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«From Inception to Implementation: How SACPA has affected the Case Processing and Sentencing of Drug Offenders in One California County DISSERTATION ...»

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Each agency ensconced in the court process adapted to the legislation in slightly different ways. The judges’ method of adapting to Prop36 was based on a respect for law in general, (even if not this particular law and regardless of whether one agreed with the version passed by the voters). The PDO’s method of adapting to Prop36 was based on a relatively positive view of the law and a desire to help their client benefit from it. Finally, the ACAO’s method of adaptation was based on a negative view of the law as well as many of the offenders it applies to. These views shaped the respective agencies responses to the legislation and the steps they took to implement it. For example, court staff was committed to make the program work using the available tools and resources at their disposal. Judges were creative when it came to figuring out how to implement shock incarceration, legally. On the other hand, Anaheim City Attorney’s Office fought the legislation to the appeals court and proactively trained police officers how to circumvent the law (based on interviews with law enforcement officers, DA’s also trained officers on circumvention methods). The end result is that the way an agency chose to adapt to Prop36 was based strongly on their opinion of the legislation from the start.

IMPACT ON OFFENDER SENTENCING AND CORRECTIONS

Staring in 2001, Proposition 36 changed the way that more than 36,000 drug offenders are punished annually in the state of California (Longshore et al., 2004).

Between 2001 and 2005, more than 193,000 offenders throughout California were sentenced to Prop36 probation (Longshore, et al., 2007). In Orange County alone between 2001 and 2007, more than 24,000 offenders were sentenced to probation under Prop36 (Orange County Probation Department PC1210 Monthly Report, December 2007). Staff at the Orange County Jail articulated that the law had no noticeable impact on their agency. Parole agents had to adapt to new policies, but not additional parolees. But for the Orange County Probation Department, which had to supervise all these offenders, the impact was nothing short of “overwhelming.” This chapter explains the sentencing changes that occurred, describes the impact that those sentencing changes had on corrections agencies in Orange County (probation, parole, and jail) and estimates the number of Orange County offenders diverted from state and local incarceration as a result of Proposition 36.

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Proposition 36 profoundly changed the sentences drug offenders receive for possession related offenses, from incarceration-based sentences to probation in the community. Prior to Proposition 36, most offenders convicted of a drug possession offense were given a combined jail and probation sentence. This means that most offenders convicted of a drug possession offense were sentenced to 90 days in jail and three years on probation. After Proposition 36, less than a quarter of all drug possession offenders received a combined jail and probation sentence. Instead, most drug possession offenders were sentenced to three years on probation (with mandatory participation in drug treatment).

Figure 6.1: Sentences for Orange County Offenders Convicted of a Drug Possession Offense, 1995 – 2005 (3 Month Moving Average)

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50% 40% 30% 20% 10%

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'0 '9 '9 '9 '9 '9 '9 '0 '0 '0 '0 '0 '0 '0 '0

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Source: California Criminal Justice Statistics Center (CJSC). 2002 data are missing due to a data entry error at CJSC that affected the OBTS database.

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occurred as a result of Proposition 36 and Table 6.1 (below) displays the changes in numerical format. Analysis of variance was conducted on each series to determine if the mean differences were different before and after SACPA implementation. As can be seen, probation and probation with jail flip-flopped as the most often given sentence. The proportion of drug offenders who were sentenced to probation with jail declined from 64.3% before Prop36 to 22% afterwards (p.001) while the percent that was sentenced to straight probation (without jail) increased from 5.7% to 53% (p.001). This is a remarkable change. Additionally, the proportion sentenced to prison decreased by half (from 26.6% to 12.7%; p.001); and although jail (without probation) is not a very popular sentence for drug possession offenders in Orange County, the percent of offenders given this sentence dropped by twothirds (from 2.4% to.8%; p.05). Furthermore, Prop36 brought the number of Orange County drug offenders sentenced to the California Rehabilitation Center (a California Department of Corrections and Rehabilitation facility for civil narcotics addicts) to near zero.

As indicated by Figure 6.1, sentencing patterns changed prior to July 1, 2001, the official start date of Proposition 36. This happened for two reasons; (1) Orange County implemented a pilot study in March 2001 that mimicked Prop36 (and thus changed sentencing patterns), and (2) some judges began sentencing offenders differently in January 2001 in anticipation of Prop36 (so that Prop36 eligible defendants would not clog the courts until July). For these reasons and based on a review of the data, March 2001 was chosen as the start date for Prop36 in Orange County for the purpose of estimating the effect of Proposition 36 on sentencing practices.





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As Table 6.1 illustrates, the percentage of offenders sentenced to probation after Prop36 is affected by the “other” sentence category, which includes cases in which no sentence was imposed, the sentence was suspended, or the sentence was stayed.

From 1995-2001, the percent of offenders sentenced as “other” was stable at.7% per month. In 2003, it was consistently less than 2% per month. Starting in January 2004, however, the percent sentenced as “other” increased to 16% and stayed at that level or higher in 2004 and 2005. On average 16%-24% of offenders convicted of a drug possession offense in 2004-2005 were sentenced as “other.” Data analysis indicates that the change only affects the “probation” sentence category (and not prison, probation with jail, jail, fine, or CRC). The reason for the change is not completely understood by this researcher, analysts at Orange County Superior Court (OCSC), or data specialists at the Criminal Justice Statistics Center (CJSC); but based on the analysis of the data along with conversations with knowledgeable persons at both OCSC and CJSC; it most likely reflects a recategorization of data at the county level. In other words, some offenders who had been classified as “sentenced to probation” prior to January 2004 were classified as sentenced to “other” starting in January 2004 due to an administrative change (not a sentencing change). Therefore, the two categories (probation and other) were combined for the purpose of time series analysis and estimating the number of offenders diverted from incarceration as a result of Prop36.

