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The Effect upon State Crime Rates of the Legalization of Recreational
Marijuana in California
Robert D. Boxerman
B.S. Chemical Engineering, Missouri University of Science and Technology, 2018
A Thesis Submitted to The Graduate School at the University of Missouri-St. Louis
in partial fulfillment of the requirements for the degree
Master of Arts in Criminology and Criminal Justice
May
2020
Advisory Committee
Stephanie Di Pietro, Ph.D.
Chairperson
Lee Slocum, Ph.D.
Adam Boessen, Ph.D.
Copyright, Robert D. Boxerman, 2020
ABSTRACT
This work examines criminal effects of the legalization of recreational marijuana in the
state of California in 2016. While multiple states have legalized marijuana for recreational
purposes, there is little empirical evidence to determine the criminal effect, if any, of introducing
marijuana products into the legal market. The research analyzes crime rates pre and post
legalization. Crime rates from the years 1990-2018 are taken from the California Attorney
General Office “Crime in California” annual report, and consist of FBI Part I offenses: murder
and nonnegligent homicide, rape, robbery, aggravated assault, burglary, motor vehicle theft,
larceny-theft, and arson. Misdemeanor drug arrests, marijuana felony arrests, and non-marijuana
felony drug arrests are also included. DUI arrests are sourced from the California Department of
Motor Vehicles DUI management information annual report. Interrupted time-series analysis is
the primary analytic strategy, in conjunction with descriptive statistics. Results suggest that
legalization has had a non-trivial impact on arson, felony marijuana arrests, and non-marijuana
felony drug arrests, although there are some data limitations which are discussed.
1
INTRODUCTION
In 2018, The U.S. Census Bureau reported that California had a population of 39.56
million. Roughly 75% of the population is at least twenty-one years old (U.S. Census Bureau,
2018), and following a narrowly approved 2016 state ballot measure, able to legally purchase
marijuana -- a federally illegal substance -- for recreational use. There are a variety of arguments
both in favor of and against the legalization of marijuana for recreational use: medical, moral,
political, and ideological. However, the criminal effects of legalizing marijuana remain relatively
unstudied. In order to provide a complete picture of the effects of legalization of marijuana,
criminal repercussions of legalizing a drug for public consumption should be taken into account.
As the national trend towards decriminalization and legalization of marijuana continues, research
and data informing upon how these laws and changes affect crime become more valuable.
There exists some research regarding the effects of marijuana upon individual criminality
(Pacula & Kilmer, 2003; Bennet and Hollway, 2008) and the effect of marijuana upon the
adolescent (Brook et. al, 2003; Philips, 2012; Reingle et. al, 2012; Aalen, 2013). Other works
explore the criminal effects of medical marijuana laws on a national scale (Morris et. al, 2014),
as well as the effect of legalization laws upon drug crime in multiple states such as Colorado and
Washington (Lu et. al, 2019). This work examines what effect (if any) the legalization of
recreational marijuana has had on crime in the state of California, and will contribute to the
growing literature regarding marijuana and crime by examining the relationship between
marijuana legalization and multiple varieties of crime, both violent and nonviolent, as well as
drug-related and DUI arrests. By restricting the analysis to the state of California, an in-depth
examination may reveal trends and relationships to inform future research and policy. Following
2
a brief discussion of the history of marijuana legislation in both the United States and California,
as well as the present-day status of the substance, I will review the extant empirical literature on
the effects of marijuana use and legislation on crime, followed by a discussion of the data used
and the analytic strategy employed. Results of descriptive and visual analyses are followed by
model construction results from time-series analysis. A conclusion section
Then summarizes this work and discusses future direction.
HISTORY OF MARIJUANA IN THE UNITED STATES
In California, marijuana production is seen in agricultural records as early as 1795, where
the plant was cultivated in order to harvest hemp, a valuable fiber source used for textile
production (Clarke, 2013). Outside of California, marijuana was being grown in North America
many years prior. Colonial America produced vast quantities of hemp for everyday usage,
including canvas, rope, sails, paper, and clothing (Robinson, 1995). Records dating back to 1611
detail significant amounts of hemp production in Jamestown, Virginia (Deitch, 2003). Several
centuries later, hemp played a significant role in World War II, as the U.S. government
subsidized American farmers to increase domestic hemp production for wartime use (Higdon,
2018). The federal government even produced a wartime film entitled Hemp for Victory
espousing the value of hemp to the Allied war effort (Robinson, 1995). In the present-day,
outside of the United States, hemp is a regulated and commercially produced material with a
variety of modern applications (Johnson, 2018). However, hemp production ceased in the United
States when a complete ban on cultivation of the marijuana plant was put into effect post-World
War II.
3
The use of marijuana as an intoxicant was first prominently seen in the States following
an expanding population of Mexican laborers in the early 20th century (Mann, 2001). The
dominant white population was apprehensive of the steadily growing Mexican community, and
beginning in the 1920’s, many Western states including California began enacting laws
prohibiting the usage of marijuana (Gieringer 1999). The Federal Bureau of Narcotics was
formed by Congress in response to concerns over marijuana usage in 1930, and by 1935, the
majority of states had enacted anti-marijuana legislation. The Marijuana Tax Act of 1937 placed
a severe tax on marijuana distribution and signaled the first significant piece of anti-marijuana
federal legislation (the act was later repealed in 1970 following the 1969 case Leary vs. The
United States .)
Anti-marijuana legislation quickly became a method of controlling the growing
1
number of Mexicans and other minority populations, such as Blacks in the South and White
immigrant classes in the East. One Texas lawmaker was on record as saying “All Mexicans are
crazy and this stuff (marijuana) is what makes them crazy” (Schlosser, 23).
While state and federal laws prohibited all use of marijuana, they were unsuccessful at
eradicating illicit marijuana consumption. The 1960’s signaled a resurgence of the drug as the
younger, college-aged generation increasingly consumed marijuana in its various forms.
Correspondingly, marijuana arrests skyrocketed to more than 100,00 yearly arrests by 1970
(Gettman, 2005). In response to public opinion and a series of Congressional hearings regarding
the significant number of citizens who often faced severe sentences at the time (from several
years to several decades), Congress passed the Controlled Substances Act of 1970, which
(temporarily) reduced the strictness of marijuana sentencing law. While sentencing guidelines
1 Timothy Leary was arrested for possession of marijuana in violation of the Tax Act of 1937. Leary made
a case that the Act required self-incrimination and was in violation of the Fifth Amendment. The court
unanimously declared the Marihuana Tax Act unconstitutional as a result.
4
generally became less severe, under this new legislation, marijuana was categorized as a
Schedule I drug, alongside narcotics such as heroin and psychedelics (LSD, mushrooms, etc.)
Drugs in this category were defined as having a high potential for abuse, no currently accepted
medical use in treatment in the United States, and a lack of accepted safety for use of the drug
under medical supervision. Following a lengthy period of study, a Congressional Commission
2
recommended that marijuana be removed from this categorization and studied further to better
understand its recreational and medicinal properties. The Nixon administration and the
soon-to-follow “War on Drugs,” however, largely ignored the Commission's findings and took a
hard stance against marijuana. By the time Nixon resigned, annual marijuana-related arrests had
risen from 119,000 in 1970, to 445,000 in 1974 (Gettman, 2005).
Beginning with Oregon in 1973, a variety of states began decriminalizing small amounts
of marijuana. The state of California decriminalized marijuana several years later in 1975
(Anderson, 1981), and several decades later, was the first state to legalize medical marijuana
with the passage of Proposition 215 (Balzar, 1996). Oregon, Alaska, Washington, and several
other mostly Western states began to follow suit (NORML, 2019). In 2012, Colorado and
Washington passed legislation legalizing recreational marijuana at the state level; a massive
departure from the Nixon-era classification of marijuana as a Schedule I drug. Most recently, in
2019, Illinois legalized recreational marijuana.
2 State laws sometimes categorize marijuana differently. For example, North Carolina recognizes marijuana as a
Schedule VI drug, which is defined as having "no currently accepted medical use in the United States, or a relatively
low potential for abuse in terms of risk to public health and potential to produce psychic or physiological
dependence liability based upon present medical knowledge, or a need for further and continuing study to develop
scientific evidence of its pharmacological effects." The federal categorization of marijuana as a Schedule I drug
remains a controversial topic. For more information, see https://norml.org.
5
THE CURRENT “STATE” OF MARIJUANA
Today, marijuana occupies ambiguous territory regarding its legality. Federally,
marijuana is still classified as a Schedule I drug, as originally designated in 1970. However, its
legal status varies within the states. Currently there are thirteen states (including Washington
D.C.) in which marijuana is fully legalized, both medically and recreationally . Figure 1 shows
3
the legal status of marijuana by state (note that the figure has not been updated since Illinois
passed recreational marijuana law into effect in 2019. At the time of writing, Illinois allows for
the recreational use of marijuana.)
Figure 1. Map of Marijuana Legality by State. DISA Global Solutions. [2019].
Retrieved from disa.com/map-of-marijuana-legality-by-state.
3 While each state has differing laws and regulations, marijuana use is typically restricted to individuals who are at
least twenty-one years of age. Use, possession, distribution and cultivation is regulated and subject to the discretion
of state agencies, lawmakers, and licensing entities. For more information regarding marijuana laws by state, visit
http://disa.com/map-of-marijuana-legality-by-state.
