Voluntary Adoption of More Stringent Governance Policy on Audit Committees: Theory and Empirical Evidence
Bibliographic record
Abstract
ABSTRACT: This study exploits an exogenous change to audit committee policy in Canada and presents new evidence on how high-quality corporate governance mitigates managerial resource diversion and improves firm values. We first examine why some firms listed on the Toronto Venture Exchange (TSX Venture) voluntarily adopted the more stringent governance policy in 2004 that requires all audit committee members to be independent and financially literate. We develop a parsimonious analytical model that shows that both compliance costs and financing needs have an impact on firms' adoption decisions. Confirming the model's predictions, we find that TSX Venture firms with low compliance costs and greater future financing needs are more likely to adopt the new policy voluntarily. The analytical model also shows that high-quality audit committees enhance firm values by reducing the likelihood of managerial resource diversion. Consistent with the predictions of our analytical model, we find that the adoption decision has a positive impact on firm value and a negative impact on firms' cost of equity capital for both Toronto Stock Exchange (TSX) and TSX Venture firms. As corroborating evidence of the economic impact of the more stringent governance policy, we also show that both TSX and TSX Venture firms have improved investment efficiency following the adoption decisions. Data Availability: Data are available from public sources identified in the paper.
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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 | 0.009 | 0.045 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| 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".