The Impact of Medical Marijuana Laws on Marijuana Use
and Other Risky Health Behaviors
Anna Choi∗
November 21, 2014
1
∗
Ph.D Candidate, Cornell University, Department of Policy Analysis and Management. Email:[email protected]
Acknowledgment: I am greatly thankful for John Cawley, Don Kenkel, Rachel Dunifon, Bryant Kim, Maria Fitzpatrick, Michael Lovenheim, Dan Grossman, and Rich Patterson for extremely helpful comments and advice on this
paper. I want to thank many seminar participants at Cornell University and participants at ASHEcon 2014 conference
who had great suggestions for my presentation and poster of this paper.
1
1
1
Introduction
As of November 2014, 23 states and the District of Columbia (D.C) passed laws to allow medical
use of marijuana for patients with certain health conditions. Maryland, Minnesota, and New York
legalized medical use of marijuana in 2014 and Ohio and Pennsylvania have pending legislations to
legalize medical marijuana. Although many states are recognizing the potential benefit of marijuana
for certain health conditions, marijuana is still illegal to use at the federal level.2 Even if it is illegal,
public support for marijuana legalization has increased in the U.S as shown in figure 1.3 This year
Alaska, Oregon, and D.C legalized recreational marijuana use and joined Colorado and Washington,
both of which legalized marijuana use in 2012. Prior to legalization, these five states already passed
medical marijuana laws (MMLs).
Given the current debate on marijuana legalization and as more states allow medical and/or
recreational marijuana use, it is important and policy relevant to examine how MMLs can affect
different health behaviors. Moreover, marijuana is the most commonly used illicit drug in the U.S
as seen in figure 2. Unlike other substances, marijuana use among individuals who are 12 or older
has been increasing since 2007. Marijuana is also the most popular illicit drug among 8th, 10th, and
12th graders as shown in figure 3. Accordingly, majority of the drug related arrests in the U.S are
related to marijuana. Out of 1.5 million drug related arrests in 2012, 48.5 percent were related to
sales, manufacturing, or possession of marijuana (FBI Uniform Crime reporting data, 2012).
Marijuana can be used to relieve symptoms rather than cure these conditions through analgesic
(pain-reducing), anxiolytic (stress-relieving), anti-spasticity, and mood enhancing properties (Fox et
al., 2013). Several clinical studies found evidence that marijuana is effective in reducing neuropathic
pain (Ellis et al., 2008), spasticity from multiple sclerosis (Rog et al., 2005), appetite loss from
chemotherapy, and nausea, vomiting, and lower intra-ocular eye pressure for glaucoma patients (Tomida et al., 2006). Although the exact list of approved health conditions vary across states, generally
2
Marijuana is classified as a Schedule I drug (Controlled Substance Act, 1970) along with heroin. Schedule I drugs
are classified as such because these drugs have no known or accepted medical purpose and pose a risk for abuse. Drug
Enforcement Administration (http://www.justice.gov/dea/druginfo/ds.shtml). According to DEA Drugs, substances,
and certain chemicals used to make drugs are classified into five (5) distinct categories or schedules depending upon
the drugs acceptable medical use and the drugs abuse or dependency potential. As the schedule increases, the drugs
abuse potential decreases (i.e. Schedule V has the least abuse potential)
3
http://www.gallup.com/poll/165539/first-time-americans-favor-legalizing-marijuana.aspx
2
patients with cancer, HIV/AIDS, glaucoma, severe or chronic pain, muscle spasm, Hepatitis B, multiple sclerosis and severe nausea are allowed to use marijuana given doctors recommendations. The
Institute of Medicine (1999) also reports potential medical value of marijuana but warns about the
possible risks associated with long term marijuana use, such as dependence and withdrawal symptoms
(Institute of Medicine, 1999; Watson, Benson and Joy, 2000).4
However, more research is necessary to fully understand the long term health consequences of
marijuana use. Some correlation studies find adverse health effects or outcomes associated with
marijuana use such as increased risk of cancer, marijuana abuse or dependence, and impaired cognitive
function (see Gordon et al., 2013; Hall and Degenhardt, 2009 for a review). According to a survey
among 1544 physicians, 69 percent said marijuana can help with certain health conditions.5 Physicians
who oppose medical use of marijuana because the long run health risks are unclear and debatable. It
is debatable whether the long term risks (if any) outweighs the current benefits. However, conducting
research for side effects, safety, and therapeutic effects of active marijuana ingredients among human
subjects is difficult and limited because marijuana is classified as a schedule I drug and illegal to
use at the federal level. Researchers also need to deal with regulations at the federal and state level
(Institute of Medicine, 1999).
Furthermore, it is particularly difficult to examine the benefits and risks associated with medical
use of marijuana when there is enormous variation in the type of marijuana products available in
dispensaries and various method of consumption (Sevigny et al., 2014). Different combinations of
marijuana strain (indica, sativa, hybrid), method of cultivation (hemp, sinsemilla, hydroponic), and
processing (herb, resin, oil) can produce different marijuana products (Sevigny et al., 2014). Consequently, different marijuana products (based on how they are raised and processed) carry different
amounts and mixtures of cannabinoids, resulting in varying degree of potency (Caulkins et al., 2012;
4
Furthermore, the Food and Drug Administration (FDA) developed and approved several drugs that use synthetic
delta-9-tetrahydrocannabinol (THC), which is a main psychoactive ingredient of marijuana. Marinol (Schedule III
drug), Nabilone (synthetic cannabinoid) and Cesamet (Schedule II drug) are available through prescription and are
developed to relieve nausea and vomiting. These are prescribed as appetite stimulants for AIDS patients or for
treatment of nausea and vomiting for cancer patients going through chemotherapy. These pills are made of synthetic
THC, cannbidiol (CBD), and other cannabinoids found in marijuana. Because of the potential for dependence and
addiction, Marinol is classified as a schedule III drug and Nabilone is classified as a schedule II drug (Svrakic et al.,
2012).
5
survey conducted by WebMD.comhttp://www.webmd.com/news/breaking-news/marijuana-on-main-street/
20140225/webmd-marijuana-survey-web
3
Sevigny et al., 2014).6 Although it was not statistically significant, Sevigny et al., (2014) find evidence that potency of marijuana increased by a half percentage point after allowing medical use of
marijuana. Anderson et al., (2013) also find lower price of high potent marijuana products among
medical marijuana states. Most medical marijuana states do not specifically regulate the potency,
specific ratio of different cannabinoids, or different forms of marijuana products (herbs, hash, oils,
or edible products). The ratio of THC to cannabidiol in marijuana products can lead to different
health consequences as cannabinoids have different effects on our body. Further research is needed
to examine the consequences of having different potency, mixture, and ratios of active compounds in
different marijuana products among patients with various helath conditions.
In the midst of this debate, it is important to study how MMLs affect marijuana use and purchase,
as well as other risky health behaviors, such as smoking, binge drinking, and use of other illicit
drugs. This is an important policy question to study because state MMLs might have unintended
consequences on non-medical use of marijuana or spillover effects on other risky health behaviors.
