Self‐Reported Cannabis Prices and Expenditures From Legal and Illegal Sources Five Years After Legalisation of Non‐Medical Cannabis in Canada
Bibliographic record
Abstract
INTRODUCTION: The price of cannabis has important implications for many outcomes discussed in legalisation debates. This paper reports on legal and illegal prices of different cannabis products. METHODS: National surveys were conducted in 2022 and 2023 among Canadians aged 16-65 years as part of the International Cannabis Policy Study. Self-reported price, purchase quantity and legal versus illegal source of the'last' cannabis purchased was examined from 2686 respondents who used cannabis in the past 12 months for nine cannabis product types. RESULTS: On average, consumers report that 78% of all their cannabis came from legal sources in the past year. Differences in the self-reported price paid between legal and illegal purchases varied by product type. Price per unit was higher from legal sources for dried flower (+23.8%, p < 0.001), vapes (+18.7%, p = 0.006) and hash (+38.4%, p < 0.001), and lower for capsules (-28.4%, p = 0.008). No statistically significant difference was found for drops (-3.3%. p = 0.76), edibles (+3.9%, p = 0.49), cannabis drinks (-8.8%, p = 0.21), concentrates (+13.8%, p = 0.15) and tinctures (-17.0%, p = 0.28). Substantial quantity discounts for dried flower were observed from both legal and illegal sources. DISCUSSION AND CONCLUSION: Differences between legal and illegal cannabis prices have narrowed considerably, likely due to declines in the price of cannabis from legal stores since the opening of legal markets in Canada. Some differences are expected across the two markets considering differences in standard quantities purchased and the presence of quantity discounts in these markets. Analyses omitting purchase quantity may overestimate the price differential between legal and illegal sources.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 source (direct Gemma or distilled Codex), 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".