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Record W4402963519 · doi:10.1016/j.intfin.2024.102063

Financial sector development and microcredit to small firms

2024· article· en· W4402963519 on OpenAlexaff
Désiré Kanga, Issouf Soumaré, Hubert Tchakouté Tchuigoua

Bibliographic record

VenueJournal of International Financial Markets Institutions and Money · 2024
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicMicrofinance and Financial Inclusion
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsEconomicsFinancial sector developmentFinancial sectorMicrofinanceFinanceFinancial systemEconomic growth

Abstract

fetched live from OpenAlex

• Negative relationship between financial development and MSME lending by MFIs. • Improvement in financial sector development decreases micro-lending to small firms. • In a less developed financial sector, MFIs lend more to MSME. This article investigates the relationship between countries’ financial sector development and the loans extended to micro, small, and medium-sized enterprises (MSMEs or small firms ) by microfinance institutions (MFIs). Using 4,801 MFI-year observations worldwide, we find a negative relationship between financial sector development and MSME lending by microfinance institutions. In other words, improvement in financial development, defined as a combination of depth, access, and efficiency, decreases micro-lending to small firms due essentially to intense competition from banks. Moreover, looking at the ownership status of microfinance institutions, we find that the intense competition between profit-oriented microfinance institutions and banks mainly drives the observed negative relationship. For nonprofit microfinance institutions, financial sector development is not significantly associated with their lending to small firms. In a less developed financial sector, microfinance institutions lend more to small firms, fulfilling their social mission.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.889
Threshold uncertainty score0.732

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.033
GPT teacher head0.241
Teacher spread0.208 · 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 designNot applicable
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

Citations8
Published2024
Admission routes1
Has abstractyes

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