Previous research found a positive association between legalization of medical marijuana use and
marijuana consumption among youth and adolescents in those states (Cerda et al., 2012; Anderson
et al., 2012; Wall et al., 2011; Lynne-Landsman, 2013). Wen et al., (2014) also finds that MMLs
are related to higher marijuana use among adults but not among youths under 21. Anderson et al.,
(2012) also do not find any significant relationship between MMLs and youth marijuana use. Other
than the direct impact of MMLs on marijuana use, previous studies investigated the impact on other
risky health behaviors such as traffic fatalities and suicide. Anderson et al., (2013) found that MML
is associated with an 8-11 percent reduction in traffic fatalities (Anderson et al., 2013). In another
study, Anderson et al. (2014), examines the effect of MMLs on completed suicides using the Vital
6
There are many different ingredients found in marijuana. THC is the main psychoactive ingredient in marijuana,
creating euphoric or high effects. THC is one of more than 60 cannabinoids (compounds that are active agents of
cannabis) found in the marijuana or cannabis plant (Caulkins et al., 2012). When THC enters the brain it binds
to cannabinoid receptors, which are found in parts of the brain related to pleasure, memory, thinking, movement,
coordination, and sensory and time perception (National Institute on Drug Abuse (NIDA) research report, 2002). THC
produces similar effects to chemicals naturally found in our brain called endogenous cannabinoids. THC overstimulates
cannabinoid receptors and disrupts the function of endogenous cannabinoids, which produces feelings of euphoria or
being high (NIDA research report, 2002). CBD is nonpsychoactive ingredient that has high therapeutic potential
for anti-nausea and anti-inflammatory effects without producing intoxicating effects from THC (Mechoulam et al.,
2007). Some argue that CBD has antipsychotic activity that may reduce anxiety caused by high concentration of THC
(Caulkins et al., 2012)
4
Statistics data. The authors find that MMLs have a negative association with the suicide rate for
men aged 20 to 29 and for men aged 30 to 39 (Anderson et al., 2014). Also, Bachhuber et al., (2014)
find that MMLs are negatively associated with opioid related deaths, which suggests that marijuana
may be a substitute for opioids (Bachhuber et al., 2014).
Building upon previous studies, I examine the effects of MMLs on marijuana use, purchase and
other substance use such as smoking, binge drinking, and other illicit drug use. I exploit the variation
in MMLs across states over time to examine how MMLs affect individuals risky health behaviors.
To examine this question, I estimate difference-in-difference (DID) models using the restricted-use
National Survey of Drug Use and Health (NSDUH) data from 2004 to 2012, which is rich in individual
level information on drug use and has detailed geographic identifiers. From 2004 to 2012, 9 states
(MI, NJ, NM, AZ, CT, RI, MT, DE, and VT) and Washington D.C. enacted MMLs. Figure 4 shows
the 9 treated MML states and control states. As shown in table 1 and table 2, the details of the
law vary by states and specific dimensions of MMLs change over time in particular states (Pacula
and Sevigny, 2013). Some MML states specify how patients can get marijuana, either through home
cultivation and/or dispensaries while others do not specify.
Previous literature that investigate the effects of MMLs on marijuana use overlooked the heterogeneous nature of MMLs (Cerda et al., 2012; Wall et al., 2011; Lynne-Landsman, 2013; Wen et al.,
2014). Pacula et al., (2014) found that general MML policy indicator was not significantly associated with marijuana use whereas home cultivation and legal dispensaries are positively associated
with marijuana use. Therefore, in addition to basic MML policy change indicator, I examine different
components of MMLs, such as when the states began to allow legal dispensaries and home cultivation.
This paper contributes to the literature and provides important evidence on effects of MMLs
along the dimension that is not extensively studied through previous research. I examine the effects
of different dimensions of the MMLs on marijuana use and other risky health behaviors. Also, this
study can help inform policymakers about possible spillover effects among states that recently passed
MMLs, which policymakers can take into account when making changes to the policy. Unlike other
studies, I also control for state level anti-marijuana legalization sentiment and political environment
of the state such as whether the state has a republican governor, and has a Republican controlled
5
legislature, or Democratic controlled legislature. These factors are related to the probability of MML
passage and thus important to account for in my DID models.
Similar to previous studies, I find positive and significant relationship between MMLs and marijuana use. In addition, MMLs are significantly and positively associated with individuals’ perception
that it is not risky to use marijuana once or twice a week. Allowing dispensaries is associated with
18.9 percent increase in past month marijuana purchase and 12.1 percent increase in other drug use.
Home cultivation is positively related to binge drinking and negatively related with driving under the
influence (DUI) of drugs. This shows that different compoenents of MMLs may have heterogeneous
effects on different risky health behaviors.
2
How can MMLs affect marijuana and other risky health
behaviors?
MMLs can affect the demand for marijuana by changing the price, availability, and perception of
risk in using marijuana. It is possible that marijuana becomes more accessible in MML states that
specifically allow home cultivation or legal dispensaries. Because MML protects the patients who
are using marijuana for medical purposes under the state law, there is a high likelihood that some
individuals will exploit loopholes in the law. It is difficult to objectively verify health conditions
like nausea or chronic pain and to check that medical marijuana is only used by registered patients
(Anderson et al., 2013). In states that have loose regulations on medical use of marijuana, such as
California, less than 5 percent of patients seeking marijuana for medical use actually have approved
health conditions (Caulkins et al., 2012). The most common conditions for which marijuana use is
recommended are non-specific (i.e. pain, insomnia, anxiety problems) and are not related to a certain
medical condition or disease, such as HIV/AIDS. As shown in table 3, among 9 treated MML states
I observe in my data, severe or chronic pain comprises the highest portion of health condition that
registered patients seek marijuana for. It is quite likely that marijuana intended for medical use is
instead used for non-medical purposes (Anderson et al., 2013). Currently, there is no system in place
that tracks how often registered patients buys marijuana in dispensaries. It is possible to sell or divert
6
supplies of medical marijuana to recreational marijuana users.7
MMLs can also affect individuals’ attitudes or perception of risk regarding marijuana use. As
more states accept marijuana for medical use over time, this can lower the perception of risk in
using marijuana because marijuana can be used as an alternative method that can relieve symptoms
associated with certain diseases and health conditions. Individuals can possibly view MMLs as an
advertising campaign of marijuana, which can lead to lower perceived risk of using marijuana (Joffe
et al., 2004).8 Carpenter and Pechmann (2011) found negative relationship between exposures to
antidrug advertisements and past month marijuana use among female youths. As shown in these
studies, exposure to drug policy or campaign can affect perception of risk as well as actual consumption
of the substance.
With increased access and supply of marijuana, price of marijuana may decrease. Anderson et
al., (2013) also find lower price of high potent marijuana products among medical marijuana states.
