The impact of cigarette prices on smoking participation and tobacco expenditure in Vietnam
Bibliographic record
Abstract
Vietnam is one of countries with the highest number of smokers in the world and the high smoking prevalence among men in the region. Although the real cigarette prices increased by around 4% during the 2010-2015 period, the prevalence of daily cigarette smoking among men decreased slightly from 31.3% to 30.7% during this period. This raises the question of whether cigarette consumption is sensitive to price. In this study, we estimated the effect of cigarette prices on smoking participation and tobacco expenditure in Vietnam. We found that a one-percent increase in the real cigarette price reduced the probability of cigarette smoking among males by 0.08 percentage points (95% CI from -0.06 to -0.10), equivalent to the price elasticity of the smoking prevalence at -0.26 (95% CI from -0.16% to -0.33%). Using this estimate, we predict that if the cigarette price is increased by 10%, the daily cigarette smoking prevalence among men would decrease from 30.7% to 29.9% and the number of male smokers would decline by around 270 thousand. Higher cigarette prices also reduced per capita tobacco expenditure of households. A one-percent increase in the cigarette price decreased per capita expenditure on tobacco consumption expenditure of households by 0.43 percent (the 95% CI from -0.029 to 0.822). This finding suggests that raising tobacco taxes and prices can be an effective measure to reduce tobacco use.
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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 | 0.005 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".