EXAMINING the POLITICS and POLICY IMPACTS Yuna C. Kim A

HARM REDUCTION AND SUBSTANCE USE: EXAMINING THE POLITICS AND POLICY IMPACTS

Yuna C. Kim

A dissertation submitted to the faculty at the University of North Carolina at Chapel Hill in partial fulfillment of the requirements of the degree of Doctor of Philosophy in the Department of Public Policy.

Chapel Hill 2016

Approved by:

Krista M. Perreira

Christine P. Durrance

Daniel P. Gitterman

Jeremy G. Moulton

Jonathan Oberlander

ii ABSTRACT

Yuna C. Kim: Harm Reduction and Substance Use: Examining the Politics and Policy Impacts (Under the direction of Krista M. Perreira)

This dissertation evaluates the impacts and political dynamics of harm reduction policies concerning substance use in North America. In doing so, this three-essay dissertation focuses on two substance use policies: medical marijuana laws (MMLs) and supervised injection services

(SISs).

The first two essays evaluate the unintended impacts that result from the implementation of MMLs in the U.S. The first essay evaluates the impact of state-level MML implementation and specific policy dimensions (i.e., dispensaries, patient registries, and in-home cultivation) in the U.S. on non-drug related arrest rates and crime rates for violent and property crimes. The second essay analyzes the effect of each MML policy dimension on the probability of cigarette smoking among U.S. adults. Considered together, these two essays highlight various unintended downstream impacts that need to be taken into consideration as policymakers adopt and implement MMLs.

The third essay is a political evaluation of the barriers to adopting SISs in North America.

The dearth of SISs in North America is puzzling considering the wealth of evidence suggesting their effectiveness in preventing overdoses and connecting injection drug users with treatment resources. Using the Canadian province of Ontario as a case study, this essay identifies the political barriers to adopting SISs and considers the limits and possibilities of establishing SISs in the North American context.

iii Taken as a whole, this three-essay dissertation provides valuable information to policymakers considering the implementation of MMLs or SISs. Policymakers considering marijuana legalization should be cognizant of the impact of particular policy dimensions on arrest rates and tobacco use to mitigate additional costs that may outweigh benefits. Moreover, this research underscores the importance of understanding the political environment in which harm reduction policies are made.

iv To my mom, Grace Soon-Ok Kim. Thank you for reminding me about juice.

v ACKNOWLEDGEMENTS

I consider myself to have the “dream team” of dissertation committees. I would like to thank my dissertation committee chair, Krista Perreira, for sharing her wisdom and expertise to help me develop as a researcher. From day one, your feedback on my work has always been incredibly insightful and I thank you for your willingness to guide me through each milestone of this program. I am also thankful to my committee members, Christine Durrance, Dan Gitterman,

Jeremy Moulton, and Jon Oberlander. Thank you for challenging me intellectually and providing the supports to help me overcome each challenge. My committee’s mentorship and support throughout the ups and downs of this program is something I treasure; I will never be able to thank each of you enough.

Thank you also to all of the new friends I made during my time in North Carolina. Thank you to Adria Molotsky, Nicole Ross, and Paige Clayton, for keeping me sane and giving me a safe space to be vulnerable. Thank you to my School of Public Health counterparts, Megan

Roberts, Sarah Lewis, Emily Gillen, Kristen Brugh, Larissa Calancie, and Lisa Selker for always being there and making this academic journey that much more fun. Thank you to my Canadian friend, Saleema Karim, for your mentorship; I promise we will be writing our Harold and Kumar series of papers soon! Thank you also to Ashley Richards for letting me join her work softball team; I hope you know how much joy that brought me. Lastly, but most certainly not least, thank you to my dear friends Evan Johnson and Lisa Spees. I think the universe must have had some plan to bring us all together in Chapel Hill. How wonderful it was that we were able to share the

vi joys and frustrations of this program together. I also feel so fortunate that I met such loving people during one of the most painful periods of my life. Thank you, quite literally, for everything.

I also want to thank the faculty, instructors, graduate, and undergraduate students in the

Department of Public Policy. I cannot think of a better place to call my academic home and I am honoured I was able to learn from each of you.

Thank you to Kim Jeffs, Patrick Jeffs, and Maureen Dunn for your patience, kindness, and guidance, and for helping me to see clearly. Thank you to Krystie and Jason Green, for being amazing roommates and friends. I look up to you both and admire how you face each obstacle life throws at you with spirituality and grace. I look forward to many more Super Bowl parties to come. Thank you to Harry MacNeill, Lenna Panou, Rhea Jankowski, and Charlsie Searle for keeping me grounded and for texting me during the 2015 Toronto Blue Jays playoff run.

I am not entirely sure how to thank adequately one very special individual in my life.

Ross Keatley, I do not know how you did it, but you carried me through my lowest lows. You managed to support me even when you were not quite sure of what to say. Despite living over

1,200 kilometres away, you made sure I never felt alone. I hope you know how much I cherish you.

Lastly, thank you to my family. Dad, thank you for spending hours with me when I was young going over math problems, editing my essays, reading Dr. Seuss to me, and playing softball with me. Like mom used to say, I am just like you, and I believe that makes me very lucky, even if a little stubborn. Thank you for supporting me unconditionally. I can act fearlessly knowing you will catch me if I fall. Danny, you are the coolest big brother. When we were kids, you once told me never to use a word if I did not know its meaning; I still remember this and

vii stick by this rule the best I can. Thank you for always keeping me humble and sticking up for me. Thank you to my aunt, Olivia Yoon, for checking in on me and providing sports updates.

Thank you to my aunt, Susie Whang, for giving me a home during American Thanksgiving.

My mom is my hero. Thank you for showing me what it means to be strong. You took some big risks in your life and sacrificed so much just so I could have the opportunity to be anything I wanted to be. Thank you for your love.

I owe this dissertation to each of you. Thank you for believing in me and supporting me.

viii

TABLE OF CONTENTS

LIST OF TABLES ...... xi

LIST OF ABBREVIATIONS ...... xiii

CHAPTER 1: EXECUTIVE SUMMARY ...... 1

Essay One...... 3

Essay Two ...... 4

Essay Three ...... 5

References ...... 6

CHAPTER 2: MEDICAL MARIJUANA LAWS, ARREST RATES, AND CRIME ...... 7

I. Introduction ...... 7

II. Background ...... 9

III. Data and Empirical Specification ...... 19

IV. Results...... 28

V. Sensitivity Analysis...... 33

VI. Conclusion ...... 36

REFERENCES ...... 40

APPENDIX TABLE 1: DEFINITIONS OF VIOLENT AND PROPERTY CRIMES ...... 62

CHAPTER 3: EXAMINING THE JOINT USE OF MARIJUANA AND TOBACCO: THE EFFECT OF MEDICAL MARIJUANA LAWS AND CIGARETTE SMOKING AMONG U.S. ADULTS ...... 63

I. Introduction ...... 63

II. Background ...... 65

III. Data and Empirical Specification ...... 68

IV. Results...... 73

V. Discussion ...... 76

REFERENCES ...... 80

CHAPTER 4: THE POLITICS OF PUBLIC HEALTH POLICY: THE CASE OF SUPERVISED INJECTION SERVICES IN ONTARIO ...... 90

I. Introduction ...... 90

II. Methods ...... 93

III. Results ...... 95

IV. Discussion ...... 102

REFERENCES ...... 106

LIST OF TABLES

Table 2.1 - States with medical marijuana laws, policy dimensions, and effective date, 1994-2012 ...... 58

Table 2.2A - Descriptive statistics of arrest rates (per 100,000) for all ages by MML implementation status ...... 59

Table 2.2B - Descriptive statistics of arrest rates (per 100,000) for adults by MML implementation status ...... 60

Table 2.2C - Descriptive statistics of arrest rates (per 100,000) for juveniles by MML implementation status ...... 61

Table 2.3 - Descriptive statistics for state-level covariates by MML implementation status ...... 62

Table 2.4 - Logged arrest rates for violent crimes as a function of MML policy dimensions, by age group ...... 63

Table 2.5 - Logged arrest rates for property crimes as a function of MML policy dimensions, by age group ...... 64

Table 2.6 - Logged arrest rates for violent crimes on restricted and unrestricted dispensary policies, by age group ...... 65

Table 2.7 - Logged arrest rates for property crimes on restricted and unrestricted dispensary policies, by age group ...... 66

Table 2.8 - Logged arrest rates for violent crimes as a function of MMLs, by age group...... 67

Table 2.9 - Non-logged arrest rates for violent crimes as a function of MMLs, by age group...... 68

Table 2.10 - Non-logged arrest rates for property crimes as a function of MMLs, by age group ...... 69

Table 2.11 - Logged arrest rates for violent crimes as a function of MMLs controlling for law enforcement, by age group ...... 70

Table 2.12 - Logged arrest rates for property crimes as a function of MMLs controlling for law enforcement, by age group ...... 71

Table 2.13 - Logged crime rates for violent and property crimes as a function of MML policy dimensions, all ages (BJS Data) ...... 72

Table 3.1 - States that enacted medical marijuana laws from 1993-2010 ...... 100

Table 3.2 - Descriptive statistics of variables, by MML status ...... 101

Table 3.3 - LPM estimates of the impact of MML policy dimensions on the probability of current smoking for adults under age 65, 1993-2010 ...... 102

Table 3.4 - Gender differences among healthy and unhealthy individuals, LPM estimates of the impact of MML policy dimensions on the probability of current smoking...... 103

Table 4.1 - Number and type of key informant interviewees ...... 129

xii

LIST OF ABBREVIATIONS

BJS Bureau of Justice Statistics

BRFSS Behavioral Risk Factor Surveillance System

CDC Centers for Disease Control and Prevention

CDSA Controlled Drugs and Substances Act

CPS Current Population Survey

FBI Federal Bureau of Investigation

HRQOL Health-related Quality of Life

IPUMS Integrated Public Use Microdata Series

LE Law Enforcement

LPM Linear Probability Model

MML Medical Marijuana Law

MOHLTC Ministry of Health and Long-Term Care

NCJJ National Center for Juvenile Justice

OLS Ordinary Least Squares

SCC Supreme Court of Canada

SIS Supervised Injection Services

TOSCA Toronto and Ottawa Supervised Consumption Assessment

UCR Uniform Crime Reporting Program

xiii

CHAPTER 1: EXECUTIVE SUMMARY

Harm reduction is an approach that emphasizes minimizing the health and social consequences associated with drug use instead of reducing consumption. Rather than viewing drug use as a criminal or immoral act, harm reduction takes a value-neutral public health perspective to substance use and focuses on minimizing risks to both the drug user and the surrounding community (i.e., reducing negative externalities) (Cheung, 2000; Marlatt, 1996).

Under this umbrella of harm reduction, this dissertation evaluates two policies that affect substance use: medical marijuana laws (MMLs) and supervised injection services (SISs).

To date, 23 states plus the District of Columbia have passed MMLs, with California being the first to implement such a law in 1996. These laws protect medical marijuana users from state-level criminal penalties and allow access to marijuana through cultivation at home or dispensaries (Hoffmann & Weber, 2010; National Conference of State Legislatures, 2014).

Patients must obtain approval from a doctor before they are allowed to acquire medical marijuana. MMLs also provide protection from prosecution for the doctors who make the recommendations (Anderson, Hansen, & Rees, 2013; Hoffmann & Weber, 2010).

MMLs embody characteristics of harm reduction. MMLs represent a shift from criminalizing certain marijuana users to providing a government-sanctioned avenue for patients to obtain medical marijuana to alleviate symptoms of a wide range of conditions. Consequently, harms associated with incarceration, enforcement costs, and the black market are potentially reduced with the legalization of medical marijuana (MacCoun & Reuter, 2001). As with any

policy, however, unintended consequences can result. The first two essays of this dissertation examine the positive and negative externalities that result from MMLs. The first essay evaluates the impact of state-level MML implementation in the U.S. on non-drug related arrest rates and crime rates for violent and property crimes. The second essay determines the effect of MMLs on the probability of cigarette smoking among U.S. adults. Considered together, these two essays highlight various unintended downstream impacts that should be taken into consideration as policymakers adopt and implement MMLs.

The contentious and often stigmatized nature of harm reduction policies, such as SISs, warrants an in-depth understanding of the politics of implementing such measures. SISs are medical clinics that provide a safe environment where injection drug users can consume pre- obtained illicit drugs. Clean and sterile equipment are provided to patients and medically trained staff are available to intervene in the event of an overdose. SISs epitomize harm reduction as they aim to reduce the transmission of infections, such as HIV/AIDS, by providing clean equipment and education on safe injection practices rather than enforcing abstinence. Such clinics, however, are extremely controversial in North America and are viewed by some as a practice that condones illicit drug use. While nearly 90 supervised injection facilities are in operation worldwide, primarily in Europe (Schatz & Nougier, 2012), currently only two supervised injection sites are in operation in North America in Vancouver, British Columbia,

Canada. The dearth of SISs in North America is puzzling considering the wealth of evidence suggesting their effectiveness in preventing overdoses and connecting injection drug users with treatment resources (DeBeck et al., 2011; Marshall, Milloy, Wood, Montaner, & Kerr, 2011;

Wood, Tyndall, Zhang, Montaner, & Kerr, 2007). Using Ontario as a case study, the third essay

identifies the political barriers to adopting SISs and considers the limits and possibilities of establishing SISs in the North American context.

Essay One

Proponents of marijuana legalization often argue that moving away from prohibition would reduce the number of marijuana-related arrests, decrease crime in the black market, and alleviate public spending on the criminal justice system. However, these arguments often do not consider the impact of such policies on non-drug related crimes and arrest rates. To examine the potential effect of legalization, this study evaluates the impact of state-level MMLs and specific policy dimensions (i.e., dispensaries, patient registries, and in-home cultivation) on the arrest rates for a variety of violent and property crimes for all ages, adults, and juveniles. This study is also the first to consider the differential impact of state dispensary policies, where some states only allow a limited number of dispensaries while others permit a less restricted market.

Depending on which MML policy dimensions are enacted within a state, MMLs could differentially impact arrest rates. Drawing upon arrest data from the Federal Bureau of

Investigation Uniform Crime Reports, I employ a fixed effects estimation strategy that uses state- fixed effects along with state-specific linear time trends. The findings suggest that states with dispensaries experience an increase in robbery and property crime arrest rates. This is especially true for states that operate an unrestricted dispensary market. Patient registries also increase arrest rates for most property crimes, but the effect is primarily driven by the adult population.

In-home cultivation allowances have protective effects, decreasing the arrest rates for theft and auto theft. Furthermore, the results indicate that changes in arrest rates are largely influenced by the effect of MMLs on crime levels rather than their impact on law enforcement allocation. The

paper concludes with a discussion of valuable lessons for policymakers and implications for states considering legalization in the future.

Essay Two

While the percent of cigarette smokers has leveled off at just under 20 percent in recent years, the prevalence of past-year marijuana use among U.S. adults more than doubled between

2001-2002 and 2012-13. The rise in marijuana use may be attributed to softening attitudes and laws towards marijuana use. Cigarette smoking due to increased access to marijuana is of particular concern as many recreational marijuana users combine tobacco with marijuana in the form of blunts or spliffs. This study evaluates the impact of MMLs on cigarette smoking among

American adults. Differences by health status are also considered given that the target population for MMLs is those suffering from chronic physical and mental health conditions.

Moreover, the study also considers differences in smoking probabilities by gender since males use cigarettes and marijuana at higher rates than females. Drawing upon data from the 1993-

2010 Behavioral Risk Factor Surveillance System surveys (N=2,722,046), I estimate linear probability models using a fixed effects identification strategy to identify the relationship between MML policy dimensions (i.e., in-home cultivation, dispensaries, and registries) and the probability an individual is a current smoker. The findings indicate that adults in states with dispensaries and registries were 1.4 and 2.0 percentage points less likely to be current smokers, respectively. In contrast, adults in states with in-home cultivation were 1.1 percentage points more likely to report current smoking. The effect of dispensaries, registries, and in-home cultivation did not differ between males and females. However, the increase in smoking probability due to in-home cultivation was largely driven by the healthy individuals. This increase in cigarette smoking among healthy individuals may be indicative of spillovers of

homegrown marijuana to recreational users. The experience from legalizing medical marijuana can be leveraged to inform other states considering marijuana legalization. In particular, public health officials seeking to discourage cigarette use should be weary of in-home cultivation allowances without a system in place to monitor and enforce cultivation limits.

Essay Three

Despite legal precedence, a demonstrated need, and considerable empirical evidence indicating several public health and fiscal benefits, SISs have yet to take root in North America beyond Vancouver, British Columbia. To understand the barriers to establishing SISs in North

America, this study looks at the case of Ontario to identify the factors that have prevented the adoption of SISs in this province. Data for this case study were obtained from 11 semi-structured key informant interviews and document reviews. Transcripts were analyzed deductively, using

Kingdon’s three-streams model and the institutions-ideas-interest (“triple-I”) framework of agenda setting to identify the factors that contribute to the success or failure of policy adoption.

The major barriers to establishing a SIS in Ontario were the result of several factors. First, SISs cross jurisdictional boundaries and require the alignment of multiple local, provincial, and federal stakeholders. Second, proponents were reluctant to submit applications for SISs due to strong opposition from the federal government and police. Third, competing political priorities put SISs low on the provincial policy agenda. The examination of the political treatment of SISs in Ontario illustrates the possibilities and limits in establishing harm reduction services for injection drug users in the North American political environment. In particular, timing and a strong understanding of concurrent political challenges are important considerations to facilitate the successful establishment of SISs in other policy contexts.

References

Anderson, D. M., Hansen, & Rees, D. I. (2013). Medical Marijuana Laws, Traffic Fatalities, and Alcohol Consumption. Journal of Law and Economics, 56(2), 333–369. http://doi.org/10.1086/668812

Cheung, Y. W. (2000). Substance abuse and developments in harm reduction. Canadian Medical Association Journal, 162(12), 1697–1700.

DeBeck, K., Kerr, T., Bird, L., Zhang, R., Marsh, D., Tyndall, M., … Wood, E. (2011). Injection drug use cessation and use of North America’s first medically supervised safer injecting facility. Drug and Alcohol Dependence, 113(2-3), 172–176. http://doi.org/10.1016/j.drugalcdep.2010.07.023

Hoffmann, D. E., & Weber, E. (2010). Medical Marijuana and the Law. New England Journal of Medicine, 362(16), 1453–1457. http://doi.org/10.1056/NEJMp1000695

MacCoun, R. J., & Reuter, P. (2001). Drug war heresies : learning from other vices, times, and places. Cambridge, UK; New York: Cambridge University Press,.

Marlatt, G. A. (1996). Harm reduction: Come as you are. Addictive Behaviors, 21(6), 779–788. http://doi.org/10.1016/0306-4603(96)00042-1

Marshall, B. D., Milloy, M.J., Wood, E., Montaner, J. S., & Kerr, T. (2011). Reduction in overdose mortality after the opening of North America’s first medically supervised safer injecting facility: a retrospective population-based study. The Lancet, 377(9775), 1429– 1437. http://doi.org/10.1016/S0140-6736(10)62353-7

National Conference of State Legislatures. (2016, January 25). State Medical Marijuana Laws. Retrieved November 21, 2014, from http://www.ncsl.org/research/health/state-medical- marijuana-laws.aspx

Schatz, E., & Nougier, M. (2012). IDPC Briefing Paper - Drug Consumption Rooms: Evidence and Practice (Briefing Paper). International Drug Policy Consortium. Retrieved from http://www.drugsandalcohol.ie/17898/1/IDPC-Briefing-Paper_Drug-consumption- rooms.pdf

Wood, E., Tyndall, M. W., Zhang, R., Montaner, J. S. G., & Kerr, T. (2007). Rate of detoxification service use and its impact among a cohort of supervised injecting facility users. Addiction, 102(6), 916–919. http://doi.org/10.1111/j.1360-0443.2007.01818.x

CHAPTER 2: MEDICAL MARIJUANA LAWS, ARREST RATES, AND CRIME

I. Introduction

As of 2013, the U.S. had the highest prison population rate in the world at 716 per

100,000 people (Walmsley, 2013). Combined with an estimated cost per inmate ranging from

$21,000 to nearly $34,000 per year (La Vigne & Samuels, 2012), incarceration costs place a great deal of pressure on the public purse. In 2013 (the most recent data available), most arrests in the U.S. were for drug abuse violations (over 1.5 million), and approximately 40 percent of these drug abuse violation arrests were for marijuana possession (Federal Bureau of

Investigation, 2013). Thus, policy interventions that remove the penalty or reduce incarceration associated with possession of small amounts of marijuana could result in substantial cost savings to the criminal justice system.

The legalization of drugs, particularly marijuana, is one policy option that is often considered as a way to reduce the incarceration rate and associated criminal justice costs.

According to one estimate, lifting the prohibition on marijuana could reduce drug enforcement costs by over $3 billion per year (Miron & Waldock, 2010). To date, Colorado, Washington,

Alaska, Oregon and the District of Columbia have legalized marijuana for recreational use in some form. However, sufficient data are not yet available to be able to determine the effect of legalization on crime and arrest rates as well as other outcomes (i.e., health and health behaviours). Instead, studies can turn to state medical marijuana laws (MMLs) to provide insights on the potential impact a legalized market for marijuana may have on crime and arrest

rates in the U.S. Currently, 23 states and the District of Columbia have passed a MML.

Although MMLs are not a perfect proxy for a fully legalized recreational market, they share many of the features of a legal market. For instance, MMLs protect qualified users from penalties for possession of small amounts of marijuana; retail stores are allowed to sell cannabis and cannabis products to eligible customers; and in some cases, medical users may be allowed to grow their own marijuana plants at home. The legalization of medical marijuana is essentially a limited legal market, which can be examined to approximate the effects of a fully legal marijuana market on crime and arrests.

The aim of this study is to evaluate the impact of MMLs on arrest rates and crime between 1994-2012, during which time 18 states enacted a MML. In doing so, this study builds on the existing literature that has examined the externalities associated with MMLs (Anderson,

Hansen, & Rees, 2013; Morris, TenEyck, Barnes, & Kovandzic, 2014; Wen, Hockenberry, &

Cummings, 2015). The contributions of the present study are threefold. First, this study examines the differences in arrest rates between adults and juveniles, which has not yet been studied in the literature. Youth are at an earlier stage of cognitive development and the motivation to commit crimes may be quite different compared to adults (Center for Child and

Family Policy, 2007). Second, this study considers the impact of individual policy dimensions as well as the impact of different state policies on dispensaries (i.e., unrestricted vs. restricted dispensary markets). This is the first known study that compares the differential effect of unrestricted and restricted dispensary policy frameworks at the state level. Third, this study explores the mechanisms through which MMLs may influence arrest rates. That is, this study examines whether changes in arrest rates were due to changes in law enforcement levels or due to changes in the level of criminal activity following the implementation of a MML

The outline of the paper is as follows: section II discusses the background literature on drugs and crime. This discussion includes an overview of existing studies on MMLs, crime, and arrest rates. The following section describes the data and analytic approach used to determine the impact of MMLs on arrest rates and crime. Section IV and V present the results and sensitivity analyses. The paper closes with a discussion of the policy implications of the study’s findings.

II. Background

Theoretical Linkages between Drug Use, Crime, and Arrests

The relationship between marijuana and crime is most widely characterized using

Goldstein’s (1985) tripartite conceptual framework, which offers three explanations for the connection between drug use and crime. First, the psychopharmacological explanation posits that the short or long-term use of drugs leads to violent and potentially criminal behaviour.

However, evidence in support of the psychopharmacological explanation with respect to marijuana is weak. While a few studies find links between prolonged or high-levels of marijuana use and violent crime and property damage, especially among youth (Baker, 1998; Fergusson &

Horwood, 1997; National Research Council, 1993; Norström & Rossow, 2014), many other studies find that marijuana use temporarily inhibits aggression and violence (Boles & Miotto,

2003; Goldstein, 1985; Markowitz, 2005; National Research Council, 1993; Pacula & Kilmer,

2003). Moreover, Pacula and Kilmer (2003) and Resignato (2000) find weak or insufficient evidence to support a causal link between marijuana use and commission of violent crimes. The psychopharmacological explanation therefore may be less applicable in explaining the link between marijuana use and criminal activity and subsequent arrests.

The second explanation, the economic compulsive model, suggests that violent crime occurs when drug users engage in income-generating crimes, such as robbery, to finance their drug use (Goldstein, 1985; National Research Council, 1993). Although the primary motivation is to acquire money to purchase drugs, additional violence often occurs due to the situation or environment in which the crime is committed (e.g., use of weapons by the offender or victim of the income-generating crime) (Goldstein, 1985). The economic compulsive model has also been applied to explain non-violent economic crimes, such as shoplifting, theft, burglary, or other property crimes that generate income. Marijuana users, especially younger or financially constrained users, may commit such crimes to obtain the resources to purchase marijuana

(Baker, 1998; Dembo et al., 1992; Goldstein, 1985; Miron & Zwiebel, 1995; Pacula & Kilmer,

2003; Popovici, French, Pacula, Maclean, & Antonaccio, 2014).

Third, the systemic model suggests that violence is embedded in the illicit drug distribution network. Examples of systemic violence include disputes over territory between rival drug dealers (e.g., “turf wars”), assaults or homicides committed to enforce hierarchies within drug dealing organizations, robberies of drug dealers, punishments for failing to pay debts, or punishments for selling adulterated drugs (Goldstein, 1985; National Research Council, 1993;

Pacula & Kilmer, 2003). Systemic violence has been attributed to the prohibition of drugs.

