Association between tax structure and cigarette consumption: findings from the International Tobacco Control Policy Evaluation (ITC) Project
Bibliographic record
Abstract
BACKGROUND: Recent studies show that greater price variability and more opportunities for tax avoidance are associated with tax structures that depart from a specific uniform one. These findings indicate that tax structures other than a specific uniform one may lead to more cigarette consumption. OBJECTIVE: This paper aims to examine how cigarette tax structure is associated with cigarette consumption. METHODS: We used survey data taken from the International Tobacco Control Policy Evaluation Project in 17 countries to conduct the analysis. Self-reported cigarette consumption was aggregated to average measures for each surveyed country and wave. The effect of tax structures on cigarette consumption was estimated using generalised estimating equations after adjusting for sociodemographic characteristics, average taxes and year fixed effects. FINDINGS: Our study provides important empirical evidence of a relationship between tax structure and cigarette consumption. We find that a change from a specific to an ad valorem structure is associated with a 6%-11% higher cigarette consumption. In addition, a change from uniform to tiered structure is associated with a 34%-65% higher cigarette consumption. The results are consistent with existing evidence and suggest that a uniform and specific tax structure is the most effective tax structure for reducing tobacco consumption.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.012 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".