Unpacking bank lending behavior: Macroeconomic and financial drivers of credit standards in the Philippines
Bibliographic record
Abstract
This study investigates the determinants of credit standards among commercial banks in the Philippines, a critical aspect of financial stability and monetary policy transmission. Utilizing data from the Bangko Sentral ng Pilipinas' Senior Bank Loan Officers' Survey and macroeconomic indicators from 2009 to 2024, a stepwise multiple regression analysis was conducted on 640 observations. The objective was to identify significant regressors of both overall and specific credit standards. Findings reveal that inflation rate and past-due ratio (PDR) lead to significant tightening of credit standards, with PDR exerting the greatest influence. Conversely, GDP growth rate, capital adequacy ratio (CAR), and return on equity (ROE) lead to significant easing. Collateral requirements and loan covenants were identified as the most regressed specific credit standards. This research offers valuable insights into bank lending behavior, providing policymakers with empirical evidence for managing credit supply, mitigating financial risks, and ensuring banking system stability.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".