Given the inability to access a formal legal conflict resolution mechanism, such as the judicial courts, underground market participants have little choice but to turn towards violence to resolve disputes under prohibition (Kleiman, Caulkins, & Hawken, 2011; Miron & Zwiebel, 1995;

National Research Council, 1993; Pacula & Kilmer, 2003; Werb et al., 2011). Since the government is not available in an illegal market to secure property rights and enforce contracts between sellers, black market participants often have to resort to violence to resolve conflicts

(Resignato, 2000). Violence can also arise because drug dealers are attractive targets for robbery since they are often carrying large amounts of cash or drugs. In an attempt to protect themselves, sellers arm themselves, which may result in violent confrontations with potential robbers (Resignato, 2000). The systemic model thus points to the illegal nature of the drug market as a contributing factor to violence and crime.

Related to the systemic model is the notion that crime increases due to drug law enforcement (MacCoun & Reuter, 2001; Werb et al., 2011). The allocation of scarce law enforcement resources towards drug-related offences inevitably leaves fewer resources available to prevent property or violent crimes committed elsewhere (Benson, Kim, Rasmussen, &

Zhehlke, 1992; Benson, Leburn, & Rasmussen, 2001; Resignato, 2000). As found in several studies of crime in the U.S., the opportunity cost of increasing police resources to enforce drug laws has been a reduction in efforts to reduce non-drug crimes and an increase in violent and property crimes (Benson et al., 2001; Resignato, 2000; Shepard & Blackley, 2005). Thus, policies that reduce the need to monitor drug law violations may lead to a decline in crime rates in other areas.

Overview of Medical Marijuana Laws

In 1996, California became the first state to pass a medical marijuana law. Since then, 22 states plus the District of Columbia have passed their own MML. As marijuana remains a

Schedule I drug at the federal level, doctors in those states are allowed to recommend the use of marijuana but are not allowed to prescribe or directly dispense marijuana (Hoffmann & Weber,

2010). States with MMLs also outline a number of conditions or symptoms for which medical marijuana can be recommended. The list of conditions, which varies by state, encompasses

Alzheimer’s disease, arthritis, asthma, cachexia or wasting syndrome, cancer, Crohn’s disease, glaucoma, Hepatitis C, HIV/AIDS, migraines, multiple sclerosis, psychological conditions (such as post-traumatic stress disorder), seizures (including epilepsy), or severe and chronic pain among other conditions (ProCon, 2015b). These state-level laws allow qualifying patients to possess limited quantities of marijuana and receive a recommendation from a doctor without the risk of criminal penalties for either party (Anderson et al., 2013; Hoffmann & Weber, 2010;

National Conference of State Legislatures, 2014). Although marijuana is still illegal according to federal law, the Federal Department of Justice issued a memorandum in 2009 (the “Ogden

Memo”) that federal resources should not be used to prosecute individuals who are complying with their states’ laws (Ogden, 2014).

MMLs could differentially impact crime and arrest rates depending on the particular policy dimensions the state enacts. MMLs often consist of a combination of the following policy dimensions: required patient registries, retail dispensaries, or in-home cultivation allowances

(Table 2.1). States with mandatory patient registries require individuals seeking to use medical cannabis to register with the state. Typically, patients are required to provide evidence of their need for medical cannabis (including a doctor’s recommendation outlining the condition for which marijuana may provide relief).1 Patients, if approved, receive an identification card, which allows the state and law enforcement officials to easily differentiate between legitimate and illegitimate users, potentially dissuading the inappropriate use of medical marijuana (Pacula,

1Chronic pain is the most (or second most) common reason for which medical marijuana is recommended (among states where registry information is publicly available). For instance, in Montana and Nevada, approximately 56 and 63 percent of patients respectively cited chronic pain as the reason for medical marijuana use (Montana Department of Health and Human Services, 2016; Nevada Division of Public and Behavioral Health, 2016). In Arizona and Hawaii, over 70 percent of patients cited chronic pain (Arizona Department of Health Services, 2014; Hawaii Department of Health, 2015). In Colorado and Oregon, over 90 percent of patients cited chronic pain (Colorado Department of Public Health and Environment, 2016; Oregon Medical Marijuana Program, 2016). In New Jersey, chronic pain was the second most popular condition with 25 percent of patients reporting chronic pain (Department of Health Medical Marijuana Program, 2016).

Powell, Heaton, & Sevigny, 2015). Moreover, given that patient identification cards easily allow users to demonstrate legal possession of medical cannabis, registered patients may be more willing to report a theft or burglary to the police in the event their medical cannabis is stolen from them. This would potentially contribute to an increase in reporting and a corresponding increase in arrests. In contrast, the issuance of patient identification cards may inadvertently result in patients or their households becoming attractive targets for criminals seeking marijuana, which becomes known via word of mouth or through internet postings (Crites, 2012; Moore,

2011; Ricker, 2014). States with mandatory registries saw a steady influx of applications; for instance, in the first year of the law, Arizona received and approved over 16,000 applications and currently has over 80,000 card holders (Arizona Department of Health Services, 2011, 2015).

By the end of 2012, Colorado received over 200,000 new patient applications.2 Thus, the number of patients who could potentially become a victim of a proeprty crime in states with patient registries is non-trivial.

In-home cultivation allowances permit qualified patients or their caregivers to grow medical cannabis at home. States individually specify the maximum number of mature and immature plants that may be cultivated at any one time (ProCon, 2015b). Cultivation at home may pose one of the more difficult challenges in terms of law enforcement. The opportunity for spillovers into the recreational market exist as medical users may use or share their medical cannabis with others for recreational purposes; or they may cultivate more plants than is legally allowed (Bostwick, 2012; Pacula et al., 2015; Thurstone, Lieberman, & Schmiege, 2011). Thus, scarce police resources may be used to ensure households abide by the state’s plant limits and cultivation rules to prevent diversion into the recreational market, potentially allowing non-drug

2One estimate by ProCon (2015a) indicated that in 2014, among states with medical marijuana laws, an average of 7.7 individuals per 1,000 state residents were legal medical marijuana users.

related crimes to continue undetected. Alternatively, since individuals have a ready supply of marijuana at home, they may rely less on purchases from illegal sources, reducing violent or property crimes associated with the black market. Or, such users may be more inclined to consume marijuana at home, decreasing crimes that may result from intoxicated confrontations in public spaces (e.g., at bars) (Anderson et al., 2013).

Retail dispensaries are storefronts that sell medical marijuana and marijuana products

(i.e., edibles, oils, tinctures, etc.) to qualified patients. Due to the federal classification of marijuana as a Schedule I drug in the Controlled Substances Act, marijuana is still illegal under federal law. The illegality of marijuana at the federal level means that financial institutions that depend on the Federal Reserve System’s money transfer system are barred from knowingly accepting money from marijuana sales (Stinson, 2015). To avoid criminal or civil liability, most financial institutions refuse to open bank accounts or accept money from the sale of marijuana.

As a result, dispensaries are forced to operate as cash-only businesses (Michiels, 2014). The substantial amount of drugs and cash on site could make dispensaries an attractive target for criminals (Kepple & Freisthler, 2012; Kovaleski, 2014; Morris et al., 2014; Nagourney &

Lyman, 2013). Among the states that have legalized medical cannabis, two distinct policy approaches towards dispensaries have been implemented. In one version (e.g., Arizona,

California, and Colorado), states permit a relatively open market. Aside from abiding by regulatory and licensing requirements (which may include licensing fees, security measures, background checks for staff, location parameters such as operating a certain distance from schools), the number of dispensaries allowed to operate in the state is not capped. In such states, the number of dispensaries has proliferated to over 80 in Arizona and several hundred in

California and Colorado by the end of 2014 (Arizona Department of Health Services, 2014;

Colorado Department of Revenue, 2015; NPR, 2009).3 In another version (e.g., Maine, New

Jersey, and New Mexico), the state government determines and controls the maximum number of dispensaries that may operate within the state. This figure may be adjusted over time, but is subject to government approval. Often these states release a request for proposals to identify and select qualified retailers. Given the increased number of targets, crime and arrest rates may be more likely to increase in states that institute an unrestricted market for dispensaries relative to states with a restricted dispensary market or without dispensaries at all.

MMLs may affect the arrest rate through two possible avenues. First, MMLs could change the crime rate, leading to corresponding changes in the arrest rate. For instance, by legalizing marijuana use under certain conditions and providing legal sources of marijuana,

MMLs may undermine part of the black market, decreasing any violent or property crime that is associated with illicit drug market activities (MacCoun & Reuter, 2001; Montgomery, 2010;

Morris et al., 2014). This, in turn, may lead to a reduced arrest rate. In contrast, however, certain features such as dispensaries might attract more crime, increasing the arrest rate. Second, changes in the arrest rate could be the result of changes in law enforcement levels due to MMLs

(Chu, 2014). Depending on which criminal activities police choose to allocate their scarce resources, arrest rates may increase or decrease for certain types of crimes. Legalizing medical marijuana may reduce the need to monitor marijuana related offences (Chu 2014), freeing up resources to address other non-drug related crimes. Alternatively, since MMLs create additional rules, it is possible police resources will need to be continuously used to enforce drug laws, ensure medical marijuana does not spill over into the recreational market, or check that storefronts or home cultivation are not fronts for illegal drug sales. Sustained police monitoring

3Arizona’s first medical marijuana dispensaries opened in 2012, at which point three dispensaries were operating (Arizona Department of Health Services, 2012). Therefore, in this study, the impact of unrestricted dispensary policies is largely driven by Colorado and California.

of marijuana rules may increase the likelihood other crimes are left unchecked (Benson et al.,

1992, 2001; Resignato, 2000).

Past Literature on MMLs, Arrests, and Crime

The literature looking specifically at medical marijuana legalization, crime, and arrest rates in the U.S. is limited. A handful of studies, however, have found that MMLs influence crime rates, but the direction of the effect varies depending on the scope and methodological approach of the study. Very few studies have looked at how arrest rates have changed as a result of MMLs.

Using the Federal Bureau of Investigation (FBI) Uniform Crime Reporting (UCR) data from 1988 to 2008, Chu (2014) examines marijuana possession arrests and finds that MMLs lead to an increase in the arrest rate for marijuana possession among males by 15 to 20 percent (after conditioning on California and Colorado). This study, however, is not a comprehensive study of the effects of MMLs on crime and arrest rates but rather a study of MMLs on illegal marijuana use. Accordingly, Chu (2014) only focuses on one type of crime and on adult males aged 18 and over.

More attention has been paid to studying the effects of MMLs on crime rates rather than arrest rates. Using the FBI UCR data (maintained the Bureau of Justice Statistics), Morris et al.

(2014) examine the effect of MMLs on violent (i.e., murder, rape, robbery, assault) and property

(i.e., burglary, larceny, and auto theft) crimes from 1990-2006. Using a state- and year-fixed effects approach with the MML exposure variable measured as a trend (i.e., equal to the number of years the state’s MML was in effect), Morris et al. (2014) find that for every year MMLs were in place, murders and assaults decrease by an additional 2.4 percent. No significant effects are

identified for economically motivated crimes (such as robbery or burglary) or property crimes, leading Morris et al. to question the notion that MMLs might increase income-generating crimes.

In a replication of the Morris et al. study, Alford (2014) uses the same data but a more recent timeframe (1995-2012) to study the impact of MMLs on crime. She finds that states that adopted MMLs followed different crime rate trends than non-adopting states, highlighting the importance of controlling for state-specific linear time trends, which are not included in Morris et al.’s analysis. After controlling for state-specific time trends, along with a number of observable state-level characteristics, Alford confirms that MMLs are associated with a decline in murder rates by approximately 10.6 percent. Where Alford and Morris et al. differ is in their estimates of property and income-generating crimes. While Morris et al. finds insignificant negative effects of MMLs on property crimes, Alford estimates that burglary and larceny significantly increase by 8.0 and 6.2 percent respectively with MMLs. In general, Alford finds that failing to control for state-specific time trends reverses the signs on the effect of MMLs on property crimes.

Another contribution of Alford’s paper (in light of work done by Pacula et al. (2015)) is the consideration of the individual MML policy dimensions, namely dispensaries and in-home cultivation allowances. Alford finds that, relative to states with no in-home cultivation, states that allowed home cultivation decrease robberies by 10.0 percent. In contrast, dispensaries increase robberies by 10.2 percent, offsetting the decrease in crime due to in-home cultivation.

Dispensaries also increase the burglary and theft rate by 13.2 and 8.0 percent respectively.

Home cultivation has no statistically significant impact on these crimes (Alford, 2014). While the consideration of the specific policy dimensions is notable, Alford did not consider the role of required patient registries. Moreover, Alford’s dispensary indicator is based on the date

dispensaries were permitted by law as opposed to the date dispensaries were actually operational within a state. As a result, the dispensary coefficients may not necessarily capture crime that resulted from storefronts open to the public.

A few other studies focus on the relationship between medical marijuana dispensaries and criminal activity. In a study of dispensaries in Sacramento, California using 2009 data from the

Sacramento Police Department, Kepple and Freisthler (2012) find that the density of medical marijuana dispensaries is not significantly associated with violent or property crime rates. This study contravenes the conventional thinking that dispensaries may serve as an attractive target for criminals seeking money or drugs. However, this study focuses on the density of dispensaries at one point in time, rather than comparing crime rates before and after dispensaries were opened

(the first legally protected dispensary opened in California in 2004). A second study of dispensaries in Sacramento shows that dispensaries that have security cameras and signs requiring patient identification cards have significantly lower levels of violence than dispensaries that do not have these security measures (Freisthler, Kepple, Sims, & Martin, 2013). Having a doorman at the front of the dispensary also deters violent crime but not at conventionally significant levels (Freisthler et al., 2013). This study does not examine the effect of security measures on property crime. A third study, using the June 2010 closures of dispensaries in Los

Angeles, finds that breaking and entering and assault (i.e., crimes sensitive to surveillance) increase after dispensaries close (Jacoboson et al., 2011). The authors suggest the on-site security provided by dispensaries deter crime in the surrounding area and subsequent closure of dispensaries may lead to an increase in crime in the community. This study, however, relied on a very limited time period (ten days before and after dispensary closures) and long-term trends may differ.

In this study, I build on the existing literature by looking at arrest rates (rather than crime rates) between juveniles and adults, examining the effect of patient registries, in-home cultivation, and legally protected dispensaries on the ground, and consider the mechanisms through which MMLs impact arrest rates.

III. Data and Empirical Specification

Data

This study employs data collected by the FBI UCR program and maintained by National

Center for Juvenile Justice (NCJJ). The data are available through the Office of Juvenile Justice and Delinquency Prevention website (Puzzanchera & Kang, 2014).4 The NCJJ database provides information on yearly state-level arrest rates from 1994 to 2012. During this timeframe,

MMLs came into effect in 18 states (Table 2.1).

The UCR program collects arrest data from thousands of law enforcement agencies on a monthly basis. The NCJJ then calculates annual state-level estimates of arrest rates. However, the NCJJ only reports data in a given year if a state’s coverage indicator is at least 90 percent.

The coverage indicator represents the proportion of the state population for which information was available and for which imputation was not required. For example, a coverage indicator of

100 percent means that all agencies reported to the UCR program for the entire year and no imputation was required. A coverage indicator of 90 percent implies either that not all agencies reported, agencies reported for only part of the year, or a combination of both. In such a case, the

NCJJ imputes the arrest rate for the remaining 10 percent of the population (Puzzanchera &

Kang, 2014). Since the NCJJ does not impute arrest rates for states falling below the 90 percent

4The Office of Juvenile Justice and Delinquency Prevention data are available at http://ojjdp.gov/ojstatbb/ezaucr/.

coverage indicator, there are some states and years with missing data.5 Seventeen of the 18 states that passed MMLs between 1994 and 2012 have sufficient information, though some states are missing observations in some years (N=73 missing observations). One state (Washington) did not have any data available for the years covered in the study (N=19) and is excluded from the sample. Among the 33 non-adopting states, 28 have sufficient data (with N=165 missing observations) and five states (Illinois, Indiana, Kansas, Mississippi, and Ohio) did not meet the coverage indicator in any year and are excluded from this analysis (N=95). The final analytic sample is an unbalanced panel consisting of 45 (17 MML and 28 non-MML) states, with 617 state-year observations (of a possible total of 969 state-year observations). Consequently, the results of this analysis should be interpreted as an average treatment effect for the states included in the sample (rather than generalizable to the entire U.S.).

Measures

Arrest Rates. Arrest rates for violent and property crimes are available for three age groups: all ages, adults (18 years and over), and juveniles (age 10 to 17 years). The rates are reported as the number of arrests made per 100,000 individuals in the referent population.

Violent crimes include murder, rape, robbery, and assault. Property crimes include burglary, theft, and auto theft.6 Definitions for each of these crimes can be found in Appendix Table 1.

5According to the NCJJ, the lower the coverage indicator, the less likely the imputed arrest rates would reflect arrest activity in a given jurisdiction. Thus, only those jurisdictions with arrest rates at or above 90% are displayed in the Easy Access to FBI Arrest Statistics database (Puzzanchera & Kang, 2014).

6Arson arrest rates are available in the NCJJ data; however, arson is excluded from the list of property crimes in this study as it is not typically considered an income-producing crime (Pacula & Kilmer, 2003). This study focuses on income-generating property crimes as they are theoretically linked to marijuana consumption and availability.

Medical Marijuana Laws. In many prior studies, MML enactment has been measured using a binary indicator equal to one when a MML came into effect in a given state. As noted by

Pacula et al. (2015), however, the use of a binary variable masks the potentially heterogeneous effects of the various policy dimensions that make up a state’s medical marijuana program. As a result, this study focuses on the effect of individual MML policy dimensions (i.e., dispensaries, registries, and in-home cultivation) on arrest rates. State implementation of dispensaries, registries, and in-home cultivation are measured as three binary indicators that equal one when the respective policy dimension comes into effect. Note that many states adopt or implement these dimensions at different times, and enactment of these dimensions does not always correspond with the date the state enacts its MML policy. These indicators take on fractional values if the policy takes effect part way through the year. Information on whether a state adopted any of these policy dimensions, and when they take effect, was drawn from ProCon.org and news releases. Any conflicting information, or information unavailable from any other source, was verified or obtained by contacting state officials directly.7

The dispensary variable measures legally protected dispensaries that were in operation in a given state. Since there is often a lag between the passage date of legislation permitting dispensaries and the actual opening of a medical marijuana dispensary, the dispensary variable in this study equals one when the first legal dispensary opened. As dispensaries may serve as targets for criminal activities, the focus is on measuring the effect of having physical storefronts that sell medical marijuana and are open to the public. Furthermore, the MML dispensary variable only includes legally protected dispensaries, although news reports have identified de facto dispensaries in states where storefronts were not permitted (Anderson & Rees, 2014b;

7The dates included in this study largely correspond with the implementation dates reported by Pacula et al. (2015) and Wen et al. (2015).

Nagourney & Lyman, 2013; Wieczner, 2013). However, following the approach of Pacula et al.

(2015), the dispensary measure captures those storefronts that were legally protected, since any state with or without legal medical marijuana laws may still find illegal de facto storefront dispensaries operating within its borders.

During the study period, six states opened legally protected medical marijuana dispensaries at various times (Table 2.1). In states with required patient registries, medical marijuana users must apply for an identification card from the state in order to legally possess and consume medical marijuana; 15 states mandated patient registries. Lastly, in states that permitted in-home cultivation, approved patients or caregivers are allowed to grow a certain number of plants at home; 14 states allowed in-home cultivation.

Covariates. A number of time-varying covariates are included in the models to control for any state-level characteristics that may influence both the state’s propensity to adopt MMLs and arrest rates. State-level demographic characteristics are obtained from the March Supplement of the Current Population Survey (CPS), which are managed and disseminated by the Integrated

Public Use Microdata Series (IPUMS) (Flood, King, Ruggles, & Warren, 2015).8 Specifically, the analysis controls for the proportion of the state population that is 45-64 years old; proportion that have at least a college degree; proportion of the population that is Black; proportion of the population that is Hispanic; and the proportion living below the poverty level. The analysis also controls for the state unemployment rate, which is obtained from the Bureau of Labor Statistics.9

A binary indicator for whether a state had previously decriminalized marijuana is also included

(Markowitz, 2005). These covariates are selected based on previous studies that demonstrated a relationship between these demographic characteristics and crime and arrest rates (Benson et al.,

8The IPUMS-CPS data are available at https://cps.ipums.org/cps/index.shtml 9Unemployment rate are available at http://www.bls.gov/data/#unemployment.

2001; Ihlanfeldt, 2007; Markowitz et al., 2012; Morris et al., 2014; Peterson, Krivo, & Harris,

2000).10

Lastly, a variable measuring beer consumption per capita in gallons is included in the analysis. There is a strong documented relationship between alcohol and crime (and arrest rates)

(Cook & Moore, 1993; Markowitz, 2005; Markowitz et al., 2012; Popovici, Homer, Fang, &

French, 2012) and evidence from natural experiments that marijuana and alcohol are substitutes

(Anderson et al., 2013; Anderson & Rees, 2014a; Crost & Guerrero, 2012; DiNardo & Lemieux,

2001). Neglecting to control for alcohol consumption would result in estimates on the effect of

MML policy dimensions that capture the effect of alcohol. To isolate the effect of MML policy dimensions independent of alcohol consumption (and alcohol-induced crimes), the models include a measure of state beer consumption per capita. Beer consumption is used since it is the most popular alcoholic beverage among Americans (World Health Organization, 2014). Beer consumption per capita is obtained from the Brewers Almanac (Beer Institute, 2013).

Analytic Approach

To identify the effect of state implementation of MMLs on arrest rates, I use a state-fixed effects model estimated using ordinary least squares (OLS). This approach exploits within-state variation to control for unobserved time-invariant factors that may otherwise bias the estimated effect of MMLs on arrest rates. The state-fixed effects control for time invariant differences between states while state-specific time trends are included to control for changes over time.

10Furthermore, these covariates had substantial within-state variation over time and accounted for additional variation in addition to state-specific linear time trends.

MMLs, however, are not uniform across states. As a result, measuring MMLs as a binary indicator (as in equation (1)) represents a naïve approach to estimating the relationship between state MMLs and arrest rates:

( ) (1)

This approach is problematic as it masks the heterogeneity of MMLs across adopting states

(Alford, 2014; Pacula et al., 2015). States with MMLs implement different combinations of policy dimensions (i.e., dispensaries, registries, and in-home cultivation), and depending on which policy dimensions a state selects, some versions of MMLs may decrease arrest rates, while others may increase it. Moreover, states implement different policy components at different times. That is, no state had all three policy dimensions in effect at the same time (see Table 2.1).

Consequently, a binary MML variable does not represent a uniform policy effect and would not accurately depict the various combinations of policy dimensions implemented in each state.

Thus, while the results of equation (1) are presented in Table 2.8, any significant effects identified by the binary MML variable are considered suspect.

Instead, the appropriate approach to evaluating the effects of MMLs is to estimate the independent effects of the individual policy dimensions, as recommended by Pacula et al. (2015).

Accordingly, the preferred approach is to estimate the following equation that includes three separate indicators for each of the three MML policy dimensions:

( )

. (2)

The outcome of interest is the log of the arrest rate for one of seven violent or property crimes,11 where the arrest rate is the number of arrests per 100,000 people in the referent population group.

Dispensaryst is a binary variable that is set equal to one in the year a state first opened a legally operating dispensary. Registryst is a binary indicator that equals one when a state mandated a required patient registry. InHomest is a binary indicator that equals one in the year a state permitted cultivation of medical cannabis at home. Zst represents a vector of time-varying state characteristics that may influence the arrest rate. Specifically, Zst contains the proportion of the state population aged 45-64; proportion with a college degree; proportion of the population that is black; proportion of the population that is Hispanic; proportion of the population under the poverty line; beer consumption per capita in gallons; the state unemployment rate; and an indicator for whether the state previously decriminalized marijuana. Lastly, denotes state- fixed effects. Examination of the unadjusted logged arrest rates revealed differential trends between MML states and non-MML states.12 Accordingly, a state-specific linear time trend

( ) is included in all models to relax the common trends assumption typically required for identification in a fixed effects model (Angrist & Pischke, 2009). The state-specific linear time trend also controls for the changes due to time within each state. Models (1) and (2) are run separately for the all ages, adult, and juvenile samples. All estimates are weighted using the average state population over the study period for the corresponding age group, and standard errors are clustered at the state level (Bertrand, Duflo, & Mullainathan, 2004).

To further explore the impact dispensaries have on arrest rates, I re-estimate equation (2) for all age groups replacing the dispensary variable with two new dispensary variables reflecting

11The log of the arrest rate is used, as the arrest data are right-skewed. This specification also ensured normally distributed residuals, which is required for hypothesis tests to be valid.

12Figures of the logged arrest rate trends are available upon request.

the two types of policy frameworks established to regulate the number of dispensaries. Among the six states that had legally protected dispensaries in operation between 1994 and 2012, three states implemented an unrestricted market where the number of dispensaries proliferated, whereas the other three states introduced a more tightly controlled dispensary market.13 The first dispensary variable added to the model (“unrestricted dispensary”) indicates whether a state allowed a more open market for dispensaries (i.e., aside from following state licensing rules and regulations, the number of dispensaries allowed to operate is relatively unrestricted). The second dispensary variable (“restricted dispensary”) represents states that permitted only a pre- determined number of medical marijuana dispensaries. Since dispensaries may serve as easy targets for property crimes, states that permit an open market for dispensaries might experience greater increases in arrest rates than states that operate a restricted market.

In some of the years, a few states have zero arrests for certain crimes, particularly for the low-incident crimes such as murder, rape, and robbery.14 Since the log of zero is undefined, the models dropped these observations, reducing the analytic sample size for murder, rape, and robbery. To check the sensitivity of the results to the omission of these observations, equation (2) is rerun using non-logged arrest rates as the outcome so that the state-years with an arrest rate of zero are retained.

