Corporate Governance Features and Efficiency: Evidence from the Saudi Arabian Banks
Bibliographic record
Abstract
The aim of this article is to examine the effect of the Corporate Governance features as measured by the Independence of the board of directors, the board size and the ownership structure (private ownership/State ownership and foreign ownership) on the banking efficiency of Saudi Arabian banks. A data set of the twelve biggest banks for the period 2000 to 2017 is used. As for banking efficiency scores, the methodology is based on the Data Envelopment Analysis (DEA). It allows for Technical Efficiency, Pure Technical Efficiency and Scale Efficiency scores. The results of this study point to the significant role of The Independence (INDEP) variable supported by a positive and significant effect on efficiency in all regressions, indicating a positive relationship with the Technical Efficiency (TE) and the Pure Technical Efficiency (PTE). In the contrary, the independence of the board directors has a negative and significant effect on scale efficiency (SE). According to Board Size variable, results related to this later reveal a negative and a significant effect on technical efficiency (TE), Pure Technical Efficiency (PTE) and Scale Efficiency (SE) in all regressions. Finally, as for the ownership structure variables, results confirm that Private Ownership (OWEN-P) provides positive and significant effects on both the Technical and the Scale Efficiency. This effect seems to be turn to be negative and significant when it is correlated to the Pure Technical Efficiency. State Ownership (OWEN-S) impacts positively and significantly the Technical Efficiency, the Pure Technical Efficiency and Scale Efficiency separately. As for the Foreign Ownership (OWEN-F) variable, except for the Pure Technical Efficiency (PTE), we note a positive and significant effect on the Technical and Scale Efficiency. This study implies better Corporate Governance practices should be supported to improve the overall efficiency and its components. This includes in particular, the Board Size and the Ownership structure variables.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".