The Effect of Marijuana Legalization on Jail Populations in Washington State

Abstract

In 2012, Washington State legalized the production, sale, and possession of marijuana through Initiative 502. Advocates of legalization argued that it would decrease the jail population and reduce the disproportionate incarceration of minorities, reasoning that the police would refocus their resources on other matters. In order to evaluate this assumption, we examined jail booking data using a set of interrupted time-series regression models. Our findings indicate that jail population trends differ among counties across time and with respect to impacts on minorities and women. With regard to ethnic and racial disproportionate impact, there appears to be little positive change.

Keywords

marijuana jails minorities women race

Introduction

During the early 1970s, in response to a growing heroin epidemic in a number of metropolitan areas, aggressive new drug laws were implemented that imposed harsh mandatory prison sentences for drug offenses and set the foundation for further punitive drug sanctions. For instance, the New York State Substance Control Act (SCA) of 1973 imposed mandatory minimum sentences of 15 years to life for persons convicted of possessing as little as four ounces of cannabis, heroin, morphine, raw or prepared opium, or cocaine (Stemen, 2017). Intended to deter and prevent drug-related crime, states nationwide implemented mandatory sentencing laws that were modeled after New York’s SCA (Walker, 2006).

Commonly referred to as the “War on Drugs” with a declared objective to achieve a “Drug Free America,” jail populations began to increase and rose dramatically from an average daily population of 223,644 in 1983 to 721,300 in 2015 (Aiken, 2017, p. 2; Banks, 2016). The Prison Policy Initiative asserts in their 2018 report that of 615,000 inmates jailed nationally, 118,000 are being held for drug offenses that have not yet been convicted, while an additional 35,000 have been convicted and jailed for drug crimes (Wagner & Sawyer, 2018). In contrast, some jail and prison population reduction did occur between 2007 and 2012 as the national recession negatively impacted local and state budgets (Garland et al., 2014; Kaeble & Cowhig, 2018). Similarly, Washington State’s jail and prison population rates mirrored national trends. However, there were notable exceptions to overall statewide jail population reductions following the passage of initiative I-692 (medical marijuana initiative) in 1998 and initiative I-502 (recreational marijuana) in 2012 (Aiken, 2017).

The public appetite for punitive drug enforcement policies and practices has been waning since 2011, particularly with regard to low-level marijuana offenses (Gallup, 2017). Public opinion polls have consistently demonstrated that support for marijuana legalization has continued to grow, reaching 64% of Americans in the Gallup poll taken in October, 2017 (Gallup, 2017, p. 1). In addition, scholars, pundits, and policymakers have long argued that the drug war led to the unprecedented building and populating of jails and prisons. A disproportionate incarceration of minority group members and the increased confinement of women were also concomitant outcomes (Brooks, 2015; The Sentencing Project, 2016). As Washington State’s 1998 and 2012 initiatives were implemented, there were expectations of reductions in the number of persons jailed, particularly involving African American, Hispanic, and female inmate populations. Hence, initiative supporters such as the Editorial Board of the New York Times editorialized in favor of the 2012 Washington and Colorado citizen initiatives due to their expected beneficial effects upon racial and ethnic disproportionality (The New York Times, 2014). In this article, we examine the jail population data in an effort to determine whether the expectations were realized.

Background

The War on Drugs included treaty agreements and legislation at the international, federal, and state levels. Drug laws affected enforcement, court room caseloads and operations, and sentencing and incarceration practices. Statutes were designed to criminalize, prevent, deter, and punish activities associated with marijuana manufacture, distribution, and possession (Robinson, 2016; Shepard & Blackley, 2016).

The Drug War and Race/Ethnicity and Gender

Race and ethnicity are deeply entangled with drug crimes. According to a number of respected scholars (e.g., Parsons-Pollard, 2011; Stohr & Foster, 2016), illicit marijuana dealing is linked with African American and Hispanic American communities, which became part of law enforcement’s focus during the War on Drugs. Police organizations broadly employed traditional “crackdown” and “get tough” approaches in efforts to counter illicit drug activity (Walker, 2006). Enforcement tactics involved brief surges of intensive police action in specific areas alongside long-term neighborhood drug enforcement that targeted small-scale drug distributors and users for particular drug offenses. Given the increased emphasis by police to increase numbers of arrests and target communities where crack cocaine possession and use were more prevalent, critics (e.g., Sentencing Project, Clark Foundation, National Criminal Justice Commission, etc.) promulgated that the War on Drugs was racially biased, destructive to inner-city communities, and increased the likelihood of juvenile violence (Walker, 2006). African Americans were found to represent 35% of all people arrested for drug offenses, yet made up only 13% of the national population (Walker, 2006, p. 15). In addition, policymakers came to believe that effective street-level drug enforcement was the best instrument for restoring order and civility to neighborhoods through increases in the number of drug offender arrests (Parsons-Pollard, 2011).

Through the early 1980s, lawmakers also initiated mandatory sentencing laws, which facilitated the mass imprisonment of thousands of African Americans and Latinos and Latinas for low-level, nonviolent drug offenses (Alexander, 2012; Robinson, 2016). Mandatory sentencing laws, such as those imposed by the Anti-Drug Abuse Act (ADAA) of 1986, resulted in disparate racial effects due in part to demographic differences in drug preference and in statutory variances pertaining to drug form and quantity. For instance, crack cocaine was less expensive to purchase than powder cocaine and became widely used during the 1980s, particularly in economically depressed and disadvantaged urban areas with significant African American and Hispanic populations (Banks, 2016). The ADAA stipulated a mandatory minimum of 5 years with a maximum of 20 years for those convicted of possessing 5 g or more of crack cocaine. This was in sharp contrast to a 5-year mandatory minimum if the amount equaled or exceeded 500 g of powder cocaine (Banks, 2016, p. 265). Although there have been decreases in the overall number of African Americans jailed, they are still incarcerated at a rate 3.5 times that of Whites (down from 5.6 times in 2000; Zeng, 2018, p. 3).

