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Record W3023347084 · doi:10.18196/jesp.21.1.5028

Total Financing of Islamic Rural Banks and Regional Macroeconomic Factors: A Dynamic Panel Approach

2020· article· en· W3023347084 on OpenAlexfundno aff
Faaza Fakhrunnas

Bibliographic record

VenueJurnal Ekonomi & Studi Pembangunan · 2020
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicIslamic Finance and Banking Studies
Canadian institutionsnot available
FundersUniversitas Islam IndonesiaAsia Pacific Foundation of Canada
KeywordsIslamPanel dataIndonesianInflation (cosmology)BusinessRural areaSample (material)EconomicsFinancial systemFinanceGeography

Abstract

fetched live from OpenAlex

Islamic rural bank is a special purpose of Islamic banks, which finances Small and Medium Enterprises (SMEs) in Indonesia. This research aims to investigate a long-run relationship of the influence of regional inflation and economic growth on the total financing of Islamic rural banks in Indonesia. By adopting panel dynamics approach, this study utilized a biggest Islamic rural bank in each Indonesian province from 2013 to 2017 based on quarterly data, which consisted of 420 observation period. The result of this study exhibited that a long-run relationship existed among regional inflation and economic growth to the total financing of Islamic rural banks. Specifically, the long-run relationship also appeared in big size Islamic rural banks, although it was not in small and medium size Islamic rural banks. Variance decompositions and Impulse response factors analysis’ result explained that the majority of all regional macroeconomic variables contributed to the influence of total financing on the Islamic rural bank. The directions of its influence were different from each sample group. According to the results, Indonesian central bank must maintain inflation rate in the safety level for financial industry by following determined inflation target through appropriate monetary policies. This recommendation for the central bank is aimed to maintain and boost Islamic rural banks’ financing that will give benefits for financial industry in Indonesia.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.220
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.023
GPT teacher head0.210
Teacher spread0.188 · 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.

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

Citations8
Published2020
Admission routes1
Has abstractyes

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