Criminal Justice Policy Review - Sage Publications

http://cjp.sagepub.com

Criminal Justice Policy Review

DOI: 10.1177/0887403406292956 2007; 18; 3 Criminal Justice Policy Review

Nancy Rodriguez and Vincent J. Webb Drug Treatment

Probation Officers, Prosecutors, and Judges Pre- and Post-Mandatory Probation Violations, Revocations, and Imprisonment: The Decisions of

http://cjp.sagepub.com/cgi/content/abstract/18/1/3 The online version of this article can be found at:

Published by:

http://www.sagepublications.com

On behalf of: Department of Criminology at Indiana University of Pennsylvania

can be found at:Criminal Justice Policy Review Additional services and information for

http://cjp.sagepub.com/cgi/alerts Email Alerts:

http://cjp.sagepub.com/subscriptions Subscriptions:

http://www.sagepub.com/journalsReprints.navReprints:

http://www.sagepub.com/journalsPermissions.navPermissions:

http://cjp.sagepub.com/cgi/content/refs/18/1/3 Citations

by Vic Strasburger on March 11, 2009 http://cjp.sagepub.comDownloaded from

http://www.hhs.iup.edu/cr/CJPR/index.htm

http://cjp.sagepub.com/cgi/alerts

http://cjp.sagepub.com/subscriptions

http://www.sagepub.com/journalsReprints.nav

http://www.sagepub.com/journalsPermissions.nav

http://cjp.sagepub.com/cgi/content/refs/18/1/3


3

Authors’Note: Preparation of this article was assisted by a grant from the Robert Wood Johnson FoundationSubstance Abuse Policy Research Program. The authors would like to thank the editor and reviewers fortheir insightful comments on an earlier draft of this article.

Criminal JusticePolicy Review

Volume 18 Number 1March 2007 3-30

© 2007 Sage Publications10.1177/0887403406292956

http://cjp.sagepub.comhosted at

http://online.sagepub.com

Probation Violations,Revocations, and ImprisonmentThe Decisions of Probation Officers,Prosecutors, and Judges Pre- and Post-Mandatory Drug TreatmentNancy RodriguezArizona State University, GlendaleVincent J. WebbSam Houston State University, Huntsville, TX

The focus of previous probation studies has been on identifying the significant predictorsof probation outcomes (e.g., violations and arrests) and probation processes (e.g., revo-cation). In this study, the authors examine how the passage of Arizona’s mandatory drugtreatment law affected probation violations and the revocation process. They rely on pro-bation, prosecution, and sentencing case file data of imprisoned low-level drug offendersto analyze how the mandatory drug treatment law influenced the decision-makingprocesses of probation officers, prosecutors, and judges. Findings indicate that the major-ity of revocations leading to incarceration involved technical violations and not the com-mission of new crimes. Furthermore, the type of violations significantly differed pre- andpostimplementation of the law, as did prosecution and sentencing decisions. Policy impli-cations for probation supervision and drug treatment laws are discussed.

Keywords: probation revocation; probation violations; mandatory drug treatment

Mandatory drug treatment laws, the latest drug control policy, require that low-level drug offenders receive substance abuse treatment while on probation

rather than be incarcerated. As studies begin to assess the impact of such laws onoffenders (Farabee, Hser, Anglin, & Huang, 2004; Hser et al., 2003; Longshore et al.,2004; Speiglman, Klein, Miller, & Noble, 2003), minimal attention has been devotedto how probation departments, prosecutors, and judges have adjusted their practicesto process low-level drug offenders. For example, it is unclear whether the types ofprobation violations that result in revocations have changed since the implementationof mandatory drug treatment laws and whether prosecutors and judges have modifiedhow they handle such revocations. Although prosecutors’ and judges’ discretion isever present in court decision-making processes, the implementation of mandatory



4 Criminal Justice Policy Review

drug treatment laws certainly reduced, and in some cases eliminated, the use of dis-cretion in particular cases (e.g., first- or second-time drug offenders). Similarly, theformal documentation of probation violations and the revocation of probation areprocesses heavily guided by probation officers’ discretion.

Although research has not examined the direct relationship among these agents,in the case of low-level drug offenses, it is certainly possible that probation viola-tions have an influence on prosecution and sentencing outcomes. A recent study onthe imprisonment of low-level drug offenders pre- and postimplementation ofArizona’s mandatory drug treatment law presents the first detailed analysis of therelationship between prosecution decisions (e.g., plea bargaining) and the sentenc-ing of low-level drug offenders (Riley et al., 2005). Findings from this study showthat 60% of offenders sentenced to prison for a low-level drug offense were offend-ers who had probation revoked (Riley et al., 2005).

To establish how mandatory drug treatment laws have affected the decision-making processes of probation officers, prosecutors, and judges, we examine theseprocesses in Arizona, which was the first state to legislate mandatory drug treatmentfor low-level drug offenders. We have set out to examine three primary decision-making processes. First, we identify the types of technical violations that lead to pro-bation revocation and imprisonment of low-level drug offenders in the state. Second,we examine how prosecutors handle revocation cases by analyzing changes in thenumber of charges filed and changes in severity of the charges. Third, we analyzethe relationship between the types of probation violations and sentence length.Specifically, we establish whether probationers who had probation revoked weretreated more severely by judges when they committed more serious (i.e., committednew crimes) or less serious (i.e., failed to report to probation officer) technical vio-lations. Lastly, we determine whether these decision-making processes changedafter the implementation of the mandatory drug treatment law.

We envision several benefits from pursuing this line of research. Given the relatively few studies focusing on mandatory drug treatment laws, this study helpsestablish how mandatory drug treatment laws have affected probation and courtdecision-making processes. This study will also expand prior research of courtprocesses by highlighting the role of prosecutors in sentencing decisions. Finally,this research will focus on identifying the significant predictors of probation revo-cation that result in the imprisonment of low-level drug offenders. Specifically, weexamine whether imprisoned low-level drug offenders were incarcerated becausethey presented a significant threat to the community (i.e., committed a new crime)or because they committed minor technical violations.

Drug Offenders and Probation Supervision

According to the Bureau of Justice Statistics (2004a, 2004b), 4 million individu-als are currently under some form of probation, and approximately 25% of them are



Rodriguez, Webb / Probation Violations 5

on probation for a drug law violation. Although the number of probationers has continued to increase over the years, few studies have examined probation officers’decision-making processes (Clear & Latessa, 1993; Fulton, Stichman, Travis, &Latessa, 1997). Rather, studies in the area of drug-related probation have focused onthe impact of drug prevention programs or policies on drug offenders (Albonetti &Hepburn, 1996; Britt, Gottfredson, & Goldkamp, 1992; Curtis, Hoctor, & Pennell,1994; W. W. Johnson & Jones, 1998; Petersilia, Turner, & Piper-Deschenes, 1992;Smith, Wish, & Jarjoura, 1989).

Studies of probation success and failure have taken various forms. For example,some studies have strictly analyzed probation success and failure (Landis, Mercer, &Wolff, 1969; Morgan, 1994; Petersilia, 1985; Sims & Jones, 1997; Vito, 1987),whereas others have focused on the probation revocation process (Albonetti &Hepburn, 1997; Clear, Harris, & Baird, 1992; Hepburn & Albonetti, 1994; Sims &Jones, 1997; Ulmer, 2001). Researchers have also placed empirical focus on the typesof violations committed by probationers (Clear et al., 1992; Gray, Fields, & Maxwell,2001; Harris, Petersen, & Rapoza, 2001; W. W. Johnson & Jones, 1998; Petersilia &Turner, 1990). For example, Clear et al. (1992) found that 39% of rule violations byprobationers were major rule violations (e.g., absconding, consumption of alcohol, andfailure to submit to urine tests). Gray et al. (2001) found that the majority of probationviolations were for minor violations that presented minimal public risk to the commu-nity. Similarly, Sims and Jones (1997) found that only 13% of felony probationers’revocations were for the commission of new crimes. Still other studies have analyzedrearrest rates while on probation and the time to probation failure (Albonetti &Hepburn, 1996; Gray et al., 2001; Hepburn & Albonetti, 1994; Mackenzie, Browning,Skroban, & Smith, 1999; Petersilia, Turner, Kahan, & Peterson, 1985; Petersilia &Turner, 1990; Sims & Jones, 1997; Vito, 1987; Whitehead, 1991).

