Staff Background Paper for the G20 Surveillance Note - Priorities for Structural Reforms in G20 Countries
Notice bibliographique
Résumé
provide a powerful lift to growth—both in the short and the long term—if they are well aligned with individual country conditions . These include an economy’s level of development, its position in the economic cycle, and its available macroeconomic policy space to support reforms. The larger a country’s output gap, the more it should prioritize structural reforms that will support growth in the short term and the long term—such as product market deregulation and infrastructure investment. Macroeconomic support can help make reforms more effective, by bringing forward long-term gains or alleviating their short-term costs . Where monetary policy is becoming over-burdened, domestic policy coordination can help make macroeconomic support more effective. Fiscal space, where it exists, should be used to offset short-term costs of reforms. And where fiscal constraints are binding, budget-neutral reform packages with positive demand effects should take priority. Some structural reforms can themselves help generate fiscal space. For example, IMF research finds that by boosting output, product market deregulation can help lower the debt-to-GDP ratio over time. Formulating a medium-term plan that clarifies the long-term objectives of fiscal policy can also help increase near-term fiscal space. With nearly all G-20 economies operating at below-potential output, the IMF is recommending measures that both boost near-term growth and raise long-term potential growth. For example: ? In advanced economies, these measures include shifting public spending toward infrastructure investment (Australia, Canada, Germany, United States (US)); promoting product market reforms (Australia, Canada, Germany, Japan, Korea, Italy) and labor market reforms (Canada, Germany, Japan, Korea, United Kingdom (UK), US); and fiscal structural reforms (France, UK, US). Where there is fiscal space, lowering employment protection is also recommended (Korea). ? Recommendations for emerging markets (EMs) focus on raising public investment efficiency ( India, Saudi Arabia, South Africa), labor market reforms (Indonesia, Russia, Saudi Arabia, South Africa, Turkey), and product market reforms (China, Saudi Arabia, South Africa), which would boost investment and productivity within tighter budgetary constraints particularly if barriers to trade and FDI were eased (Brazil, India, Indonesia). Governance (China, South Africa) and other institutional reforms are also crucial. Where policy space is limited, adjusting the composition of fiscal policy can create space to support reforms ( Argentina, India, Mexico, Russia). ? Some commodity-exporting EMs (Brazil, Russia, Saudi Arabia, South Africa) are facing acute challenges, with output significantly below potential and an urgent need to rebuild fiscal buffers. To bolster growth, Fund staff recommends product market and legal reforms to improve the business climate and investment; trade and FDI liberalization to facilitate diversification; and financial deepening to boost credit flows. IMF advice also aims to promote inclusiveness and macroeconomic resilience. The Fund recommends a targeted expansion of social spending toward vulnerable groups (Mexico), social spending for the elderly poor ( Korea), and upgrading social programs for the nonworking poor (US). Recommendations to bolster macrofinancial resilience include expanding the housing supply (UK), resolving the corporate debt overhang (China, Korea), coordinating a national approach to regulating and supervising life insurers (US), and reforming monetary frameworks (Argentina, China).
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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 | 0,001 |
| 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 ».