6
Some states, such as New York, have not allowed for the recreational use of marijuana
but have decriminalized possession of small amounts of marijuana. For example, New York
Article 221 states “Unlawful possession of marihuana is a violation punishable only by a fine of
not more than one hundred dollars” (New York, 221.05) There are stipulations surrounding the
decriminalization of marijuana, however; in New York, possession of marijuana in a public place
is a Class B misdemeanor. Possession of a marijuana product (including edible marijuana
products) in excess of twenty-five grams is also a Class B misdemeanor. Most states have also
established their own laws regarding the sale and distribution of marijuana. In New York,
punishments for violations of the state’s marijuana laws vary from a Class B misdemeanor to a
Class C felony, with permissible sentence lengths from fifteen days to one year (Class B
misdemeanor) to greater than one year (Class C felony) (New York, 10.00)
States also have discretion regarding the legal status of medical
marijuana. For example,
in Missouri, a citizen may apply for a medical marijuana license through the Missouri
Department of Health and Senior Services. Citizens are able to apply under a broad range of
medical conditions, many of which are explicitly listed on the Department’s website: cancer,
4
epilepsy, and glaucoma (Missouri DHSS, 2019), to list a few. While some permissible medical
conditions are explicitly defined, the state allows for an individual to apply if “in the professional
judgment of a physician, any other chronic, debilitating or other medical condition, including,
but not limited to, hepatitis C” (Missouri DHSS, 2019), marijuana may provide medical benefit.
Essentially, the Department allows a physician discretion to prescribe medical marijuana in
4 https://health.mo.gov/safety/medical-marijuana/how-to-apply-pi.php
7
virtually any circumstance in which it may provide benefit or relief to a patient. In addition to
providing medical marijuana licenses to citizens for personal medical use, the Department also
issues cultivation and distribution licenses to qualified entities and individuals.
Clearly, there is no national legal consensus on the status of marijuana. While marijuana
may be legal medically and recreationally in certain states, such as California, the fact that
marijuana is still federally illegal has caused conflict in the past and continues to provide a
barrier to proponents of legal marijuana and legal marijuana providers/distributors. To name but
one example, the Rohrabacher–Farr amendment, introduced first in 2001 and passed in late 2014,
was a legislative attempt to prohibit the United States Department of Justice to utilize funds in
order to interfere with the enactment of state medical marijuana laws (113th Congress, Rec. 160
No. 82, 2014). Signaling significant progress for medical marijuana advocates, the amendment
was contested by federal officials, who argued that it protected state officials but not private
entities or individuals. The Department of Justice proceeded to prosecute businesses and private
citizens under their interpretation of the amendment, until U.S. District Judge Charles Breyer
confirmed the original meaning and intent of the amendment, and ruled the DOJ’s actions
"counterintuitive and opportunistic", claiming it "defies language and logic" and "tortures the
plain meaning of the statute" (Ingraham, 2015).
An individual from one state may cross a state line and find that they are in violation of
state marijuana laws. For example, if an adult over the age of twenty-one were to step from
Oregon (where marijuana is legal recreationally and medically) into Idaho, they could be subject
to a year in jail, a $1,000 fine, or both, for possession of less than two ounces of marijuana.
Greater amounts could lead to a felony conviction and longer sentence lengths. This discrepancy
8
between state and federal law, as well as state-to-state law, is representative of the unclear
position of marijuana and its legal standing in the United States.
MARIJUANA AND CALIFORNIA
Some of the earliest reports of marijuana usage by Mexican, Arab, and Hindu immigrants
originate from California (Duvall, 2014) in the late 1800’s. Legislation enacted in 1915 restricted
the sale and possession of marijuana products without a prescription, and by 1925, possession
and sale sentencing were made much stricter, with sentences reaching up to 6 years in
prison--climbing to up to 10 years in 1929. By the mid 1950’s, sentences of up to 15 years for
sale, and a minimum of 1-10 years for possession were in effect (Gieringer, 1999).
California has been known for a liberal and progressive population and culture, typically
associated with drug usage--marijuana in particular (Cerdá et. al, 2019). In 1964, Lowell
Eggemeier publicly protested California’s ban on marijuana by smoking a marijuana joint on the
steps of the San Francisco Hall of Justice (NORML, 2014). Eggemeier would go on to serve
nearly a year in jail, but had started the movement towards marijuana reform in California.
Eggemeier’s attorney, James R. White III, initiated a series of legal contests under the argument
that prohibition of marijuana violated both the 8th and the 14th amendments. White also founded
the LeMar (Legalize Marijuana) advocacy group which would launch a failed ballot initiative in
1972 to legalize marijuana. Marijuana became more popular and prevalent throughout the 70’s,
until California decriminalized the substance in 1975, and eventually legalized medical
marijuana in 1996 and recreational marijuana in 2016.
In 2018, California produced roughly 15.5 million pounds of marijuana, and about $2.5
billion of legal marijuana products were sold in the state according to a report published by the
9
California Department of Food and Agriculture (MacEwan et. al, 2017). The legalization of both
medical and recreational marijuana has had significant impacts on the state, with ramifications
for its economy and polity. A California Department of Tax and Fee Administration memo states
that as of January 1st, 2018, the purchase of marijuana products is taxed at a rate of 15% in
California, while producers, distributors, and cultivators pay an additional “$9.25 per dry-weight
ounce of cannabis flowers, and $2.75 per dry-weight ounce of cannabis leaves” with additional
taxes and fees potentially to come--in addition to local, city, and municipal taxes (CDTFA,
2019). Revenue from these taxes goes to a variety of sources: general services, education, public
health, and others. The 2016 legalization initiative stated that “the revenues will provide funds to
invest in public health programs that educate youth to prevent and treat serious substance abuse,
train local law enforcement to enforce the new law with a focus on DUI enforcement, invest in
communities to reduce the illicit market and create job opportunities, and provide for
environmental cleanup and restoration of public lands damaged by illegal marijuana cultivation”
(Adult Use of Marijuana Act, 2016).
Currently, California is struggling to curb illegal marijuana sales and growth for a variety
of reasons. Lack of funding for enforcement, staffing issues within regulatory agencies,
discrepancy between state and local regulations, unclear enforcement/agency responsibilities,
and the sheer scale of the marijuana market have proven significant obstacles for creation of an
efficient and regulatable legal market. Governor Gavin Newsom recently stated that illegal
growing operations in California are worsening (Fuller, 2019), and tax revenue from legal
marijuana sales have been far below projections. Consequently, California has created a variety
of agencies to cope with the injection of marijuana into the state economy. The 2015 passage of
10
the Medical Cannabis Regulation and Safety Act (MCRSA) was an attempt to create a statewide
framework to effectively regulate and tax the sale and distribution of medical marijuana (CAC,
2018). Several years later, through Senate Bill 94, MCRSA was merged with existing legislation
in order to manage both medical and recreational marijuana regulation throughout the state
(CAC, 2018). The Bureau of Cannabis Control (known as the Bureau) is the primary regulatory
agency, but other agencies such as the Manufactured Cannabis Safety Branch, CalCannabis
Cultivation Licensing (CalCan), and the Cannabis Advisory Committee (CAC) provide
important services in the regulation of marijuana. A CAC report released in 2018 details the
difficulties the state has had in the enforcement of effective marijuana regulation. The federal
classification of marijuana as an illegal substance, the relative youth of legal marijuana in the
United States, and the task of integrating an established industry into a new regulatory
framework have proven to be serious obstacles to effective marijuana policy (CAC, 2018).
LITERATURE REVIEW: MARIJUANA AND CRIME
The relationship between marijuana and crime is one that is often fueled by anecdotal
evidence or no evidence at all (Lu et. al, 2019). Marijuana was first “discussed” from a criminal
perspective in the early to mid 1900’s by opponents of its use. At the time of expanding
sanctions for marijuana use and possession, opponents of marijuana employed fearmongering
and exaggeration to push their prohibitive agenda. Wichita police officer L.E. Bowery asserted in
1933 that marijuana gave criminals inhuman strength, endurance, sexual aggression and
immunity to pain (Artamento et. al, 2009). Many lawmakers and law enforcement agents alike
insisted that marijuana trafficking was a significant concern within the U.S., echoing the bold
claims of Bowery on the effects of marijuana on the criminal individual and the implications for
11
public safety. Dr. William C. Woodward addressed Congress to refute these claims, asserting
that the Bureau of Prisons had yet to provide any evidence that any of their prisoners were
marijuana addicts (Artamento et. al, 2009), but his arguments fell largely upon deaf ears.
Leading the charge into marijuana prohibition was Henry Anslinger, the appointed head of the
Federal Bureau of Narcotics. Anslinger utilized inflammatory and factually unevidenced
statements regarding the criminal potential of marijuana to persuade federal lawmakers to
continue the crusade against marijuana in the States.
What empirical evidence is there to inform on the effect of marijuana upon crime? On an
individual level, marijuana has been subject to a moderate amount of research to determine its
violent or criminal effects. Pacula and Kilmer (2003) explored the link between marijuana use
and individual crime by analyzing arrest data from the Arrestee Drug Abuse Monitoring
(ADAM) Program and the FBI’s Uniform Crime Reports (UCR). The authors cite Taylor and
Bennett (1999) and Makkai et al.’s (2000) findings that “reports from the United States, England,
and Australia...all show that approximately 60% of arrestees test positive for marijuana use and
that marijuana is the drug whose metabolites are most frequently found in arrestees’ urine”
(Pacula & Kilmer, 2003) as a basis for further exploration of the link between marijuana and
crime, as they argue no inferences can be drawn from this finding as most arrestees are not using
marijuana exclusively, but are frequently under the influence of other substances and narcotics as
well. They also argue that urine tests for THC are indicative of THC use anytime in the
preceding month, and that the short-term influence of marijuana on criminal activity can not be
established from this method of testing.
12
Results show that of all arrestees in the sample, marijuana users are more likely to be
charged with a drug-related crime. The authors acknowledge that due to the sample being
comprised of only those who have been arrested, the finding is not surprising, agrees with
previous research, and likely not generalizable to the greater population. Results also indicated
that individuals who had been arrested for a violent crime were more likely to have used
marijuana in the past 30 days, but that this relationship can not be inferred to be causal in nature.