The overwhelming focus of this line of research has been on identifying the signif-icant predictors of probation outcomes (e.g., violations and arrests) or probationprocesses (e.g., revocation). For example, sociodemographic variables such as age,sex, race, education level, social disadvantage, and employment status have all beenshown to affect probation violations, arrests while on probation, and overall probationsuccess (Albonetti & Hepburn, 1997; Gray et al., 2001; W. W. Johnson & Jones, 1998;Landis et al., 1969; Morgan, 1994; Petersilia, 1985; Sims & Jones, 1997; Ulmer, 2001;Whitehead, 1991). Legal criteria such as prior record and offense type (i.e., property,drug, and assaults) have also been shown to be significant factors in probation out-comes (Gray et al., 2001; Johnson & Jones, 1998; Landis et al., 1969; Morgan, 1994;Sims & Jones, 1997; Ulmer, 2001; Whitehead, 1991). Not surprisingly, prior drug useand probation requirements such as drug testing and mandatory drug or alcohol treat-ment play an important role in probation failure (Albonetti & Hepburn, 1997; Gray et al., 2001; Sims & Jones, 1997; Ulmer, 2001; Whitehead, 1991). Taxman andCherkos (1995) indicate that the increase in unsuccessful probation completions overthe years may be directly linked to probation requirements that increase the likelihoodof probation violations (e.g., drug testing). Although this may certainly be the case, it



is important to note that probation revocations may be the product of a technical rule violation and/or the commission of a new crime (Morgan, 1994).

The Relationship Among Probation Officers,Prosecutors, and Judges

There are two competing perspectives regarding the relationship between proba-tion officers and courtroom actors. One perspective presents the probation officer ina “quasijudicial” role, especially in highly discretionary processes such as revoca-tions. Czajkoski (1973) claims prosecutors call on probation officers to documenttechnical violations when probationers have been arrested for a new crime. Seekinga probation revocation in such cases rather than preparing for a new trial is not onlymore efficient and cost-effective but often results in incarceration (Czajkoski, 1973).A second perspective views the actions of probation officers as highly dependent onthe cooperation of courtroom actors (e.g., judges) and institutional resources (Clearet al., 1992). Clear et al. (1992) note that social and political context and the sen-tencing court play instrumental roles in revocation decisions. Furthermore, they findprobation officers may have “little control” over the decision to revoke as there isgreat variability in the behavior and standard of proof regarding revocations (p. 6).

Kingsnorth, Cummings, Lopez, and Wentworth (1999) and Kingsnorth, MacIntosh,and Sutherland (2002) have conducted several studies on the relationship between pro-bation officers and prosecutors that have produced valuable insight into this relation-ship. For example, Kingsnorth et al. (1999) found that probation officers’ presentenceinvestigation report recommendations often differed with the sentences that were pro-posed by prosecuting and defense attorneys. Furthermore, prosecutors’ recommenda-tions were far more influential than probation officers’ recommendations in sentencingoutcomes. Kingsnorth et al. (2002) found great variation in the number of probationviolation hearings filed by prosecutors post-passage of California’s mandatory drugtreatment law. Interestingly, prosecutors reported pursuing probation violation hear-ings in cases that were either rejected or dismissed to secure prison time. Kingsnorthet al. (2002) recommend that studies of prosecution processes include the initiation ofprobation revocation hearings. The authors argue that this process may not be uniqueto one offense, but, as found in their study, it is certainly applicable to drug cases.

With regard to the relationship between revocations and sentencing outcomes,researchers have called attention to the minimal analytical focus placed on theseprocesses (Clear et al., 1992; Harris et al., 2001). Among the few studies in this area areClear et al.’s (1992) study that shows that the consequences of revocations vary withinand between jurisdictions and that probation officers often consider prison overcrowd-ing when responding to probation violators. Probation officers also reported leaving thepunishment of new offenses committed by probationers to the court because revocationprocesses would produce a less severe penalty than a new conviction (Clear et al.,




1992). Although some studies have found that probation officers’ recommendationshave a significant effect on judicial decisions and that probation officers maintain a highdegree of independence from judges (Hagan, Hewitt, and Alwin, 1979; Walsh, 1985),other studies have found that judges rarely overrule plea agreements in favor of a con-flicting probation officer recommendation (Kingsnorth et al., 1999). In fact, MacDonaldand Baroody-Hart (1999) indicate that the number of formal probation grants without apresentence report is indicative of the minimal communication and cooperation thatexists between probation officers and judges.

In sum, prior work in this area has guided the development of some generalassumptions regarding probation officers’, prosecutors’, and judges’ decision-makingprocesses, yet few empirical studies of such processes have been conducted. Not surprisingly, these largely descriptive accounts provide minimal insight into howprosecutors respond to certain probation violations and how such violations affectsentencing outcomes. Kingsnorth et al. (1999) indicate probation officers perceivethemselves as “independent professionals” trying to distance themselves from pros-ecutors and defense attorneys to maintain legitimacy within the courtroom. Studiesto date have been unable to ascertain whether probation officers’ decisions are in factendorsed by courtroom actors or are negated and play a minimal role in prosecutors’and judges’ decisions.

Mandatory Drug Treatment: Arizona’s Latest Drug Control Policy

Arizona became the first state to pass a mandatory drug treatment law for low-level drug offenders. Proposition 200 (i.e., the Drug Medicalization, Prevention andControl Act of 1996) mandates that first- and second-time, nonviolent offenders con-victed of personal possession or use of a controlled substance be sentenced to pro-bation and drug treatment (Arizona Revised Statutes, 13-901.01).1 Eligible drugoffenders are placed on probation with a condition that they participate in an appro-priate drug treatment or drug education program in lieu of incarceration. Since itsimplementation during fiscal year 1998 (July 1997-June 1998), the Arizona law hasbeen subject to several changes. First, the Arizona Supreme Court has ruled thatmandatory drug treatment also applies to drug paraphernalia cases (State v. Estrada,2001). Second, a state law now enables prosecutors to recommend jail time for drugoffenders who violate their terms of probation, commit a subsequent drug-relatedoffense while on probation, or refuse probation for drug treatment.

Mandatory drug treatment laws have been described as an extension of the drugcourt movement that began in 1989. Like drug courts, mandatory drug treatmentreduces imprisonment rates of drug offenders and enables offenders who suffer fromsubstance abuse problems to receive community-based treatment. Given the manda-tory nature of these laws, some have questioned whether states have the capacity to




address the varying drug offending population and whether states can secure suffi-cient resources to provide treatment to all eligible drug offenders (Riley, Ebener,Chiesa, Turner, & Ringel, 2000).

Most of the information on Arizona’s drug treatment law comes from state-mandated reports. The Arizona Supreme Court’s Administrative Office of the Courtsreports that diverting drug offenders from prison after 1 year of the law produced a$2.5 million cost savings for the state and a $6.7 million reduction after its 2nd year(Arizona Supreme Court, 2000). A recent report from the Administrative Office of the Courts shows that a total of 19,070 probationers (during mid-2000 throughmid-2004) participated in substance abuse treatment funded by the Drug Treatmentand Education Fund (Arizona Supreme Court, 2005). This report also shows thatonly 55% of probationers complied with the treatment requirements during 2003 and2004. To date, the state has not conducted a process or impact evaluation ofArizona’s mandatory drug treatment law.

The most detailed account on the processing of low-level drug offenders pre- andpost-mandatory drug treatment comes from Riley et al. (2005). This study focusedexclusively on incarcerated low-level drug offenders in an effort to ascertain howoriginal offenses, prior record, and race/ethnicity influenced plea bargaining negoti-ations before and after Proposition 200. Findings from this study show that plea bar-gaining practices play a significant role in the classification and imprisonment oflow-level drug cases. Interestingly, the study found that most of the low-level drugoffenders in prison (i.e., 60%) were former probationers whose probation had beenrevoked. In fact, plea negotiations of low-level drug offenders were different for pro-bationers and nonprobationers.

The Current Study

Although a number of states have enacted sentencing laws that mandate drugtreatment for low-level drug offenders, few studies have extended an empirical focusto such laws. To expand studies of sentencing policies to include mandatory drugtreatment laws and to provide a more comprehensive review of the relationship thatexists among probation officers, prosecutors, and judges in revocation cases, weexplore the following four research questions:

1. What types of technical violations lead to probation revocation and imprisonmentof low-level drug offenders?

2. What is the relationship between technical violations and prosecutors’ decision-making processes?

3. What is the relationship between technical violations and judicial outcomes (i.e.,sentence length)?

4. Did probation officers’, prosecutors’, and judges’ decision-making processeschange after the implementation of the mandatory drug treatment law?




It is important to note that the revocation of probation not only is a measure ofagency surveillance and control activities (Albonetti & Hepburn, 1997) but also has adirect impact on prison populations. As policies now mandate community treatmentfor low-level drug offenders, it is important for research to examine how probationofficers respond to violators and how probation revocations affect the prosecution andimprisonment of offenders.2

Prior studies have relied on the categorization of probation violations to examinetheir influence in case outcomes. For example, Clear et al. (1992) placed both offensesand technical violations into categories (i.e., major, moderate, and minor) and foundthat the majority of offenses committed by probationers included minor, nonseriousoffenses. Gray et al. (2001) also collapsed violations into groups (i.e., major,3 medium,and less serious violations) and found that most violations committed by probationersare not serious and do not pose a threat to the community.

