Determinants of Bank Profitability—Do Institutions, Globalization, and Global Uncertainty Matter for Banks in Island Economies? The Case of Fiji
Bibliographic record
Abstract
The objective of this study is to examine the influences of institutions, globalization, and world uncertainty on bank profitability in small developing economies. Consequently, we emphasize the significance of both bank-specific and other external factors influencing bank profitability. The empirical estimation is based on seven banks in Fiji—a small island economy—over the period 2000–2021. Together with bank-specific and macro factors, we account for institutions, globalization, and world uncertainty in analyzing the determinants of bank profitability. The study uses the fixed-effect estimation method. From the results, we observe that bank-specific variables, like the net interest margin, non-interest income, bank size, and capital adequacy ratio, are positively associated with bank profitability. Non-performing loans and credit risk are negatively associated with bank profitability. Macro variables, such as real GDP growth and remittances, have positive effects on bank profitability. Institutional factors, such as government effectiveness and voice and accountability, are positively associated with bank profitability. Regarding globalization, we find that it supports bank profitability. Global uncertainty and the Global Financial Crisis (2007–2008) are positively associated with profitability, whereas the global pandemic (COVID-19) is negatively associated. This study underscores the need to analyze the bank performance with factors beyond those reported in financial statements to derive a comprehensive understanding and appreciation of the complex nature of banking operations.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".