Prices, taxes and alcohol use: a systematic umbrella review
Bibliographic record
Abstract
AIM: To measure the impact of taxes and prices on alcohol use with particular attention to the different context of rising alcohol consumption in low- and middle-income countries. METHODS: Systematic review: we searched MEDLINE, Embase, EconLit and LILACS, grey literature, hand-searched five specialty journals and examined references of relevant studies. We considered all reviews that included studies that quantitatively examined the relationship between alcohol prices or taxes and alcohol use. At least two reviewers independently screened the articles and extracted the characteristics, methods and main results and assessed the quality of each included study. We identified 30 reviews. RESULTS: There was overwhelming evidence that higher alcohol prices and taxes were associated with lower total alcohol consumption and that price responsiveness varied by beverage type. Total own-price elasticities of alcohol demand were consistently negative and substantial enough to be policy meaningful; total own-price elasticities for beer, wine and spirits were found to be approximately -0.3, -0.6 and -0.65. Reviews generally concluded that higher taxes and prices were associated with lower heavy episodic drinking and heavy drinking, although the magnitude of these associations was generally unclear. Reviews provided no evidence that alcohol price responsiveness differed by socioeconomic status, mixed and contradictory evidence with respect to age and sex and limited evidence that price responsiveness in low- and middle-income countries was approximately the same as in high-income countries. CONCLUSIONS: Taxes are effective in reducing alcohol use. Moreover, increasing the price of alcohol by increasing taxes can also be expected to increase tax revenue, because the demand for alcohol is most certainly inelastic.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.011 | 0.059 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.006 |
| Bibliometrics | 0.022 | 0.020 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".