unum: Data and Operations Integration in the Cal ifornia Criminal Justice System.” Stanford Law and Policy Review (21): 277– 309. Ball, W. David, and Robert Weisberg. 2010. ”Criminal Justice Information Sharing: A Legal Primer for Criminal Practitioners in California.” Santa Clara Law Digital Commons. Available at http://digita lcommons.law.scu.edu/facpubs/551 . California Department of Justice. 2013. “Attorney General Kamala D. Harris Launches Initiative to Reduce Recidivism in California.” Office of the Attorney General, Press Release (November 20). Chettiar, Inimai, Lauren-Bro oke Eisen, and Nicole Fortier , with Timothy Ross. 2013. Reforming Funding to Reduce Mass Incarceration. Brennan Center for Justice at New York University School of Law . Available at www.brennancenter.org/sites/default/files/pu blications/REFORM_FUND_MASS_INCARC_web_0.pdf . Clawson, Elyse, and Meghan Guevara. 2011. Putting the Pieces Together: Practical Strategies for Implementing Evidence-Based Practices. U.S. Department of Justice, National Institute of Corrections, NIC Accessio n Number 024394. Crime and Justice Institute. 2004. Implementing Evidence-Based Practice in Community Corrections : The Principles of Effective Intervention. U.S. Department of Justice, National Institute of Corrections. Durlauf, Steven N., and Daniel S. Nagin. 2011. “Imprisonment and Crime: Can Both Be Reduced?” Criminology and Public Policy 10 (1): 13 –54. Lofstrom, Magnus, and Steven Raphael. 2013a. Impact of Realig nment on County Jail Populations. Public Policy Institute of California . Available at www.ppic.org/main/publication.asp?i=1063 . Lofstrom, Magnus, and Steven Raphael. 2013b. Public Safety Realignment and Crime Rates in California. Public Policy Institute of California . Available at www.ppic.org/main/publication.asp?i=1075 . Santos, Michael. 2013. “California’s Realignment: Real Prison Reform or Shell Game?” Crime, The Blog, Huffington Post, (March 11 ). A vailable at www.huffingtonpost.com/michael -santos/california-prison-realignment_b_2841392.html . http://www.ppic.org /main/home.asp Corrections Realignment and Data Collection in California 20 About the Authors Sonya Tafoya is a research associate at the Public Policy Institute of California . Her current work focuses on California’s criminal justice system. Before rejoining PPIC, she conducted research on chil dren in foster care at the Administrative Office of the Courts, and worked as a research associate at the Pew Hispanic Center, where she focused on Latino demographic trends. Her work has been published by PPIC, the Pew Hispanic Center, the Russell Sage Fo undation, the Levy Economics Institute at Bard College, and the Harvard Journal of Hispanic Policy . She holds an M.S. in plant biology from the University of California, Davis. Ryken Grattet is a research fellow at the Public Policy Institute of California and a p rofessor of sociology at the University of California, Davis . He previously served as assistant secretary of r esearch in the California Department of Corrections and Rehabilitation. His current work focuses on California correctional policy at the state and local levels. He is the author of Making Hate a Crime: From Social Movement to Law Enforcement (with Valerie Jenness), Parole Violations and Revocations in California (with Joan Petersilia and Jeffrey Lin), and numerous articles in professional a nd policy publications. He holds a Ph.D. in sociology from the University of California, Santa Barbara. Mia Bird is a research fellow at the Public Policy Institute of California , specializing in research regarding c orrections and h ealth and human services. Her current projects evaluate the effects of p ublic safety r ealignment on reentry and recidivism outcomes. Before joining PPIC, she served as a research and evaluation consultant with the San Francisco Office of the Public Defender and the San Fra ncisco Superior Court. She holds a Ph.D. in public policy, an M.A. in demography , and an M.P.P. from the University of California, B erkeley. She also serves on the faculty at the Goldman School of Public Policy at the University of California, Berkeley. Acknowledgments This report has benefited from numerous conversations with state and county officials and staff around California. PPIC colleagues Paul Warren, Joseph Hayes, Daniel Krimm, and Lynette Ubois offered helpful advice and feedback on early drafts . Susan Mauriello, CAO of the County of Santa Cruz, and W. David Ball of Santa Clara University School of Law provided insightful and encouraging reviews of the report. Any errors in this work are our own. http:// www.ppic.org /main/home.asp Corrections Realignment and Data Collection in California 21 PUBLIC POLICY INSTITUTE OF CALIFORNIA Board of Directors Donna Lucas, Chair Chief Executive Officer Lucas Public Affairs Mark Baldassare President and CEO Public Policy Institute of California Ruben Barrales President and CEO GROW Elect María Blanco Vice President, Civic Engagement California Community Foundation Brigitte Bren Attorney Walter B. Hewlett Member , Board of Directors The William and Flora Hewlett Foundation Phil Isenberg Vice Chair Delta Stewardship Council Mas Masumoto Author and Farmer Steven A. Merksamer Senior Partner Nielsen, Merksamer, Parrinello, Gross & Leoni, LLP Kim Polese Chairman ClearStreet, In c. Thomas C. Sutton Retired Chairman and CEO Pacific Life Insurance Company The Public Policy Institute of California is dedicated to informing and improving public policy in California through independent, objective, nonpartisan research on major economic, social, and political issues. The institute’s goal is to raise public awar eness and to give elected representatives and other decisionmakers a more informed basis for developing policies and programs. The institute’s research focuses on the underlying forces shaping California’s future, cutting across a wide range of public poli cy concerns, including economic development, education, environment and resources, governance, population, public finance, and social and health policy. PPIC is a public charity. It does not take or support positions on any ballot measures or on any local, state, or federal legislation, nor does it endorse, support, or oppose any political parties or candidates for public office. PPIC was established in 1994 with an endowment from William R. Hewlett. Mark Baldassare is President and Chief Executive Officer of PPIC. Donna Lucas is Chair of the Board of Directors. Short sections of text, not to exceed three paragraphs, may be quoted without written permission provided that full attribution is given to the source. Research publications reflect the views of the authors and do not necessarily reflect the views of the staff, officers, or Board of Directors of the Public Policy Institute of California. Copyright © 201 4 Public Policy Institute of California All rights reserved. San Francisco, CA PUBLIC POLICY INSTITUTE OF CALIFORNIA 500 Washington Street, Suite 600 San Francisco, California 94111 phone: 415.291.4400 fax: 415.291.4401 www.ppic.org PPIC SACRAMENTO CENT ER Senator Office Building 1121 L Street, Suite 801 Sacramento, California 95814 phone: 916.440.1120 fax: 916.440.1121" } ["___content":protected]=> string(102) "

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" ["_permalink":protected]=> string(100) "https://www.ppic.org/publication/corrections-realignment-and-data-collection-in-california/r_414str/" ["_next":protected]=> array(0) { } ["_prev":protected]=> array(0) { } ["_css_class":protected]=> NULL ["id"]=> int(8908) ["ID"]=> int(8908) ["post_author"]=> string(1) "1" ["post_content"]=> string(0) "" ["post_date"]=> string(19) "2017-05-20 02:42:00" ["post_excerpt"]=> string(0) "" ["post_parent"]=> int(4353) ["post_status"]=> string(7) "inherit" ["post_title"]=> string(8) "R 414STR" ["post_type"]=> string(10) "attachment" ["slug"]=> string(8) "r_414str" ["__type":protected]=> NULL ["_wp_attached_file"]=> string(12) "R_414STR.pdf" ["wpmf_size"]=> string(6) "296512" ["wpmf_filetype"]=> string(3) "pdf" ["wpmf_order"]=> string(1) "0" ["searchwp_content"]=> string(62413) "Corrections Realignment and Data Collection in California April 2014 Sonya Tafoya, Ryken Grattet, and Mia Bird with research support from Brandon Martin Summary The Public Safety Realignment Act of 2011 (AB 109) shifted authority for managing tens of thousands of lower-level felony offenders from the state to the counties. With a strong focus on reducing California’s high recidivism rates, the act encouraged counties to consider alternatives to incarceration and to adopt evidence- based practices. By implementing these new practices, the counties would achieve improved public safety