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Although the dramatic change in sentencing is clearly visible to the naked eye, time series analysis is useful because it can provide a statistical likelihood that Prop36 is responsible for the changes observed in the time series’ before and after implementation of the law. Time series analysis was performed on the four main sentences (prison, jail, probation with jail, and probation [plus “other”]). Analyses reveal that not all of the sentences are statistically significantly different before and after Prop36 (see Appendix J for a discussion of time series methods, a description of ARIMA models, and significance levels). Time series analysis reveals that the sentence “jail” is statistically significantly different after Prop36 was implemented;

but that the “prison,” “probation with jail,” and “probation and other” sentences are not statistically different after Prop36 implementation. Strictly speaking, this suggests that Prop36 was partially (if not wholly) responsible for the change in the number of offenders sentenced to “jail” before and after Prop36, but that it was not responsible for much (if any) of the change in the number of offenders sentenced to “prison,” “probation” or “probation with jail.” This finding is peculiar, and contradicts Figure 6.1 and Table 6.1 that illustrate the dramatic changes in sentencing that occurred in the beginning of 2001.

There are three reasons why time series analysis does not indicate a Prop36 impact on some of the sentences, despite the impact being visually obvious. First, the series’ contain monthly interval data and the interval time periods are not equivalent. Second, some of the series’ are subject to large fluctuations in the number of observations from month to month. Third, Prop36 was implemented in phases in Orange County, not as a single point in time. All of these issues affect time series analysis and are discussed in turn below.

The results of the time series analyses are a function of the data used, both in terms of quality and power and reflect the extremely complicated data series’.

Because data are in monthly intervals (and not evenly spaced 28 days or weekly intervals), convictions sometimes vary dramatically between months due to the number of working days in each month (for example, there are fewer working days in December than in March. This serves to increase the variance between months, which diminishes the likelihood of finding a statistically significant impact.

Furthermore, some of the series have large fluctuations in the number of observations from month to month – partly a result of unequal intervals and seasonality, but also other unidentified county level trends, and small numbers of observations in individual months. For example, although there are at least 3,000 drug possession offenders convicted and sentenced each year, the data are analyzed by sentence category and month. This affects the number of the observations each month and thus the power to detect differences. This was a particularly significant issue in the “probation” series because there were few offenders sentenced to this sanction prior to Prop36 and the low cell counts inflated the variance, which disguised the impact of the legislation and caused time series analysis to not identify an impact, when in fact there was one (this is an issue of low power).

Finally, although Prop36 did not “officially” go into effect until July 1, 2001, sentences in Orange County began changing in January 2001. Some judges in Orange County’s felony panel courts started sentencing offenders to probation (instead of probation with jail, jail, or prison) in January 2001 in anticipation of Prop36 and a fear that these cases would remain on the courts’ calendar until July and cause a backlog in cases. Additionally, Orange County implemented a pilot study in March 2001 that mimicked Prop36. Hence, Prop36 did not begin on July 1, 2001; it began in January 2001, gained momentum in March and was fully implemented in the following months. It was implemented in stages, not at a single point in time. Such series’ are not well-suited for time series analysis because time series analysis is designed to assess the impact of an intervention at a single point in time – not an ongoing change that occurs in several stages. Although time series analysis is able to detect incremental changes in most time series, the problem with these data series is that they suffer from multiple issues, each one compounds the others and renders some of the series unsuitable for this statistical technique72.

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The main goal of Proposition 36 was to divert drug offenders from incarceration. The previous section outlined how sentences changed as a result of The presence of these data issues clearly demonstrates the inherent benefits of using dual research methodologies to investigate a question and corroborate findings.

the law and revealed that a smaller percentage of drug offenders are sentenced to incarceration (prison, probation with jail, jail, CRC) as a result of Proposition 36.

The question is, how many offenders have been diverted as a result of the legislation? The California Legislative Analyst’s Office originally estimated that 36,000 offenders throughout the state would be diverted from incarceration each year as a result of Proposition 36 (LAO, 2000). UCLA studies confirm that approximately 36,000 drug offenders per year are sentenced under Proposition 36 (Longshore, et al., 2006), but the question remains whether all of these offenders would have been sentenced to incarceration if it was not for Proposition 36.

The current research calculates a diversion estimate using data on “possession of narcotics” and “possession of dangerous drugs” convictions. This method allows for a rough estimate of the number of Orange County drug possession offenders diverted from incarceration as a result of Proposition 36. The caveat being that it cannot estimate the diversion impact for the state of California, only Orange County. Moreover, the data are limited, and the reader should be cautioned that this method will yield a diversion estimate that is much lower than the actual diversion impact because it includes only two Prop36 eligible offenses, possession of narcotics and possession of dangerous drugs. By necessity, it excludes “possession of paraphernalia” as well as “transportation for personal use.” Because of data limitations, it is unknown how many of these Prop36-eligible arrests take place each year, but based on probation data approximately 13% of Prop36 probationers are on probation for paraphernalia charges. Furthermore, the estimate excludes convictions for “under the influence of a controlled substance” arrests because “under the influence” is a misdemeanor and the state does not track conviction information for misdemeanor arrests. This is important because there are approximately 2,500 “under the influence” arrests each year in Orange County (which is higher than the number of narcotics possession arrests) and the standard sentence given to most of these offenders prior to Prop36 was a combined probation with jail sentence. The actual number of Orange County drug offenders diverted, therefore, is likely 30% - 60% higher than the possession-only estimate.



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