The impact of cigarette prices on smoking onset and cessation: evidence from Vietnam
Bibliographic record
Abstract
BACKGROUND: Vietnam is a country with very high smoking rates among men. According to a Global Adult Tobacco Survey (GATS) conducted in 2015, the daily smoking prevalence among Vietnamese men was 39%. METHODS: We used data from the 2010 and 2015 Vietnamese GATSs and cigarette price data from General Statistics Office of Vietnam. Since smoking prevalence is low among women, we only considered men. Using discrete-time hazard models, we estimated the effect of cigarette prices on smoking onset and cessation. Sensitivity analyses are conducted using different model specifications. RESULTS: We find that higher cigarette prices reduce the probability of smoking onset. A 1% increase in the cigarette price reduces the hazard of smoking onset by 1.2% (95% CI -2.12% to -0.28%). This suggests that increases in tobacco taxation, which translate to price increases, can reduce smoking onset. We did not find evidence that cigarette prices impact smoking cessation among men in Vietnam. CONCLUSION: Vietnam should continue to increase excise taxes on tobacco products to reduce smoking onset. Since smokers are resilient to excise tax increases, other tobacco control policies, such as smoke-free areas and tobacco advertisement bans, should be better enforced to encourage people to quit. Other policies not yet implemented, such as plain packaging of tobacco products, may also encourage smokers to quit.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".