Changes in retail cigarette price after tax increase: Findings from the 2018–2020 ITC Vietnam surveys
Bibliographic record
Abstract
INTRODUCTION: No longitudinal study has investigated the impact of cigarette tax increases on retail prices in Vietnam. This study aims to describe changes in the purchase price of cigarettes following an excise tax increase from 70% to 75% in January 2019. METHODS: Data were collected from people who currently smoke cigarettes in the longitudinal ITC Vietnam surveys: 1870 participants in Wave 1 (pre-increase), 1564 in Wave 2 (post-increase), and 1308 in Wave 3 (post-increase). Weighted mean self-reported prices of a cigarette pack (with standard error) were calculated for participants who were successfully followed up across three waves. These mean prices were calculated for domestic and international brands, categorized by specific cigarette brands. Percentage changes in mean prices were also measured, and significant differences in mean prices between follow-up waves (Waves 2 and 3) and the baseline (Wave 1) were assessed using paired t-tests. For brands with very small sample sizes, we used non-parametric tests, specifically the Wilcoxon signed-rank test instead of paired t-tests. RESULTS: The weighted mean price of a cigarette pack remained low and stable: VND 12330 (US$0.54) in 2018, VND 12700 (US$0.55) in 2019, and VND 12120 (US$0.53) in 2020 (1000 Vietnamese Dongs about US$0.04345, at 2018). International brands were substantially more expensive than domestic brands, but prices for both remained constant across all waves. Among domestic brands, Thang Long and Sai Gon showed slight price increases of around 3% and 5%, respectively (p<0.05). Among international brands, no statistically significant increase in mean prices was observed. CONCLUSIONS: The retail price of cigarettes remains low, indicating that the slight tax increase was insufficient to raise the current retail price significantly. Therefore, a substantial increase in cigarette prices by adding a specific tax is necessary.
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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 | 0.000 | 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) | 0.002 | 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".