Up in Smoke: the Effects of Recreational Marijuana Dispensaries on Regional Crime in Colorado and Washington, 2009-2014

UP IN SMOKE: THE EFFECTS OF RECREATIONAL MARIJUANA DISPENSARIES ON REGIONAL CRIME IN COLORADO AND WASHINGTON, 2009-2014

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A THESIS

Presented to

The Faculty of the Department of Economics and Business

The Colorado College

In Partial Fulfillment of the Requirements of the Degree Bachelor of the Arts

Philip Cieplak

February 2015

UP IN SMOKE: THE EFFECTS OF RECREATIONAL MARIJUANA DISPENSARIES ON REGIONAL CRIME IN COLORADO AND WASHINGTON, 2009-2014

Philip Cieplak

February 2015

Economics

Abstract

This paper examines whether the legalization of marijuana has contributed to crime rates around Colorado and Washington. Specifically, the primary objective of this paper is to analyze incident based crime rates in a 1,400-ft radius around each dispensary before and after legalization. I collected incident-based crime data from cities across Colorado and Washington from January 2009 to November 2014. For the control variables, I primarily used census block groups around each dispensary and employment data collected from the Bureau of Labor Statistics (BLS). After sorting each variable into sub-categories, I am able to test my hypothesis at different levels across the dataset. Aggregate data analysis supports the hypothesis that legalizing recreational marijuana would have no statistically significant effect on crime in Colorado and Washington. Categorizing my results suggest crimes such as Burglary and Robbery increased by up to 111%, while Vandalism and ‘All Other Crimes’ decreased by up to 130% since legalization.

KEYWORDS: (Recreational Marijuana, Crime, Dispensary, Colorado, Washington)

ON MY HONOR, I HAVE NEITHER GIVEN NOR RECEIVED UNAUTHORIZED AID ON THIS THESIS

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Contents I. INTRODUCTION AND MOTIVATION ...... 1 II. LITERATURE REVIEW ...... 5 INCIDENCE OF MARIJUANA CONSUMPTION ...... 5 DRIVING WHILE UNDER THE INFLUENCE OF MARIJUANA ...... 6 MARIJUANA’S EFFECTS ON THE BODY ...... 7 MARIJUANA AS A GATEWAY DRUG ...... 8 CAUSES OF CRIME ...... 8 1) Retail Locations ...... 9 III. RELEVANT DATA: ...... 10 CONTROLS: ...... 12 IV. THEORY ...... 14 METHODOLOGY: ...... 14 ECONOMETRIC EQUATION: ...... 15 V. RESULTS: ...... 17 HYPOTHESIS 1: ...... 17 Table 5.1: Change in crime as a result of legalization ...... 18 Table 5.2: Change in crime as a result of legalization ...... 20 HYPOTHESIS 2: ...... 21 SHORTCOMINGS: ...... 21 VI. CONCLUSIONS: ...... 23 VII. REFERENCES: ...... 25 APPENDIX: ...... 27 Table 7.1: Crime Categorizations ...... 27 Figure 7.2: Violent and Non-Violent Crimes ...... 28 Table 7.3: Violent and Non-Violent Crimes Table ...... 29 Table 7.4: Summary Table ...... 30 Table 7.5: Crimes by Zone ...... 31 Figure 7.6: Crimes by Year ...... 32

ACKNOWLEDGEMENTS

I would like to thank Dr. Kevin Rask for his data analysis expertise. I thank Mr. Van Skilling for his donation making the data collection possible. I want to thank the crime analysis units of Bellingham, Boulder, Denver, Fort Collins, Longview and Spokane for their support in data acquisition. I thank Madison Sink for her assistance using GIS.

I would also like to thank Dr. Dan Johnson for not only balancing being Chair of the Economics Department, my Thesis Advisor and my Academic Advisor; but also for helping me become a life-long learner along the way.

I. Introduction and Motivation

Colorado and Washington voted to conduct statewide social experiments: legalizing recreational marijuana, coming into effect in 2015. In November 2012,

Colorado voters passed Amendment 64 which legalized marijuana for recreational purposes for anyone over 21. The amendment allowed for commercial operations such as licensed marijuana retail stores, manufacturing of edibles and mass cultivation operations; as well as personal use including legally owning up six plants for personal consumption, gifting up to one ounce to another adult and possessing up to one ounce. Washington's Initiative 502 has essentially the same provisions. Under these new laws, marijuana would be treated similarly to alcohol in that marijuana cannot be consumed in public and cannot be distributed without a permit. In addition, individuals driving under the influence are subject to a DUI (FCGov 2012).

Legalizing recreational use has the potential to generate significant tax revenues accruing primarily to public health care, substance-abuse clinics, and education, and can create further taxation opportunities as a new profitable industry is created. As of the end of 2014, Colorado and Washington combined tax revenue is about $75 million while the marijuana industry is projected to grow to become a $35 billion dollar industry by 2020 (Johnson 2015).

