Self-reported price of cigarettes, consumption and compensatory behaviours in a cohort of Mexican smokers before and after a cigarette tax increase
Bibliographic record
Abstract
OBJECTIVE: To assess the impact of a 2007 cigarette tax increase from 110% to 140% of the price to the retailer on cigarette price and consumption among Mexican smokers, including efforts to offset price increases. METHODS: Data were analysed from the 2006 and 2007 administrations of the International Tobacco Control (ITC) Policy Evaluation Survey in Mexico, which is a population-based cohort of adult smokers. Self-reported price of last cigarette purchase, place of last purchase, preferred brand, daily consumption and quit behaviour were assessed at baseline and follow-up. RESULTS: Self-reported cigarette prices increased by 12.7% after the tax increase, with prices for international brands increasing more than for national brands (13.5% vs 8.7%, respectively). Although the tax increases were not fully passed onto consumers particularly on national brands, no evidence was found for smokers changing behaviour to offset price increases. Consistent declines in consumption across groups defined by sociodemographic and smoking-related psychosocial variables suggest a relatively uniform impact of the tax increase across subpopulations. However, decreased consumption appeared limited to people who smoked relatively more cigarettes a day (>5 cigarettes/day). Average daily consumption among lighter smokers did not significantly decline. A total of 13% (n=98) of the sample reported being quit for a month or more at follow-up. In multivariate models, lighter smokers were more likely than heavier smokers to be quit. CONCLUSIONS: Results suggest that the 2007 tax increase was passed on to consumers, whose consumption generally declined. Since no other tobacco control policies or programmes were implemented during the period analysed, the tax increase appears likely to have decreased consumption.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".