Comparing states before and after MML enactment to states without such laws, they find that MML enactment leads to a significant reduction of about 16% in the fatality rate for individuals aged 20-40 from motor vehicle accidents. The authors also find that legalization of medical marijuana leads to a significant 13.2% decrease in fatalities from alcohol-involved accidents. Pacula et al. recognized that heterogeneity in MML policy may result in heterogeneous effects, and they repeated the analysis of Anderson et al. distinguishing between MMLs with and without legal dispensary allowances. Their results confirm that MML enactment is negatively associated with alcohol-involved traffic fatality rates, but they show that this negative relationship is almost entirely offset in states that allow dispensaries. These results are supported by Choi , who uses individual-level data from the National Survey of Drug Use and Health from 2004-2012 and finds that allowing marijuana dispensaries is associated with a 9.1 and 23.9 percent increase in reporting driving under the influence of alcohol and drugs respectively. Why should there be differences in the effects of MMLs on traffic fatalities depending on the allowance of dispensaries? For one, Downey et al. finds that high tetrahydrocannabinol levels significantly increase driving impairment,hydroponics flood tray and MML states that allow dispensaries have been shown to have greater diffusion of high-potency cannabis . Comparing Colorado to non-MML states, Salomonsen-Sautel, Min, et al. find no significant increase in the propor-tion of drivers involved in a fatal motor vehicle crash who tested positive for marijuana until the period when medical marijuana commercialization expanded .The evidence from Table 3.1 can help explain the different findings of past research. There is wide variation in medical marijuana penetration depending on the supply regulations established by state MML policies and the level of federal enforcement.

Using the timing of state MML enactment to identify the effects of medical marijuana legalization on traffic fatalities misses important changes in the availability of marijuana that occur long after initial MML passage. These omitted effects may be of particular importance when assessing the impact on youths, who are more likely to self-report willingness to drive after consuming cannabis or after consuming both marijuana and alcohol . Irrespective of its effects on traffic accidents, marijuana’s role as a substitute for alcohol has important health implications. The question of whether alcohol and marijuana are substitutes or complements has received substantial attention in the literature, but findings have varied. Given the breadth of work, I focus here on studies using marijuana liberalization policies to identify the relationship between marijuana and alcohol use. Empirical evidence on the effect of marijuana decriminalization policies is mixed for a review of the literature, and more recent work specifically examining the effects of MMLs on alcohol consumption has found similarly varied outcomes. Using data from the 2004-2012 National Survey of Drug Use and Health , Wen et al. report a positive effect of MML enactment on frequency of binge drinking for adults over 20 years of age but no effect on alcohol use by individuals under age 21. In contrast, Anderson et al. examine data from the 1993-2010 Behavioral Risk Factor Surveillance System and find that MML enactment has a significant negative impact on past-month drinking and binge-drinking among young adults. Differences in these findings may in part be driven by the fact that the studies cover different years, and hence different state laws are used in the identification of the treatment effects. Pacula, MacCoun, et al. caution against using a binary indicator for marijuana decriminalization laws, as there is substantial variation in how these laws are implemented, enforced, and hence understood by citizens.

As discussed by Pacula et al. and demonstrated in section 3.2, state MML regulations vary greatly and can thus be expected to generate heterogeneous effects. Additionally, the categorical MML measure used in past analyses does not capture the later evolution of medical marijuana markets shown in Figure 3.2. The inclusion of state-specific trends in the empirical specification will thus confound preexisting trends with the dynamic effects of the policy . By using medical marijuana registration rates instead of a binary MML indicator as the policy variable of interest, this paper overcomes these limitations. While there are only a few economic studies of the relationship between marijuana and opioid use, clinical studies by Cichewicz and Welch and Ramesh et al. suggest that smoked cannabis and cannabinoids have opioid sparing properties and may prevent the development of tolerance to opiates. Additionally, studies of opioid dependent patients indicate that moderate use of cannabis or synthetic cannabinoids leads to significantly improved outcomes for medication compliance, opioid withdrawal symptoms, and retention in treatment . The potential for medical marijuana to reduce opioid abuse is supported by Bachhuber et al. , who find that MML enactment is associated with a significant 20% decrease in age-adjusted prescription opioid-related mortality, with these effects strengthening several years post-enactment. Powell et al. find no effect of MMLs on opioid abuse or mortality, but they find that legalizing dispensaries reduces opioid abuse and mortality by about 15%. While these results suggest that increased medical marijuana availability offers the benefit of significantly reducing opioid use, past work has not examined to whom these benefits are accruing. While all age groups have seen significant growth in opioid-related deaths, the rise in mortality rates has been most pronounced for adults aged 45-64 .

