Differences in self-reported cannabis prices across purchase source and quantity purchased among Canadians
Bibliographic record
Abstract
Background: In October 2018, Canada legalized non-medical cannabis. A primary goal of legalization is to reduce illicit market transactions; however, there is little ‘baseline’ data on the price and purchase sources of cannabis prior to legalization in Canada. This study examined the self-reported price of dried cannabis, quantity purchased, and sources used before retail stores opened. Methods: Data come from the baseline wave of the International Cannabis Policy Study (ICPS), a prospective cohort survey conducted in August–October 2018, immediately before legalization. Respondents were 1227 Canadians aged 16–65 years who reported purchasing dried cannabis in the past 12 months. Respondents were recruited using the Nielsen Consumer Insights Global Panel. A linear regression model examined price-per-gram by quantity purchased, source used, and socio-demographics. Results: Overall, the mean self-reported price-per-gram among cannabis users was C$9.56 (standard errors of the mean [SEM] = 0.2). The price-per-gram of cannabis significantly decreased as quantity purchased increased. For example, the mean price of cannabis purchased in smaller quantities (<3.5 g) ($12.81/g, SEM = 0.5) was more than double the price of cannabis purchased in larger quantities (>28 g) ($5.60/g, SEM = 0.2). The estimated quantity discount elasticity was −0.21 (95% CI: −0.25, −0.18). The most common purchase sources used were family member/friends (53.0%) and illicit street dealers (51.7%). Price-per-gram varied across sources; however, variation was largely accounted for by consumers purchasing different quantities at different sources. Conclusions: Variations in the price of dried cannabis were largely determined by the quantity purchased. The findings highlight the importance of accounting for purchase quantity when assessing cannabis prices, particularly in illicit markets.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| 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) | 0.005 | 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".