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Research Proposal: The Association of Recreational Marijuana Policy and
Opioid Overdoses
Dana George
Department of Economics, University of Colorado Denver, March 2016
Objective: My objective is to evaluate the effect of recreational marijuana policy on opioid
overdoses. Methods: The National Vital Statistics System multiple cause-of-death mortality
files from the Centers for Disease Control and Prevention will be used to identify
state-level opioid overdose deaths. Ideally, I would like to use a data set containing
county-level data on opioid overdoses. I will exploit the variation in recreational marijuana
policy across states to identify the causal effect of policy legalizing recreational marijuana
on deaths caused by opioid overdose. I will use pre-tend and post-trend data in standard
differences-in-differences analyses. Hypothesis: I suspect that in those states that have
legalized recreational marijuana, we will see a decrease in opioid overdose deaths. If I do
find that the two are negatively related, this would provide evidence that recreational
marijuana and dangerous painkillers are substitutes.
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1. Introduction
Given that the 2013 National Survey on Drug Use and Health reported that marijuana is the most
widely used illicit drug in the U.S, a critical public health issue is whether cannabis (marijuana) might be a
substitute for opioid painkillers such as Oxycontin, Vicodin and Dilaudid. Understanding the behavioral and
public health implications of this evolving regulatory environment is critical for the ongoing
implementation of recreational marijuana laws and future iterations of marijuana policy reform.
To date, four states, Alaska, Oregon, Colorado and Washington, and the District of Columbia have
legalized marijuana for recreational use. In addition, pro-recreational marijuana initiatives are expected in
six other states in 2016: Arizona, California, Maine, Massachusetts, Montana and Nevada. In November
2014, Portland, Maine, followed Washington and Colorado's lead and legalized recreational use of the drug,
while the Michigan cities of Lansing, Jackson and Ferndale resoundingly voted to let people older than 21
possess an ounce of marijuana on private property (Sanchez, Martinez, 2014).
In 2014, The Center for Disease Control and Prevention (CDC) reported that opioids, primarily
prescription pain relievers and heroin, are the main drugs associated with overdose deaths. In 2014, opioids
were involved in 28,647 deaths, or 61% of all drug overdose deaths; the rate of opioid overdoses tripled
from 2000 to 2014. The 2014 data demonstrates that the United States' opioid overdose epidemic includes
two distinct but interrelated trends: a 15-year increase in overdose deaths involving prescription opioid
pain relievers and a recent surge in illicit opioid overdose deaths.
Natural and semisynthetic opioids, which include the most commonly prescribed opioid pain
relievers, oxycodone and hydrocodone, continue to be involved in more overdose deaths than any other
opioid type. Although this category of opioid drug overdose death had declined in 2012 compared with
2011, and had held steady in 2013, there was a 9% increase in 2014. In 2014 Daniel M. Sosin, M.D., M.P.H.,
F.A.C.P., acting director of CDC’s National Center for Injury Prevention and Control said that “improving
how opioids are prescribed will help us prevent the 46 prescription painkiller overdose deaths that occur
each day in the United States.” Clearly there is an issue regarding deaths due to overdosing on prescription
painkillers. This paper will provide evidence as to whether recreational marijuana, known to have pain
reducing side effects, and opioids are being used as substitutes, and intern affecting the count of opioid
overdose deaths.
Research from Berkeley, as reported by High Times, the leading cannabis-related magazine in the
United States, reports that cannabis is well known to work synergistically with opioids to kill pain; medical
marijuana patients reported cutting back their opioid prescriptions by a third to a half. The hypothesis is
that by legalizing recreational marijuana, people who are not prescribed painkillers, and therefore likely
unaware of the dangers of overdosing or the amount that causes an overdose, can choose to use cannabis as
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a safer substitute. CDC announced in 2010 that 60 percent, over 22,000, of overdose deaths in the United
States were related to prescription pharmaceuticals and of those, three-out-of-four related to opioids (such
as Oxycontin, Vicodin and Dilaudid) and three-out-of-ten related to benzodiazepenes (like Valium, Xanax
and Ambien) and some overdoses included both opioids and bensodiazepenes, making overdoses on drugs
not called cannabis the leading cause of injury death in the United States.
