Evidence that a tax on sugar sweetened beverages reduces the obesity rate: a meta-analysis
Bibliographic record
Abstract
BACKGROUND: Excess intake of sugar sweetened beverages (SSBs) has been shown to result in weight gain. To address the growing epidemic of obesity, one option is to combine programmes that target individual behaviour change with a fiscal policy such as excise tax on SSBs. This study evaluates the literature on SSB taxes or price increases, and their potential impact on consumption levels, obesity, overweight and body mass index (BMI). The possibility of switching to alternative drinks is also considered. METHODS: The following databases were used: Pubmed/Medline, The Cochrane Database of Systematic Reviews, Google Scholar, Econlit, National Bureau of Economics Research (NBER), Research Papers in Economics (RePEc). Articles published between January 2000 and January 2013, which reported changes in diet or BMI, overweight and/or obesity due to a tax on, or price change of, SSBs were included. RESULTS: Nine articles met the criteria for the meta-analysis. Six were from the USA and one each from Mexico, Brazil and France. All showed negative own-price elasticity, which means that higher prices are associated with a lower demand for SSBs. Pooled own price-elasticity was -1.299 (95% CI: -1.089 - -1.509). Four articles reported cross-price elasticities, three from the USA and one from Mexico; higher prices for SSBs were associated with an increased demand for alternative beverages such as fruit juice (0.388, 95% CI: 0.009 - 0.767) and milk (0.129, 95% CI: -0.085 - 0.342), and a reduced demand for diet drinks (-0.423, 95% CI: -0.628 - -1.219). Six articles from the USA showed that a higher price could also lead to a decrease in BMI, and decrease the prevalence of overweight and obesity. CONCLUSIONS: Taxing SSBs may reduce obesity. Future research should estimate price elasticities in low- and middle-income countries and identify potential health gains and the wider impact on jobs, monetary savings to the health sector, implementation costs and government revenue. Context-specific cost-effectiveness studies would allow policy makers to weigh these factors.
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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 | 0.014 | 0.046 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.016 | 0.054 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".