However, because marijuana is still illegal to use at the federal level, full price of marijuana includes
search cost, monetary costs of purchasing marijuana, as well as non pecuniary costs such as health risks
and legal risks of using marijuana (Pacula et al., 2014). In MML states that allow dispensaries or home
cultivation, search cost of marijuana will decrease. Monetary cost of marijuana sold from dispensaries
may decrease if the number of dispensaries rise and increases competition among dispensaries. Because
of the disagreement between the federal and state governments regarding legalization of medical
marijuana use, dispensaries that operate in MML states face legal risks and these uncertainties can
affect the number of dispensaries operating in MML states. Similarly, registered medical marijuana
patients may be prosecuted under the federal law. Subsequent to the announcement by the U.S.
Attorney General that the Justice department would not raid marijuana dispensaries that complied
with state MMLs the number of dispensaries increased (Ogden, 2009). As seen in figure 5, Montana
experienced a boom in marijuana patients and dispensaries after 2009. This shows that legal risks
play an important role in the number of registered patients as well as dispensaries.
7
In states like Colorado and Oregon, recreational and medical marijuana markets are closely related and a substantial
amount of medical marijuana is overproduced and leaked into the recreational markets (Sevigny and Pacula, 2014;
Finlaw and Brohl, 2013; Rendon, 2013; Wirfs-Brock, Seaton, and Sutherland, 2010).
8
In New Jersey, cable subscribers can see TV advertisement of webpage marijuanadoctors.com which is a like
yellow-page for medical marijuanaphysicians.http://www.nj.com/business/index.ssf/2014/03/marijuana_tv_ad_
debuts_to_new_jersey_comcast_subscribers.html
7
Because MMLs can affect both medical and recreational use of marijuana, marijuana can have
unintended consequences on other risky health behaviors. First, marijuana may act as a gateway drug,
leading to consumption of other hard drugs. Although not causal, many previous studies interpret
the fact that young adult cocaine users have previously used marijuana as an evidence of the gateway
effect of marijuana (Mills and Noyes, 1984; Newcomb and Bentler, 1986). DeSimone (1998) found
that previous consumption of marijuana raised the probability of current cocaine consumption by 29
percentage points among young adults (DeSimone, 1998). Similarly, Deza (2012) found the largest
stepping stone effect among young adults where previous marijuana use led to an increased chance of
future marijuana and harder drug consumption (Deza, 2012). It is possible that MMLs can lead to
higher illicit drug use because marijuana may act as a gateway drug.
In addition, marijuana may be a substitute or complement to other substances such as alcohol,
cigarettes, and other drugs. In that case, passing MMLs will not only affect marijuana consumption
but can have spillover effects on other substance use. Studies found evidence for complementarity
between cigarettes and marijuana use particularly among youths (Farrelly et al., 2001; Cameron and
Williams, 2001; Chaloupka et al., 1999). Saffer and Chaloupka (1999) find negative and significant
cross price effects of cocaine and heroin with marijuana, suggesting they are complements to marijuana
(Saffer and Chaloupka, 1999). Between marijuana and alcohol, evidence is mixed. Some studies find
evidence that they are complements (Farrelly et al., 1999; Pacula, 1998; Williams et al., 2004; Yoruk
and Yoruk, 2011), while others find that they are substitutes (Anderson et al., 2013; Crost and
Guerrero, 2012; Cameron and Williams, 2001; Dinardo and Lemiuex, 2001). As Pacula and Sevigny
(2013) note, the results of different studies examining the effect of raising the Minimum Legal Drinking
Age on marijuana use may vary due to use of different data sets, sample restrictions, and outcome
variables. If the substance is a complement to marijuana, then consumption of that substance may
increase subsequent to MML passage. On the other hand, if the substance is a substitute, then
consumption of substitutable substance will decrease after MML passage.
8
3
Data
I use the restricted-use NSDUH data with state identifiers from 2004 to 2012, which is a repeated
cross sectional data. During 2004 to 2012, 9 states and D.C. passed MMLs. This is an annual survey
that collects data from about 70,000 individuals, age 12 and older, who are randomly selected from
U.S. civilian non-institutionalized population. NSDUH collects data from residents of households,
and non-institutional group quarters (dorm, rooming houses, shelters, etc.) but does not include
homeless individuals who do not use shelters or residents of institutional group quarters (jails and
hospitals). NSDUH is a well suited data set for this study because it contains detailed questionnaires
about individuals illicit drug, tobacco, and alcohol use and frequency, dependency, mental health and
some other health behaviors.9
Because NSDUH is a repeated cross sectional dataset, it does not follow individuals over time.
Hence, I cannot observe or compare individuals behaviors before and after the policy change. Survey
responses are self-reported so it might be susceptible to underreporting, especially because these are
very detailed and sensitive questionnaires about individuals drug use and other risky health behaviors.
To a certain extent this can be minimized by the way survey responses are collected. The survey is
done in a private setting using a laptop computer, instead of having an interviewer administer the
survey. NSDUH uses audio computer-assisted self-interviewing (ACASI), which gives respondents
more privacy, ensures anonymity, and in turn can help reduce underreporting. This can help reduce
under reporting of risky health behaviors due to social desirability bias, which can lead some better
educated individuals to under report because they might know better what the socially acceptable
norm is and have more desire to provide others with a favorable impression of themselves and be more
embarrassed to admit engaging in illegal behaviors (Edwards, 1957). Another source of reporting error
is miscomprehension of the survey questionnaires or false memory recalls. NSDUH contain information
about whether the respondent had any difficulty understanding the questionnaire and I control for
this factor in the analysis.
The Substance Abuse and Mental Health Services Administration (SAMHSA) and the National
9
http://www.samhsa.gov/data/NSDUH/2012SummNatFindDetTables/NationalFindings/NSDUHresults2012.
htm#ch1.1
9
Institute of Drug Abuse (NIDA) supported a study that investigates the validity of self-reported
responses on illicit drugs in the NSDUH data among individuals of age 12 to 25 (Harrison et al.,
2007). This study on the validity of self reported drug use responses in NSDUH had representative
sample of the U.S. population10 but was limited to those of age 12 to 25. More than 4400 respondents
completed the interview from the initial sample of 6000 respondents.11
For marijuana use, 89.8 percent of respondents had congruent interview and urine test results,
which was mostly driven by 82.9 percent of respondents who reported no use and tested negative.
However, 4.4 percent reported no use and tested positive and 5.8 percent reported use but tested
negative.12 There is higher rate of false negative reports, where individuals report no use but test
results show otherwise. But also, there are instances where individuals report use but test result is
negative, which could be due to reporting error or this could indicate that they are not heavy drug
users. Given this study’s result, I suspect that NSDUH will have some instances of under reporting
of drug use for those of age 12 to 25. However, the rates of false negative or positive responses are
quite low and I control for whether the individual had complete privacy during the interview, which
can account for reporting error to some extent.
Another study examined the validity of self-reported marijuana use data using the data Arrestee
Drug Abuse Monitoring Program (ADAM) from 1998 to 2002. Kuhns and Miller (2012) find higher
validity levels of self-reported marijuana use among juvenile arrestees in MML states. This may
suggest that passage of MMLs can affect validity of self-reported marijuana use. It is possible that
because medical use of marijuana is allowed, individuals living in MML states might have lower
probability of under reporting marijuana use. If this is true in the NSDUH data, marijuana use
results may be upwardly biased because the increase in marijuana use might be driven mostly by
10
except for Alaska and Hawaii
The response rate of 74.3 percent and among those who completed the interview, 89.4 percent consented to givine
urine or hair samples. After the interview was completed, respondents learned about biological specimen collection.
