Marijuana medicalization and motor vehicle fatalities: a synthetic control group approach

  • Bradley J. BartosEmail author
  • Carol Newark
  • Richard McCleary



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.


Medical 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.


  1. Abadie, A., Diamond, A., & Hainmueller, J. (2010). Synthetic control methods for comparative case studies: Estimating the effect of California’s tobacco control program. Journal of the American statistical Association, 105(490), 493–505.CrossRefGoogle Scholar
  2. Abadie, A., Diamond, A., & Hainmueller, J. (2011). Synth: an r package for synthetic control methods in comparative case studies. Journal of Statistical Software, 42(13).Google Scholar
  3. Abadie, A., Diamond, A., & Hainmueller, J. (2015). Comparative politics and the synthetic control method. American Journal of Political Science, 59(2), 495–510.CrossRefGoogle Scholar
  4. Abadie, A., & Gardeazabal, J. (2003). The economic costs of conflict: a case study of the Basque Country. American Economic Review, 113–132.CrossRefGoogle Scholar
  5. Anderson, D. M., Hansen, B., & Rees, D. I. (2013). Medical marijuana laws, traffic fatalities, and alcohol consumption. Journal of Law and Economics, 56, 333–369.CrossRefGoogle Scholar
  6. Asbridge, M., Hayden, J., & Cartwright, J. (2012). Acute cannabis consumption and motor vehicle collision risk: Systematic review of observational studies and meta-analysis. British Medical Journal, 344, e536.CrossRefGoogle Scholar
  7. Bates, M. N., & Blakely, T. A. (1999). Role of cannabis in motor vehicle crashes. Epidemiologic Reviews, 21, 222–232.CrossRefGoogle Scholar
  8. Braun, B. L., Tekawa, I. S., Gerberich, S. G., & Sidney, S. (1998). Marijuana use and medically attended injury events. Annals of Emergency Medicine, 32, 353–360.CrossRefGoogle Scholar
  9. Busby, A.M. (2010) Seeking a second opinion: how to cure Maryland’s medical marijuana law. University of Baltimore Law Review, 139–181.Google Scholar
  10. Chaloupka, F. J., & Laixuthai, A. (1997). Do youths substitute alcohol and marijuana? Some econometric evidence. Eastern Economic Journal, 23, 253–276.Google Scholar
  11. Crost, B., & Guerrero, S. (2012). The effect of alcohol availability on marijuana use: evidence from the minimum legal drinking age. Journal of Health Economics, 31, 112–121.CrossRefGoogle Scholar
  12. DiNardo, J., & Lemieux, T. (2001). Alcohol, marijuana, and American youth: the unintended consequences of government regulation. Journal of Health Economics, 20, 991–1010.CrossRefGoogle Scholar
  13. Fairman, B. J. (2016). Trends in registered medical marijuana participation across 13 US states and District of Columbia. Drug and Alcohol Dependence, 159, 72–79.CrossRefGoogle Scholar
  14. Farrelly, M. C., Bray, J. W., Zarkin, G. A., Wendling, B. W., & Pacula, R. L. (1999). The effects of prices and policies on the demand for marijuana: evidence from the National Household Surveys on drug abuse. working paper 6940. National Bureau of Economic Research. Google Scholar
  15. Gerberich, S. G., Sidney, S., Braun, B. L., Tekawa, I. S., Tolan, K. K., & Quesenberry, C. P. (2003). Marijuana use and injury events resulting in hospitalization. Annals of Epidemiology, 13, 230–237.CrossRefGoogle Scholar
  16. Greene, W. H. (2003). Econometric analysis (5th ed.). Upper Saddle River, NJ: Prentice-Hall.Google Scholar
  17. Heckman, J. J., Ichimura, H., & Todd, P. E. (1997). Matching as an econometric evaluation estimator: Evidence from evaluating a job training programme. The Review of Economic Studies, 64, 605–654.CrossRefGoogle Scholar
  18. Holland, P. W. (1986). Statistics and causal inference. Journal of the American statistical Association, 81(396), 945–960.CrossRefGoogle Scholar
  19. Im, K. S., Pesaran, M. H., & Shin, Y. (2003). Testing for unit roots in heterogeneous panels. Journal of Econometrics, 115, 53–74.CrossRefGoogle Scholar
  20. Kann, L., Warren, C. W., Harris, W. A., Collins, J. L., Williams, B. I., Ross, J. G., & Kolbe, L. J. (1996). Youth risk behavior surveillance—United States, 1995. Journal of School Health, 66, 365–377.CrossRefGoogle Scholar
  21. Mayr, E. (1997). This is biology: the science of the living world. Cambridge, MA: Harvard University Press.Google Scholar
  22. McCleary, R., McDowall, D., & Bartos, B. (2017). Design and analysis of time series experiments. Oxford University Press.Google Scholar
  23. National Highway Traffic Safety Administration. (2014). FARS analytic reference guide 1975 to 2014. Washington, DC: Department of Transportation.Google Scholar
  24. Pacula, R. L. (1998). Does increasing the beer tax reduce marijuana consumption? Journal of Health Economics, 17, 557–585.CrossRefGoogle Scholar
  25. Pacula, R. L., Powell, D., Heaton, P., & Sevigny, E. L. (2015). Assessing the effects of medical marijuana laws on marijuana use: the devil is in the details. Journal of Policy Analysis and Management, 34(1), 7–31.Google Scholar
  26. Powell, D., Pacula, R. L., & Jacobson, M. (2015). Do medical marijuana laws reduce addictions and deaths related to pain killers? Working Paper 21345, National Bureau of Economic Research. July, 2015.Google Scholar
  27. Rubin, D. B. (1974). Estimating causal effects of treatments in randomized and nonrandomized studies. Journal of Educational Psychology, 66, 688–701.CrossRefGoogle Scholar
  28. Rubin, D. B. (2005). Causal inference using potential outcomes. Journal of the American Statistical Association, 100, 322–331.CrossRefGoogle Scholar
  29. Saffer, H., & Chaloupka, F. J. (1999). The demand for illicit drugs. Economic Inquiry, 37, 401–411.CrossRefGoogle Scholar
  30. Vitiello, M. (1998). Proposition 215: de facto legalization of pot and the shortcomings of direct democracy. University of Michigan Journal of Law Reform, 31, 707–776.Google Scholar
  31. Williams, J., Pacula, R. L., Chaloupka, F. J., & Wechsler, H. (2004). Alcohol and marijuana use among college students: Economic complements or substitutes? Health Economics, 13, 825–843.CrossRefGoogle Scholar
  32. Windelband, W. (1894). Geschichte der alten Philosophie: Nebst einem anhang: abriss der Geschichte der Mathematik und Naturwissenschaften. In Altertum von Siegmund Gunter. Munich: Beck.Google Scholar

Copyright information

© Springer Nature B.V. 2018

Authors and Affiliations

  1. 1.Department of Criminology, Law and SocietyUniversity of CaliforniaIrvineUSA

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