Affordability of Alcoholic Beverages in the European Union
Bibliographic record
Abstract
BACKGROUND: From a public health perspective, alcohol taxation should be designed to reduce alcohol affordability and thus alcohol consumption and related harms. OBJECTIVES: In this brief report, we estimate alcohol affordability in European Union Member States and associated countries and investigate whether affordability is related to national alcohol excise duties. METHOD: Beverage-specific affordability for beer, wine, and spirits were estimated based on the number of standard drinks a household could purchase based on their median monthly disposable household income in 2020. To determine the pooled affordability of alcohol, the beverage-specific estimates were weighted by the share of the beverage-specific per capita consumption in total recorded consumption. Pearson and Spearman rank correlations were calculated to establish the association between alcohol affordability and alcohol excise duty rates. All data were retrieved from official sources. RESULTS: On average, a European household can purchase 1,628 standard drinks of alcohol with its monthly income, with affordability being highest in Germany, Austria, France, and Luxembourg. The affordability of spirits, but not that of beer or wine, was inversely correlated with the beverage-specific excise duty rates. CONCLUSIONS: Alcohol is affordable in the Member States of the European Union and associated countries, and low levels of excise duties on beer and wine appear to be unrelated to their affordability. Alcohol taxes should be increased to effectively reduce the affordability of alcoholic beverages in order to lower the alcohol-related health burden in Europe.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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) | 0.002 | 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".