In other words, it is plausible that individuals who commit violent crimes are also likely to
engage in marijuana use, but the violent act can not be attributed to using marijuana. Pacula and
Kilmer do find, however, that within the sample, marijuana users (regardless of time of use or
method of measurement of use) are more likely to commit property crime than non-marijuana
users. The authors acknowledge that their sample is limited (only individuals arrested and
processed through law enforcement entities participating in the ADAM program are included)
and may not be generalizable to the overall arrestee population. Pacula and Kilmer also propose
that they are unable to confirm how the individuals arrested
for crime are behaviorally similar to
individuals who commit
crime, and that the associations between marijuana use and arrest may
merely point to the fact that using marijuana decreases the likelihood of successfully committing
a crime, and increases the likelihood of apprehension and arrest. When re-running the model
using crime rates rather than arrest rates, there was no causal link between violent crime and
marijuana use. The relationship between property crime and marijuana use was less obvious, as
results were only statistically significant when endogenous measures of enforcement were used.
The authors claim that their work supports a causal link between proximal marijuana use and
property crime, although whether marijuana use increases the individuals likelihood of
13
participation in crime is unclear. Overall, Pacula and Kilmer’s study suggests that arrestees for
property crime are more likely to be marijuana users, although a causal link is difficult to
establish.
Another examination of the link between individual marijuana use and crime is seen in
Bennet and Hollway’s (2008) meta-analysis of drug use and offending. Of all the studies
reviewed, 10 examined the link between marijuana use and crime within the United States.
Overall, the authors find that marijuana users are about 1.5 times more likely to offend than
non-marijuana users (Bennet & Holloway, 2008; Lu et. al, 2019) . Does this establish causality
5
or does it merely suggest association? In order to answer this question, research examining
marijuana use among adolescents may provide some insight. Similar to prior research suggesting
that early marijuana use in juveniles may adversely affect cognitive development, Brook et. al
(2003) determined that adolescent marijuana use is related to problem behavior later in
adolescence in Colombian youth, and Philips (2012) determined that marijuana use among
high-risk youth was a predictor of violent behavior. Reingle et. al (2012) examined the
relationship between marijuna use and intimate partner violence, finding that marijuana use
during adolescence is related to intimate partner violence later in life. These studies present
evidence of, at the very least, an association between marijuana use and crime, particularly when
use occurs among adolescents. Some studies, however, such as Aalen (2013) suggest that
marijuana use may act as a substitute among adolescents for more criminal or dangerous acts. I
am concerned that these studies typically examine youth in lower SES contexts or youth who are
relatively at greater risk to become involved in offending. Weis (1974) described this issue,
5 Lu et. al make the important point that this study is not sensitive to the legal status of marijuana.
14
writing that beyond measuring prevalence, most studies regarding adolescent drug use focus on
low-income, African-American children. Just as Philips (2012) and Reingle et. al (2012) focused
on Colombian and at-risk American youth (respectively), these studies often neglect to examine
adolescent marijuana use from a broader perspective, across greater swathes of populations.
Patrick et. al (2012) determined that adolescent marijuana use is related to higher family SES
when compared to alcohol or tobacco use, and yet in the course of research, I found that very few
researchers examined marijuana use among middle-class adolescents, and focused rather on
minority adolescents in urban environments. This represents a serious and concerning deficit in
the effort to establish the relationship between adolescent marijuana use and offending later in
life. Further research examining the effect of marijuana use in adolescence among a greater
diversity of samples (such as middle or upper class Caucasian youth in higher SES areas) would
help increase the confidence of stating that adolescent marijuana use does indeed have a causal
effect upon crime.
In recent years, literature regarding marijuana laws and crime on a state or national scale
has become more prevalent. Most recently, a 2019 JRSA report analyzed the impact of marijuana
legalization and decriminalization upon state criminal justice resources. Farley and Orchowsky
(2019) used state-level data to assess three measures: impacts of marijuana legalization and
decriminalization on criminal justice resources in Colorado, Washington, and Oregon; the
impacts on criminal justice resources in states that border those states that have legalized
marijuana (Nebraska, Nevada, Oklahoma, Utah, and Kansas), and the impacts of marijuana
legalization and decriminalization on drug trafficking through northern and southwest border
states (Arizona, California, Idaho, Oregon, and Washington.) The authors note that there are
15
serious data limitations in that state agencies do not adhere to a uniform, standardized measure of
reporting regarding marijuana arrests and seizures, as well as difficulty obtaining required data
from state/local agencies in interpretable or manageable ways. The authors also note that many
of their analyses examine a pre- and post- trend with a short timeframe being used for post-
analyses, given that legalization of marijuana is a recent phenomenon, and that a greater amount
of time may be required to draw conclusions on causality and/or associations regarding the
legalization of marijuana.
While keeping these limitations in mind, the authors offer some tentative findings.
Legalization of marijuana for recreational purposes lowered the number of marijuana-related
arrests and court cases. When arrests and court case filings were quantified in Oregon and
Washington (two states with legal recreational marijuana), a decrease was observed in the years
following legalization. Secondly, although subject to significant data limitations, no observable
increase in drug crime-related indicators was observed in the states bordering states with legal
marijuana. Third, no indicators of increased drug trafficking were observed in border states,
6
although the authors point to qualitative interviews conducted which suggest that in some states
trafficking may be increasing while it may be decreasing in other states, and that longer time
periods are potentially required to address this particular topic.
Morris, TenEyck, Barnes, and Kovandzic (2014) examined state medical
marijuana laws
across the country and whether they had an effect upon crime between 1990-2016. FBI Part I
offenses (homicide, rape, robbery, assault, burglary, larceny, and auto theft) were used as the
dependent variables). When examining unconditioned crime trend data, the authors found that
6 The authors state that their efforts to address this research question were severely hampered by data
limitations, and that this finding should be interpreted with caution.
16
states that enacted medical marijuana laws saw a reduction in crimes in general. Given that the
nation as a whole was experiencing declining crime rates, the authors compared these states to
states without medical marijuana laws and found that the rate of reduction in crimes for states
with medical marijuana laws was greater than those states without. Using a fixed-effects panel
design in conjunction with ordinary least-squares regression, findings suggested that states with
medical marijuana laws experienced a 2.4% yearly reduction in homicide and assault, with
effects compounding annually for each year that medical marijuana laws were in effect.
Lu et. al (2019) performed a quasi-experimental study that is most similar to my
proposed work. The authors used UCR Part I Offense data in Washington and Colorado in
conjunction with an interrupted time-series analysis and found that the legal status of marijuana
in these states had little to no effect on major indicators of offending for both violent and
property crime.
THEORETICAL LINKS BETWEEN MARIJUANA AND OFFENDING
Regarding theoretical explanations as to why
marijuana (and by extension laws regarding
its legality) may affect offending, Goldstein (1985) suggests that drugs contribute to crime in
three possible ways: psychopharmacologically (an individual commits crime as a direct result of
the drugs effects upon the body and mind), economic compulsively (committing crime for
material/monetary gain in order to acquire more drugs), and/or systemically (violence as a result
of the drug market or drug-related interactions with others such as sales and distribution.) My
propositions for the causes of marijuana-related crime align well with Goldstein’s framework
and are presented as follows.
17
Marijuana may inhibit an individual's cognitive abilities and lead him/her to make
poor decisions or engage in criminal activity (psychopharmacological) . Literature regarding
7
how marijuana directly engenders crime is sparse at best. Association between marijuana use and
crime has been established, but causality remains difficult to confirm or deny. Most research
regarding marijuana pertains to its medical effects, and “hard” drugs such as crack-cocaine and
heroin are more prevalent in the literature due to their more visible health and criminal impacts.
As has been documented, marijuana impacts an individual’s decision-making ability and
other mental functions. A person under the influence of marijuana may do things they otherwise
would not do--engaging in vandalism/petty crime, violent behavior, etc. Laboratory research
typically shows that marijuana use inhibits violent behavior. Mizcek et. al (1994) as well as
White & Gorman (2000) both show this to be the case. However, a study by Baker (1998) found
that juveniles who engage in marijuana use are significantly more likely to engage in violent acts
and vandalism. Bushman (1990) performed laboratory trials in which marijuana users were seen
to be more aggressive when compared to the placebo group, and the U.S. National Research
Council (1993) suggests that prolonged marijuana usage may engender changes in the nervous
system, leading to increased aggression.
Increases in driving while under the influence of marijuana may lead to more accidents,
injuries, and deaths. Driving under the influence of marijuana is illegal in California, although
difficult to test, as traces of marijuana remain in the body for weeks after use, and is not as easily
testable in the same way as alcohol (Farley and Orchowsky, 2019). An interviewee in the JRSA
2019 report claimed an increase of 55-60% in drugged driving in the state of Oregon (Farley and
7 For a discussion of the physical effects of marijuana, see Appendix I.
18
Orchowsky, 2019). Some research shows that driving while under the influence of marijuana
increases risky or dangerous driving behaviors (Hartman et. al, 2015; Lenné et. al, 2010).
Marijuana users may turn to criminal activity to fund their purchasing of the drug
(economic compulsive). It is logical to extrapolate that an individual who is willing to commit a
crime for economic purposes such as purchasing marijuana is likely to attempt to acquire
marijuana regardless of its legal status --thus, the legalization of marijuana may have a negligible
8
effect on this variety of crime, especially given that illegal marijuana is often significantly
cheaper in California when compared to legal marijuana (McGreevy, 2019). However, the
legality of recreational marijuana may increase the ease and availability of purchase (purchasing
from a legal dispensary may be easier than purchasing through illicit channels) leading to an
increased demand and higher counts of property or violent crime.
