The Impact of Raising Alcohol Taxes on Government Tax Revenue: Insights from Five European Countries
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVE: Reducing the affordability of alcoholic beverages by increasing alcohol excise taxation can lead to a reduction in alcohol consumption but the impact on government alcohol excise tax revenue is poorly understood. This study aimed to (a) describe cross-country tax revenue variations and (b) investigate how changes in taxation were related to changes in government tax revenue, using data from Estonia, Germany, Latvia, Lithuania and Poland. METHODS: For the population aged 15 years or older, we calculated the annual per capita alcohol excise tax revenue, total tax revenue, gross domestic product and alcohol consumption. In addition to descriptive analyses, joinpoint regressions were performed to identify whether changes in alcohol excise taxation were linked to changes in alcohol excise revenue since 1999. RESULTS: In 2022, the per capita alcohol excise tax revenue was lowest in Germany (€44.2) and highest in Estonia (€218.4). In all countries, the alcohol excise tax revenue was mostly determined by spirit sales (57-72% of total alcohol tax revenue). During 2010-20, inflation-adjusted per capita alcohol excise tax revenues have declined in Germany (- 22.9%), Poland (- 19.1%) and Estonia (- 4.2%) and increased in Latvia (+ 56.8%) and Lithuania (+ 49.3%). In periods of policy non-action, alcohol consumption and tax revenue showed similar trends, but tax level increases were accompanied by increased revenue and stagnant or decreased consumption. CONCLUSIONS: Increasing alcohol taxation was not linked to decreased but increased government revenue. Policymakers can increase revenue and reduce alcohol consumption and harm by increasing alcohol taxes.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".