The drug war, along with the “get tough on crime” movement, also disproportionately affected women, bringing more of them into both jails and prisons in historically unprecedented numbers (Gilliard & Beck, 1997; Zeng, 2018). For instance, the number of female inmates in jails climbed from 8% in 1985, to 10.8% in 1996, and to 14.5% by 2016 (slightly down from the all-time high of 14.7% in 2014) (Gilliard & Beck, 1997, p. 6; Zeng, 2018, p. 4). Women, particularly African American and Latina women, were more likely to be affected by the drug war because lower level offenses, which they tend to commit, were punished severely (Owen, 1998; Pollock, 2002). Therefore, as the state and federal drug war laws sanctioned low-level possession, sale, and distribution crimes, women were at an increased risk to be caught up in the criminal justice system. Yet, some states are showing reductions in such trends. For example, since 2009, demographic transitions involving drugs of choice and legislative changes focused more on violent rather than non-violent offenders have contributed to 38% fewer African American women being incarcerated in Georgia state correctional facilities (Rankin, 2018, p. 1).

Citizen Initiatives

Although drug enforcement policies influenced jail booking rates for persons charged with controlled substance violations, subsequent initiatives acted to soften local enforcement practices. For instance, in Washington State, Initiative 692 (I-692), which passed in November 1998, allowed qualified patients with certain illnesses access to marijuana if, in the judgment of their physicians, such patients would benefit from its medical use. As an impetus to I-692, Seattle voters passed Initiative 75 (I-75) in 2003, directing through city ordinance that the Seattle Police Department and City Attorney’s Office make the “investigation, arrest and prosecution of marijuana offenses, where the marijuana was intended for adult personal use, the City’s lowest law enforcement priority” (SMC, 12A.20.060, Sect. A). Enacted later in 2011, Tacoma Washington passed the Cannabis Reform Act, or Initiative 1 (I-1), directing the “police chief and city attorney [to] make the investigation, arrest, and prosecution of cannabis (a/k/a “marijuana”) offenses the lowest enforcement priority” (Reform Act ORG, 2017, p. 1). Finally, Washington State’s I-502 (2012) legalized the production, sale, and possession of marijuana for adults 21 years and older.

Federal Policy

In 2013, the U.S. Department of Justice implemented a non-preemptive policy toward state laws legalizing marijuana, but stipulated the federal government’s right to intervene and challenge legalization laws when national priorities necessitate intervention (Cole, 2013). However, Attorney General (AG) Session’s January 2018 memorandum reasserts the prohibition of marijuana cultivation, distribution, and possession (21 USC § 801 et seq.), as well as noting the penalties (21 USC § 841 et seq.) for violating the U.S. Code (USC). In addition, the Session’s memorandum serves as a powerful reminder that marijuana remains classified as a dangerous drug, and that unlawful marijuana activity is still a serious crime under federal law (Sessions, 2018).

Jails and Correctional Centers in Washington State

There are 39 counties in Washington State, with 38 of them operating jails or correctional centers where jail bookings are conducted (the San Juan County Sheriff’s Office operates a temporary holding facility and transfers offenders awaiting trial or serving a sentence to the Yakima County Correctional Facility) (Washington Jail and Inmate Records Directory [WJIRD], 2017). Local jails are frequently used as holding facilities for state and federal inmates convicted of felonies and awaiting transfer to prison (Robinson, 2016). Washington’s incarceration practices generally reflect national trends, with laws pertaining to drug use, possession, sale, manufacture, and distribution providing sanctions at the misdemeanor, gross misdemeanor, and felony levels.

This Study

No prior studies have been conducted to assess the effects of I-502 on jail populations. Rather, prior empirical investigation has focused on the relationship between medical and recreational marijuana legalization and crime, crime clearance rates, sentencing patterns, state and federal incarceration levels, marijuana use by minors, and influences upon traffic collision and related fatality rates (Dills et al., 2016; Makin et al., 2018; Shepard & Blackley, 2016; Washington Traffic Safety Commission [WTSC], 2016). Consequently, there are empirical gaps in knowledge regarding the effects of marijuana legalization on the criminal justice system in Washington State, and on jails in particular. Researchers do not know the extent to which marijuana legalization has positively or negatively impacted justice system institutions. Such research is a necessary component of broader efforts to calculate the cost-benefit analysis of marijuana legalization (Washington State Institute for Public Policy [WSIPP], 2017).

Method

I-502 is a paradigm-shifting piece of legislation which legalized the recreational use of marijuana in Washington State. Since only a small number of states have legalized the recreational use of marijuana, with Washington State and Colorado being the first to do so in 2012, empirical policy evaluation pertaining to the effects of I-502 on criminal justice agencies generally—or on jail populations in particular—is notably limited. Based on the limited knowledge we have on the potential effects of recreational marijuana legalization on jail populations (with the expectation that some of the drug war effects will be reversed), our core research question is: What has been the effect of I-502 and its implementation on Washington State jail populations? To investigate this research question, we hypothesize that:

Hypothesis 1 (H1): I-502 and its implementation will result in a decrease in local jail populations.

