Willingness to pay for small‐quantity lipid‐based nutrient supplements for women and children: Evidence from Ghana and Malawi
Bibliographic record
Abstract
Small-quantity lipid-based nutrient supplements (SQ-LNS) are designed to enrich maternal and child diets with the objective of preventing undernutrition during the first 1,000 days. Scaling up the delivery of supplements such as SQ-LNS hinges on understanding private demand and creatively leveraging policy-relevant factors that might influence demand. We used longitudinal stated willingness-to-pay (WTP) data from contingent valuation studies that were integrated into randomized controlled nutrition trials in Ghana and Malawi to estimate private valuation of SQ-LNS during pregnancy, postpartum, and early childhood. We found that average stated WTP for a day's supply of SQ-LNS was more than twice as high in Ghana than Malawi, indicating that demand for SQ-LNS (and by extension, the options for effective delivery of SQ-LNS) may be very context specific. We also examined factors associated with WTP, including intervention group, household socioeconomic status, birth outcomes, child growth, and maternal and child morbidity. In both sites, WTP was consistently negatively associated with household food insecurity, indicating that subsidization might be needed to permit food insecure households to acquire SQ-LNS if it is made available for purchase. In Ghana, WTP was higher among heads of household than among mothers, which may be related to control over household resources. Personal experience using SQ-LNS was not associated with WTP in either site.
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 | 0.009 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".