returns on the state’s correctional investment. Yet, despite the promotion of an evidence -based approach, the act did not require or provide direct support for data collection, research, or evaluation. More than two years into realignment, some data collection efforts have been established and others are emerging, but the work of creating integrated data systems that can be used to demonstrate which correctional strategies are most effective remains largely undone. The Public Policy Institute of California is coordinating with the Board of State and Community Corrections and ele ven counties to address this need. In this report, we make a case for fully embracing the data -driven approach to corrections envisioned in AB 109. Based on our work with the eleven counties, we establish data collection priorities that will enable countie s to implement evidence- based practices. We make the following four recommendations for improving the immediate quality and availability of county data: (1) ensure that relevant data are captured, (2) link data across systems, (3) standardize definitions o f key measures, and (4) upgrade information technology systems to make the collection, extraction, and sharing of data easier. Counties can undertake some of these recommendations on their own, but other recommendations require further statewide leadership, additional technological investments, and collaboration and cooperation among justice partners. The state has made recent investments in new jail construction to increase the physical capacity for community corrections, and we suggest an analog ous investment to upgrade information technology systems and increase the capacity for evidence -based practice. Because the legislature has charged the Board of State and Community Corrections with providing leadership, coordination, and technical assistance to promote effective and evidence -based corrections practices, the responsibility for overseeing the recommended improvements in this report fit within the current role of the board. http:// www.ppic.org /main/home.asp Corrections Realignment and Data Collection in California 2 Contents Summary 2 Abbreviations 4 Introduction 5 A Vision of Data-Driven Community Corrections 6 Current State Data Collection Efforts 9 Data Priorities for Identifying Effective Strategies 11 Recommendations for an Improved Data Collection System 12 Capturing Data 12 Linking Data Across Systems 14 Standardizing Definitions and Measures 15 Upgrading Information Technology Systems 16 The State’s Role in Supporting County Improvements 17 Conclusion 19 References 20 About the Authors 21 Acknowledgments 21 A technical appendix to this paper is available on the PPIC website: www.ppic.org/content/pubs/other/414STR_appendix.pdf Abbreviations 1170(h) 1170(h) refers to the penal code designation for felony offenders convicted of non- violent, non-serious, non-sexual crimes. Under realignment, these offenders will serve their sentences in county jails rather than in state prisons. The term “1170(h)s” is often used colloquially to refer to these offenders. AB 109 Enacted into law in 2011, Assembly Bill 109 mandated the implementation of public safety realignment beginning on October 1, 2011. AB 109 shifted responsibility for certain low -level offenders from the state to the counties. AB 900 Enacted into law in 2007, Assembly Bill 900 authorized approximately $7.7 billion for prison construction and rehabilitation initiatives in order to relieve the significant overcrowding problems facing state prisons ; it also included $1.2 billion in lease revenue bond finan cing for the construction of county jails. AB 1050 Enacted into law in 2013, Assembly Bill 1050, among other mandates, required the Board of State and Community Corrections to develop standardized definitions for data collected by county probation departments and sheriff’s departments for the purpose of ev aluating the implementation of evidence -based practices and programs. AOC (California) Administrative Office of the Courts BSCC Board of State and Community Corrections CDCR California Department of Corrections and Rehabilitation CII Criminal Identification and Information number CPOC Chief Probation Officers of California CSAC California State Association of Counties DOJ (California) Department of Justice LAO Legislative Analyst’s Office PRCS Post-Release Community Supervision, as defined in realignment legislation, is the county-based supervision of offenders released from state prison, replacing the state- based parole program for a majority of released prisoners. SB 678 Enacted into law in 2009, The California Community Corrections Performance Incentive Act established a system of performance- based funding for county probation departments. SB 1022 Enacted into law in 2012, Senate Bill 1022 authorized $500 million in state lease revenue bond financing to fund local adult correctional facilit ies. http://www.ppic.org /main/home.asp Corrections Realignment and Data Collection in California 4 Introduction Public Safety Realignment (AB 109) fundamentally changed the correctional system in California, shifting responsibility for tens of thousands of lower -level felony offenders from the state correctional system to county systems. A central principle of AB 109 is that counties should have a strong hand in designing their own approach to managing offenders who are now under their purview. The statute declares that “fiscal policy and correctional practices should align to promote a just ice reinvestment strategy that fits each county,” and defines “justice reinvestment” as a “data -driven approach to reduce corrections spending and reinvest savings” using “evidence - based strategies designed to increase public safety.” 1 It is laudable that the state endorsed the use of a data -driven approach and evidence -based strategies; however, counties need additional support to meet this challenge. Realignment legislation did not dedicate funds for an evaluation of the effects of realignment subsequent to its implementation, nor did it provide counties with specific funds for assessing the success rates of their local correctional strategies. This was a missed opportunity. Through realignment, the state effectively created 58 county - level policy laborat ories. The variation across counties in correctional practices creates an ideal opportunity to identify cost -effective strategies and to disseminate these best practices across the state. Yet, without a consistent framework for data collection and evaluati on, weeding out failing strategies and identifying successful ones will be a haphazard process. Without strong evidence that adoption of another county's approach will be effective, counties will understandably be reluctant to change policies and practices that are familiar. This barrier to change is important because the long -term stability and sustainability of California’s criminal justice system depends not on the success of a few counties, but on the broad statewide adoption of successful correctional strategies that promote public safety and reduce reliance on California’s overextended prison system. Drawing on the goals laid out in AB 109, we begin our report by describing the features of a data -driven community corrections system as envisioned by th e legislation. We illustrate how counties can use data to improve the quality of the local corrections systems. More specifically, we show how the collection and use of these data will help counties to identify effective and efficient programs, match inter ventions to offender types, exchange info rmation on program successes and failures, hold service providers more accountable, and equip system leaders with an impartial basis for targeting resources. The community corrections system we describe also finds s upport among the national community of corrections professionals. Central to the vision is the idea that data and evidence should be integrated into policy decisions, that data should improve accountability, and that management and technological systems sh ould reinforce the use of data for ongoing improvements. We then turn to the current data collection efforts in California and discuss their strengths and limitations. Based on our work with policy makers, criminal justice analysts, and information techno logy staff from eleven counties, we identify the specific areas of data collection that are necessary for counties to identify effective approaches to reducing recidivism among realigned offenders. 