Marijuana medicalization and motor vehicle fatalities: a synthetic control group approach
This paper reports a quasi-experimental evaluation of California’s 1996 medical marijuana law (MML), known as Proposition 215, on statewide motor vehicle fatalities between 1996 and 2015.
To infer the causal impact of California’s MML enactment on statewide motor vehicle fatalities, we construct a synthetic control group for California (i.e., California had it NOT enacted MMLs) as a weighted sum of annual traffic fatality time series from a donor pool of untreated (no MML) states. The post-MML difference between California and its constructed counterfactual reflects the net effect of MMLs on statewide traffic fatalities. The synthetic control group design avoids the problematic homogeneity assumptions intrinsic to panel regression models, which have been employed in prominent studies of this topic.
California’s 1996 MML appears to have produced a large, sustained decrease in statewide motor vehicle fatalities amounting to an annual reduction between 588 and 900 vehicle fatalities. This finding is consistent across a wide range of model specifications and donor pool restrictions. In-sample placebo test results suggest that the estimated intervention effect is unlikely to be a spurious artifact and the “leave-one-out” sensitivity analysis demonstrates that the effect is not being driven by an individual or ensemble of influential donor pool states.
Our focus on California as a case study limits our ability to generalize our estimate of the MML intervention on motor vehicle fatalities in California to other MML states; however, state-level MML interventions have major differences in their policy dimensions that seem unlikely to “average out” through aggregation.
KeywordsMedical marijuana Traffic fatalities Synthetic control Policy evaluation Time series Drug policy
This research was conducted using publicly available data and did not receive any external support or funding.
Data availability statement
The datasets generated and analyzed during the current study are available in the Harvard Dataverse repository, [URL temporarily censored to avoid compromising blind review].
Compliance with ethical standards
Conflict of interest
The authors declare that they have no conflict of interest.
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