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Record W4403132232 · doi:10.15353/rea.v15i3-4.5123

Capital Inflows and Domestic Credit Growth: Empirical Evidence from Emerging Market and Developing Economies

2023· article· en· W4403132232 on OpenAlexvenueno aff
Kongchheng Poch, Cristopher Gan, Baiding Hu

Bibliographic record

VenueReview of Economic Analysis · 2023
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicGlobal Financial Crisis and Policies
Canadian institutionsnot available
Fundersnot available
KeywordsEmerging marketsEconomicsCapital marketDeveloping countryMonetary economicsCapital (architecture)Empirical evidenceInternational economicsFinancial systemMacroeconomicsFinanceEconomic growthGeography

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.232
Threshold uncertainty score0.941

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.057
GPT teacher head0.307
Teacher spread0.250 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2023
Admission routes1
Has abstractyes

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