2 We note some of the common obstacles to collecting this information, and we make four recommendations for improving the quality and availability of data, highlighting the benefits that would accrue from successfully addressing these issues. Finally, we review the state’s role in ensuring that the goals of real ignment are met. The state has invested in new jail construction to increase the physical capacity for community corrections, and we suggest an analogous investment in technical capacity and research capability. 1 California Penal Code § 3450. 2 This report is based on our experience reviewing and working with existing state -level data sources. A summary of these sources is provided in Appendix A . Our county -level findings are based on meetings with county policymakers, criminal justice analysts, and information technology staff. We conducted meetings in 11 counties participating in the BSCC -PPIC Multi-County Study. The meetings took place in the fall of 2013. http://www.ppic.org /main/home.asp Corrections Realignment and Data Collection in California 5 A Vision of Data-Driven Community Correctio ns California parole services, county probation departments, and sheriff’s offices have a variety of strategies to help an offender address the source of his or her criminal behavior, be it substance abuse, mental health problems, employability, lack of education, deficits in basic social skills, or other underlying circumstances. They also have at their disposal procedures, some newly available since the passage of AB 109, to sanction offenders when they fail to comply with the conditions of their supervis ion. Yet only a small fraction of these strategies ha s been rigorously researched and, even among the well -researched strategies, their applicability to California’s offender populations remains untested. As a result, practitioners have little basis for co nfidence that the selection of services and sanctions they make will result in the desired outcomes. Collecting data on both the characteristics of offenders and the correctional interventions those offenders receive would allow counties and the state to properly evaluate the effectiveness of specific interventions in reducing recidivism outcomes for particular offenders. To give practitioners a data -driven basis for their choices, and to thereby improve the odds that offenders will benefit from targeted i nterventions, we envision practitioners having readily available access to high -quality data on the characteristics of offenders and the underlying needs driving their criminal behavior. Based on these data, and given an adequate array of evidence- based interventions, practitioners could select a set of interventions demonstrated to be most effective among similar offenders. This approach stands in contrast to the “one- size-fits -all” solution commonly applied when information on offender characteristics and needs is not fully integrated into practice or when programming resources are scarce. In recent years, the movement to adopt evidence- based practices has expanded beyond the use of offender risk and needs data and the selection of research -based services to focus on the importance of integrating data and evidence into organizational practices (Clawson and Guevara 2011; Crime and Justice Institute 2004). This change reflects the recognition among community corrections experts that adoption of the latest evi dence -based program models without organizational support and oversight may not be sufficient to produce the desired results. Using an “integrated model” in evidence- based practices means that data are used not only to inform service and sanctioning choice s and to focus practitioners on the highest-priority outcomes, but also to provide feedback for managers about the ongoing performance of the organization, increasing transparency and the degree of accountability. Realignment legislation emphasizes the ne ed for effectiveness and efficiency in local corrections practices. If the state as a whole is going to move in this direction, then each county must not only be technologically capable of carrying out its own data -driven strategies but must also be able to contribute to the state’s understanding of what works. Currently, even as county agencies experiment with innovative approaches, there is no standardized means for demonstrating the effectiveness of these innovations such that counties both inform their own practices and share their findings. It is true that these problems pre- date the recent reforms, but state funding for realignment and the corresponding renewed public attention have made the shortcomings of existing data collection efforts even more pronounced. To resolve these shortcomings, we envision a system with a level of standardization that allows the state to capitalize on the experiences of various counties. With a common understanding of the data needed to identify effective strategies, count ies would be better positioned to learn from each other. http:// www.ppic.org /main/home.asp Corrections Realignment and Data Collection in California 6 The data priorities for identifying effective strategies that we describe below are not explicitly written into AB 109, but they are critical to the vision of a data-driven correctional system. Whil e AB 109 avoided mandates for the adoption of specific correctional policies and programs, it set the expectation that the system as a whole would move toward greater reliance on data. 3 Several associated developments reinforce this expectation and emphasi ze the need for greater coordination, interagency collaboration, and standardization. For example, the legislature established the Board of State and Community Corrections (BSCC) to provide “leadership, coordination, and technical assistance to promote eff ective state and local efforts and partnerships in California's adult and juvenile criminal justice system.” 4 The BSCC also has the broad duty “to collect and maintain available information and data about state and community correctional policies, practices, capacities, and needs.” 5 AB 1050, passed in 2013, further operationalized some of these functions by requiring the board “to develop definitions of specified key terms in order to facilitate consistency in local data collection, evaluation, and impleme ntation of evidence -based programs in consultation with stakeholders and experts .” The terms include but are not limited to “ recidivism, average daily population, and treatment program completion rates.” In response to the legislation, the BSCC has establi shed an Executive Steering Committee to develop definitions and a Data and Research Standing Committee to meet the data reporting requirements of AB 1050. The legislature has also mandated that the Administrative Office of the Courts (AOC) collect informa tion from the local trial courts on the implementation of realignment. 6 In recognition of the importance of data -driven correctional practices and data standardization, the attorney general launched the Division of Recidivism Reduction and Reentry in Novem ber 2013. The division is intended to “support counties and District Attorneys by partnering on best practices, such as the development of a statewide definition of recidivism, identifying grants to fund the creation and expansion of innovative anti -recidi vism programs and using technology to facilitate more effective data analysis and recidivism metrics”(California Department of Justice 2013). Data collection and evaluation are also central to some of the cost -saving, incentive -based policy initiatives th at have emerged in recent years. These initiatives are attractive because they hold the promise of furthering policy objectives while containing general fund expenditures. For example, under California’s SB 678 (the California Community Corrections Perform ance Incentives Act), the state provides counties with financial incentives to reduce probation failures that result in costly re -incarcerations in state prisons. Data on probation revocations are used to calculate the statewide savings resulting from lowe ring probation failures at the county level. The state then shares a portion of the savings with successful counties. In the first year of the program, the probation revocation rate dropped by 1.8 percentage points, saving the state $179 million dollars, a portion of which was shared with counties (AOC 2011). Social impact bonds represent another model of incentive- based funding that relies on the capacity to collect and evaluate data. Under this model, a private investor enters into an agreement with the s tate to provide and fund a social service and to deliver a mutually agreed upon and quantifiable program outcome. If the outcome is achieved, the state repays the investor with a return on the investment. For the state to benefit from social impact bonds f or corrections services, it would be necessary to expand the capacity for data collection in order to estimate returns on program investments. 