Because of the risk entailed with legalized marijuana, the industry is under heavy scrutiny. Workers in every grower and dispensary are under constant video surveillance while each plant is recorded at each stage of the growth process.

However even under these conditions, laws under this amendment can be hard to

enforce. There is no reliable THC Breathalyzer-equivalent on the market so it is difficult to track driving while under the influence. Also, there is no effective way to enforce the rule limiting adults to up to six plants in their home, which makes controlling the supply of legal marijuana difficult. With this growing supply in one state, trafficking marijuana across borders will become more and more of a problem.

Another issue with current legalization is that since marijuana distribution is not nationally legal, banks cannot hold a dispensary’s money. In response, each dispensary only accepts cash and every person visiting the dispensary must have cash in hand. (Parker 2014). Criminals are attracted to cash transactions and dispensaries have responded by instituting heavy security.

Marijuana consumption has always been a controversial issue primarily due to different perceptions of its cognitive and physiological effects. Even today, there are still disagreements in the medical world over its effects on the body. Research has yet to show that recreational use of marijuana has harmful consequences but consistent, long-term use has ambiguous effects that the medical field is just now discovering.

Even so, to date no one has ever died from a THC overdose (DrugAbuse 2013).

Marijuana legalization bills in Colorado mandated that a significant portion of marijuana sales go to taxes, with a focus on schools. These figures take into account revenue as well as licensing fees with a portion going to local governments. As of

August of this year, recreational marijuana sales have surpassed those of medical marijuana, and continuing to climb each month. Washington had seen similar effects, only opening its doors on July 9th, has made $12 million in revenue and is collecting

$3 million in tax revenue (Reilly 2013). The benefits are obvious with higher state

revenue from what would have gone to black markets. The legalization of marijuana should also reduce policing costs because law enforcement officers no longer have to focus on the most commonly used illicit drug. If states continue to legalize recreational use, several—not all—of the aforementioned problems could be solved: banks could see the legitimacy of the marijuana industry and open marijuana electronic bank accounts; government agencies could develop a national THC

Breathalyzer; and trafficking would be far less of an issue.

Like any social experiment, legalizing a Federal Schedule I drug (the DEA ranks marijuana on par with heroin and LSD) needs to have checks to ensure that this new liberty will not have higher costs to society than its added benefits. As Colorado and Washington progress through their experiment, they will continue to modify their rules. Dispensaries have had to reduce edible production because of their potency. An unthreatening THC-laced cookie can contain as much THC as an ounce of marijuana which can cause panic attacks in some individuals. Since marijuana dispensaries are now a relatively common sight in Colorado and Washington, it is important to find any trends in crime and any other societal detriments that these dispensaries may create. Legislation came into practice this year and there has been little to no legitimate research on recreational marijuana dispensaries and their correlation with crime rates.

After reading extensively about this topic, I see two prevailing hypotheses on how marijuana will affect crime rates: It will either (1) have no effect on crime rates because marijuana has sedative effects and has not been shown to cause criminal tendencies and (2) marijuana is an untested drug that if legalized will cause unknown

dangers to society, especially with its popularity among youth. This paper will investigate any crime trends that legalization may have inadvertently created.

II. Literature Review

Incidence of Marijuana Consumption

Historically marijuana consumption declined from the late 90s until the mid-

2000s (DrugAbuse 2013). Research indicates that individuals, teens in particular, are seeing marijuana as less dangerous and are therefore more prone to consumption.

Over time, the public view has changed enough to legalize medicinal marijuana across several states and now Colorado and Washington have voted to legalize recreational consumption.

Marijuana is the most common illicit drug used today. In 2013, 12% of people ages 12 and above have reported use, with particularly high rates among young adults (Baler 2014). Medical Marijuana Laws and Teen Marijuana Use (Anderson,

Hansen, Rees 2012) uses data from national and state Youth Risk Behavior Surveys

(YRBS), the National Longitudinal Survey of Youth 1997 and the Treatment Episode

Data Set, and finds no correlation between legalization and teen marijuana consumption. Similar results found in the paper The Effect of Medical Marijuana Laws on Crime: Evidence from State Panel Data, used crime statistics obtained by the

Federal Bureau of Investigation's Uniform Crime Reporting (UCR) Program. The authors find no correlation between increased crime and medicinal marijuana legality (Morris RG, TenEyck M, Barnes JC, Kovandzic TV). In fact, their data suggest a slight decrease in criminality in states where medical marijuana is legal.

Anderson, Hansen, and Reese show that the probability of teen marijuana use within 30 days of medicinal marijuana legalization actually decreases by about 5%

with a 3.5% decrease in its frequent use using an unweighted OLS model. Within a

95% confidence interval, their estimates suggest that the impact of legalizing medical marijuana on the probability of marijuana use in the past 30 days is no larger than a

0.8 percentage point change. They also found that the rate of marijuana consumption between genders was indistinguishable from zero. Regardless of age, frequent use also declined after legalization of medical marijuana.