There are two potential mechanisms through which MMLs can affect arrest rates. First,

MMLs could lead to a change in crime levels, thus affecting the arrest rate. Second, MMLs could change the level of law enforcement, enforcement effort, or the allocation of law enforcement

13The three states with an unrestricted dispensary policy are Arizona, California, and Colorado. The three states with a restricted dispensary policy are Maine, New Jersey, and New Mexico.

14The number of state-years with a murder arrest rate of zero is 10 in both the all ages and adult samples, and 92 for the juvenile sample. The number of state-years with a rape arrest rate of zero is 2 for the juvenile sample. The number of state-years with a robbery arrest rate of zero is 1 for both the all ages and adult samples, and 2 for the juvenile sample. The resulting sample sizes can be seen in Table 2.4.

resources, increasing the arrest rate for crimes to which resources were allocated. To determine which mechanism is acting on arrest rates, two sensitivity checks are run: first, I include a measure of law enforcement (LE) in equation (2); and second, I compare the arrest rates to changes in the crime rates.

LE is measured as the number of sworn police officers per 1,000 persons.15 Inclusion of a LE variable serves two functions: first, the inclusion of the LE variable serves as a sensitivity check of the results after including a potentially endogenous variable (since MMLs may induce a change in law enforcement levels); second, inclusion of the LE variable controls for the effect of increased police surveillance on the probability of getting arrested. After controlling for law enforcement levels, the estimated impact of the MML policy variables would largely reflect changes in criminal activity on arrest rates.

To further explore whether the changes in arrest rates are the result of changes in crime levels rather than changes in law enforcement, model (2) is re-estimated using the logged crime rate from 1994-2012, using FBI UCR data maintained by the Bureau of Justice Statistics (BJS).

These data estimate the crime rate (rather than the arrest rate) based on the number of crimes reported to police at the state level and are recorded as the crime rate per 100,000 persons. If the coefficients from the BJS models operate in the same direction as the primary arrest models, this would indicate that the changes in arrest rates are likely due to changes in the crime rate.

15The number of sworn police officers was obtained from Table 77 of the annual UCR reports, available on the FBI website (https://www.fbi.gov/about-us/cjis/ucr/ucr-publications#Crime). Law enforcement officers were defined as individuals who have arrest powers, carry a badge and firearm, and are paid from government funds reserved for sworn law enforcement officers; this does not include civilian employees in the police service (e.g., correctional officers, radio dispatchers, meter attendants, etc.).

IV. Results

Descriptive Statistics

The descriptive statistics show that the implementation of MMLs is associated with a lower murder arrest rate for all ages (Table 2.2A), adults (Table 2.2B) and juveniles (Table

2.2C). For juveniles only, MML enactment is associated with a lower level of all other violent and property crime arrest rates (except robbery).

These differences in means, however, do not account for the various observed and unobserved state-level factors that may influence arrest rates. Indeed several state-level characteristics significantly differed by MML implementation status (Table 2.3). The unadjusted associations also do not reflect the various MML policy dimensions, which could differentially influence arrest rates. Accordingly, the following sections report the estimated effect of MML policy dimensions on arrest rates conditional on a number of observed and unobserved state- level characteristics.

MML Policy Dimensions and Arrests for Violent Crime

In the results from the preferred models that examine each policy dimension, legally operating dispensaries have the most notable impact on certain violent crime arrest rates (Table

2.4). In particular, holding all else equal, a state with a dispensary significantly increases the robbery arrest rate among adults and juveniles relative to states that do not have legally protected dispensaries (Table 2.4, column 4). Among adults, dispensaries lead to an increase in robbery arrest rates by 14.6 percent, while the juvenile robbery arrest rate is increased by 35.7 percent.16

The magnitude of these estimated results are generally in line with the effect sizes found by

16These percentage changes are calculated using ( ) .

Alford (2014) and Chu (2014). These findings are also consistent with media reports that dispensaries have been attractive targets for income-generating crimes (Montgomery, 2010;

Nagourney & Lyman, 2013; Volz, 2010). Dispensaries do not significantly impact the arrest rate for any other type of violent crimes, other than the murder rate. In this case, dispensaries lead to an increase in the murder arrest rate, but only among adults. However, murder is a low-incident arrest rate, as seen in Table 2.2; thus, small changes in the arrest rate correspond with large percentage changes and should be interpreted with caution.17

MML Policy Dimensions and Arrests for Property Crime

Turning to property crime arrests, states with legally operating dispensaries generally experience an increase in arrest rates for each type of property crime compared to states without legal dispensaries (Table 2.5). While dispensaries lead to an increase in the theft arrest rate for adults and juveniles by 16.3 percent and 8.8 percent respectively (Table 2.5, column 3), the burglary (column 2) and auto theft (column 4) arrest rates increase significantly only among adults. These findings suggest that adults are the main drivers behind the increase in property crime arrest rates when dispensaries are open. The increased property crime arrest rates are in line with the notion that dispensaries act as prime targets for income-generating crimes such as burglary and theft.

Required patient registries also increase burglary and theft arrest rates relative to states without required registries but only among the adult sample. States with registries experienced an

17Though the independent effects of these policy dimensions were statistically significant for some arrest rates, these results should be interpreted with some caution. These policy dimensions do not exist in isolation; rather, states often implement a combination of these dimensions when instituting their MMLs. In fact, no state had a legally operating dispensary without already having either in-home cultivation or a required patient registry already in place. Thus, the dispensary coefficient should be interpreted with caution and as an offsetting effect to one of the other policy dimensions.

increase in the adult arrest rate for burglary and theft by 15.9 and 11.6 percent respectively

(Table 2.5, columns 2 and 3). In contrast, the impact of registries on juvenile arrest rates for burglary and theft are negative though not statistically significant. The increase in theft and burglary arrest rates among adults may potentially be explained by the implications of having a registry rather than the registry itself. For instance, with registries, patients are afforded legal protections to possess medical marijuana in their home or on their person. These individuals or persons may then be targeted for property crimes, particularly if knowledge of their possession becomes known by word of mouth (Ricker, 2014; Trotter, 2014). Furthermore, the patient registry essentially creates a property right to medical marijuana. As a result, registered patients who were victims of property crimes (i.e., had their medical marijuana stolen) may be more inclined to report crimes involving marijuana than before registries were established, potentially contributing to an increase in arrests for theft and burglary (Moore, 2011). An alternative explanation may be related to the lag between permitting medical marijuana use and the creation of legal avenues to obtain marijuana in many states (Alford, 2014). Due to the lack of legal sources for medical cannabis, patients would have to resort to illegal means to obtain marijuana, perpetuating the underground market and the property crime associated with it (Washington

State Department of Health, 2008).

Compared to states without in-home cultivation allowances, states that permit in-home cultivation experience a decrease in arrest rates for select property crimes. Auto theft arrest rates decrease significantly for both adults and juveniles (Table 2.5, column 4). In-home cultivation also leads to a statistically significantly decline in the theft arrest rate but only among adults

(Table 2.5, column 3). This decline in arrest rates suggests the enactment of in-home cultivation may have offset the increase in property crime arrests that occurred due to dispensaries and

registries. It is possible that by having a supply of marijuana at home, this reduces the need for users to commit property crimes to generate revenue for drugs or to source marijuana from the illicit market.

Restricted versus Unrestricted Dispensaries

The proliferation of medical marijuana dispensaries may affect crime and arrest rates due to an increased number of potential targets for crime. Certain states place a cap on the number of medical marijuana dispensaries that are legally allowed to operate. Other states permit a more open market whereby an interested business is allowed to apply for a business license to operate a medical marijuana dispensary, provided they abide by certain regulations (i.e., payment of a licensing fee, security measures, operating a certain distance from schools, etc.). In such unrestricted markets, the number of dispensaries have proliferated (e.g., by the end of 2012,

Colorado had nearly 300 licensed medical marijuana dispensaries across the state (Colorado

Department of Revenue, 2015)) potentially creating more opportunities for crime or more facilities to monitor for illegal activity. In contrast, Maine, for instance, permits only 8 dispensaries to operate (Maine Department of Health and Human Services, 2013).

With respect to violent crimes, states with an unrestricted dispensary market experience a statistically significant increase in the murder and robbery arrest rates for adults and juveniles

(Table 2.6, columns 2 and 4). In contrast, enacting a controlled dispensary market does not result in statistically significant differences in the violent crime arrest rates.18 Among the six states with operational dispensaries within the study period, these findings suggest that the increased

18To determine whether the effect of unrestricted dispensaries was different from restricted dispensaries, I tested whether the coefficient on unrestricted dispensary was equal to the coefficient on restricted dispensary. The null hypothesis was that the two coefficients are equal. The tests rejected the null hypothesis of equality for the all age murder arrest rate, and the robbery arrest rate for all three samples, confirming that the two coefficients were statistically different at the 0.05 level.

violent crime (murder and arrest rates) were largely driven by the states that allowed a more open market for dispensaries to operate.

Turning to property crime arrest rates, states that implement an unrestricted dispensary policy again experience increases in various arrest rates for property crimes relative to states without dispensaries (Table 2.7). Unrestricted dispensary policies result in a statistically significant increase in the theft arrest rate for both adults and juveniles (column 3), while burglary and auto theft arrest rate increased only among adults (columns 2 and 4). In contrast, relative to states without dispensaries, states with restricted dispensary policies largely do not experience any increase in property crime arrest rates. The exception was auto theft, where states with restricted dispensary policies experienced a statistically significant increase in the auto theft rate among adults and juveniles (column 4). For burglary and theft, however, tests of equality indicate that unrestricted dispensary policies indeed result in larger positive effects on arrest rates than restricted dispensary policies for both adults and juveniles. These results indicate that increases in property crime arrest rates were likely driven primarily by states with unrestricted dispensary policies.

These results should be interpreted with caution, however, since specifying two separate dispensary variables reduces the number of states that are used to estimate each dispensary coefficient. That is, only three states had an open dispensary market and three states had a controlled market in the analytic sample, so the estimates rely on limited variation.19

Nevertheless, these results can be viewed as preliminary evidence of the effect of having a restricted or unrestricted dispensary market on arrest rates.

19Many of the dispensaries also open near the end of the time period studied, again limiting the number of “post- dispensary” years to derive estimates.

V. Sensitivity Analysis

Sample Size Attrition

A few of the state-year observations reported an arrest rate of zero for murder, rape, and robbery. Since the outcome of interest was the logged arrest rate, this resulted in a small number of observations being dropped from the samples, depending on the type of crime (reductions in sample size ranged from 1 to 92 observations). To determine whether the reduction in sample size affects the results, equation (2) is re-estimated using the non-logged arrest rate as the outcome, which retained all 617 observations. With respect to violent crimes, the non-logged estimates are comparable to the logged results in both direction and significance (Table 2.9). One notable difference is seen in the juvenile murder arrest rate, which has the largest reduction in sample size in the logged model. In the non-logged results, the increase in the juvenile murder arrest rate due to dispensaries is statistically significant whereas it is insignificant in the logged estimates. Despite this, the direction of effects on the juvenile murder arrest rate is consistent in both models. Overall, the reduction in sample size does not appear to substantially alter the interpretation of results for violent crime arrest rates.

The non-logged models for violent crime arrests were also run using the reduced sample used in the logged models for violent crime arrest rates (see Table 2.4 for corresponding sample sizes). No substantial differences exist between the full and reduced samples for the non-logged estimates (results available upon request), supporting the conclusion that the reduced sample size does not greatly impact the interpretation of the logged models.

The data for the property crime arrest rates do not suffer from sample size attrition.

Accordingly, the non-logged estimates confirm the direction and significance of the logged estimates (Table 2.10). One difference is the effect of in-home cultivation becomes statistically

significant for both the adult and juvenile burglary arrest rates in the non-logged model whereas it is insignificant in the logged models. Nevertheless, these results support the conclusions drawn from the logged models for property crime arrest rates.

Mechanisms Linking MML Policy Dimensions and Arrest Rates

MML policy dimensions can impact arrest rates through one of two mechanisms: by changing the level of police resources made available to monitor other non-drug crimes or by changing the level of crimes committed. By controlling for law enforcement levels, the effect of the MML policy dimensions would largely reflect changes in crimes committed. The estimated relationships between the MML policy dimensions and arrest rates are generally robust to the inclusion of a LE variable. In the violent and property crime arrest rate models, controlling for

LE results in coefficients that are qualitatively similar to the main estimates (Table 2.11 and

Table 2.12). The lack of meaningful differences between the estimates with and without LE suggests that the policy dimensions influenced arrest rates through a corresponding impact on crime rates rather than changes in police resources.

To further explore whether the changes in the arrest rates are a reflection of changes in criminal activity or changes in police resources, I compare the all ages arrest rates to estimates of the crime rates using FBI UCR data compiled by the Bureau of Justice Statistics (BJS) (N=969).

If MML enactment leads to a reallocation of policing resources away from drug law enforcement and towards monitoring and deterring other crimes, then an increase in arrest rates would likely accompany a decrease in the estimated crime rates. In contrast, if the changes in arrest rates were a response to changes in crime rates, the arrest rates and crime rates should move in the same direction.

Looking at violent crime rates for all ages, the presence of dispensaries significantly increases the murder and robbery crime rates relative to states without dispensaries (Table 2.13, columns 1 to 5). This suggests that the increase in murder and robbery arrest rates due to dispensaries is likely a response to a corresponding increase in crime levels. In contrast, states with in-home cultivation allowances experience declines in the crime rates for murder, robbery and assault relative to states without in-home cultivation allowances, but there was no statistically significant change in arrest rates for these crimes. This suggests that although crimes decreased in states with in-home cultivation (relative to states without in-home cultivation), police may have maintained pre-established levels of surveillance on these violent crimes.

Registries are associated with a statistically significant increase in rape, robbery, and assault crime rates relative to states without registries. However, registries do not lead to a corresponding change in the arrest rates for these crimes. In this case, the lack of change in arrest rates despite significant increases in crime rates due to registries may be explained by a continued need to enforce MML registry rules and ensure marijuana users were legitimate medical users (Wieczner, 2013). That is, in states with required patient registries, police resources may not have been made available to make arrests for these violent crimes due to their need to monitor MML compliance.

Turning to property crime rates, changes in property crime arrest rates more closely mirror changes in the property crime arrest rates (Table 2.13, columns 6 to 9). States with dispensaries, relative to states without dispensaries, experience a statistically significant increase in the burglary, theft, and auto theft crime rates, which correspond with statistically significant increases in the arrest rates for these crimes. Similarly, the effect of registries significantly increases both crime rates and arrest rates for burglaries. Registries also lead to an increase

(though statistically insignificant) in theft and auto theft crime rates, which was consistent with the positive effect of registries on these arrest rates. Compared to not permitting in-home cultivation, home cultivation decreases the crime rate and arrest rate for property crimes, though most coefficients are not significant at conventional levels. The similarities between the changes in crime rates and arrest rates support the interpretation that MML policy dimensions influence property arrest rates by changing the level of crime rather than by shifting policing resources.

Taken together, it is likely that many of the increases in arrest rates are due to corresponding increases in crime rates. Though, these results cannot completely rule out the possibility that changes in police resource allocation had a partial impact on the changes (or lack of changes) in the arrest rates, especially with respect to violent crimes. While I have controlled for LE levels, I am not able to measure on which crime areas police resources are focused.

Therefore, I am not able to confirm with certainty as to whether MMLs divert police resources away from non-drug related crimes. Nevertheless, this analysis provides some evidence that crimes (particularly property crimes) increased with the introduction of certain MML policy dimensions, and arrest rates increased accordingly.

VI. Conclusion

This study examines the impact of various aspects of MMLs on arrest rates for a number of violent and property crimes in the U.S. In doing so, this study adds to the literature on the externalities associated with MML implementation in three ways. First, this study examines the effects of MMLs on arrest rates by age, for adults and juveniles; second, this is the first known study to examine the differential impact of state policies on dispensaries (i.e., restricted versus

unrestricted dispensary markets); and third, this study considers the different mechanisms through which MML policy dimensions impact arrest rates.

With the exception of robbery arrest rates, the adult population is responsible for most of the changes in violent and property crime arrest rates. Such a finding may alert policymakers and law enforcement officials in states that have enacted or will enact MMLs, particularly those with dispensaries and registries, to divert most resources towards deterring criminal activity among the adult population.

The results of this study also indicate which specific MML policy dimensions lead to increases in arrest rates. Legally protected dispensaries and required patient registries are shown to increase arrest rates, especially for property crimes. In particular, the increase in arrest rates due to dispensaries is primarily driven by states with unrestricted dispensary markets. This is consistent with media reports that dispensaries have acted as targets for income-generating crimes, such as robberies, burglary, and theft (Nagourney & Lyman, 2013; Volz, 2010;

Wieczner, 2013). To curtail the increase in crime, state policymakers may consider implementing additional restrictions on the number of dispensaries that may be allowed to operate within the state, as states with restricted dispensary markets experience no significant changes in arrest rates relative to states without dispensaries. Moreover, policy changes that allow dispensaries to open accounts with banks so that they do not have to operate as cash-only businesses may decrease the attractiveness of dispensaries as a target.

Conversely, in-home cultivation offsets the increase in arrest rates suggesting beneficial outcomes associated with allowing individuals to cultivate medical marijuana at home. This, however, should not be interpreted as a wholesale endorsement of in-home cultivation allowances since other negative externalities may result from such a provision. Further study and

consideration of potential spillovers into the recreational market (Bostwick, 2012; Thurstone et al., 2011) and impacts on other health behaviours and social outcomes need to be considered before determining the full array of benefits and drawbacks of in-home cultivation.

The primary mechanism through which MML policy dimensions impact arrest rates appears to be through their impact on the crime rate. In turn, the crime rate may have increased for several reasons. For instance, crime rates may have increased due to the establishment of new targets for crime (i.e., dispensaries). Alternatively, crimes unrelated to drug use or possession may have increased because police resources are required to enforce MML rules, prevent spillovers into the recreational market, verify that users are legitimate medical patients, and ensure dispensaries or in-home cultivators are not fronts for grow-ops (Chu, 2014; Nagourney &

Lyman, 2013; Volz, 2010; Wieczner, 2013). These changes in crime associated with the enactment of various MML policy dimensions are likely the main drivers of changes in arrest rates.

This study benefits from a long timeframe from 1994 to 2012, which includes the first state to adopt an MML as well as the first six states to open dispensaries to the public. One drawback of the study is the attrition in the NCJJ dataset, which limits the generalizability of results to those states included in the sample. Nevertheless, these results provide insights as to the expected impacts of different types of MMLs on crime and arrest rates. The results of this study provide guidance on the design features of MMLs to states considering the legalization of medical cannabis. The lessons learned from the medical marijuana experience can also inform the creation of legal recreational markets, as many of the features of MMLs can be transferred to a recreational market. While legal markets hypothetically reduce arrests for most marijuana possession charges, new opportunities for criminal activity may arise. Policymakers can thus

take precautionary action to safeguard against increases in crime and arrest rates that may arise from legalizing medical (or recreational) marijuana.

REFERENCES

Alford, C. (2014, September). How Medical Marijuana Laws Affect Crime Rates. Working Paper, Charlottesville, VA. Retrieved from http://people.virginia.edu/~cea9e/website_files/alford_mml_and_crime.pdf

Anderson, D. M., Hansen, Benjamin, & Rees, D. I. (2013). Medical Marijuana Laws, Traffic Fatalities, and Alcohol Consumption. Journal of Law and Economics, 56(2), 333–369.

Anderson, D. M., & Rees, D. I. (2014a). The Legalization of Recreational Marijuana: How Likely Is the Worst-Case Scenario? Journal of Policy Analysis and Management, 33(1), 221–232.

Anderson, D. M., & Rees, D. I. (2014b). The Role of Dispensaries: The Devil is in the Details. Journal of Policy Analysis and Management, 33(1), 235–240.

Angrist, J., & Pischke, J.-S. (2009). Mostly harmless econometrics : an empiricist’s companion. Princeton: Princeton University Press.

Arizona Department of Health Services. (2011). Application Monthly Report - Arizona Medical Marijuana Program. Phoenix. Retrieved from http://www.azdhs.gov/documents/licensing/medical- marijuana/reports/2011/111125_Patient-Application-Report.pdf

Arizona Department of Health Services. (2012). Arizona Medical Marijuana Act (AMMA) End of Year Report, 2012. Phoenix. Retrieved from http://www.azdhs.gov/documents/licensing/medical-marijuana/reports/2012/arizona- medical-marijuana-end-of-year-report-2012.pdf

Arizona Department of Health Services. (2014). Arizona Medical Marijuana Act (AMMA) End of Year Report, 2014. Phoenix. Retrieved from http://www.azdhs.gov/documents/licensing/medical-marijuana/reports/2014/arizona- medical-marijuana-end-of-year-report-2014.pdf

Arizona Department of Health Services. (2015). Arizona Medical MArijuana Program August 2015 Monthly Report. Phoenix. Retrieved from http://www.azdhs.gov/documents/licensing/medical-marijuana/reports/2015/2015-august- monthly-report.pdf

Baker, J. (1998). Juveniles in Crime-Part 1: Participation Rates & Risk Factors. NSW Bureau of Crime Statistics and Research Sydney.

Beer Institute. (2013). Brewers Almanac, 2013. Washington, DC: Beer Institute. Retrieved from http://www.beerinstitute.org

Benson, B. L., Kim, I., Rasmussen, D. W., & Zhehlke, T. W. (1992). Is property crime caused by drug use or by drug enforcement policy? Applied Economics, 24(7), 679–692.

Benson, B. L., Leburn, I. S., & Rasmussen, D. W. (2001). The Impact of Drug Enforcement on Crime: An Investigation of the Opportunity Cost of Police Resources. Journal of Drug Issues, 31(4), 989–1006.

Bertrand, M., Duflo, E., & Mullainathan, S. (2004). How Much Should We Trust Differences-In- Differences Estimates? The Quarterly Journal of Economics, 119(1), 249–275.

Boles, S. M., & Miotto, K. (2003). Substance abuse and violence: A review of the literature. Aggression and Violent Behavior, 8(2), 155–174.

Bostwick, J. M. (2012). Blurred Boundaries: The Therapeutics and Politics of Medical Marijuana. Mayo Clinic Proceedings, 87(2), 172–186.

Center for Child and Family Policy. (2007). Juvenile or Adult? Adolescent Offenders and the Line Between the Juvenile and Criminal Justice Systems. Duke University. Retrieved from https://childandfamilypolicy.duke.edu/pdfs/familyimpact/2007/BriefingReport_07.pdf

Chu, Y.-W. L. (2014). The effects of medical marijuana laws on illegal marijuana use. Working Paper Series, School of Economics and Finance, Victoria University of Wellington. Retrieved from http://researcharchive.vuw.ac.nz.libproxy.lib.unc.edu/handle/10063/3212

Colorado Department of Public Health and Environment. (2016). Medical Marijuana Registry Program Statistics. Denvoer, CO: Colorado Department of Public Health and Environment. Retrieved from https://www.colorado.gov/pacific/sites/default/files/CHED_MMR_Report_Statistics_Feb ruary_2016.pdf

Colorado Department of Revenue. (2015, August 3). MED Licensed Facilities. Retrieved September 13, 2015, from https://www.colorado.gov/pacific/enforcement/med-licensed- facilities

Cook, P. J., & Moore, M. J. (1993). Violence reduction through restrictions on alcohol availability. Alcohol Health & Research World, 17(2), 151–156.

Crost, B., & Guerrero, S. (2012). The effect of alcohol availability on marijuana use: Evidence from the minimum legal drinking age. Journal of Health Economics, 31(1), 112–121.

Crites, N. (2012, May 11). Police: AZ medical marijuana card holders becoming targets. CBS 5. Phoenix. Retrieved from http://www.cbs5az.com/story/18314237/medical-marijuana- grow-houses-bring-crime-to-neighborhoods

Dembo, R., Williams, L., Schmeidler, J., Wish, E. D., Getreu, A., & Berry, E. (1992). Juvenile Crime and Drug Abuse: Journal of Addictive Diseases, 11(2), 5–31.

Department of Health Medical Marijuana Program. (2016). 2015 Annual report and 2015 Biennial Report. Trenton, NJ: State of New Jersey Department of Health. Retrieved from http://www.state.nj.us/health/medicalmarijuana/documents/annual_biennial_report2.pdf

DiNardo, J., & Lemieux, T. (2001). Alcohol, marijuana, and American youth: the unintended consequences of government regulation. Journal of Health Economics, 20(6), 991–1010.

Federal Bureau of Investigation. (2013). Persons Arrested. Retrieved August 28, 2015, from https://www.fbi.gov/about-us/cjis/ucr/crime-in-the-u.s/2013/crime-in-the-u.s.- 2013/persons-arrested/persons-arrested

Fergusson, D. M., & Horwood, L. J. (1997). Early onset cannabis use and psychosocial adjustment in young adults. Addiction, 92(3), 279–296.

Flood, S., King, M., Ruggles, S., & Warren, J. R. (2015). Integrated Public Use Microdata Series, Current Population Survey: Version 4.0. [Machine-readable database]. Minneapolis: University of Minnesota. Retrieved from https://cps.ipums.org/cps/

Freisthler, B., Kepple, N. J., Sims, R., & Martin, S. E. (2013). Evaluating Medical Marijuana Dispensary Policies: Spatial Methods for the Study of Environmentally-Based Interventions. American Journal of Community Psychology, 51(1-2), 278–288.

Goldstein, P. J. (1985). The Drugs/Violence Nexus: A Tripartite Conceptual Framework. Journal of Drug Issues, 15(4), 493–506.

Hawaii Department of Health. (2015). Medical Marijuana (329) Registry Program. Honolulu, HI: Department of Health. Retrieved from http://health.hawaii.gov/medicalmarijuanaregistry/wp- content/blogs.dir/97/files/2015/12/FY16-October-31-Statistics-FINAL-11-30-15.pdf

Hoffmann, D. E., & Weber, E. (2010). Medical Marijuana and the Law. New England Journal of Medicine, 362(16), 1453–1457.