These opioids, whether prescribed or received from a friend or relative, are intended to medically
treat mood, anxiety, pain and insomnia, all things that marijuana users report cannabis has positive a
positive effect. In contrast, in 2014, zero deaths were reported as a result of overdosing on marijuana and, a
2016 article published in The Economist reported that from 2014 to 2016, there were just three deaths
associated with cannabis use in Colorado (The Economist, 2016). The question still remains, does allowing
citizens access to legal, controlled marijuana lower the 22,000 death count related to opioid overdoses?
Legislative and public attention have focused on these issues. My research intent is to exploit the policy
variation in Colorado and Washington, and add to the limited empirical evidence on this topic.
2. Literature Review Background
One current issue that makes studying recreational marijuana difficult is access to data. There are a
multitude of surveys and datasets that report prescription rates of medical marijuana, but because policy
reform regarding recreational marijuana is relatively new, the data is scarce. Arguments for marijuana
policy reform center around social justice, public safety and the economic impacts. Some policy reformists
argue that legalizing recreational marijuana will eliminate the incarceration of nonviolent users and shrink
existing illicit markets. Others advocate for reform somewhere in the middle, for decriminalization of
possessing a certain amount of cannabis, and lesser penalties for production and sales (Caulkins J. 2014).
Without a doubt, this debate is now a part of the political mainstream, and this paper seeks to fill a gap that
has yet to be explored.
In 2009, Hans Melberg, Andrew Jones and Anne Bretteville-Jensen found that for “troubled youths”
there is a gateway effect of marijuana that leads these teens to the use of harder drugs, like amphetamines,
cocaine or heroin. However, they also found that in a larger fraction of youths, previous cannabis use had
less of an impact on being a gateway to harder drugs. This gateway research contributes to the public safety
literature, however because it was published in 2009, there have since been changes in who is consuming
cannabis and an increase in its’ use. My study adds to the available marijuana literature by introducing legal
recreational marijuana use as my independent variable, rather than street marijuana or medical marijuana.
The difference here lies in the idea that legal recreational marijuana is now being treated like alcohol - a
controlled substance for social use, rather than strictly medical use. As reported by the National Survey and
Drug Use and Health in Colorado and Washington, this is a significant distinction because of the high
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increase in recreational usage rates after reform as well as a high increase in first time usage rates. In
addition, researchers report that the impact of decriminalization is concentrated among minors, who have a
higher uptake in the first five years following the policy (Williams, Jensen 2014). I will include this
heterogeneity for age groups and report what the effect is when a county has a higher concentration of
18-25 year olds on the rates of opioid overdoses.
In 2012, Washington and Colorado passed initiatives to legalize the recreational use of cannabis.
Europe, the UK and Australia have also removed criminal sanctions under a policy of decriminalization. The
key argument for many is that eliminating criminal sanctions for marijuana use will eliminate expensive
and ineffective policies. Costs for law enforcement and criminal justice resources that are argued to be
unnecessary (Caulkins, Kilmer, 2014). Opponents, however, argue that recent research proves that there are
potential harms of cannabis use (Ammerman, 2014); however, as reported by High Times in 2014, cannabis
has been successful in treating a myriad of debilitating conditions including cancer, epilepsy and pain. High
Times also comments on the studies that suggest marijuana has negative health effects but state that these
studies are insignificant, with sample sizes of 20. My research seeks to bridge the gap and report whether or
not marijuana is being used as a substitute for dangerous pain killers, potentially lowering the death count.
Another topic that is important to address is how medical marijuana has affected substance use.
Researchers conclude that medical marijuana laws (MML) implementation has insignificant impacts on
non-medical use of prescription pain medication (Wen, Hockenberry, Cummings 2015). This is an
important discovery; however, it is likely that some medical marijuana prescriptions and opioid
prescriptions are prescribed to those that falsely report chronic pain, in order to obtain the prescription or
obtain the medicines for friends and/or family. As such, MML’s should only provide restricted legal
protection and access to marijuana for a select group of patients; however, in practice, it is likely that the
laws have a spillover effect on marijuana use in the non-patient population, those seeking its’ recreational
use, akin to how prescription opioids eventually find their way into the street drug market. Although the
literature seems to be conclusive that medical marijuana does not affect the non-medical use of
prescription pain medication, there has been limited conclusion on whether legal recreational marijuana
has the same effect. It could be that the main population overdosing on opioids are those people that are
more likely to seek recreational marijuana as a substitute.
