Illicit trade and real prices of cigarettes in Chile
Bibliographic record
Abstract
INTRODUCTION: The tobacco industry claims that tobacco taxes are responsible for increased illicit trade in Chile, which they estimated at 37% in 2022. However, the evolution of cigarette consumption, estimated from population surveys, and of tax-paying cigarettes shows a decreasing penetration of illicit trade since 2018. METHODS: A gap analysis was used to estimate the evolution of illicit trade based on an arithmetic identity stating that total national cigarette consumption over a given period is equal to the registered consumption as paying taxes plus the cigarettes that are consumed nationally without paying taxes. RESULTS: Illicit trade penetration in Chile was around 10% in 2020, less than half of what the tobacco industry claimed. In addition, the evolution of real prices of cigarettes, calculated using tax collection data, indicates that real prices net of tobacco taxes increased significantly during 2015-2021, a period with no changes in tobacco taxation. The cheapest cigarettes, presumably competing with illicit cigarettes, registered the most significant price increase. CONCLUSIONS: Claims of increasing illicit trade penetration in Chile are unfounded and are not supported by data on consumption and tax-paying cigarettes.
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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 | 0.003 |
| 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 | 0.001 |
| 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".