3 California Penal Code § 3450. 4 California Penal Code § 6024 . 5 California Penal Code § 6027. 6 California Penal Code § 1315 5. http:// www.ppic.org /main/home.asp Corrections Realignment and Data Collection in California 7 Viewed in the context presented above, data collection and evaluation cannot be divorced from effective offender management, from ongoing system improvement, or from state cost -saving efforts. Improving the state’s capacity for data collection and evaluation must be given a higher priority because it is central to all aspects of improving corrections in California. If recognizing the centrality of data collection to the long -term success of community corrections in California is the first step toward achieving a data -driven correctional system, the next step is to examine the data collection efforts already under way and to map out a course for addressing any shortcomings. http://www.ppic.org /main/home.asp Corrections Realignment and Data Collection in California 8 Current State Data Collection Efforts Looking at the state’s current data collection efforts informs our understanding of the additional workload pressures brought on by realignment and allows us to assess the effects of realignment. Below we describe these efforts and highlight their strength s and weaknesses. We close this section by describing why enhanced data collection is critical for fully estimating the effects of realignment and for differentiating between those correctional interventions that merit replication and those that do not. Gauging Workload. The primary purpose of data collection in this area is to document the new workload pressures presented by realignment. 7 For example, the BSCC modified its Jail Profile Survey to include a variety of measures about the traffic of realigned offenders through local county jails. The Realignment Dashboard from the Chief Probation Officers of California (CPOC) is another instance of a data collection that documents workload. It provides counts of post -release community supervision (PRCS) and 1 170(h) cases, along with the movement into or out of a particular status (jail, new convictions for those under PRCS, new bookings into jail, PRCS violation cases, etc.). Yet another example of this type of data is an effort by the A OC to gather data on fe lony dispositions and petitions to revoke probation, PRCS, mandatory supervision, and parole. 8 Workload data collection allows us to examine how frequently counties are using some of the new practices made available since realignment’s implementation. For example, some workload data track the use of alternative custody programs, split sentences, jail -only sentences, and “flash incarcerations.” 9 Because all counties collect these workload data on an ongoing basis, we can examine the variation across countie s and the change over time in the use of these practices. The BSCC, CPOC, and AOC data are also important because they give counties the information they need to document the burdens that realigned offenders are placing on various components of the local c orrections system. The weakness of these data sources stems from the fact that they are summary data rather than individual - level data. When the summary numbers rise or fall, it is difficult to determine what is driving the change. For example, if a count y experiences an uptick in bookings among offenders on PRCS , it could be attributed to a failure of policy or practice. Alternatively, the uptick could be the result of a change in the overall composition of the PRCS population in terms of risk. Summary da ta alone do not provide a basis for discerning among possible explanations. Moreover, summary measures often cannot be broken down further to reveal the forces that underlie observed differences across counties. As a result, summary data may invite inappro priate comparisons across counties. Perhaps most limiting of all, summary data do not provide a link between the services and sanctions offenders are receiving and their recidivism outcomes. Assessing Impact. Existing state -level data sources, including b oth the new summary-level data we describe above, and the ongoing individual -level data collection by the California Department of Corrections and Rehabilitation and the Department of Justice, enable researchers to begin to assess the impact of realignment on crime and recidivism (Lofstrom and Raphael 2013a; Lofstrom and Raphael 2013b). However, some 7 A ppendix A provides references, links, and a summary of the kinds of measures included in the BSCC AB 109 Jail Profile Supplement, CPOC Dashboard, and the AOC report. 8 California P enal Code § 13155 . 9 A "flash incarceration" is defined in California Penal Code § 3454 as a period of detention (1 –10 consecutive days) in county jail due to a violation of an offender's conditions of post -release community supervision . http:// www.ppic.org /main/home.asp Corrections Realignment and Data Collection in California 9 questions still cannot be fully answered using existing state-level sources. For example, it is currently not possible to analyze the recidivism patterns of 11 70(h) offenders because these offenders are either not tracked or are not identified in state- level data. Additionally, available sources do not capture the data necessary to identify effective correctional interventions. Improved data collection and analy sis is required to tell this important part of the realignment story. 10 Identifying Effective Strategies. While realignment impact studies look backward to discern the effects of this major policy shift, studies that identify effective interventions look f orward, seeking to identify practices that work so they can be adopted more broadly in the future. These data are essential to building the capacity for data- driven practices, but data collection in this area is currently the least developed of the three a reas of data collection described here. Data collection that identifies effective strategies would enable the analysis of specific correctional interventions aimed at reducing recidivism and enhancing successful reintegration into society. These data would link the recidivism outcomes of individual offenders with the services and sanctions they have received. Below we present the data priorities for a data collection effort that equips counties to identify effective strategies. 10 Some counties have collected data on realigned offenders and have produced workload reports and analyses of the effectiveness of specific interventions. Because these studies have been undertaken entirely within agencies or counties, with little attempt t o coordinate or standardize their approach with other counties, their benefit to audiences external to the county are limited. http:// www.ppic.org /main/home.asp Corrections Realignment and Data Collection in California 10 Data Priorities for Identifying Effective Strategies Evaluating the effectiveness of interventions on recidivism outcomes requires three major types of data. These types include (1) offender characteristics, including criminal histories; (2) the correctional interventions individuals experience at the county level through jail and probation systems; and (3) recidivism outcomes . Crucially, these data need to be collected at the individual level, and, once collected, they need to be linked together to provide a picture of ea ch offender as he or she moves through different agencies within the system. These three data types are described in further detail below. 11 Offender Characteristics. Keeping track of specific characteristics of offenders, such as demographic characteristic s and criminal histories, allows researchers and practitioners to identify subpopulations of interest and account for the role of offenders’ characteristics in outcomes. It is not sufficient to compare the outcomes of those who received a treatment (i.e., an intervention involving a service or sanction) with the outcomes of those who did not. In order to isolate the effect of a treatment, it is necessary to adjust for differences between the group that received the treatment and the group that did not. There are multiple ways of doing this, including using offender characteristics to match offenders between treatment and control groups, or using such data to control for differences between groups in a regression model. Collecting individual- level information about offender characteristics is essential to making appropriate “apples to apples” comparisons when assessing the effectiveness of an intervention. Interventions. Counties use a wide variety of intervention tools to reduce recidivism and maintain publi c safety. There are any number of reentry services and alternatives to incarceration . For example, job training is a reentry service commonly provided to offenders. To measure the effect of job training on recidivism outcomes, it is necessary to know which offenders were referred to the training. It is also useful to know if the referred offender entered and completed the training program. Additional details about the program, such as the duration, intensity ( e.g., dosage), and underlying approach (e.g., tr eatment model), are also helpful in making comparisons across program sites. Recidivism Outcomes. Finally, in order to assess the effects of realignment on crime and recidivism, and to identify effective practices, we need to capture the full range of rec idivism outcomes (including rearrest, reconviction, and return to prison or jail custody). 