Similar to NSDUH, this study used computer-assisted interviewing (CAI) to collect the data and used ACASI for
sensitive questions. In addition to 30 day window of drug use, additional questions were asked within a shorter time
period, such as marijuana use within the past 7 days or 3 days, to be able to compare and detect the drug use with
urine or hair samples (Harrison et al., 2007).
12
For 3 or 7 day period, the agreement rate between self reported use and urine test results was higher but also
had similar patterns of false positive and negative responses. Cocaine results were similar to marijuana’s. 7 day selfreported response had 98.5 percent agreement with urine drug test results, mostly driven by non-users, who comprised
98.2 percent.
11
10
users who lied or under reported marijuana use before the passage of MMLs.
To investigate that possibility, I proxy for under reporting of marijuana use by creating an indicator
variable that equals 1 if you used marijuana in the past year but not in the past month. I create the
same indicator variable for cigarettes and alcohol use. We might suspect that because marijuana is
still illegal to use under the federal law, people might be under reporting their use and that passage
MMLs might reduce under reporting. This would not necessarily be true for other legal substances
like alcohol or cigarettes. If the percent of respondents who used marijuana in the past year but not
in the past month increases in the years subsequent to MML passage and I do not observe similar
trends for alcohol or cigarettes, then this might suggest that there are more respondents are reporting
marijuana use due to passage of MMLs. However, as shown in figure 6a, 6b, and 6c, I do not observe
a sharp increase of individuals who report past year marijuana use but not past month use. Overall,
the trend look similar across marijuana, cigarettes, and alcohol. Even though this is not a conclusive
evidence of more respondents suddenly reporting marijuana use after the passage of MMLs, these
figures suggest that marijuana does not show a sharp increase of recent quitters subsequent to MML
passage.
4
Empirical strategy
First, I will exploit the variation of medical marijuana laws across states over time to identify the
impacts of MMLs on risky health behaviors. Over the period that restricted use NSDUH data covers,
2004-12, 9 states and D.C. enacted legislation allowing medical marijuana use. Treatment states are
these 9 states and D.C. that passed MMLs between 2004 and 2012. Control states are those states
that never had MMLs. Always treated states are the states that passed MMLs prior to 2004.
Figures 8a to 8d show that treatment states that passed MMLs from 2004 to 2012 have higher
baseline rates of marijuana use outcomes. However, I cannot conclude that MMLs produced higher
marijuana use rates. It is possible that states with higher rates of marijuana users may be more likely
to pass these laws. Figures 8a to 8d show that treatment states that passed MMLs from 2004 to
2012 have higher baseline rates of marijuana use outcomes. However, I cannot conclude that MMLs
11
produced higher marijuana use rates. It is possible that states with higher rates of marijuana users
may be more likely to pass these laws. As seen in figure 8b, mariuana use among users is increasing
in both treatment and control states.13 Figure 8a shows that baseline marijuana use rate is is higher
among MML states from 2004-2012 compared to control states and the gap between treatment and
control states widens subsequent to MML passage.
Similarly, figure 8d displays that both treatment and control states have increasing number of
respondents who think that it is not risky to use marijuana once or twice a week over time but treatment states have higher baseline rates than control states. Figure 8c shows that baseline marijuana
purchase rate is also higher among treatment than among control states. There is no MML state
that has unusual trend of marijuana use or any other concurrent shocks that affected marijuana use
outcome as shown in figure 7. Similar to marijuana use trends, figures 9a and 9c show that treatment
states have higher baseline rates of other drug use or binge drinking but the rate is not increasing
over time for either treatment or control states. Smoking rate seem to be similar across treatment
and control states as shown in figure 9b.
I use the difference-in-differences strategy to estimate the effects of medical marijuana use policies:
Yist = β0 + β1 M M Lst + β2 σist + β3 γst + Ts + Zt + Ts × Zt + st
(1)
Yist is a measure of various risky health behaviors such as previous month marijuana use, purchase,
binge drinking, illicit drug use excluding marijuana marijuana, pain reliever use, past year driving
under the influence of alcohol or drugs, illicit drug abuse, alcohol abuse, and marijuana abuse for
individual i in state s at time t. The main explanatory variable of interest is M M Lst , an indicator
variable that becomes one in the year that the state legalizes medical marijuana use and also equals
one for all subsequent years. For the first year of MML implementation, I use the fraction of the year
that was under the influence of MMLs since the effective date.14 . M M Lst equals one throughout the
data period for states that previously passed MMLs prior to 2004. M M Lst equals zero for all control
13
I randomly assigned years of MMLs to control states. I used the distribution of law changes in the NSDUH sample
from 2004 to 2012 to inform how to randomize event time for the controls
14
i.e. If a state passes MML in January of 2004 and the law becomes effective in July of 2004, M M Lst equals 0.5 in
2004 and 1 in subsequent years
12
states that never implemented MMLs.
σist is a vector of individual characteristics (age, race, sex, marital status, education, income,
whether or not an individual has any health insurance, whether they have excellent self-reported
health status, whether you have a child, whether you live in a urban area). I also control for whether
the individual has any problem understanding the questionnaire and whether the survey was done in
the most private setting possible to minimize reporting error. In order to account for any anticipation
effect, I control for an indicator variable for individuals who are interviewed between the MML enacted
and effective dates.
γst is a vector of socioeconomic characteristics and political environment of the state (per capita
income, unemployment rate, Democratic governor, Democratic controlled legislature, split legislature
where Republican controlled legislature is the omitted category) as well as state level policy variables relevant to marijuana use (state cigarette tax, state beer tax, whether the state decriminalized
possession of marijuana (small amount), and drug related arrests per capita, state level marijuana
legalization sentiment).
Ts and Zt are state and year fixed effects respectively. State fixed effects remove time-invariant
differences between treatment and control states and year fixed effects absorb common time shocks.
Standard errors are clustered at the state level to adjust for the serial correlation. All of the regressions are weighted by NSDUH sampling weights. Number of observations is rounded to the nearest
hundreds due to disclosure review protocols. In addition to this basic model specification, I estimate
another model controlling for state specific linear time trends (to take into account unobserved state
level characteristics that change over time linearly such as other substance use) and another model
controlling for both state-specific pre-trends and post-trends relative to when MML is passed. I run
three model specifications for all of the outcome variables.15 The pre trend variable ranges from -7
to 0 incrementally, where 0 indicates the year that MML becomes effective in that state. The post
trend variable ranges from 0 to 7 incrementally, where 0 indicates the effective date of MML.16 I estimate OLS models for continuous dependent variables and linear probability models for dichotomous
15
I report the results from this model, as the point estimates are similar across different model specifications.
For the states that passed MML law before 2004, post trend variables are calculated based on the years since they
passed the law and pre trend variables are all set to 0.
16
13
dependent variables. I estimate this basic model separately for those under 21 and over 21 following
previous literature that investigate the effects of MMLs.
