The economics of tobacco control (Part 2): evidence from the ITC Project
Bibliographic record
Abstract
The empirical evidence for the effectiveness of excise tax increases as a tool for tobacco control and for generating government revenue is overwhelming. Although initially this evidence was generated primarily in high-income countries, the past two decades has seen much evidence originating in low-income and middle-income countries. The findings are broadly similar. The answer to the question, “Are excise taxes effective as a tobacco control instrument?” is an unambiguous “Yes.” This supplement considers secondary questions. For example, how do tax and/or price increases affect different demographic groups’ smoking behaviour? How does the tax structure influence the effectiveness of excise tax increases? Do certain individual or community characteristics impact the effectiveness of excise tax increases? Are minimum price laws (MPLs) equally/more/less effective than tobacco excise tax increases to reduce smoking? How do smokers avoid tax or price increases? These topics do not question the effectiveness of excise tax increases as a tobacco control instrument, but they allow researchers and policymakers to gain a deeper understanding of the complexities associated with tax and price increases. While time series data and cross-sectional data were sufficient to establish the effectiveness of excise tax increases as a tobacco control tool, many of the secondary questions require more sophisticated data. Longitudinal data allow researchers to do exactly that. The International Tobacco Control Policy Evaluation Project (ITC Project), founded in 2002, systematically evaluates key policies of the WHO Framework Convention on Tobacco Control (FCTC). Starting with four countries (Canada, the USA, the UK and Australia), it has expanded rapidly and is currently in 22 countries, containing more than 50% of the world's population, 60% of the world's smokers and 70% of the world's tobacco users. This supplement consists of 13 papers. Of these, 10 consider individual countries (China (2), Malaysia, Mauritius, Mexico (2), South Korea, Uruguay, the …
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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 | 0.020 | 0.062 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.009 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".