Effectiveness of subsidies in promoting healthy food purchases and consumption: a review of field experiments
Bibliographic record
Abstract
OBJECTIVE: To systematically review evidence from field interventions on the effectiveness of monetary subsidies in promoting healthier food purchases and consumption. DESIGN: Keyword and reference searches were conducted in five electronic databases: Cochrane Library, EconLit, MEDLINE, PsycINFO and Web of Science. Studies were included based on the following criteria: (i) intervention: field experiments; (ii) population: adolescents 12–17 years old or adults 18 years and older; (iii) design: randomized controlled trials, cohort studies or pre–post studies; (iv) subsidy: price discounts or vouchers for healthier foods; (v) outcome: food purchases or consumption; (vi) period: 1990–2012; and (vii) language: English. Twenty-four articles on twenty distinct experiments were included with study quality assessed using predefined methodological criteria. SETTING: Interventions were conducted in seven countries: the USA (n 14), Canada (n 1), France (n 1), Germany (n 1), Netherlands (n 1), South Africa (n 1) and the UK (n 1). Subsidies applied to different types of foods such as fruits, vegetables and low-fat snacks sold in supermarkets, cafeterias, vending machines, farmers’ markets or restaurants. SUBJECTS: Interventions enrolled various population subgroups such as school/ university students, metropolitan transit workers and low-income women. RESULTS: All but one study found subsidies on healthier foods to significantly increase the purchase and consumption of promoted products. Study limitations include small and convenience samples, short intervention and follow-up duration, and lack of cost-effectiveness and overall diet assessment. CONCLUSIONS: Subsidizing healthier foods tends to be effective in modifying dietary behaviour. Future studies should examine its long-term effectiveness and cost-effectiveness at the population level and its impact on overall diet intake.
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 | 0.031 | 0.086 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.005 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".