Hypothesis 2 (H2): I-502 and its implementation will result in a greater decrease in the female jail population than the male jail population.

Hypothesis 3 (H3): I-502 and its implementation will result in a greater decrease in the African American jail population than the White jail population.

Hypothesis 4 (H4): I-502 and its implementation will result in a greater decrease in the Hispanic jail population than the non-Hispanic jail population.

Data

To answer the research question and address the associated hypotheses, we obtained individual-level jail booking data from October 1, 2009 to December 31, 2016 from the Washington State Office of Financial Management (OFM; 2018). The data initially included information from 38 county jails with 2,772,328 bookings records (the King County Department of Adult and Juvenile Detention [DAJD] was not represented in the OFM data and therefore not included in this study). Importantly, about 44.0% of the total number of cases does not contain any information regarding offense types, while the remaining 56.0% of the cases yielded 30,847 unique values representing offense type. The OFM data are inconsistent due in part to the procedures through which various offenses were documented across Washington State counties. This resulted in the lack of meaningful offense categorization during the data cleaning process.

Consequently, when addressing the research question and testing the derivative hypotheses, we examined fluctuations in the trend of overall jail bookings, and overall jail bookings disaggregated by groups (race and gender). Given that marijuana arrests were numerous across the state prior to I-502, an overall shift in booking rates is expected to occur post I-502. Data drawn from the UCR substantiates this, as there was a 64% decline in marijuana possession arrests from 2012 to 2013 in Washington. Moreover, Washington State’s arrest experience has approximated national trends. Whereas the number of marijuana arrests nationally between 2001 and 2010 equated to more than 8.2 million arrests, with African Americans 3.73 times more likely than Whites to be arrested for a marijuana offense, Washington State during the same period underwent approximately 129,000 marijuana arrests with African Americans arrested on marijuana charges at 2.9 times the rate of Whites (American Civil Liberties Union [ACLU], 2013, p. 8). In brief, the prevalence of marijuana offenses indicates that legalization could have an impact on overall jail populations, as well as minority and gender effects.

The data obtained are in long-format and consist of individual persons booked. This study is concerned with the number of jail bookings (per month and per facility) as opposed to the number of unique individuals being jailed. Individuals who are booked at jails for multiple offenses are counted as a single jail booking. This action is completed by counting each unique statewide booking number for each booking event. When a person was jailed for three offenses, the data presented three entries, but with only one unique incident booking number. However, if a person was booked three times in a given month, the person would be assigned three unique incident booking numbers; as a result, we counted three unique booking numbers. We removed duplicated entries, and reduced the total number of cases by identifying and selecting unique booking numbers into our sample. Using the unique incident booking number, we calculated the total number of bookings per month for each jail. The current research covers 87 months since the beginning of the study (October 1st, 2009) and includes individuals with multiple arrests and multiple jail bookings at multiple times. After engaging in this preliminary form of data cleaning, the data used for the analyses reported below contained 1,148,377 unique bookings from 38 of 39 counties in Washington State.

The univariate analyses revealed that the total number of jail bookings per month were often likely misreported and, specifically, under reported in the OFM data. By examining the face validity (jails missing data for a 12-month period were excluded from further analysis) of the number of monthly bookings for each of the county jails, the research team deemed that only eight county jails could be used to conduct analysis with confidence that the results would not be biased. The other 30 counties did not maintain monthly booking data sufficiently reliable to permit their use in the analysis, despite specific state statutory language requiring them to do so. We will return to this issue in our concluding discussion.

The team further tested the quality of the data from the rest of the selected eight facilities by comparing monthly and seasonal spikes in population to determine if they fell within an acceptable 10% range of more or fewer inmates from the documented monthly average patterns. Hence, these eight counties reported an occasional extreme fluctuation and had some degree of missing data. Recognizing that changes in jail populations can be caused by changes of the population in the community, we calculated jail population rates. The county populations for the investigated trend variables were obtained from the Washington State Office of Financial Management-Population and Demographics and U.S. Census county population data. To address the issues of missing data after the ratio variables were computed, outlier values were identified using Tukey’s 1.5*IQR rule and were replaced with imputed values using Kalman Smoothing, as were any existing missing data points for these eight counties (Durbin & Koopman, 2012). This process is a state space time-series approach in which imputed values are generated based on a variety of components, including trends, seasonality, and disturbance terms using the imputeTS package in R (Moritz & Bartz-Beielstein, 2017). The results of the jail population change will be presented in the form of jail population per capita for standardized cross-referencing.

