Recreational Cannabis Use Proven To Reduce Opioid Mortality Rate

In the following research study published only days ago we find that cannabinoid medicine has drastic positive effects on consumers that believe they're using it for recreational purposes. While many state that all use is medicinal, there is a degree of misuse as well. While many recreational users are enjoying the legalization of cannabis as a trend, others are using large amounts of cannabis concentrates that would seem to have a negative effect. This study confirms that the legalization for recreational use has impacted the number of people that are falling victim to the Opioid Epidemic in overdose deaths.

THE EFFECTS OF RECREATIONAL MARIJUANA LEGALIZATION AND DISPENSING ON OPIOID MORTALITY

Nathan W. Chan Jesse Burkhardt Matthew Flyr

First published: 06 August 2019 ~ Wiley Online Library

Abstract

This study documents how the changing legal status of marijuana has impacted mortality in the United States over the past two decades. We use a difference‐in‐difference approach to estimate the effect of medical marijuana laws (MML) and recreational marijuana laws (RML) on fatalities from opioid overdoses, and we find that marijuana access induces sharp reductions in opioid mortality rates. Our research corroborates prior findings on MMLs and offers the first causal estimates of RML impacts on opioid mortality to date, the latter of which is particularly important given that RMLs are far more expansive in scope and reach than MMLs. In our preferred econometric specification, we estimate that RMLs reduce annual opioid mortality in the range of 20%–35%, with particularly pronounced effects for synthetic opioids. In further analysis, we demonstrate how RML impacts vary among demographic groups, shedding light on the distributional consequences of these laws. Our findings are especially important and timely given the scale of the opioid crisis in the United States and simultaneously evolving attitudes and regulations on marijuana use. (JEL I18, K32, H75)

To read the entire study please see: https://doi.org/10.1111/ecin.12819

Let's jump to the discussion and read the findings:

VI. DISCUSSION

In this paper, we investigate how changes to the legal status of marijuana affect opioid mortality. This research is especially timely given simultaneous trends in the U.S. opioid epidemic and growing calls for marijuana legalization throughout North America. Using a variety of empirical specifications along with a battery of robustness and placebo tests, we show that marijuana legalization reduces opioid mortality.

Our findings corroborate past work on the effects of MMLs, and they also present wholly novel results on the impacts of RMLs. This latter point is especially important because RMLs have much wider scope than MMLs and will therefore affect a much larger population. Our results have direct relevance for policy—including state ballot propositions in the midterm elections of 2018 and Canada's recent legalization of recreational marijuana nationwide—as they indicate that recent expansions to marijuana access have significant co‐benefits in the form of reduced opioid mortality. States with legal access to marijuana were far less affected by the opioid mortality boom of the past decade than those without. Thus, our work provides important food for thought for state and federal authorities that continue to mull medical and/or recreational legalization of marijuana.

Our work also opens up additional questions for future research. First and foremost, future work can examine the precise mechanism that drives our results. Although we demonstrate a robust reduction in opioid deaths due to marijuana legalization, we can only speculate on the underlying relationship between this treatment and outcome. Is it the case that individuals with legal access are more likely to use marijuana for pain management, displacing highly addictive opioids and thereby reducing mortality risk from opioid use? This hypothesis is consistent with our primary findings as well as broader demographic trends in opioid use. Miller and Seo (2018) find substitutability between marijuana and alcohol using scanner data, which lends some evidence, albeit suggestive, that substitution between substances (in our case, marijuana for opiates) may underlie the observed reductions in mortality. Alternatively, marijuana legalization may improve a state's economic situation, particularly if it spurs economic activity or raises tax revenue. As Hollingsworth, Ruhm, and Simon (2017) show, opioid use and overdoses decline as macroeconomic conditions improve. Thus, the question of mechanism merits more detailed analysis. Moreover, it would be useful to further examine the consequences of recreational marijuana legalization on other outcomes, especially those with important distributional consequences. Along these lines, future work may build upon our work on opioid mortality to investigate the effect of marijuana legalization on outcomes like crime, incarceration, employment, and long‐term health.