To gain insight on the types of violations that result in the revocation and impris-onment of low-level drug offenders, we also categorize violations reported by pro-bation officers and identify which violations most frequently led to revocation andimprisonment. W. W. Johnson and Jones (1998) conducted one of the most compre-hensive studies of revocations by analyzing the significant predictors of revocations(e.g., commission of a new crime, technical violation) and found that the predictorsof probation revocation differed for technical violators and for probationers whocommitted new crimes. Consistent with the work of W. W. Johnson and Jones, weidentify those legal and extralegal factors that significantly influence the type of pro-bation violation committed by low-level drug offenders who had probation revokedand were subsequently incarcerated.

Researchers have recommended that studies of probation officers and their decision-making processes include the legal courtroom context (Clear et al., 1992;Harris et al., 2001). To determine the relationship that exists between revocationcases and prosecutors, we focus on how prosecutors respond to those violations thatinvolve a new arrest. We explore whether prosecutors file additional charges, resortto modifying the severity of the offense, or merely accept and process the revocationcase without any case adjustments.

The study of prosecutorial decision-making processes has been largely limited tocase dismissal and plea bargaining practices in relation to sentencing outcomes(Albonetti, 1991; Albonetti & Hepburn, 1996; Kautt & Spohn, 2002; Moore & Miethe,1986; Myers, 1982; Myers & LaFree, 1982; Rhodes, 1991; Spohn, Gruhl, & Welch,1987; Ulmer & Kramer, 1996). Most recently, studies have begun to expand this focusto include more appropriate categorizations of case dismissal (Kingsnorth et al., 2002)and of plea bargaining (Johnson, 2003). One study that has examined prosecutorial dis-cretion in charging decisions is Miethe’s (1987) analyses of Minnesota’s sentencingguidelines pre- and postimplementation. He found no evidence that prosecutors were“over charging” postguidelines to encourage defendants into pleading guilty to a




reduced charge. Like Miethe, we examine whether violations involving new crimesresult in additional filed charges.

Sentencing decisions continue to be one of the most examined areas of the judicialprocess. Much of our understanding of sentencing decisions comes from research thathas captured the direct and indirect effects of extralegal (e.g., race, age, and, sex) andlegal (e.g., offense seriousness and prior record) variables (Crawford, Chiricos, &Kleck, 1998; Kramer & Steffensmeier, 1993; Myers & Talarico, 1986; Petersilia,1983; Souryal & Wellford, 1997; Spohn, DeLone, & Spears, 1998; Steffensmeier &Demuth, 2000; Tonry, 1995; Ulmer & Kramer, 1996; Zatz, 1987). Although the major-ity of sentencing studies examine both in-out decisions and sentence length decisions,in this study we focus exclusively on sentence length because the low-level drugoffenders under examination were incarcerated after having their probation revoked.4

We examine not only which extralegal and legal factors of probation cases influencesentencing length decisions but also whether particular violations are sanctioned moreseverely by judges in sentencing decisions. To gain further knowledge of probationviolations and sentencing outcomes, we also measure the effect that number of proba-tion violations presented by probation officers has in sentence length decisions.

The proposed analytical focus is consistent with Kingsnorth et al.’s (2002) rec-ommendation that violations of probation be regarded as distinct from felony andmisdemeanor convictions in judicial studies. Furthermore, Kingsnorth et al. (1999)stress the need to “locate the role of probation officer in a larger framework of court-room work group and contemporary sentencing structure” (p. 266). We not onlyexamine these processes among these courtroom actors but also determine if proba-tion officers’, prosecutors’, and judges’ decision-making processes changed after theimplementation of Arizona’s mandatory drug treatment law.

Data and Method

Data for this study come from a larger research project on the plea bargaining prac-tices of imprisoned low-level drug offenders (Riley et al., 2005). In that particular study,a quasiexperimental design was used to measure the changes in prosecutors’ decision-making processes pre- and post-Proposition 200. Data included commitments to theArizona Department of Corrections (AZDOC) involving drug use, possession, andparaphernalia from July 1996 through June 2000 (fiscal years 1997-2000) from the fourcounties (Maricopa, Pima, Yuma, Mohave) that produced the overwhelming majority(89%) of drug commitments to AZDOC. A stratified sample based on race/ethnicity,sex, drug offense, county, and year of imprisonment was used, and sample weightswere constructed to ensure that the sample matched the population on selected factors(e.g., race/ethnicity, sex, offense).5 Because the focus of this current study is exclusiveto probationers, we analyze only those low-level drug offenders who were on probationprior to their incarceration. This subpopulation included 2,840 cases.




Data Collection Procedures

Various strategies were used to acquire probation, prosecution, and sentencing dataof imprisoned low-level drug offenders. Electronic data were provided by AZDOC(e.g., demographic indicators, sentencing dates, state admission dates, jurisdiction ofcase, drug type, and sentence length) and the Arizona Department of Public Safety(e.g., criminal history variables). After a review of the electronic data, a data abstrac-tion form was constructed and used to collect data from prosecution case files, proba-tion records, county clerks’ files, and prison record files.

Intervention Period

The implementation of Arizona’s mandatory drug treatment law began in fiscalyear 1998 (July 1997-June 1998). However, counties were not able to fully implementthe law until contracts were in place to provide drug treatment to offenders. Becausethe establishment of treatment provider contracts occurred throughout the 1998 fiscalyear, three time-reference points across the 1998 fiscal year (i.e., July 1997, January1998, June 1998) were created and examined to determine whether they produced sig-nificant differences in the analysis before and after Proposition 200. Minimal differ-ences were found in coefficients and standard errors among models using the threetime reference points. Based on this, June 1998 was selected as the date of implemen-tation (preimplementation = 0, postimplementation = 1) because all counties in thestudy would have implemented the law by the end of the fiscal year.

Measurement

Probation violations. It is important to note that the probation violation data comefrom violations reported in probationers’ case files. It follows that violations notknown to probation officers and known violations that officers did not report in casefiles are not included in this study. Although violations may result in various out-comes (e.g., no action, modification of probation terms, or revocations), we placeexclusive focus on those violations that led to revocation and imprisonment.

We measure probation violations in several ways. Consistent with prior work(e.g., Clear et al., 1992; Gray et al., 2001), violations were collapsed into three categories—most serious violations, medium serious violations, and least seriousviolations—and probationers were categorized based on the most serious probationviolation. Most serious violations include new crimes, possession of a weapon, andfailure to surrender. Medium serious violations primarily include failure to attendtreatment and positive urinalysis. Less serious violations include violations such asfailure to report to probation officer and change in residence without notice.6 Also,we measure the number of probation violations reported in each case to assess theircumulative influence in case processing.




Dependent variables. To examine probation, prosecution, and sentencing processes ofimprisoned low-level drug offenders, we measure several variables. First, we analyzewhich factors predict probation revocation violations. Violations are measured as anominal variable (most serious violation = 1, medium serious violations = 2, and lessserious violations = 3). To examine prosecution processes, we construct several mea-sures. Consistent with Miethe’s (1987) work, we used an offense charging measure tocapture whether the number of charges from probation revocation increased (coded 1),decreased (coded 2), or remained the same (coded 3) at the time of prosecution. Thesame measure was used to identify whether charges changed from prosecution to sen-tencing. We next assigned a severity score to all offenses at the stages of probationrevocation, prosecution, and sentencing to construct a sum severity score of offensesat each stage.7 As with the charging measure, the sum severity score from probationrevocation to prosecution and from prosecution to sentencing was assessed to capturewhether the score had increased (coded 1), decreased (coded 2), or remained the same(coded 3) from one stage to the next. Lastly, we analyze sentence outcome by mea-suring the sentence length (in months) received by revoked probationers.

Independent variables. We rely on several extralegal and legal control variables for this study. Extralegal factors include probationers’ gender (male = 1, female = 0),race/ethnicity (a set of five dummy variables: White, Black, Hispanic/Latino, NativeAmerican, Other), age at sentencing, employment status (yes = 1, no = 0), and resid-ing county (set of four dummy variables: Maricopa, Pima, Mohave, and Yuma). Legalvariables include drug offense (a set of five dummy variables: marijuana, dangerousdrug, narcotic drug, vapors, and paraphernalia), number of counts, number of probationviolations, prior probation reinstatement (yes = 1, no = 0), probation term (in months),and case disposition (pleading guilty = 1, bench/jury trials = 0). To measure the impactof criminal history on probation, prosecution, and sentencing decisions, several priorrecord measures were constructed and analyzed. In this investigation, we utilize the totalnumber of prior arrests and total number of the offenses of prior arrests.8

Analytical Techniques

We relied on descriptive statistics to establish the bivariate relationship betweenvariables across periods (i.e., pre- and postproposition) and to examine certain rela-tionships of interest (e.g., probation violation type and prosecutors’ charging deci-sions). Given the nominal scale of probation violation type, we relied on multinomiallogistic regression to examine the effects of predictors on probation violation type.Multivariate ordinary least squares regression models were estimated to establish howprobation variables influence sentence length. Separate analyses were conducted forcases before and after the implementation of Proposition 200 to assess possiblechanges resulting from the mandatory drug treatment law. To compare whether coef-ficients vary statistically across implementation periods, a z test was performed.9




Findings

Descriptive statistics for the sample are presented in Table 1. Males composed76% to 77% of probationers pre- and post-Proposition 200. There were minimalchanges in the racial/ethnic composition of imprisoned probationers across periods.The majority of these probationers were White (52% pre and 55% post), followed byHispanics/Latinos (27% pre and 26% post) and Blacks (16% pre and post). Theiraverage age was 32 to 33 years old, and approximately one third of the probationerswere employed prior to sentencing. County variation across implementation periodswas found in 3 of the 4 counties. Specifically, Pima County experienced an increasein the number of probationers revoked and incarcerated for low-level drug offensespost-Proposition 200, whereas all other counties experienced a reduction in the pro-portion of incarcerated probationers. The majority of revoked probationers wereunder supervision for narcotics or paraphernalia offenses. Paraphernalia offenseswere the only drug cases that increased post-Proposition 200.

