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Predicting State-Level Marijuana Legalization
Nicholas Athey, Jason Ahn, and Piper Jackson
Simon Fraser University
Executive Summary
Legal status of marijuana by state
This project is an ongoing endeavor among members of the Modelling of
Complex Social Systems (MoCSSy) group. This program supports
interdisciplinary modelling through a system of mentoring, skills training, and
knowledge exchange. Since inception of the program in 2008, MoCSSy projects
have resulted in more than 100 peer-reviewed publications, including 7 books.
Our goal is the application of innovative mathematical and computational
modelling techniques to social phenomena of concern.
The US has witnessed a major political shift since 1996 when California
became the first state to legalize medical marijuana. Nearly 20 years later,
23 states have decriminalized medical marijuana use and 4 in particular
(Alaska, California, Oregon, Washington, and Washington, D.C.) have passed
statutes legalizing recreational use as well. Recently, three senators have
introduced legislation that would legalize medical marijuana at the federal
The current project incorporates state-level demographic variables, opinion
polls about marijuana policy, and aspects of proposed policy to try and predict
which states are likely to decriminalize (or legalize) next.
level and allow individual states to establish their own regulatory
framework without fear of persecution With the 2016 elections quickly
approaching,
speculation
about
which
states
will
be
next
to
Project Outline
•t
decriminalize/legalize are rising; yet, to date, rigorous predictive models
Team Members
are lacking.
Nicholas Athey is a PhD Student in the School of Criminology and member of
the MoCSSy group. He received his BS and MS in Criminal Justice from
California State University, Long Beach where he studied performanceenhancing drug production, trafficking, and use. His current research looks at
(medical) marijuana policy, illicit drug markets, and the effect of drug policy
on drug users’ lives.
Decriminalization/Legalization
Policy Variables
What’s in a Measure?
• Prevalence of marijuana use
• How to regulate possession, production, and distribution
• Opinion polls about citizens’ support for decriminalization
• Whether to permit home cultivation
• Whether proposal is a citizen initiated ballot or legislative enactment
• How it will be taxed
• Whether proposing to legalize, decriminalize, or depenalize
• Whose allowed to use it? (e.g., age restrictions, medical, recreational)
• Whether the state already has a form of decriminalization or
depenalization in place
• Whether to decriminalize, depenalize, or legalize
• Type(s) of use being proposed (e.g., recreational vs. medicinal)
• State’s need for tax revenue
Pros and Cons of Legalization/Decriminalization:
• Pros
State Demographics
- Reduce correlated harms (e.g., illicit market, crime, violence)
• Age composition (e.g., % able to vote, % in adolescence, % 60+)
- Regulate production and distribution (e.g., quality control)
• Religiosity (e.g., % self-identified, # of state religious institutions,
etc.)
- Implement principles of harm reduction for problematic use
• State population
• Political ideology (e.g., Republican vs. Democratic)
• Geographical proximity to states with some form of
decriminalization
- Tax revenue
- Empirical investigation of harms/(medical) benefits of marijuana
- Reduce production costs
Through our literature search and review of expert opinions, we have constructed
the following research questions:
- Reduce social harms and monetary expenditures associated with
the ‘War on Drugs’
• Which state-level and policy relevant variables are most influential in
determining whether a state will decriminalize or legalize marijuana?
How to Model Policy Change?
- ‘Leakage’ or ‘Spillover’ to illicit market
•
- Send the message that drug use is not ‘immoral’
•
•
Regression Analyses: E.g., multinomial regression predicting states’
legal status of marijuana
Fuzzy Cognitive Mapping: Map the interaction of factors and
iteratively compute influence using fuzzy logic
Piper Jackson is the Postdoctoral Fellow in Complex Systems Modelling at the
IRMACS Centre and the Gerontology Research Centre at SFU. In addition to
directing MoCSSy, Piper is part of the Ambient Assistive Living Technologies
for Older Adults with Mild Cognitive Impairment (AAL-WELL) project.
Research Strategy
• Cons
Decision Tree or Classification Analysis: Identify most and least
influential variables
Jason Ahn is currently a 4th year undergraduate student at Simon Fraser
University pursuing a degree in Arts and Social Sciences in Criminology.
Being an active member of various organizations and school clubs, he is
highly motivated to make changes to the world, especially about legal and
social justice issues.
• Is there a linear progression from criminalization to legalization, or a nonlinear
‘Tipping Point’ that will speed up the process (e.g., legalization at the federal
level)?
• Can the aforementioned information be used to construct a predictive model?
- ‘Gateway’ effect where initial marijuana use leads to subsequent,
harder, drug use
- Initiating process of decriminalizing/legalizing other drugs
References
- Commercialization (including advertisement) of production and
distribution—similar to tobacco and alcohol.
Husak, D., & de Marneffe, P. (2005). The legalization of drugs. New York, NY: Oxford University Press.
Room, R., Fischer, B., Hall, W., Lenton, S., & Reuter, P. (2010). Cannabis policy: Moving beyond stalemate. New York, NY: Oxford
University Press.