The macroeconomic implications of healthcare. Bruegel Policy Contribution Issue n˚11 | August 2018
Notice bibliographique
Résumé
Health-care systems play a crucial role in supporting human health. They also have major macroeconomic implications, an aspect that is not always properly acknowledged. Countries spend very different amounts on healthcare, with spending in North America (Canada and the United States) more than twice as much per capita as in the European Union on average, and there are significant differences between EU countries too. Various explanatory factors such as income levels, population age structures and epidemiological profiles cannot explain the differences between countries. Decisions on the optimal level of spending should also consider various others factor, including the macroeconomic implications of health-care systems. Whatever amount is spent on health care, it should be spent efficiently, in order not to waste resources and to improve the macroeconomic impacts. We demonstrate that there are threshold effects whereby certain quantitative indicators of health tend to improve with increased spending only up to certain amount of spending, but not further. Using a standard method to measure efficiency, data envelopment analysis (DEA), we find significant differences between countries, suggesting that not all countries use existing technologies and best practices to their full potential. This finding calls for policy responses. Health-care systems matter for the macroeconomy because of their large size in output, employment and research. They also have direct fiscal implications in terms of the long-term sustainability of public finances, while health-care spending decisions influence short-term economic development through the fiscal multiplier effect, which is substantial. Most southern European countries cut health-care spending aggressively in recent years, likely amplifying the depth of their recessions and possibly causing hysteresis effects from long-term unemployment and reduced productivity. Fiscal consolidation strategies should aim to preserve spending items that have large fiscal multipliers, including health-care expenditures. Health-care systems also influence labour force participation, productivity and human capital formation through various channels, and thereby have an influence on overall macroeconomic outcomes. They also play an important role in inequality, and we find that inequality of access to health care is particularly high in about one-third of EU countries, which calls for policy responses. It is essential that discussions of health systems consider both the opportunity cost and the economic value of investing in health. Such an approach can help policymakers resist the temptation to default to the potentially inefficient status quo.
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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 | 0,001 | 0,001 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 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 ».