Economic costs of childhood stunting to the private sector in low- and middle-income countries
Notice bibliographique
Résumé
Background: Stunting during childhood has long-term consequences on human capital, including decreased physical growth, and lower educational attainment, cognition, workforce productivity and wages. Previous research has quantified the costs of stunting to national economies however beyond a few single-country datasets there has been a limited number of which have used diverse datasets and have had a dedicated focus on the private sector, which employs nearly 90% of the workforce in many low- and middle-income countries (LMICs). We aimed to examine (i) the impact of childhood stunting on income loss of private sector workforce in LMICs; (ii) to quantify losses in sales to private firms in LMICs due to childhood stunting; and (iii) to estimate potential gains (benefit-cost ratios) if stunting levels are reduced in select high prevalence countries. Methods: This multiple-methods study engaged multi-disciplinary technical advisers, executed several literature reviews, used innovative statistical methods, and implemented health and labor economic models. We analyzed data from seven longitudinal datasets (up to 30+ years of follow-up; 1982-2016; Peru, Ethiopia, India, Vietnam, Philippines, Tanzania, Brazil), 108 private firm datasets (spanning 2008-2020), and many global datasets including Joint Malnutrition Estimates, and World Development Indicators to produce estimates for 120+ LMICs (with estimates up to 2021). We studied the impact of childhood stunting on adult cognition, education, and height as pathways to wages/productivity in adulthood. We employed cloud-based artificial intelligence (AI) platforms, and conducted comparative analyses using three analytic approaches: traditional frequentist statistics, Bayesian inferential statistics and machine learning. We employed labour and health economic models to estimate wage losses to the private sector worker and firm revenue losses due to stunting. We also estimated benefit-cost ratios for countries investing in nutrition-specific interventions to prevent stunting. Findings: Across 95 LMICs, childhood stunting costs the private sector at least US$135.4 billion in sales annually. Firms from countries in Latin America and the Caribbean and East Asia and Pacific regions had the greatest losses. Totals sales losses to the private sector accumulated to 0.01% to 1.2% of national GDP across countries. Sectors most affected by childhood stunting were manufacturing (non-metallic mineral, fabricated metal, other), garments and food sectors. Sale losses were highest for larger sized private firms. Across regions (representing 123 LMICs), US$700 million (Middle East and North Africa) to US$16.5 billion (East Asia and Pacific) monthly income was lost among private sector workers. Investing in stunting reduction interventions yields gains from US$2 to US$81 per $1 invested annually (or 100% to 8000% across countries). Across sectors, the highest returns were in elementary occupations (US$46) and the lowest were among agricultural workers (US$8). By gender, women incurred a higher income penalty from childhood stunting and earned less than men; due to their relatively higher earnings, the returns for investing in stunting reduction were consistently higher for men across most countries studied. Interpretation: Childhood stunting costs the private sector in LMICs billions of dollars in sales and earnings for the workforce annually. Returns to nutrition interventions show that there is an economic case to be made for investing in childhood nutrition, alongside a moral one for both the public and private sector. This research could be used to motivate strong public-private sector partnerships to invest in childhood undernutrition for benefits in the short and long-term.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».