Corporate Governance and Agency Cost: Empirical Evidence from Vietnam
Bibliographic record
Abstract
This study examines the impact of corporate governance, reflecting a wide spectrum of board characteristics and ownership structure on agency costs in 281 listed companies on Ho Chi Minh Stock Exchange (HOSE) in Vietnam in the period 2013–2018. For this purpose, three board characteristics were chosen: (1) the size of board of directors, (2) equilibrium between non-executive and executive members of the board of directors, (3) the CEO chair duality and three types of ownership structures were chosen: (1) management ownership, (2) government ownership, (3) foreign ownership. An inverse proxy of agency costs is used: asset utilization ratio (asset turnover), which reflects the managerial efficiency. The research methodology includes three statistical approaches: Ordinary least squares (OLS), fixed effects model (FEM) and random effects model (REM) are considered to employ to address econometric issues and to improve the accuracy of the regression coefficients. The results can create effective corporate governance mechanisms in controlling the managerial opportunistic behavior to lower agency conflicts, and hence lower agency costs.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".