Own price elasticities of the demand for sugar-sweetened beverages in Bangladesh
Bibliographic record
Abstract
BACKGROUND: Consumption of sugar-sweetened beverages (SSB) is a major global public health problem. Increasing the price of SSBs through taxation is an effective tool to reduce SSB consumption. Price-elasticity estimates are useful in measuring the effect of taxation on consumption. We estimated the own price elasticities of demand for SSBs in Bangladesh, which will inform how SSB taxes could affect behaviour. METHODS: We used Household Income and Expenditure Survey (HIES) 2016 data, which is a nationally representative dataset at the household level across the country and is conducted using stratified random sampling method. Deaton's method was used to estimate the price elasticities for SSBs in Bangladesh. RESULTS: We found that the own price elasticity for SSBs varied between - 0.53% to -1.17% by types of SSBs in Bangladesh. The price elasticity for soft drinks was - 1.17, indicating that if the price of soft drinks increases by 10% via taxes, the quantity consumed of these beverages would reduce by 11.7%. CONCLUSION: This is the first study that estimates the own price elasticities of demand for SSBs in Bangladesh. Our results suggest to raise SSB prices through increased taxation in order to reduce SSB consumption and ensure public health gains in Bangladesh.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".