Modelling the expected impact of cigarette tax and price increases under Vietnam’s excise tax law 2015–2020
Bibliographic record
Abstract
BACKGROUND: Vietnam's national tobacco control strategy aims to reduce the rate of smoking among male adults from 45% in 2015 to 39% by 2020. The aim of this paper is to assess what contribution cigarette tax increases under Vietnam's current excise tax plan can be expected to make to this target, and to discuss what additional measures might be implemented accordingly. METHODS: This study uses a mix of administrative datasets and predictive modelling techniques to assess the expected impact of tax and price increases on cigarette consumption, tobacco tax revenues and the rate of smoking between 2015 and 2020. FINDINGS: The average retail price of cigarettes is estimated to have increased by 16% (sensitivity analysis: 14%-18%) in inflation-adjusted terms between 2015 and 2020, while cigarette consumption is projected to decrease by 5.1% (4.5%-5.5%). The rate of smoking among males is projected to decrease to 42.8% (42.1%-43.6%) compared with the target of 39%. Total tax revenues from cigarettes are projected to increase by 21% (19%-23%), reflecting an extra ₫3300 billion in inflation-adjusted revenues for the government. CONCLUSION: The current excise tax law is expected to have only a modest impact on the rate of smoking in Vietnam, though it has generated tax revenues. If Vietnam is to achieve its tobacco control targets, the government should implement a mixed excise system with a high-specific component to promote public health by raising the price of cigarettes more significantly.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".