The Crowding-out Effect of Tobacco Expenditure on Health Expenditure: Evidence From a Lower-Middle-Income Country
Notice bibliographique
Résumé
INTRODUCTION: Poor people have remarkably lower health expenditures than rich people in Vietnam. According to the 2016 Vietnam Household Living Standard Survey (VHLSS), per capita health expenditure of the top quintile households is around 6 times higher than that of the bottom quintile households. AIMS AND METHODS: We analyze economic inequalities in health expenditure using the concentration index approach and data from the VHLSS 2010-2016. Next, we use the instrumental-variable regression analysis to examine the crowding-out effect of tobacco expenditure on health expenditure. Finally, we use decomposition analysis to explore whether economic inequality in tobacco expenditure is associated with an economic inequality in health expenditure. RESULTS: We find a crowding-out effect of tobacco expenditure on health expenditure of households. The share of health expenditure of households with tobacco spending is 0.78% lower than that of households without tobacco spending. It is estimated that a one-VND increase in tobacco expenditure results in a 0.18 Vietnamese Dong (VND) (95% CI: -0.30 to -0.06) decrease in health expenditure. There is a negative association between economic inequality in tobacco expenditure and economic inequality in health expenditure. This means that if the poor consume less tobacco, their expenditure on health can be increased, resulting in a decrease in inequality in health expenditure. CONCLUSIONS: Findings from this study suggest that reducing tobacco expenditure could improve health care of the poor and reduce inequality in health care in Vietnam. Our study recommends that the government continuously increase the tobacco tax in order to effectively reduce tobacco consumption. IMPLICATIONS: Empirical studies show mixed results on the effect of tobacco expenditure on health expenditure. We find a crowding-out effect of tobacco expenditure on health expenditure of poor households in Vietnam. It implies that if the poor reduce their expenditure on tobacco, economic inequality in health expenditure can be reduced. Our findings suggest that reducing tobacco consumption in poor households can increase their health expenditure, therefore, decreasing inequality in health expenditure. Different policies to reduce tobacco consumption such as tobacco taxation, smoke-free areas, and tobacco advertisement bans should be strengthened.
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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 | 0,012 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,003 |
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 ».