Looking at what has already been written about the recreational marijuana, The

Rocky Mountain High Intensity Drug Trafficking Area released a paper in August

2014 entitled The Legalization of Marijuana in Colorado: The Impact. Although the paper voices real concerns about potential societal costs such as increased trafficking across state borders and increased use among teens and young adults, there are obvious anti-legalization political motivations. It collects data from irrelevant studies that combine DUI data with marijuana and alcohol and includes anecdotal examples as ‘Findings’ (RMHIDTA 2014).

Driving While Under the Influence of Marijuana

According to the National Highway and Traffic Administration, marijuana consumption can both damage and benefit driving ability. On one hand there is decreased car handling performance, impaired time and distance estimation, and impaired sustained vigilance; while on the other, there can be increased reaction times with some drivers improving driving performance for brief periods by overcompensating for self-perceived impairment (DrugAbuse.org 2013). However, a

paper written by the Journal of Safety Research suggested that there was a positive correlation between frequency of marijuana consumption and teen car accidents

(Hingson 1982). Another issue with driving under the influence is that unlike alcohol, again there is no standardized Breathalyzer. At this point, arrest is under the policeman’s discretion.

Marijuana’s Effects on the Body

Short-term effects of THC (the active ingredient in marijuana products) can include euphoria, lack of concentration, increased appetite, distorted senses, drowsiness, and mood changes such as panic reactions and paranoia. Increased doses intensify these effects. Marijuana is classified as a hallucinogen but has depressive qualities, generally sedating its users. The act of smoking anything increases risk of lung cancer but THC in infrequent, recreational doses have yet to show health maladies (DrugAbuse.org 2013).

Long-term marijuana use and its effect on the brain are unclear due to differing research methodologies used over time. Recent research conducted by

Proceedings of the National Academy of Sciences indicates that long-term use decreases gray matter (regions of the brain that are involved in muscle control, sensory perception and memory). They also found that these changes depend on age

(effects are more severe in teens) and duration of use (Fibley 2014). Animal testing indicates chronic THC consumption leads to a physical dependence and withdrawal symptoms include irritability, hyperactivity and increased dreaming (DrugAbuse.org

2013). 7

Marijuana as a Gateway drug

Research indicates that marijuana is a gateway drug but there are disagreements over severity and impact. Crost and Guerrero (2012)’s paper found that marijuana consumption declines after turning 21, suggesting that alcohol and marijuana are substitutes among young adults (Crost and Gruerrero 2012). Anderson, Hansen, &

Rees’s research agrees with Crost and Guerrero’s findings, but add that they found no evidence that medical legalization increases use among teens. Rosalie Pacula finds the opposite findings in her paper (Ludwig 2014). Her research suggests that increases in beer prices also reduce the demand for marijuana, indicating that marijuana and alcohol are complementary. Further, she suggests that alcohol and tobacco consumption leads to increased likelihood of marijuana consumption (Pacula 1998).

Additional research finds evidence that marijuana users are more likely to develop drug habits at a future age, supporting the idea that marijuana can be a gateway drug

(Saffer and Chaloupka 1999; Williams et al. 2004; Yörük and Yörük 2011).

Causes of Crime

Looking at the current research on reported criminality, the most obvious factors associated with higher crime rates are the availability of drugs such as cocaine in low- income areas and race Reefer Madness: Broken Windows Policing and

Misdemeanor Marijuana Arrests in New York City, 1989-2000 found that African

Americans and Hispanics were 2.66 and 1.85 times more likely to be convicted of marijuana use (and received 4 and 3 times more jail time, respectively) in New York

than their white counterparts (Harcourt, Bernard, Jens 2007). They also cite that most criminologists believe that the correlation between marijuana’s psychopharmacological effects and criminal or violent activity is much lower than with most other drugs, including alcohol and crack cocaine. Markowitz supports this with his research on cocaine, finding that low cocaine prices have been shown to increase the incidence of violent crime in a given area. This was partially due to the stimulant nature of cocaine (Markowitz 2000). Since marijuana has sedative properties, it may have a different effect on user’s crime ambitions.

Criminality, Social Cohesion and Economic Performance examines the effects of crime on society and finds that higher wealth is associated with proportionally higher levels of property crime and drug-related offenses (Entorf & Spengler, 2000). They also find that regional GDP growth has a negative correlation on criminality, meaning that with lower levels of income, people will be more driven to commit crime. After doing a series of regressions, they find that the largest contributors of robbery are long-term unemployment, youth unemployment, and slow real GDP growth. Using these as controls will be essential in my own research.

1) Retail Locations

Looking at the reasons why a certain marijuana dispensary owner would select a given location, I look at current retail location research. Formulating Retail Location

Strategy in a Changing Environment (Craig & Gosh, 1983) models successful strategies in finding the best locations for retailers. It goes through site desirability, a criterion for selecting among alternative properties, and a heuristic to facilitate computational

procedure. With limited cities that issue dispensary licenses, dispensaries are forced to open in areas they may not have previously considered.