Analytic Approach

To examine the effects of I-502 on jail bookings in Washington State, we estimated a set of interrupted time-series regression models. Interrupted time-series analysis is a quasi-experimental technique in which the immediate and long-term effects of an intervention can be analyzed (Cook et al., 1979), with some scholars arguing that interrupted time series are the strongest such design available when experiments are not feasible (Wagner et al., 2002). In the context of I-502, we employ an interrupted time-series approach to examine whether the legalization of marijuana had immediate and/or long-term effects on overall and disaggregated jail booking rates. As noted by Bernal et al. (2017), the general form of the interrupted time-series analysis is given as: $Y_{t} = β_{0} + β_{1} T_{t} + B_{2} X_{t} + B_{3} X_{t} T_{t}$ , where $Y_{t}$ is the outcome variable (the number of jail bookings per 100,000 people per month), $T_{t}$ is the time since the start of the study (centered to facilitate interpretation), and $X_{t}$ is a dummy variable (1 = post intervention). As such, $β_{0}$ is the starting jail booking rate in September of 2009, $β_{1}$ is the trajectory of jail bookings prior to I-502, $B_{2}$ is the immediate change in the level of jail bookings upon legalization, and $B_{3}$ is the difference between pre and post I-502 slopes for monthly jail booking rates. We extend this model to account for multiple interruption points (Linden, 2015), as it is possible that both the legalization and the start of retail sales could have effects on the jail population. Moreover, preliminary analyses discovered evidence of seasonality, which Bernal et al. (2017) note can bias interrupted time-series results. As a result, we include a set of monthly fixed-effects to account for regular monthly variation. Our final general model is as follows: $Y_{t} = β_{0} + β_{1} T_{t} + B_{2} X_{t} + B_{3} X_{t} T_{t} + B_{4} Z_{t} + B_{5} Z_{t} T_{t} + α$ , where $X_{t}$ is the passage of I-502 ( $X_{t} = 1$ from December 2012 onward), $Z_{t}$ is the start of retail sales of recreational marijuana in Washington State ( $Z_{t} = 1$ from July 2014 onward), and $α$ is a matrix of 11 fixed-effects dummy variables to control for monthly variation. Therefore, $β_{1}$ is the pre-legalization trend in jail booking rates, $B_{2}$ is the immediate legalization effect on jail booking rates, $B_{3}$ is the post-legalization trend difference in jail booking rates, $B_{4}$ is the immediate effect of retail sales on jail booking rates, and $B_{5}$ is the post-retail sales trend difference in jail booking rates. We also estimate 11 additional coefficients for the fixed-effects monthly indicator variables in the model (not presented to save space, available upon request).

Findings

To examine the effects of legalization on jail booking rates, we used the previously described model to examine trends in overall jail booking rates, as well as disaggregating by county, gender, race/ethnicity. We present three time-series plots below in Figures 1 to 3 demonstrating overall jail populations for these eight counties in Washington and disaggregated jail populations by race/ethnicity and gender. Each plot includes two dashed vertical lines to indicate the intervention points: December, 2012 (the passage of I-502) and July, 2014 (the start of retail marijuana sales). In general, this time-series plot seems to suggest that there has been little overall change in jail populations from October 1, 2009 to December 31, 2016 (see Figure 1). Although there are fairly regular cyclical shifts and some evidence that rates were on a downward trend following legalization, the trends post-retail sales seems to be flat or perhaps even increasing. It is important to note that the dashed line faithfully tracks the observed jail population, suggesting that our time-series approach does a good job of accounting for both the cyclical nature of jail populations and the overall trend in jail rates.

Figure 1.

Overall predicted and observed jail booking rates.

Figure 2.

Predicted jail booking rates by race.

Figure 3.

Predicted jail booking rates by gender.

In terms of race, the most noteworthy result from Figure 2 is that African Americans are jailed at a substantially higher per capita rate than Whites in these eight counties (Hispanics are omitted due to missing data from one of the eight counties). This plot also suggests that legalization might have decreased jail population rates for African Americans, though the rates seem to have increased again after the start of retail sales. Jail rates for females are displayed in Figure 3 with the overall jail rate for purposes of comparison. These results visually indicate that our model predicts females to generally be jailed at a lower rate than males and, moreover, that male rates may have increased in a nonlinear fashion since the start of retail sales.

Overall, the visual depiction of jail rates over-time suggests that if legalization influenced jail population rates in Washington State, it was a modest effect. To more formally examine the effects of legalization and retail sales, we present the results of the regression models in Table 1. While these models include monthly dummy variables to account for seasonal variation, we omit those from the table to improve interpretability (results are available upon request).

Table 1.

Interrupted Time-Series Regression Results for All Eight Counties.

Variables	Overall jail rates	White	Black	Hispanic	Female
Pre-intervention trend	−0.288 (0.491)	−0.073 (0.350)	1.582 (2.451)	−1.307** (0.439)	0.137 (0.133)
Immediate effect of legalization	16.442 (13.084)	19.632* (9.633)	44.448 (75.762)	4.472 (14.187)	3.716 (4.417)
Trend after legalization	−0.418 (1.343)	−0.895 (0.966)	−17.039** (7.065)	0.044 (1.295)	−0.665^† (0.399)
Immediate effect of sales	−18.259 (21.698)	−26.058^† (15.618)	−223.759** (112.710)	−47.833* (20.353)	−5.857 (6.202)
Trend after start of retail sale	1.095 (1.472)	1.595 (1.056)	17.607** (7.598)	3.703** (1.379)	0.475 (0.422)
Constant	326.909** (14.474)	258.579** (10.427)	1,254.355** (77.619)	222.457** (14.683)	167.425** (4.795)
Log likelihood	−301.061	−282.545	−443.284	−339.305	−271.865
AIC	640.122	603.090	924.569	716.611	581.731

Note. N = 87. Unstandardized regression coefficients with standard errors in parentheses. Hispanic model based on seven counties due to missing data from Clark county. AIC = Akaike information criterion.

†

p < .1. *p < .05. **p < .01.