More than half (55% pre and 52% post) of probationers had probation reinstatedat least once for the current offense. Not surprisingly, almost all probationers pleadguilty at sentencing. Probationers’ term of probation for original offenses averaged36 to 37 months. The average number of probation violations filed by probation offi-cers was 2 pre- and postproposition. Prior to Proposition 200, probationers averaged7.5 total prior arrests and 14.7 total prior offenses of arrests.10 There were minimalchanges between the number of charges presented at revocation, prosecution, andconviction. Sentence lengths of revoked probationers appear to have become shorterpostproposition (22 months vs. 19 months). Because nearly all probationersaccepted plea agreements, all subsequent analyses are restricted to guilty plea cases.

Categories of Probation Violations Among Revocation Cases

A review of the categories of violations shows that most probationers had theirprobation revoked for medium serious violations (e.g., positive urinalyses tests andfailure to attend treatment; see Table 2). Although the average severity of violationsdid not change across periods, the violations became more concentrated around themedium level and were less likely to be most serious (e.g., new crimes) or least seri-ous (e.g., failing to report to probation officer and change in residence without noti-fying the probation officer) after the law change.

To examine which factors predict certain probation violations and whether pre-dictors changed after the implementation of Proposition 200, we estimated multino-mial regression models to examine such effects.

Predictors of probation violations—Pre-Proposition 200. Regression estimates inthe first two columns of Table 3 reveal that among defendants who had their proba-tion revoked, male probationers were more likely than females to have committed





Table 1Description of Low-Level Drug Offenders Revoked

From Probation and Incarcerated

Preproposition 200 Postproposition 200

% n % n

GenderMale 75.8 1,285 77.1 882Female 24.2 411 22.9 262

Race/ethnicityWhite 51.7 877 54.7 625Hispanic/Latino 27.2 462 25.7 294Black 16.0 272 15.7 179American Indian 3.9 66 3.1 35Other 1.1 18 0.8 9

Age at sentencing 31.96 7.73 33.29 8.37(in years; M, SD)**

EmployedYes 32.9 534 32.2 354No 67.1 1,088 67.8 745

CountyMaricopa** 72.4 1,228 67.6 773Pima** 14 238 23.1 264Yuma** 6.8 115 4.0 46Mohave 6.7 114 5.3 61

Probation offenseMarijuana 14.5 245 12.1 138Dangerous drugs** 25.4 430 17.2 197Narcotic drug 28.9 490 25.7 294Paraphernalia** 29.1 493 44.1 504Vapor* 2.2 37 1.0 12Yes 54.7 923 51.8 588No 41.9 708 40.1 455

Mode of dispositionPlead guilty 99.6 1,689 99.5 1,137Bench trial 0.1 1 0.1 1Jury trial 0.3 5 0.4 5

M SD M SD

Counts presented by probation department** 1.10 0.33 1.25 0.49

Term of probation (in months)** 37.67 6.57 36.18 6.42Number of probation violations 2.10 1.7 2.22 1.6Total arrests in prior record** 7.52 5.96 9.62 8.17Total offenses in prior arrests** 14.69 11.13 19.81 16.04



less serious violations than most and medium serious violations. Racial/ethniceffects were found in violation outcomes. Specifically, Hispanics/Latinos were morelikely than Whites to have committed a medium serious violation than a less seriousviolation. Also, Black probationers were less likely than Whites to commit a most


Table 1 (continued)


M SD M SD

Counts filed by prosecution* 1.26 0.65 1.31 0.58Counts at conviction** 1.26 0.65 1.32 0.58Sentence length (in months)** 22.65 16.85 19.17 11.99

Note: N = 2,840. Statistical differences of variables across periods are presented here.*p < .05. **p < .01.

Table 2Categories of Probation Violations


% n % n

Most serious violations 12.4 183 8.8 89New crimes 165 78Failed to self-surrender 2 13Possession of a weapon 17 1

Medium serious violations 48.2 713 56.6 569Urine analysis results 453 345Failed to attend treatment 352 248Failed to complete community service 165 140Failed to remain employed 126 125Be a law abiding citizen 11 3Left residence without permission 81 81Violated terms of probation 61 60Failed to abide by special conditions 42 9

Least serious violations* 39.4 582 34.6 348Failed to report to probation officer 723 467Failed to pay fee 182 79Change in residence without notification 344 301Failed to keep probation officer informed 37 43Violated curfew 46 26Rejected further terms of probation 17 23

Total 100.0 1,478 100.0 1,006

Note: Specific violations within categories are not mutually exclusive.*Violation seriousness significant across periods at the .05 level.



16

Tabl

e 3

Mul

tino

mia

l Est

imat

es o

f T

ypes

of

Pro

bati

on V

iola

tion

s in

Rev

ocat

ion

Cas

es

Prep

ropo

sitio

n 20

0Po

stpr

opos

ition

200

ZTe

sts

Mos

t Ser

ious

Vio

latio

nM

ediu

m S

erio

us V

iola

tion

Mos

t Ser

ious

Vio

latio

nM

ediu

m S

erio

us V

iola

tion

Mos

t M

ediu

mSe

riou

sSe

riou

s B

SEE

xp(B

)B

SEE

xp(B

)B

SEE

xp(B

)B

SEE

xp(B

)V

iola

tion

Vio

latio

n

Inte

rcep

tb–3

.600

0.95

1–5

.129

0.73

8–3

.442

1.59

91.

321

0.93

1M

ale

–0.4

97*

0.24

40.

608

–0.3

71*

0.18

10.

690

2.20

1**

0.56

09.

039

1.18

0**

0.27

43.

254

–4.4

2**

–4.7

2**

His

pani

c/L

atin

o0.

299

0.24

41.

349

0.45

5**

0.18

81.

576

–2.6

87**

0.52

10.

068

–1.8

39**

0.27

50.

159

5.19

**6.

88**

Bla

ck–1

.306

**0.

409

0.28

60.

071

0.22

51.

074

0.12

20.

460

1.13

0–0

.277

0.28

20.

758

–2.3

2**

Am

eric

an I

ndia

n–1

.251

0.67

60.

286

–0.5

560.

416

0.57

3–2

.905

*1.3

710.

055

–2.0

09**

0.78

30.

134

Age

at s

ente

ncin

g–0

.008

0.01

40.

992

0.05

1**

0.01

11.

053

–0.1

23**

0.02

30.

884

–0.1

17**

0.01

50.

890

4.27

**9.

03**

Em

ploy

men

t0.

386

0.22

01.

471

0.37

3*0.

162

1.45

20.

531

0.34

91.

701

–0.6

71**

0.22

50.

511

3.76

**D

ange

rous

dru

gs0.

619

0.37

21.

857

0.34

60.

269

1.41

4–1

.103

*0.

541

0.33

2–1

.361

**0.

387

0.25

62.

62**

3.62

**N

arco

tic d

rugs

0.70

8*0.

365

2.03

10.

237

0.25

61.

267

–3.6

68**

0.82

00.

026

–0.7

87*

0.35

70.

455

4.88

**2.

33**

Para

pher

nalia

0.08

50.

342

1.08

9–0

.514

*0.

245

0.59

8–0

.525

0.49

60.

592

–0.0

170.

319

0.98

3C

ount

s–0

.029

0.34

70.

972

0.62

90.

248

1.87

50.

145

0.55

01.

156

0.19

90.

306

1.22

0Pr

ior

offe

nses

–0.0

150.

010

0.98

5–0

.007

0.00

70.

993

0.05

1**

0.01

01.

053

0.01

9**

0.00

71.

019

–4.6

7**

–2.6

2**

Term

of

prob

atio

n0.

007

0.01

81.

007

0.01

60.

013

1.01

60.

015

0.03

01.

015

–0.0

020.

019

0.99

8Pr

ior

prob

atio

n 0.

172

0.09

21.

188

0.07

60.

080

1.07

90.

052

0.07

41.

054

–0.1

53*

0.06

30.

858

2.25

**re

inst

ated

Num

ber

prob

atio

n 1.

565*

*0.

142

4.78

31.

445

0.13

84.