11 This report is limited to the minimal data necessary to identify effective correctional strategies. We do not address the issue of relative program costs and benefits. Program evaluations based on the type of data we describe in this report are the prerequisites for cost -benefit analyses. For further information see the Bureau of Justice Assistance, Center for Program Evaluatio n and Performance Measurement at www.bja.gov/evaluation/guide/gs6.htm . http:// www.ppic.org /main/home.asp Corrections Realignment and Data Collection in California 11 Recommendations for an Improved Data Collection System While researchers and evaluators readily agree that the data types discussed above comprise the relevant research components needed to identify effective strategies, there are currently a number of obstacles to compiling such data. In this section, we identify the impediments that limit the capacity for evaluating effective strategies at the county level, and we suggest four areas for improvement. Developing a fully functional county data collection and evaluation system will require (1) capturing the necessary data, (2) linking data across systems, (3) standardizing definitions across counties, and (4) upgradin g information technology systems to capture, integrate, and extract the data. We describe the implications of these challenges both in terms of practical decision -making and in terms of research into effective correctional strategies. Capturing Data Offender Characteristics. Risk and needs assessments produce data on the characteristics of individual offenders. These data enable community corrections practitioners to gauge whether an individual will reoffend (i.e., assess the risk) and understand the facto rs that drive the offending behavior (i.e., assess the needs). These data have practical importance because limited service and supervision resources often compel corrections practitioners to focus resources on those offenders who pose the greatest risk an d to direct these offenders to interventions that address their specific criminogenic needs. A risk and needs assessment requires information drawn from an offender’s criminal history and, in some cases, from structured interviews with the offender. In addition to the direct practical guidance that these data provide to practitioners, this information is also critical to evaluators because, without these data, evaluators lack the ability to account for the role of offender characteristics. Despite the impo rtance of these data, practitioners do not always gather or draw on these assessments. Although widely used across p robation departments (AOC 2012) , their use still varies across sheriff’s departments . Broader adoption of risk and needs assessments is the first step, but even when risk and needs assessments are used, there may be impediments to data compilation. For example, assessment data are often collected using proprietary standalone software. With out full integration of these data with case management systems, many probation departments have limited capacity to use these data to influence decisions. In the case of sheriff’s departments, there is much to gain by the wider adoption and use of these t ools. For example, if sheriff’s departments collected assessment data on a consistent basis, and fully integrated these data into case management systems, then decisions regarding release plans , alternative custody placement s, and placement s in custodial p rograms could more easily be informed by an assessment of needs and prioritized by a consideration of risk . This is the standard of practice in corrections nationwide. California should support broader adoption of these tools, leveraging risk and needs dat a to enhance effectiveness in offender management and improve consistency in decisions. Interventions. Capturing data that tracks the use of services by offenders is another challenge. As with the risk and needs data, some agencies lag further behind others in the collection of use -of -service data. A few county probation departments do not currently track probation referrals to mental health programs, employment services, or other kinds of services designed to address the sources of the offender’s criminal behavior. For these counties, the first step is to initiate a tracking system. For counties that do track referrals, http:// www.ppic.org /main/home.asp Corrections Realignment and Data Collection in California 12 the data trail often stops with the referral date. In some cases, this is because the service provider does not have the capacity to colle ct additional data. This is a particularly acute problem for nonprofit community - based organizations operating on shoestring budgets. However, in other instances, partnering county agencies are not tracking the service data, or they may be tracking the dat a but not feeding the data back to probation departments. At times, the use of paper files limits the ability of providers to feed information on client progress back to the probation department. Privacy considerations may also limit data sharing for some service providers. In all cases, the result is that it is time- consuming for probation officers to confirm whether referred offenders ever entered or completed a program . Capturing and relaying program entry data back to probation officers has practical i mportance. Without such data, probation officers cannot identify offenders who require additional attention or motivation to participate in programs. Knowing how likely offenders are to succeed in specific programs also assists probation officers in decidi ng whether they should continue making referrals to the program . Moreover, creating a data feedback loop not only aid s probation decision-making and improves the quality o f supervision but also increases the accountability of service providers . At present, counties often trust that providers are using evidence- based practices. At a minimum, in order to ensure accountability, referral data, entry data, and exit data ought to be collected and shared between probation departments and service providers. Service provider data are also important for the purposes of comprehensive evaluations, particularly for creating basic measures of program performance. For example, h igh attrition from referral to entry—the first step in accessing services— undermines the chances that the program will positively affect offender recidivism outcomes. If those referred to a program fail to show up, otherwise effective programs will appear to underperform in research studies . Similarly , data on who receives particular sanctions in response to non -compliant behavior is frequently scant . The lack of data on sanctions creates problems for probation officers and managers alike. For example, having access to dates of non -compliance as well as sanctioning dates is important because existing research shows that the swiftness and certainty of sanctions are essential to their effectiveness (Durlauf and Nagin 2011). In addition, capturing this kind of data facilitates good practices because the information is necessary for carry ing out graduated sanctioning. For example, probation officers should know if offender s have been given multiple referrals to a day reporting center before a flash incarceration is imposed . It is also important for managers to track the patterns of use for particular sanctions to assess whether they are applied swiftly, consistently , and appropriately across the agency . It is clear that capturing data in these areas facilitates research and evaluation. However, it is equally important that the captured data give corrections professionals the ability to monitor key aspects of their own work. To the extent that practitioners