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Record W4366826451 · doi:10.59276/tckhdt.2023.04.2488

The effectiveness of monetary policy transmission through trade credit channel in Vietnam

2023· article· en· W4366826451 on OpenAlexaboutno aff
Hà Trang Lê, Ngọc Thắng Đoàn, Thùy Linh Vũ, Ngọc Yến Mạnh

Bibliographic record

VenueTạp chí Khoa học và Đào tạo Ngân hàng · 2023
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicWorking Capital and Financial Performance
Canadian institutionsnot available
Fundersnot available
KeywordsCredit channelMonetary policyQuarter (Canadian coin)Economic shortagePanel dataBusinessChannel (broadcasting)EconomicsFinancial systemContext (archaeology)Monetary economicsInflation targeting

Abstract

fetched live from OpenAlex

Existing research in Vietnam mainly focuses on the traditional transmission channels of monetary policy. This article investigates the effectiveness of monetary policy transmission through an alternative funding channel for bank credit, which is the trade credit channel. Panel data regression was conducted with datasets from 501 Vietnamese enterprises in the period of the second quarter of 2010 to the second quarter of 2020. The results show that most enterprises in Vietnam do not have the ability to use trade credit as an alternative fund for bank credit in the context of monetary tightening, thereby helping to maintain or even increase the transmission efficiency of monetary policy. The transmission of monetary policy through the trade credit channel is most effective for enterprises with low credit ratings and is partly weakened for large enterprises, which can use trade credit as complementary short-term financing in the situation of credit supply shortage.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.010
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.033
Threshold uncertainty score0.066

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0030.002
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.013
GPT teacher head0.229
Teacher spread0.216 · 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 source (direct Gemma or distilled Codex), 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

Citations0
Published2023
Admission routes1
Has abstractyes

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