Reasons for Purchasing Cannabis From Illegal Sources in Legal Markets: Findings Among Cannabis Consumers in Canada and U.S. States, 2019–2020
Bibliographic record
Abstract
Objective: Nonmedical cannabis is legal in Canada and several U.S. states. Displacing the illegal market is a primary goal of legalization; however, there are little data on factors that predict consumers’ transition from the illegal to the legal market. The current study aimed to examine reasons for purchasing illegal cannabis and, thus, potential barriers to purchasing legal cannabis among consumers in Canada and U.S. states. Method: Data are from the 2019 and 2020 International Cannabis Policy Study, a repeat cross-sectional survey conducted among 16- to 65-year-olds. Reasons for purchasing illegally in the past 12 months were asked of male and female cannabis consumers in Canada and U.S. legal states (n = 11,659). Changes over time in reasons for illegal purchasing were tested. Analyses among Canadians also examined associations between reasons for illegal purchasing and objective data on cannabis prices and retail density. Results: In both years, the most commonly reported barriers to legal purchasing were price (Canada: 35%–36%; United States: 27%) and inconvenience (Canada: 17%–20%; U.S.: 16%–18%). In 2020 versus 2019, several factors were less commonly reported as barriers in Canada, including inconvenience (17% vs. 20%, p = .011) and location of legal sources (11% vs. 18%, p < .001). Certain barriers increased in the United States, including slow delivery (5% vs. 8%, p = .002) and requiring a credit card (4% vs. 6%, p = .008). In Canada, consumers in provinces with more expensive legal cannabis were more likely to report price as a barrier, and those in provinces with fewer legal retail stores were more likely to report inconvenience as a barrier (p < .001). Conclusions: Higher prices and inconvenience of legal sources were common barriers to purchasing legal cannabis. Future research should examine how perceived barriers to legal purchasing change as legal markets mature.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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 | 0.001 |
| 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".