Ihlanfeldt, K. R. (2007). Neighborhood Drug Crime and Young Males’ Job Accessibility. Review of Economics and Statistics, 89(1), 151–164.

Jacobson, M., Chang, T., Anderson, J. M., MacDonald, J., Bluthenthal, R. N., & Ashwood, J. S. (2011). Regulating Medical Marijuana Dispensaries. Retrieved March 28, 2016, from http://www.rand.org/pubs/technical_reports/TR987.html

Kepple, N. J., & Freisthler, B. (2012). Exploring the Ecological Association Between Crime and Medical Marijuana Dispensaries. Journal of Studies on Alcohol and Drugs, 73(4), 523– 530.

Kleiman, M. A. R., Caulkins, J. P., & Hawken, A. (2011). Drugs and Drug Policy: What Everyone Needs to Know. Oxford University Press.

Kovaleski, S. F. (2014, February 14). U.S. Issues Marijuana Guidelines for Banks. The New York Times. Retrieved from http://www.nytimes.com/2014/02/15/us/us-issues-marijuana- guidelines-for-banks.html

La Vigne, N. G., & Samuels, J. (2012). The Growth & Increasing Cost of the Federal Prison System: Drivers and Potential Solutions. Urban Institute Justice Police Center. Retrieved from http://www.urban.org/research/publication/growth-increasing-cost-federal-prison- system-drivers-and-potential-solutions

MacCoun, R. J., & Reuter, P. (2001). Drug war heresies : learning from other vices, times, and places. Cambridge, UK ;New York: Cambridge University Press,.

Maine Department of Health and Human Services. (2013). Rules Governing the Maine Medical Use of Marijuana Program. Retrieved from http://www.maine.gov/dhhs/dlrs/mmm/documents/MMMP-Rules-144c122.pdf

Markowitz, S. (2005). Alcohol, Drugs and Violent Crime. International Review of Law and Economics, 25(1), 20–44.

Markowitz, S., Nesson, E., Poe-Yamagata, E., Florence, C., Deb, P., Andrews, T., & Barnett, S. B. L. (2012). Estimating the Relationship between Alcohol Policies and Criminal Violence and Victimization. German Economic Review, 13(4), 416–435.

Michiels, M. (2014). The Cannabis Conundrum. American Bankers Association. ABA Banking Journal, 106(2), 32–33,35.

Miron, J. A., & Waldock, K. (2010). The Budgetary Impact of Ending Drug Prohibition (White Paper). Washington D.C.: Cato Institute. Retrieved from September 27, 2010.

Miron, J. A., & Zwiebel, J. (1995). The Economic Case Against Drug Prohibition. The Journal of Economic Perspectives, 9(4), 175–192.

Montana Department of Public Health and Human Services. (2016). Montana Marijuana Program (MMP) February 2016 Registry Information. Helena, MT: Montana Department of Public Health and Human Services. Retrieved from http://dphhs.mt.gov/Portals/85/qad/documents/LicensureBureau/MarijuanaProgram/Febr uary%202016%20MMP%20Registry%20Information.pdf?ver=2016-03-03-092256-220

Montgomery, M. (2010, October 27). Will Legalizing Pot Wipe Out The Black Market? [California’s Proposition 19: The Politics of Pot]. Retrieved September 14, 2015, from http://www.npr.org/templates/story/story.php?storyId=130832710

Moore, D. (2011, September 27). Second medical marijuana theft reported in Sagadahoc County. Retrieved from http://bangordailynews.com/2011/09/27/news/police-beat/second- medical-marijuana-theft-reported-in-sagadahoc-county/

Morris, R. G., TenEyck, M., Barnes, J. C., & Kovandzic, T. V. (2014). The Effect of Medical Marijuana Laws on Crime: Evidence from State Panel Data, 1990-2006. PLoS ONE, 9(3), e92816.

Nagourney, A., & Lyman, R. (2013, October 26). Few Problems With Cannabis for California. The New York Times. Retrieved from http://www.nytimes.com/2013/10/27/us/few- problems-with-cannabis-for-california.html

National Conference of State Legislatures. (2014, October 20). State Medical Marijuana Laws. Retrieved November 21, 2014, from http://www.ncsl.org/research/health/state-medical- marijuana-laws.aspx#1

National Research Council. (1993). Understanding and preventing violence. (A. J. Reiss & J. A. Roth, Eds.). Nat Acad Pr. Retrieved from http://search.proquest.com/docview/59612447?pq-origsite=summon

Nevada Division of Public and Behavioral Health. (2016). Nevada Medical Marijuana Program Statistics (p. Carson City, NV). Department of Health and Human Services. Retrieved from http://dpbh.nv.gov/uploadedFiles/dpbhnvgov/content/Reg/MM-Patient-Cardholder- Registry/dta/Monthly_Reports/MMPFebruary2016.pdf

Norström, T., & Rossow, I. (2014). Cannabis use and violence: Is there a link? Scandinavian Journal of Public Health, 42(4), 358–363.

NPR. (2009, October 15). In California, Marijuana Dispensaries Outnumber Starbucks. Tell Me More. Retrieved from http://www.npr.org/templates/story/story.php?storyId=113822156

Ogden, D. W. (2014, September 15). Memorandum for Selected United State Attorneys on Investigations and Prosecutions in States Authorizing the Medical Use of Marijuana. Retrieved September 13, 2015, from http://www.justice.gov/opa/blog/memorandum- selected-united-state-attorneys-investigations-and-prosecutions-states

Oregon Medical Marijuana Program. (2016). The Oregon Medical Marijuana Program Statistical Snapshot. Portland, OR: Oregon Health Authority. Retrieved from https://public.health.oregon.gov/DiseasesConditions/ChronicDisease/MedicalMarijuanaP rogram/Documents/OMMP%20Statistic%20Snapshot%20-%2001-2016.pdf

Pacula, R. L., & Kilmer, B. (2003). Marijuana and Crime: Is there a Connection Beyond Prohibition? (Working Paper No. 10046). National Bureau of Economic Research. Retrieved from http://www.nber.org/papers/w10046

Pacula, R. L., Powell, D., Heaton, P., & Sevigny, E. L. (2015). Assessing the Effects of Medical Marijuana Laws on Marijuana Use: The Devil is in the Details. Journal of Policy Analysis and Management, 34(1), 7–31.

Peterson, R. D., Krivo, L. J., & Harris, M. A. (2000). Disadvantage and Neighborhood Violent Crime: Do Local Institutions Matter? Journal of Research in Crime and Delinquency, 37(1), 31–63.

Popovici, I., French, M. T., Pacula, R. L., Maclean, J. C., & Antonaccio, O. (2014). Cannabis Use and Antisocial Behavior among Youth. Sociological Inquiry, 84(1), 131–162.

Popovici, I., Homer, J. F., Fang, H., & French, M. T. (2012). Alcohol Use and Crime: Findings from a Longitudinal Sample of U.S. Adolescents and Young Adults. Alcoholism: Clinical and Experimental Research, 36(3), 532–543.

ProCon. (2015a, April 27). Number of Legal Medical Marijuana Patients. Retrieved October 14, 2015, from http://medicalmarijuana.procon.org/view.resource.php?resourceID=005889

ProCon. (2015b, July 1). 23 Legal Medical Marijuana States and DC. Retrieved August 13, 2015, from http://medicalmarijuana.procon.org/view.resource.php?resourceID=000881

Puzzanchera, C., & Kang, W. (2014). Easy Access to FBI Arrest Statistics 1994-2011. Retrieved November 19, 2014, from http://www.ojjdp.gov/ojstatbb/ezaucr/

Resignato, A. J. (2000). Violent crime: a function of drug use or drug enforcement? Applied Economics, 32(6), 681–688.

Ricker, N.-N. (2014, September 30). Maine police say they have leads on law enforcement impersonators who robbed medical marijuana patient. The Bangor Daily News. Retrieved from http://bangordailynews.com/2014/09/30/news/bangor/police-say-they-have-solid- leads-on-law-enforcement-impersonators-who-robbed-winterport-medical-marijuana- patient/

Shepard, E. M., & Blackley, P. R. (2005). Drug Enforcement and Crime: Recent Evidence from New York State. Social Science Quarterly, 86(2), 323–342.

Stinson, J. (2015). States Find You Can’t Take Legal Marijuana Money to the Bank. Retrieved March 28, 2016, from http://bit.ly/1BeJAXo

Thurstone, C., Lieberman, S. A., & Schmiege, S. J. (2011). Medical marijuana diversion and associated problems in adolescent substance treatment. Drug and Alcohol Dependence, 118(2–3), 489–492.

Trotter, B. (2014, November 10). Two Ellsworth High School students charged in $5,000 medical marijuana theft. The Bangor Daily News. Retrieved from http://bangordailynews.com/2014/11/10/news/police-beat/two-ellsworth-high-school- students-charged-in-5000-medical-marijuana-theft/

Volz, M. (2010, July 21). Medical Marijuana Facing Backlash. Retrieved February 12, 2015, from http://www.huffingtonpost.com/2010/05/21/medical-marijuana-facing- _n_585546.html

Walmsley, R. (2013). World Prison Population List (tenth edition). London: International Centre for Prison STudies. Retrieved from http://www.prisonstudies.org/sites/default/files/resources/downloads/wppl_10.pdf

Washington State Department of Health. (2008). Information Summary: Patient Access to Medical Marijuana in Washington State (No. Publication Number 631-001). Olympia,

WA. Retrieved from http://www.doh.wa.gov/portals/1/Documents/2000/PatientAccess.pdf

Wen, H., Hockenberry, J. M., & Cummings, J. R. (2015). The effect of medical marijuana laws on adolescent and adult use of marijuana, alcohol, and other substances. Journal of Health Economics, 42, 64–80.

Werb, D., Rowell, G., Guyatt, G., Kerr, T., Montaner, J., & Wood, E. (2011). Effect of drug law enforcement on drug market violence: A systematic review. International Journal of Drug Policy, 22(2), 87–94.

Wieczner, J. (2013, August 15). 10 Things Medical Marijuana Biz Won’t Say. Retrieved September 9, 2015, from http://www.marketwatch.com/story/10-things-medical- marijuana-wont-tell-you-2013-08-09?page=2

World Health Organization. (2014). Global Alcohol Report United States of America Country Profile. Retrieved September 13, 2015, from http://www.who.int/substance_abuse/publications/global_alcohol_report/profiles/usa.pdf

Table 2.1 States with medical marijuana laws, policy dimensions, and effective date, 1994-2012 1st Legal Required In-Home MML in Dispensary Patient Cultivation State Effect Opened Registry Allowed Alaska 1999 1999 1999 Arizona 2011 2012† 2011 2011 California 1996 2004† 1996 Colorado 2001 2004† 2001 2001 Connecticut 2012 2012 District of Columbia 2010 2010 Delaware 2011 2011 Hawaii 2000 2000 2000 Maine 1999 2011 2009 1999 Michigan 2008 2008 Montana 2004 2011 2004 Nevada 2001 2001 2001 New Jersey 2010 2012 2010 New Mexico 2007 2009 2007 2007 Oregon 1998 2007 1998 Rhode Island 2006 2006 2006 Vermont 2004 2004 2004 Washington 1998 1998 Notes: Dispensaries with a † indicate that the state permits an open dispensary market. All other states put limits on the number of dispensaries that may operate within the state. In Colorado, the first reported dispensary open in 2004; however a majority of the dispensaries did not open until 2009/2010 (Anderson and Rees, 2014b; Weinstein, 2010).

Table 2.2A Descriptive statistics of arrest rates (per 100,000) for all ages by MML implementation status Full Sample MML=0 MML=1 Mean s.d. Mean s.d. Mean s.d. All Ages Overall Violent Crime 176.78 (12.86) 178.72 (12.45) 169.84 (31.99) 0 Murder 4.18 (0.38) 4.50 (0.44) 3.03 (0.52) ** Rape 9.22 (0.51) 9.52 (0.61) 8.13 (0.70) 0 Robbery 34.27 (3.50) 35.31 (3.97) 30.56 (5.37) 0 Assault 129.12 (10.00) 129.42 (8.96) 128.03 (27.47) 0 Overall Property Crime 614.73 (22.94) 628.89 (24.23) 564.19 (39.08) * Burglary 96.84 (5.74) 98.22 (5.95) 91.93 (11.48) 0 Theft 470.58 (19.36) 483.97 (19.91) 422.78 (36.01) * Auto Theft 41.44 (3.50) 40.74 (3.54) 43.95 (5.98) 0 N 617 482 135 *** p<0.01; ** p<0.05; * p<0.1.

Notes: Estimates are unweighted and clustered at the state level.

Table 2.2B Descriptive statistics of arrest rates (per 100,000) for adults by MML implementation status Full Sample MML=0 MML=1 Mean s.d. Mean s.d. Mean s.d. Adults Overall Violent Crime 196.75 (14.95) 197.07 (13.76) 195.59 (39.35) 0 Murder 5.01 (0.44) 5.36 (0.51) 3.76 (0.63) ** Rape 10.32 (0.56) 10.62 (0.66) 9.25 (0.86) 0 Robbery 33.83 (3.26) 34.57 (3.58) 31.20 (5.41) 0 Assault 147.63 (12.24) 146.57 (10.50) 151.42 (34.82) 0 Overall Property Crime 563.32 (23.37) 571.41 (24.81) 534.41 (38.42) 0 Burglary 89.02 (6.20) 89.46 (6.35) 87.44 (12.38) 0 Theft 434.50 (18.73) 444.00 (19.31) 400.58 (34.49) 0 Auto Theft 36.08 (3.52) 34.16 (3.16) 42.95 (6.67) 0 N 617 482 135 49

*** p<0.01; ** p<0.05; * p<0.1. Notes: Estimates are unweighted and clustered at the state level.

Table 2.2C Descriptive statistics of arrest rates (per 100,000) for juveniles by MML implementation status Full Sample MML=0 MML=1 Mean s.d. Mean s.d. Mean s.d. Juveniles Overall Violent Crime 257.96 (20.99) 272.47 (24.54) 206.13 (30.42) * Murder 3.80 (0.40) 4.24 (0.46) 2.21 (0.52) *** Rape 13.03 (1.01) 13.73 (1.26) 10.53 (0.82) ** Robbery 79.23 (10.56) 83.78 (12.54) 62.96 (13.53) 0 Assault 161.90 (11.57) 170.71 (13.20) 130.46 (18.77) * Overall Property Crime 1666.70 (82.56) 1736.73 (97.49) 1416.67 (106.75) ** Burglary 261.64 (13.42) 271.30 (15.29) 227.17 (24.93) * Theft 1252.73 (72.44) 1306.01 (84.02) 1062.48 (97.87) ** Auto Theft 125.43 (10.50) 132.39 (13.23) 100.60 (10.53) ** N 617 482 135

50 *** p<0.01; ** p<0.05; * p<0.1.

Notes: Estimates are unweighted and clustered at the state level.

Table 2.3 Descriptive statistics for state-level covariates by MML implementation status Full Sample MML=0 MML=1 Mean s.d. Mean s.d. Mean s.d. Proportion Age 45-64 0.24 (0.00) 0.24 (0.00) 0.26 (0.01) *** Proportion with College Degree 0.31 (0.01) 0.30 (0.01) 0.33 (0.01) ** Proportion Black 0.09 (0.01) 0.11 (0.02) 0.04 (0.01) *** Proportion Hispanic 0.09 (0.02) 0.08 (0.02) 0.13 (0.04) 0 Proportion Under Poverty 0.12 (0.00) 0.12 (0.00) 0.12 (0.01) 0 Beer consumption per capita (in gallons) 22.35 (0.61) 22.12 (0.68) 23.16 (0.96) 0 Unemployment Rate 5.60 (0.17) 5.29 (0.17) 6.71 (0.37) *** Decriminalized Marijuana 0.28 (0.07) 0.18 (0.07) 0.61 (0.13) *** Law Enforcement Officers (per 1,000) 2.28 (0.08) 2.31 (0.09) 2.15 (0.12) 0 N 617 482 135 *** p<0.01; ** p<0.05; * p<0.1. Notes: Estimates are unweighted and clustered at the state level.

Table 2.4 Logged arrest rates for violent crimes as a function of MML policy dimensions, by age group (1) (2) (3) (4) (5) Overall Violent Crime Murder Rape Robbery Assault A) All Ages Dispensaries 0.0510 0.227*** 0.0296 0.181*** 0.0221 (0.03) (0.07) (0.04) (0.04) (0.04) Registries 0.036 -0.130 -0.012 0.014 0.058 (0.06) -(0.10) (0.08) (0.09) (0.06) In -home Cultivation 0.030 -0.008 0.098* -0.051 0.033 (0.05) (0.19) (0.06) (0.05) (0.05) Observations 617 605 617 616 617 Mean arrest rate 176.78 4.18 9.22 34.27 129.12 B) Adults Dispensaries 0.047 0.232*** -0.020 0.136*** 0.030 (0.04) (0.07) (0.04) (0.05) (0.04) Registries 0.039 -0.106 0.001 0.022 0.066 (0.06) (0.08) (0.08) (0.07) (0.06) In -home Cultivation 0.036 0.054 0.061 -0.053 0.033 (0.05) (0.18) (0.06) (0.05) (0.05) Observations 617 607 617 616 617 Mean arrest rate 196.75 5.01 10.32 33.83 147.63 C) Juveniles Dispensaries 0.105*** 0.185 -0.055 0.305*** -0.006 (0.04) (0.11) (0.04) (0.06) (0.05) Registries -0.010 -0.511** -0.017 0.017 0.001 (0.10) (0.21) (0.12) (0.16) (0.09) In -home Cultivation 0.002 0.013 0.086 -0.081 0.044 (0.08) (0.20) (0.09) (0.08) (0.07) Observations 617 525 615 612 617 Mean arrest rate 257.96 3.80 13.03 79.23 161.90 *** p<0.01; ** p<0.05; * p<0.1. Notes: Coefficients are weighted by average state population during the time period and standard errors (in parentheses) are clustered at the state level. All arrest rates are logged. The mean arrest rate is per 100,000 people of the relevant population. State-level covariates include proportion 45-64 year olds, proportion with a college degree, proportion Black; proportion Hispanic; proportion under poverty; beer consumption per capita in gallons; unemployment rate; and an indicator for whether state decriminalized marijuana. All models include state fixed effects and state-specific linear time trends.

Table 2.5 Logged arrest rates for property crimes as a function of MML policy dimensions, by age group (1) (2) (3) (4) Overall Property Crime Burglary Theft Auto Theft A) All Ages Dispensaries 0.167*** 0.139*** 0.135*** 0.205** (0.03) (0.03) (0.03) (0.10) Registries 0.108*** 0.126*** 0.0882** 0.118 (0.02) (0.04) (0.04) (0.12) In -home Cultivation -0.043 -0.038 -0.017 -0.247*** (0.03) (0.03) (0.04) (0.06) Observations 617 617 617 617 Mean arrest rate 614.73 96.84 470.58 41.44 B) Adults Dispensaries 0.195*** 0.179*** 0.151*** 0.225** (0.03) (0.03) (0.03) (0.10) Registries 0.130*** 0.148** 0.110*** 0.084 (0.03) (0.06) (0.03) (0.13) In -home Cultivation -0.060*** -0.054 -0.048** -0.237*** (0.02) (0.04) (0.02) (0.07) Observations 617 617 617 617 Mean arrest rate 563.32 89.02 434.50 36.08 C) Juveniles Dispensaries 0.086*** 0.029 0.085** 0.122 (0.03) (0.05) (0.03) (0.09) Registries -0.014 -0.003 -0.034 0.065 (0.09) (0.07) (0.11) (0.08) In -home Cultivation 0.005 0.008 0.059 -0.239*** (0.08) (0.05) (0.10) (0.06) Observations 617 617 617 617 Mean arrest rate 1666.70 261.64 1252.73 125.43 *** p<0.01; ** p<0.05; * p<0.1. Notes: Coefficients are weighted by average state population during the time period and standard errors (in parentheses) are clustered at the state level. All arrest rates are logged. The mean arrest rate is per 100,000 people of the relevant population. State-level covariates include proportion 45-64 year olds, proportion with a college degree, proportion Black; proportion Hispanic; proportion under poverty; beer consumption per capita in gallons; unemployment rate; and an indicator for whether state decriminalized marijuana. All models include state fixed effects and state-specific linear time trends.

Table 2.6 Logged arrest rates for violent crimes on restricted and unrestricted dispensary policies, by age group (1) (2) (3) (4) (5) Overall Violent Crime Murder Rape Robbery Assault A) All Ages Unrestricted Dispensaries 0.054 0.240*** 0.037 0.195*** 0.023 (0.04) (0.07) (0.05) (0.05) (0.04) Restricted Dispensaries -0.014 -0.095 -0.134 -0.149 0.001 (0.09) (0.15) (0.12) (0.14) (0.09) Registries 0.037 -0.125 -0.010 0.019 0.058 (0.06) (0.09) (0.08) (0.09) (0.06) In-home Cultivation 0.030 -0.008 0.097* -0.052 0.032 (0.05) (0.19) (0.06) (0.05) (0.05) Observations 617 605 617 616 617 Mean arrest rate 176.78 4.18 9.22 34.27 129.12 B) Adults Unrestricted Dispensaries 0.049 0.239*** -0.005 0.148*** 0.030 (0.04) (0.07) (0.05) (0.05) (0.04) Restricted Dispensaries -0.011 0.061 -0.363*** -0.123 0.026 (0.08) (0.13) (0.10) (0.11) (0.09) Registries 0.039 -0.103 0.006 0.026 0.066 (0.06) (0.08) (0.07) (0.06) (0.06) In-home Cultivation 0.036 0.054 0.060 -0.054 0.033 (0.05) (0.18) (0.06) (0.05) (0.05) Observations 617 607 617 616 617 Mean arrest rate 196.75 5.01 10.32 33.83 147.63 C) Juveniles Unrestricted Dispensaries 0.114** 0.199* -0.059 0.324*** -0.002 (0.04) (0.11) (0.04) (0.07) (0.05) Restricted Dispensaries -0.106 -0.403 0.031 -0.174 -0.105 (0.10) (0.40) (0.32) (0.21) (0.09) Registries -0.007 -0.503** -0.019 0.024 0.003 (0.10) (0.21) (0.12) (0.16) (0.09) In-home Cultivation 0.001 0.013 0.086 -0.082 0.044 (0.08) (0.20) (0.09) (0.08) (0.07) Observations 617 525 615 612 617 Mean arrest rate 257.96 3.80 13.03 79.23 161.90 *** p<0.01; ** p<0.05; * p<0.1. Notes: Coefficients are weighted by average state population during the time period and standard errors (in parentheses) are clustered at the state level. All arrest rates are logged. The mean arrest rate is per 100,000 people of the relevant population. State-level covariates include proportion 45-64 year olds, proportion with a college degree, proportion Black; proportion Hispanic; proportion under poverty; beer consumption per capita in gallons; unemployment rate; and an indicator for whether state decriminalized marijuana. All models include state fixed effects and state-specific linear time trends.

Table 2.7 Logged arrest rates for property crimes on restricted and unrestricted dispensary policies, by age group (1) (2) (3) (4) Overall Property Crime Burglary Theft Auto Theft A) All Ages Unrestricted Dispensaries 0.173*** 0.146*** 0.140*** 0.202* (0.03) (0.04) (0.03) (0.10) Restricted Dispensaries 0.033 -0.011 0.014 0.270*** (0.07) (0.08) (0.07) (0.09) Registries 0.110*** 0.128*** 0.090** 0.117 (0.02) (0.04) (0.04) (0.12) In -home Cultivation -0.043* -0.039 -0.017 -0.247*** (0.03) (0.03) (0.04) (0.06) Observations 617 617 617 617 Mean arrest rate 614.73 96.84 470.58 41.44 B) Adults Unrestricted Dispensaries 0.202*** 0.186*** 0.157*** 0.227** (0.03) (0.03) (0.03) (0.10) Restricted Dispensaries 0.029 0.010 -0.004 0.187** (0.06) (0.08) (0.06) (0.08) Registries 0.132*** 0.151** 0.112*** 0.084 (0.03) (0.06) (0.03) (0.13) In -home Cultivation -0.060*** -0.054 -0.048** -0.237*** (0.02) (0.04) (0.02) (0.07) Observations 617 617 617 617 Mean arrest rate 563.32 89.02 434.50 36.08 C) Juveniles Unrestricted Dispensaries 0.095*** 0.039 0.094*** 0.109 (0.03) (0.06) (0.03) (0.10) Restricted Dispensaries -0.128* -0.215*** -0.130 0.438** (0.08) (0.04) (0.09) (0.20) Registries -0.011 0.001 -0.031 0.060 (0.08) (0.07) (0.10) (0.08) In -home Cultivation 0.005 0.008 0.058 -0.238*** (0.08) (0.05) (0.10) (0.06) Observations 617 617 617 617 Mean arrest rate 1666.70 261.64 1252.73 125.43 *** p<0.01; ** p<0.05; * p<0.1. Notes: Coefficients are weighted by average state population during the time period and standard errors (in parentheses) are clustered at the state level. All arrest rates are logged. The mean arrest rate is per 100,000 people of the relevant population. State-level covariates include proportion 45-64 year olds, proportion with a college degree, proportion Black; proportion Hispanic; proportion under poverty; beer consumption per capita in gallons; unemployment rate; and an indicator for whether state decriminalized marijuana. All models include state fixed effects and state-specific linear time trends.

