A study of the disparate prosecution of drug possession across California in 2010 is illustrative, and the case to which we will return: charging policies and decisions were influenced by community and judicial attitudes toward the crime and the political and philosophical beliefs of district attorneys and charging deputies . Studies of the use of prosecutorial discretion to mitigate or maximize penalties in the context of three strikes laws have also found that more politically conservative environments tend to be more punitive, and counties with a high case flow relative to the budget for prosecution have lower average sentence severities . Prosecutors and judges may appropriately use discretion to align a punishment with the characteristics of a case and the local community’s priorities for law enforcement. However, unequal application of the law to equivalent cases calls into question the integrity and equity of the law, and can undermine public trust in law enforcement . For example, after controlling for case characteristics, third strike sentences in California were disproportionately imposed upon black defendants, with the largest gaps evident for offenses that could be charged as felonies or misdemeanors at the prosecutor’s discretion . These geographic differences may stabilize or exacerbate social and health inequalities. Community principles of proportionality that inform prosecution policies and practices may differ even within a county, and represent those of wealthier suburban populations with political power and the ability to prioritize crime reduction without bearing the costs of punishment . Those costs are high. Criminal records, particularly felony convictions, create a broad range of legal and social barriers that persist long after time is served. Restrictions range from the loss of voting rights, parental rights,mobile grow systems public benefits that support health and education, employment and occupational licensing, and housing – creating conditions that can in turn impact mental and physical health .

Associations between the risk of criminal justice exposure and place of residence present the possibility that collateral consequences will be unequally distributed and exacerbate inequalities by race and location . Considering the significance of criminal history for the severity of punishment for subsequent offenses, including eligibility to receive drug diversion rather than a felony conviction and incarceration, the effects of living in a punitive location are likely to compound over time. Geographic differences in conviction rates also have implications for costs. Punitive charging and sentencing decisions are made by counties but costs are passed on to the state; a felony conviction can receive a sentence to state prison, while county jails and probation supervise those with misdemeanors . In essence, the decisions of more punitive counties to impose higher rates of imprisonment are subsidized by less punitive counties .Drug law enforcement has been especially susceptible to differential justice by geography in California. Prior to the passage of Prop 47 in 2014, possession of a controlled substance and possession of concentrated cannabis were classified as “wobbler” offenses, which are charged as felonies by default, but provide prosecutors with the discretion to reduce them to misdemeanors. This discretion was introduced through a penal code amendment 17 in 1969 to reduce caseloads at overburdened superior courts responsible for hearing felony cases, by allowing lesser felonies to be adjudicated as misdemeanors in municipal courts at the prosecutor’s discretion . Research conducted in 2010 found the proportion of arrests for possession of a controlled substance that were charged as felonies varied across California counties from 25 to 100 percent . Even after controlling for case characteristics and criminal history, county of residence was a strong predictor of felony filings following arrest . California law does not dictate how wobblers should be prosecuted, nor does it define the quantity of drug that differentiates possession from sale, the latter of which is always a felony. Charging policies are established by district attorneys and differ across counties; the study found that some simply charged all possession cases as felonies, and others considered the quantity of the drug, prior criminal record, and concurrent charges. The extant research has found that Prop 47 led to fewer arrests, bookings, and custody time on average for Prop 47 offenses , and identified county variation in changes in arrests and jail populations following passage . However, how Prop 47 impacted geographic disparities in the severity of case dispositions has not been investigated. While the reclassification of drug possession offenses to misdemeanors may have reduced county variation in felony convictions for drug possession, felony convictions for concurrent offenses or for drug offenses that remained felonies may have increased in more punitive counties, potentially offsetting a reduction in geographic disparities.

This study will assess the effect of Prop 47 on county variation in felony convictions in two ways. First, we will test whether there was a change in county variation in the probability of a felony conviction for those arrested for drug possession. Within this group, we will examine whether there was an increase in felony convictions for concurrent offenses, which would suggest mitigation of Prop 47’s effects. Second, we will assess the change in felony conviction probability for individuals arrested for non-Prop 47 felony drug offenses, such as sale and transport, which may also result in Prop 47 convictions. California law does not specify the amount of drug that differentiates sale from possession, and those arrested for sale might have their charges reduced to possession. If this group of defendants continues to have charges reduced to possession, which is now a misdemeanor, we would see a reduction in their felony convictions.For example, the practice of reducing sale to possession during plea bargaining could decline in these counties, potentially increasing cross-county variation in felony convictions following these arrests. We extracted criminal records from the California Department of Justice’s Automated Criminal History System , which records all arrests and corresponding convictions and sentences within California. The analysis includes all Prop 47 arrests , or non-Prop 47 felony drug arrests, for which the arrest or first court event was within one year prior to or post Prop 47 passage. Arrests including both Prop 47 and non-Prop 47 felony drug offenses concurrently were classified as non-Prop 47 felony drug. Non-Prop 47 felony drug included an extensive list of offenses, the most common of which were possession of a controlled substance for sale , transport of a controlled substance , possession of marijuana for sale , transport of narcotics , possession of narcotics for sale , and transport of marijuana .

