Distributional benefits of tobacco tax and smoke–free workplaces in China: A modeling study
Bibliographic record
Abstract
BACKGROUND: Tobacco taxation and smoke-free workplaces reduce smoking, tobacco-related premature deaths and associated out-of-pocket health care expenditures. We examine the distributional consequences of a price increase in tobacco products through an excise tax hike, and of an implementation of smoke-free workplaces, in China. METHODS: We use extended cost-effectiveness analysis (ECEA) to evaluate, across income quintiles of the male population (the large majority of Chinese smokers), the premature deaths averted, the change in tax revenues generated, and the financial risk protection procured (eg, poverty cases averted, defined as the number of individuals no longer facing tobacco-related out-of-pocket expenditures for disease treatment, that would otherwise impoverish them), that would follow a 75% increase in cigarette prices through substantial increments in excise tax fully passed onto consumers, and a nationwide total implementation of workplace smoking bans. RESULTS: A 75% increase in cigarette prices would avert about 24 million premature deaths among the current Chinese male population, with a third among the bottom income quintile, increase additional tax revenues by US$ 46 billion annually, and prevent around 9 million poverty cases, 19% of which among the bottom income quintile. Implementation of smoking bans in workplaces would avert about 12 million premature deaths, with a fifth among the bottom income quintile, decrease tax revenues by US$ 7 billion annually, and prevent around 4 million poverty cases, 12% of which among the bottom income quintile. CONCLUSIONS: Increased excise taxes on tobacco products and workplace smoking bans can procure large health and economic benefits to the Chinese population, especially among the poor.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".