Total Financing of Islamic Rural Banks and Regional Macroeconomic Factors: A Dynamic Panel Approach
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".