The overall jail population model is displayed in the first column of Table 1. This model indicates that neither legalization nor the start of retail sales had immediate or over-time effects on jail population rates in general. Yet, aggregated data can make more subtle and nuanced trends difficult to observe. Indeed, Model 3 suggests that legalization and retail sales are related to changes in the jail population for African Americans. Specifically, while there was no immediate treatment effect for I-502, jail population trends for African Americans declined significantly (b = -17.039) following I-502. Moreover, there was a statistically significant immediate treatment effect (b = -223.76) for the start of retail sales on African American jail rates, though post-retail trends appear to rise and return to normal rates (b = 17.607).

Similar results are not found for other racial or ethnic groups. There is no evidence that trends in White jail population rates changed as a result of legalization or retail sales. Model 2 does indicate that there was an immediate increase (b = 19.632) in jail population rates following legalization and a borderline statistically significant decrease (b = -26.058) following sales; however, these two effects are similar in magnitude and largely cancel each other out. For Hispanics, Model 4 indicates that there was an immediate decline in jail population rates following the start of retail sales (b = -47.833), though trends after sales appear to be significantly rising (b = 3.703) and approaching pre-sale levels.

Aside from these results, there is little evidence that legalization had a large effect on jail population rates for other groups. The female jail rate trend declined in a marginally significant manner following legalization, but this effect is quite small (b = −0.665) and falls short of the standard cutoff for statistical significance (indeed, this would only indicate a decline of a little more than one female per 100,000 every 2 months following legalization).

To further examine the trends in these models, we also estimated the eight interrupted time-series models presented in Table 2 disaggregated by county. The over-time results are summarized in Table 2, which indicate whether there was a statistically significant shift in jail population trends following legalization (December 2012) and sales (July 2014) and the direction of this shift by county.

Table 2.

Shift in Jail Population Trends by County.

County	Overall	Race/ethnicity	Females
Clark	N/S	N/S for Whites; decline following legalization and increase following sales for African Americans	Decline following legalization
Ferry	Decline following legalization; increase following sales.	Decline for Whites and African Americans following legalization; increase for Whites, African Americans, and Hispanics following sales	Decline following legalization; increase after sales
Grant	Decline following sales.	N/S for Whites; increase following legalization and decline following sales for African Americans and Hispanics	N/S
Klickitat	Increase following sales; decline following legalization.	N/S for African Americans or Hispanics; decline following legalization and increase following sales for Whites	Decline following legalization; increase after sales
Okanogan	N/S	N/S for African Americans or Hispanics; decline following legalization for Whites	N/S
Skagit	N/S	N/S	N/S
Stevens	N/S	N/S for Whites or Hispanics; increase following legalization and decrease following sales for African Americans	Increase after sales
Yakima	N/S	N/S for Whites; decline following legalization for African Americans and increase following sales for African Americans or Hispanics	N/S

N/S = not significant.

The county-disaggregated results support our primary aggregated finding—that is, legalization and retail sales of marijuana have not had any apparent effect on jail population rates as a whole. Race disaggregated jail populations did appear to change following legalization and sales. However, these results are somewhat inconsistent. For example, there are no significant shifts post legalization for African American jail population rates in three counties (Klickitat, Okanogan, and Skagit), significant declines following legalization in three other counties (Clark, Ferry, and Yakima), and a significant increase in two other counties (Grant and Stevens).

Contrary to our first hypothesis (H1), there was no decrease in jail populations as a whole. Overall aggregated and county disaggregated jail population rates indicate no immediate or longer term temporal effects related to I-502, or attributable to the commencement of retail sales. This may be because offenders in possession of small amounts of marijuana are typically cited and released, not booked into jails or, alternatively, many agencies might have already deprioritized marijuana offenses. Our analysis suggests insignificant overall change in jail populations from October 1, 2009 to December 31, 2016. However, jail population trends differ between counties, and with the exception of Ferry County, are in line with their respective demographic make-up. Although inconsistent, minority county jail populations were somewhat likely to decline immediately following legalization, with subsequent increases noted after legal retail sales began in the state.

County differences were also observed in findings surrounding female jail populations, with some results differing from our hypothesis (H2) that I-502 would result in a greater decrease in female than male jail populations. Specifically, in contrast to the male jail populations, Clark, Ferry, and Klickitat counties exhibited significant reductions in female jail populations following I-502, while four counties (i.e., Grant, Skagit, Okanogan, and Yakima) showed no such change in their female jail populations. However, after the opening of retail sales, Ferry, Klickitat, and Stevens counties reflected increases in female jail population rates. Of particular interest, Ferry and Klickitat counties were the only two counties to demonstrate both a significant reduction in female jail populations following I-502 and significant increases in female jail populations starting with the onset of commercial sales.

Similarly, African American jail populations also differed, with some counties (i.e., Clark, Ferry, and Yakima) showing reductions following I-502. However, these trends were not reflected in the other five counties, which either indicated no change (i.e., Klickitat, Okanogan, and Skagit) in jail population rates for African Americans, or demonstrated significant increases (i.e., Grant and Stevens). In comparison, White jail population rates showed significant decreases following legalization in Ferry, Klickitat, and Okanogan counties, but reported significant increases following dispensary sales in Ferry and Klickitat counties. Therefore, our projection (H3) that I-502 would result in a greater decrease for African American than White jail populations is supported in only two (i.e., Clark and Yakima) of the eight counties studied. Notably, the proportion of African Americans to non-African Americans in each of the eight counties ranges from 0.5% to 2.2% and is likely not a dominant influence upon significant fluctuations in county jail populations.