do not receive feedback from the service providers they rely on to alter the behavior of offenders under their supervision, they not onl y operate in the absence of potentially critical information, but they also allow service providers to avoid accountability for their work with offenders. Capturing these currently missing data elements is essential to supporting better outcomes for offend ers. These data enable better case management, promote accountability among service providers, and form the basis of high- quality evaluations. http://www.ppic.org /main/home.asp Corrections Realignment and Data Collection in California 13 Linking Data Across Systems The capacity to link and share data across agencies within local correctional systems is similarly in need of improvement. In many counties, officials have reported that realignment has intensified the need for collaboration among community corrections age ncies, in addition to increasing county agencies’ interactions with municipal police departments, city governments, and state agencies. This, in turn, has stimulated the desire to share data across different departments and levels of government. Data shari ng among agencies has long been a challenge for law enforcement agencies (Ball 2010; Ball and Weisberg 2010). Even though the Department of Justice has initiated some work in this area, there remains more work to be done. Criminal I dentification and I nformation Number. D ata must be collected on individual offenders as they move between different parts of the criminal justice system . To forge these data links, common identifying information on offenders must be used consistently across all parts of the syst em. Counties possess the Criminal Identification and Inf ormation (CI&I) number, an identification number assigned to every person arrested. The CI&I number is the ideal bridge between many different parts of the system. Probation departments and s heriff’s departments often have their own uni que identifiers that can usually be linked to CI&I numbers and then used to link to other sources. However, sometimes when risk and needs assessment data are collected using standalone software, offender names rather than identification numbers may be stored in the system. This makes it difficult to link these data to probation or s heriff’s departments, which primarily use local identification number s or the CI& I. Because data have historically been s iloed in different parts of the criminal justice system , some counties have developed data -sharing arrangements to link these data. Those counties that have not shared data across agencies in the past have noted that realignment has led to greater appreciation of the import ance of data linking and sharing. Integration of County and State Data. While data- sharing efforts are progressing among county agencies, the state currently lacks a system for data integration of state -level data with data held at the county level. The C alifornia Department of Corrections and Rehabilitation and the Department of Justice hold substantial criminal justice data. Historically, these two agencies have shared data in order to produce a comprehensive picture of the criminal histories, institutio nal experiences, and recidivism outcomes of the felony offender population held in and released from state prison. Since the passage of realignment, however, a large and increasing portion of the felony population will never reach state prison. Those popul ations are “off the radar” of state tracking systems and their information is not available to be shared for either law enforcement or research purposes. The result is that some counties cannot link data on their populations to state -level records to compu te recidivism measures. Efforts to close this gap are under way, but are not yet operational. Validation of Risk and Needs Assessments. Beyond the previous discussion on the adoption and integration of data from risk and needs assessments into routine pra ctice, validation of these instruments is also necessary. Developing the capacity to link risk assessments to recidivism outcomes on an ongoing basis is the minimum necessary step to determining whether a risk assessment method is performing adequately or requires improvement. Risk assessments that lack predictive validity may nonetheless provide a structure for decision -making and ensure equity in treatment; however, if they fail to adequately forecast who will reoffend, their usefulness as a mechanism for reducing recidivism and maintaining public safety remains open to question. Moreover, if risk assessments are not used or if they fail to identify the highest -risk groups, probation resources may be misdirected. For example, counties without access to hig h-quality risk information may rely more heavily on higher- cost incarceration strategies in cases where lower -cost alternatives may be adequate. http://www.ppic.org /main/home.asp Corrections Realignment and Data Collection in California 14 Standardizing Definitions and Measures A third area for improvement, one that is sometimes contentious, is the effort to standardize definitions and outcome measures. Standardizing the terminology used across counties and across agencies would allow the state to capitalize on the knowledge gained by individual counties as they put a variety of approaches into prac tice. As indicated above, the legislature mandated that the Board of State and Community Corrections address this issue, and this work is commencing at the BSCC. In spite of these developments, however, there is still widespread disagreement concerning the definition of key measures. Recidivism. Perhaps the topic that generates the greatest degree of contention is the measurement of recidivism. Recidivism rates can vary widely according to whether arrests, convictions, or returns to custody are used to det ermine whether an individual has recidivated. Recidivism rates will also vary with the length of the follow -up period under consideration. Re- arrests, re-convictions, and returns to custody each reveal something different about the intersection between off ender behaviors and local justice system practices. Hence, the key to creating a comprehensive system for evaluating recidivism is for counties and the state to collect data at each step along the path. Using this “building block” approach will allow maxim al flexibility for calculating a full array of recidivism measures. Because t he state, the CPOC , or law enforcement groups may favor one definition over others, it should be routine to collect data that facilitate the construction of multiple measures over different observational periods. Intervention. Attempts to standardize definitions in other areas can be equally problematic. In particular, what counts as an “ intervention” can be the source of considerable ambiguity. If programs are not directly design ed to reduce recidivism, are they truly interventions? For example, individuals might be assigned to an alternative custody program in which jail inmates are released from custody to work on roadside litter collection crews. Is the central goal of this pro gram to reduce recidivism, or is it a mechanism for relieving pressure on the jail population? Is it fair to assess such a program for its effect on recidivism when recidivism reduction is not the goal of the program? Program components matter as well. Fo r example, the components of a “life skills” class may vary widely from place to place, but these classes may be grouped together and judged as one form of treatment. Thus, ”apples to apples” comparisons are not just a concern when comparing offenders, but also when comparing treatment programs. In order to appropriately judge the effectiveness of particular interventions, we not only need to know the characteristics of the participants, but we also need to ensure that interventions are defined and implemen ted consistently within and across counties. Whereas some established correctional interventions benefit from a great deal of definitional consensus (e.g., cognitive behavioral therapy), more loosely defined interventions, along with local innovations supp orted under realignment, present challenges to collecting standardized data. Service providers in several counties have raised concerns that high -quality programs may be lumped together with lower- quality programs of the same type, resulting in findings th at underestimate the effectiveness of their programs. Settling on common definitions for intervention types will be challenging, but will ultimately benefit the state by enabling researchers to generalize findings from one context to the next. http://www.ppic.org /main/home.asp