242

2.08

4**

0.22

38.

033

1.87

4**

0.20

46.

513

–1.9

6*vi

olat

ions

Pim

a0.

051

0.34

11.

052

0.79

0**

0.25

72.

204

1.35

8**

0.54

13.

888

1.17

6**

0.36

23.

242

–2.0

4**



17

Yum

a1.

219*

0.53

23.

384

2.33

4**

0.42

510

.320

——

Moh

ave

0.58

10.

667

1.78

92.

211*

*0.

514

9.12

70.

374

0.81

41.

453

0.17

30.

609

1.18

92.

56**

–2 lo

g lik

elih

ood

1923

.089

1603

.296

χ271

5.67

661

5.35

2df

3232

Not

e:Sa

mpl

e si

ze p

repr

opos

ition

=1,

478;

sam

ple

size

pos

tpro

posi

tion

=1,

006.

Oth

er r

acia

l/eth

nic

grou

ps w

ere

excl

uded

fro

m th

ese

anal

yses

.a.

zsc

ores

cal

cula

ted

usin

g z

=(b

1–

b 2) /

Sqrt

(SE

12+

SE22

); s

ee P

ater

nost

er,B

ram

e,M

azer

olle

,and

Piq

uero

(19

98).

zsc

ores

rep

orte

d w

ith s

tatis

tical

sig

-ni

fica

nce

dete

rmin

ed b

y 2-

taile

d te

sts.

b. R

efer

ence

cat

egor

y in

clud

es le

ss s

erio

us v

iola

tions

,Whi

tes,

mar

ijuan

a ca

ses,

and

Mar

icop

a C

ount

y.*p

<.0

5. *

*p<

.01.



serious violation than a less serious violation. Age and employment status displayeda significant positive effect in the revocation of medium serious violations.

Legal characteristics also play a significant effect on violations. Narcotic offend-ers who had probation revoked were more likely than marijuana offenders to com-mit a most serious violation. Also, paraphernalia offenders were less likely thanmarijuana offenders to commit a medium serious violation. The number of proba-tion violations had a significant positive effect on most serious violations. Analysesalso show county variation in violation outcomes. Probationers from Pima, Yuma,and Mohave counties were more likely than probationers from Maricopa County tohave committed a medium serious violation.

Predictors of probation violations—Post-Proposition 200. Post-Proposition 200analyses show that among defendants who had probation revoked, males were morelikely than females to have committed most and medium serious violations (seeTable 3). Hispanics/Latinos were more likely than Whites to commit less seriousviolations than most and medium violations. Although violation outcomes did notdiffer for American Indians and White probationers preproposition, AmericanIndians were more likely than Whites to have committed a less serious violationpost-Proposition 200. The effect of age postproposition is negative for most andmedium serious violations. With regard to drug offense, both dangerous drug andnarcotic drug offenders were less likely than marijuana offenders to have committedmost and medium serious violations. Although prior record was not a significant pre-dictor of violations prior to the implementation of the law, it had a positive effect onboth most and medium serious violations postimplementation of the law. Amongdefendants who had probation revoked, probation reinstatement had a negative effectin medium serious violations. The effect of number of probation violations remainedpositive post-Proposition 200, as did the effect of Pima County in violations.

To establish whether the coefficients vary statistically across implementationperiods, z test coefficient comparisons between the two groups were calculated. Asshown in Table 3, gender, Hispanic/Latino, age, dangerous drugs, narcotics drugs,and prior offenses effects are significantly different pre- and postproposition for esti-mates of both most serious and medium serious violations. The coefficients ofBlacks, number of violations, and Pima County are significantly different for mostserious violators, whereas employment, prior probation reinstatement, and MohaveCounty effects are also significantly different across implementation periods formedium serious violations.

Prosecutors’ Charging Decisions

To examine the relationship between technical violations and prosecutors’ decision-making processes, we review prosecutors’ charging decisions in cases where proba-tioners’ violation involved the commission of a new crime (see Table 4). Analyses




show that the majority of offenders were on probation for a low-level drug offense or offenses and were imprisoned for those particular offenses. Given the minimal vari-ation across outcomes, we were unable to model the predictors across prosecution out-comes and therefore present the bivariate statistics of these outcomes pre- andpost-Proposition 200.

We present the findings from the four measures that capture prosecutorial pro-cessing. Findings indicate that in more than three fourths of probationers’ cases,prosecutors did not increase or reduce the number of charges from probation revo-cation to prosecution (86% pre vs. 78% postproposition). Preproposition, some pro-bationers (7.2%) had the number of charges increase from probation to prosecution.This proportion increased after the implementation of Proposition 200 (22%). Theremainder of probation cases had the number of charges drop from probation revo-cation to prosecution (7% pre vs. 0% postproposition). A review of charges from


Table 4Change in Offense Charges During Case Processing—New Crime Violations Only


% n % n

Charges from probation to prosecution**

Increased 7.2 12 22.2 16No change 86.1 143 77.8 56Decreased 6.6 10 0.0 0

Charges from prosecution to sentencing


Sum severity score from probation to prosecution**


Sum severity score from prosecution to sentencing**


Note: Statistical differences of variables across periods are presented here.*p < .05. **p < .01.



prosecution to sentencing shows that when prosecutors file probationers’ charges,the number does not change at sentencing.

Analyses of sum severity scores reveal a similar pattern. The majority of cases(77% pre vs. 76% postproposition) experienced no change in sum severity scorefrom probation revocation to prosecution. Also, the proportion of cases with anincrease in sum severity score increased postproposition (6.7% vs. 22.2%), and theproportion of cases with a decrease in sum severity score dropped after the law wasimplemented (17.0% vs. 1.4%). Although probationers preproposition were morelikely to have the sum severity score of their offense or offenses increase rather thandecrease from prosecution to sentencing, the proportion of cases with a decrease insum severity score increased (1.2% vs. 14.1%) post-Proposition 200.

Sentence Length and Probation Violations

Judicial outcomes and probation violations—Pre-Proposition 200. Analyses of sen-tence length show that most serious violators (i.e., probationers who committed newcrimes) received shorter sentences than less serious violators (see Table 5). As withprobation outcomes, extralegal factors are significant predictors of sentence length.For example, male probationers received longer sentences than female probationers.Also, Hispanic/Latino probationers received longer sentences and American Indianprobationers received shorter sentences than did White probationers. Age had a pos-itive effect on sentence length as older probationers received longer sentences thanyounger probationers. Legal criteria also play an important role in judicial sentenc-ing decisions. Probationers convicted of dangerous drug and narcotic drug offensesreceived longer sentences than did marijuana offenders. Counts filed, prior offenses,term of probation, concurrent probation, and the number of probation violations filedby probation officers had a positive effect on sentence length. With regard to countyeffects, probationers from Pima and Mohave counties received shorter sentencesthan probationers from Maricopa County.

Judicial outcomes and probation violations—Post-Proposition 200. Post-Proposition200 findings show that medium serious violators (i.e., probationers who tested pos-itive for drugs or failed to attend drug treatment) received longer sentences than lessserious violators. The effects of males, Hispanics/Latinos, dangerous drugs, narcoticdrugs, and prior record remained positive postproposition. However, unlike the preproposition period, the effect of age was negative postproposition. Employmentstatus had a significant effect on sentence length postproposition. Specifically, pro-bationers who were employed received shorter sentences than probationers whowere unemployed. The significant positive effect of term of probation remained pos-itive postproposition. Although the number of probation violations served to increase




sentence length prior to the implementation of the mandatory drug treatment law,postproposition analysis shows that the number of violations no longer serves toaffect sentence length. Lastly, probationers from Pima County received longer sen-tences than did probationers from Maricopa County.

z scores of coefficients indicate that the effect of both most serious and mediumserious violations on sentence length are significantly different pre- and postpropo-sition. Also, age, prior probation reinstatement, number of probation violations, andPima County effects are significantly different across implementation periods.


Table 5Linear Regression Estimates of Sentence Length


b SE β b SE β z Testsa

Interceptb –1.503 1.791 6.706 1.733Most serious violation –1.543* 0.735 –.047 0.908 0.802 .029 –2.25**Medium serious violation –0.717 0.534 –.032 1.132* 0.486 .064 –2.56**Male 1.904** 0.510 .073 1.152* 0.484 .054Hispanic 1.653** 0.521 .067 1.101* 0.479 .055Black –0.131 0.648 –.004 –0.068 0.531 –.003American Indian –2.549* 1.155 –.043 –1.632 1.223 –.028Age at sentencing 0.100*** 0.027 .071 –0.004** 0.00 –.042 3.85**Employed –0.195 0.453 –.008 –0.836* 0.408 –.045Dangerous drugs 14.346** 0.729 .564 12.934** 0.717 .566Narcotic drugs 11.744** 0.715 .485 10.053** 0.669 .513Paraphernalia –1.246 0.669 –.052 0.100 0.622 .006Counts 1.694** 0.604 .054 0.410 0.491 .022Prior offenses in record 0.052** 0.020 .052 0.079*** 0.012 .144Term of probation 0.207** 0.036 .126 0.154** 0.031 .119Prior probation

reinstatement 0.572** 0.185 .059 –0.054 0.097 –.012 2.98**Number of probation

violations 0.377** 0.136 .060 –0.347 0.138 –.062 3.73**Pima –5.780** 0.656 –.176 –4.004** 0.540 –.188 –2.09**Yuma –1.463 0.886 –.034 –0.828 1.063 –.017Mohave –4.709** 0.961 –.100 –1.840 0.970 –.041 –2.10**Adjusted R2 .533 .613Df 19 19

Note: Sample size preproposition = 1,478; sample size postproposition = 1,006.a. Reference category includes les serious violations, Whites, marijuana cases, and Maricopa County.b. z scores calculated using z = (b1 – b2) / Sqrt (SE1

2 + SE22); see Paternoster, Brame, Mazerolle, and

Piquero (1998). z scores reported with statistical significance determined by 2-tailed tests.*p < .05. **p < .01.



