Alcohol Excise Taxation, Tax Share and Revenue in the European Union and the United Kingdom in 2022: An Overview and Modelling Analysis
Bibliographic record
Abstract
INTRODUCTION: Although increases in alcohol excise taxation have been identified as one of the 'best buys' of the World Health Organization to reduce alcohol consumption and attributable harm, excise tax shares-the proportion of excise tax included in retail prices of alcoholic beverages-remain low in Europe. Revenue derived from alcohol excise taxation, and how it is affected by changes in alcohol excise taxation, has not yet been widely explored. METHODS: We conducted a search for revenues generated from alcohol excise taxation in all European Union (EU) countries and the United Kingdom between 2017 and 2022. We then calculated the average excise tax share for alcoholic beverages for 2022. Using regression analysis, we predict tax revenue per capita from the tax share, type of alcohol excise taxation, recorded and unrecorded consumption and prevalence of past-year drinking. To illustrate the potential for revenue increases, we conducted a case study on Germany. RESULTS: In 2022, average revenue from alcohol excise taxation (119 euros per capita) and excise tax share (17.3%) were low in the EU countries and the United Kingdom, but showed sizable variation. The association between excise tax share and revenue from excise taxation was very high, with a Pearson correlation of 0.888 (0.720-0.958; df = 16; p < 0.0001). In regression analyses, only the excise tax share significantly predicted tax revenue. DISCUSSION AND CONCLUSIONS: Marked revenue gains could be achieved in several countries having low tax shares by instituting increases in excise tax share, with only small effects on consumer prices.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".