Investigating Indicators to Assess and Support Alcohol Taxation Policy: Results From the International Alcohol Control (IAC) Study
Bibliographic record
Abstract
Alcohol taxation is a key policy to reduce consumption and alcohol harm but evidence on tax design and indicators to assess taxation policy are lacking. Tax design and two indicators: tax as a share of lowest retail price and affordability, were investigated in eight high-income and nine middle-income jurisdictions. Collaborators populated the International Alcohol Control (IAC) study online Alcohol Policy Tool, providing measures of tax design, tax rates; and typical lowest prices available for retail take-away alcohol. These data were used to calculate tax/share of retail price. Affordability of alcohol was assessed against gross national income (GNI) per capita. High-income jurisdictions had higher tax/share and higher affordability on average compared with middle-income jurisdictions. Over the sample as a whole there was no association between these two indicators of tax policy. The tax designs used also varied with high-income jurisdictions more likely to use specific excise tax reflecting potency and middle-income jurisdictions more likely to utilise ad valorem and specific volume based taxes and to use more than one method across a beverage. Increased alcohol taxation to reduce alcohol consumption and harm is established as a high impact policy and is believed to work by affecting affordability. However, less is known about the best taxation methods to reduce affordability or the best measures to monitor and compare alcohol taxation between countries and over time. In this sample of high- and middle-income jurisdictions tax/price share was not found to predict affordability, suggesting the need to further research indicators of alcohol affordability.
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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 | 0.026 | 0.073 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".