Discussion

Mandatory drug treatment laws represent the latest policy aimed at dealing with theincreasing representation of drug offenders in prison. The laws are expected to have asignificant impact not only on offenders’ substance abuse but also on probation offi-cers, prosecutors, and judges who are now mandated to process and oversee the treat-ment of offenders while on probation. Although drug-related policies and programshave been studied in the past (Curtis et al., 1994; W. W. Johnson & Jones, 1998;Petersilia et al., 1992; Smith et al., 1989), few studies have examined how such poli-cies affect the decisions of probation officers and courtroom actors. In this context, ourstudy set out to (b) examine whether the types of probation violations among defen-dants who had probation revoked changed after the implementation of a mandatorydrug treatment law and (b) identify how prosecutors and judges processed these cases.

Prior research indicates that the majority of violations committed by probationersinclude minor technical infractions (Gray et al., 2001; Sims & Jones, 1997). Consistentwith previous work, we found that 12% of violations preproposition and less than 9%of violations postproposition involved the commission of new crimes. Thus, the major-ity of revoked probationers were incarcerated for technical violations, many of whichpresent a minimal threat to the community. Interestingly, we found that the proportionof medium serious violations (e.g., urinalyses results, failing to attend treatment)increased post-Proposition 200. It appears that after the implementation of the law,more than half of revocations (57%) involved such violations. This finding may beattributed to an increase in drug offenders who simply fail to comply with treatmentplans and/or suffer from extensive substance abuse problems. This latter explanation isconsistent with findings from a recent study of California’s mandatory drug treatmentlaw that found state officials underestimated the seriousness of drug offenders’ sub-stance abuse problems (Longshore et al., 2004). However, the increase in treatment-related violations may also be because of an increase in reporting by probation officerswho recognize that under the new law, probationers have been afforded an opportunityto remain in the community and receive drug treatment. Probationers who do not com-ply with treatment plans may be perceived by probation officers as not deserving oftreatment, who may, in turn, seek to revoke such cases and recommend imprisonmentwhere an incarceration outcome is nearly certain.

Multivariate analyses of probation violations reveal that legal and extralegal pre-dictors of violations differ among violation types and across implementation periods.Although marijuana offenders were less likely than narcotic offenders to be revokedfor a most serious violation preproposition, they were more likely to be revoked formost and medium serious violations postproposition. In other words, marijuanaoffenders were not only more likely to commit new crimes but also more likely to berevoked for failing treatment-related provisions while on probation. The less seriousnature of this drug offense calls attention to treatment modalities of specific offenders.Although revocation may be more likely among more serious drug offenders, findings




here show that less serious drug offenders are also at risk of reoffending and failing tomeet the terms of probation. Prior record also had a significant effect on violation typespostimplementation of the law indicating increased reliance on legal criteria post-Proposition 200. Extralegal variables appear to be as important as legal criteria in revo-cation processes. The different influence of gender and race/ethnicity across periodsserves to highlight how the effect of these variables is not constant but rather interre-lated with the mandatory drug treatment law.

The analyses of revocation cases reveal that prosecutors overwhelmingly do notfile additional charges in cases where probationers committed new crimes; therewere only a few instances where prosecutors adjust case charges. However, after theimplementation of Proposition 200, prosecutors were more likely to file additionalcharges and increase the sum severity of the offense or offenses. Although cautionshould be exercised given the small number of cases across prosecutorial outcomes,the more proactive response by prosecutors after the implementation of the law indi-cates prosecutors are pursuing more punitive outcomes for probationers who com-mit new crimes. Findings also show that at the time of sentencing, judges were morelikely to reduce the sum severity score of the offense or offenses postpropositionthan preproposition. Judges may be directly responding to prosecutors’ filing ofcharges by seeking a balance between proactive, punitive outcomes and inactivity byprosecutors in cases where a new crime has been committed.

These findings support the argument that probation officers and not prosecutorsplay the most significant role in revocation decisions. This was confirmed in a dis-cussion with a representative of a county prosecutor’s office, who indicated thatinformal policy precluded the filing of charges as this would consume staff andresources that were simply not available. Instead, on notice of an arrest that involvedan individual who was on probation, the office would wait for the probation depart-ment to file the petition of revocation. Unlike Kingsnorth et al. (2002), who exam-ined rejected and dismissed cases, our focus was on probation cases where newarrests were overwhelmingly processed as revocations and not as new charges. Theincrease in the number of charges filed and sum severity of score of offense oroffenses postproposition may be because of increased pressure to identify those pro-bationers who have exhausted their treatment opportunity.

A review of sentencing decisions of revoked probationers preproposition shows thatmost serious violators (i.e., probationers who committed new crimes) received shortersentences than did less serious violators. It appears that prior to the law, probationerswho committed new crimes were not penalized but rather treated less harshly than pro-bationers who committed less serious violations (e.g., failed to report). This findingmay be directly related to prosecutors not filing additional charges for cases involvingthe commission of a new crime. Although the commission of a new crime wouldappear to deserve more severe punishment, relative to less serious violations, judgesmay question why charges were not filed and assume probationers are not guilty of thearresting offense. Under these circumstances, probationers who failed to report to their




probation officer may be seen as deserving of more punitive treatment than probation-ers who were arrested but whose offense did not result in a new charge.

Post-Proposition 200 findings show that medium serious violators, including pro-bationers who tested positive for drugs or failed to attend drug treatment, receivedlonger sentence than did less serious violators. This finding might indicate thatjudges respond to policies in ways that enable them to sentence certain violatorsmore harshly than others. Since the implementation of the mandatory drug treatmentlaw, violators who failed to comply with treatment-related provisions of probationmay be seen as most deserving of longer sentences given they had the opportunityto receive substance abuse treatment.

Consistent with previous studies, we found that both legal (i.e., drug offense, priorrecord) and extralegal (e.g., gender, age, race/ethnicity) variables affect sentences ofdrug offenders. For example, the more punitive treatment of Hispanic/Latino proba-tioners supports a growing body of research that shows Hispanics may be subject tomore severe treatment than other racial/ethnic groups (Demuth, 2003). Previous rein-statement of probation and number of probation violations had a positive effect on sen-tence length preproposition yet were not significant predictors postproposition. Thedifferent effect of probation-related variables demonstrates how new policies can affectthe factors used by judges in making sentencing decisions.

The findings from this study have both theoretical and policy implications. From atheoretical perspective, conceptual frameworks such as focal concerns (Kramer &Steffensmeier, 1993; Ulmer & Kramer, 1996) and causal attribution (Albonetti, 1991)can be used to explain how probation officers and prosecutors respond to probationviolators in light of new policies. For example, the racial/ethnic disparities found inprobation violations are consistent with a vast body of sentencing research that showsracial/ethnic disparities in sentencing outcomes. The county effects in probation viola-tions and sentence length outcomes highlight the importance of examining character-istics of community context in studies of court decision-making processes. Socialdisorganization measures (e.g., poverty, residential mobility) may provide insight onhow community dimensions affect the sentencing of probation violators.

The policy relevance of this study centers on the incarceration of probation vio-lators. Findings indicate that defendants who had probation revoked were a minimalthreat to the community. Also, post-mandatory drug treatment law, an increasingnumber of incarcerated defendants had probation revoked for drug-treatment provi-sions. In light of these findings, it may be worthwhile for probation departments toidentify appropriate levels of supervision of drug offenders given local practices(i.e., therapeutic vs. social control practices). The revocation of these offenders alsotranslates into more drug offenders entering prison with substance abuse problemsthat were not addressed during probation supervision. Resources must be availableto expand in-prison drug treatment programs along with reentry programs that facil-itate drug offenders’ reintegration into society.




