Department of Political Science, 2824University of Dayton, Dayton, OH, USA.
School of Public Policy, 311285Pennsylvania State University, University Park, PA, USA.
Eval Rev. 2022 Apr;46(2):165-199. doi: 10.1177/0193841X221077795. Epub 2022 Feb 23.
American states have used different approaches in adoption of cannabis policies and continue to modify those policies after approval. States also differ in how long it takes to implement such policies, and this influences the availability of legal marijuana. Such policy differences and implementation timelines could influence usage of marijuana and other illicit drugs by adolescents, young adults, and older adults. We develop an original coding scheme for marijuana legalization policies by classifying policy bundles characterized by three views of marijuana: as a pharmaceutical; as a permissive drug, or as a state fiscal revenue source. We test the impact of state legal marijuana policy characteristics on age group rates of marijuana use with panel regression models including control variables and fixed effects for 2000-2019. This design moves beyond a dichotomous construct of marijuana legalization and accounts for the dynamic adaptation of policies beyond their initial adoption. States with a higher pharmaceutical score experienced lower marijuana usage rates for adolescents and young adults while states with a permissive approach or fiscal approach experienced higher rates of marijuana use for all age groups. We find no consistent spillover effect of the pharmaceutical or permissive marijuana policy bundles on other illicit drug use for any age group, but fiscal bundles show some association with greater illicit drug use for adults. These more nuanced measures better reflect state policies as implemented and provide more clarity of the policy impact on target populations' marijuana usage.
美国各州在采用大麻政策方面采取了不同的方法,并在批准后继续修改这些政策。各州在实施这些政策所需的时间上也存在差异,这影响了合法大麻的供应。这种政策差异和实施时间表可能会影响青少年、年轻人和老年人使用大麻和其他非法药物。我们通过将大麻合法化政策分类为三种观点的政策包来制定一个原始的编码方案:作为一种药物;作为一种放任的药物,或作为国家财政收入来源。我们使用面板回归模型测试了 2000-2019 年各州合法大麻政策特征对各年龄组大麻使用率的影响,其中包括控制变量和固定效应。这种设计超越了大麻合法化的二分法结构,并考虑了政策在初始采用之外的动态调整。药物评分较高的州,青少年和年轻人的大麻使用率较低,而采取放任或财政方法的州,所有年龄组的大麻使用率都较高。我们没有发现药物或放任政策包对任何年龄组的其他非法药物使用有一致的溢出效应,但财政政策包与成年人更多的非法药物使用有一定关联。这些更细致的措施更好地反映了实施中的州政策,并更清楚地说明了政策对目标人群大麻使用的影响。