Drug-market violence and crime may be affected by the legalization of marijuana
(systemic). It is doubtful that marijuana fuels drug violence to the same extent that other drugs
such as cocaine and methamphetamines do; Goldstein et. al (1998) found that of 414 New York
City drug-related homicides, only 6 were related to marijuana use. There is some evidence that
legalization of marijuana has even reduced Mexican drug-trafficking organization activity (CBC,
2017). However, in a 2019 JRSA report, some law enforcement officials expressed concern that
illegal marijuana home-grow operations have led to violence as individuals attempt to establish
turf (Farley and Orchowsky, 2019), and that individuals from out-of-state commute into states
with legal marijuana and create illegal growing and distributing operations. Law enforcement
officials also express frustration over the difficulty of differentiating between legal and illegal
8 By this, I mean that the illegality of marijuana would not be a barrier to purchase.
19
marijuana, as illegal distributors are able to slip under the radar in states with legal marijuana
now that the substance is not scrutinized as heavily. For example, the smell of marijuana is no
longer as useful to law enforcement officers as an indicator of criminal activity (Farley and
Orchowsky, 2019) and in some cases can not be used as probable cause to lead to searching a
vehicle or for an arrest. This could prevent officers from removing criminal or dangerous
individuals from the street, and may be expressed in greater crime rates.
If marijuana is legal, buyers may turn to legal outlets to acquire the drug and may avoid
contributing to the illicit drug market, which could decrease violence and illicit drug sales.
Conversely, drug dealers may find that they are struggling to sell as much illegal marijuana and
may attempt to increase sales of other more dangerous narcotics such as methamphetamines or
heroin. They may also make bolder and more dangerous efforts to sell marijuana to a greater
market, and may infringe upon the turf of rival drug dealers, which may incite greater levels of
violence.
Marijuana dispensaries typically deal only in cash and offer potential targets for
crime. Felson and Cohen’s 1979 routine activity theory proposes that there are three necessary
elements for crime to occur: a motivated offender, a suitable target, and the absence of a capable
guardian. Revenue from legal marijuana sales is recognized under federal law as drug
trafficking, and many dispensaries are unable to establish accounts with banking institutions
(Chemerinsky et. al, 2015). As a result, most dispensaries operate solely in cash transactions and
have significant amounts of cash on hand, as well as a steady influx of customers who have both
cash and marijuana on their person; hence, a suitable target for motivated offenders (which are
generally accepted to be present in virtually all situations). As law enforcement agents or other
20
capable guardians are not always present or effective, there may be greater rates of property
9
and/or violent crime in areas immediately surrounding marijuana dispensaries which may also
10
be reflected in greater counts of state crime.
HYPOTHESES
This work seeks to determine whether legalizing recreational marijuana in the state of
California has had an appreciable effect on crime at the state level. My hypotheses are as
follows:
●There will be little or no effect of legislation on violent crime or property crime.
There is not enough research affirming the causal role of marijuana in crime to believe
that legalization will engender a significant amount of new criminal activity. Existing
state-level research such as Lu et. al (2019) has found that legal marijuana has not yet
produced a noticeable change in crime, and Orchowsky & Farley (2019) were unable to
find noticeable state-level effects as a result of legalization.
●There will be fewer marijuana related arrests, particularly felony-level arrests,
following legalization. Currently, California marijuana laws contain only two marijuana
violations which are classified as felonies: sale or delivery to individuals under 14 years,
or between 14-17 years. While it is possible that there will be more arrests for these two
11
specific felony violations, there are no possession, cultivation, or paraphernalia felony
charges for marijuana--charges which are likely the most applicable to the general
9 A capable guardian need not be a law enforcement agent or even a human being but may be a safe, a
locked door, a security camera, etc. The “capability” of the guardian reflects their ability to effectively deter
or prevent crime.
10 Lu et. al (2019) propose the same mechanism.
11 https://norml.org/laws/item/california-penalties
21
population. Orchowsky & Farley (2019) also found a decrease in marijuana-related court
cases in states that legalized marijuana.
●There may be an increase in misdemeanor-level drug arrests. I believe that marijuana
will be more readily available and that the general individuals attitude about the
substance will be more relaxed and may lead them to consume the product
inappropriately (in excess, in public spaces, etc.) or they may inadvertently violate laws
such as transportation of marijuana or may be in possession of a greater amount of
marijuana than is legally permissible, although these minor legal transgressions may not
be visible on a state scale, or may not occur frequently.
●There may be an increase in DUI arrests. As the DUI arrest count incorporates
marijuana and
alcohol, it may be difficult to disentangle arrests for the two, and as
Orchowsky et. al (2019) pointed out, testing drivers for marijuana use is difficult and
costly. For this reason, while it is possible more drivers are under the influence of
marijuana, this may not be reflected in a greater number of arrests.
DATA
The California Attorney General’s Office publishes an annual “Crime in California”
report, which the state Department of Justice purports to be the most comprehensive report on
crime in the state. Reports from the years 1990-2018 include the following crimes, which will be
the primary dependent variables: homicide, rape, robbery, aggravated assault, burglary, vehicle
theft, larceny, and arson, all measured as the total number of reported crimes for each year. The
reports are available on the Department of Justice website. The reports also provide annual
12
12 https://oag.ca.gov/
22
counts of felony marijuana arrests, felony non-marijuana drug arrests, and misdemeanor drug
arrests.
The California Department of Motor Vehicles releases an annual DUI management
information report which is publicly available on the Department website . The report is the
13
state’s attempt to compile a comprehensive DUI data reference and monitoring system, and
contains information such as total number of annual DUI arrests and convictions, drug or alcohol
related license violations, and recidivism estimates for DUI offenders.
As the population of California has increased steadily since 1990, I controlled for the
annual population as measured by the U.S. Census Bureau’s Decennial Census, as well as the
American Community Survey. For the years 1991-1999 and 2001-2009, there is no survey data
available for counts of the state population. In order to provide control values for these years,
linear interpolation was performed. For the year 2018, linear extrapolation was performed to
provide an estimate for the state population.
14
Dependent Variables
Property Crime (burglary, vehicle theft, larceny, and arson) Source: CA DOJ
Report
Violent Crime (homicide, rape, robbery, aggravated assault)
Source: CA DOJ Report
Drug Arrests (marijuana and nonmarijuana felony, misdemeanor)
Source: CA DOJ Report
DUI Arrests
Source: CA DMV Report
Independent Variables
Post-Legalization (dichotomous dummy variable)
Post-SB 1449 (dichotomous dummy variable)
Post-47 (dichotomous dummy variable)
Control Variables
State Population
Source: U.S. Census Bureau
METHODS
13 https://www.dmv.ca.gov/portal/dmv
14 At the time of writing, ACS surveys are only available up until the year 2017.
23
Descriptive statistics for crime rates (property, violent
) and arrests (DUI, drug
) in the
periods before and following legalization laws were calculated, as well as t-tests for pre- and
post- periods. Underlying trends are taken into account in order to isolate potential causality
between marijuana policy changes and resulting changes in indicators of offending. There are
three independent variables. A dichotomous variable “Post-Legalization” is coded 0 for the years
1990-2016 , and coded 1 for the years 2017-2018. In 2010, California passed SB 1449, in which
15
possession of less than one ounce of marijuana was reclassified as a misdemeanor rather than a
felony. This represents a stringent? piece of legislation, and may have effects upon crime
(particularly drug-related arrests.) For this reason, the second dichotomous independent variable,
“Post-1449,” is given a value of 0 for the years 1990-2010 , and a value of 1 for the years
16
2011-2018. Similarly, Proposition 47, passed in 2014, reduced the classification of some drug
and nonviolent crimes from felonies to misdemeanors. This may have a non-trivial effect on
arrest data for drug offenses. “Post-47” will be coded 0 for the years 1990-2014 , and 1 for the
17
years 2015-2018.
Limitations to a purely descriptive and pre- post- analysis are two-fold. There is no
comparative case with which to compare results, in that this comparison is limited to the state of
California. While there have been other states such as Colorado and Washington that have
legalized marijuana recreationally, California has a vastly different social, economic, and
population landscape, and as previously mentioned, has greater marijuana production and a
larger marijuana economy than other states. However, existing literature has performed similar
15 Prop 64 did not pass until November 2016, and for this reason, 2016 is being included in the
pre-legalization period and will receive a 0 value for the Post-Legalization variable.
16 SB 1449 was passed in Congress in September 2010. 2010 will be coded as a 0 for “Post-1449” as a
result.
17 Prop 47 was not passed until November 2014.
24
analyses in these states (Lu et. al, 2019; Farley & Orchowsky, 2018) and the results of their
analyses may be used for comparative purposes. Additionally, descriptive and pre- post-
post-analysis do little to determine causality and make inferences about the effect of legalization
discretely from underlying, pre-existing trends in crime and arrest data. Crime rates have
generally been declining in the United States in recent decades (UCR, 2017) and by relying on
descriptive analysis, it would be incorrect to assign the observed decreasing trend in crime and
arrests to marijuana legalization. In order to address this concern, time-series analysis was
performed.
According to Corsaro (2018) time-series analysis is a method of analysis used to
ascertain whether an intervention at a known point and time had an effect upon an outcome,
where the effect of the intervention is greater than a pre-existing underlying trend. This allows
researchers to determine with greater certainty that changes in the outcome are in fact due to the
intervention rather than the existing trend. In order for time-series analysis to be performed, there
are several requirements that must be met. The first is that the intervention must occur at a
known point in time; as Bernal et. al (2017) state, there must be clear differentiation. In this case,
the first requirement is met in that the date of legalization legislation is known, and effective
immediately statewide. Next, the outcome must change immediately following the intervention.
Given that all data is provided in yearly counts, it is likely a reasonable assumption that changes
in crime and arrest data should be “instantaneous” at an annual level and immediately visible (to
some degree) in the yearly aggregation of crime and arrests. If data were provided weekly or
monthly, however, or should the previous assumption be found to be incorrect, there are more
complicated time-series models that allow for a delay between the intervention and changes in
25
the outcome. Another requirement for time-series analysis is that there is a clear pre-intervention
function. A cursory visual inspection of the data shows that pre-2016 data follow fairly clear
trends and a functional relationship can be found. There must also be an acceptable amount of
pre-intervention observations; here, by beginning analysis in 1990, there is a reasonable amount
of pre-intervention points available . Lastly, there must be no alternative factor causing changes
18
in the outcome. By taking non-legalization legislation (such as SB 1449 and Prop 47) into
account, as well as controlling for population in California, spuriousness can be minimized.