In relation to our fourth hypothesis (H4) that posits a greater decrease in Hispanic jail populations than non-Hispanic jail populations following I-502, such an outcome did not manifest itself in our findings. Rather than a decrease following I-502, Grant County exhibited an increase in Hispanic jail population, while four other counties (i.e., Klickitat, Okanogan, Skagit, and Stevens) showed no substantial change in Hispanic jail populations. In addition, Ferry and Yakima counties demonstrated increases in Hispanic jail populations following retail sales. The only noteworthy decrease occurred in Grant County after retail sales were affected. Grant County’s Hispanic jail population fluctuation might be more evident due to its larger Hispanic population (41%) in proportion to the statewide average of 12.4% (U.S. Census, 2017). This may also be a factor in Yakima County where the Hispanic population makes up 48.8% of the population, but is likely to be much less of an influence in Ferry County, accounting for just 4.5% of the population (U.S. Census, 2017).

Discussion and Conclusion

In general, aggregated and disaggregated county jail population outcomes vary between counties. County demographic variables illustrate inconsistent temporal effects related to I-502 and the subsequent implementation of retail sales. However, as previously noted, identifiable trends were observed among some demographic-specific county jail populations that indicated decreases following legalization, with subsequent stability or increases after the employment of retail sales. These results suggest the necessity for determining why counties have such variances and apparent inconsistencies in booking rates following statutory and policy change, and its effects upon diverse demographics. Counties enjoying reduced jail booking and incarceration rates following I-502 or retail sales might benefit from an exchange of dialogue with one another to delineate factors influencing varied charging, booking, and jail incarceration rates. Such factors might involve differences in social support services, law enforcement policies and practices, city and county policy influences, dispensary availability, cultural variability, or class and economic distinctions, to name a few. Clearly, increasing jail populations or heightened levels of incarceration associated with specific demographic groups is counterproductive to the limited resources available to municipal and county governments, as well as creating an environment for an escalation of tensions between varied racial, ethnic, or gender groups.

Our results, which indicate that legalization has not had a substantial or consistent impact on jail populations, also suggest that researchers interested in parsing out the full effects of legalization on criminal justice outcomes might do better to examine other outcomes, like arrest rates for minor offenses and incarceration rates for more serious offenses, as jail booking data may miss minor marijuana offenders. While this study does not seek to reveal or address the underlying causes of increasing, decreasing, or stable jail population rates separate from the influence of marijuana legalization, it does provide a foundation from which to view county differences. It also provides a platform for communities to identify further data needs or ask questions previously left unstated for purposes of gaining increased understanding related to local jail populations. Given additional information, communities might have the opportunity to explore interventions designed to reduce or eliminate unnecessary jail population growth.

Limitations

This study used an interrupted time-series approach to examine whether the legalization of marijuana had immediate and/or long-term effects on overall and disaggregated jail population rates. The purpose of this analysis was to examine and contrast fluctuations and trends in aggregated and disaggregated jail bookings pertaining to various demographic groups delineated by gender and race/ethnicity. However, due to a number of data issues related to jail misreporting and underreporting, eight Washington State counties of the 38 counties were selected for analysis with confidence that the county data presented were reliable for analysis.

A central limitation to this study pertains to the unreliable jail data obtained from the Washington State OFM. The data held by OFM and available for public release rely upon accurate reporting from Washington State county jails. Yet, data from the 38 counties that operate jails or maintain a temporary holding facility (i.e., San Juan County) in Washington State suffered some level of non-reporting, underreporting, and inaccurate reporting. Such flawed data hindered or prevented broader assessments of the data available for this study, leaving eight counties with enough consistent data from which to produce results that were limited to the aforementioned demographics (i.e., race/ethnicity, gender) and determined to be reliable. Although the inclusion of 8 counties is a genuine limitation, the Vera Institute’s “Incarceration Trend” tool (which presents yearly jail rates, based on Bureau of Justice Statistics data) shows that these counties cover a wide range of county types in Washington, including those with both high and low jail population rates and those with both high and low county population densities (Henrichson, 2018). Moreover, these eight counties cover all major regions of the state, though the lack of data from the largest county by population (King) limits our ability to describe how these trends play out in the largest metropolitan areas.

Revised Code of Washington 70.48.100 (1977) stipulates the maintenance of a jail register in which the name of each person confined in a jail is to be entered in a timely manner. In addition, the register is to contain the hour, date, cause of confinement, and manner of each person’s discharge. The data held by OFM from 38 Washington State counties fails to meet this standard. Therefore, this study also serves to highlight the statutorily required documentation shortcomings of Washington State’s county jails. However, more investigation needs to be conducted into the reasons underlying data deficiencies and flaws that potentially involve faulty documentation processes, problems in data transference between agencies, or some additional cause that necessitates a dependable and consistent remedy.

Notwithstanding these limitations, this study has meaningful policy implications that begin with improvement in data recording, processing, and transmittal between agencies. Secondarily, those counties exhibiting increases in jail populations in the heretofore described demographics might first seek to understand the core reasons surrounding the growth and subsequently develop and implement strategies and interventions for reducing incarceration rates. In turn, policymakers and their communities would likely enjoy reductions in jail costs and improved relationships with the demographic groups most affected by increased charging, booking, and rates of confinement.

