Prices and Purchase Sources for Dried Cannabis Flower in the United States, 2019–2020
Bibliographic record
Abstract
Introduction: The price of cannabis has major implications for public health, public safety, social equity, and government revenues. This article examines prices and sources of purchased dried cannabis flower among consumers facing different state laws in the United States. Methods: Repeat cross-sectional survey data were collected from the International Cannabis Policy Study in 2019 and 2020. U.S. respondents were recruited through online commercial panels, ages 16–65, and purchased dried flower in the past year ( n =9766). Weighted binary logistic regression models examined legal purchasing in states that had legalized recreational cannabis. Results: Compared with respondents in states with recreational stores, respondents living in “illegal,” “medical,” and “recreational” states without stores were associated with paying a higher unit price of dried flower (+20.5%, +23.6%, +27.4%, respectively; all p <0.05). The majority of respondents in states with recreational stores last purchased from stores/dispensaries (2019: 66.6%; 2020: 62.0%) and the odds of purchasing legally was greater with each additional year after stores opened (adjusted odds ratio=1.48, 95% confidence interval: 1.37, 1.60). Conclusions: Cannabis prices and purchase behaviors are strongly influenced by its legal status and presence of stores. After states legalize for recreational purposes, it takes multiple years for the legal market to become established as the number of retail stores increase and prices decrease. The findings demonstrate that consumers use sources that they are legally allowed to access, suggesting an increased number of physical retail stores and online delivery services could expand uptake of legal sources in states with recreational cannabis laws.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".