The Contribution of Islamic Banking to Indonesia’s Economic Growth: The Evidence from the Vector Error Correction and Variance Decomposition Methods
Bibliographic record
Abstract
Purpose: The aim of this study is to empirically understand whether Islamic banks have a positive relationship to economic growth in Indonesia. Methodology: This study examines the causal relationship amongst several selected variables: real GDP (RY), total deposit (TD), the change in the Consumer Price Index as an inflation proxy (INF), and the ratio of total imports and exports to nominal GDP (OE). In order to accomplish the research objectives of this study, a time series quarterly data spanning from the first quarter of 2003 to the last quarter of 2011 comprising of 36 data points has been used to perform an effective analysis. Findings: The inference deduced here is twofold; First Islamic banks in Indonesia are still unable to contribute significantly to Indonesia’s economic growth. Second, the relationship between Islamic banks and economic growth in Indonesia is positively but weakly correlated. Research Limitations: For this time series research, the researcher is limited by the small amount of data (2003.Q1 to 2011.Q4).
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 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 | 0.009 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".