Craig and Gosh make their model based off of competitive equilibrium. They look at basing the ideal location on what is feasible, as opposed to prior literature stating that retailers can be placed at any point in a region. Craig and Gosh suggest having to adapt to not only today’s market but also for the market of the future. The retailer has to make a decision of where to put all future locations simultaneously, rather than using a sequential process, while still taking into account factors affecting profitability. The reason for this is that the retailer needs to already know the profitability of the market (ie locating the stores closest to customer origin) and plan accordingly

Gentrification may also decrease crime around a dispensary. With the growth of

“Marijuana Tourism”, people from all over the country are sending an influx of wealth into the part of the city where the dispensary is located. Shifting wealth to an area has been shown to decrease crime rates over the long-run (Kolko 2007).

III. Relevant Data:

a) Crime Data. Incident based crime data comes from each crime analysis unit of

the following six cities around Colorado and Washington: Bellingham,

Washington; Boulder, Colorado; Denver, Colorado; Fort Collins, Colorado;

Longview, Washington; and Spokane, Washington. To compensate for the

relatively short time frame, I included these six cities to reduce error. Not

every city in Colorado and Washington distributes retail recreational

marijuana licensees so my selection was more limited. Since Washington cities

were comparatively less populated and opened their dispensaries later than

that of Colorado, the Washington dataset is smaller.

To control for time, crime data spans back to pre-legalization January 2009

(with the exception of Spokane, which started in 2011) and stops after

dispensaries opened their doors. My test period in Colorado beings in January

2014 and progresses through post-legalization, November 2014. Washington

permitted dispensaries to open on July 7th so my test period spans from this

date to November 2014. My test data consists of only crimes that happened

within 1,400-ft of each dispensary. After combining the data, my dataset

included approximately 300,000 incidents of crime. When further condensed

to include crime within the 1,400-ft radius around each dispensary, my dataset

totaled approximately 40,000 crime incidents. I then collapsed the data so it

can be organized by number of crimes into 10,999 rows.

b) Dispensary Addresses. I used WeedMaps.com to collect the addresses of each

dispensary around Colorado and Washington. Despite its name, it is an

updated online database that gives the addresses and details about every

recreational and medicinal marijuana dispensary in the country (Kincaid

2014). I organize the distances of dispensaries and crimes that happen around

them into 200 foot zones up to 1,400 feet radii around each dispensary.

Controls:

c) Population Demographics Estimates. I collected the following monthly block

data from the census, focused around each dispensary: estimated population,

estimated population density, estimated median age, estimated alcoholic

consumption, estimated tobacco consumption, estimated white population,

estimated percentage of population that have a high school education,

estimated percent population between 15 and 34, estimated percent of houses

that are vacant.

The literature indicates that higher populations and population density

increase crime rates simply because criminals are within closer proximity to

others. Alcohol and tobacco can be complements to marijuana so this should

serve as a control for that variable.

Using block demographics should give a more accurate control than using zip

code or city based data. Monthly data also increases accuracy of these controls

instead of using annualized reports.

a. The yearly data is factored in from the crime dates and controls for any

annual crime spikes within the last four years.

b. Each city has differing cultures and views of consuming marijuana and

for this reason it can be difficult to attribute marijuana consumption to

any changes in crime. In order to counter this, I have included a variety

of cities to control for these cultural differences.

d) Labor Statistics and Income Estimates. I used monthly block data on estimated

median income and estimated unemployment rate available from the Bureau

of Labor Statistics (BLS). According to the literature, these data should control

for some of the largest contributors to crime.

IV.Theory

Methodology:

Since it is impossible to track who consumes recreational marijuana and where they go after they consume it, I have broken the locations of my data into 200- foot concentric zones around each dispensary. In this way, I can control for countless variables contributing to delinquency. This methodology will account for essentially unlimited marijuana supply in a given area including people who have consumed the drug close-by and are coming back for more; if these people have marijuana-induced criminal tendencies (as seen in cocaine and heroin); and since low income is a strong indicator of crime, if these visitors are spending all of their money on marijuana products. Also, it accounts for criminals attracted to the transactions being in all cash.

I ran regressions on my data using the following variables: (1) before and after recreational legality, (2) type of crime, (3) year, (4) violent versus non-violent crime, and (5) 200-foot zones around each dispensary. These regressions include all of my aforementioned demographic controls. I applied a model that is a combination of a model constructed in Ludwig’s research on Concealed-Gun Laws and Violent crime

(Ludwig 1998) and another panel-based model in Morris, TenEyck, Barnes, and

Kovandzic’s model in a medical marijuana research paper (Morris, TenEyck, Barnes,

Kovandzic 2014).

