Bank Loan Loss Provision Determinants in Non-Crisis Years: Evidence from African, European, and Asian Countries
Bibliographic record
Abstract
Loan loss provision is an important accounting accrual in the banking sector. There have been numerous debates about the determinants of loan loss provision in several contexts. This study extends the debate by investigating the determinants of bank loan loss provision in non-crisis years for 28 countries from 2011 to 2018. The non-crisis years cover the periods after the global financial crisis and the periods before the COVID-19 pandemic while the countries consist of African, European, and Asian countries. Using the generalized linear model regression and the quantile regression methodologies, the results show that institutional quality is a significant determinant of bank loan loss provision, indicating that the presence of strong institutions decreases the size of bank loan loss provision in non-crisis years. In the regional analyses, it was found that economic growth is a significant determinant of bank loan loss provisions in African and Asian countries. Loan loss provision is higher in times of economic prosperity in African and Asian countries. Bank overhead cost is a significant determinant of bank loan loss provisions in Asian countries. Meanwhile, bank loan loss provision determinants are insignificant in European countries.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".