Taxation of tobacco, alcohol, and sugar-sweetened beverages: reviewing the evidence and dispelling the myths
Bibliographic record
Abstract
The article reviews the large body of evidence on how taxation affects the consumption of tobacco, alcohol, and sugar-sweetened beverages (SSB). There is abundant evidence that demand for tobacco, alcohol, and SSB is price-responsive and that tax changes are quickly passed on to consumers. This suggests that taxes can be highly effective in changing consumption and reducing the burden of diseases associated with consuming these products. Tobacco, alcohol, and SSB industries oppose taxation on similar grounds, mostly on the regressivity of taxes since regressive taxes take a larger percentage of income from low income earners than from middle and high income earners; but also on the effects taxes might have on employment and economic activity; and, in the case of tobacco, the effects taxation has on illicit trade.Contrary to industry arguments, evidence shows that taxation may have short-term negative financial consequences for low-income households. However, medium and long-term financial benefits from reduced healthcare costs, better health, and welfare largely compensate for such consequences. Moreover, taxation does not negatively affect aggregate economic activity or employment, as consumers switch demand to other products that generate employment and may compensate for any employment loss in taxed sectors. Evidence also shows the revenues generated are generally spent on labour-intensive services. In the case of illicit trade in tobacco, evidence shows that illicit trade has not increased globally (rather the opposite) despite increases in tobacco taxes. Profit-maximising smugglers increase illicit cigarette prices along with the increases in licit cigarette prices. This implies that even when increased taxes divert some demand to the illicit market, they push prices up in the illicit market, discouraging consumption.
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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 | 0.011 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 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".