Micro and Macro Determinants of Non-Performing Loan (NPL) in Banking Sector of Bangladesh
Bibliographic record
Abstract
This study seeks to determine the drivers of Non-performing Loans in the Bangladeshi banking system. To achieve this, panel data from four types of Bangladeshi banks from 2008 to 2021 are utilized. It has employed a fixed effect regression model to analyze the influence of bank-related variables and variables related to macroeconomics on the NPL ratio. This study utilizes return on assets (ROA), return on equity (ROE), and capital to risk-weighted assets (CRAR) as bank-specific variables, whereas GDP Growth, broad money supply, real interest rate, and domestic credit to private sector by banks are employed as macroeconomic variables. The study demonstrates that ROA and ROE have little bearing on the NPL situation of banks, however an increase in the CRAR ratio can enhance the NPL position of banks. Furthermore, the analysis demonstrates that GDP growth and domestic credit to the private sector are the most influential macroeconomic determinants on the NPL condition of the banking system. In addition, the study gives some recommendations that could be crucial in addressing the NPL situation in the Bangladeshi banking system.
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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 | 0.000 | 0.002 |
| 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 | 0.000 |
| 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".