Alcohol taxes’ contribution to prices in high and middle‐income countries: Data from the International Alcohol Control Study
Bibliographic record
Abstract
INTRODUCTION: Taxation is increasingly being used as an effective means of influencing behaviour in relation to harmful products. In this paper we use data from six participating countries of the International Alcohol Control Study to examine and evaluate their comparative prices and tax regimes. METHODS: We calculate taxes and prices for three high-income and three middle-income countries. The data are drawn from the International Alcohol Control survey and from the Alcohol Environment Protocol. Tax systems are described and then the rates of tax on key products presented. Comparisons are made using the Purchasing Power Parity rates. The price and purchase data from each country's International Alcohol Control survey is then used to calculate the mean percentage of retail price paid in tax weighted by actual consumption. RESULTS: Both ad valorem and specific per unit of alcohol taxation systems are represented among the six countries. The prices differ widely between countries even though presented in terms of Purchasing Power Parity. The percentage of tax in the final price also varies widely but is much lower than the 75% set by the World Health Organization as a goal for tobacco tax. CONCLUSION: There is considerable variation in tax systems and prices across countries. There is scope to increase taxation and this analysis provides comparable data, including the percentage of tax in final price, from some middle and high-income countries for consideration in policy discussion.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.010 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".