There is also no other known legislation or extraneous event occurring in 2016 (the year that the
intervention takes place) that would affect crime and arrest data.
A typical time-series analysis uses the following model:
β β TβXβT XY t= o+ 1+ 2t+ 3t
Here, T = the time that has passed since the beginning of the study, coded in the same
frequency with which observations are taken. βo represents the independent variable at T= 0, β1
shows the increase associated with a unit increase in T (the underlying pre-intervention trend), β2
represents a level or “step” change following the intervention, and β3 represents a slope change
following the intervention (Bernal et. al, 2017). As mentioned previously in this section, the
model may be simplified according to the predicted outcome.
For this analysis, a negative binomial regression was used. Time series analysis requires
the use of maximum likelihood estimation when outcomes are not normal. Preliminary analysis
of the data shows that, similar to much count data (Bernal et. al, 2017), normality is absent.
Corsaro recommends use of a Poisson or negative binomial regression in these instances. In this
18 Corsaro states that a “rule of thumb” is 40-60 total observations.
26
case, a negative binomial regression is preferable to address an issue commonly occurring in
count data in that the current data does not fit a Poisson distribution; the variance is not equal to
the mean (Ford, 2016), and use of a Poisson regression would result in a poorly fitted model.
When performing time-series analysis, both seasonality and stationarity of the data must
be addressed. Seasonality refers to seasonal variations in data; for example, crime rates
increasing in warmer summer months and decreasing during the winter (Lauritsen, 2014). Given
that the data is available in yearly counts, seasonality is not an issue; visual inspection of the data
confirms this, and additional statistical verification can be performed if deemed necessary .
19
Stationarity refers to mean and variance stability. A visual inspection of the data clearly shows
that stationarity must be accounted for, which was addressed through the addition of a trend
variable in the later stages of model development (Corsaro, 2018).
The Huber-White sandwich estimator is a method of adjusting the standard errors for lack
of consistent variance across observations (non-normality among the residuals) leading to
heteroscedastic-consistent errors. This estimator was used in the present analysis as well; Slocum
et. al (2019) utilized the Huber-White sandwich estimator when analyzing police count data over
time, a similar analysis to the one at hand.
The use of time-series design for this research question is potentially problematic, as
there are only 2 observations for the post-intervention period, although Corsaro states that
typically there are far fewer observations available for the post-period in a time-series analysis,
and Bernal et. al (2017) believe it is rarely practical for pre- and post-intervention observations to
be equal in number. As with most statistical analysis, exogenous variables remain potentially
19 Accounting for seasonality by adding a “seasonal” variable into the model had no effect when analysis
was performed, verifying that the data does not display seasonal variation.
27
problematic in time-series analysis. These may be identified and included within the model;
population has been accounted for in the proposed model, as well as exogenous interventions
(Prop 47, SB 1449). Other variables which are frequently included in criminological models
include SES and racial composition. I omitted these variables from the model for the reason that
the entire state is the unit of analysis, and inclusion of an SES variable across time would require
adjustment for multiple extraneous factors such as inflation and the dynamics of the California
economy. Also, as the state is the sole “case” and data is taken not across different groups but
across different points in time, SES seems unnecessary to include. Racial composition was
20
excluded for the same reason; analysis of racial composition of California (as reported in the
ACS and Census surveys) shows that the state has experienced relative racial proportion
stability.
RESULTS
The first step of analysis was visual examination of the time-series crime data.
Underlying trends in national offending have been consistently moving downwards in the past
several decades. Figures 2 through 13 present a series of graphs which show national arrest data
sourced from the FBI UCR juxtaposed with arrests in California. There is no nationally available
data for marijuana-specific arrests or misdemeanor drug arrests, therefore marijuana felony
arrests, non-marijuana felony drug arrests, and misdemeanor drug arrests are not included. DUI
21
20 If the state of California as a whole were to experience SES changes on a macroscopic scale across
the periods of observation, SES would likely need to be included. A cursory visual analysis of California
median income in the period of observation shows that this is not the case (Data USA, 2019). Alternately,
if crime and arrest data was being examined county-to-county or city-to-city, SES would vary, and would
likely contribute to variance in the dependent variables, and would thus require inclusion in the model. As
this is not the case, neither SES nor racial composition are included in the model.
21 There is information regarding marijuana arrests and other drug arrests by year but they are not
separable by felony or misdemeanor.
28
arrest data is only available from 1995 onwards. Juxtaposition of the California and national
offense data helps to determine whether changes in offending in California are unique to the state
and attributable to changes in legislation, or whether they follow national patterns.
Homicide (Figure 2) counts are seen to decrease greatly since the beginning of the
observation period, with some variation throughout. Homicide is on an incline from the year
2015 through 2016, and declines in 2017 and 2018, the years in which recreational marijuana is
legal. As mentioned prior, crime has generally been decreasing in the United States during the
period of observation. Figure 2a displays homicide in california juxtaposed with homicide counts
for the entire United States. National homicide counts are seen to follow a very similar trend to
that of California. In 2014. National counts increase at a rate even greater than that of California.
Similar to California, they also decline in 2017 and 2018, at a lower rate than in California.
Figure 2. Homicide per 100,000 Residents, California
29
Figure 2a. National vs CA Homicide
Rape (Figure 3) declines until the year 2015, after which counts increase through 2018.
The slope of the increase is virtually unchanged in 2017 and 2018 compared to 2016, and is a
less steep slope than from 2015-2016. In Figure 3a, it is evident that national and California
patterns are similar. There is a large jump in national counts from 2012-2013 due to the FBI
revising their definition of rape to include more circumstances. National rape counts follow the
California trend of increasing in 2014 through 2018.
30
Figure 3. Rape per 100,000 Residents, California
Figure 3a. National vs CA Rape
31
Robbery (Figure 4) also faces a general decline from 1990. The data levels out between
2013-2018 and is roughly at the same level during these years. There is a slight decline from
2017-2018. The national robbery trends are similar to those in California, although the curve is
flattened somewhat and changes in robbery show less rapid increases and decreases. It is worth
noting that California consistently displays higher levels of robbery per capita.
Figure 4. Robbery per 100,000 Residents, California
32
Figure 4a. National vs California Robbery
33
Aggravated assault (Figure 5) consistently decreases until 2013, after which it gradually
increases until leveling out from 2016-2018. National aggravated assault trends mirror those of
California almost exactly.
Figure 5. Aggravated Assault per 100,000 Residents
34
Figure 5a. National vs CA Aggravated Assault
Burglary (Figure 6) declines fairly consistently throughout the entire period of
observation. The slope of the data is virtually unchanged from the years 2014-2018. The
California trend is extremely similar to national burglary counts.
Figure 6. Burglary per 100,000 Residents, California
35
Figure 6a. National vs CA Burglary
Vehicle thefts (Figure 7) declined overall from 1990, but with much greater variation.
There is a peak in the year 2005, after which a decline occurs until 2011. From 2011-2018, the
36
data oscillates slightly, with declines occurring from 2016-2018. National vehicle theft counts
display less oscillation overall. National counts also show an uptick in 2014, and similar to
California, decrease gradually from 2016-2018.
Figure 7. Vehicle Theft per 100,000 Residents, California
37
Figure 7a. National vs California Vehicle Theft
Larceny (Figure 8) decreases overall. There is a slight increase from 2014-2015, followed
by a gradual decline through 2018. Unlike California, national larceny counts do not increase
from 2014-2015, but decline consistently from 1990-2018.
38
Figure 8. Larceny per 100,000 Residents, California
Figure 8a. National vs CA Larcen
39
Arson (Figure 9) trends downwards until beginning a shallow incline from 2014 through
2018. Arson counts in California gradually decrease 2017-2018. National arson counts have a
slight uptick from 2014-2015, but contrary to California, decrease from 2016-2018.
Figure 9. Arson per 100,000 Residents, California
40
Figure 9a. National vs CA Arson
Similar to the UCR offenses, DUI (Figure 10) arrests drop considerably throughout the
observation period. There is a slight increase from 2005 to 2008, followed by a decrease until
2017. There is a very slight increase from 2017 to 2018 (roughly 9 more DUI arrests per 100,000
residents.) National trends also reflect a decrease from 2012-2017, until a slight uptick in 2018.
41
Figure 10. DUI Arrests per 100,000 Residents, California
Figure 10a. National vs California DUI Arrests
42
Felony marijuana arrests (Figure 10) declined greatly from 1990 to 2001 until peaking in
2008. Between 2008-2018, arrests go from 48 per 100,000 to 4 in 2018. This is consistent with
the passing of SB-1449 and Prop 47, which both reduced many marijuana offenses from felonies
to misdemeanors.
Figure 11. Felony Marijuana Arrests per 100,000 Residents, California
43
Non-marijuana felony arrests (Figure 12) gradually declined from 1990 to 2011, with
significant oscillation throughout. Arrests were at a local peak in 2013 before steeply declining
from 2014-2016 with the passing of Prop 47. Declines continued gradually through 2018.
Passage of SB 1449 in 2010 does not immediately appear to affect non-marijuana felony drug
arrests.
Figure 12. Non-Marijuana Felony Drug Arrests per 100,000 Residents
Misdemeanor drug arrests (Figure 13) contrary to other offense data, gradually increased
from the years 1990-2007, before sharply dropping 2007-2012 to the lowest levels throughout
the entire period of observation. Arrests then sharply rose through 2018. This is consistent with
my hypothesis that misdemeanor drug arrests, relatively minor offenses, would likely increase.
The sharpest increase occurs between 2014-2016--the years in which Prop 47 took effect. I
44
hypothesize that the arrests which had previously been classified as felonies were recategorized
as misdemeanors, which is reflected by sharp decreases in felony arrests and corresponding
increases in misdemeanor arrests. This is displayed for clarity in Figure 13. The vertical red line
represents the passage of Prop 47.