In contrast to the concern for marijuana’s “gateway” effect, there is recent evidence that increased
access to medical marijuana resulting from MMLs may benefit certain individuals by reducing their opioid
use. For instance, marijuana may provide analgesia for patients with chronic pain (Lynch and Campbell,
2011). Thus, those who have already received opioid pain medication may experience improved pain relief
and lower their opioid dose after they commence marijuana use. In addition, those who would have
otherwise initiated opioid analgesics may choose marijuana instead. Furthermore, marijuana may also
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benefit those with opioid misuse by easing withdrawal symptoms and facilitating recovery (Scavone,
Sterling Bockstaele, 2013). If predisposing factors and exposure opportunities are the primary mechanisms
that cause opioid use, MML’s should not result in a change in hard drug use because the predisposing
factors and exposure opportunities for hard drug use remain unaffected. However, although important to
compare MML literature to retail marijuana literature, there are distinct differences. As of March 1, 2016
there were 424 retail marijuana stores open in Colorado. Increased access to retail marijuana should limit
the results that Wen, Hockenberry and Cummings found to apply only to medical marijuana’s effect on
opioid use.
Based on the statistics from the Colorado Department of Health and Environment, Colorado had
only 5051 registered medical marijuana patients in January 2009, but the number skyrocketed to 99,902 by
July 2010, implying that about 2.6% of adults were legal patient's (Chu, 2014). Data from the NSDUH also
sees a significant sharp increase in total marijuana use from 2012 to 2013. The conclusive research is that
MML’s and retail marijuana reform have increased the total use of marijuana.
The 2012 legalization of recreational marijuana in Colorado and Washington gives my research the
ability to use the two states as something of a laboratory in which the effects of legalizing recreational
marijuana use can be studied. This proposal describes an important topic that has yet to be researched. This
study would advance the existing literature by: (i) providing one of the first estimates of the effect of
recreational marijuana policy; (ii) estimating the effect of recreational marijuana policy on marijuana use
based on the most recent data; (iii) estimating the effect of recreational marijuana policy on opioid
overdoses; (iv) estimating the state-level variation in policy effects of marijuana reform between different
age groups at the county level.
3. Data and Empirical Specification
3.1 Data Sources
Data from the National Survey on Drug Use and Health (NSDUH), an annual nationwide survey
involving interviews with approximately 70,000 randomly selected individuals aged 12 and older, will be
reported in Figure 1 with data from 2010-2015. The NSDUH provides state-level estimates on the use of
tobacco products, alcohol, illicit drugs and mental health in the United States. The White House Office of
National Drug Control Policy and the U.S. Department of Justice use the NSDUH to support their research. I
will be assuming I have data at both the state and county level and would report both.
Opioid overdose death data will come from the Mortality Detail Files, produced by the National
Vital Statistics System (NVSS). This data contains information on year of death, gender, age group, and
underlying cause of death for U.S. residents and are aggregated to the state level by the NVSS; however, I
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will be assuming I have data at the county level. Drug overdose deaths involving opioids are drug overdose
deaths with a multiple cause-of-death code of T40.0, T40.1, T40.2, T40.3, T40.4, or T40.6. Approximately
one fifth of drug overdose deaths lack information on the specific drugs involved. Some of these deaths
might involve opioids.
3.2 The impact of the 2012 marijuana reform on marijuana use
Figure 2 will plot estimates of marijuana use for all states from the National Survey on Drug Use
and Health (NSDUH), an average of several comparison states and the entire United States between 2000
and 2015. Here, I will find the differences in comparison states and the treatment states Colorado and
Washington and look at trends before the 2012 reform and before the 2014 dispensary openings.
Comparison states will be chosen that are similar to Colorado and Washington, based upon average income,
average age, political standing and GDP per capita. I expect to see a mild upward trend in marijuana use
across the United States and a large sharp increase after 2012 and another sharp increase after 2014 for
Colorado and Washington. For the comparison states and states not in the treatment group, I expect them
to remain on the same trend after the 2012 reform and after the 2014 dispensary openings. While the law
substantially increased recreational marijuana use in Colorado and Washington, the effect of the law may
have been different in counties that had a higher fraction of their population aged 18-25, as the National
Institute on Drug Abuse reports that they are the highest users of the substance.