Second, I estimate the same difference-in-differences model as above with two different treatments
of MMLs, home cultivation and dispensaries.
Yist = β0 + β1 Dispensaryst + β2 σist + β3 γst + Ts + Zt + Ts × Zt + st
(2)
The main variable of interest is Dispensaryst , which is a dichotomous variable that equals 1 in
year that state allows retail sales of marijuana through dispensaries (and in all subsequent years) and
the variable equals 0 otherwise (following the definition of Sevigny and Pacula, 2014). During the
study period from 2004 to 2012, 6 states amended or enacted laws to have state licensed dispensaries
and 2 states and D.C. passed laws to allow dispensaries (but in these states, first dispensary opened
in 2013, so do not count these as states that legally allowed dispensary before 2012; see tables 1 and 2
for details). This is an important dimension of MMLs because dispensaries are important supplies of
marijuana for patients in addition to home cultivation. It is also the case that in many MML states,
boom in dispensaries are positively correlated with the number of patients and caregivers in the state.
For example, as shown in figure ??, dispensaries in Montana exhibit this pattern. I also estimate the
same equation where the treatment indicator is allowing home cultivation.
Identification relies on the assumption that MML passage is uncorrelated with unobserved determinant of marijuana use or other risky health behaviors. One of the potential threats to identification
is policy endogeneity. There might be certain unobserved characteristics that distinguish states that
allow medical use of marijuana as opposed to other states that do not. It is possible that states with
more liberal political environment and/or states that already have higher baseline marijuana use are
more likely to pass these legislations. In that case, these policy changes across states over time are not
exogenous. If unobserved factors that are correlated with both legalization of medical marijuana use
and other risky health behaviors do not change over time, then controlling for state fixed effects will
remove the bias. Controlling for year fixed effects will remove time variant trends that are common
across states.
In addition, I control for state level marijuana legalization sentiment by merging the aggregated
14
Simmons National Consumer Survey (NCS) data from 2004 to 2009.17 Similar to DeCicca et al.
(2007), it is important to account for sentiment for marijuana legalization because this can be correlated with the passage of MMLs as well as baseline marijuana use in the states (DeCicca et al.,
2007). NCS is an annual survey of adults of age 18 or older and measures various consumer behavior
and opinions to products, brands, media, and other social issues. One of the questionnaires asked
participants, Do you think marijuana should be legalized? Participants choose a response from five
categoriesdisagree a lot, disagree a little bit, neither disagree nor agree, agree a little bit, and agree a
lot. I created an indicator variable that equals 1 if respondent disagrees a lot or disagrees a little bit
with the marijuana legalization question and aggregated it at the state level. For this questionnaire,
there is no respondent from Alaska or Hawaii. I weight this variable based on the sampling weights
given by NCS before merging to NSDUH data.
It is possible that variation in passing MMLs are not exogenous. In order to examine that possibility, I estimate an auxiliary model to examine the likelihood of a state enacting legislation regarding
legalization of medical marijuana use. I follow the empirical strategy of Cawley and Liu (2008) in
estimating this model. Using discrete time hazard model, I examine what factors are correlated with
likelihood of MML passage. I find that having a Democrat controlled state legislature is significantly
and positively related to MML passage. I control for state political environment and state level
marijuana legalization sentiment which can affect the passage of MMLs.18
17
These are the only available years for the data that I could access. I linearly extrapolate the data for 2010 to 2012.
The dependent variable is whether the state enacted MML. I collected the information about legalization of marijuana from the National Conference of State Legislatures during 2004-12. I control for political environment of the state
because this can affect state legislative actions (Cawley and Liu, 2008). I created an indicator variable for whether the
governor is a Democrat, whether Democrats control the state legislature, and whether Republicans control the state
legislature, whether the state has a split legislature (the two chambers have different majority parties), and the omitted
category is Republican controlled legislature. State legislations may be strongly associated with various socioeconomic
characteristics of the state. I control for various socioeconomic characteristics of the state such as percent of population
with bachelors degree or higher, racial composition (white is the omitted category), log per capita income, and percent
of population enrolled in Medicaid, poverty. Data for log per capita income comes from Bureau of Economic Analysis, Department of Commerce. I retrieved the rest of the data on state socioeconomic characteristics from Current
Population Survey (annual average).
I also control for per capita drug related arrests and per capita DUI arrests each year. The annual data comes
from Federal Bureau of Investigations Uniform Crime Reports. I use per capita arrests to account for variation in
state population. Potentially, states that have higher number of drug arrests are more likely to enact MMLs to lower
enforcement costs related to drugs. In addition, I control for the percentage of subjects admitted to treatment due to
alcohol use, marijuana use, or other drugs (except marijuana or alcohol) using the Treatment Episodes Data Set (TEDS)
data from 2004 to 2010. I also control for state level marijuana legalization sentiment calculated from the Simmons
data and whether the state decriminalized small possession of marijuana. These are relevant state characteristics that
could affect the baseline marijuana use as well as the likelihood of passing MMLs.
18
15
5
Results
All models include state and year fixed effects, state pre and post trends and standard errors are
clustered at the state level. Table 4 shows the descriptive statistics and the treatment states have
higher baseline marijuana use than control states. However, among marijuana users, defined as those
who used marijuana in the past month or year, average number of days used marijuana is slightly
higher among control states. Also, treatment states have higher rates of other illicit drug use and
driving under the influence of alcohol or drugs. On the other hand, treatment and control states are
similar in terms of smoking, binge drinking, and illicit drug abuse prevalence rates.
5.1
Direct impacts on marijuana use and purchase
I examine the impact of MMLs on both the extensive and intensive margins of overall marijuana use.
As seen in table 5, implementing MMLs is positively associated with marijuana use in the overall
sample and over 21 sample. This result is similar to Wen at al., (2014) but the magnitude of the
point estimate is larger. MMLs are associated with 23.7 percent and 29.6 percent increase in the
likelihood of using marijuana in the past month for the overall and over 21 samples respectively.
Also, MMLs are associated with lower perception of risk using marijuana once or twice a week. It
is possible that because states passing MMLs recognize the potential use of marijuana for relieving
symptoms of various health conditions, individuals’ may not perceive marijuana as harmful. I also
find positive and significant relationship between MMLs and marijuana use among users in terms of
States that have passed medical marijuana use legislations during 2004 to 2011 are more likely to have Democrats
in control of the state legislature than states that have not passed these legislations (and the opposite in terms of
Republican being in control of the state legislature). Also, MML states have higher portion of population enrolled in
Medicaid, poverty, and higher portion of Hispanic population but the difference is not statistically significant. Similarly,
drug arrest per capita is 0.1 percent higher among non MML states and the difference is significant at 10 percent level
(see table 4 for descriptive statistics).
I use discrete time hazard models to estimate the association between state political and socioeconomic characteristics
and passing legislations that allow medical marijuana use. Dependent variable is an indicator variable for whether the
state passed legislations legalizing medical marijuana use from 2004 to 2012. I only include year fixed effects in all of
the models.
