Market structure and Islamic banking performance in Indonesia: An error correction model
Bibliographic record
Abstract
The research aims to highlight the importance of market structure and behavior on Islamic banking performance by using the Structure Conduct Performance (SCP) analysis to approximate the growth of Islamic banking performance in the short and long term using error correction model. The data used in this study is time series obtained from the financial statement of each Islamic bank, Fi-nancial Service Authority and Bank of Indonesia on Islamic banking statistics monthly reported from April, 2015 to October, 2018. Population of this study covers all Islamic commercial banks in Indonesia. The purposive sampling method is used to select samples based on criteria to get samples that are feasible to be analyzed. Error correction model is applied to characterize the joint dynamic of variables in both in the long and short term relationships. The Johansen co-integration results indicate a stable long term relation between market structure and Islamic banking performance. Results show that Market structure variable outside market share of financing in the long term had a significant effect on the Islamic banking performance, but in the short term market structure, the variable had no significant effect on the Islamic banking performance 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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.003 | 0.001 |
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".