Factors Affecting Return on Assets (ROA) in the Banking Sector of Selected Arab Countries: Is There a Role for Financial Inclusion and Technology Indicators?
Bibliographic record
Abstract
The objective of this study is to examine, using a dynamic panel data framework, the effects of financial inclusion on the performance of the banking sector, as measured by the return on assets, for eleven Arab countries during the period 2012-2019. In addition to financial inclusion and technologies indicators, our analysis incorporates banking and macroeconomic variables. The study reveals that bank-specific variables have a greater impact on the profitability of banks than macroeconomic variables. The results show that there is a positive and significant impact of the bank’s assets, the bank solvency, the credit growth, the economic growth rate, and the inflation rate on the profitability of the banking sector. However, the return on assets is unaffected by fluctuations in nonperforming loans and the interbank lending rate. Regarding indicators of financial inclusion and technologies, the study finds no evidence of significant effects of automated teller machines (ATM) distribution and bank branch density on return on assets.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| 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 | 0.001 |
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".