Bibliographic record
Abstract
There is widespread consensus that higher cigarette taxes are the most effective policy tool in reducing population smoking rates and tobacco-induced mortality, but the efficacy of such taxes is tempered by the possibility of a rise in smuggling and the availability of contraband tobacco. Understanding the extent to which stronger law enforcement affects the consumption of contraband tobacco is key given the significant tobacco tax increases recently implemented by the federal, Ontario and Quebec governments. Concerns have been raised about lost tax revenue and even the funnelling of black-market revenue to organized crime and terrorist activities. The study employs rigorous econometric methods in order to estimate the amount of smuggled cigarette cartons, along with associated lost tax revenues, in Quebec and Ontario from 2006 to 2014. While the amount of contraband has been quite significant in both provinces, it has been particularly high for Ontario, with lost tax revenue of approximately $816 million to $900 million in 2014. But the amount of contraband has declined over time for both provinces and coincided with an increase in excise cigarette taxes. The reduction in contraband since 2008 has been especially dramatic in Quebec. Lost tax revenue from current levels of contraband in Quebec is roughly a tenth of corresponding amounts in Ontario. The decline in illegal sales can be at least partially attributed to additional federal and provincial resources devoted to law enforcement. Given the magnitude of the decrease in estimated lost tax revenues as a likely consequence of stronger policing, and the risks to higher tobacco taxes undermining fruitful enforcement efforts, it appears that Ontario in particular would be better off by focusing on strengthening enforcement and regulation.
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 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 | 0.007 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.009 |
| Scholarly communication | 0.012 | 0.010 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.012 | 0.011 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".