Corrections Realignment and Data Collection in California 15 Upgrading Information Technology Systems Many county information technology (IT) systems will require improvements to enable the kind of data collection, data linkage, and data extraction we have described. Counties may face one or more of the following technical challenges: (1) they may be using programming languages that are no longer supported or operating on systems that were built by companies that h ave gone out of business; (2) they may be using systems that were purchased “off the shelf,” and hence reliant on vendors and additional funds for system upgrades; or (3) they may be using locally developed systems that may not be integrated across agencies. For counties with outdated programming languages or systems from vendors who have gone out of business, f inding staff who are capable of making program changes can be very difficult. With these systems, seemingly simple tasks such as extracting data and producing a list of specific offender subpopulations may require substantial effort. Overall data storage capac ity can also be a problem with aging information system s. For example, the systems may allow a limited number of data elements to be captured, and thus adding a new field might require removing an existing one. If it is not possible to make adequate progra m changes, counties with outdated systems will eventually need to invest in newer systems. In counties with newer, locally developed systems, technologically altering the system for new purposes may be relatively easy . However, to execute these upgrades, a gency administrators must agree on the alterations, define the alterations clearly for programmers, devote resources for staff programming time, and , in some cases, provide additional training for end users . Similarly, in counties with “off -the -shelf” syst ems, upgrades may be technically straightforward, but they may require expensive contracting arrangements with vendors and close project oversight. In cases where counties are in the process of converting old IT systems or adding new components, they shoul d be encouraged to fully and systematically upgrade their systems so as to be able to capture and exchange relevant data. In particular, the adoption of new electronic case management systems in probation departments presents an opportunity for capturing c ritically important individual-level data on service referrals, participation, and outcomes. http://www.ppic.org /main/home.asp Corrections Realignment and Data Collection in California 16 The State’s Role in Supporting County Improvements In the preceding section, we made four recommendations for improving the quality and availability of corrections data: (1) capture the relevant data, (2) link data across systems, (3) standardize definitions of key measures, and (4) upgrade information technology systems to make the collection, sharing, and extraction of data easier and more effective. Here we foc us on the state role in supporting these county improvements, and provide examples of how these efforts could be funded. Given its legislative mandate, the BSCC could assume responsibility for coordinating the implementation of these improvements. In response to prison overcrowding, and to meet the increased need for county jails after the implementation of realignment, the state passed AB 900 and SB 1022 to fund the expansion and enhancement of local correctional facilities. The BSCC administered the proc ess of allocating these funds. We recommend a similar state investment to enhance the technological capacity for data -driven strategies and evidence -based practices at the county level. Unlike the long -term operational costs associated with expanded jail c apacity, these targeted IT changes would be short -term efforts to support counties in enhancing their data infrastructure. And, unlike the unsuccessful attempt to develop a statewide system for court case management, these technological improvements would be “grassroots” in nature. Given a well - designed and standardized set of project requirements, participating counties could not only improve their internal capacity but could also contribute a standardized set of data elements to a state -level research dat abase. This state -level coordination would maximize the opportunity for counties to share findings and allow the state to evaluate the statewide effects of realignment on recidivism and public safety outcomes. A voluntary and competitive grant program woul d allow the state to provide guidance to counties as they design their data infrastructure improvements, ensuring that new data collection systems meet minimum standards and reporting requirements. In addition to state funding, federal funds might also be identified to support these efforts. For example, the federal Edward Byrne Memorial Justice Assistance Grant (JAG) program includes “Planning, evaluation, and technological improvements” as one of the seven major JAG funding categories. In recent years, ho wever, only 8 to 12 percent of JAG funds nationwide have gone into this area. 12 In the past, California has opted to use these funds primarily to support the Marijuana Suppression Program, the Campaign Against Marijuana Planting program, and multi -community crime task forces. The fact that evaluation and technological improvements are presented as funding areas in competition with programs and direct services, rather than as integral components of the corrections system, may explain why this category has rec eived such a small share of JAG funding. The state could take a more active role in directing a share of each JAG award for county planning, evaluation, and technological improvements. With this integrated approach, the state could maintain flexibility in prioritizing specific program areas, while still expanding county capac ity for data-driven practices. Adopting this latter approach would also be consistent with national trends. For example, a recent report by New York University’s Brennan Center for Jus tice has recommended that when allocating JAG funds, the 12 Program areas for JAG funding include (1) law enforcement; (2) prosecution, court, and defense; (3) prevention and educati on programs; (4) corrections and community corrections; (5) drug treatment and enforcement; (6) crime victim and witness initiatives; and (7) planning, evaluation, and technology improvement. http://www.ppic.org /main/home.asp Corrections Realignment and Data Collection in California 17 U.S. Department of Justice should not only adopt “success-oriented” performance measures but should also encourage recipients to use funds to implement the data collection systems necessary to gather the information and to construct the proposed measures. The report also recommends that the U.S. Department of Justice should “provide as much technical assistance and training as possible to recipients,” noting that “this would make reporting on performa nce far easier” (Chettiar et al. 2013). Last , although this report focuses on policy issues related to AB 109, the data priorities we present and the recommendations we make will be applicable to future criminal justice practice reforms. The legislature may wish to revise future corrections funding models to reward counties for adopting practices that save the state money while maintaining public safety. If so, we recommend that the state begin investing in improvements that enable counties to track offenders, intervention strategies, and recidivism outcomes. http://www.ppic.org /main/home.asp Corrections Realignment and Data Collection in California 18 Conclusion Enacted in response to a series of federal court rulings and occurring in the midst of a state budget crisis, AB 109 represents a significant shift in responsibilities from the state to the counties. It has been characterized as “t he biggest penal experiment in modern history” (Santos 2013). This experiment has led to the adoption of a wide range of county strategies and has created a unique opportunity to make significant advances in reducing recidivism, increasing public safety, a nd conserving public resources. As it stands, the state has announced its commitment to these goals and its intent to meet them by relying more heavily on evidence -based practices, but many challenges remain before counties can evaluate their progress towa rd achieving these goals. The central problem is that community corrections practitioners lack the necessary information to make the best service and sanctioning decisions. Local- level policymakers lack objective data on program performance that could be used to direct course adjustments, and officials at the state level do not know