The Impact of Bank Specific and Macro-Economic Factors on Non-Performing Loans in the Banking Sector: Evidence from an Emerging Economy
Bibliographic record
Abstract
The current study examines macro-economic and bank specific determinants of non-performing loans (NPLs) for commercial banks from 2008–2018. The Pakistani banking sector has observed a significant increase in NPLs. In addition, the current study is undertaken to fill this gap in the literature as most of the prior studies focus on the developed markets. In the current study, we prefer the system GMM estimator. Its reliability depends on the validity of the instruments. To testing the second-order serial correlation, we apply the J test for testing the validity of the instruments and the Arellano–Bond AR (2) test. Using dynamic-GMM estimations, we find that credit growth, net interest margin, loan loss provision, and bank diversification significantly increase NPLs, while operating efficiency, bank size, and ROA lower NPLs. In addition, higher interest rates, exchange rates, and political risk significantly increase NPLs, while GDP growth decreases NPLs. This paper provides a timely insight to management and policy makers about the determinants of NPLs. The findings help management to take corrective actions and policy makers may take into consideration the significance of macro-economic conditions while formulating policy regarding NPLs. Likewise, the study provides insight to potential investors to consider the findings while selecting better investment opportunity. The current study is the first of its kind focusing on the link among bank specific, macroeconomic variables, and non-performing loans within the specific context of an emerging economy, Pakistan.
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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".