Indeed, recent work by Case and Deaton shows that drug poisoning deaths have significantly contributed to the reversal in mortality improvement experienced by US white non-Hispanics aged 45-54 between 1999 and 2013. By estimating the effects of increased medical marijuana availability on opioid poisoning mortality separately by age group, this paper contributes toward further understanding whether medical marijuana can serve to improve the deteriorating mortality outcomes for older individuals. To assess whether increased cannabis use results in more automobile accidents, data was compiled from the Fatal Accident Reporting System for 1990-2013. FARS, collected by the National Highway Traffic Safety Administration ,hydro flood table contains detailed information on the circumstances of the accident, in addition to information on the characteristics of occupants and non-occupants. In order to most precisely identify which age groups experience increased risk of causing fatal accidents,traffic fatalities are analyzed separately by age of the driver involved in single-vehicle accidents only. Summary statistics are given in Appendix H-. It should be noted that the traffic fatality variables used in this analysis differ slightly from that used in Anderson et al. . Their analysis separates traffic fatalities by age of the deceased, while my empirical model analyzes fatalities by age of the driver involved. While these two variables should be correlated, focusing on age of the driver involved provides a better indicator of which individuals are changing their alcohol and cannabis consumption in response to increased medical marijuana availability.To investigate substitution between marijuana and other addictive substances, substance related poisoning mortality data from 1990-2013 was downloaded from the Center for Disease Control’s Wide-ranging Online Data for Epidemiologic Research interface. Alcohol poisonings are defined as deaths with ICD-10 code X45, X65, Y15, or F10.0.5 Opioid analgesic poisonings are defined as those under ICD-10 codes X40-X44, X60-X64, or Y10-Y14, where a prescription opioid was also coded. Heroin-related poisonings are defined as those under ICD-10 codes X40-X44, X60-X64, or Y10-Y14, where heroin was also coded. Summary statistics are given in Appendix H-. Deaths are coded based on multiple-cause reporting instead of the underlying cause, since this can provide a more complete representation of all conditions that contributed to the death . A death is counted if the specified condition is listed on the death certificate as a contributing factor, but the condition need not be the specified as the underlying cause of death. Thus, these counts do not necessarily represent unique deaths on all fatal traffic accidents, but these effects are only statistically significant for daytime accidents. For older adult drivers, increased registration rates do not predict any significant change in fatal traffic accidents. However, while not statistically significant, the effects on drivers aged 45-64 are all negative, with the largest effects for nighttime accidents. While Table 3.2 indicates that greater marijuana availability leads to increased traffic fatalities involving young drivers, it is unclear whether these effects are driven by cannabis use alone or the joint use of cannabis with other substances. To disentangle the role of alcohol and marijuana in generating motor vehicle fatalities, Table 3.3 presents estimates of the effects of registration rate growth on traffic fatalities seperately by substance involvement.Panel A reports estimates of the effects of legal market growth on fatalities in which the driver’s blood alcohol content was tested and found to be equal to zero.

For all age groups, the estimates are insignificant. In contrast, for accidents in which the driver had a positive BAC value, Panel B shows that increased medical marijuana availability is associated with a significant 11.6% increase in traffic fatalities involving a driver aged 15-20 and an insignificant 5.3% decrease for drivers aged 45-64. While the results from Panel C are consistent with increased prevalence of cannabis use for drivers of all ages, Panel D suggests that it is the joint use of alcohol and marijuana that generates negative externalities in the form of increased traffic fatalities caused by drivers aged 15-24. The results of Table 3.3 are consistent with the experimental evidence from driving simulator studies, but caution should be taken in interpreting these results. Drivers are not regularly tested for cannabinoids, and the decision to test may well be endogenous with expansion in the medical marijuana market. Also, because THC is lipid-soluble and excreted slowly over time into urine, a positive test for cannabinoids does not necessarily mean the individual has used marijuana recently — let alone that cannabis-impairment caused the accident . Still, the results suggest that differences between youths and older adults in the decision to use marijuana and alcohol jointly may generate different health consequences caused by greater marijuana availability. To further examine substitution behavior, Table 3.4 presents estimates of the effects of registration rates on poisoning mortality involving alcohol , prescription opioid analgesics , and heroin . Panel A shows that higher registration rates predict a large and significant decline in alcohol-related poisoning mortality for adults aged 45-64. With increased medical marijuana access, older adults appear to substitute away from the heavy use of opioids. Registration rates have a significant negative effect on opioid-analgesic poisoning mortality for adults aged 45-64 of 11-15%. These results are smaller but in line with the findings of Powell et al. . In contrast, there is suggestive evidence of complementarity between alcohol and marijuana for youths aged 15-24, consistent with the evidence from Tables 3.2 and 3.3. As stated earlier, the Poisson specification was preferred over the more intuitive loglinear specification and the commonly-used negative binomial regression model. Since in many years, there are relatively few single-vehicle traffic fatalities, a loglinear specification will introduce considerable noise in the analysis, and will result in biased estimates under heteroskedasticity . While the negative binomial regression estimator can account for the over-dispersion apparent in the data, violation of the model’s assumptions about the underlying data-generating process will produce biased coefficients . Still, these alternative models can provide specification checks for the primary analyses. Tables 3.5 and 3.6 thus present coefficients on the registration rate variable from the log-linear and negative binomial specifications for completeness. In line with the estimates from Tables 3.2 and 3.4, these alternative specifications confirm that growth in the legal medical marijuana leads to a significant increase in weekend and nighttime traffic fatalities caused by drivers aged 15-20, and significant declines in alcohol and opioid analgesic poisoning deaths for adults aged 45-64. As the final set of sensitivity analyses, Tables 3.7 and 3.8 estimate the effects of medical marijuana market growth including only states that had enacted an MML as of 2015. The effects are thus estimated from differences in market size within the set of states that presently provide legal protections for medical marijuana. Since all of these states eventually passed laws, they may be considered more similar. For all outcomes, the results are largely unchanged. One difference of note is that the negative effects on traffic fatalities involving a driver aged 45-64 are larger and significant when the sample is restricted to MML states.