Comparing taxes on alcoholic beverages in the Region of the Americas
Bibliographic record
Abstract
BACKGROUND AND AIMS: Excise taxes represent one of the most cost-effective policies to reduce the harmful use of alcohol. Existing information about their design is limited and no standardized metric has been used to compare tax levels in the Region of the Americas. This study aimed to compare alcohol excise tax policies throughout the Americas, compare tax levels and consider opportunities to improve the impact of excise taxes on alcohol consumption and health. DESIGN AND SETTING: Descriptive analysis using a method developed by the Pan American Health Organization and adapted from the World Health Organization's tobacco tax monitoring. Data were collected by surveying ministries of finance and reviewing tax legislation in effect as of November 2020 in the Region of the Americas. MEASUREMENTS: Tax policy design indicators, taxes as a percentage of the retail price of the most-sold brand of beer, wine and spirits, including a weighted average indicator across beverage types, and tax levels per standard drink (10 g ethanol) in international dollars at purchasing power parity. FINDINGS: Thirty-three countries in the Americas (94%) apply excise taxes on alcoholic beverages, with Argentina and Uruguay not applying them to wine. There is significant heterogeneity in excise tax design across countries and beverage types. Only a third of amount-specific excise taxes are regularly adjusted to avoid erosion. Regional median excise taxes represent the highest share of the price for spirits (21.4%) and the lowest for wine (11.0%). The regional median consumption-weighted average excise tax share across all beverage types is 12.0%. Excise tax shares are generally higher in Latin America than in the Caribbean and Canada. Excise tax levels per standard drink are generally lower for spirits than for other beverages. CONCLUSIONS: Alcohol excise tax policies vary significantly across the Americas, often reflecting national consumption patterns. To maximize their public health impact, tax rates could be increased and tax designs improved, particularly to ensure higher tax burdens on high-strength drinks.
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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 | 0.000 |
| 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".