Econometric Equation:

When I use the crime incidence data, my econometric specification is as follows:

Δ Crime (ε)=β0+ Arson_crime0+Assault_crime0+…Weapon_Violation0+ Δ

Controls ε + error ε

β0: Before Dummy variable

Crime Type Dummy: Types of crime Dummy Variables: Arson, Burglary, Domestic

Violence, Drug Crime, Forgery, Fraud, Harassment, Homicide, Malicious Mischief,

Obstructing Justice, Sex Crimes, Stolen Vehicle, Larceny, Unlawful imprisonment,

Trespassing, Vehicular crimes, Vandalism, Weapon Violation, and All other crimes.

Controls: Population Demographics that potentially impact crime rates: type of crime, 200-feet concentric zones around each dispensary, estimated population, estimated population density, estimated median age, estimated median income, estimated alcoholic consumption, estimated tobacco consumption, estimated white population, estimated percentage of population that have a high school education, estimated percent population between 15 and 34, estimated percent of houses that are vacant, estimated unemployment rate, and the year of occurrence.

Error ε: Time demeaned, crime specific error term

I use fixed effects on the assumption that criminality is not random over time and panel data to control for variables (ie cultural differences) that I cannot directly observe, avoiding unobserved heterogeneity. Under this model, the unit of observation is the specific crime observation. My dependent variable is the number of crimes that occurred and my explanatory variable is the before/after legalization crime dummy variable. The rest of my controls are: type of crime, 200-foot concentric zones around each dispensary, estimated population, estimated population density, estimated median age, estimated median income, estimated alcoholic consumption, estimated tobacco consumption, estimated white population, estimated percentage of population that have a high school education, estimated percent population between

15 and 34, estimated percent of houses that are vacant, estimated unemployment rate, and the year of occurrence. The standard error is clustered by city to avoid non- independence of data at that level.

The fixed effect model is ideal for my analysis however, like any other econometric analysis, it has limitations. This model handles unobserved factors, however, it cannot account for time-variant variables across locations. Also, I am already using concentric rings as my geographic dummy variable and I can’t control for state in this model, even though I do have three of my six cities in both states. Nonetheless, the model controls for most of the known and unknown contributors of criminal activity.

V. Results:

Hypothesis 1:

Marijuana use should not affect crime because of its sedative effects on the body.

My analysis indicates that the legalization of marijuana has had no statistically significant effect on crime. Table 4.1 shows consolidated number of crimes before and after legalization variable (bolded and italicized) controlled by the demographic and income variables, revealing a p-value of .292. This value indicates that we can only be

69.8% sure that the coefficient will be accurate in describing the relationship between criminal activity and the legalization of marijuana. Most of my controls are effective in reducing bias in my results, as indicated by the low p-values. To make any conclusive statements, I would need to be at least 90% certain that my analysis revealed a correlation.

There could be a variety of reasons for this result including (1) marijuana does not induce aggressive tendencies or (2) users obey the law and consume the drug in the safety of their homes.

Table 5.1: Change in crime as a result of legalization

VARIABLE Coef. Std. Error t-value p-value 95% Confidence Interval

BEFORE/AFTER LEGALIZATION -.2702081 0.256351 -1.05 0.292 -.772703 .2322868

ESTIMATED POPULATION .0000337 6.45E-05 0.52 0.601 -.0000927 .0001601

ESTIMATED POPULATION DENSITY -.0000746 1.61E-05 -4.63 0*** -.0001061 -.000043

ESTIMATED MEDIAN AGE .0318205 0.017562 1.81 0.07* -.0026037 .0662447

ESTIMATED MEDIAN INCOME .0000159 8.25E-06 1.93 0.054* -2.89e-07 .0000321

ESTIMATED ALCOHOLIC CONSUMPTION -.0020567 0.001769 -1.16 0.245 -.0055235 .0014101

ESTIMATED TOBACCO CONSUMPTION .0032605 0.002772 1.18 0.24 -.0021734 .0086944

ESTIMATED WHITE POPULATION -.0039754 0.005399 -0.74 0.462 -.0145592 .0066085

ESTIMATED PERCENTAGE OF .0049925 0.001166 4.28 0*** .0027063 .0072786 POPULATION THAT HAVE A HIGH SCHOOL EDUCATION ESTIMATED PERCENT POPULATION .0479636 0.016666 2.88 0.004*** .0152951 .0806322 BETWEEN 15 AND 34 ESTIMATED PERCENT OF HOUSES THAT -.0431885 0.016091 -2.68 0.007*** -.0747305 -0.01165 ARE VACANT ESTIMATED UNEMPLOYMENT RATE -.0060555 0.008292 -0.73 0.465 -.0223099 .0101988

YEAR .2310386 0.105899 2.18 0.029** .0234567 .4386205

Note: *90% Confidence, **95% Confidence, ***99% Confidence

To further analyze my results, I have separated crimes into categories and regressed 2014 data against all previous years, as shown in Table 4.2. On the aggregate, there was no general trend, but on a selected scale, the data show that rates of burglary, malicious mischief and robbery increased while Vandalism and All

Other Crimes (see Table 7.1) declined.

