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Record W2997619343 · doi:10.6000/1929-7092.2019.08.110

Financial Sector Development and Poverty Alleviation in the SADC Region

2019· article· en· W2997619343 on OpenAlexvenueno aff
Samkele Leve, Forget Mingiri Kapingura

Bibliographic record

VenueJournal of Reviews on Global Economics · 2019
Typearticle
Languageen
FieldComputer Science
TopicEconomic Growth and Development
Canadian institutionsnot available
Fundersnot available
KeywordsPovertyBusinessFinanceFinancial sector developmentFinancial sectorFinancial systemEconomicsEconomic growthDevelopment economics

Abstract

fetched live from OpenAlex

Financial development is widely regarded as another conduit through which poverty can be reduced.The study empirically examines the relationship between financial sector development and poverty reduction in SADC countries utilising the Generalised Method of Moments technique for the period 1980 to 2017.The empirical results indicate that the effect of the different measures of financial sector development on poverty in the SADC region is mixed.Six out of nine financial development variables have a negative effect on poverty in the SADC region.In terms of financial depth, the empirical results reveal mixed outcomes.Results on financial system stability confirm the notion that a stable financial system is beneficial to the poor.The results also reveal that financial inclusion or access to financial services significantly reduces poverty in the SADC region.The results thus suggest that financial sector development is beneficial to the poor when it is inclusive and stable.The results imply that policies aimed at ensuring a stable financial system, which is also inclusive, should be pursued if the poor are to benefit from the financial system.

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 score0.811
Threshold uncertainty score0.282

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.021
GPT teacher head0.218
Teacher spread0.197 · 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

Citations0
Published2019
Admission routes1
Has abstractyes

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