Figure 13. Misdemeanor Drug Arrests per 100,000 Residents, California
45
Figure 14. Drug Arrests per 100,000 Residents
Descriptive statistics (Table 1) were calculated, as well as t-scores for pre- and post-
legalization. For all comparisons other than misdemeanor drug arrests, the mean number of
yearly offenses was significantly lower after legalization. The descriptive statistics suggest that
the legalization of marijuana correlated to fewer offenses, with the exception of misdemeanor
drug offenses. Visual examination of homicide, robbery, aggravated assault, burglary, vehicle
theft, larceny, arson, marijuana felony arrests and non-marijuana felony drug arrests also appear
to decrease post-legalization, while rape and misdemeanor drug arrests appear to increase
post-legalization.
46
Table 1. Comparison of Crime Count Data Before and After Legalization
Changes in offending have been drastic since the start of the period of observation. Table
2 displays the percent change in offending from 1990 to 2016, the year before legalization
occured in California. It is evident that on a national scale, offending has dropped significantly
since 1990. Figure 2a shows changes in offending for California. California also displays
significant decreases in offending from 1990-2016.
Figure 2. Percent Change in Offenses, National 1990-2016
47
Figure 2a. Percent Change in Offenses, California 1990-2016
48
In order to detangle changes in offending from pre to post marijuana legalization, percent change
in offending from 2016 to 2018 is presented for national (Table 3) and California (Table 3a)
counts. A comparison of percent changes shows that for all FBI Part I offenses with the
exception of arson, both California and national offenses have decreased from a range of -.4% to
-19.8%. California-specific offense data (drug arrest information) shows that felony drug arrests
have decreased significantly, while misdemeanor drug arrests have increased by more than 80%.
Compared to drug arrest differences from 2016-2018, the majority of the decrease in
non-marijuana felony drug arrests occurred from 1990 as opposed to 2016 (-81.8% to -15.2%).
Similarly, the increase in misdemeanor drug arrests (31.3%) from 1990 is far less from 2016 to
2018 (4.3%).
Table 3. Percent Change in Offenses, National 2016-2018
49
Figure 3a. Percent Change in Offenses, California 2016-2018
50
National and California offense data shows frequent increases in offending post-2014. In
order to assess this, Table 4 and Table 4a present percent changes in offenses of the mean value
between 2012-2016 compared to the mean value for 2017-2018. Results show that national
counts for homicide and vehicle theft have increased, while California counts have gone down.
National robbery and arson counts have decreased, while California counts have increased. For
all other FBI offenses, California and national levels have trended in parallel, with some
variation in slope, ranging from most variation (difference of 9.7%) to least (2.9%).
Table 4. Percent Change in Offenses, National 2012-2016 and 2017-2018
51
Table 4a. Percent Change in Offenses, California 2012-2016 and 2017-2018
52
Further t-tests were performed between 2012-2016 and 2017-2018 in order to
detangle changes in offending from underlying trends. Table 5 and 5a show the results
for t-tests performed on the 5-year period preceding legalization to the 2 year
post-legalization. Results suggest that changes in offending for national offenses were
only significant for robbery and larceny. For California, only arson and marijuana felony
arrest changes were significant. Other changes in offending are possibly only attributable
to random variation or oscillation.
Table 5. T-tests, 2012-2016 and 2017-2018, National
53
Table 5a. T-tests, 2012-2016 and 2017-2018, California
Interrupted time-series analysis was also performed in order to determine whether
changes in offending were attributable to legalization, or merely due to existing trends. Initial
model construction, where all change in homicide is attributed to legalization, is presented as an
example in figure 15. This negative binomial model displays the issue with disregarding
22
underlying trends as well as external disruptions (Prop 47, SB-1449). Clearly, this is not an
accurate model (pseudo R2 = .018). This model suggests that legalization of marijuana is strongly
significant (p ≤ 0.000) and has a negative effect on homicide (coef. = -.504 or roughly 60%
impact).
22Stages of model construction are shown here in order to display the utility of time series analysis for
count data such as this; full models only are provided for other dependent variables. Partial models are
available in the supplementary data file that accompanies this document.
54
Figure 15. Homicide Attributed only to Legalization
Inclusion of a trend variable improves the model (Figure 16.) The trend variable is
negative and strongly significant (coef. = -.044, p ≤ 0.000) while the effect of legalization is less
powerful, positive, and still significant (coef. = .193, p ≤ 0.000). The model accuracy is also
improved significantly (pseudo R2 = .183).
55
Figure 16. Homicide Attributed to Legalization and the Existing Trend
External disruptions should also be included whenever possible in order to increase the
accuracy of the model, and to avoid attributing too much change in the dependent variable to the
intervention. In this case, SB-1449 and Prop-47 were both considered to be important outside
influences. Including these had significant impacts upon the model; legalization of marijuana
was no longer seen to be significant (coef. = .023, p ≤ .435). The trend variable was still
significant and negatively related to homicide (coef. = -.049, p ≤ 0.000) and SB-1449 (coef. =
.099, p ≤ 0.042) as well as Prop-47 were both significant and positively related to homicide. The
56
trend variable is responsible for roughly 4.75% of the variance, while SB-1449 and Prop 47 were
9.45% and 18.32%, respectively. The intervention (legalization of marijuana), SB-1449, and
Prop-47 explain about 32.5% of all variance, and is an improvement upon previous models with
a pseudo R2 value of .186. For this and all following models, a squared trend term was initially
added, but was not found to be significant in any of the models, and was dropped. The same is
true for inclusion of a slope-change term ( ) and neither term was included.β T X 3t
Figure 17. Homicide, Full MLE Model
57
Figure 18. Rape, Full Model
Using the MLE model to examine rape data shows legalization to be strongly significant
(p ≤ 0.000) and positively related (coef. = .181). Prop-47 (coef. = .212, p ≤ .003) is also
signficant, and positively related to rape counts. SB1449 (p ≤ .194) is not significant. This model
has a pseudo R2 value of .216, and is reasonably close-fitting to the count data for the region of
interest (2010-2018).
58
Figure 19. Robbery, Full Model
The model for robbery shows that legalization is positively correlated to robbery counts
(coef. = .117, p ≤ 0.000). The same is true for Prop-47 (coef. = .123, p ≤ 0.000), although
SB1449 is not significant at alpha = .05 (p ≤ .071). The trend variable is significant (p ≤ 0.000)
and negatively related to robbery (coef. = -.05).
59
Figure 20. Aggravated Assault, Full Model
The model for aggravated assault shows that legalization is significant (p ≤ .001) and
positively related to aggravated assault (coef. = .119). SB1449 (coef. = .072, p ≤ .005) and
Prop-47 (coef. = .208, p ≤ 0.000) are also both significant and positively related to counts for
aggravated assault.
60
Figure 21. Burglary, Full Model
The model for burglary suggests that all variables are significant. Legalization is
negatively related (coef. = -.049, p ≤ 0.000), as is the trend variable (coef. = -.044, p ≤ 0.000) and
Prop-47 (coef. = -.072, p ≤ .024). SB1449 is positively related to burglaries (coef. = .199, p ≤
0.000)
61
Figure 22. Vehicle Theft, Full Model
The model for vehicle theft shows that legalization (p ≤ .819) and SB1449 (p ≤ .803) are
both not significant. The trend variable (coef. = -.04, p ≤ 0.000) and Prop-47 (coef = .151, p ≤
0.000) are both significant. The trend variable is negatively related, while Prop-47 is positively
related.
62
Figure 23. Larceny, Full Model
The model for larceny fits very well for the years 2010-2018. All variables are
significant, with p ≤ 0.000. Legalization (coef. = .034), SB1449 (coef. = .122), and Prop-47
(coef. = .117) are all positively related to larceny counts. The trend variable (coef. = -.035) is
negatively related.
63
Figure 24. Arson, Full Model
All variables are significant in the model for arson. Legalization (coef. = .217, p ≤ 0.000)
and Prop-47 (coef. = .139, p ≤ 0.000) are both positively related to arson. The trend variable
(coef. = -.053, p ≤ 0.000) and SB1449 (coef. = -.117, p ≤ 0.021) are both negatively related.
64
Figure 25. DUI Arrests, Full Model
The model for DUI arrests shows that legalization (p ≤ .218) and SB1449 (p ≤ .915) are
not significant. The trend variable (coef. = -.03, p ≤ 0.000) and Prop-47 (coef. = -.142, p ≤ 0.000)
are both significant and negatively related to DUI arrests.
65
Figure 26. Marijuana Felony Arrests
Marijuana felony arrests are seen to be negatively related to legalization (coef. = -1.505,
p ≤ 0.000), as is the case with the trend variable (coef. = -.011, p ≤ .003) and Prop-47 (coef. =
-.466, p ≤ 0.000). SB1449 is not significant in this model (p ≤ .286).
66
Figure 27. Non-Marijuana Felony Drug Arrests, Full Model
The model for non-marijuana felony drug arrests shows that SB1449 is not significant
(p ≤ .621). All other variables are significant and negatively related to non-marijuana felony drug
arrests. Legalization (coef. = -.183, p ≤ 0.000), trend variable (coef. = -.016, p ≤ 0.000), and
Prop-47 (coef. = -1.244, p ≤ 0.000).
67
Figure 28. Misdemeanor Drug Arrests, Full Model
The last model, for misdemeanor drug arrests, shows that legalization (p ≤ .079) and the
trend variable (p ≤ .674) are not significant. SB1449 (coef. = -.529, p ≤ 0.000) is negatively
related to misdemeanor drug arrests, and Prop-47 (coef. = .688, p ≤ 0.000) is positively related.
68
Table 6. Summary of Model Parameters by Crime Type
69
National offense data for FBI offenses and DUI arrests were also submitted into the
time-series model for the reason that changes in offending in California captured by the model
may reflect national trends and may not be due to legislative changes. These models are
presented in Figures 29-37 and summarized in Table 7.