Table 2.8 Logged arrest rates for violent crimes as a function of MMLs, by age group (1) (2) (3) (4) (5) (1) (2) (3) (4) Overall Overall Violent Property Auto Crime Murder Rape Robbery Assault Crime Burglary Theft Theft A) All Ages MML 0.016 -0.085 0.073 -0.089*** 0.029 -0.031 -0.018 -0.012 -0.195* (0.03) (0.15) (0.05) (0.03) (0.03) (0.04) (0.04) (0.03) (0.10) Observations 617 605 617 616 617 617 617 617 617 Mean arrest rate 176.78 4.18 9.22 34.27 129.12 614.73 96.84 470.58 41.44 B) Adults MML 0.026 -0.025 0.045 -0.073* 0.033 -0.041 -0.027 -0.032 -0.202* (0.03) (0.14) (0.05) (0.04) (0.03) (0.05) (0.06) (0.04) (0.10) Observations 617 607 617 616 617 617 617 617 617

56 Mean arrest rate 196.75 5.01 10.32 33.83 147.63 563.32 89.02 434.50 36.08

C) Juveniles MML -0.041 -0.180 0.087 -0.151*** 0.022 -0.027 -0.012 0.013 -0.199** (0.05) (0.16) (0.08) (0.05) (0.06) (0.05) (0.04) (0.07) (0.07) Observations 617 525 615 612 617 617 617 617 617 Mean arrest rate 257.96 3.80 13.03 79.23 161.90 1666.70 261.64 1252.73 125.43 *** p<0.01; ** p<0.05; * p<0.1. Notes: Coefficients are weighted by average state population during the time period and standard errors (in parentheses) are clustered at the state level. All arrest rates are logged. The mean arrest rate is per 100,000 people of the relevant population. State-level covariates include proportion 45-64 year olds, proportion with a college degree, proportion Black; proportion Hispanic; proportion under poverty; beer consumption per capita in gallons; unemployment rate; and an indicator for whether state decriminalized marijuana. All models include state fixed effects and state-specific linear time trends.

Table 2.9 Non-logged arrest rates for violent crimes as a function of MMLs, by age group (1) (2) (3) (4) (5) Overall Violent Crime Murder Rape Robbery Assault A) All Ages Dispensaries 14.070* 1.598*** 0.310 9.462*** 2.983 (7.18) (0.53) (0.37) (1.82) (5.53) Registries 6.168 -0.341 -0.166 -0.005 5.822 (9.35) (0.63) (0.75) (4.46) (5.64) In -home Cultivation 4.338 -0.025 0.520 -4.308 8.160 (8.45) (1.39) (0.62) (3.65) (5.71) Observations 617 617 617 617 617 Mean arrest rate 176.78 4.18 9.22 34.27 129.12 B) Adults Dispensaries 11.920 1.769** 0.080 7.212*** 3.644 (8.64) (0.67) (0.42) (1.73) (6.63) Registries 5.588 -0.493 0.214 0.720 5.522 (9.13) (0.73) (0.61) (3.36) (6.20) In -home Cultivation 5.478 0.569 0.344 -4.462 8.997 (9.14) (1.72) (0.71) (3.31) (6.39) Observations 617 617 617 617 617 Mean arrest rate 196.75 5.01 10.32 33.83 147.63 C) Juveniles Dispensaries 39.800*** 1.607** -0.411 36.930*** 0.575 (13.51) (0.66) (0.62) (7.37) (8.43) Registries 19.040 -0.351 -0.833 -0.437 20.68** (23.51) (1.09) (2.58) (17.64) (8.67) In -home Cultivation -4.705 -1.771 -0.163 -13.070 10.020 (22.22) (1.09) (1.44) (14.16) (11.21) Observations 617 617 617 617 617 Mean arrest rate 257.96 3.80 13.03 79.23 161.90 *** p<0.01; ** p<0.05; * p<0.1. Notes: Coefficients are weighted by average state population during the time period and standard errors (in parentheses) are clustered at the state level. All arrest rates are non-logged. The mean arrest rate is per 100,000 people of the relevant population. Models include state-level covariates: proportion 45-64 year olds, proportion with a college degree, proportion Black; proportion Hispanic; proportion under poverty; beer consumption per capita; unemployment rate; and an indicator for whether state decriminalized marijuana. All models include state fixed effects and state-specific linear time trends.

Table 2.10 Non-logged arrest rates for property crimes as a function of MMLs, by age group (1) (2) (3) (4) Overall Property Crime Burglary Theft Auto Theft A) All Ages Dispensaries 96.60*** 19.97*** 57.89*** 18.74*** (13.60) (4.09) (11.82) (5.80) Registries 90.93*** 15.53** 64.87** 10.52* (32.63) (6.77) (31.76) (5.43) In -home Cultivation -54.57** -14.31*** -20.480 -19.79*** (21.03) (4.47) (24.10) (5.11) Observations 617 617 617 617 Mean arrest rate 614.73 96.84 470.58 41.44 B) Adults Dispensaries 104.9*** 23.39*** 60.32*** 21.21*** (16.50) (3.45) (13.53) (6.25) Registries 89.73*** 16.88** 65.94** 6.909 (27.77) (8.06) (26.54) (6.61) In -home Cultivation -53.36*** -15.71** -22.780 -14.87*** (16.73) (6.99) (16.60) (5.15) Observations 617 617 617 617 Mean arrest rate 563.32 89.02 434.50 36.08 C) Juveniles Dispensaries 170.3*** 25.170 116.5** 28.65** (55.39) (18.65) (49.39) (11.71) Registries 211.400 26.180 139.000 46.25** (154.90) (26.08) (136.20) (18.95) In -home Cultivation -156.500 -31.10** -44.890 -80.49*** (114.00) (13.64) (111.80) (13.63) Observations 617 617 617 617 Mean arrest rate 1666.70 261.64 1252.73 125.43 *** p<0.01; ** p<0.05; * p<0.1. Notes: Coefficients are weighted by average state population during the time period and standard errors (in parentheses) are clustered at the state level. All arrest rates are non-logged. The mean arrest rate is per 100,000 people of the relevant population. Models include state-level covariates: proportion 45-64 year olds, proportion with a college degree, proportion Black; proportion Hispanic; proportion under poverty; beer consumption per capita; unemployment rate; and an indicator for whether state decriminalized marijuana. All models include state fixed effects and state-specific linear time trends.

Table 2.11 Logged arrest rates for violent crimes as a function of MMLs controlling for law enforcement, by age group (1) (2) (3) (4) (5) Overall Violent Crime Murder Rape Robbery Assault A) All Ages Dispensaries 0.0613* 0.214*** 0.034 0.192*** 0.034 (0.03) (0.07) (0.04) (0.04) (0.03) Registries 0.053 -0.153 -0.007 0.033 0.076 (0.05) (0.11) (0.08) (0.08) (0.05) In-home Cultivation 0.019 0.006 0.095 -0.063 0.021 (0.05) (0.19) (0.06) (0.05) (0.05) Law Enforcement 0.132*** -0.181** 0.044 0.148* 0.147*** (0.04) (0.08) (0.13) (0.08) (0.04) Observations 616 604 616 615 616 Mean arrest rate 176.78 4.18 9.22 34.27 129.12 B) Adults Dispensaries 0.056 0.218*** -0.017 0.145*** 0.041 (0.03) (0.07) (0.04) (0.04) (0.03) Registries 0.053 -0.129 0.003 0.036 0.083* (0.05) (0.09) (0.07) (0.06) (0.04) In-home Cultivation 0.027 0.068 0.060 -0.062 0.022 (0.05) (0.18) (0.06) (0.05) (0.05) Law Enforcement 0.116*** -0.183* 0.0145 0.110* 0.143*** (0.04) (0.10) (0.12) (0.06) (0.04) Observations 616 606 616 615 616 Mean arrest rate 196.75 5.01 10.32 33.83 147.63 C) Juveniles Dispensaries 0.127*** 0.164 -0.052 0.326*** 0.014 (0.03) (0.11) (0.04) (0.05) (0.04) Registries 0.025 -0.548** -0.012 0.053 0.033 (0.08) (0.24) (0.12) (0.14) (0.07) In-home Cultivation -0.021 0.032 0.082 -0.103 0.024 (0.06) (0.21) (0.09) (0.07) (0.06) Law Enforcement 0.284*** -0.245 0.046 0.274* 0.250*** (0.09) (0.17) (0.24) (0.16) (0.08) Observations 616 525 614 611 616 Mean arrest rate 257.96 3.80 13.03 79.23 161.90 *** p<0.01; ** p<0.05; * p<0.1. Notes: Coefficients are weighted by average state population during the time period and standard errors (in parentheses) are clustered at the state level. All arrest rates are logged. Law enforcement is measured as the number of sworn police officers per 1,000 persons. The mean arrest rate is per 100,000 people of the relevant population. Models include state-level covariates: proportion 45-64 year olds, proportion with a college degree, proportion Black; proportion Hispanic; proportion under poverty; beer consumption per capita; unemployment rate; and an indicator for whether state decriminalized marijuana. All models include state fixed effects and state-specific linear time trends.

Table 2.12 Logged arrest rates for property crimes as a function of MMLs controlling for law enforcement, by age group (1) (2) (3) (4) Overall Property Crime Burglary Theft Auto Theft A) All Ages Dispensaries 0.166*** 0.138*** 0.142*** 0.159* (0.03) (0.03) (0.03) (0.08) Registries 0.106*** 0.123*** 0.0981*** 0.043 (0.03) (0.04) (0.03) (0.09) In-home Cultivation -0.042 -0.036 -0.023 -0.201*** (0.03) (0.03) (0.04) (0.05) Law Enforcement -0.015 -0.026 0.079 -0.594*** (0.06) (0.05) (0.08) (0.18) Observations 616 616 616 616 Mean arrest rate 614.73 96.84 470.58 41.44 B) Adults Dispensaries 0.189*** 0.172*** 0.151*** 0.179** (0.03) (0.03) (0.03) (0.08) Registries 0.118*** 0.136** 0.110*** 0.010 (0.03) (0.05) (0.04) (0.11) In-home Cultivation -0.0525** -0.046 -0.0471* -0.191*** (0.02) (0.04) (0.02) (0.05) Law Enforcement -0.093 -0.0961* -0.003 -0.592*** (0.06) (0.05) (0.06) (0.20) Observations 616 616 616 616 Mean arrest rate 563.32 89.02 434.50 36.08 C) Juveniles Dispensaries 0.107*** 0.049 0.113*** 0.090 (0.03) (0.04) (0.03) (0.08) Registries 0.019 0.029 0.012 0.013 (0.06) (0.06) (0.07) (0.07) In-home Cultivation -0.016 -0.012 0.030 -0.205*** (0.07) (0.04) (0.08) (0.07) Law Enforcement 0.259** 0.250** 0.360*** -0.413* (0.10) (0.11) (0.10) (0.21) Observations 616 616 616 616 Mean arrest rate 1666.70 261.64 1252.73 125.43 *** p<0.01; ** p<0.05; * p<0.1. Notes: Coefficients are weighted by average state population during the time period and standard errors (in parentheses) are clustered at the state level. All arrest rates are logged. Law enforcement is measured as the number of sworn police officers per 1,000 persons. The mean arrest rate is per 100,000 people of the relevant population. Models include state-level covariates: proportion 45-64 year olds, proportion with a college degree, proportion Black; proportion Hispanic; proportion under poverty; beer consumption per capita; unemployment rate; and an indicator for whether state decriminalized marijuana. All models include state fixed effects and state-specific linear time trends.

Table 2.13 Logged crime rates for violent and property crimes as a function of MML policy dimensions, all ages (BJS Data) (1) (2) (3) (4) (5) (6) (7) (8) (9) Overall Overall Violent Property Auto Crime Murder Rape Robbery Assault Crime Burglary Theft Theft Dispensaries 0.026 0.126** -0.018 0.109*** -0.018 0.111*** 0.150*** 0.066*** 0.129* (0.03) (0.05) (0.02) (0.02) (0.04) (0.02) (0.02) (0.02) (0.07) Registries 0.103*** 0.127 0.0544* 0.109** 0.106*** 0.0937* 0.140** 0.078 0.115 (0.03) (0.08) (0.03) (0.04) (0.04) (0.06) (0.06) (0.05) (0.13) In -home Cultivation -0.0937*** -0.170** 0.007 -0.143*** -0.0795*** -0.048 -0.058 -0.043 -0.091 (0.03) (0.07) (0.01) (0.05) (0.02) (0.04) (0.04) (0.04) (0.10)

Observations 969 969 969 969 969 969 969 969 969 Mean crime rate 454.11 5.77 34.47 125.80 288.07 3583.03 750.13 2472.85 360.06 *** p<0.01; ** p<0.05; * p<0.1. Notes: Coefficients are weighted by average state population during the time period and standard errors (in parentheses) are clustered at the state level. All crime

61 rates are logged. Mean crime rates are per 100,000 persons. All models include state-level covariates: proportion 45-64 year olds, proportion with a college

degree, proportion Black; proportion Hispanic; proportion under poverty; beer consumption per capita in gallons; unemployment rate; and an indicator for whether state decriminalized marijuana. All models include state fixed effects and state-specific linear time trends. Estimated crime rates were obtained from the Bureau of Justice Statistics.

APPENDIX TABLE 1: DEFINITIONS OF VIOLENT AND PROPERTY CRIMES

Crime Definition Violent Crimes Murder (and non-negligent The wilful (non-negligent) killing of one human being by another. Deaths caused by negligence, manslaughter) attempts to kill, suicides, accidental deaths, and justifiable homicides are excluded.

The carnal knowledge of a female forcibly and against her will. Included are rapes by force and Forcible rape attempts or assaults to rape. Statutory offenses (no force used - victim under age of consent) are excluded. The taking or attempting to take anything of value from the care, custody, or control of a person or Robbery persons by force or threat of force or violence and/or by putting the victim in fear. An unlawful attack by one person upon another for the purpose of inflicting severe or aggravated Aggravated assault bodily injury. This type of assault usually is accompanied by the use of a weapon or by means likely to produce death or great bodily harm. Simple assaults are excluded.

Property Crimes Burglary (breaking and The unlawful entry of a structure to commit a felony or a theft. Attempted forcible entry is included. entering) (Except motor vehicle theft) The unlawful taking, carrying, leading, or riding away of property from the possession or constructive possession of another. Examples are thefts of bicycles or automobile Larceny-theft accessories, shoplifting, pocket-picking, or the stealing of any property or article, which is not taken by force and violence, or by fraud. Attempted larcenies are included. Embezzlement, "con" games, forgery, worthless checks, etc. are excluded. The theft or attempted theft of a motor vehicle. A motor vehicle is self-propelled and runs on the Auto theft surface and not on rails. Specifically excluded from this category are motorboats, construction equipment, airplanes, and farming equipment. Notes: Information obtained from the Federal Bureau of Investigation (https://www.fbi.gov/about-us/cjis/ucr/crime-in-the-u.s/2013/crime-in-the-u.s.- 2013/persons-arrested/persons-arrested) and National Center for Juvenile Justice. (http://ojjdp.gov/ojstatbb/ezaucr/asp/dictionary.asp).

CHAPTER 3: EXAMINING THE JOINT USE OF MARIJUANA AND TOBACCO: THE EFFECT OF MEDICAL MARIJUANA LAWS AND CIGARETTE SMOKING AMONG U.S. ADULTS

I. Introduction

Each year, cigarette smoking generates over $300 billion in health-related costs and is responsible for more than 480,000 deaths in the U.S., making it the leading cause of preventable death (Bonnie, Stratton, & Wallace, 2007; Center for Disease Control and Prevention (CDC),

2015). While the percent of cigarette smokers has leveled off at just below 20 percent in recent years (Center for Disease Control and Prevention, 2013, 2015), the prevalence of past-year marijuana use among U.S. adults has more than doubled from 4.1 to 9.5 percent between 2001-

02 to 2012-13 (Hasin, Saha, Kerridge, & et al., 2015). The rise in marijuana use may be attributed to softening attitudes towards marijuana use (Jones, 2015); however, it is important to ensure that policy changes that result in the increase in marijuana use do not inadvertently encourage other harmful health behaviours.

Cigarette smoking due to increased access to marijuana is of particular concern as many recreational marijuana users combine tobacco with marijuana in the form of blunts or spliffs. The combined use of marijuana and tobacco contributes to an increased desire to smoke cigarettes when marijuana is neither available nor publicly permitted (Agrawal, Budney, & Lynskey, 2012;

Amos, Wiltshire, Bostock, Haw, & McNeill, 2004; Bélanger, Akre, Kuntsche, Gmel, & Suris,

2011; Highet, 2004). Studies have also demonstrated the potential for a reverse gateway effect, where marijuana use preceded tobacco use for a subset of users (Agrawal, Madden, Bucholz,

Heath, & Lynskey, 2008; Amos et al., 2004; Bélanger et al., 2011; Highet, 2004; Patton, Coffey,

Carlin, Sawyer, & Lynskey, 2005; Timberlake et al., 2007). Public health officials therefore need to consider the impact of increased access to marijuana on cigarette use.

Medical marijuana laws (MMLs) are one type of policy that increases access to marijuana. While four states (Colorado, Washington, Alaska, and Oregon) have legalized recreational marijuana since 2012, sufficient data to study the effects of recreational marijuana legalization are not yet available. Consequently, MMLs have been studied to determine the impact of increased access to marijuana on a number of downstream health outcomes (Anderson,

Hansen, & Rees, 2013; Anderson, Rees, & Sabia, 2014; Bachhuber MA, Saloner B, Cunningham

CO, & Barry CL, 2014; Chu, 2015; Morris, TenEyck, Barnes, & Kovandzic, 2014; Wen,

Hockenberry, & Cummings, 2015). Indeed, spillovers of medical marijuana into the recreational market and an increase in marijuana use among adults following medical marijuana legalization has been documented (Bostwick, 2012; Chu, 2014a; Pacula, Powell, Heaton, & Sevigny, 2015;

Thurstone, Lieberman, & Schmiege, 2011).

A growing body of literature is examining the impact of MMLs not only on marijuana use (Chu, 2014a; Harper, Strumpf, & Kaufman, 2012; Lynne-Landsman, Livingston, &

Wagenaar, 2013; Pacula et al., 2015) but on other second stage behaviours including traffic fatalities, alcohol consumption, and opioid overdose deaths (Anderson et al., 2013, 2014;

Bachhuber MA et al., 2014; Chu, 2015; Morris et al., 2014; Wen et al., 2015). However, very few studies have looked into the relationship between MMLs and cigarette use, particularly among U.S. adults. This study first aims to fill this void by estimating the impact of MMLs on the probability of cigarette use among U.S. adults. Additionally, this study examines differences in smoking probabilities by health status (as defined by four health-related quality of life

(HRQOL) measures developed by the Centers for Disease Control). Lastly, this paper examines differences in marijuana and cigarette use by gender.

II. Background

A Brief Background on MMLs

To date, 23 states and DC have passed MMLs. In general, MMLs provide protection from state-level sanctions for individuals who use medical marijuana and for the doctors who recommend it (Anderson et al., 2013; National Conference of State Legislatures, 2016). The ailments for which medical marijuana use is permitted are listed in state laws, and include chronic conditions such as HIV/AIDs, post traumatic stress disorder, cancer, glaucoma, cachexia, chronic pain, among other ailments (ProCon, 2015). While the particulars of MMLs vary across states, at least one of the following three policy dimensions is typically implemented: required patient registries, in-home cultivation allowances, or storefront dispensaries (Table 3.1). States with required patient registries mandate that qualified patients seeking to use medical marijuana prove medical need (i.e., with a doctor’s recommendation) and register with the state. In return, individuals receive a patient ID card allowing them to legally possess and consume medical marijuana. States permitting in-home cultivation allow patients or their caregivers to grow a certain number of mature and immature plants on their property. Although the intended use of the plants is medical, spillovers into recreational use are difficult to monitor and prevent

(Bostwick, 2012; Pacula et al., 2015; Salomonsen-Sautel, Sakai, Thurstone, Corley, & Hopfer,

2012; Thurstone et al., 2011; Wirfs-Brock, Seaton, & Sutherland, 2010). States with dispensaries permit the operation of storefronts to sell medical marijuana and marijuana products to qualified patients.

Theorized Links between MMLs and Cigarette Use

Previous studies have found that, on average, MMLs increase marijuana use among adults (Chu, 2014a; Pacula et al., 2015; Wen et al., 2015). In particular, certain MML policy dimensions, namely dispensaries and in-home cultivation allowances, have been found to increase marijuana use (Pacula et al., 2015). Registries, on the other hand, were not found to change marijuana use, though some studies identified a negative but statistically insignificant impact (Pacula et al., 2015; Wen et al., 2015). The varied impact of each policy dimension underscores the differential impact MMLs have on marijuana use depending on the policy dimensions that have been included in state law.

Past studies have also found that marijuana and cigarettes are economic complements where a decrease in the (pecuniary or non-pecuniary) price of one substance leads to an increase in demand for the other (Cameron & Williams, 2001; Chaloupka, Pacula, Farrelly, Johnston, &

O’Malley, 1999; Farrelly, Bray, Zarkin, & Wendling, 2001). Indeed, MMLs have been found to decrease the price of high quality marijuana, suggesting an increase in the supply of marijuana

(Anderson et al., 2013). Accordingly, the MML policy dimensions that increase access to and consumption of marijuana are expected to result in an increase in cigarette use as well.

Specifically, in-home cultivation can be expected to increase the probability of cigarette use, while registries may have minimal or potentially a negative effect on cigarette use.

The effect of dispensaries on cigarette smoking, however, is less clear. Although states with dispensaries experience an increase in marijuana use, two points should be considered.

First, dispensaries increase access to non-smoked marijuana products such as edibles, tinctures, oil, and lotions (National Institute on Drug Abuse, 2014; O’Connell & Bou-Matar, 2007; Simon,

2010; Vandrey R et al., 2015; Wang G, Roosevelt G, & Heard K, 2013; Wardarski, 2015).

Second, the complementary relationship between marijuana and cigarettes may only hold when both substances are smoked. Smoked marijuana and cigarettes share a common route of administration that may serve as a link contributing to sustained tobacco use among cannabis smokers (Agrawal et al., 2012; Agrawal & Lynskey, 2009). In contrast, smokeless tobacco use is not associated with marijuana use, likely due to differing routes of administration (Agrawal &

Lynskey, 2009). The corollary may also be true in that edible marijuana may not be used with smoked tobacco because they lack a common route of administration. Thus, the effect of dispensaries on cigarette smoking could be positive or negative. On the one hand, dispensaries may lead to an increase in cigarette smoking if it increases access to smoked forms of marijuana.

Alternatively, dispensaries may discourage smoking if it encourages the consumption of non- smoked forms of marijuana.

Given that the target population of MMLs is individuals experiencing chronic physical and mental health conditions, it is important to understand how MMLs differentially impact individuals with different health statuses. This analysis provides insights on the implementation of MMLs (i.e., impacts on the target population; unintended impacts on the non-targeted population). MMLs should result in no increase in cigarette smoking among unhealthy (i.e., targeted) individuals (Hoffmann & Weber, 2010; National Conference of State Legislatures,

2016), since cannabis products are presumably being used as medicine and because medical users are more likely to prefer non-smoked forms of marijuana (Pacula, Jacobson, &

Maksabedian, 2016). Further, although MMLs target a chronically ill population, and healthy individuals have no legitimate claim to medical marijuana use, considerable evidence exists of spillovers from medical marijuana markets to recreational markets (Chu, 2014b; Pacula et al.,

2016; Thurstone et al., 2011; Wirfs-Brock et al., 2010). Given these spillovers, and to the extent that healthy individuals represent a potential pool of recreational marijuana users, MML policy dimensions that increase access to marijuana (particularly in-home cultivation) may lead to an increase in cigarette smoking among healthy individuals.

Lastly, concerning differences by gender, men are more likely to smoke both marijuana and cigarettes than women (Center for Disease Control and Prevention, 2015; Centers for

Disease Control and Prevention, 2001). For instance, among adults, 19 percent of males smoke while only 15 percent of females smoke cigarettes. Likewise, 9.7 percent of males are current users of marijuana, while only 5.6 percent of women report current marijuana use (Substance

Abuse and Mental Health Services Administration, 2014). Consequently, males are expected to experience an increase in smoking in states with MML policy dimensions that increase access to marijuana (i.e., in states with in-home cultivation and possibly dispensaries).

III. Data and Empirical Specification

Data

Data on individual smoking behaviour are drawn from the 1993-2010 Behavioral Risk

Factor Surveillance System (BRFSS) surveys administered by the Centers for Disease Control and Prevention (CDC). Telephone-based surveys are administered annually in each state using a randomly selected cross-sectional sample of individuals age 18 and over. Surveys collect information on demographic characteristics, health status, and health behaviours. This study combined the annual surveys into a pooled cross-sectional dataset consisting of 2,722,046 observations of adults aged 18-64 with sampling weights and full information on key covariates across the 18 years. The sample was restricted to adults aged 18-64 since the smoking rate and

illicit drug use declines considerably among individuals 65 and older (Center for Disease Control and Prevention, 2015; Substance Abuse and Mental Health Services Administration, 2013).

Measures

Current smoking. In accordance with the CDC, current smokers were defined as individuals who reported smoking at least 100 cigarettes in their lifetime and smoking on at least one day in the past 30 days (Center for Disease Control and Prevention, 2015; Centers for

Disease Control and Prevention, 2011). Those who reported smoking less than 100 lifetime cigarettes or no smoking in the past 30 days were categorized as current non-smokers.

Medical Marijuana Laws. States enacted various combinations of MML policy dimensions, and often policy dimensions were enacted at different times within each state.

Rather than using a binary indicator to capture MML enactment, which masks potentially heterogeneous impacts on cigarette use (Pacula et al., 2015), MMLs were coded as a set of three binary variables (0/1 variable) indicating whether a state had a required patient registry, a legally operating dispensary, or in-home cultivation. The registry variable captured states where qualified medical marijuana patients were required to register; this measure excluded states that implemented voluntary registries. The dispensary variable included states that have legally protected dispensaries in operation. Moreover, the dispensary indicator reflected the date on which dispensaries opened, rather than the date legislation was passed. As a result, the analysis isolated the effect of an increase in supply of marijuana, rather than any anticipatory effects associated with a law change (Anderson & Rees, 2014). Lastly, the in-home cultivation variable included those states that permitted patients to grow plants at home.

