Does Corporate Governance Compliance Increase Company Value? Evidence from the Best Practice of the Board
Bibliographic record
Abstract
Drawing upon agency theory, we address the limitations of best practice code in the context of emerging governance, emphasizing the role of concentrated ownership. While the code provisions were formulated in developed countries, the transfer of one-size-fits-all guidelines may not address the characteristics and challenges of emerging and post-transition economies. Specifically, we emphasize that provisions of corporate governance codes are aimed at solving the principal–agent conflict between shareholders and managers. These guidelines may remain limited in addressing principal–principal conflicts between majority and minority shareholders and have either a lesser effect on valuation or none at all. Using a unique sample of 155 companies listed on the Warsaw Stock Exchange during the period 2006–2015, with hand-collected data from declarations of conformity, we tested the hypotheses on the link between corporate governance compliance (with board) practice and company value. The period of 2006–2015 was chosen deliberately, due to the relative stability of corporate governance code recommendations over this time. The results of our panel model reveal a negative and statistically significant relation between corporate governance compliance and company value. We contribute to the existing literature providing new evidence on compliance practice in the context of concentrated ownership, and the limited effect of code provisions in addressing structural challenges of corporate governance in emerging post-transition economies and hierarchy-based control systems.
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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 | 0.012 | 0.104 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".