Figure 29. Homicide, National Model
70
Figure 30. Rape, National Model
71
Figure 31. Robbery, National Model
72
Figure 32. Aggravated Assault, National Model
73
Figure 33. Burglary, National Model
74
Figure 34. Vehicle Theft, National Model
75
Figure 35. Larceny, National Model
76
Figure 36. Arson, National Model
77
Figure 37. DUI Arrests, National Model
78
Table 7. Summary of Model Parameters, National Model
79
DISCUSSION
Overall, results suggest that legalization of marijuana has had a mostly impact on crime
in California. Model impacts for rape, robbery, aggravated assault, burglary, larceny, arson,
felony marijuana arrests, and felony non-marijuana arrests were all significant at p ≤ .05 for all
cases and p ≤ .001 for some cases, while homicide, vehicle theft, DUI arrests, and misdemeanor
drug arrests were not significant at the p ≤ .05 level. These findings should be interpreted with
caution, for several reasons. First, an examination of impact sizes shows that there is variability
in the estimated impact of each crime type. Table 7 displays the percent change in expected
counts for each significant variable. This percentage change is calculated by the following
formula:
ercentage C hange in Expected Counts 00 exp(b) 1]P= 1 *[ −
Where ‘b’ is the coefficient from the negative binomial regression model. This approach takes
advantage of the multiplicative nature of the relationship between the predictor and outcome
variables; regression coefficients represent the expected change in the outcome variable when a
one-unit change occurs in the predictor variable (holding other variables constant.) The
logarithmic nature of the relationship requires exponentiating the coefficient in order to produce
an estimate for the percentage change in the expected outcome as a result of a change in the
predictor.
80
Table 7. Percentage Change in Expected Counts, Time-series Analysis
The model suggests that legalization of marijuana increased counts for arson by 24.23%.
Rape (19.84%), robbery (12.41%), aggravated assault (12.64%), burglary (5.02%), and larceny
(3.46%) all increased as a result of legalization. Felony marijuana arrests (-77.8%) and felony
non-marijuana arrests (-16.72%) both decreased as a result of legalization.
These percentage changes should be interpreted with extreme caution. The reliability of
the time-series models in this work are questionable largely for the reason that there are very few
observations for the post-legalization period. Corsaro recommends that this type of time-series
analysis be performed on a total of 40-60 observations, roughly split across the pre and post
periods; these models utilize a total of 29 observations total, with only 2 observations occurring
in the post-legalization period. This makes it difficult to examine any true trend that may be
occurring, and changes in counts may merely reflect fluctuations in data. The models included in
this analysis attempt to measure whether there is an immediate step-change increase in crime
counts--an upward or downward spike as a result of fluctuation would be reported by the model
as an indicator of a step-change occurring, resulting in a “false-positive” although no true change
has taken place.
81
Time-series analysis also allows for inclusion of a slope-change measurement in the
outcome variable as a result of the intervention. This can be determined by inclusion of another
time variable; in this case, as data is taken yearly, this variable would increase by 1 for every
year that legalization has been in place. This variable was initially included in the model,
however, inclusion of this variable had virtually no effect upon the model whatsoever and was
consequently excluded. This is likely again due to the short post- period, in which 2 data points
(2017 and 2018) are insufficient to determine a slope change.
Visual analysis of the graphed data can serve to reiterate the above limitations, and to
strengthen conclusions. All of the Part I and Part II UCR offenses show trends which do not
change drastically in either slope or step change in 2017 or 2018, but tend to follow trends which
were established in previous years, or have spikes and drops which are consistent with
fluctuation in data for the previous period of observation. Counts for misdemeanor drug arrests
show a slightly more significant increase in the post period, although the model suggests that
legalization was not significant. Examination of national data shows that most changes in
offending in California appear to mirror national trends.
Inclusion of national offense data in the time-series model is useful in order to determine
whether changes in offending as a result of legalization are due to legislation or are merely
in-line with national trends. When examining offenses that were significant in the
California-only time-series analysis model in conjunction with the national model, it is revealed
that rape, aggravated assault, and burglary are likely due to national trends and are independent
of legislative changes in offending. This was determined by comparing coefficients and
significance levels for significant California offenses to offenses in the national model. Robbery,
82
larceny, and arson still appear to be significant in the California model when compared to the
national model. Robbery and larceny are unlikely to have changed as a result of legalization
when their t-tests and percent changes are examined. Their t-test values are not consistently
significant, suggesting that changes in mean offending levels are likely due to random variation.
Examination of their percent change values also shows that it is highly likely that changes in
these offenses are reflective of national trends.
However, when analyzing percent changes and t-test values for these “significant”
offenses, only arson appears to be affected as a result of legalization. Arson was significant in
both national and California time-series models. The national percent change in arson counts
between 2016 and 2018 was negative, and it was also negative between the 5-year period before
legalization and the mean value post-legalization. This is in contrast to positive changes in both
percent change models for California. Finally, changes in arson counts were significant in the
pre- and post- legalization t-tests for both the entire period of observation and the 5-year period
prior to legalization. This suggests that legalization of recreational marijuana may have
contributed to an increase in arson in the state of California post-legalization. There is little to no
existing literature to inform as to why this may be. Jayaraman & Frazer (2006) examined a
sample of convicted arsonists in the United Kingdom and found that roughly half of the sample
had cannabinoids in their system at the time of arrest; however, causality is nearly impossible to
establish, and mere correlation may be responsible. All of the individuals were under the
influence of alcohol, and a third had opioids in their system as well. Some arson investigators
and fire department officials believe that arson is on a rise since legalization, although there is no
peer-reviewed or academic literature to support this (Pitawanich, 2016).
83
Regarding the validity of time-series models in this work, Lu et al. (2019) performed a
similar time-series analysis to the methods in this study using FBI data in the Offenses Known
and Clearances by Arrest dataset which is offered monthly; however, this dataset has not yet
been released for the years 2017 and 2018. By using a dataset which offers monthly counts, a
more reliable model can be created, however the timeliness of recreational legalization in
California limits the available data that can be analyzed. State-wide datasets offering crime
information in monthly or weekly increments do not seem to be available at this time. The Los
Angeles Police Department has recorded every reported crime in the city for the years
2010-2019; San Francisco offers COMPSTAT reports for monthly crime data in the city as well.
Other large cities in the state have similar datasets available for public access. Future actions
could increase the reliability of the models in this work by including new UCR state-wide data
once the datasets are released; they may also use city-wide data which is often available in
monthly or weekly increments.
CONCLUSION
A combination of visual, descriptive, and time-series analysis suggests that
legalization of recreational marijuana in California has had a significant and non-trivial impact
on several types of offending: arson, felony marijuana arrests, and felony non-marijuana arrests.
Arson appears to have increased post-legalization, while felony marijuana arrests and felony
non-marijuana drug arrests have decreased. Theoretical links between legalization and arson are
difficult to establish. Future directions for this may include an aggregate analysis of all states in
which marijuana has been legalized in order to determine whether these results are generalizable
outside of California. Qualitative interviews with officials in these states such as investigators,
84
retail distributors, and fire department officials may also expose the potential link between
legalization and arson.
It is not surprising that felony marijuana arrests have decreased greatly; as mentioned in
the literature review of this study, California has reduced penalties for marijuana possession, to
the point where felony marijuana charges are relatively unlikely to occur (compared to previous
years and other violations) and are largely only allocated in the case of illicit distribution of
marijuana to minors. It is not necessarily the case that fewer individuals are committing the acts
formerly defined as felony marijuana violations, but rather that the state has reclassified these
actions as misdemeanors, ordinance violations, or entirely legal acts. It is also possible that law
enforcement officers exercise discretion and no longer or ticket arrest individuals in California
for marijuana violations, given the changing social environment regarding marijuana use. This
also brings to mind the continuing debate between positivistic and theological crime, or the clash
between “immoral” and criminal acts.
The summary of marijuana legislation in this work outlined how marijuana prohibition
began as a form of social control directed at specific populations. The act itself was not seen as
abhorrent in the same way that a crime such as murder or rape is universally immoral. This clash
between legal and moral crime is explored by Fuller in his 1942 publication, “Morals and the
Criminal Law.” Fuller writes that some practitioners of the time believed that the law was
determined by custom and public opinion. Fuller suggests that society enacts new criminal
legislation in order to cater to the moral standards of certain groups, and that criminal legislation
does not always reflect a universal moral standard, but rather the moral standard of whichever
group is able to enact their agenda. Perhaps most expressive of his sentiment in his work is his
85
statement: “Criminal and Immoral are not always synonymous.” Mewett (1962) argues that
criminal law is socially constructed, and that the law should only be concerned with acts that
have demonstrable negative effects on society. Mewett believes that all laws should seek to
minimize societal harm, and that morality and law are essentially separate. He describes the
relationship between criminality and morality as complex, “an immoral person can not be
equated with a criminal person. Even if one accepts that all criminals are immoral, one can not
suggest that all criminal acts are immoral. In any case, it is doubtful whether all criminal acts are
immoral acts” (Mewett, 214). Cohen (1940) echoes this sentiment, arguing that some define an
action as a crime only when those with legislative power determine it to be a crime, while others
argue that an action is a crime when it is so in nature, or goes against the nature of society.
Cohen describes the former view of crime as positivistic,and the latter as a theological viewpoint.
Cohen inserts his own thoughts on the subject, saying “Laws must often be changed if our rules
of conduct are to facilitate the good life under changing conditions. How this is to be brought
about in any given determinate social situation is not something known in advance, but must be
determined in the processes of adjustment of our economic and political life” (Cohen, 997).
Cohen clearly takes a more positivistic view on crime, arguing that laws must change in order to
adjust to evolving social and political environments and norms. This debate still rages on more
than half a century later, as the overall lack of credible evidence suggesting that marijuana incites
violent or property crime points to the positivistic rather than theological nature of marijuana
violations.
