BANK LENDING DECISION AND BUSINESS CYCLES: ARE INDONESIAN ISLAMIC BANKS DIFFERENT?
Bibliographic record
Abstract
This paper evaluates the relationship between bank lending decisions and the business cycle in Indonesia where both Islamic and the conventional banks operate side by side. Moreover, the paper also seeks to determine whether the response between the two types of banks is significantly different from each other with regards to the financing behavior observed during the business cycles. The current research paper employs a sample of 73 Indonesian banks (64 conventional and 9 Islamic commercial banks) and studies their financing behavior over a period of 16 years (2005 to 2019). The findings of the paper can be summarized as follows. First, bank credit in Indonesia is pro-cyclical, indicating that the financing behavior of Indonesian banks is positively correlated with the business cycle(s). In other words, during prosperous times, banks lend more while restricting the same during times of economic crisis. Overall, the findings show that the financing behavior of Indonesian commercial banks can aggravate the crisis as they reduce credit during adverse times. Secondly, and more importantly, the findings also reveal that the Islamic banks’ financing is less cyclical in nature, highlighting the potential smoothing abilities of Islamic banks. Not surprisingly, these fundings are fueled by the deposits, as is the case with any developing country. The findings are found stable to the following robustness tests: a) alternate proxy of loan growth, b) inclusion of competition proxy in the regression and the c) use of HP filter to get an alternate proxy of business cycles
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".