Taxes and front-of-package labels improve the healthiness of beverage and snack purchases: a randomized experimental marketplace
Bibliographic record
Abstract
BACKGROUND: Sugar taxes and front-of-package (FOP) nutrition labelling systems are strategies to address diet-related non-communicable diseases. However, there is relatively little experimental data on how these strategies influence consumer behavior and how they may interact. This study examined the relative impact of different sugar taxes and FOP labelling systems on beverage and snack food purchases. METHODS: A total of 3584 Canadians 13 years and older participated in an experimental marketplace study using a 5 (FOP label condition) × 8 (tax condition) between-within group experiment. Participants received $5 and were presented with images of 20 beverages and 20 snack foods available for purchase. Participants were randomized to one of five FOP label conditions (no label; 'high in' warning; multiple traffic light; health star rating; nutrition grade) and completed eight within-subject purchasing tasks with different taxation conditions (beverages: no tax, 20% tax on sugar-sweetened beverages (SSBs), 20% tax on sugary drinks, tiered tax on SSBs, tiered tax on sugary drinks; snack foods: no tax, 20% tax on high-sugar foods, tiered tax on high-sugar foods). Upon conclusion, one of eight selections was randomly chosen for purchase, and participants received the product and any change. RESULTS: Compared to those who saw no FOP label, participants who viewed the 'high in' symbol purchased less sugar (- 2.5 g), saturated fat (- 0.09 g), and calories (- 12.6 kcal) in the beverage purchasing tasks, and less sodium (- 13.5 mg) and calories (- 8.9 kcal) in the food tasks. All taxes resulted in substantial reductions in mean sugars (- 1.4 to - 4.7 g) and calories (- 5.3 to - 19.8 kcal) purchased, and in some cases, reductions in sodium (- 2.5 to - 6.6 mg) and saturated fat (- 0.03 to - 0.08 g). Taxes that included 100% fruit juice ('sugary drink' taxes) produced greater reductions in sugars and calories than those that did not. CONCLUSIONS: This study expands the evidence indicating the effectiveness of sugar taxation and FOP labelling strategies in promoting healthy food and beverage choices. The results emphasize the importance of applying taxes to 100% fruit juice to maximize policy impact, and suggest that nutrient-specific FOP 'high in' labels may be more effective than other common labelling systems at reducing consumption of targeted nutrients.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 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".