Reshaping Corporate Boards Through Mandatory Gender Diversity Disclosures: Evidence from Canada
Bibliographic record
Abstract
We study whether and how standardized gender diversity disclosure requirements affect women’s representation on corporate boards. Our analysis takes advantage of Canada’s 2014 regulation, which requires disclosures of consistent and comparable information on boards’ gender diversity policies and guidelines. Using a difference-in-differences design, we find that the disclosure regulation increases women’s representation on boards. The increase is more pronounced among firms with stronger shareholder oversight as proxied by ownership from environmental, social, and governance (ESG)–friendly investors, poor diversity performance preregulation, Toronto Stock Exchange index membership, and U.S. cross-listing. In addition, after the regulation, firms that make stronger diversity commitments (e.g., disclosing the adoption of written diversity policies and diversity targets) experience a higher increase in ownership by foreign ESG-friendly investors. Together, our findings suggest that standardized disclosure requirements enable shareholders to be better monitors, thereby improving board gender balance. This paper was accepted by Suraj Srinivasan, accounting. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2023.00509 .
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".