A decrease in non-felony marijuana arrests is less easily explained. It is plausible that
users of other drugs such as opiates or amphetamines have increased their marijuana
86
consumption and reduced usage of these other non-marijuana substances, leading to a decrease in
arrests for these drugs. It is also possible that enforcement has changed in California, and while
there are fewer arrests, the actual level of drug violations has not changed as a result of
legalization. This tentative hypothesis is based on the history of California in decriminalizing and
reducing penalties for many non-violent and drug offenses, and would require further
investigation to verify. Mixed-methods consisting of qualitative interviewing with law
enforcement and judicial officials in conjunction with analysis of more detailed drug-arrest data
would likely reveal the veritability of this hypothesis.
Proposition 47, which both reduced penalties for non-violent drug offenses (and some
low-level property offenses), has been the subject of a significant amount of empirical attention.
Prop-47 was seen to be significant in many of the time-series models. While not the focal point
of this work, Prop-47 has been seen to have no effect on violent crime and a minor increase in
larceny--roughly 9% (Bird et. al, 2018). The results of this analysis reinforce the findings of Bird
et al., 2018. The ramifications of reducing penalties for these kinds of offenders may include a
focus on treatment alternatives rather than incarceration. Proponents of labeling theory would
also likely argue that by keeping individuals out of the penal system, they are less likely to
reoffend. In the context of marijuana and marijuana users in California, Proposition 47 should
theoretically reduce crime according to this ideological perspective. SB-1449 (2011) set the tone
for future legalization in California. The time-series models in this work suggest that SB-1149
may have had a non-trivial impact on larceny, arson, aggravated assault, and burglary. However,
when combined with visual analysis of offense data, it seems likely that construction of a more
complex time-series model would be necessary to verify this. This line of inquiry was considered
87
to be out of the scope of this work and was not pursued, but has potential for future research.
Given, however, that the results of this work suggest that (other than arson) legalizing marijuana
did not affect offending, it seems fairly unlikely that reducing penalties for marijuana possession
would have significantly affected offending.
Lu et. al (2019) determined that legalizing marijuana did not have any significant effects
upon crime in Washington in Colorado; Farley and Orchowsky (2019) then interviewed law
enforcement and government officials in these states. These interviewees believed that
legalization had attracted crime from out of state, increased violence in drug markets, and
increased DUI rates, although the “hard” data suggests otherwise. Reliance merely on
quantitative methods and large datasets likely does not tell the whole story. Conversely,
interviews, even with qualified respondents, have limited utility if they can not be backed with
reliable data. Future research in this vein which integrates and encourages cooperation between
practitioners and academics could utilize the experience and specific knowledge of both groups
in order to construct new and improved datasets which may lend themselves to future research.
Policy implications for future iterations of this work may include focused policing on
varieties of crime which increase post-legalization. For example, should burglary be found to
increase post-legalization, law enforcement agencies may wish to concentrate efforts on
anti-burglary enforcement. These results could be strengthened by geospatial analysis of the
relationship between dispensary location and crime, or other analyses which are performed in
order to determine how marijuana-related crime occurs geographically, so that law enforcement
is better able to plan for how to allocate their resources to most efficiently combat increases in
88
crime post-legalization. This work also suggests that enforcement and policy may change as
some variants of crime may decrease, such as felony marijuana arrests.
89
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APPENDIX I
THE EFFECT OF MARIJUANA UPON THE INDIVIDUAL
The word “marijuana” is Mexican in origin, and refers to the entire plant as a whole.
There are three species of marijuana: cannabis sativa, cannabis indica,
and cannabis ruderalis.
The plant can be grown both indoors and outdoors, and under optimal conditions, can reach
maturity within sixty days (Armentano et. al, 2009). The stalks of the plant (which can grow up
to twenty feet tall), are the source of hemp. The psychoactive components of the plant are found
in the flowers of a female plant, also known as buds, and the leaves of plants of both genders.
While marijuana plants contain a variety of psychoactive and non-psychoactive (but still active)
chemicals, the plant is best known for the psychoactive compound delta-9-tetrahydrocannabinol
(THC). Other non-psychoactive but still active compounds include cannabigerol (CBG),
cannabinol (CBN), and cannabidiol (CBD), which provide a variety of effects when ingested, but
do not provide an intoxicant effect (Armentano et. al, 2009). Similar to the alcohol content of
liquor or beer, the concentration of THC within the product determines the strength of the
intoxicant effect. Ingesting a product with a high concentration (10-15% THC) will induce the
effects faster and more intensely than a low concentration product (most domestic marijuana
products contain a THC concentration of around 5%).
The intended effects of marijuana ingestion can vary by individual and by product but
most often include pleasant mental and/or physical sensations such as relaxation. Some
marijuana users report decreased anxiety and an increased ability to interact with others in social
situations. Users also often report that marijuana heightens some of their body’s senses such as
touch, sound, or hunger and taste (hence the term “munchies.”) (Armentano et. al, 2009; NIH,
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2019). Negative side-effects of marijuana ingestion include rapid heart beat, paranoia, and
dry-mouth. These effects are typically felt by a first-time user or when too much marijuana
product is ingested in a short period of time. While unpleasant, these effects pose minimal
physiological risk.
Marijuana can be ingested in a variety of ways. Inhalation and oral consumption are the
primary methods. Other products include topical applications such as lotions (although these
products are not typically used for intoxicant purposes) (DPA, 2019). Physical effects are felt
when the active compounds in marijuana pass into the user’s bloodstream, whether ingestion
occurs through consumption, inhalation, or other means. Inhalation produces the fastest
response, one that is almost instantaneous (DPA, 2019).
BENEFITS OF MARIJUANA USAGE
A 1988 DEA hearing concluded that marijuana usage “in strict medical terms” is safer
than many commonly consumed food in America, and is “one of the safest” therapeutically
active substances available (Young, 1988). Marijuana is frequently used for medical purposes,
with an estimated three million legal
medical marijuana users in the nation--and an unknown
number of citizens using illegal
marijuana for medical purposes (MPP, 2019). Marijuana is used
to treat symptoms corresponding to a variety of conditions including mild to moderate pain,
nausea, and eating disorders (Grinspoon, 2018), and has been seen in clinical studies to
significantly improve the negative effects of glaucoma (Mack & Joy, 2000). One study found
that marijuana has the potential to reduce inflammation in individuals suffering from HIV (Rizzo
et. al, 2018). Non-THC marijuana products such as CBD oils have been applauded in the past for
their ability to provide relief in cases of severe epilepsy (FDA, 2018) anxiety, neural
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degeneration (Artamento et. al, 2009), and pain (Grinspoon, 2018). The American Kennel Club
even acknowledges anecdotal evidence of CBD oil providing medical benefits for dogs (Burke,
2018).
The medical effects of marijuana may be corroborated by significant amounts of
anecdotal evidence and some clinical research, but compared to other recognized forms of
therapy and medication such as prescription opiates, the quantity of empirical research and
clinical study is fairly sparse. As a result, the medical potential of marijuana is relatively
uncertain in the scientific domain. Medical marijuana clinical research typically is not funded at
the federal level due to the classification of marijuana as a Schedule I drug (MAPS, 2019). In
order to remove marijuana from this classification, it would be necessary to provide evidence
that it does indeed have definitive and proven medical benefits This creates a situation in which
23
it is difficult to provide compelling clinical evidence in favor of the medical benefits of
marijuana.
RISKS OF MARIJUANA USAGE
Marijuana has some established medical and recreational benefits, both anecdotal and
clinical. However, there are proven as well as potentially unknown risks to marijuana users.
Marijuana can temporarily impair an individual's cognitive and motor abilities (Crean et. al,
2011). Individuals should not drive or operate heavy machinery while under the influence of
marijuana. Decision-making may also be adversely affected by marijuana use (Kauert et. al,
2006), as well as short-term memory impairment (Cherek et. al, 2005). The CDC acknowledges
that more research is necessary to make concrete conclusions, but advises pregnant women not to
23 I am not suggesting that marijuana does not have potential medical benefits, but rather that the present
categorization of marijuana as a Schedule I drug in the view of the federal government does.
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consume marijuana (ACOG, 2017), and that doing so may be related to low infant birth-weight
(Heard et. al, 2013) and developmental issues (Gunn et. al, 2016). There is some evidence as
well that men may lower their sperm count as a result of marijuana ingestion (Whan et. al, 2006).
Inhalation of marijuna can lead to serious negative health risks, including damage to the
airway and potential predisposition to infections and cancer (Tashkin, 2001). The American
Lung Association states that “smoke from marijuana combustion has been shown to contain
many of the same toxins, irritants and carcinogens as tobacco smoke” (ALA, 2019). There are
also concerns that cannabis may increase blood pressure (Harvard Medical School, 2017) and
contribute to fatty buildup in the liver (Chhabra et. al, 2018).
24
Perhaps one of the most significant dangers of marijuana is the dearth of literature on its
potential long-term effects . For a substance that enjoys such widespread use, there is a
25
relatively small amount of literature on potential complications. A National Institute of
Health-supported evidence-based review of the effects of marijuana use delivered a variety of
concerning conclusions. “Research on the effects of cannabis on cognition has generally lagged
behind studies on the cognitive effects of alcohol, cocaine, methamphetamine and heroin...few
controlled studies have investigated the effects of acute doses of cannabis on impulsive
behavior...this area of the literature has been fraught with inconsistencies in findings and is
complicated by discrepant definitions of what constitutes “long-term effects”” (Crean et. al,
2011). The lack of reliable literature and academic consensus has proven to be a persistent
24 Like much of the medical information surrounding marijuana use, this is a contested point, and some research
points to the opposite (Chhabra et. al, 2018).
25 A point that bears repeating again and again in this argument is not that literature is entirely absent regarding the
potential negative effects of marijuana, but that compared to other substances such as alcohol and opiates, the
existing knowledge base is insufficient to draw a consensus in the medical and scientific community.
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obstacle for advocates of medical and recreational marijuana, and a roadblock to effective policy
creation.
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