The Effect of Non-Performing Loans on Credit Availability: Insight from the Moroccan Banking System.
Bibliographic record
Abstract
In recent years, Morocco has witnessed a notable and troubling increase in non-performing loans (NPLs). Data from Bank Al-Maghrib, the central bank of Morocco, reveals that nonperforming loans accounted for over 8% of the total credit volume in 2022.These loans present significant challenges, not only for the resilience of the banking sector but also because they can result in tighter credit conditions, making it increasingly difficult for businesses and consumers to obtain financing. This study conducts a comprehensive analysis of the impact of non-performing loans on credit supply within the Moroccan banking system, covering the period from the first quarter of 2009 to the fourth quarter of 2022. Findings from the ARDL model indicate that the rate of non-performing loans substantially impedes credit availability, both in the short term and the long term. This underscores the critical relationship between financial stability and economic stability.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".