Using Price Promotions to Drive Children's Healthy Choices in a Developing Economy
Bibliographic record
Abstract
This research examines how price discounts—a classic marketing incentive—drive children's healthy choices in the understudied context of a developing economy. The authors partnered with UNICEF to launch three field experiments in Panamá among 2,418 children to examine four pillars of price discount promotions for children: what to discount (product selection), how to discount (message design), whom to target (children's age), and whether to discount again (repetition). They uncovered four previously undocumented insights. First, price discounts alone effectively increase demand among children 6–11 years of age, reconciling conflicting findings in prior literature. Second, product selection based on relative price—a particularly crucial factor in developing regions—drives opposing postpromotion effects: ironically, marketers should not discount expensive healthy options but rather moderately priced ones. Third, different from prior literature's practice of directly communicating final prices, discount messages that require older children to derive final prices are more effective. Fourth, repetition can amplify or undermine discounts’ efficacy depending on message complexity and children's age. This research offers concrete guidelines for researchers and practitioners, uncovering both positive and negative effects of price promotions on children, and shedding light on price promotion interventions that most powerfully nudge children of different ages to act.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".