Alcohol excise taxes as a percentage of retail alcohol prices in 26 OECD countries
Bibliographic record
Abstract
BACKGROUND: Many countries have implemented alcohol excise taxes. However, measures of excise taxes as a percentage of alcohol prices have not been systematically studied. METHODS: Data on the retail prices of alcoholic beverages sold in stores and excise taxes in 26 countries during 2003-2018 was from the Economist Intelligence Unit price city data and the Organization for Economic Co-operation and Development (OECD) tax database. The percentages of excise taxes in off-premise retail prices were derived as the ratio of taxes to prices at different price levels. Changes of excise taxes over time were assessed using negative binominal regressions. RESULTS: The percentage of excise taxes in average off-premise alcohol prices was from 5 % in Luxembourg to 59 % in Iceland for beer, and from 0 % in France to 26 % in Iceland for wine. Excise taxes accounted for 5% of discount liquor prices in Czech Republic to 41 % in Sweden for Cognac, for 19 % in the United States (US) to 67 % in Sweden for Gin, for 13 % in the US to 63 % in Australia for Scotch Whisky six years old, and for 6 % in Iceland to 76 % in Sweden for Liqueur Cointreau. There were no significant changes in the percentage of excise taxes in alcohol prices over time in most countries except for Nordic countries. While wine had the lowest excise taxes, liquors had the highest tax burden. CONCLUSION: Tax burden on alcoholic beverages is low in OECD countries, indicating ample room for increasing alcohol excise taxes, particularly for beer and wine in those countries.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".