Minority investor protection mechanisms and agency costs: An empirical study using a World Bank–developed approach
Bibliographic record
Abstract
This study aims to examine the impact of minority investor protection mechanisms on agency costs. All relevant indicators of minority investor protection adapted from the World Bank’s annual ‘Doing Business’ reports, along with concentrated government ownership, are employed with a panel data sample of 135 Vietnamese listed firms during the period 2014–2018. It is found that the following mechanisms are effective in mitigating agency costs and hence agency problems at the firm level: 1) review and approval requirements for related-party transactions; 2) minority shareholders’ ability to sue and hold directors liable for their duties; 3) minority shareholders’ access to internal corporate documents; 4) investors’ rights to approve major corporate investment and sale of asset decisions; and 5) disclosure in annual reports of salaries, bonuses and other forms of remuneration to directors and management. Interestingly, board independence and controlling government shareholders are not confirmed to play significant roles in addressing agency problems. To the best of the authors’ knowledge, this is the first attempt at testing for the impact of minority investor protection mechanisms developed by the World Bank on agency costs at the firm level, hence providing empirical evidence for the adoption of the minority investor protection mechanisms promoted by the World Bank. This study also provides policy implications for selecting effective mechanisms to mitigate agency conflicts between controlling shareholders and minority investors in order to enhance the financial performance of firms in an Asian emerging market.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".