Although the outcome of this study indicates that jail trends vary from county to county, they also suggest jail populations were more likely to decline after legalization and to increase after sales. If legalization did affect jail bookings, the effects were not consistent. There were few increases in jail population rates after I-502 and a limited number of decreases in jail populations following the start of retail sales. With the exception of specific demographics, most of the counties maintained stability in overall jail population rates both after I-502 and the implementation of commercial sales. Thus, there is no evidence of an aggregate reduction in disproportionate minority incarceration. As a final note, crimes and incidents associated with marijuana continue to demand police resources in the form of Driving Under the Influence, marijuana-related traffic crashes, trafficking in unlawful amounts of marijuana product, unlawful production of marijuana products, and numerous other illicit activities (Northwest High Intensity Drug Trafficking Area [NWHIDTA], 2017; Washington Association of Sheriffs & Police Chiefs [WASPC], 2015; WTSC, 2016). Perhaps jail populations have remained stable or increased as the result of police organizations refocusing their resources to address these persistent and increasingly demanding community needs.

Footnotes

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

Author Biographies

Duane Stanton, PhD, is an instructor in the Washington State University Pullman Department of Criminal Justice and Criminology. His research focuses on the legalization of marijuana, as well as issues related to police interrogation and recording approaches and their related outcomes.

Xiaohan Mei, PhD, is an assistant professor in the School of Criminal Justice at California State University, Los Angeles. His research interests are corrections and risk assessment.

Sohee Kim is a doctoral student in the Washington State University Pullman Department of Criminal Justice and Criminology. Her research agenda includes adolescent delinquency, confidence in the police, and crime victimization.

Dale Willits, PhD, is an assistant professor in the Washington State University Pullman Department of Criminal Justice and Criminology. His research studies include drug policy, policing, and violence.

Mary Stohr, PhD, is a professor in the Washington State University Pullman Department of Criminal Justice and Criminology. She has published seven books and more than 100 publications on a variety of criminal justice topics, particularly in corrections and management in justice-dispensing agencies.

Craig Hemmens, PhD, is a professor in the Washington State University Pullman Department of Criminal Justice and Criminology. He has published 20 books and more than 200 articles and other writings on a variety of criminal justice topics.

Guangzhen Wu, PhD, is an assistant professor in the Department of Sociology, University of Utah. His research interests include comparative criminal justice/criminology, police behavior and decision-making, and the impact of marijuana legislation.

Ruibin Lu, PhD, is an assistant professor of Criminal Justice, Stockton University. Her research focuses on criminal court and law, specialty courts, public policy and administration evaluation, and drug crime and usage.

David Makin, PhD, is an assistant professor in the Washington State University Pullman Department of Criminal Justice and Criminology. His research studies the impact of technology on public safety, body-worn cameras, and refinement and evaluation of public safety training.

Nicholas Lovrich, PhD, is a Regents professor emeritus at Washington State University Pullman and currently on research affiliate appointment in the Department of Criminal Justice and Criminology.

References

Aiken

(2017). Era of mass expansion: Why state officials should fight jail growth. Prison Policy Initiative. https://www.prisonpolicy.org/reports/jailsovertime.html#introduction

Alexander

(2012). The new Jim Crow: Mass incarceration in the age of colorblindness. The New Press.

American Civil Liberties Union. (2013, June). The war on marijuana in black and white: Billions of dollars wasted on racially biased arrests. https://www.aclu.org/sites/default/files/field_document/1114413-mj-report-rfs-re11.pdf

Banks

(2016). Criminal justice ethics: Theory and practice (4th ed.). SAGE.

Bernal

J. L.

Cummins

Gasparrini

(2017). Interrupted time series regression for the evaluation of public health interventions: A tutorial. International Journal of Epidemiology, 46(1), 348–355. https://doi.org/10.1093/ije/dyw098

Brooks

(2015). The prison problem. The New York Times. https://www.nytimes.com/2015/09/29/opinion/david-brooks-the-prison-problem.html

Cole

(2013). Guidance regarding marijuana enforcement. U.S. Department of Justice Office of the Deputy Attorney General. https://www.justice.gov/iso/opa/resources/3052013829132756857467.pdf

Cook

T. D.

Campbell

D. T.

Day

(1979). Quasi-experimentation: Design & analysis issues for field settings (Vol. 351). Houghton Mifflin.

Dills

Goffard

Miron

(2016, September 16). Dose of reality: The effect of state marijuana legalization (Policy Analysis-CATO Institute, No. 799). https://www.cato.org/publications/policy-analysis/dose-reality-effect-state-marijuana-legalizations

10.

Durbin

Koopman

S. J.

(Eds.). (2012). Time series analysis by state space methods (Vol. 38). Oxford University Press. https://www.oxfordscholarship.com/view/10.1093/acprof:oso/9780199641178.001.0001/a cprof-9780199641178

11.

Gallup. (2017). Illegal drugs (Data set). https://news.gallup.com/poll/1657/illegal-drugs.aspx

12.

Garland

Hogan

Wodahl

Hass

Lambert

Stohr

M. K.

(2014). The issue of decarceration and its possible effects on inmates, staff, and communities. Punishment and Society, 16(4), 448–473.

13.

Gilliard

D. K.

Beck

A. J.

(1997). Prison and jail inmates at midyear, 1996. U.S. Department of Justice, Bureau of Justice Statistics. https://www.bjs.gov/index.cfm?ty=pbdetail&iid=879

14.

Henrichson

(2018). Incarceration trends. https://trends.vera.org/incarceration-rates?data=pretrial

15.

Kaeble

Cowhig

(2018). Correctional populations in the United States, 2016. U.S. Department of Justice, Office of Justice Programs, Bureau of Justice Statistics. https://www.bjs.gov/index.cfm?ty=pbdetail&iid=6226

16.