I find that having a Democrat controlled state legislature compared to Republican controlled legislature is associated
with a 3 percentage point higher probability of passing MMLs. This significant and positive relationship remains as I
add more controls such as marijuana legalization sentiment, drug arrests per capita and treatment admissions due to
alcohol, marijuana, or other drugs. On the other hand, decriminalization of small marijuana possession is negatively
associated with the likelihood of passing MMLs. I control for political environment of the state and anti-marijuana
legalization sentiment in my main analysis.
16
the intensive margin. This may suggest that individuals who have been using marijuana in the past
are now increasing the frequency of marijuana use possibly because of increased supply and reduced
legal risks associated with using marijuana. Because long term health risks are unclear, it might
be important to regulate and track how much users consumer over time similar to prescription drug
monitoring program.
I find similar results when I use a different treatment indicator, namely home cultivation and
dispensaries. As shown in table 9, marijuana use among users is positively and significantly associated
with dispensaries and home cultivation. I find stronger relationship at the intensive margin, which
may suggest that MMLs have greater impact on those who have been using marijuana. Other studies
like Wen et al., (2014) find that positive relationship between marijuana use and MMLs is mostly
driven by adults over 21.
Also, allowing dispensaries in a state is positively and significantly associated with marijuana
purchase in the past month. Having legal dispensaries is associated with 18.9 percent increase in
marijuana purchase in the past month. This shows that clearly specifying the supply channel of marijuana is associated with increased purchase. Unlike using the basic MML policy change indicator, low
risk perception of frequent marijuana use has positive but insignificant association with dispensaries
or home cultivation. This might be due to the fact that passage of the MMLs matter for individuals’
perception and not subsequent changes to the policy.
5.2
Spillover effects on other substances, and alcohols, cigarettes
As table 6 and table 7 show, with respect to various risky health behaviors, such as smoking, binge
drinking, and other drug use, I do not find any statistically significant relationship with MMLs except
for alcohol abuse or dependence. This may suggest that alcohol is a complement for marijuana, and
perhaps among heavy drinkers. Wen et al., (2014) finds positive relationship between MMLs and
binge drinking but I find higher alcohol abuse or dependence instead. This relationship holds true
for other treatment indicators such as home cultivation and dispensaries as table 11. I find that
MMLs, dispensaries, and home cultivation have positive and significant relationship with marijuana
use among users at the intensive margin. Similarly, MMLs, dispensaries, and home cultivation have
17
positive and significant relationship with alcohol abuse or dependence. Taken together, this may
suggest that MMLs can give already users or long term users of marijuana free rein in accessing
and using marijuana. If marijuana and alcohol are complements, then possibly alcohol abuse or
dependence may increase among heavy users of alcohol and/or marijuana.
In addition, I find significant relationship with other substance use and dispensaries. I find that
allowing dispensaries is positively and significantly associated with other drug use (excluding marijuana), driving under the influence (DUI) of alcohol, and DUI of drugs. Allowing dispensaries leads to
a 12.1 percent increase in other drug use excluding marijuana. It is possible that individuals maybe
exposed to more concentrated marijuana products than smoked marijuana which home growers of
marijuana would use. This might be suggestive evidence of gateway effect. Previous studies find
some suggestive evidence that in states that allow medical use of marijuana, potency of marijuana
also increases (Sevigny et al., 2014).
On the other hand, allowing home cultivation, where registered patients are allowed to grow
marijuana in their homes, has negative and significant relationship with DUI of drugs by 12.8 percent.
It is possible that individuals who grow marijuana at home have lower probability of DUI of drugs
because of accessibility and as MMLs usually do not allow registered patients to use marijuana in
public areas. However, because dispensaries sell variety of marijuana products such as marijuana
brownies, juices, and oils, it may increase the probability of drugged driving. It is difficult to identify
whether someone used marijuana if the users did not smoke marijuana and consumed marijuana
infused products such as brownies.
6
6.1
Extensions
Effects of MMLs on probability of moving to a MML state
It is possible that individuals, particularly patients who want to use marijuana for their medical
conditions, move to a state where medical marijuana use is allowed. I examine this by estimating
a linear probability model of drug magnet as shown in table 8. From 2006, NSDUH asked the
respondents in which state they lived in a year ago and two years ago. Based on this questionnaire,
18
I created an indicator variable that equals 1 if the respondent moved to a MML state from a state
that does not allow medical use of marijuana in any period. As shown in table 8, I find positive
but insignificant relationship between MMLs and the probability of individuals moving to a MML
state. When I control for all of the individual and state level control variables, passage of MMLs is
associated with 6.3 percentage point increase in the probability of moving to a MML state from a non
MML state but this is statistically insignificant. If I find that passage MMLs lead more individuals to
migrate to these states, then it will upwardly bias the estimates in DID analysis as these individuals
are moving to use marijuana in MML state.
7
Effects of MMLs on cross border shopping
Furthermore, it is possible that individuals living nearby MML states may cross the state border to
purchase medical or non-medical marijuana. I created a dichotomous variable for individuals living in
counties that border MML states and estimated the model on the likelihood of marijuana purchase in
the past month. In any of the model specifications, living in MML bordering counties does not lead
to a significant increase in marijuana purchase. This does not provide any significant evidence that
individuals driving across the state border to get marijuana or migrate to be able to use marijuana.
8
Conclusions
This study examines the early effects of state MMLs on individuals marijuana use and other risky
health behaviors. Using rich individual level data on drug use, binge drinking, DUI, smoking, and drug
and alcohol abuse, this paper contributes to the literature by demonstrating how MMLs might affect
different health behaviors. Furthermore, I use a different definition of MMLs using specific dimension
of the law such as allowing dispensaries and/or home cultivation. Passing MMLs is positively and
significantly associated with marijuana use, but does not have any significant effect on other risky
health behaviors such as binge drinking, smoking, driving under the influence of drugs, and illicit
drug use. Findings related to marijuana is similar to previous studies (Wen et al., 2014).
Overall, I find suggestive evidence that two components of MMLs may play a different role on
19
spillover effects. Even though, basic MMLs treatment indicator is not significantly related with other
risky health behaviors, allowing dispensaries in a MML state is positively and significantly associated
with marijuana purchase and DUI of drugs, and allowing home cultivation has the opposite effect on
DUI of drugs. Although this does not mean that MMLs do not affect other risky health behaviors
at all, more careful examination of what each state’s law entails seems important. Also, alcohol
abuse or dependence was positively and significantly related to MML passage, dispensaries, and home
cultivation, suggesting that allowing medical use of marijuana may affect heavy users and that alcohol
and marijuana can be complements.
This study examines NSDUH data from 2004 to 2012, and because this is not a panel data set, I
cannot examine the effects of MMLs on individuals’ behavior change over time. Also, this paper cannot
examine the long term effects of the law for the most recent MML states. Collecting longitudinal
data with longer time periods and more careful examination of how MMLs in each state evolve over
time can improve future research on the effects of MMLs on various health outcomes. Furthermore,
it will be useful to have objective verification of self-reported responses to sensitive questions among
a smaller group of individuals within the sample. I attempted to examine the possibility of policy
endogeneity through estimating a model of likelihood of MML passage. I find that state’s political
environment is positively and significantly associated with hte probability of MML passage. Thus, I
control for state level political environment and also state level anti-marijuana legalization sentiment
in the main models to account for this issue.