Though arrests were also made for offenses such as obtaining prescriptions by fraud, cultivating marijuana, or possession while armed, for simplicity, we will hereafter refer to non-Prop 47 felony drug offenses as sale/transport since these make up the vast majority of arrests. The dataset is organized in “cycles,” each of which holds a collection of related events, including the initial arrest and all subsequent court actions associated with the arrest. Some arrests were coded as having dispositions in a separate arrest cycle,cannabis grow supplies and could not be linked with their disposition in the available deidentified dataset. We therefore imposed the assumption that the arrest cycle that contained the missing disposition for these arrests was that which contained the next chronological case disposition. A total of 6.1% of Prop 47 drug arrests and 4.4% of non-Prop 47 felony drug arrests were reassigned based on this rule. After reclassifying concurrent Prop 47 drug and non-Prop 47 felony drug arrests, a total of 327,719 Prop 47 drug arrests and 123,726 non-Prop 47 felony drug arrests occurred during the two-year analytic period. Of the Prop 47 arrests, we excluded .02% due to missing county, and .01% due to missing gender. We also dropped Sierra and Alpine Counties, which made only 14 and 2 Prop 47 arrests during the analytic period, respectively. Of non-Prop 47 felony drug arrests, we excluded .07% due to missing county, and .02% due to missing gender. We again dropped Sierra and Alpine Counties, which made five and two arrests of this type, respectively. The remaining sample included 327,610 Prop 47 arrests, ranging from 53 to 65,341 across counties , and 123,599 non-Prop 47 felony drug arrests, ranging from 54 to 24,973 across counties . Separately for Prop 47 arrests and non-Prop 47 felony drug arrests, we determined whether the event resulted in a felony conviction for any offense associated with the arrest. We used any felony conviction as our primary outcome, because prosecutors have the discretion to consolidate arrest charges into an individual filing, or to alter offenses to negotiate a plea, and the charges prosecutors file may have been affected by Prop 47. For example, it is possible that prosecutors were more likely to file felony charges for non-Prop 47 offenses after passage, to counteract the drop in felonies due to reduced classification of Prop 47 offenses. By defining the outcome as any felony conviction, we attempted to account for possible changes in specific charges filed, and capture the severity of the overall case disposition following the arrest. Arrests with no disposition were assumed not to have been prosecuted. If Prop 47 shifted law enforcement practices, some individuals arrested during the pre-Prop 47 period might not have been arrested had they committed their crimes during the post-Prop 47 period. To assess the plausibility of such compositional changes in the populations arrested, we first compared pre- and post-policy groups on demographic characteristics, concurrent charges, and criminal histories, separately for Prop 47 offenses and non-Prop 47 felony drug offenses. Pearson’s chi-squared tests were used for categorical variables and Wilcoxon rank-sum tests for skewed continuous variables.

In the presence of compositional changes, we cannot estimate the effects of Prop 47 on arrest outcomes for individuals who would only have been arrested under pre-Prop 47 conditions, because a comparable group is not represented in the post-Prop 47 period. Furthermore, estimating the effect of reclassification on individuals unlikely to be arrested under the new laws would be of little value. Therefore, propensity score matching was used to assess the effect of the “treatment on the treated,” comparing arrest outcomes only among individuals who were likely to be arrested regardless of the reclassification of offenses. Each individual who was arrested after Prop 47 was matched with an individual who was approximately as likely, given their covariates, to have been arrested after Prop 47 was adopted, but was in fact arrested pre-Prop 47. We generated propensity scores using a logit model predicting the log odds that an arrest occurred during the post-Prop 47 vs. pre-Prop 47 period. Predictors included all available demographic variables, and concurrent arrest and criminal history variables likely to affect the arrest disposition. These consisted of age, gender, race/ethnicity; county and calendar month of arrest; any concurrent arrest, separately for felony or misdemeanor classifications: property, violent, sex, weapons, and other; whether the arrest included a probation or parole violation; number of prior arrests; prior arrest for a Prop 47 drug offense ; a measure of the severity of conviction history ; dummies for types of prior felony convictions, including drug, property, violent, sex, weapons, and other; any prior prison sentence and any prior jail sentence. For sale/transport arrests, we also include whether there was a concurrent Prop 47 drug offense. To accommodate non-linearities in age and the number of prior arrests, we use restricted cubic splines with five knots at equally spaced percentiles of each variable’s distribution. Propensity scores were estimated separately for arrests for Prop 47 and sale/transport offenses.Using within-county one-to-one matching without replacement, post-Prop 47 arrestees were matched on the logit of their propensity score to pre-Prop 47 arrestees, within a maximum of 0.2 of the standard deviations of the logit of the propensity score . For Prop 47 drug arrests, 5.6% of the post-Prop 47 group was dropped due to insufficient matches. For sale/transport arrests, 7.8% of the postProp 47 group was dropped. Covariate balance across propensity-score matched treatment and control groups was checked to assess the adequacy of the propensity score models. Standardized mean differences in all covariates were less than 5% in both samples. For each arrest category, we used a set of mixed logit models to examine the variance in county probabilities of felony conviction pre- and post-Prop 47 among propensity score matched samples. First, we specified the model to include county-specific random intercepts and random coefficients for the policy effect with an unstructured covariance structure. This generated an estimate of the covariance of pre-Prop 47 mean felony conviction probability with Prop 47 effects on conviction probability, which would indicate whether counties with higher pre-Prop 47 means declined to a greater degree, thus reducing variance in the outcome. Second, models were specified such that counties had separate random intercepts for pre- and post-Prop 47 periods, which generated an estimate of the variance in county probability of felony conviction in each period.