Linden

(2015). Conducting interrupted time-series analysis for single-and multiple-group comparisons. The Stata Journal, 15(2), 480–500. https://doi.org/10.1177/1536867X1501500208

17.

Makin

Willits

DuBois

Stohr

Koslicki

Stanton

Hemmens

Snyder

Lovrich

(2018). Marijuana legalization and crime clearance rates: Testing proponent assertions in Colorado and Washington State. Police Quarterly, 22(1), 31–55. https://doi.org/10.1177/10986111786255

18.

Moritz

Bartz-Beielstein

(2017). Package “imputeTS: Time series missing value imputation in R.” The R Journal, 9(1), 207–218. https://journal.r-project.org/archive/2017/RJ-2017-009/index.html

19.

The New York Times . (2014, July 27). Repeal prohibition, again. Editorial Board. https://www.nytimes.com/interactive/2014/07/27/opinion/sunday/high-time-marijuana-legalization.html

20.

Northwest High Intensity Drug Trafficking Area. (2017, August). Washington State marijuana impact report (Vol. 2). https://wsnia.org/wp-content/uploads/2017/11/NWHIDTA-Marijuana-Impact-Report-Volume-2_August-2017.pdf

21.

Office of Financial Management. (2018). About: What we do. https://ofm.wa.gov/about/what-we-do

22.

Owen

(1998). In the mix: Struggle and survival in women’s prison. State University of New York Press.

23.

Parsons-Pollard

(2011). Disproportionate minority contact: Current issues and policies. Carolina Academic Press.

24.

Pollock

(2002). Women, prison & crime (2nd ed.). Wadsworth.

25.

Rankin

(2018). Number of African Americans sent to Georgia prisons hits historic lows. The Atlanta Journal—Constitution. https://www.myajc.com/news/local/proportion-black-people-sent-state-prison-hits-historic-lows/kSVudQ2MbdLJHu4WUWmtxJ/

26.

Reform ActORG. (2017). Complete text of Tacoma Initiative No. 1. https://www.cannabisreformact.org/initiative-text/

27.

Revised Code of Washington. (1977). Section 70.48.100. https://app.leg.wa.gov/rcw/default.aspx?Cite=70.48.100

28.

Robinson

(2016). Race, ethnicity, crime and justice. Carolina Academic Press.

29.

Seattle Municipal Code. (2003). 12A.20.060, Sect. A. clerk.ci.seattle.wa.us/~public/initref/init75.htm

30.

The Sentencing Project. (2016). Fact sheet: Trends in U.S. corrections. The Sentencing Project. https://sentencingproject.org/wp-content/uploads/2016/01/Trends-in-US-Correction

31.

Sessions

(2018, January 4). Marijuana enforcement (Press release).https://www.justice.gov/opa/pressrelease/file/1022196/download

32.

Shepard

Blackley

(2016). Medical marijuana and crime: Further evidence from the western states. Journal of Drug Issues, 46(2), 122–134. https://doi.org/10.1177/0022042615623983

33.

Stemen

(2017). Beyond the war: The evolving nature of the U.S. approach to drugs. Harvard Law and Policy Review, 11, 375–418.

34.

Stohr

M. K.

Foster

(2016). No tokes over the line: Marijuana decriminalization, medicalization and legalization as it affects decarceration. In Marion

N. E.

Hill

J. B.

(Eds.), Legalizing marijuana: A shift in policies across America (pp. 119–134). Carolina Academic Press.

35.

U.S. Census. (2017). United States government printing office, Washington, D.C.

36.

Wagner

A. K.

Soumerai

S. B.

Zhang

Ross-Degnan

(2002). Segmented regression analysis of interrupted time series studies in medication use research. Journal of Clinical Pharmacy and Therapeutics, 27(4), 299–309.

37.

Wagner

Sawyer

(2018, March 4). Mass incarceration: The whole pie 2018. Prison Policy Initiative. https://www.prisonpolicy.org/reports/pie2018.html

38.

Walker

(2006). Sense and nonsense about crime and drugs: A policy guide (6th ed.). Thomson Higher Education.

39.

Washington Association of Sheriffs & Police Chiefs. (2015). Crime in Washington 2015 annual report. https://www.waspc.org/assets/CJIS/crime%20in%20washington%202015.small.pdf

40.

Washington Jail and Inmate Records Directory. (2017). https://publicrecords.onlinesearches.com/Washington-Jail-and-Inmate-Records.htm

41.

Washington Initiative 502 (I-502). (2012). https://lcb.wa.gov/publications/Marijuana/I-502/i502.pdf

42.

Washington State Institute for Public Policy. (2017, September). I-502 evaluation and benefit-cost analysis second required report. https://www.wsipp.wa.gov/ReportFile/1670/Wsipp_I-502-Evaluation-and-Benefit-Cost-Analysis-Second-Required-Report_Report.pdf

43.

Washington Traffic Safety Commission. (2016). Washington State 2016 traffic safety annual report. https://wtsc.wa.gov/wpcontent/uploads/dlm_uploads/2014/12/AnnualReportFFY2016FINAL.pdf

44.

Zeng

(2018, February 22). Jail inmates in 2016 (NCJ 251210). U.S. Department of Justice, Office of Justice Programs, Bureau of Justice Statistics. https://www.bjs.gov/index.cfm?ty=pbdetail&iid=6186