Association between non-medical cannabis legalization and alcohol sales: Quasi-experimental evidence from Canada
Bibliographic record
Abstract
BACKGROUND: There is increasing interest in understanding the impact of non-medical cannabis legalization on use of other substances, especially alcohol. Evidence on whether cannabis is a substitute or complement for alcohol is both mixed and limited. This study provides the first quasi-experimental evidence on the impact of Canada's legalization of non-medical cannabis on beer and spirits sales. METHODS: We used the interrupted time series design and monthly data on beer sales between January 2012 and February 2020 and spirits sales between January 2016 and February 2020 across Canada to investigate changes in beer and spirits sales following Canada's cannabis legalization in October 2018. We examined changes in total sales, nationally and in individual provinces, as well as changes in sales of bottled, canned and kegged beer. RESULTS: Canada-wide beer sales fell by 96 hectoliters per 100,000 population (p=0.011) immediately after non-medical cannabis legalization and by 4 hectoliters per 100,000 population (p>0.05) each month thereafter for an average monthly reduction of 136 hectoliters per 100,000 population (p<0.001) post-legalization. However, the legalization was associated with no change in spirits sales. Beer sales reduced in all provinces except the Atlantic provinces. By beer type, the legalization was associated with declines in sales of canned and kegged beer but there was no reduction in sales of bottled beer. CONCLUSIONS: Non-medical cannabis legalization was associated with a decline in beer sales in Canada, suggesting substitution of non-medical cannabis for beer. However, there was no change in spirits sales following the legalization.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".