In sum, the findings reported in this study reveal the importance of examining pro-bation revocations in the context of probation officers’, prosecutors’, and judges’decision-making processes. We found not only that their decision-making processeschanged after the implementation of the mandatory drug treatment, but also that char-acteristics of revocation cases appear to be far more interrelated with court outcomesthan previously documented. Unfortunately, our findings are restricted to probationviolations that resulted in revocation and incarceration. Therefore, we are unable toascertain the degree to which the findings from this study are representative of non-revocation cases or revocation cases that do not result in incarceration. Furthermore,our probation data comes from official data reported in probation, prosecution, andcorrections case files formally documented by officials when processing probation-ers. Therefore, our analysis is based on the assumption that such documentation isrepresentative of standard decision-making processes of revocations cases. Even withsuch limitations, we feel our study shows not only how mandatory drug treatmentlaws have affected probation and court decision-making processes, but also expandsprior research of court processes by highlighting the role of prosecutors and judges inrevocation cases.

Conclusion

The study of probation violations and revocations has been subject to many inves-tigations. Although such studies have expanded our understanding of probationcases, findings from this study show that the implementation of mandatory drugtreatment laws has the capacity to alter the decisions made by probation officers,prosecutors, and judges in low-level drug cases. We encourage future empiricalfocus on these dynamic processes to gain a better insight into how mandatory drugtreatment affects the processing of offenders and on how criminal justice officialsuse their discretion to process such offenders.

We see several ways in which future studies can expand the current focus of thiswork. For example, to gain a better understanding to why probation officers revokecertain cases, future research should rely not only on official probation case file databut also surveys of the attitudes and beliefs of probation officers to capture how theirindividual values and organization and social climate affect the revocation of cases.Such surveys might also be used to gain a better sense of how gender and race/ethnicity influence the revocation process. Of particular importance is the futurestudy of prosecutors’ decision-making processes. The significant difference in howprosecutors handled low-level drug probation cases after the implementation of thelaw suggests that prosecutors might respond to the reduction of discretion in waysthat ultimately increase sentence severity for certain probationers.

With regard to sentencing outcomes, studies of sentencing processes must expandtheir focus to include the examination of mandatory drug treatment laws. Such studies




could generate important insights into how courts respond to rehabilitative policies.Lastly, future research should examine the consequences of probation violations in dif-ferent jurisdictions as these processes can vary significantly within and between juris-dictions (Clear et al., 1992; Kingsnorth et al., 1999; Vito, 1987).

Notes

1. The act was sponsored by The People Have Spoken (formerly Arizonans for Drug Policy Reform)and funded by George Soros, Peter Lewis, and John Sperling.

2. Although the study of timing of failure on probation is certainly important, it is a distinctly differentfocus from the one proposed here. The purpose of our investigation is to examine the decision-makingprocesses of probation officers, prosecutors, and judges in revocation cases that result in imprisonment.

3. Arrest for a new offense was included in the major violation category.4. We recognize that without analyzing data on low-level drug offenders who were not imprisoned,

it is unclear whether the changes in violations, prosecution, or sentence length over time and the predic-tors of these outcomes at each period are a function of the legal agents or an artifact of the sample ofdefendants. Unfortunately, data on defendants who were not imprisoned were not available.

5. For readers who may be interested, effective sample size (ESS) was used to obtain the samplewhere, the ESS for estimating statistics associated with subpopulation j is ESSj = nj (Nj – 1)/Nj – nj. Wedenote the size of the jth subpopulation of the offenders with Nj. From subpopulation j, we randomlyselected nj offenders. If n = 0, then ESS = 0, indicating that we have no information on that subpopula-tion. If we select all the observations from subpopulation (nj = Nj), then ESS is infinite, indicating that wehave perfect information on that subset of offenders. Once the subpopulations were defined, we countedthe number of offenders in each category to obtain Nj. Second, we set nj = 0 to initialize the process. Third,we computed the ESS for each subpopulation until the sum of the njs equaled 1,600. We determined that400 cases per year would yield the ability to detect a 10% difference in proportion means with more than80% probability for a factor with four categories (i.e., four counties). A sample size of 1,600 cases pro-duced excellent power for factors (e.g., criminal history, offense, race/ethnicity, and gender) with a lownumber of categories.

6. See Table 2 for a full review of the categorizations of violations.7. We relied on California’s Bureau of Criminal Statistics to create a severity score for offenses in

Arizona. We matched all offenses in the Arizona Revised Statutes with a corresponding offense code inCalifornia. We worked with researchers in California who have vast experience in using the severityscores and with prosecutors in Arizona to ensure that offenses across states corresponded as much as pos-sible. For every offense code in Arizona, we assigned a severity score ranging between 1 and 74, withlower numbers representing more severe offenses. Murder, for example, is the most severe offense inArizona criminal law, with a severity score of 1. Serious offenses against persons (e.g., rape, robbery, kid-napping) are rated 1 to 7, major property crimes such as burglary or forgery are rated 8 to 11, and majordrug felonies (e.g., possession or narcotics or drug trafficking) are rated 12 to 16. Misdemeanor drugoffenses are assigned severity scores of 34 to 36. Examples of severity scores for less serious offensesinclude trespassing (49), possession of burglary tools (61), and driving with a suspended license (74). Foreach offense in the current arrest and for each offense in the criminal history, we assigned a correspond-ing severity score. For analytical purposes, we reversed the scale so that higher severity scores were asso-ciated with more severe offenses. To summarize the severity of a group of charges, we computed the sumseverity score of those charges.

8. Criminal history rap sheets provided by the Arizona Department of Public Safety included prior arrests and convictions. Officials indicated that they only maintain data submitted to their office byothers; therefore, these data are by no means a valid measure of offenders’ prior criminal activity. Based




on informal discussions with agency officials, approximately 70% of all arrest data lack disposition out-comes, making convictions in these data an underestimate of offenders’ true prior convictions. Given theunreliable nature of prior convictions in these data, severity index scores were constructed for offenses ofprior arrests. Agency officials noted far more confidence in the accuracy of arrest data, given the report-ing practices of local law enforcement agencies.

9. Given that few studies have examined the impact of violations on prosecutors’ and judges’ deci-sions, we caution on the generalizations of findings across jurisdictions outside of Arizona.

10. Although not reported here, as with the sum severity score of offenses, offenses at prior arrestswere also assigned a severity score ranging from 1 to 74. Not only were probations’ criminal historyrecords postproposition more extensive, but they were also more serious in nature based on the sum sever-ity score of prior offenses (M = 531.64 pre vs. M = 769.41 post).

References

Albonetti, C. (1991). An integration of theories to explain judicial discretion. Social Problems, 38, 247-266.Albonetti, C. A., & Hepburn, J. R. (1996). Prosecutorial discretion to defer criminalization: The effects of

defendant’s ascribed and achieved status characteristics. Journal of Quantitative Criminology, 12, 63-81.Albonetti, C. A., & Hepburn, J. R. (1997). Probation revocation: A proportional hazards model of the con-

ditioning effects of social disadvantage. Social Problems, 44, 124-318.Arizona Supreme Court. (2000). Drug treatment and education fund. Phoenix: Arizona Supreme Court,

Administrative Office of the Courts, Adult Probation Services Division.Arizona Supreme Court. (2005). Drug treatment and education fund, reporting detailing years 2001-

2004. Phoenix: Arizona Supreme Court, Administrative Office of the Courts, Adult Probation ServicesDivision.

Britt, C., Gottfredson, M., & Goldkamp, J. S. (1992). Drug testing and pre-trial misconduct: An experi-ment on the specific deterrent effects of drug monitoring defendants on pretrial release. Journal ofResearch in Crime and Delinquency, 29, 62-78.

Bureau of Justice Statistics. (2004a). Prisoners in 2003. Washington, DC: Department of Justice.Bureau of Justice Statistics. (2004b). Probation and parole in the United States, 2003. Washington, DC:

Department of Justice.Clear, T. R., Harris, P. M., & Baird, S. C. (1992). Probationer violations and officer response. Journal of

Criminal Justice, 20, 1-12Clear, T. R., & Latessa, E. J. (1993). Probation officers role in intensive supervision: Surveillance versus

treatment. Justice Quarterly, 10, 441-462.Crawford, C., Chiricos, T., & Kleck, G. (1998). Race, racial threat, and sentencing of habitual offenders.

Criminology, 36, 481-511.Curtis, C., Hoctor, D., & Pennell, S. (1994) Intensive supervision for drug-involved probationers. In C. B.

Fields (Ed.), Innovative trends and specialized strategies in community-based corrections (pp. 87-122).New York: Garland.

Czajkoski, E. H. (1973). Exposing the quasi-judicial role of the probation officer. Federal Probation, 37,9-13.

Demuth, S. (2003). Racial and ethnic differences in pretrial release decisions and outcomes: A compari-son of Hispanic, Black, and White felony arrestees. Criminology, 41, 873-907.

Farabee, D., Hser, Y.-I., Anglin, M. D., & Huang, D. (2004). Recidivism among an early cohort ofCalifornia’s Proposition 36 offenders. Criminology and Public Policy, 3, 563-583.

Fulton, B., Stichman, A., Travis, L., & Latessa, E. (1997). Moderating probation and parole officers atti-tudes to achieve desired outcomes. The Prison Journal, 77, 295-312.