The results from this paper suggest that all MMLs are not created equal. More research is necessary
to examine how various combinations of different ingredients in marijuana can affect individuals with
certain symptoms. Policy makers should consider regulating the potency of marijuana products sold
in dispensaries as they might or might not be related to efficacy of symptom reliefs for patients.
Because long term health risks of marijuana use is still debatable, it might be necessary to create a
quota or limit the potency level of marijuana products until we learn about the potential long run
risks. Policymakers can consider adopting a monitoring system similar to prescription drug monitoring
program. Also, it might be important to make sure that dispensaries label different ingredients in
various marijuana products in non plant forms because patients may not necessarily understand the
20
long run consequences of using such products.
21
Figure 1: Public opinion on marijuana legalization
Figure 2: Past month illicit drug use (age 12 or older), NSDUH 2002-12
22
Figure 3: Past year substance use (8th, 10th, and 12th grades)
Figure 4: Map of MML states
23
Figure 5: Data on medical marijuana patients, providers, doctors in Montana
24
Figure 6: ”Recent quitters” who used the substance in the past year but not past month
(a) Marijuana
(b) Cigarettes
(c) Alcohol
25
Figure 7: Event study- Marijuana use among each MML states
26
Figure 8: Event study- Marijuana Use
(a) Past month marijuana use
(b) Past month marijuana among users (#days)
(c) Past month marijuana purchase
(d) % who think it’s not risky to use marijuana often
27
Figure 9: Event study- Other substance use
(a) Other illicit drug (excluding marijuana)
(b) Cigarette use
(c) Binge drinking
(d) Illicit drug abuse
28
Table 1: MMLs 2004 to 2012
State
Effective date
Allows dispensaries
Allows home
cultivation
2010
NA
NA
2008
2005
NA
2008
2006
2004
Approved by
ballot initiative
Yes
No
No
Yes
Yes
No
No
No
No
Arizona
November 2, 2010 2010
Delaware
July 1, 2011
2011
D.C
July 27, 2010
2010 (first opened in 2013)
Michigan
December 4, 2008 2010 (de facto)
Montana
November 2, 2004 2009 (de facto)
New Jersey
October 1, 2010
2010
New Mexico July 1, 2007
2009
Rhode Island January 3, 2006
2010 (first opened in 2013)
Vermont
July 1, 2004
2010 (first opened in 2013)
NA: Not legally allowed per state law.
Source: procon.org, Sevigny et al., (2014), Pacula and Sevigny (2013), Anderson et al. (2013)
Table 2: MMLs prior to 2004
State
Effective date
Legally allows Dispensaries
Allows home
dispensaries
de facto operating cultivation
Alaska
March 4, 1999
No
No
1999
California
November 6, 1996
2005
1997
1997
Colorado
June 1, 2001
2010
2005
2001
Hawaii
December, 28, 2000 No
No
2001
Maine
December 22, 1999 No
No
2000
Nevada
October 1, 2001
No
No
2002
Oregon
December 3, 1998
No
2009
1999
Washington November 3, 1998
No
2009
1999
Source: procon.org, Sevigny et al., (2014), Pacula and Sevigny (2013), Anderson et al. (2013)
29
Approved by
ballot initiative
Yes
Yes
Yes
No
Yes
Yes
Yes
Yes
Table 3: State registry data
Arizona
(2012)
Montana
(2010)
Rhode Island
(2010)
Registered
patients
35641
Providers Severe or
Chronic pain (%)
3
71
18,012
395
3073 patients
(1948 caregivers)
Hepatitis C (%) HIV/AIDS (%)
Cancer (%)
Male (%)
Average Age
Age 18-40 years (%)
1.83
1.75
0.49
73
42.8
48.40
43
46
3.70
4.26 (Cancer,
glaucoma, or
HIV/AIDS)
5.87
65.1 (Severe or
chronic pain only?)
19.51 (Chronic pain)
38.94 (Chronic or
Debilitating
Disease or Condition*)
5.74
Vermont
385 patients
(Aug, 2011)
(67 caregivers)
Source: State medical marijuana registry (data directly from the state department or the website), Anderson et al. (2013)
? If I add multiple conditions that involve severe or chronic pain
this number is 84.6% (severe or chronic pain and nausea OR muscle spasms OR seizures)
For RI: 16.05% (muscle spasms), 5.68 %(severe nausea)
* Chronic or debilitating disease or conditions include cachexia or wasting syndrome, severe,
debilitating, chronic pain, severe nausea, seizures, including,
but not limited to, those characteristic of epilepsy, severe persistent muscle spasms
including, but not limited to, those characteristic of multiple sclerosis or Crohns disease
or agitation of Alzheimers disease.
30
Table 4: Descriptive statistics
Dependent variables
Past month marijuana use
Past year marijuana use
# days used marijuana past month
# days used marijuana past year
Marijuana purchase
No risk using marijuana
Past month binge drinking
Past month heavy drinking
Past month binge drinking (#days)
Past month smoking
Past year smoking
# days smoked in the past month
Past month other drug use
Past year other drug use
DUI alcohol
DUI drugs
Any illicit drug abuse
Marijuana abuse or dependence
Alcohol abuse or dependence
Pain reliever use (100 days or more)
All
MML 2004-12
[N=614000]
[N=107000]
0.064
0.070
0.110
0.118
13.303
13.095
32.316
30.975
0.395
0.043
0.262
0.271
0.231
0.244
0.671
0.068
1.116
1.122
0.237
0.237
0.278
0.280
5.882
5.869
0.035
0.038
0.082
0.085
0.123
0.129
0.041
0.044
0.004
0.004
0.017
0.018
0.073
0.077
0.007
0.007
Source: 2004-2012 restricted use NSDUH data
31
Never MML
[N=416000]
0.059
0.102
13.209
31.029
0.037
0.249
0.232
0.069
1.142
0.247
0.288
6.245
0.034
0.078
0.123
0.040
0.004
0.016
0.071
0.007
Always MML
[N=91000]
0.082
0.133
13.652
36.535
0.048
0.303
0.219
0.060
1.018
0.199
0.242
4.554
0.039
0.094
0.118
0.045
0.005
0.020
0.080
0.007
Table 5: Marijuana use
Extensive margins
Dependent Past month
variables
use
Overall
MML
0.0145*
(0.00636)
N
[569800]
0.061
Mean
Under 21
MML
0.0138
(0.0170)
N
[256000]
Mean
0.119
Over 21
MML