Health status. Health status was defined according to the CDC’s Health-Related Quality of Life (HRQOL) variables, which include measures of self-rated health, physical functioning, psychological well-being, and disability or lost productivity (Hennessy 1994; Mody and Smith,

2006). Four questions are asked in the BRFSS that obtain information on each dimension of

HRQOL: (1) (self-rated health) “Would you say that in general your health is poor, fair, good, very good, or excellent?” (2) (physically unhealthy days) “Now thinking about your physical health, which includes physical illness and injury, for how many days during the past 30 days was your physical health not good?” (3) (mentally unhealthy days) “Now thinking about your mental health, which includes stress, depression, and problems with emotions, for how many days during the past 30 days was your mental health not good?” (4) (activity limitation days/disability) “During the past 30 days, for about how many days did poor physical or mental health keep you from doing your usual activities, such as self-care, work, or recreation?” These four measures have good construct validity and reliability, which has been documented elsewhere (Idler & Benyamini, 1997; Kapp, Jackson-Thompson, Petroski, & Schootman, 2009;

Newschaffer, 1998).

In accordance with CDC recommendations (Brown et al., 2003, 2004; Centers for

Disease Control and Prevention, 2015b; Ford, Moriarty, Zack, Mokdad, & Chapman, 2001;

Mody & Smith, 2006), the self-rated health variable was measured as a dichotomous variable

(excellent, very good, and good versus fair and poor). The remaining three healthy days variables were dichotomized as less than 14 days (relatively healthy) and greater than or equal to 14 days

(relatively unhealthy). Individuals who reported zero physically and mentally unhealthy days were coded as having no activity limitation days. Four separate measures of health were used

since state MMLs cover a range of chronic conditions that cover both physical and mental health.

Accordingly, these measures of health capture multiple dimensions of health.

Individual demographic controls. Analyses controlled for the respondent’s age, race and ethnicity (coded categorically as non-Hispanic White, non-Hispanic Black, Hispanic, or Other), and gender. The respondent’s marital status (married/in a relationship or single) and educational attainment (high school dropout, high school graduate, some college, or college graduate) were also included as covariates.

State-level characteristics. Models included a dichotomous variable for whether the state decriminalized marijuana (1=yes, 0=no) (Marijuana Policy Project, 2015; Scott, 2010) as well as the sum of the state and federal beer tax adjusted to 2010 dollars (Beer Institute, 2013). The state unemployment rate and income per capita (adjusted to 2010 dollars) were obtained from the

Bureau of Labor Statistics (BLS) and Bureau of Economic Analysis (BEA), respectively. To capture state attitudes towards smoking, analysis controlled for state cigarette excise taxes, which were obtained from the Tax Burden on Tobacco (adjusted to 2010 dollars) (Orzechowski &

Walker, 2015), and a binary measure of whether states had implemented a smoking ban in bars

(American Lung Association, 2015; Centers for Disease Control and Prevention, 2015a). The model also included state-fixed effects to control for time-invariant state-level heterogeneity, while year-fixed effects were included to control for secular time trends common across all states. A state-specific linear time trend variable controlled for differing trends in smoking across states over time.

Analytic Approach

The bivariate relationship that illustrates the unadjusted proportion of current smokers for the full sample by MML implementation status was first estimated. To control for potential confounders, the following multivariate state-fixed effects model was estimated using ordinary least squares (OLS):

where Smokeist is the binary smoking status for individual i in state s at year t. Dispst, InHomest, and Registryst are the various state-level MML policy dimension indicators. Xist and Zst represent a vector of individual- and state-level covariates, respectively. State-fixed effects are denoted by

, and state-specific linear time trends are indicated by , which control for changes in smoking in each state due to time. The fixed effects approach exploits within-state changes, thus controlling for unobserved time-invariant state-level confounders (Angrist & Pischke, 2009). To account for the sampling procedures of the BRFSS, all estimates are weighted and standard errors are clustered at the state level (Bertrand, Duflo, & Mullainathan, 2004). Since the outcome of interest is dichotomous, the model was estimated as a linear probability model, which provides a more intuitive percentage point interpretation of the estimated coefficients than a logistic regression model. In sensitivity analyses (not shown), results from the logistic regression did not qualitatively differ from OLS estimates.

The multivariate fixed effects model was first estimated for the full sample, which provided the independent effect of MML policy dimensions on the probability of current smoking. Next, the sample was stratified by health status to compare the results between relatively healthy and unhealthy individuals with respect to overall self-reported health, physical health, mental health, and activity limitations. The coefficients for healthy and unhealthy

individuals were compared for each HRQOL measure by estimating a single model and interacting health status with each covariate (not shown) and testing the significance of these interactions.

Since the specific illnesses constituting poor health were not reported in the BRFSS surveys, and because the HRQOL measure of healthy individuals includes people who experienced 1 to 13 unhealthy days, it is possible some of the “healthy” respondents may be part of the MML targeted population. As a test of sensitivity, the HRQOL models were re-estimated using only individuals who reported zero unhealthy days and 30 unhealthy days. Those respondents who indicated 30 unhealthy days (in the past 30 days) more likely represent those with a significant chronic illness for which medical marijuana may be useful. Individuals who reported 0 unhealthy days likely represented healthy individuals without chronic illness and who were not the target audience for MMLs.

To determine differences by gender, the models were run separately for males and females and a fully interacted model was estimated to test for significant differences between males and females. Lastly, the behaviours of males and females were again compared by health status, which was done by stratifying the sample by health status and gender. All estimates were weighted and standard errors were clustered at the state level (Bertrand, Duflo, & Mullainathan,

2004).

IV. Results

Among all adults in the sample, 23 percent reported being current smokers. In the state- years with MMLs, 18 percent were current smokers whereas 24 percent of respondents were current smokers in state-years without MMLs (Table 3.2). This lower smoking prevalence

associated with MMLs did not account for omitted factors that affect the probability of smoking and ignored heterogeneity across state MML policies that may differentially impact smoking rates.

Turning to the multivariate analysis of all adults, dispensaries and required patient registries had a protective effect on smoking while in-home cultivation encouraged smoking

(Table 3.3, Panel A). Relative to states without dispensaries and registries, adults in states with dispensaries and registries were 1.4 and 2.0 percentage points less likely to be current smokers, respectively. At the weighted sample mean, this represented a decline in the probability of smoking by 6.1 (-0.014/0.231=-0.061) and 8.7 (-0.020/0.231=-0.087) percent respectively. In contrast, states with in-home cultivation experienced a significant increase in the probability of smoking by 1.1 percentage points (or 4.8 percent), partially offsetting the protective effect of dispensaries and registries on cigarette smoking.

Looking at differences by health status, dispensaries and registries were also consistently protective for both healthy and unhealthy individuals across all health status measures (Table

3.3, Panel B). In contrast, the increase in smoking probability due to in-home cultivation was largely driven by the healthy population. For all four measures of poor health status, in-home cultivation did not significantly impact the probability of smoking. Conversely, in-home cultivation led to a significant increase in the probability of smoking ranging from 1.2 to 1.4 percentage points among relatively healthy individuals. Tests of equality showed that the difference between those with good mental health versus those with poor mental health was statistically significant.

In the sensitivity analysis (not shown), which only included those who reported 0 or 30 unhealthy days, the coefficients on in-home cultivation and registries were consistent with the

main results. Dispensaries were also largely consistent with main results, with the exception of an increase in the probability of smoking among physically unhealthy adults. The reversal in signs among the physically ill suggests the dispensary estimates should be considered with caution, particularly as only three states passed MMLs with dispensaries within the study timeframe (i.e., California, Colorado, and New Mexico).

Turning to differences by gender (Table 3.3, Panel C), states with dispensaries and registries experienced significant declines in smoking probabilities among both men and women, while in-home cultivation led to significant increases in the probability of smoking. Consistent with the hypothesis that men would be more responsive to increased access to marijuana, the increase in smoking probabilities due to home cultivation for men was larger than that for women (1.8 and 0.4 percentage points respectively), though the difference was not statistically significant.

Although it was expected that unhealthy individuals would be more likely to use marijuana medicinally and therefore be less likely to smoke cigarettes upon passage of MMLs, the estimates from the models stratified by health status and gender revealed otherwise (Table

3.4). Among physically unhealthy and limited activity males (Table 3.4, Panel A), dispensaries led to an increase in the probability of smoking by 2.2 and 4.8 percentage points, respectively. In contrast, dispensaries led to a decline in the probability of smoking among unhealthy females, consistent with the estimates for all measures of unhealthy individuals. These results suggest that dispensaries largely encouraged males to smoke, regardless of health status.

Another striking difference emerged in states that permitted in-home cultivation.

Specifically, unhealthy males with poor self-rated health, poor physical health, and limited activity experienced an increase in the probability of smoking as a result of in-home cultivation.

These males also were significantly more likely to smoke than their female counterparts (who experienced declines in the probability of smoking). Thus, the coefficients on in-home cultivation for unhealthy males and females largely offset each other, which resulted in the null findings in the main health status models (Table 3.3, Panel B). Moreover, the positive coefficients on unhealthy and healthy males suggests that in-home cultivation encouraged cigarette smoking among all males, regardless of health status.

Registries were associated with declines in smoking probabilities among healthy and unhealthy men and women, which was consistent with the findings in the overall health status estimates. The results for healthy males and females across all measures of HRQOL were consistent with the results for healthy adults overall (Table 3.4, Panel B).

V. Discussion

As more states debate the merits of medical and recreational marijuana policies, it is important to understand the potential unintended consequences such laws might have on other health behaviours. This study highlighted the differential impact specific MML policy dimensions had on cigarette use. On average, dispensaries and registries decreased the probability of cigarette smoking.

The magnitude of the decline in smoking probabilities associated with registries and dispensaries was comparable to the implementation of smoke-free policies, such as workplace bans or public smoking bans, which reduce tobacco use by 3.4 percentage points (Hopkins et al.,

2010). Although MML policy dimensions were not enacted with the intention of impacting cigarette smoking, the reduction in smoking probabilities was an added public health benefit.

The negative coefficient on dispensaries among adults supports the hypothesis that dispensaries increase access to non-smoked forms of marijuana products, which may not encourage cigarette use due to differing routes of administration. Although dispensaries on average protected against cigarette smoking, it is concerning that dispensaries were associated with an increase in the probability of smoking among unhealthy males. Further research is needed to understand the factors contributing to the increase in smoking among this population.

Policymakers, however, should be wary of the increase in the probability of smoking among relatively healthy adults due to in-home cultivation. Given that healthy adults have few health-related reasons to cultivate medical marijuana, this increase may be indicative of recreational use. In-home cultivation is difficult for law enforcement to monitor to ensure individuals abide by cultivation rules and plant limits. Consequently, spillovers of medical marijuana for recreational use are difficult to prevent.

Study Strengths and Limitations

This study contributes to the limited empirical literature exploring the relationship between marijuana and cigarettes. In addition to estimating the effects of individual MML policy dimensions, this research used a policy change that altered the availability of marijuana as a way to better understand the interrelationship between marijuana and cigarette use. This study also employed an identification strategy that controlled for unobserved state-level time-invariant covariates. Furthermore, the study covered a relatively long time period with a nationally representative sample that included the early MML adopters.

A few limitations of this study should be noted. First, due to data limitations, this study only considered the extensive margin of cigarette use. This measure did not capture the intensity

of use, such as the number of cigarettes smoked by existing and new smokers. Second, only three jurisdictions had legally operating dispensaries within the study time period (i.e., California,

Colorado, and New Mexico), limiting the amount of variation that could be used to identify causal effects; the dispensary results should therefore be interpreted with caution. Third, this study was unable to identify the exact mechanisms linking MMLs and cigarette use. Marijuana consumption is one potential mechanism through which MMLs affect cigarette use. However, marijuana use was not directly observed in BRFSS, limiting the ability to determine conclusively that MMLs affected cigarette use through changes in marijuana use. Moreover, given that dispensaries and in-home cultivation led to an increase in smoking probabilities among unhealthy males, there may be other unexplored factors linking medical marijuana availability and cigarette smoking that warrant further study.

Conclusion and Policy Implications

Although smoking rates have declined in the past 20 years (Center for Disease Control and Prevention, 2013, 2015), policymakers need to be sure MMLs do not inadvertently counteract the downward trend in smoking. In particular, policymakers seeking to discourage cigarette use should be weary of in-home cultivation allowances without a system in place to monitor and enforce cultivation limits. Required patient registries were associated with declines in smoking, indicating the added benefit of tracking medical marijuana users.

The decline in cigarette smoking probabilities associated with dispensaries is suggestive of potential public health benefits. However, the exact mechanism linking dispensaries and cigarette smoking is not yet clear. Moreover, the public health benefits should be balanced against the potential risks related to the consumption (or over-consumption) of edible products,

spillovers to children, or the inability to identify the concentration of tetrahydrocannabinol

(THC, the active ingredient in marijuana) in edible products purchased at dispensaries (Vandrey et al., 2015; Wardarski, 2015). The potential impact of dispensaries on other societal consequences, such as crime (Alford, 2014; Morris et al., 2014), should also be considered.

Thus, before endorsing dispensaries as a means to reduce cigarette smoking, the public health benefits should be weighed against potential risks, and measures to mitigate these risks also need to be put in place.

REFERENCES

Agrawal, A., Budney, A. J., & Lynskey, M. T. (2012). The co-occurring use and misuse of cannabis and tobacco: a review: Cannabis and tobacco review. Addiction, 107(7), 1221– 1233. http://doi.org/10.1111/j.1360-0443.2012.03837.x

Agrawal, A., & Lynskey, M. T. (2009). Tobacco and Cannabis Co-occurrence: Does Route of Administration Matter? Drug and Alcohol Dependence, 99(1-3), 240–247. http://doi.org/10.1016/j.drugalcdep.2008.08.007

Agrawal, A., Madden, P. A. F., Bucholz, K. K., Heath, A. C., & Lynskey, M. T. (2008). Transitions to regular smoking and to nicotine dependence in women using cannabis. Drug and Alcohol Dependence, 95(1-2), 107–114. http://doi.org/10.1016/j.drugalcdep.2007.12.017

American Lung Association. (2015). American Lung Association’s Battle Against Tobacco Use Milestones. Retrieved October 30, 2015, from http://www.stateoftobaccocontrol.org/tobacco-timeline.html

Amos, A., Wiltshire, S., Bostock, Y., Haw, S., & McNeill, A. (2004). “You can”t go without a fag . . . you need it for your hash’—a qualitative exploration of smoking, cannabis and young people. Addiction, 99(1), 77–81. http://doi.org/10.1111/j.1360-0443.2004.00531.x

Anderson, D. M., Hansen, & Rees, D. I. (2013). Medical Marijuana Laws, Traffic Fatalities, and Alcohol Consumption. Journal of Law and Economics, 56(2), 333–369. http://doi.org/10.1086/668812

Anderson, D. M., & Rees, D. I. (2014). The Role of Dispensaries: The Devil is in the Details. Journal of Policy Analysis and Management, 33(1), 235–240. http://doi.org/10.1002/pam.21733

Anderson, D. M., Rees, D. I., & Sabia, J. J. (2014). Medical Marijuana Laws and Suicides by Gender and Age. American Journal of Public Health, 104(12), 2369–2376. http://doi.org/10.2105/AJPH.2013.301612

Angrist, J., & Pischke, J.-S. (2009). Mostly harmless econometrics : an empiricist’s companion. Princeton: Princeton University Press.

Bachhuber MA, Saloner B, Cunningham CO, & Barry CL. (2014). Medical cannabis laws and opioid analgesic overdose mortality in the united states, 1999-2010. JAMA Internal Medicine, 174(10), 1668–1673. http://doi.org/10.1001/jamainternmed.2014.4005

Beer Institute. (2013). Brewers Almanac, 2013. Washington, DC: Beer Institute. Retrieved from http://www.beerinstitute.org

Bélanger, R. E., Akre, C., Kuntsche, E., Gmel, G., & Suris, J.-C. (2011). Adding Tobacco to Cannabis—Its Frequency and Likely Implications. Nicotine & Tobacco Research, ntr043. http://doi.org/10.1093/ntr/ntr043

Bertrand, M., Duflo, E., & Mullainathan, S. (2004). How Much Should We Trust Differences-In- Differences Estimates? The Quarterly Journal of Economics, 119(1), 249–275. http://doi.org/10.1162/003355304772839588

Bonnie, R. J., Stratton, K. R., & Wallace, R. B. (2007). Ending the tobacco problem : a blueprint for the nation. Washington, DC: National Academies Press,.

Bostwick, J. M. (2012). Blurred Boundaries: The Therapeutics and Politics of Medical Marijuana. Mayo Clinic Proceedings, 87(2), 172–186. http://doi.org/10.1016/j.mayocp.2011.10.003

Brown, D. W., Balluz, L. S., Giles, W. H., Beckles, G. L., Moriarty, D. G., Ford, E. S., & Mokdad, A. H. (2004). Diabetes mellitus and health-related quality of life among older adults: Findings from the behavioral risk factor surveillance system (BRFSS). Diabetes Research and Clinical Practice, 65(2), 105–115. http://doi.org/10.1016/j.diabres.2003.11.014

Brown, D. W., Balluz, L. S., Heath, G. W., Moriarty, D. G., Ford, E. S., Giles, W. H., & Mokdad, A. H. (2003). Associations between recommended levels of physical activity and health-related quality of life Findings from the 2001 Behavioral Risk Factor Surveillance System (BRFSS) survey. Preventive Medicine, 37(5), 520–528. http://doi.org/10.1016/S0091-7435(03)00179-8

Cameron, L., & Williams, J. (2001). Cannabis, Alcohol and Cigarettes: Substitutes or Complements? Economic Record, 77(236), 19–34. http://doi.org/10.1111/1475- 4932.00002

Center for Disease Control and Prevention. (2013, November 14). Trends in Current Cigarette Smoking Among High School Students and Adults, United States, 1965–2011. Retrieved March 4, 2016, from http://www.cdc.gov/tobacco/data_statistics/tables/trends/cig_smoking/

Center for Disease Control and Prevention. (2015, December 8). Current Cigarette Smoking Among Adults in the United States. Retrieved June 25, 2015, from http://www.cdc.gov/tobacco/data_statistics/fact_sheets/adult_data/cig_smoking/

Center for Disease Control and Prevention. (2015, December 11). Smoking and Tobacco Use; Fact Sheet; Fast Facts. Retrieved December 21, 2015, from http://www.cdc.gov/tobacco/data_statistics/fact_sheets/fast_facts/

Centers for Disease Control and Prevention. (2001). Women and smoking : a report of the Surgeon General. Rockville, MD: Department of Health and Human Services.

Centers for Disease Control and Prevention. (2015a). State Tobacco Activities Tracking and Evaluation (STATE) System. Retrieved October 30, 2015, from http://nccd.cdc.gov/STATESystem/rdPage.aspx?rdReport=OSH_State.CustomReports

Centers for Disease Control and Prevention. (2015b, June 23). Health-Related Quality of Life (HRQOL) Frequently Asked Questions. Retrieved March 4, 2016, from http://www.cdc.gov/hrqol/faqs.htm

Centers for Disease Control and Prevention. (2011). Vital signs: current cigarette smoking among adults aged ≥18 years--United States, 2005-2010. MMWR. Morbidity and Mortality Weekly Report, 60(35), 1207–1212.

Chaloupka, F. J., Pacula, R. L., Farrelly, M. C., Johnston, L. D., & O’Malley, P. M. (1999). Do Higher Cigarette Prices Encourage Youth to Use Marijuana? (Working Paper No. 6939). National Bureau of Economic Research. Retrieved from http://www.nber.org/papers/w6939

Chu, Y.-W. L. (2014a). The effects of medical marijuana laws on illegal marijuana use. Working Paper Series, School of Economics and Finance, Victoria University of Wellington. Retrieved from http://researcharchive.vuw.ac.nz.libproxy.lib.unc.edu/handle/10063/3212

Chu, Y.-W. L. (2014b). The effects of medical marijuana laws on illegal marijuana use. Journal of Health Economics, 38, 43–61. http://doi.org/10.1016/j.jhealeco.2014.07.003

Chu, Y.-W. L. (2015). Do Medical Marijuana Laws Increase Hard-Drug Use? Journal of Law and Economics, 58(2), 481–517. http://doi.org/10.1086/684043

Farrelly, M. C., Bray, J. W., Zarkin, G. A., & Wendling, B. W. (2001). The joint demand for cigarettes and marijuana: evidence from the National Household Surveys on Drug Abuse. Journal of Health Economics, 20(1), 51–68. http://doi.org/10.1016/S0167- 6296(00)00067-9

Ford, E. S., Moriarty, D. G., Zack, M. M., Mokdad, A. H., & Chapman, D. P. (2001). Self- Reported Body Mass Index and Health-Related Quality of Life: Findings from the Behavioral Risk Factor Surveillance System. Obesity Research, 9(1), 21–31. http://doi.org/10.1038/oby.2001.4

Harper, S., Strumpf, E. C., & Kaufman, J. S. (2012). Do Medical Marijuana Laws Increase Marijuana Use? Replication Study and Extension. Annals of Epidemiology, 22(3), 207– 212. http://doi.org/10.1016/j.annepidem.2011.12.002

Hasin, D. S., Saha, T. D., Kerridge, B. T., & et al. (2015). Prevalence of marijuana use disorders in the united states between 2001-2002 and 2012-2013. JAMA Psychiatry, 72(12), 1235– 1242. http://doi.org/10.1001/jamapsychiatry.2015.1858

Hennessy, C. H., Moriarty, D. G., Zack, M. M., Scherr, P. A., & Brackbill, R. (1994). Measuring health-related quality of life for public health surveillance. Public Health Reports, 109(5), 665–672.

Highet, G. (2004). The role of cannabis in supporting young people’s cigarette smoking: a qualitative exploration. Health Education Research, 19(6), 635–643. http://doi.org/10.1093/her/cyg089

Hoffmann, D. E., & Weber, E. (2010). Medical Marijuana and the Law. New England Journal of Medicine, 362(16), 1453–1457. http://doi.org/10.1056/NEJMp1000695

Hopkins, D. P., Razi, S., Leeks, K. D., Priya Kalra, G., Chattopadhyay, S. K., & Soler, R. E. (2010). Smokefree Policies to Reduce Tobacco Use. American Journal of Preventive Medicine, 38(2), S275–S289. http://doi.org/10.1016/j.amepre.2009.10.029

Idler, E. L., & Benyamini, Y. (1997). Self-Rated Health and Mortality: A Review of Twenty- Seven Community Studies. Journal of Health and Social Behavior, 38(1), 21–37. http://doi.org/10.2307/2955359

Jones, J. M. (2015, October 21). In U.S., 58% Back Legal Marijuana Use. Retrieved March 4, 2016, from http://www.gallup.com/poll/186260/back-legal-marijuana.aspx

Kapp, J. M., Jackson-Thompson, J., Petroski, G. F., & Schootman, M. (2009). Reliability of health-related quality-of-life indicators in cancer survivors from a population-based sample, 2005, BRFSS. Public Health, 123(4), 321–325. http://doi.org/10.1016/j.puhe.2008.10.005

Lynne-Landsman, S. D., Livingston, M. D., & Wagenaar, A. C. (2013). Effects of state medical marijuana laws on adolescent marijuana use. American Journal of Public Health, 103(8), 1500–1506. http://doi.org/10.2105/AJPH.2012.301117

Marijuana Policy Project. (2015). State Decriminalization Laws. Retrieved October 29, 2015, from https://www.mpp.org/issues/decriminalization/state-laws-with-alternatives-to- incarceration-for-marijuana-possession/

Mody, R. R., & Smith, M. J. (2006). Smoking Status and Health-related Quality of Life: Findings From the 2001 Behavioral Risk Factor Surveillance System Data. American Journal of Health Promotion, 20(4), 251–258. http://doi.org/10.4278/0890-1171- 20.4.251

Morris, R. G., TenEyck, M., Barnes, J. C., & Kovandzic, T. V. (2014). The Effect of Medical Marijuana Laws on Crime: Evidence from State Panel Data, 1990-2006. PLoS ONE, 9(3), e92816. http://doi.org/10.1371/journal.pone.0092816

National Conference of State Legislatures. (2016, January 25). State Medical Marijuana Laws. Retrieved November 21, 2014, from http://www.ncsl.org/research/health/state-medical- marijuana-laws.aspx

National Institute on Drug Abuse. (2014, December). Monitoring the Future Survey, Overview of Findings 2014. Retrieved June 29, 2015, from http://www.drugabuse.gov/related- topics/trends-statistics/monitoring-future/monitoring-future-survey-overview-findings- 2014

Newschaffer, C. J. (1998). Validation of Behavioral Risk Factor Surveillance System (BRFSS) HRQOL Measures in a Statewide Sample. Atlanta: Centers for Disease Control and Prevention.

O’Connell, T. J., & Bou-Matar, C. B. (2007). Long term marijuana users seeking medical cannabis in California (2001–2007): demographics, social characteristics, patterns of cannabis and other drug use of 4117 applicants. Harm Reduction Journal, 4(1), 16. http://doi.org/10.1186/1477-7517-4-16

Orzechowski, W., & Walker, R. (2015). The Tax Burden on Tobacco, 2014 (No. Volume 49). Arlington, VA: Orzechowski and Walker.