Gray, M. K., Fields, M., & Maxwell, S. R. (2001). Examining probation violations: Who, what, and when.Crime & Delinquency, 47, 537-557.




Hagan, J., Hewitt, J. D., & Alwin, D. F. (1979). Ceremonial justice: Crime and punishment in a looselycoupled system. Social Forces, 58, 506-527.

Harris, P. M., Petersen, R. D., & Rapoza, S. (2001). Between probation and revocation: A study of inter-mediate decision making. Journal of Criminal Justice, 29, 307-318.

Hepburn, J. R., & Albonetti, C. A. (1994). Recidivism among drug offenders: A survival analysis of theeffects of offender characteristics, type of offense, and two types of intervention. Journal ofQuantitative Criminology, 10, 159-179.

Hser, Y.-I., Teruya, C., Evans, E., Longshore, D., Grella, C., & Farabee, D. (2003). Initial findings froma five-county study on the impact of California’s Proposition 36 on the treatment system and patientoutcomes. Evaluation Review, 27, 479-505.

Johnson, B. (2003). Racial and ethnic disparities in sentencing departures across modes of conviction.Criminology, 41, 449-490

Johnson, W. W., & Jones, M. (1998). Probation, race, and the war on drugs: An empirical analysis of drugand non-drug felony probation outcomes. Journal of Drug Issues, 28, 985-1004.

Kautt, P., & Spohn, C. (2002). Cracking down on Black drug offenders? Testing for interactions amongoffenders’ race, drug types, and sentencing strategy in federal drug sentences. Justice Quarterly, 19, 1-36.

Kingsnorth, R., Cummings, D., Lopez, J., & Wentworth, J. (1999). Criminal sentencing and the court pro-bation office: The myth of individualized justice revisited. The Justice System Journal, 20, 255-273.

Kingsnorth, R. F., MacIntosh, R. C., & Sutherland, S. (2002). Criminal charge or probation violation?Prosecutorial discretion and implications for research in criminal court processing. Criminology, 40,553-577.

Kramer, J., & Steffensmeier, D. (1993). Race and imprisonment decisions. The Sociological Quarterly,34, 357-376.

Landis, J. R., Mercer, J. D., & Wolff, C. E. (1969). Success and failure of adult probationers in California.Journal of Research in Crime and Delinquency, 6, 34-40.

Longshore, D., Uranda, D., Evans, E., Hser, Y.-I., Prendergast, M., Hawkins, A., et al. (2004). Evaluationof the Substance Abuse and Crime Prevention Act 2003 report. Sacramento: California Health andHuman Services Agency, Department of Alcohol and Drug Programs.

MacDonald, S. S., & Baroody-Hart, C. (1999). Communication between probation officers and judges:An innovative model. Federal Probation, 63, 42-50.

Mackenzie, D. L., Browning, K., Skroban, S. B., & Smith, D. A. (1999). The impact of probation on thecriminal activities of offenders. Journal of Research in Crime and Delinquency, 36, 423-453.

Moore, C. A., & Miethe, T. D. (1986). Regulated and unregulated sentencing decisions: An analysis of first-year practices under Minnesota’s felony sentencing guidelines. Law and Society Review, 20, 253-277.

Miethe, T. D. (1987). Charging and plea bargaining practices under determinate sentencing: An investi-gation of the hydraulic displacement of discretion. The Journal of Criminal Law and Criminology, 78,155-176.

Morgan, K. D. (1994). Factors associated with probation outcome. Journal of Criminal Justice, 22, 341-353.Myers, M. A. (1982). Common law in actions: The prosecution of felonies and misdemeanors. Sociological

Inquiry, 52, 1-15.Myers, M. A., & LaFree, G. D. (1982). Sexual assault and its prosecution: A comparison with other

crimes. Journal of Criminal Law and Criminology, 73, 1282-1305.Myers, M. A., & Talarico, S. (1986). The social contexts of racial discrimination in sentencing. Social

Problems, 33, 236-251.Paternoster, R., Brame, R., Mazerolle, P., & Piquero, A. (1998). Using the correct statistical test for the

equality of regression coefficients. Criminology, 36, 859-866.Petersilia, J. (1983). Racial disparities in the criminal justice system. National Institute of Corrections.

Santa Monica, CA: RAND.Petersilia, J. (1985). Probation and felony offenders. National Institute of Justice research in brief.

Washington, DC: National Institute of Justice.




Petersilia, J., & Turner, S. (1990). Intensive supervision for high risk probationers: Findings from threeCalifornia experiments. Santa Monica, CA: RAND.

Petersilia, J., Turner, S., Kahan, J., & Peterson, J. (1985). Granting felons probation: Public risk and alter-natives. Santa Monica, CA: RAND.

Petersilia, J., Turner, S., & Piper-Deschenes, E. (1992). The costs and effects of intensive supervision fordrug offenders. Federal Probation, 56, 12-17.

Rhodes, W. (1991). Federal criminal sentencing: Some measurement issues with application to pre-guidelinesentencing disparity. Journal of Criminal Law and Criminology, 81, 1002-1033.

Riley, K. J., Ebener, P. A., Chiesa, J., Turner, S., & Ringel, J. (2000). Drug offenders and the criminal jus-tice system: Will Proposition 36 treat or create problems? Santa Monica, CA: RAND.

Riley, K. J., Rodriguez, N., Barnes-Proby, D., Griffith Forge, N., Ridgeway, G., & Webb, V. (2005). Justcause or just because? Prosecution and plea bargaining among low-level-drug offenders inCalifornia and Arizona. Santa Monica, CA: RAND.

Sims, B., & Jones, M. (1997). Predicting success or failure on probation: Factors associated with felonyprobation outcomes. Crime & Delinquency, 43, 314-327.

Smith, D. A., Wish, E. D., & Jarjoura, G. R. (1989). Drug use and pretrial misconduct in New York City.Journal of Quantitative Criminology, 5, 101-126.

Souryal, C., & Wellford, C. (1997). An examination of unwarranted sentencing disparity under Maryland’svoluntary sentencing guidelines. Baltimore: Maryland Commission on Criminal Sentencing Policy.

Speiglman, R., Klein, D., Miller, R., & Noble, A. (2003). Early implementation of Proposition 36: Criminaljustice and treatment system issues in eight counties. Journal of Psychoactive Drugs, 35, 133-141.

Spohn, C., DeLone, M., & Spears, J. (1998). Race/ethnicity, gender and sentencing severity in DadeCounty, Florida: An examination of the decision to withhold adjudication. Journal of Crime andJustice, 21, 111-138.

Spohn, C., Gruhl, J., & Welch, S. (1987). The impact of the ethnicity and gender of defendants on thedecision to reject or dismiss felony charges. Criminology, 25, 175-191.

State v. Estrada, 201 Ariz. 347, 34 P.3d 356 (Ariz. 2001).Steffensmeier, D., & Demuth, S. (2000). Ethnicity and sentencing outcomes in U.S. federal courts: Who

is punished more harshly? American Sociological Review, 65, 705-729.Taxman, F. S., & Cherkos, R. (1995). Intermediate sanctions: Dealing with technical violators.

Corrections Today, 57, 46-57.Tonry, M. (1995). Malign neglect: Race, crime and punishment in America. New York: Oxford University

PressUlmer, J. T. (2001). Intermediate sanctions: A comparative analysis of the probability and severity of

recidivism. Sociological Inquiry, 71, 164-193.Ulmer, J. T., & Kramer, J. H. (1996). Court communities under sentencing guidelines: Dilemmas of for-

mal rationality and sentencing disparity. Criminology, 34, 383-408.Vito, G. F. (1987). Felony probation and recidivism: Replication and response. Federal Probation, 50, 17-25.Walsh, A. (1985). The role of the probation officer in the sentencing process: Independent professional or

judicial hack? Criminal Justice and Behavior, 12, 289-303.Whitehead, J. T. (1991). The effectiveness of felony probation: Results from an eastern state. Justice

Quarterly, 8, 525-543.Zatz, M. (1987). The changing forms in racial/ethnic biases in sentencing. Journal of Research in Crime

and Delinquency, 24, 69-92.

Nancy Rodriguez is an associate professor in the Department of Criminal Justice and Criminology atArizona State University. Her research interests include sentencing policies, juvenile court processes,restorative justice, and drug abuse. Her recent work has appeared in Crime & Delinquency, JusticeQuarterly, and Criminology & Public Policy.




Vincent J. Webb is dean and director of College of Criminal Justice and the George Beto Criminal JusticeCenter at Sam Houston State University. He has also held academic posts at the University of Nebraskaat Omaha, Arizona State University, and Southern Illinois University. His recent research and publishedscholarship has addressed a variety of criminal justice policy issues including the police response togangs, sex offender residential clustering, the incarceration of low-level drug offenders, juvenile drugcourts, and the police response to human trafficking.




Criminal Justice Policy Review - Sage Publications

Documents

Transcript of Criminal Justice Policy Review - Sage Publications