Basic economic gap related to smoking: reconciling tobacco tax receipts and economic costs of smoking-attributable diseases
Bibliographic record
Abstract
BACKGROUND: Tobacco tax rates set by various governments are not based on the idea that tax receipts should cover the costs incurred by smoking. It can be assumed that tobacco tax receipts (TTR) differ from the costs of smoking. The aim is to determine the global basic economic gap (BEG) between TTR and the economic costs of smoking-attributable diseases (ECS). METHODS: BEG is described as the difference between the ECS and TTR. A total of 124 countries representing 94% of global tobacco consumption were included in the research by means of the creation of a database, the adjustment of input data and the identification of their intersection. RESULTS: The global BEG reaches US$1438 billion per year. The global ECS are US$1911 billion per year. The global TTR are US$473 billion per year and compensate for only one quarter of the ECS. Within countries with the highest consumption of cigarettes, especially the USA but also Russia and Germany, the proportion of the ECS covered by the TTR is even lower, although private health expenditures have been taken into account. CONCLUSIONS: Our findings suggest that tobacco taxes would have to be globally increased by more than four times on average in order to cover the ECS or between two and two-and-a-half times if we take private health expenditures into account. The informational pressure concerning health risks associated with smoking aimed at reducing harmful consumption and improving global health can also be supported with these economic facts.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 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".