Pacula, R. L., Jacobson, M., & Maksabedian, E. J. (2016). In the weeds: a baseline view of cannabis use among legalizing states and their neighbours. Addiction. http://doi.org/10.1111/add.13282

Patton, G. C., Coffey, C., Carlin, J. B., Sawyer, S. M., & Lynskey, M. (2005). Reverse gateways? Frequent cannabis use as a predictor of tobacco initiation and nicotine dependence. Addiction, 100(10), 1518–1525. http://doi.org/10.1111/j.1360- 0443.2005.01220.x

ProCon. (2015, July 1). 23 Legal Medical Marijuana States and DC. Retrieved August 13, 2015, from http://medicalmarijuana.procon.org/view.resource.php?resourceID=000881

Salomonsen-Sautel, S., Sakai, J. T., Thurstone, C., Corley, R., & Hopfer, C. (2012). Medical Marijuana Use Among Adolescents in Substance Abuse Treatment. Journal of the American Academy of Child & Adolescent Psychiatry, 51(7), 694–702. http://doi.org/10.1016/j.jaac.2012.04.004

Scott, E. M. (2010). Marijuana Decriminalization (No. 2010-R-0204). Connecticut General Assembly Office of Legislative Research. Retrieved from https://www.cga.ct.gov/2010/rpt/2010-R-0204.htm

Simon, S. (2010, November 23). Colorado’s Medical-Pot Rules: ID, Video and a Vast Paper Trail. Wall Street Journal. Retrieved from http://www.wsj.com/articles/SB10001424052748703559504575630760766227660

Substance Abuse and Mental Health Services Administration. (2013). Results from the 2012 National Survey on Drug Use and Health: Summary of National Findings (NSDUH Series H-46 No. HHS Publication No. (SMA) 13-4759). Rockville, MD: Substance Abuse and Mental Health Administration. Retrieved from http://archive.samhsa.gov/data/NSDUH/2012SummNatFindDetTables/NationalFindings/ NSDUHresults2012.htm#ch2.4

Substance Abuse and Mental Health Services Administration. (2014). Results from the 2013 National Survey on Drug Use and Health: Summary of National Findings (NSDUH Series H-48 No. HHS Publication No. (SMA) 14-4863). Rockville, MD: Substance Abuse and Mental Health Administration.

Thurstone, C., Lieberman, S. A., & Schmiege, S. J. (2011). Medical marijuana diversion and associated problems in adolescent substance treatment. Drug and Alcohol Dependence, 118(2–3), 489–492. http://doi.org/10.1016/j.drugalcdep.2011.03.031

Timberlake, D. S., Haberstick, B. C., Hopfer, C. J., Bricker, J., Sakai, J. T., Lessem, J. M., & Hewitt, J. K. (2007). Progression from marijuana use to daily smoking and nicotine dependence in a national sample of U.S. adolescents. Drug and Alcohol Dependence, 88(2-3), 272–281. http://doi.org/10.1016/j.drugalcdep.2006.11.005

Vandrey R, Raber JC, Raber ME, Douglass B, Miller C, & Bonn-Miller MO. (2015). Cannabinoid dose and label accuracy in edible medical cannabis products. JAMA, 313(24), 2491–2493. http://doi.org/10.1001/jama.2015.6613

Wang G, Roosevelt G, & Heard K. (2013). Pediatric marijuana exposures in a medical marijuana state. JAMA Pediatrics, 167(7), 630–633. http://doi.org/10.1001/jamapediatrics.2013.140

Wardarski, J. (2015, August 19). Edible Marijuana is Booming, But These Aren’t Your Father’s Pot Brownies. NBC News. Retrieved from http://www.nbcnews.com/health/health- news/these-are-not-your-fathers-pot-brownies-n411881

Weinstein, J. (2010, October 7). Growing Pains: Entrepreneurs cash in on medical marijuana. Retrieved February 12, 2015, from http://www.steamboattoday.com/news/2010/oct/07/growing-pains-entrepreneurs-cash- medical-marijuana/

Wirfs-Brock, J., Seaton, L., & Sutherland, A. (2010, July 31). Colorado medical marijuana surplus leaks to black market. Daily Camera News. Retrieved from http://www.dailycamera.com/news/ci_15644376

Table 3.1 States that enacted medical marijuana laws from 1993- 2010 Legal In home Required MML law dispensary cultivation registry State in effect opened allowed opened Alaska 1999 1999 1999 California 1996 2004 1996 Colorado 2001 2004 2001 2001 DC 2010 2010 Hawaii 2000 2000 2000 Maine 1999 1999 2009 Michigan 2008 2008 Montana 2004 2004 Nevada 2001 2001 2001 New Jersey 2010 2010 New Mexico 2007 2009 2007 2007 Oregon 1998 1998 2007 Rhode Island 2006 2006 2007 Vermont 2004 2004 2004 Washington 1998 1998 Notes: In Colorado, the first reported dispensary opened in 2004; however a majority of the dispensaries did not open until 2009/10.(Anderson & Rees, 2014; Weinstein, 2010)

Table 3.2 Descriptive statistics of variables, by MML status Full Sample MML not in effect MML in effect Mean/% (s.d.) Mean/% (s.d.) Mean/% (s.d.) Primary Outcome Smoker (%) 0.23 (0.01) 0.24 (0.00) 0.18 (0.01) *** Individual-level Characteristics Age (mean) 39.65 (0.13) 39.66 (0.12) 39.60 (0.29) White (%) 0.70 (0.04) 0.73 (0.02) 0.57 (0.08) ** Black (%) 0.10 (0.01) 0.11 (0.01) 0.04 (0.01) *** Other (%) 0.06 (0.01) 0.05 (0.00) 0.11 (0.01) *** Hispanic (%) 0.14 (0.03) 0.11 (0.02) 0.28 (0.07) ** Female (%) 0.50 (0.00) 0.50 (0.00) 0.49 (0.00) *** Married/In a relationship (%) 0.65 (0.01) 0.65 (0.01) 0.64 (0.01) Less than HS (%) 0.11 (0.01) 0.10 (0.01) 0.15 (0.03) * HS Graduate (%) 0.29 (0.01) 0.30 (0.01) 0.24 (0.01) *** Some College (%) 0.28 (0.00) 0.28 (0.00) 0.28 (0.01) 87 College Graduate (%) 0.32 (0.01) 0.32 (0.01) 0.33 (0.01) State-level Characteristics Marijuana decriminalized (%) 0.34 (0.12) 0.25 (0.09) 0.81 (0.15) *** Unemployment Rate (mean) 5.93 (0.19) 5.73 (0.12) 6.93 (0.22) *** Income per capita (mean) 37,414.76 (804.46) 36,817.18 (892.78) 40,374.80 (624.97) *** Beer tax, cents (mean) 0.96 (0.03) 0.97 (0.03) 0.90 (0.02) * Cigarette state excise tax, cents (mean) 84.86 (8.11) 79.25 (9.47) 112.66 (15.22) * Bar smoking ban (%) 0.24 (0.08) 0.14 (0.04) 0.75 (0.12) *** Health Status Self-rated Healthy (%) 0.88 (0.00) 0.88 (0.00) 0.86 (0.01) * ≥14 Physically Unhealthy Days (%) 0.09 (0.00) 0.08 (0.00) 0.09 (0.00) *** ≥14 Mentally Unhealthy Days (%) 0.10 (0.00) 0.10 (0.00) 0.11 (0.00) ** ≥14 Limited Activity Days (%) 0.06 (0.00) 0.05 (0.00) 0.06 (0.00) *** N 2,722,046 2,222,559 499,487 Percentage of Full Sample 82% 18% *** p<0.01, ** p<0.05, * p<0.1 Notes: All estimates are weighted and adjusted for clustering at the state level. All N's are unweighted.

Table 3.3 LPM estimates of the impact of MML policy dimensions on the probability of current smoking for adults under age 65, 1993-2010 MML Dimensions In Home Patient Prop. Dispensary Cultivation Registry Smoker N A) All Adults Adults -0.014 *** 0.011** -0.020 *** 0.231 2,722,046 (0.003) (0.005) (0.006)

B) Health Status Self-Rated Unhealthy -0.012 *** -0.004 -0.022 * 0.335 369,438 (0.003) (0.010) (0.013) Self-Rated Healthy -0.015 *** 0.014 ** -0.019 *** 0.216 2,352,608 (0.004) (0.006) (0.007)

≥14 Unhealthy Days, -0.010 *** 0.005 -0.011 0.336 279,279 Physical Health (0.004) (0.008) (0.009) <14 Unhealthy Days, -0.014 *** 0.012 ** -0.021 *** 0.221 2,442,767 Physical Health (0.004) (0.005) (0.006)

≥14 Unhealthy Days, -0.037 *** -0.009 -0.028 ** 0.388 305,242 Mental Health (0.003) (0.008) (0.014) <14 Unhealthy Days, -0.011 *** 0.013 ** -0.018 *** 0.212 2,416,804 Mental Health (0.003) (0.005) (0.006)

≥14 Limited Activity Days -0.014 *** 0.010 -0.028 *** 0.378 186,274 (0.005) (0.007) (0.010) <14 Limited Activity Days -0.014 *** 0.012 *** -0.020 *** 0.222 2,535,772 (0.003) (0.004) (0.006)

C) Gender Males -0.014 *** 0.018 ** -0.026 *** 0.251 1,094,643 (0.003) (0.009) (0.009) Females -0.015 *** 0.004 * -0.014 ** 0.210 1,627,403 (0.004) (0.002) (0.006) *** p<0.01; ** p<0.05; * p<0.1. Notes: Covariates include individual age, sex, race/ethnicity, marital status, student status, retirement status, educational attainment, and state-level variables for marijuana decriminalization, unemployment rate, per capita income, state and federal beer tax, state cigarette tax, and smoking bans in bars. All regressions include state fixed effects and state-specific linear time trends. Regressions are weighted using BRFSS "_finalwt." Standard errors, in parentheses, are clustered at the state level. Sample includes adults aged 18-64. Bolded values indicate significant differences in smoking probabilities (at p<0.1) between the healthy and unhealthy statuses. No significant differences in smoking probabilities exist between males and females. Unhealthy is defined as ≥14 unhealthy or limited activity days. Healthy is defined as <14 unhealthy or limited activity days.

Table 3.4 Gender differences among healthy and unhealthy individuals, LPM estimates of the impact of MML policy dimensions on the probability of current smoking MML Dimensions In Home Patient Prop. Dispensary Cultivation Registry Smoker N A) Unhealthy Individuals Self-Rated Unhealthy Males -0.007 0.033 *** - 0.054*** 0.372 139,726 (0.008) (0.009) (0.015) Self -Rated Unhealthy Females -0.027*** -0.039 ** 0.007 0.301 229,712 (0.009) (0.017) (0.021) Physically Unhealthy, Males 0.022 0.047 *** -0.041** 0.363 97,215 (0.014) (0.017) (0.018) Physically Unhealthy, Females -0.036*** -0.031 0.015 0.316 182,064 (0.011) (0.021) (0.015) Mentally Unhealthy, Males -0.019** 0.002 -0.045*** 0.419 93,765 (0.008) (0.013) (0.016) Mentally Unhealthy, Females -0.049*** -0.015 -0.015 0.367 211,477 (0.009) (0.009) (0.018) Limited Activity, Males 0.048*** 0.053 ** -0.053** 0.399 65,736 (0.010) (0.025) (0.021) Limited Activity, Females -0.061*** -0.027 ** -0.008 0.362 120,538 (0.011) (0.011) (0.018) B) Healthy Individuals Self Rated Healthy Males -0.018*** 0.016 * -0.021** 0.235 954,917 (0.004) (0.009) (0.009) Self -Rated Healthy Females -0.012*** 0.011 *** -0.017** 0.197 1,397,691 (0.004) (0.003) (0.007) Physically Healthy, Males -0.017*** 0.016 * -0.024*** 0.242 997,428 (0.004) (0.008) (0.008) Physically Healthy, Females -0.012*** 0.008 *** -0.017*** 0.199 1,445,339 (0.004) (0.002) (0.006) Mentally Healthy, Males -0.014*** 0.019 * -0.023** 0.235 1,000,878 (0.004) (0.010) (0.009) Mentally Healthy, Females -0.009** 0.006 *** -0.012* 0.188 1,415,926 (0.004) (0.002) (0.006) Limited Activity, Males -0.018*** 0.017 ** -0.025 *** 0.243 1,028,907 (0.003) (0.008) (0.009) Limited Activity, Females -0.011*** 0.007 *** -0.015** 0.200 1,506,865 (0.004) (0.002) (0.006) *** p<0.01; ** p<0.05; * p<0.1. Notes: Covariates include individual age, sex, race/ethnicity, marital status, student status, retirement status, educational attainment, and state-level variables for marijuana decriminalization, unemployment rate, per capita income, state and federal beer tax, state cigarette tax, and smoking bans in bars. All regressions include state fixed effects and state-specific linear time trends. Regressions are weighted using BRFSS "_finalwt." Standard errors, in parentheses, are clustered at the state level. Sample includes adults aged 18-64. Bolded values indicate significant differences in smoking probabilities (at p<0.1) between males and females within each health status. Unhealthy is defined as ≥14 unhealthy or limited activity days. Healthy is defined as <14 unhealthy or limited activity days.

CHAPTER 4: THE POLITICS OF PUBLIC HEALTH POLICY: THE CASE OF SUPERVISED INJECTION SERVICES IN ONTARIO

I. Introduction

An evidence-based approach to policymaking, particularly in public health policy, follows a linear and predictable path: a public problem is identified; reliable data are collected and research is conducted; and an optimal solution based on evidence is proposed, adopted, and implemented. Such a model implies that the availability of evidence and research is a sufficient condition for the adoption of appropriate policy interventions (Bernier & Clavier, 2011; Fafard,

2012). Supervised injection facilities are one intervention that has considerable peer-reviewed evidence of public health benefits (Rapid Response Service, 2014).

Supervised injection services (SISs) are legally sanctioned clinics or facilities where injection drug users can consume pre-obtained illicit drugs in a medically supervised environment. Such sites are intended to reduce health and public order problems associated with illicit drug use and also connect drug users to resources and referrals to treatment, housing, and other social supports (Dolan, Kimber, Fry, Fitzgerald, & al, 2000; Rapid Response Service,

2014). Over 90 supervised injection sites exist worldwide, primarily in Europe (Drug Policy

Alliance, 2016). Yet North America has only two supervised injection sites, both in Vancouver,

British Columbia (CBC News, 2016). Even in Canada, despite empirical evidence citing benefits, and support from front-line workers and scientists, a supervised injection service has yet to open in other parts of the country. To understand the barriers to establishing SISs in North

America, this study looks at Ontario to identify the factors that have prevented the adoption of

SISs in this province.

History of Supervised Injection Facilities in Canada

In 2003, Insite opened in Vancouver as North America’s first supervised injection site. In

2012 (the most recent data available), over 9,200 unique individuals visited the site and there were 497 overdose incidents but no overdose deaths have occurred (Schatz & Nougier, 2012;

Vancouver Coastal Health, n.d.). Numerous peer-reviewed studies have found that Insite reduced overdose mortality rates (Marshall, Milloy, Wood, Montaner, & Kerr, 2011); reduced needle sharing (Kerr, Tyndall, Li, Montaner, & Wood, 2005) and publicly discarded needles (Wood,

Tyndall, Montaner, & Kerr, 2006); increased entry into detoxification programs (DeBeck et al.,

2011; Wood, Tyndall, Zhang, Montaner, & Kerr, 2007); did not increase the rate of drug use

(Kerr, 2006; Wood et al., 2006); and was cost effective, with conservative estimates suggesting savings of $14 million over 10 years (Bayoumi & Zaric, 2008).

To operate legally in Canada, Insite required the federal government to grant an exemption from the Controlled Drugs and Substances Act (CDSA). Despite evidence of the public health benefits, the federal government at the time (led by Stephen Harper’s Conservative

Party) refused to extend the exemption past 2008 due to concerns that Insite did not effectively reduce drug use and addiction (goals outside of Insite’s purview). Moreover, the Conservative

Party had made it a priority not to use public money to “fund drug use” (Dooling & Rachlis,

2010). The party was vocal in its view that illicit drug use was unacceptable (Woods, 2005) and was concerned that government-funded SISs would send mixed messages to young people about drug use (Galloway, 2008). The refusal to extend the exemption led to a lengthy court challenge

between Insite and the federal government that eventually reached the Supreme Court of Canada

(SCC).

In a unanimous decision delivered in September 2011, the SCC ordered the federal government to grant Insite an exemption from the CDSA. The basis for the ruling was that Insite offered life-saving services for injection drug users and denying these services constituted a violation of the right to life and security of the person of potential clients (Canada (Attorney

General) v. PHS Community Services Society, 2011). The Supreme Court’s decision laid the groundwork for future supervised injection services to open across Canada. The decision was celebrated by Ontario’s harm reduction proponents who had been working to establish the province’s first supervised injection facility.

Following the decision, researchers from the University of Toronto published a feasibility study exploring whether the cities of Toronto and Ottawa would benefit from supervised consumption sites. The Toronto and Ottawa Supervised Consumption Assessment (TOSCA) study noted that Toronto had the highest number of drug users in Ontario and that Ottawa had the highest new rate of HIV infections among injection drug users in Ontario (Bayoumi et al.,

2012). The TOSCA study recommended three supervised injection sites for Toronto and two sites for Ottawa, and that these sites be integrated into existing organizations that serve drug users (Bayoumi et al., 2012). The TOSCA authors concluded that benefits would arise from averting hepatitis C infections and cost savings associated with preventing disease contraction

(Bayoumi et al., 2012; Enns et al., 2015).

This case study explores why Ontario has not yet opened a SIS, despite legal precedence, a demonstrated need, and considerable empirical evidence indicating several public health and fiscal benefits. In doing so, we build upon literature exploring the role of politics in public health

policymaking (Gollust, Baum, & Jacobson, 2008; Oliver, 2006) and apply it to the case of supervised injection sites and injection drug use issues more broadly, a highly stigmatized public health issue (Des Jarlais, 2000; MacCoun, 2012; Moss, 2000; Olsen & Sharfstein, 2014). By exploring the case of SISs in Ontario, this study considers the clash between science and politics and why empirical evidence does not always result in corresponding policy changes. This study also discusses the limits and possibilities in adopting harm reduction policies especially as they pertain to illicit drug use. While past studies have assessed the prospects of establishing SISs in other parts of North America (Beletsky, Davis, Anderson, & Burris, 2008; Hyshka, Bubela, &

Wild, 2013), the Ontario experience represents an opportunity to explore the actual decision making process surrounding SISs. As advocates across Canada (i.e., Victoria, Montréal) and the

U.S. (i.e., New York City, Ithaca, Boston, Seattle) call for supervised injection facilities

(Anderson, 2016; Bebinger, 2016; CBC News, 2015; Chen, 2016; Drug Policy Alliance, 2015;

Valiante, 2015), the lessons learned from Ontario highlight the challenges that arise when navigating controversial public health recommendations through the policymaking process.

II. Methods

Data for this case study were obtained from semi-structured key informant interviews and document reviews. Key informants were identified through an initial web search of actors involved in drug policy development in Toronto and Ottawa. A snowball approach was used to recruit additional key stakeholders, resulting in a total of 11 participants. Key informants represented municipal and provincial levels of government as well as various policy spheres (i.e., policymakers, agency/program delivery, researchers, grassroots advocates) (Table 4.1).

Interview participants were asked a series of open-ended questions about key events that influenced SIS development in Ontario, public opinion surrounding SISs, responses by the provincial and federal governments, and critical barriers and facilitators to establishing SISs in

Ontario. Interviews were conducted between August 2015 and February 2016 by the first author, recorded, and transcribed verbatim. Each interview lasted approximately 30-45 minutes. The questions primarily focused on events occurring between 2003 to January 2016 to capture changes that resulted from the October 2015 federal election, which was won by Justin

Trudeau’s Liberal Party. Select participants interviewed prior to the election were consulted again to inquire about the impact of the new federal government.

Interview transcripts were analyzed drawing upon Kingdon’s Multiple Streams model

(Kingdon, 2003) and the institutions-ideas-interest (“triple-I”) framework of agenda setting to identify potential factors that may prevent successful policy adoption (Bhatia & Coleman, 2003;

Lavis, 2013; Lavis et al., 2002; Oberlander, 2003; Weller, 1980). Kingdon’s model highlights the key role of policy entrepreneurs in bringing together three streams (i.e., policy solutions, politics, and problems) in a window of opportunity to place an issue prominently on the policy agenda.

The triple-I framework highlights the interaction of political institutions, ideas and ideologies, and interest groups in determining which issues receive greater attention among decision makers.

Taken together, these two frameworks highlight the importance of favourable institutional rules, political climate, and timing in getting policies adopted. Therefore, falling short on any of these factors represent challenges to establishing SISs.

To complement interview data, public documents were obtained and analyzed.

Specifically, relevant internal government documents were obtained through a Freedom of

Information request to the Ontario Ministry of Health and Long-Term Care (MOHLTC). The

transcripts of provincial and federal legislative debates, municipal reports pertaining to SISs, and research reports were also reviewed.

III. Results

Several factors explain why progress towards opening a SIS in Ontario has been slow.

These barriers can be grouped into three categories: challenges related to the interjurisdictional policy context; resistance to SISs from the governing Conservatives; and factors related to provincial politics and timing.

Interjurisdictionality

One of the challenges in establishing a supervised injection site in Canada related to the interjurisdictional nature of injection drug use. Injection drug use engages both federal and provincial jurisdictions and cuts across health and criminal policy domains. The federal government is able to govern drug use through its constitutional jurisdiction over criminal laws while the province has constitutional authority to provide health services for injection drug users via its jurisdiction over health (Canada (Attorney General) v. PHS Community Services Society,

2011). The interjurisdictionality of injection drug use creates challenges to policy adoption by virtue of engaging multiple levels of government who may have divergent interests.

During the study period, the Conservative federal government took actions that made it particularly difficult to establish SISs in Ontario and elsewhere. In 2015, the Conservative government enacted the Respect for Communities Act (Bill C-2), which created 26 requirements that needed to be met in order to receive an exemption from the CDSA. These requirements included, but were not limited to, presenting scientific evidence of the medical benefit of SISs

and obtaining letters of opinion on SISs from the provincial minister of health, minister responsible for public safety, local police force, and community stakeholders (Government of

Canada, 2015). Additionally, upon review of an application for exemption, Bill C-2 did not compel the federal health minister to grant an exemption from the CDSA (Butler & Phillips,

2013). Rather, Bill C-2 used permissive rather than mandatory language, allowing the Minister to exercise his or her discretion in granting exemptions. The passage of Bill C-2 was viewed by stakeholders as a clear signal of the Conservative government’s unwillingness to cooperate in the establishment of SISs:

[The passage of Bill C-2] made it much more difficult for people to do the exemption application. I went to Health Canada and met with the [civil servants] who were in charge of the exemption application and they seemed really very – this is pre passing of the Bill – the people we met with were extremely helpful…. But then, subsequently, with the passage of the bill, that all fell by the wayside. – Ottawa stakeholder

Bill C-2 added significant layers of work and obstacles before SISs could be approved

(Eggertson, 2015). Ultimately, the federal government’s authority to grant exemptions highlights the need for support from the federal level in order to establish SISs in Ontario.

In addition to federal support, cooperation from the provincial government also contributes to the success (or failure) in establishing SISs. Given that health matters are under provincial purview, the operation of SISs would require provincial funding. However, opposition from the provincial government was not considered a critical obstacle that would end progress on

SISs. According to internal MOHLTC documents, should the federal government grant an exemption, the province would not have the authority to prohibit the establishment of SISs

(Ministry of Health and Long-Term Care, 2012a). Nevertheless, the lack of recognition of the benefits of SISs and an unwillingness to provide funding for SISs effectively stalled what could have been more rapid progression in setting up an SIS in Ontario. As noted by two stakeholders:

[A]s health is a provincial responsibility, I think if our Minister [of Health and Long- Term Care] could be very clear that this was something that is going to help Ontario get ahead of the HIV/Hepatitis C epidemic—and it would have to come with funding—I think that would be excellent. I think lack of funding … it’s been a barrier, let’s put it like that. It’s been a barrier. – Ottawa Stakeholder

I think funding is the big one as well. There’s not a lot of extra money for anything. These programs can’t run on nothing. …[T]hey would need staff to supervise the injection, and manage the service, and monitor and counsel and refer; so there’s a budget associated with the implementation of these programs. I think that’s definitely going to be another barrier. – Toronto Stakeholder

While there was consensus among stakeholders that the Ontario government did not necessarily

“shut the door” on supervised injection sites, it also did not take actions to facilitate their establishment in Ontario. Thus, provincial cooperation (or lack thereof) added yet another element to address in the efforts to SIS establishment in Ontario.

As a result of various layers of jurisdictional authority over this issue, and the need for cooperation from multiple stakeholders, establishing an SIS in Ontario required the coordination of several moving parts. According to another stakeholder:

[O]ne of the policy challenges I think that we face here is that you’re trying to get federal, provincial, and local politics also aligned on the controversial political issue. So that’s one of the real challenges in getting something like this established. – Ottawa Stakeholder

Support for SISs, even if implicit, was therefore required at local, provincial, and federal levels of governance, which was not in accord in Ontario during the study period. As illicit drug use intersected both criminal and health domains, establishing SISs required coordination of many actors in a complex policymaking environment.

Conservative Party Resistance to SISs

Another major reason for the lack of SISs in Ontario was the absence of applications for exemptions from agencies or clinics seeking to host a supervised injection facility. Part of the difficulty in submitting applications was related to Bill C-2. Through the creation of multiple

requirements, Bill C-2 in itself was a barrier for interested agencies to move forward with applications to establish SISs in Ontario. Agencies involved in delivering services for drug users noted that the number of requirements to submit an application for an exemption created a significant burden. Meeting the requirements required ample time and resources, which cash- strapped community agencies often did not have to put together an application expediently.