whether the funds they have provided to counties are yielding the outcomes envisioned under realignment. Acquisition of the data necessary to identify effective practices is a goal that is within reach. However, it will require counties to make improvements in four areas: capturing data, linking data across systems, standardizing definitions, and upgrading technology to facilitate extraction of data for multiple purposes. Addre ssing these obstacles will require leadership and a directed use of available resources. But, if counties can make these adjustments, there will be significant benefits, including an improved ability to identify the most effective strategies and target res ources toward those correctional interventions, an expanded base of evidence to support difficult policy choices, and an increased ability to share successful interventions. For the state as a whole, increasing the capacity for data -driven practices at the county level will result in a more efficient, effective, and sustainable corrections system. It will also enable the state to better track the overall results of realignment and to more easily implement incentive- based funding in the future. http://www.ppic.org /main/home.asp Corrections Realignment and Data Collection in California 19 References Administrative Office of the Courts. 2011. SB 678 Year 1 Report: Implementation of the California Community Corrections Performance Incentive Act . Available at www.courts.ca.gov/documents/SB678 -Year-1-Report -FINAL.pdf . Administrative Office of the Courts. 2012. SB 678 Year 2 Report: Implementation of the California Community Corrections Performance Incentives Act. Available at www.courts.ca.gov/documents/SB678 -Year-2-report.pdf . Ball, W. David. 2010. “ E Pluribus Unum: Data and Operations Integration in the Cal ifornia Criminal Justice System.” Stanford Law and Policy Review (21): 277– 309. Ball, W. David, and Robert Weisberg. 2010. ”Criminal Justice Information Sharing: A Legal Primer for Criminal Practitioners in California.” Santa Clara Law Digital Commons. Available at http://digita lcommons.law.scu.edu/facpubs/551 . California Department of Justice. 2013. “Attorney General Kamala D. Harris Launches Initiative to Reduce Recidivism in California.” Office of the Attorney General, Press Release (November 20). Chettiar, Inimai, Lauren-Bro oke Eisen, and Nicole Fortier , with Timothy Ross. 2013. Reforming Funding to Reduce Mass Incarceration. Brennan Center for Justice at New York University School of Law . Available at www.brennancenter.org/sites/default/files/pu blications/REFORM_FUND_MASS_INCARC_web_0.pdf . Clawson, Elyse, and Meghan Guevara. 2011. Putting the Pieces Together: Practical Strategies for Implementing Evidence-Based Practices. U.S. Department of Justice, National Institute of Corrections, NIC Accessio n Number 024394. Crime and Justice Institute. 2004. Implementing Evidence-Based Practice in Community Corrections : The Principles of Effective Intervention. U.S. Department of Justice, National Institute of Corrections. Durlauf, Steven N., and Daniel S. Nagin. 2011. “Imprisonment and Crime: Can Both Be Reduced?” Criminology and Public Policy 10 (1): 13 –54. Lofstrom, Magnus, and Steven Raphael. 2013a. Impact of Realig nment on County Jail Populations. Public Policy Institute of California . Available at www.ppic.org/main/publication.asp?i=1063 . Lofstrom, Magnus, and Steven Raphael. 2013b. Public Safety Realignment and Crime Rates in California. Public Policy Institute of California . Available at www.ppic.org/main/publication.asp?i=1075 . Santos, Michael. 2013. “California’s Realignment: Real Prison Reform or Shell Game?” Crime, The Blog, Huffington Post, (March 11 ). A vailable at www.huffingtonpost.com/michael -santos/california-prison-realignment_b_2841392.html . http://www.ppic.org /main/home.asp Corrections Realignment and Data Collection in California 20 About the Authors Sonya Tafoya is a research associate at the Public Policy Institute of California . Her current work focuses on California’s criminal justice system. Before rejoining PPIC, she conducted research on chil dren in foster care at the Administrative Office of the Courts, and worked as a research associate at the Pew Hispanic Center, where she focused on Latino demographic trends. Her work has been published by PPIC, the Pew Hispanic Center, the Russell Sage Fo undation, the Levy Economics Institute at Bard College, and the Harvard Journal of Hispanic Policy . She holds an M.S. in plant biology from the University of California, Davis. Ryken Grattet is a research fellow at the Public Policy Institute of California and a p rofessor of sociology at the University of California, Davis . He previously served as assistant secretary of r esearch in the California Department of Corrections and Rehabilitation. His current work focuses on California correctional policy at the state and local levels. He is the author of Making Hate a Crime: From Social Movement to Law Enforcement (with Valerie Jenness), Parole Violations and Revocations in California (with Joan Petersilia and Jeffrey Lin), and numerous articles in professional a nd policy publications. He holds a Ph.D. in sociology from the University of California, Santa Barbara. Mia Bird is a research fellow at the Public Policy Institute of California , specializing in research regarding c orrections and h ealth and human services. Her current projects evaluate the effects of p ublic safety r ealignment on reentry and recidivism outcomes. Before joining PPIC, she served as a research and evaluation consultant with the San Francisco Office of the Public Defender and the San Fra ncisco Superior Court. She holds a Ph.D. in public policy, an M.A. in demography , and an M.P.P. from the University of California, B erkeley. She also serves on the faculty at the Goldman School of Public Policy at the University of California, Berkeley. Acknowledgments This report has benefited from numerous conversations with state and county officials and staff around California. PPIC colleagues Paul Warren, Joseph Hayes, Daniel Krimm, and Lynette Ubois offered helpful advice and feedback on early drafts . Susan Mauriello, CAO of the County of Santa Cruz, and W. David Ball of Santa Clara University School of Law provided insightful and encouraging reviews of the report. Any errors in this work are our own. http:// www.ppic.org /main/home.asp Corrections Realignment and Data Collection in California 21 PUBLIC POLICY INSTITUTE OF CALIFORNIA Board of Directors Donna Lucas, Chair Chief Executive Officer Lucas Public Affairs Mark Baldassare President and CEO Public Policy Institute of California Ruben Barrales President and CEO GROW Elect María Blanco Vice President, Civic Engagement California Community Foundation Brigitte Bren Attorney Walter B. Hewlett Member , Board of Directors The William and Flora Hewlett Foundation Phil Isenberg Vice Chair Delta Stewardship Council Mas Masumoto Author and Farmer Steven A. Merksamer Senior Partner Nielsen, Merksamer, Parrinello, Gross & Leoni, LLP Kim Polese Chairman ClearStreet, In c. Thomas C. Sutton Retired Chairman and CEO Pacific Life Insurance Company The Public Policy Institute of California is dedicated to informing and improving public policy in California through independent, objective, nonpartisan research on major economic, social, and political issues. The institute’s goal is to raise public awar eness and to give elected representatives and other decisionmakers a more informed basis for developing policies and programs. The institute’s research focuses on the underlying forces shaping California’s future, cutting across a wide range of public poli cy concerns, including economic development, education, environment and resources, governance, population, public finance, and social and health policy. PPIC is a public charity. It does not take or support positions on any ballot measures or on any local, state, or federal legislation, nor does it endorse, support, or oppose any political parties or candidates for public office. PPIC was established in 1994 with an endowment from William R. Hewlett. Mark Baldassare is President and Chief Executive Officer of PPIC. Donna Lucas is Chair of the Board of Directors. Short sections of text, not to exceed three paragraphs, may be quoted without written permission provided that full attribution is given to the source. Research publications reflect the views of the authors and do not necessarily reflect the views of the staff, officers, or Board of Directors of the Public Policy Institute of California. Copyright © 201 4 Public Policy Institute of California All rights reserved. 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