These changes are likely attributed to cash-carrying marijuana consumers attracting criminals in the area. Table 7.5 would support this theory because most of the crimes occur within 1,000 to 1,200 feet from the dispensaries, suggesting that criminals prey on unsuspecting victims outside of the safety of the dispensaries. This

could be why there was a 66% increase in robbery. Likewise with a 98% confidence, my data suggest that burglary has increased by 53%. The literature suggested that when areas have sources of income [a dispensary], crime begins to decline over time.

If the marijuana industry continues at its current growth rate, it should bring more wealth into the area and reduce crime in the long run. In the sort-run, however, surrounding businesses may benefit from the added traffic from the dispensaries and become more vulnerable to burglary. Malicious Mischief increased 111% perhaps due to inhibited forward thinking abilities associated with THC consumption. Stolen

Vehicles increase at a similar rate as Malicious Mischief (Legal-Dictionary defined as: reckless destruction of property and the willful perpetration of injury to its owner), perhaps for the same reason. Negatively correlated crimes such as Vandalism and All

Other Crimes variables could be related to the sedative properties of marijuana consumption.

Table 5.2: Change in crime as a result of legalization

VARIABLE Coef. Std. Error t-value p-value 95% Confidence Interval Arson 0.1688 0.146972 1.15 0.256 -0.12594 0.463631

Assault 0.63962 0.508891 1.26 0.209 -0.35931 1.638545 Burglary 0.5347 0.224437 2.38 0.017** 0.094352 0.975096

Domestic Violence 0.1165 0.234848 0.5 0.62 -0.34537 0.578311

Drug crime -2.05879 1.315549 -1.56 0.118 -4.64136 0.523787 Forgery 0.7482 1.387718 0.54 0.59 -1.99118 3.48758 Fraud -0.0597 0.335471 -0.18 0.859 -0.71913 0.599735 Harassment 0.148243 0.204113 0.73 0.468 -0.25339 0.54988 Homicide ......

Larceny 1.069086 1.09839 0.97 0.331 -1.08528 3.223449 Malicious Mischief 1.1141 0.34293 3.25 0.001*** 0.44119 1.787038

Obstructing Justice 0.061532 0.301261 0.2 0.838 -0.5339 0.656962 Robbery 0.664625 0.199667 3.33 0.001*** 0.272274 1.056976 Sex Crime 1.9169 2.09836 0.91 0.362 -2.21291 6.046748

Stolen Vehicle 1.1893 0.211525 5.62 0*** 0.774158 1.604504

Unlawful Imprisonment 0.1449 0.131617 1.1 0.284 -0.1296 0.419498

Trespassing -1.32195 0.91547 -1.44 0.15 -3.12227 0.478373 Vandalism -1.247 0.424612 -2.94 0.003*** -2.08129 -0.413

Vehicular Crimes -0.113 0.380346 -0.3 0.766 -0.86039 0.634321

Violation of Court Order 0.1971 0.226811 0.87 0.386 -0.24989 0.644021

Weapon Violation 0.4531 0.198283 2.28 0.023** 0.062556 0.843547

All Other Crimes -6.79207 3.819216 -1.78 0.076* -14.3106 0.726427 Note: *90% Confidence, **95% Confidence, ***99% Confidence

Hypothesis 2:

Marijuana is an untested drug that if legalized will cause unknown dangers to society, especially with its popularity among youth.

Notwithstanding my results, this hypothesis also has some legitimacy. My data only includes one year and marijuana’s long-term effects on criminality may have not yet fully appeared. Table 4.2 shows the increases in crimes such as Burglary and

Robbery—an unintended side-effect of marijuana legalization. Figure 4.3 also shows a spike in Sex Crimes which, if it continues, could be alarming. Otherwise, if factored into the rest of my data, it is not statistically significant.

Shortcomings:

Because Colorado and Washington instituted Recreational Marijuana so recently, there will naturally be shortcomings to my research. Primarily, the largest drawback of my research is that my test period only consists of crime data for eleven months. I controlled for current crime trends by having four years of control crime rates (with the exception of Spokane, Washington where I have two years), but I would need more forward looking years to make more concrete correlations. Future marijuana researchers should use a similar research method and include more data.

Hopefully as more and more cities adopt recreational marijuana, crime analysis units will keep more standardized record systems that economists can use. It was time- consuming and difficult to manually ensure that each city’s crimes used the same data

format (ie addresses instead of coordinates and vice versa for the locations of their crimes) as well as the fact that it introduced the possibility of human error.

Another issue I encountered when combining my dataset was the inability to run a large regression for a decrease in drug crimes simply because marijuana was legalized, since each city had a different way of accounting for drug crimes. Since data are not available on marijuana purchasers and purchase locations, I could not use a larger dataset that may have yielded a more accurate model of how marijuana affects crime rates. Nonetheless, collecting crime data around each dispensary is still one of the best ways to account for trends in criminal trends following legalization because each dispensary is the source of the drug in question.

