Capital Inflows and Domestic Credit Growth: Empirical Evidence from Emerging Market and Developing Economies
Bibliographic record
Abstract
This study investigates the extent to which capital inflows and their composition affect domestic credit growth in emerging market and developing economies (EMDEs) and the role of institutional quality in mediating the capital inflows and domestic credit growth nexus. Using a sample of 130 EMDEs from 1991-2015, the study uses generalized method of moments to control for endogeneity issues. The study makes notable contributions to the literature and policy discourse. First, this is the first empirical studies that documents the persistence of domestic credit growth in EMDEs. Second, the study provides a granular analysis of the capital inflows – domestic credit growth nexus. Whereas gross capital inflows significantly exert a positive impact on domestic credit growth, disaggregated-level analyses showed that only foreign direct investment positively affects domestic credit growth whereas portfolio equity has a negative effect; and portfolio debt and other investment do not. Third, the study adds novel evidence that institutional quality plays a crucial role in mediating the capital inflows – domestic credit growth nexus. Fourth, this study crystallises the lens used to investigate the interactions between capital inflows and institutional quality in analysing the capital inflows – domestic credit growth nexus. Finally, the findings are helpful for designing and implementing macro-financial policy and strengthening institutions, especially in managing capital flows and financial sector.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".