Determinants of Non-Performing Loans in a Small Island Economy of Fiji: Accounting for COVID-19, Bank-Type, and Globalisation
Bibliographic record
Abstract
An increase in non-performing loans and bad debts in the banking sector can make banks vulnerable to a loss of confidence among customers and other banks and a banking collapse. The recent pandemic (COVID-19) and the evolving globalisation can affect bank operations, although the effects may depend on the type of banks and other bank-specific factors. In this paper, we revisit the topic on the determinants of non-performing loans of banks in a small island economy of Fiji over the period 2000 to 2022. We apply a fixed-effect method and consider seven banks (five commercial banks and two non-bank financial institutions). In our estimations, we examine the effect of bank-specific factors and control for the social and economic globalisation, the GFC, the COVID-19 pandemic, and bank-type effects, as well as the effect of the interaction between the bank type and the pandemic, as key contributions of the study. Overall, our results are consistent in terms of the effects noted from the bank-specific factors. From the extended model estimations, we note that COVID-19 had a more adverse effect on loan losses than the GFC, and the interaction between the bank type and COVID-19 indicates that non-banks were highly vulnerable to loan losses, whereas commercial banks exhibited greater preparedness. Economic globalisation reduces bank losses, whereas social globalisation exacerbates NPLs.
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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 | 0.003 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".