0.0151*
(0.00664)
[314000]
N
0.051
Mean
Intensive margins
Past month
Past year
Past year
use among
use among
use
users (#days) users (#days)
Past month
purchase
No risk using
marijuana
0.00440
(0.00596)
[569800]
0.110
1.194**
(0.418)
[56200]
12.292
27.23***
(7.504)
[38100]
23.284
0.479
(0.647)
[457000]
0.033
0.0257*
(0.0104)
[563500]
0.235
0.00433
(0.00990)
[256000]
0.213
1.815***
(0.326)
[27000]
12.180
9.258
(7.001)
[19000]
18.313
0.00724
(0.0113)
[206000]
0.076
0.0126
(0.0130)
[253000]
0.259
0.005
(0.00632)
[314000]
0.092
1.151
(0.684)
[29000]
12.337
31.47***
(8.357)
[19300]
25.142
-0.0012
(0.00488)
[251000]
0.026
0.0285*
(0.0116)
[310000]
0.230
Source: 2004-2012 restricted use NSDUH data
All regressions include state and year fixed effects and state specific pre and post trends
Standard errors in parentheses *p<0.05 **p<0.01 *** p<0.001
32
Table 6: Drinking and smoking
Drinking (past month)
days of
Binge
Heavy
binge
drinking drinking drinking
Smoking
(past month)
Smoking
(past year)
Number of days
smoked
(past month)
0.00327
(0.00625)
0.066
0.134
(0.163)
1.040
0.0210
(0.0164)
0.235
0.0209
(0.0163)
0.279
0.0678
(0.252)
5.291
0.0139
(0.00987)
0.057
0.111
(0.0874)
0.818
-0.00424
(0.0219)
0.185
-0.0114
(0.0342)
0.263
-0.0707
(0.173)
3.151
0.00145
(0.00853)
0.068
0.126
(0.206)
1.080
0.0244
(0.0180)
0.244
0.0257
(0.0158)
0.282
0.0590
(0.350)
5.673
Dependent
variables
Overall
MML
0.0176
[N ≈ 569000] (0.00982)
Mean
0.242
Under 21
MML
-0.00401
[N ≈236000]
(0.0170)
0.194
Mean
Over 21
MML
0.0207
[N ≈ 206000] (0.0120)
Mean
0.250
Smoking
Source: 2004-2012 restricted use NSDUH data
All regressions include state and year fixed effects and state specific pre and post trends
Standard errors in parentheses *p<0.05 **p<0.01 *** p<0.001
33
Table 7: Other drug use
Other drug use
Dependent Past month
variables
use
Overall
MML
0.00394
[N≈569000]
(0.00539)
Mean
0.038
Under 21
MML
0.00335
[N≈256000]
(0.00695)
0.061
Mean
Over 21
MML
0.00401
(0.00622)
[N≈314000]
Mean
0.033
Past year
use
Driving under
the influence
Alcohol
Drugs
past year past year
0.00361
(0.00493)
0.084
0.00637
(0.00565)
0.130
0.00856
(0.0177)
0.152
0.00265
(0.00408)
0.072
Abuse or
dependence
Any illicit
drug abuse
Marijuana
Alcohol
-0.00307
(0.00282)
0.043
0.00181
(0.00316)
0.004
0.000547
(0.00403)
0.017
0.00863**
(0.00296)
0.077
0.00275
(0.00937)
0.082
-0.00136
(0.00385)
0.075
-0.000665
(0.00496)
0.010
0.00494
(0.00920)
0.053
0.000250
(0.00975)
0.092
0.00653
(0.00609)
0.138
-0.00388
(0.00312)
0.037
0.00184
(0.00255)
0.003
0.000985
(0.00356)
0.011
0.0149***
(0.00275)
0.075
Source: 2004-2012 restricted use NSDUH data
All regressions include state and year fixed effects and state specific pre and post trends
Standard errors in parentheses *p<0.05 **p<0.01 *** p<0.001
34
Table 8: Probability of moving to a MML state
Overall
MML
[N =319000]
Mean
Under 21
MML
[N =206000]
Mean
Over 21
MML
[N =92000]
Mean
Move to a
MML state
cross border
shopping
0.109
(0.0546)
0.007
0.0487
(0.0713)
0.047
0.124
(0.062)
0.0069
0.0689
(0.059)
0.046
0.108
(0.054)
0.0069
0.0689
(0.059)
0.048
Source: 2004-2012 restricted use NSDUH data
All regressions include state and year fixed effects and state specific pre and post trends
Standard errors in parentheses *p<0.05 **p<0.01 *** p<0.001
35
Table 9: Marijuana use: dispensaries and home cultivation
Extensive margins
Dependent Past month
variables
use
Legal
0.00839
Dispensaries
(0.00627)
Home
0.00542
Cultivation
(0.00284)
N
[570000]
0.061
Mean
Past year
use
0.0152***
(0.00385)
-0.00418
(0.00397)
[570000]
0.110
Intensive margins
Past month
Past year
use among
use among
users (#days) users (#days)
0.652
17.53***
(0.448)
(4.789)
1.439*
21.22**
(0.583)
(7.055)
[38000]
[38000]
12.292
23.284
Past month
purchase
0.00625**
(0.00228)
-0.00169
(0.00428)
[563000]
0.033
No risk using
marijuana
0.0207
(0.0119)
0.0188
(0.00972)
[563000]
0.235
Source: 2004-2012 restricted use NSDUH data
All regressions include state and year fixed effects and state specific pre and post trends
Standard errors in parentheses *p<0.05 **p<0.01 *** p<0.001
Table 10: Drinking and smoking: dispensaries and home cultivation
Dependent
variables
[N ≈ 569000]
Legal
Dispensaries
Home
Cultivation
Mean
Drinking (past month)
days of
Binge
Heavy
binge
drinking drinking drinking
Smoking
Smoking
(past month)
Number of days
Smoking
smoked
(past year)
(past month)
0.0101
(0.0107)
-0.00458
(0.00367)
0.0501
(0.0687)
0.0242***
(0.00591)
0.0201*
(0.00845)
0.326
(0.163)
0.0265*
(0.0104)
0.242
0.0101
(0.00554)
0.066
0.240
(0.193)
1.040
0.00390
(0.00772)
0.235
0.00552
(0.0103)
0.279
-0.183
(0.181)
5.291
Source: 2004-2012 restricted use NSDUH data
All regressions include state and year fixed effects and state specific pre and post trends
Standard errors in parentheses *p<0.05 **p<0.01 *** p<0.001
36
Table 11: other drug use: dispensaries and home cultivation
Other drug use
Dependent Past month
variables
use
[N ≈ 569000]
Legal
0.00667
Dispensaries
(0.00385)
Past year
use
Driving under
the influence
Alcohol
Drugs
past year past year
0.0102*
(0.00407)
0.0118**
(0.00381)
Home
Cultivation
Mean
0.000748
(0.00449)
0.084
0.000797
(0.00669)
0.038
Abuse or
dependence
Any illicit
drug abuse
Marijuana
Alcohol
0.0103*
(0.00467)
0.00334
(0.00374)
-0.0027
(0.00186)
0.0172*
(0.00779)
0.00898* -0.00554*
(0.00420) (0.00216)
0.130
0.043
-0.00071
(0.00086)
0.004
-0.00133
(0.0051)
0.017
0.0111**
(0.00307)
0.077
Source: 2004-2012 restricted use NSDUH data
All regressions include state and year fixed effects and state specific pre and post trends
Standard errors in parentheses *p<0.05 **p<0.01 *** p<0.001
37
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