Tax awareness and perceived cost of sugar-sweetened beverages in four countries between 2017 and 2019: findings from the international food policy study
Bibliographic record
Abstract
Abstract Background The public health benefits of sugar-sweetened beverage (SSB) taxes often rely on, among other things, changes to consumer purchases. Thus, perceived cost of SSBs and signalling effects—via awareness of the tax—may impact the effectiveness of SSB taxes on consumer purchases. Objective The study sought to examine perceived cost of SSBs, tax awareness, and changes in beverage purchasing over time and across four countries with and without SSB taxes. Methods The study used data from the 2017, 2018 and 2019 waves of the International Food Policy Study. Annual cross-sectional online surveys were conducted in Australia, Mexico, UK and US, which captured perceived cost of SSBs relative to non-SSBs in all countries (with Australia as a no-tax comparator), and measures of tax awareness and participants’ reported changes in beverage purchasing in response to SSB taxes in Mexico (tax implemented in 2014), UK (tax implemented in 2018) and US (subnational taxes since 2015). Logistic regression models evaluated the measures across years and socio-demographic groups. Results Perceived cost of SSBs relative to non-SSBs was higher in Mexico (all three years) and the UK (2018 and 2019 following tax implementation) than Australia and the US. Tax awareness was higher in UK than Mexico, and decreased over time among Mexican respondents. Patterns of reported beverage purchasing changes in response to the tax were similar across Mexico, UK and US, with the largest changes reported by Mexican respondents. Respondents with characteristics corresponding to lower socioeconomic status were less likely to be aware of an SSB tax, but more likely to perceive SSBs to cost more than non-SSBs and report changes in purchasing in response to the tax, where there was one. Conclusions This study suggests that in countries where a national SSB tax was present (Mexico, UK), perceived cost of SSBs and tax awareness were higher compared to countries with no SSB tax (Australia) or subnational SSB taxes (US), respectively, and suggests that perceived cost and tax awareness represent distinct constructs. Improving the ‘signalling effect’ of existing SSB taxes may be warranted, particularly in tax settings where consumer behaviour change is a policy objective.
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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 | 0.002 | 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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 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".