VI.Conclusions:

The lack of significant changes in criminality indicates that marijuana is not a violent drug. Importantly, the data may also show that legalization primarily appeals to individuals who previously purchased marijuana through illegal channels. A benefit of legalization is that instead of funneling revenue to the black market, tax revenue can be shifted towards programs that benefit society.

In the future, policy-makers could focus on transitioning consumers away from medicinal consumption to recreational use as a means of increasing tax revenue

(recreational marijuana is taxed at a higher rate). The results of this study indicate that neither medicinal nor recreational marijuana have much effect on criminality.

One way to increase tax revenue would be to make it more difficult to get a medical card. Medical card standards have not yet adapted to the new recreational system; medical cards are easily obtained by simply claiming short-sightedness or a history of panic-attacks. Treating marijuana more like an over-the-counter drug would result in far less marijuana arbitrage between medical to recreational users (Parker 2013).

Although this research suggests that there is no correlation between marijuana consumption and changes in criminal behavior, further research in this topic is essential to discover how marijuana changes societal behaviors—especially as states continue to legalize the drug. Since I completed this study, Oregon and Alaska both legalized recreational consumption of marijuana products.

My primary concern is that since marijuana may have the potential to be a gateway drug, we have yet to see true effects on criminality. This study only includes

11 months of data. Since research suggests that marijuana and alcohol can be substitutes, will there be lower rates of alcohol poisoning and alcohol induced delinquency in states where recreational marijuana is legal? This question will be particularly important if marijuana use increases among teens. On the other hand, will marijuana legalization result in higher rates of car accidents and/or increased use of other drugs? Only time and further research will tell.

VII. References: Anderson, Hansen, & Rees. (2012, January 1). Medical Marijuana Laws and Teen Marijuana Use. Retrieved February 11, 2015, from http://depts.washington.edu/phenom/docs/Anderson_Hansen_Rees_2012.pdf

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Appendix:

Table 7.1: Crime Categorizations

When I received the raw crime data, I organized them into categories to make them easier to interpret: • All other Crimes (animal abuse, accessory to crime, reckless endangerment, illegal gambling, possession of contraband in prison) • Assault (simple and aggravated) • Domestic Violence (Non-violent and Violent, child-abuse) • Drug crime (Possession, distributions, drug equipment violations, underage possession of alcohol, drunkenness, narcotics violations, liquor law violations) • Forgery (Counterfeit, false signature) • Fraud (Identity Theft, impersonation, impersonating a policeman, wire fraud, prescription fraud) • Harassment (Stalking, threat, extortion, blackmail) • Obstructing Justice (resisting arrest) • Sex Crime (Forcible Fondling, statutory rape, prostitution, pimping, indecent exposure) • Larceny (Embezzlement, stealing from coin operating machine, pick- pocketing, purse snatching, motor vehicle parts, from motor vehicle, of a bicycle, possession of stolen property, Shoplifting) • Vehicular Crimes (Hit and run, DUI, traffic violations, vehicle assault, Vehicle Prowl) • Vandalism (Graffiti, environmental damage) • Violation of Court Order (Restraining order, custody violation) • Weapon Violation (Shots fired, possession of an unlicensed firearm)

I did not categorize the following crime types because they were unique: • Arson • Burglary • Homicide • Malicious Mischief • Stolen Vehicle • Unlawful Imprisonment • Trespassing

Figure 7.2: Violent and Non-Violent Crimes

Violent Crime: • Homicide • Sex crime • Robbery • Assault

Property Crime: • Burglary • Larceny • Auto Theft • Larceny • Malicious Mischief • Arson

Table 7.3: Violent and Non-Violent Crimes Table

VIOLENT CRIMES Coef. Std. Error t-value p-value 95% Confidence Interval BEFORE/AFTER LEGALIZATION 0.487231 0.434451 1.12 0.262 -0.36484 1.3393 ESTIMATED POPULATION 1.04E-05 0.000108 0.1 0.923 -0.0002 0.000222 ESTIMATED POPULATION DENSITY -7.13E-06 2.86E-05 -0.25 0.804 -6.3E-05 4.91E-05 ESTIMATED MEDIAN AGE 0.040337 0.03056 1.32 0.187 -0.0196 0.100274 ESTIMATED MEDIAN INCOME 1.15E-05 1.43E-05 0.8 0.422 -1.7E-05 3.94E-05 ESTIMATED ALCOHOLIC CONSUMPTION 0.001445 0.003191 0.45 0.651 -0.00481 0.007703 ESTIMATED TOBACCO CONSUMPTION -0.00358 0.00476 -0.75 0.452 -0.01292 0.005753 ESTIMATED WHITE POPULATION -0.00037 0.009589 -0.04 0.969 -0.01918 0.018432 ESTIMATED PERCENTAGE OF POPULATION THAT HAVE A HIGH SCHOOL EDUCATION -0.00109 0.002083 -0.52 0.602 -0.00517 0.002999 ESTIMATED PERCENT POPULATION BETWEEN 15 AND 34 0.034228 0.02925 1.17 0.242 -0.02314 0.091595 ESTIMATED PERCENT OF HOUSES THAT ARE VACANT 0.025469 0.030339 0.84 0.401 -0.03403 0.084972 ESTIMATED UNEMPLOYMENT RATE -0.00209 0.014814 -0.14 0.888 -0.03114 0.026964 YEAR 0.282518 0.19085 1.48 0.139 -0.09179 0.656823

