Saving not Spending: Indonesia’s Domestic Demand Problem
Notice bibliographique
Résumé
Indonesian income per capita has risen rapidly in the past 10 years. The growth in income, combined with an expanding middle class, has corresponded with strong growth in retail sales. Recently, however, this trend has started to change. Consumption growth has been relatively stable, but retail sales are growing more slowly than in the past. In order to develop a clearer picture of consumer spending in Indonesia, we discuss differences in spending behaviour across two income groups—lower-middle income and upper-high income. Consumption varies across income groups, so saving and investment patterns may also vary. We find that the upper-high income group, despite having more income than in the past, is less willing to invest and borrow than previously, and that the lower-middle income group continues to suffer from a lack of purchasing power. Meanwhile, investors are simply postponing investments, preferring to take a ‘wait and see’ approach. Excess saving can be economically problematic. If effective demand is too weak, it can have negative consequences for long-term economic growth. We begin, however, by surveying recent economic developments in Indonesia, focusing on the third quarter of 2017. Indonesia’s current rate of economic growth (5.1% year on year) places it among the world’s fastest-growing large economies, but the lack of acceleration is a concern: growth has not exceeded 6.0% since the second quarter of 2012. Despite this lack of acceleration, Indonesia has achieved macroeconomic and financial stability. The balance of payments has been improving since early 2016, with a narrow current account deficit—well below 3.0% of GDP—and a surplus trade balance. Exports grew by 17.3% in the third quarter of 2017, owing to rising commodity prices (which boosted export growth in both value and volume), while imports grew by 15.1%, although the impact on economic growth has so far been more moderate than in the commodity boom of 2000–2011. The growth in commodity exports has also benefited Kalimantan, Sumatra, and other commodity-rich regions. However, rising commodity prices come with some caveats. They might boost growth for a short period, but they raise the challenge of making this growth sustainable. We have seen this many times in the past. Increasing institutional capacity to better implement policy initiatives, for one, will help to deliver sustainable, high-quality economic growth.
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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 | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 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 | 0,000 | 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 ».