Colorado and Washington legalized recreational marijuana on November 6, 2012; however, the first
legal recreational marijuana dispensaries did not open until January 1, 2014 and July 8, 2014, respectively. I
will look at trends 3 years before and 3 years after the policy was enacted on November 6, 2012 as well as 1.5
years before and 1.5 years after each state opened their first dispensaries. The 2012 recreational marijuana
reform substantially increased marijuana use in Colorado and Washington. Trends before the policy in both
Washington and Colorado were on an upward trend. Following the policy, you see a sharp increase in
marijuana use in Washington and Colorado.
3.3 Impact of the 2012 marijuana reform on opioid overdoses
My analysis will use two types of variation of identify the effect of the policy on opioid overdoses.
First, I plan to analyze the relative change in opioid overdoses in Colorado and Washington, by age groups
12+, 12-17, 18-25 and 26+, as these are the age groups that the National Institute on Drug Abuse uses to
classify. Because the policy was issued statewide, I will expect to see larger effects in areas with higher
concentrations of the 18-25 year olds. Second, I plan to compare the variation in opioid overdoses in
Colorado and Washington with variation in the comparison states.. I will use robust estimates to Colorado
and Washington specific shocks and differential trends in opioid overdoses between Colorado and
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Washington and the comparison states.
3.4 Empirical Specification
For all data sets, I will use state-level changes in recreational marijuana policy to identify the
relationship between the policy and a measure of opioid overdoses leading to death. I plan to employ
differences-in-differences models that include county and year fixed effects in all regressions. I will adjust
standard errors by clustering at the county-level (Bertrand, Duflo, Mullainathan, 2004). I will evaluate
gender as an independent variable, as it has been shown that gender plays a role in opioid abuse. Also, it is
important to compare age groups, as certain age groups may have been more or less affected by the reform.
For the NVSS data, I plan to model the number of opioid overdoses as a function of state recreational
marijuana policy, county fixed effects, year fixed effects, and a vector of state-level time-varying controls. If
recreational marijuana policy causes people to overdose on opioids less, opioid overdoses should decrease
in Colorado and Washington relative to other states.
3.5 Colorado and Washington Analysis
I begin by evaluating opioid overdose trends across Colorado and Washington at the county level.
My identification strategy relies on the assumption that, if the reform had not taken place, opioid overdoses
in Colorado and Washington and comparison states would have evolved similarly. Therefore, it is important
to evaluate whether pre-reform trends in opioid overdoses were similar across counties in Colorado and
Washington. I do this by evaluating trends in opioid overdoses across counties with different 2010
marijuana use rates. To test this, I estimate the following for both Colorado and Washington
opioidoverdosect= I(countyc) + (βt1*I(Yeart) +βt2*MarijUse2010c* I(Yeart)) +εct∑
2015
t=2008
(1)
The dependent variable opioidoverdosect is the per-capita opioid overdose deaths in county c in
year t. I will generate per-capita opioid overdose deaths by dividing number of opioid overdose deaths in a
given county by the Census Bureau’s estimated county population, by zip code of residence. I include
county fixed effects I(countyc), year fixed effects I(Yeart), and the interaction term between year fixed
effects and the 2010 marijuana use rate in a given county, in 2010, two years before the policy. Standard
errors will be clustered by county to account for correlation at the county-level errors over time. This
regression will then be done once more for Colorado and Washington, replacing MarijUse2010 with
MarijUse2013, to report results in response to dispensaries opening. Later, I will look at percentages of
counties that are over age 25 and under age 25, to test whether age is an indicator of the policy effect.
If I find that counties more and less affected by the reform have similar trends before the reform,
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and change course only after the reform, this will provide strong evidence that these changes were caused
by the policy rather than a pre-existing differential time trend. If this is true, βt2 should be zero for the
years prior to the policy and negative for the years after the policy. I will report results for total opioid
overdose deaths at the state level and county level. I will display the results of regression (1) in figure 3,
with coefficients on the interaction term MarijUse2010c* I(Yeart), coefficients for the interaction term will
be on the y-axis and the x-axis will denote the year. I hypothesize that from regression (1) I will report
results that indicate that the 2010 marijuana use rate will predict a significant increase in opioid overdoses.