• 1 We speculate that marijuana displaces opioids through substitution. However, the reduction in opioid mortality may also work through an indirect channel, for example, marijuana legalization may improve economic conditions, which in turn reduces abuse of opioids. Indeed, this mechanism would also be consistent with our findings and would accord with prior work by Hollingsworth, Ruhm, and Simon (2017) that documents the relationship between macroeconomic conditions and opioids.
• 2 We present estimates without any of these controls in Table 7. We have also run specifications that exclude the beer tax, but we omit these results for brevity. Our estimates prove robust to such variations.
• 3 Throughout the paper, percentages are obtained by applying the formula %Δ = exp(δ) − 1, where δ is the coefficient of interest.
• 4 As shown in Figure 1, 2013 was roughly the year in which opioid mortality began to swell.
• 5 The dropped states are Connecticut, District of Columbia, Delaware, Florida, Illinois, Indiana, Kentucky, Massachusetts, Maryland, Maine, Michigan, Missouri, North Carolina, New Hampshire, New Jersey, New York, Ohio, Pennsylvania, Rhode Island, Tennessee, Virginia, Vermont, and West Virginia.
• 6 This procedure drops all of the same states as our preferred specification; in addition, it further drops Alabama, Arizona, California, Georgia, Louisiana, Minnesota, Oklahoma, South Carolina, Texas, and Wisconsin. Results are presented in Table 5, columns 1–3.
• 7 The states omitted from this sample are Connecticut, District of Columbia, Delaware Kentucky, Massachusetts, Maryland, Maine, Michigan, New Hampshire, New Jersey, Ohio, Pennsylvania, Rhode Island, and West Virginia, that is, a subset of those dropped in our preferred specification. The results of this separate analysis are presented in columns 4–6 of Table 5.
• 8 Other researchers have used a synthetic control approach to address issues of selection (Hansen, Miller, and Weber 2018a).
• 9 Regressions that include all states, including those with large growth in opioid mortality, generate very large treatment effects, as shown in Table 3. We focus on our subsample analyses, as they provide more plausible and conservative estimates and are more likely to reflect the true causal effect.
• 10 For simplicity, we will henceforth refer to the RML Dispensary effect more generically as the “RML effect.”
• 11 Continuing the back of the envelope calculations above, an RML would avert up to 70 opioid deaths per year for a state the size of Colorado (5.6 million inhabitants).
• 12 This differs from our preferred specification because in our primary model, we omit states that exhibited statistically significant increases in synthetic opioid mortality rates between 2012 and 2013, 2013 and 2014, 2014 and 2015, 2015 and 2016, or 2016 and 2017.
• 13 Thus, the effect is identified from changes in Oregon and Alaska, which implemented RMLs in 2015, and California and Nevada, which implemented RMLs in 2017.
• 14 Because we include a comprehensive set of dummy variables covering all years in our sample, we must drop one from our regression. We drop the year immediately prior to treatment (consistent with the standard described by Schmidheiny and Siegloch 2019), so that all coefficients can be interpreted relative to this norm. Other papers that include only an incomplete set of lags and leads (e.g., Anderson, Hansen, and Rees 2013) need not do this, as their effects are estimated relative to the unaccounted years. Both approaches yield similar insights, but we follow our approach because it provides greater ease of interpretation for the coefficients of interest.
• 15 Pacula and Sevigny (2014) note that the composition of treated states changes over time, introducing potential issues with selection. We present a robustness check for these parallel trends in Table S4. This alternative specification estimates the parallel trends using a balanced panel of states with at least 2 years of recreational marijuana legalization (treated states include only Colorado and Washington). The results are qualitatively similar for our primary outcome of interest, Synthetic Opioids, as the coefficients become more negative and statistically significant after dispensary openings. However, the trends for All Opiates and Prescription Opiates are less clear‐cut.
• 16 We use the same mortality codes as those used in Bachhuber et al. (2014).
• 17 The ACS data are available from 2005 onward. For years before 2005, we use demographic data from the 2000 decennial census. To generate demographic specific summary statistics, we divide the number of opioid deaths for the particular demographic, for example males, by the number of males in the population and multiply by 100,000.
• 18 Results for the broader categories of All Opiates and Prescription Opioids are provided in Tables S1–S4, although results are more difficult to interpret given the wider array of factors affecting these outcomes. recreational cannabis