Governance Quality in a “Comply or Explain” Governance Disclosure Regime
Bibliographic record
Abstract
Abstract Manuscript Type Empirical Research Question/Issue Do firms take advantage of the flexibility of the “comply or explain” corporate governance disclosure regime to adopt governance practices that are best suited to their needs and value‐added to the firms as predicted by economic theories of the firm? Using the Canadian “comply or explain” corporate governance disclosure regime, we construct a board score measure based on the Canadian code's 47 “best practices.” We employ a unique approach by positing that the “explain” disclosures indicate higher agency costs of best practice adoption or indicate the ability of the firm to improve its governance practices relative to “best practices” in light of firm specific circumstances. Research Findings/Insights We find that our measure is strongly and positively associated with higher firm value and weakly and positively associated with better operational performance. Further, our measure is more strongly associated with both than best practice adoption measures. Theoretical/Academic Implications Our unique measure of governance quality reveals differences in governance efficiency and effectiveness that are consistent with the theorized advantages of “comply or explain” governance disclosure regimes. Further, our results suggest that firms in a “comply or explain” regime are not employing, on average, the discretion permitted by such a regime to avoid improvements to their corporate governance practices. Practitioner/Policy Implications Our results support the proposition that the flexibility of a “comply or explain” governance regime provides tangible financial benefits to shareholders in terms of higher firm value and returns on shareholders' equity investment.
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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 | 0.016 | 0.083 |
| 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 | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".