If I find these results, I can conclude that opioid overdoses, in Colorado and Washington in counties more
affected by the reform, were not growing at a different rate than in other counties in Colorado and
Washington. Therefore, I will be able to attribute the potential reduction in the growth of opioid overdoses
to the change in the policy rather than a differential trend.
To measure the effect of the reform, I model per capita opioid overdoses in county c and quarter t
in both Colorado and Washington as
Opioidoverdosect =I(Countyc ) + (⍺q1+I(Quarterqt) + ⍺q2*I(Quarterqt)*MarijUse2010c) + ⍺1xct+∑
3
q=1
⍺2Postt+ ⍺3Legalt+ ⍺4Legalt*MarijUse2010c+ (2)
⍺5Postt*MarijUse2010c+ ctμ
Table 3 will show the estimates of regression (2) where the dependent variable is total per capita
opioid overdoses. From (2) I will be able to report the effects during the policy period and compare them to
the post-policy period. The variable xct are county demographic characteristics: the unemployment rate and
the median income. The variable Legalt equals 1 during the period between November 6, 2012 and January
1, 2014 and July 8, 2014 for Colorado and Washington, respectively. During this period for each state,
cannabis was legalized recreationally, but it was not until the 2014 dates that recreational cannabis was
sold at retail stores. The variable Postt equals 1 during the post-policy period, through 2015. The standard
errors are clustered by county to account for correlation in error terms within counties over time. I will run
one regression with controls for county demographics and one regression without the controls for county
demographics. I will be able to use the second form of the regression, without controls, to compare with the
regression with the controls to confirm the results. The coefficient of interest from regression (2) is ⍺5, the
interaction between marijuana use rates in 2010 and the post-policy indicator that measures the relative
change in opioid overdoses in counties. ⍺4 will also be an important indicator of the interaction between
marijuana rates in 2010 and the period between legalizing recreational marijuana and the first retail stores
opening. I will be able to use this data to confirm or deny what the legalization of marijuana did to
per-capita opioid overdoses. I will report these findings in Table 3.
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From Table 3, I will conclude whether or not, under the assumption that the policy only affected
opioid overdoses through its’ expansion of marijuana use, predicted reduction in opioid deaths can be
directly interpreted as the treatment effect of recreational marijuana reform. This will then measure the
average treatment effect for Coloradoans and Washingtonians, and the average treatment effect on the
treated for the United States. If the policy caused some Colorado or Washington residents to move from a
county with less access to the cannabis to somewhere with more access, my estimate will over-estimate the
treatment effect of legal recreational marijuana on opioid overdoses.
Lastly, it may be helpful to evaluate further difference-in-difference models for rubustness. For
instance, it may be helpful to include a binary variable, replacing the continuous measure of county rates,
to look at differences between different marijuana use for different counties containing certain age groups,
for instance below 25 and above 25, as 18-25 year olds are potentially the most affected by the policy. With
this model, I would be able to report whether or not counties with low (decided upon with further research
on how “low” would be categorized) percentages of people below the age of 25 (affectedc = 0) captures the
Colorado and Washington specific trends. Here, I would need to gather pre-trend data on both groups. I
could then go on to report whether or not the policy caused opioid overdose deaths to rise or fall in the
below 25 age group relative to the above 25 age group. In addition, evidence suggests gender differences in
the abuse of prescription opioids, and therefore likely gender differences in the abuse of non-prescription
opioids (Green, Serrano, Licari, Budman, Butler, 2009). It would be helpful for the current literature to add a
gender variable and an interaction term with gender and marijuana use to see if this holds true. Lastly, it
would be beneficial to present another regression with estimates using the dependent variable
ln(opioidoverdosect), to account for any measurement errors in the estimate of the county populations.