PROPERTY CRIMES Coef. Std. Error t-value p-value 95% Confidence Interval BEFORE/AFTER LEGALIZATION 0.173851 0.317247 0.55 0.584 -0.44805 0.795755 ESTIMATED POPULATION 2.81E-05 8.09E-05 0.35 0.728 -0.00013 0.000187 ESTIMATED POPULATION DENSITY -5E-05 1.92E-05 -2.58 0.01*** -8.7E-05 -1.2E-05 ESTIMATED MEDIAN AGE 0.031165 0.021155 1.47 0.141 -0.01031 0.072634 ESTIMATED MEDIAN INCOME 2.86E-05 9.96E-06 2.87 0.004*** 9.03E-06 4.81E-05 ESTIMATED ALCOHOLIC CONSUMPTION -0.00421 0.0021 -2.01 0.045** -0.00833 -9.5E-05 ESTIMATED TOBACCO CONSUMPTION 0.005281 0.003394 1.56 0.12 -0.00137 0.011935 ESTIMATED WHITE POPULATION 0.001254 0.006471 0.19 0.846 -0.01143 0.013939 ESTIMATED PERCENTAGE OF POPULATION WITH HIGH SCHOOL EDUCATION 0.001796 0.001411 1.27 0.203 -0.00097 0.004561 ESTIMATED PERCENT POPULATION BETWEEN 15 AND 34 0.041074 0.020396 2.01 0.044** 0.001092 0.081057 ESTIMATED PERCENT OF HOUSES THAT ARE VACANT -0.06218 0.018822 -3.3 0.001*** -0.09908 -0.02528 ESTIMATED UNEMPLOYMENT RATE 0.008944 0.009926 0.9 0.368 -0.01051 0.028402 YEAR 0.100748 0.126163 0.8 0.425 -0.14657 0.348067

Note: *90% Confidence, **95% Confidence, ***99% Confidence Out of all of my variables, there was no statistically significant correlation with Violent Crime. Property Crimes were similar but the only variable that seems to indicate a shift is the estimated percent of the population between 15 and 34 and the estima ted percent of vacant houses seems to increase crime by 4% and to decrease property crime by about 6%, respectfully. Noticeably, marijuana legalization does nothing here, with incredibly high p-values. 29

Table 7.4: Summary Table

SUMMARY: Obs Mean Std. Dev Min Max 200-FT ZONE 10999 4.43704 1.814405 1 7

ESTIMATED 10999 1664.283 1300.919 0 12950 POPULATION ESTIMATED 10999 6654.346 5517.533 0 31325 POPULATION DENSITY ESTIMATED 10999 43.07238 7.838386 0 84.1 MEDIAN AGE ESTIMATED 10999 43867.65 21386.88 0 141332 MEDIAN INCOME ESTIMATED 10999 388.2654 117.0689 0 709.1 ALCOHOLIC CONSUMPTION ESTIMATED 10999 318.6609 55.48773 0 413 TOBACCO CONSUMPTION ESTIMATED 10999 109.682 20.54896 0 135 WHITE POPULATION ESTIMATED 10999 151.9918 96.40152 0 355 PERCENTAGE OF POPULATION THAT HAVE A HIGH SCHOOL EDUCATION ESTIMATED 10999 24.19517 8.458415 0 72.59786 PERCENT POPULATION BETWEEN 15 AND 34 ESTIMATED 10999 7.462919 5.522141 0 39.34108 PERCENT OF HOUSES THAT ARE VACANT ESTIMATED 10999 10.64394 12.18541 0 84.7 UNEMPLOYMENT RATE YEAR 10999 2011.752 1.852989 2009 2014

BEFORE/AFTER 10999 0.263569 0.440589 0 1 LEGALIZATION NUMBER OF 10999 3.986908 8.119729 1 205 CRIMES Note: *90% Confidence, **95% Confidence, ***99% Confidence

Table 7.5: Crimes by Zone

Interestingly Figure 7.5 how most of the crimes around each dispensary happened 1,000-1,200 feet away. There is a noticeable decline at a 1,400-foot distance. This means that the crimes occur within a relatively safe distance away, yet still close enough to each dispensary—the source of marijuana.

Figure 7.6: Crimes by Year

Looking at Figure 7.6, we can see no strong correlation over time. Each point is a category of crime, starting in 2009. As time progressed, the sheer volume of crime shifted slightly, but not enough to create a statistically significant shift. The highest point in 2014 can be attributed to a spike in Sex Crimes. It may look high on the graph but it is not weighted heavily enough to modify any trends because I had so much data.