A number of other factors were also responsible for discouraging community agencies from submitting exemption applications. One major deterrent was the federal Conservative government’s well-known opposition to SISs. During their tenure from 2006 to 2015, the

Conservatives challenged the legality of Insite; called SISs a form of “harm addition” (Picard,

2008); framed SISs as “heroin injecting sites” (Parliament of Canada, 2015; Postmedia News,

2015); and removed harm reduction from the National Drug Strategy (Dooling & Rachlis, 2010).

As discussed by several stakeholders, such opposition to harm reduction and SISs set a discouraging tone, indicating to community agencies in Ontario that the timing was not ideal to present a case for additional SISs:

I think the current [Conservative] federal government has not been supportive of harm reduction. And that has created barriers itself. That flows through and so it makes it increasingly difficult to have the local level to implement anything if the federal government doesn’t have it under its umbrella. –Toronto Stakeholder

It’s been frustratingly long to get here, but realistically we needed to change the federal [Conservative] government before anything was going to happen. … [The Dr. Peter Centre in Vancouver] sent [their application] in and it’s been about 18 months that it’s been in there, and they haven’t heard back. And they met all the criteria of the new law [Bill C-2] as well because they’ve been operating for so long, they have all the things they asked for, and they still weren’t getting an answer. So the message was pretty clear: Don’t bother with this [Conservative] government. They were just not interested in supporting this at all. So there really was no point in submitting until now. –Ottawa Stakeholder

I think they [the federal Conservatives] had a chilling effect because of all the opposition. You know, fighting it in the Supreme Court, developing an exemption process that was onerous. Bill C-2, which was very, very specific about the criteria you had to meet for an exemption, and they set the bar very high. – Toronto Stakeholder

Moreover, the Conservative government was often dismissive of research findings (Wood, Kerr,

Tyndall, & Montaner, 2008), citing mixed research results and insufficient evidence to support continued operation of SISs (Galloway, 2008; Parliament of Canada, 2008). Harm reduction proponents, in contrast, interpreted the federal government’s reaction as a refusal to acknowledge empirical evidence of the public health benefits of SISs. As expressed by a Toronto stakeholder,

“Our federal [Conservative] government doesn’t talk about science. It doesn’t talk about facts and research, and in fact has been very open in silencing science and research.” Consequently, harm reduction advocates in Ontario largely felt discouraged from submitting an application exemption while the Conservative Party was in power.

A clear consensus emerged among key informants that a change in the federal government was needed in order for progress to be made in Ontario. In fact, with the election of the Liberal Party, stakeholders expect to see Ontario agencies begin to move forward more assertively with exemption applications. The Liberal Minister of Health recognized drug use as a significant issue and expressed openness to expanding SISs across Canada (Duggan, 2016).

Stakeholders agreed that the change in federal leadership would improve the chances of establishing an SIS in Ontario, even if some local-level opposition remained. As an Ottawa stakeholder noted, “[I]f an oppositional letter from the police or mayor may have been a knockout punch before, it is no longer so.” Illustrating the extent to which Conservative opposition posed a barrier to SIS establishment, a second SISs in Vancouver was granted an exemption from the CDSA within three months of the Liberal government forming office

(Health Canada, 2016).

In addition to federal considerations, incongruent views from police leadership further discouraged applications from SIS proponents. Given the illegality of drug use, Toronto and

Ottawa police preferred abstinence through treatment and enforcement. Thus, creating a space for individuals to consume drugs was contrary to police goals of reducing illegal drug use.

Moreover, police in Ontario favoured professional experience and anecdotal evidence to inform their opinion of SISs (Watson et al., 2012). As a result, police from Ontario’s two largest cities were not convinced SISs would consistently be used, undermining their effectiveness, and would potentially lead to new or worse problems in the community (Watson et al., 2012).

The opposition among police placed a damper on applications particularly among agencies in Ottawa. Due to the unwavering opposition from Ottawa police, proponents were concerned that police could interfere with the operation of any new SISs located in Ottawa. As a result of the lack of support from the federal Conservatives and Ottawa police coupled with opposition from the Ottawa mayor and a lack of leadership from the provincial government, agencies in Ottawa were not encouraged to submit an exemption application.

Among proponents for SISs in Ontario, there was agreement that the Conservative Party leadership did not value harm reduction services. The Conservative Party’s ideological preference for abstinence, and a persistent unwillingness to accept empirical evidence made for a hostile environment for the establishment of SISs in Ontario.

Provincial Politics and the Importance of Timing

Even with the SCC decision and the TOSCA study results, and growing public support for SIS in Ontario (Strike et al., 2014), SISs remained low on the provincial agenda. The lack of funding and public endorsement was a key barrier and greater initiative on the part of the Ontario government was noted as an ingredient that would certainly facilitate forward progress on SISs.

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In response to the TOSCA report, the Minister of Health and Long-Term Care released the following statement: “Experts continue to be divided on the value of the sites. We have no plans to pursue supervised sites at this time” (Ministry of Health and Long-Term Care, 2012b).

Individuals close to the MOHLTC explained that the lack of initiative from the Minister was largely due to an unfavourable political climate at the time. At the time the TOSCA report was released, the provincial Liberal government was under fire for a number of controversial policy decisions that had evolved into public scandals. The governing Liberals had been criticized for a number of controversial decisions, including the eHealth spending scandal (Office of the Auditor

General of Ontario, 2009); issuing promising lucrative renewable energy contracts to a consortium of Korean companies (without detailed economic analysis or the normal due diligence process) (Office of the Auditor General of Ontario, 2011); and moving gas power plants that were seemingly motivated by electoral considerations, resulting in the loss of over $1 billion in public money (Office of the Auditor General of Ontario, 2013a, 2013b). Moreover, the

Liberals held a minority government that was at risk of losing confidence, and did not have sufficient political capital to take on a policy issue as highly contentious as supervised injection.

These issues are highlighted in the following stakeholder quotations:

So there was a risk, a perceived political risk of supporting something like this [SISs] for a government that was already feeling a little bit sensitive about its opportunity for reelection with some of the previous scandals that tend to accumulate for governments that have been in power for a period. There were several big things that meant that they didn’t have a lot of political capital to spend on something like supervised injection services. –Ottawa Stakeholder

It’s [SISs] going to be a controversial public conversation. And governments tend to limit the number of controversies that they’re involved in as much as they can. And they were looking at it thinking they had enough on their plate at that time without adding a new controversial issue to deal with. –Toronto Stakeholder

The Government at the time was dealing with a lot of other pressures. You had a minority government. This was a hot button issue that the opposition could always use against you, because it’s very easy to blow it up in the media and in public opinion. … The

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government at the time was dealing with a lot of other pressures and issues. There had been a couple of other major things blow up reflecting on their judgment and management. So a lot of things it’s a question of timing, right? If you really want to influence policy, you have to think about timing and what else is coming at them, and how important an issue this is going to be. –Toronto Stakeholder

Thus, despite recommendations and support for SISs in Ontario, political pressures from competing policy interests had a considerable impact on the negative outcome of SISs in

Ontario.

IV. Discussion

Using Ontario as an example, this case study informs the possibilities and limits of adopting harm reduction policies related to injection drug use. This study extends the literature exploring the political determinants of health policy to supervised injection services and to harm reduction initiatives more generally. By focusing on why SISs have yet to be established in

Ontario despite evidence of benefits and support among injection drug users and stakeholders, we identify key factors that may help or hinder the establishment of harm reduction services in the North American context.

Political scientists have long noted the diffusion of public policy within and across countries, including in policy realms related to public health (Berry & Berry, 1990; Dolowitz &

Marsh, 2000; Shipan & Volden, 2008; Studlar, 2002; Walker, 1969). Yet in North America, SISs have not taken root anywhere other than British Columbia. Given the challenges faced in transferring Vancouver’s innovation to Ontario, SISs represent a compelling case of policy non- diffusion.

The reasons for the lack of diffusion to Ontario can be attributed to the inherently complex nature of policymaking, particularly with respect to illegal drugs. The interjurisdictional nature of illicit drug use crossing both health and criminal elements necessitates multiple levels

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of government involvement in order for the successful adoption of SISs in any province.

Moreover, opposition from the federal government (a critical actor in granting approval for SISs) and police placed a damper on the willingness of agencies to submit proposals for SISs in

Ontario. Lastly, the political environment at the provincial level was not conducive to shouldering another contentious issue.

Inopportune timing posed a significant challenge in getting SISs established in Ontario.

In the case of Ontario, provincial authorities did not pursue SISs because of the immediate pressure to deal with competing political priorities (in arenas outside of public health, no less).

This illustrates how such policies, particularly contentious harm reduction issues, are not considered in isolation. The success or failure of other seemingly unrelated policies had an impact on the prospects of SISs in Ontario. Thus, given that policymaking happens in a web of decision-making, rather than isolated silos, sound empirical evidence calling for SISs may not be sufficient to move such sensitive issues to the top of the policy agenda. This represents an important lesson for public health advocates: the presence of sound empirical evidence may not be enough; timing and a suitable political environment is critical to pushing a controversial and stigmatized public health program higher on the policy agenda (Kingdon, 2003).

Although illicit drug use continues to be a stigmatized behaviour among the public

(MacCoun, 2012), key informants did not consider this to be the major barrier to adoption.

Rather, key informants noted that public opinion on harm reduction services and drug use gradually shifted towards a willingness to accept SISs if its goal was to provide health related services (Strike et al., 2014). Nevertheless, Conservative opposition to SISs was rooted in a moral opposition to illicit drug use, which was characterized as unacceptable and criminal

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behaviours. These sentiments motivated several policy actions that made the adoption of SISs in

Ontario far more challenging.

One of the limitations of a single case study focused on Ontario is the potential for limited generalizability to other contexts. Advocates in many other North American cities have called for supervised injection facilities. However, the particular policy context (including pre- existing programs available for injection drug users) and the nature of the drug use problem may not be the same in other jurisdictions considering supervised injection services. Furthermore, while the key informants represented stakeholders involved in various aspects of the policymaking process, they were geographically concentrated in southern Ontario, particularly in

Ottawa and Toronto. The experience in smaller Ontario markets, though exposed to the same provincial and federal rules, may differ according to variations in local politics.

Nevertheless, the experience in Ontario provides considerable information on the conditions that would facilitate SIS establishment in a North American context. The Ontario case represents a federal system with multiple layers of governance, which is applicable to other

Canadian cities considering SISs as these cities operate in the same federal-provincial policy context. The Ontario case is also informative for advocates and health policy officials in the

U.S., as state-sanctioned injection drug sites would require cooperation among federal lawmakers in order to operate (Beletsky et al., 2008). Moreover, the impact of political timing transcends national boundaries, and is a valuable lesson for proponents in Canada and the U.S.

The Ontario case thus highlights the barriers to overcome in order for a stigmatized health issue to reach the top of the policy agenda.

As opioid overdoses climb in major metropolitans across North America (Rudd, Aleshire,

Zibbell, & Gladden, 2016), policy interventions to address the problem are in high demand.

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Harm reduction programs such as supervised injection services are one potential policy tool to address this problem. In order to successfully implement such programs dealing with a highly stigmatized issue, policy actors need to be equipped not only with sound empirical evidence of need, feasibility, and cost effectiveness; but also with a solid understanding of the broader political environment in which policymaking occurs. In the political arena, where policy priorities constantly compete with each other, timing is everything.

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REFERENCES

Anderson, R. (2016). Seattle activist plans mobile drug haven to encourage safe use. Los Angeles Times. Seattle. Retrieved from http://www.latimes.com/nation/la-na-heroin-on-wheels- 20160111-story.html

Bayoumi, A., & Zaric, G. S. (2008). The cost-effectiveness of Vancouver’s supervised injection facility. Canadian Medical Association Journal, 179(11), 1143–1151. http://doi.org/10.1503/cmaj.080808

Bayoumi, A., Strike, C., Jairam, J., Watson, T., Enns, E., Kolla, G., … Brandeau, M. (2012). Report of the Toronto and Ottawa Supervised Consumption Assessment Study. Toronto, Ontario: St. Michael’s Hospital and the Dalla Lana School of Public Health, University of Toronto. Retrieved from http://www.toscastudy.ca/TOSCA_Report.html

Bebinger, M. (2016, March 1). Boston’s heroin users will soon get a safer place to be high. NPR.org. Retrieved from http://www.npr.org/sections/health- shots/2016/03/01/468572534/bostons-heroin-users-will-soon-get-a-safer-place-to-be-high

Beletsky, L., Davis, C. S., Anderson, E., & Burris, S. (2008). The Law (and Politics) of Safe Injection Facilities in the United States. Am J Public Health, 98(2), 231–237. http://doi.org/10.2105/AJPH.2006.103747

Bernier, N. F., & Clavier, C. (2011). Public health policy research: making the case for a political science approach. Health Promotion International, 26(1), 109–116. http://doi.org/10.1093/heapro/daq079

Berry, F. S., & Berry, W. D. (1990). State Lottery Adoptions as Policy Innovations: An Event History Analysis. The American Political Science Review, 84(2), 395.

Bhatia, V., & Coleman, W. D. (2003). Ideas and Discourse: Reform and Resistance in the Canadian and German Health Systems. Canadian Journal of Political Science / Revue Canadienne de Science Politique, 36(4), 715–739.

Butler, M., & Phillips, K. (2013). Bill C-2: An Act to amend the Controlled Drugs and Substances Act (No. Publication No. 41-2-C2-E). Ottawa, ON: Parliamentary Information and Research Service.

Canada (Attorney General) v. PHS Community Services Society, 3 SCR 134 (SCC September 30, 2011).

CBC News. (2015, December 28). Supervised injection site needed in Victoria, say drug advocates. CBC. Retrieved from http://www.cbc.ca/news/canada/british- columbia/victoria-drug-overdoses-1.3382665

CBC News. (2016, January 15). Dr. Peter Centre approved to operate second supervised injection site in Canada. Retrieved March 2, 2016, from

106

http://www.cbc.ca/news/canada/british-columbia/dr-peter-centre-approved-to-operate- second-supervised-injection-site-in-canada-1.3345252

Chen, A. (2016, February 24). Ithaca’s Plan To Open A Safe Site For Heroin Users Faces Legal Hurdles. NPR.org. Retrieved from http://www.npr.org/sections/health- shots/2016/02/24/467958912/ithacas-plan-to-open-a-safe-site-for-heroin-users-faces- legal-hurdles

Des Jarlais, D. C. (2000). Research, Politics, and Needle Exchange. American Journal of Public Health, 90(9), 1392–4.

Dolan, K., Kimber, J., Fry, C., Fitzgerald, J., & al, et. (2000). Drug consumption facilities in Europe and the establishment of supervised injecting centres in Australia. Drug and Alcohol Review, 19(3), 337.

Dolowitz, D. P., & Marsh, D. (2000). Learning from Abroad: The Role of Policy Transfer in Contemporary Policy-Making. Governance, 13(1), 5–23. http://doi.org/10.1111/0952- 1895.00121

Dooling, K., & Rachlis, M. (2010). Vancouver’s supervised injection facility challenges Canada’s drug laws. Canadian Medical Association Journal, 182(13), 1440 –1444. http://doi.org/10.1503/cmaj.100032

Drug Policy Alliance. (2015, December 8). Public Health Advocates Call for NYC to Authorize Supervised Injection Facilities to Fight Overdose Crisis | Drug Policy Alliance. Retrieved March 2, 2016, from http://www.drugpolicy.org/news/2015/12/public-health-advocates- call-nyc-authorize-supervised-injection-facilities-fight-overdo

Drug Policy Alliance. (2016, February). Supervised Injection Facilities. Retrieved March 2, 2016, from http://www.drugpolicy.org/sites/default/files/DPA%20Fact%20Sheet_Supervised%20Inj ection%20Facilities%20%28Feb.%202016%29.pdf

Duggan, K. (2016, February 10). Philpott expects more applications for safe injection sites. iPolitics. Ottawa. Retrieved from http://ipolitics.ca/2016/02/10/philpott-expects-more- applications-for-safe-injection-sites/

Eggertson, L. (2015). Legislation jeopardizes safe injection sites. Canadian Medical Association. Journal, 187(8), E225–E226.

Enns, E. A., Zaric, G. S., Strike, C. J., Jairam, J. A., Kolla, G., & Bayoumi, A. (2015). Potential cost-effectiveness of supervised injection facilities in Toronto and Ottawa, Canada. Addiction. http://doi.org/10.1111/add.13195

107

Fafard, P. (2012). Public Health Understandings of Policy and Power: Lessons from INSITE. Journal of Urban Health : Bulletin of the New York Academy of Medicine, 89(6), 905– 914. http://doi.org/10.1007/s11524-012-9698-2

Galloway, G. (2008, May 29). Ottawa wants safe-injection site shut down. The Globe and Mail, p. A1. Ottawa, ON.

Gollust, S. E., Baum, N. M., & Jacobson, P. D. (2008). Politics and Public Health Ethics in Practice: Right and Left Meet Right and Wrong. Journal of Public Health Management and Practice, 14(4), 340–347. http://doi.org/10.1097/01.PHH.0000324561.42039.47

Government of Canada. Respect for Communities Act, SC 2015, Second Session, Forty-first Parliament, c22 (2015). Retrieved from http://laws- lois.justice.gc.ca/eng/annualstatutes/2015_22/

Health Canada. (2016, January 15). Statement from the Minister of Health - Health Canada Authorizes Dr. Peter Centre to Operate Second Supervised Consumption Site in Canada [Statements]. Retrieved February 11, 2016, from http://news.gc.ca/web/article- en.do?nid=1027839&_ga=1.196648108.183764168.1455209604

Hyshka, E., Bubela, T., & Wild, T. C. (2013). Prospects for scaling-up supervised injection facilities in Canada: the role of evidence in legal and political decision-making. Addiction, 108(3), 468–476. http://doi.org/10.1111/add.12064

Kerr, T. (2006). Impact of a medically supervised safer injection facility on community drug use patterns: a before and after study. BMJ, 332(7535), 220–222. http://doi.org/10.1136/bmj.332.7535.220

Kerr, T., Tyndall, M., Li, K., Montaner, J., & Wood, E. (2005). Safer injection facility use and syringe sharing in injection drug users. The Lancet, 366(9482), 316–8.

Kingdon, J. W. (2003). Agendas, alternatives, and public policies (2nd ed.). New York: Longman,.

Lavis, J. (2013). Chapter 2. In J. Church, P.-G. Forest, H. Lazar, & J. Lavis (Eds.), Paradigm freeze : why it is so hard to reform health-care policy in Canada. Montreal: Institute of Intergovernmental Relations: School of Policy Studies, Queen’s University.

Lavis, J., Ross, S. E., Hurley, J. E., Hohenadel, J. M., Stoddart, G. L., Woodward, C. A., & Abelson, J. (2002). Examining the Role of Health Services Research in Public Policymaking. The Milbank Quarterly, 80(1), 125–154. http://doi.org/10.1111/1468- 0009.00005

MacCoun, R. J. (2012). Moral Outrage and Opposition to Harm Reduction. Criminal Law and Philosophy, 7(1), 83–98. http://doi.org/10.1007/s11572-012-9154-0

Marshall, B. D., Milloy, M.-J., Wood, E., Montaner, J. S., & Kerr, T. (2011). Reduction in overdose mortality after the opening of North America’s first medically supervised safer

108

injecting facility: a retrospective population-based study. The Lancet, 377(9775), 1429– 1437. http://doi.org/10.1016/S0140-6736(10)62353-7

Ministry of Health and Long-Term Care. (2012a, April 10). Toronto and Ottawa Supervised Consumption Assessment (TOSCA) Study - Key Messages and Q&A.

Ministry of Health and Long-Term Care. (2012b, April 11). Statement From Ontario’s Health Minister On Supervised Drug Injection Sites Report. Retrieved March 2, 2016, from http://news.ontario.ca/mohltc/en/2012/04/statement-from-ontario-s-health-minister-on- supervised-drug-injection-sites-report.html

Moss, A. R. (2000). Epidemiology and the Politics of Needle Exchange. American Journal of Public Health, 90(9), 1385–7.

Oberlander, J. (2003). The Politics Of Health Reform: Why Do Bad Things Happen To Good Plans? Health Affairs. http://doi.org/10.1377/hlthaff.w3.391

Office of the Auditor General of Ontario. (2009). Ontario’s Electronic Health Records Initiative (Special Report). Toronto, ON: Office of the Auditor General of Ontario. Retrieved from http://www.auditor.on.ca/en/reports_en/ehealth_en.pdf

Office of the Auditor General of Ontario. (2011). 2011 Annual Report (Annual Report). Toronto, ON: Office of the Auditor General of Ontario. Retrieved from http://www.auditor.on.ca/en/reports_en/en11/2011ar_en.pdf

Office of the Auditor General of Ontario. (2013a). Mississauga Power Plant Cancellation Costs (Special Report). Toronto, ON: Office of the Auditor General of Ontario. Retrieved from http://www.auditor.on.ca/en/reports_en/mississaugapower_en.pdf

Office of the Auditor General of Ontario. (2013b). Oakville Power Plant Cancellation Costs (Special Report). Toronto, ON: Office of the Auditor General of Ontario. Retrieved from http://www.auditor.on.ca/en/reports_en/oakville_en.pdf

Oliver, T. R. (2006). The Politics of Public Health Policy. Annual Review of Public Health, 27(1), 195–233. http://doi.org/10.1146/annurev.publhealth.25.101802.123126

Olsen Y, & Sharfstein JM. (2014). Confronting the stigma of opioid use disorder—and its treatment. JAMA, 311(14), 1393–1394. http://doi.org/10.1001/jama.2014.2147

Parliament of Canada. (2008). 39th Parliament, 2nd Session Edited Hansard (Hansard No. Number 101). Ottawa, ON: Parliament of Canada. Retrieved from http://www.parl.gc.ca/HousePublications/Publication.aspx?Language=E&Mode=1&DocI d=3529225#Int-2497812

Parliament of Canada. (2015). 41st Parliament, 2nd Session Edited Hansard (Hansard No. Number 224). Ottawa, ON: Parliament of Canada. Retrieved from http://www.parl.gc.ca/HousePublications/Publication.aspx?Language=E&Mode=1&DocI d=8027414#Int-8734532

109

Picard, A. (2008, August 6). Clement’s Insite attack leaves WHO red-faced; Health Minister’s denunciation occurs at event strongly advocating safe injection. The Globe and Mail, p. A1. Mexico City.

Postmedia News. (2015, October 14). Tory ads in Chinese newspaper accuse Trudeau of wanting to open brothels, make pot accessible to kids. National Post. Retrieved from http://news.nationalpost.com/news/canada/canadian-politics/conservative-ads-in-chinese- language-newspaper-accuse-trudeau-of-wanting-to-open-brothels-drug-injection-sites

Rapid Response Service. (2014). Effectiveness of supervised injection services. Toronto, Canada: Ontario HIV Treatment Network. Retrieved from http://www.ohtn.on.ca/wp- content/uploads/rapid-response/RR83_effectiveness-of-SIS.pdf

Rudd, R. A., Aleshire, N., Zibbell, J. E., & Gladden, R. M. (2016). Increases in Drug and Opioid Overdose Deaths - United States, 2000-2014 (No. 64(50)) (pp. 1378–1382). Atlanta, United States: U.S. Center for Disease Control. Retrieved from http://search.proquest.com/docview/1756550757/abstract/791EE17D726344ABPQ/1

Shipan, C. R., & Volden, C. (2008). The Mechanisms of Policy Diffusion. American Journal of Political Science, 52(4), 840–857. http://doi.org/10.1111/j.1540-5907.2008.00346.x

Strike, C., Jairam, J. A., Kolla, G., Millson, P., Shepherd, S., Fischer, B., … Bayoumi, A. (2014). Increasing public support for supervised injection facilities in Ontario, Canada. Addiction, 109(6), 946–953. http://doi.org/10.1111/add.12506

Studlar, D. (2002). Tobacco control : comparative politics in the United States and Canada. Peterborough, Ont.: Broadview Press.

Valiante, G. (2015, November 15). Advocates of Quebec safe drug injection sites expect Liberal approval. CBC. Retrieved from http://www.cbc.ca/news/canada/montreal/safe-injection- sites-supervised-trudeau-canada-government-quebec-1.3319725

Vancouver Coastal Health. (n.d.). Supervised Injection - User Statistics. Retrieved February 13, 2016, from http://supervisedinjection.vch.ca/research/supporting-research/user-statistics

Walker, J. L. (1969). The Diffusion of Innovations among the American States. The American Political Science Review, 63(3), 880–899. http://doi.org/10.2307/1954434

Watson, T. M., Bayoumi, A., Kolla, G., Penn, R., Fischer, B., Luce, J., & Strike, C. (2012). Police Perceptions of Supervised Consumption Sites (SCSs): A Qualitative Study. Substance Use & Misuse, 47(4), 364–374. http://doi.org/10.3109/10826084.2011.645104

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Weller, G. R. (1980). The Determinants of Canadian Health Policy. Journal of Health Politics, Policy and Law, 5(3), 405–418. http://doi.org/10.1215/03616878-5-3-405

Wood, E., Kerr, T., Tyndall, M. W., & Montaner, J. S. G. (2008). The Canadian government’s treatment of scientific process and evidence: inside the evaluation of North America’s first supervised injecting facility. The International Journal on Drug Policy, 19(3), 220– 225.

Wood, E., Tyndall, M. W., Montaner, J. S., & Kerr, T. (2006). Summary of Findings from the Evaluation of a Pilot Medically Supervised Safer Injecting Facility. Canadian Medical Association Journal, 175(11), 1399–1404. http://doi.org/10.1503/cmaj.060863

Woods, A. (2005, December 4). Harper vows tough drug policy: Tory leader would set mandatory sentences, scrap Liberals’ plan to decriminalize marijuana. Ottawa Citizen, p. A4. Burnaby, BC.

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Table 4.1 Number and type of key informant interviewees Type of Key Informant N Policymaker 2 Agency/Program Delivery 5 Researchers/Academic 2 Grassroots Advocates 2 Total 11

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