3.6 Comparison State Analysis
In this section I will expand upon the previous analysis by comparing opioid overdoses in Colorado
and Washington, my comparison states and the United States. The addition of comparison states allows me
to control for Colorado and Washington specific trends, as well as unobservable changes among counties. If
the policy decreases opioid overdoses, rates should fall in Colorado and Washington counties relative to
counties in the comparison states, with the highest reductions occurring in counties that experienced the
largest effect of the reform. I will estimate the following model for both Colorado and Washington
Opioidoverdosect=I(Countyc)+⍺1xct+⍺2Postt+⍺3Legalt+⍺4COs*Postt+⍺5COs*Legalt+ (3)
⍺6MarijUse2010c+⍺7Legalt*MarijUse2010c+⍺8Postt*MarijUse2010c+
⍺9*Postt*MarijUse2010c*COs+ ⍺10Legalt*MarijUse2010c*COs+ cμ
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This regression will allow me to report if the treated counties experience reductions or increases in
opioid overdoses, relative to untreated counties. In this model, ⍺9 will estimate the effect of the policy on
opioid overdoses for each change in county marijuana use. The variable COs, or WAs in the second
regression, equals 1 for counties in CO, or 1 for counties in WA in the second regression. I control for
differences in trends in opioid overdoses between CO, WA and the comparison states with the interaction
term COs*Postt and I control for trends associated with 2010 marijuana use with the interaction term
Postt*MarijUse2010c. It would be beneficial to include the full analysis above with the log of total opioid
overdoses as the dependent variable. These results will be reported in a table 4.
4. Did the legalization of recreational marijuana cause a change in opioid overdoses?
From the empirical analysis of the three regressions and their subsequent regressions, I intend to
add to the limited literature on this topic. The results will be able to confirm or deny my claims and be an
important addition to policy and health research as referenced throughout this proposal.
5. References
1. Ammerman, S. “Marijuana. Adolescent Medicine: State of the Art Reviews.” 2014.
2. Bertrand, M., Duflo, E., & Mullainathan, S. (2004). “How much should we trust
differences-in-differences estimates?” The Quarterly Journal of Economics. 119, 249–275.
3. Caulkins J. How to Regulate Cannabis: A Practical Guide. Addiction. 2014.
4. Caulkins JP., Kilmer B., “Criminal Justice Costs of Prohibiting Marijuana in California.” 2014.
5. Centers for Disease Control and Prevention. “Increases in Drug and Opioid Overdose Deaths --
United States, 2000-2014. January 1, 2016.
6. Chu, Yu-Wei Luke. “The Effects of Medical Marijuana Laws on Illegal Marijuana Use.” 2014. Journal
of Health Economics. 38. 43-61.
7. Green T., Serrano J., Licari A., Budman S., Butler S. 2009. “Women Who Abuse Prescription
Opioids.” Drug and Alcohol Dependence. 103. 65-73.
8. Hefei Wen, Jason M. Hockenberry, Janet R. Cummings (2015). “The Effect of Medical Marijuana
Laws on Adolescent and Adult Use of Marijuana, Alcohol, and other Substances.” Journal of Health
Economics, 42, 64-80.
9. Lynch and Campbell, 2011 M.E. Lynch, F. Campbell. “Cannabinoids for treatment of chronic
non-cancer pain: a systematic review of randomized trials.” British Journal of Clinical
Pharmacology, 72 (5) (2011), pp. 735–744.
10. Marijuana Statistics and Trends. Results from the National Institute on Drug Abuse. 2014
National Center for Injury Prevention and Control. WISQARS injury mortality reports, 1999---2007.
11. Sanchez, R., Martinez M., CNN. 2014. “Colorado pot law called springboard for other states.”
12. Scavone, R.C. Sterling, E.J. Van Bockstaele “Cannabinoid and opioid interactions: implications for
opiate dependence and withdrawal.” 2013. Neuroscience. 637-654.
13. Substance Abuse and Mental Health Services Administration. Results from the 2013 National
Survey on Drug Use and Health: summary of national findings. Rockville, MD: Substance Abuse and
Mental Health Services Administration; 2014. HHS Publication No. (SMA) 14-4887. NSDUH Series
H-49.
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14. The Economist. “Reeferegulatory Challenge.” February 13, 2016.
15. The National Survey on Drug Use and Health (NSDUH), funded by the Substance Abuse and Mental
Health Services (SAMHSA) and the U.S. Department of Health and Human Services (DHHS).
16. Williams, Jensen., 2014. “Does Liberalizing Cannabis Law Increase Cannabis Use?” Journal of
Health Economics, 36, 20-31.
