Levels and trends in cigarette contraband in Canada
Bibliographic record
Abstract
BACKGROUND: There is overwhelming evidence that increases in tobacco taxes reduce tobacco use, save lives and increase government tax revenue. High taxes, however, create an incentive to devise ways to avoid or evade tobacco taxes through contraband tobacco. The associated consequences are significant and call for an accurate measurement of contraband's magnitude. However, its illegal nature makes the levels and trends in contraband intrinsically difficult to measure accurately. OBJECTIVE: To examine levels and trends in cigarette contraband in Canada. METHODS: We employed 2 approaches: first, we contrasted estimates of tax-paid cigarettes sales with consumption estimates based on survey data; second, we used data from several individual-level surveys that examined smokers' purchasing and use behaviours. We placed a particular emphasis on the provinces of Québec and Ontario because existing research suggests that cigarette contraband levels are far higher than in any other province. RESULTS: The estimates presented show a clear upward trend from the early 2000s in cigarette contraband in Québec and Ontario followed by, on the whole, a decreasing trend from about 2007 to 2009. None of the data presented provide support to the narrative that cigarette contraband has been increasing in recent years. Of note are Québec estimates which suggest relatively low levels of cigarette contraband since 2010, at levels no higher than in the early 2000s. CONCLUSIONS: The data presented suggest that policies to tackle cigarette contraband introduced from the mid-2000s to late 2000s, at both federal and provincial levels, may have dampened the demand for contraband cigarettes.
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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 | 0.000 | 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".