Mandatory Disclosure and Female Representation in Corporate Leadership: Evidence from NASDAQ
Bibliographic record
Abstract
Over the course of the summer, I had the privilege to work with Bhargav Gopal and his team of researchers on the topic of a disclosure rule introduced by NASDAQ in 2021. With increasing regulatory focus on corporate board diversity, prior research has focused on gender quotas, but not on mandatory disclosure. His paper contributes to research regarding the effect of mandatory disclosure on corporate female diversity, short-term and long-term effects on financial performance, and explores why some firms choose compliance and some do not. I participated in a review of previous quota and disclosure literature, contributed to writing the literature review, and researched datasets on heterogeneity and investor reputation sensitivity. The sample used in the research included NASDAQ- and NYSE-listed firms, focusing on U.S. firms present in all three datasets: CRSP, Compustat, and BoardEx. There were two main approaches: a Difference-in-Differences (DiD) design for Board Composition and Long-Term Financial Outcomes, and an analysis of Short-Term Financial Outcomes. The short-term outcomes were studied using an Event Study Methodology and a Portfolio Approach. Throughout my work on the literature review and supporting disclosure knowledge, I kept these methodologies in mind. There was a moderate increase in gender diversity in response to NASDAQ’s requirement. However, point estimates were much smaller relative to gender quotas, diversity campaigns led by institutional investors, and other mandatory diversity disclosure policies. The relatively small increase in female diversity suggests minimal reputational consequences for disclosing no diversity. I look forward to contributing more to this paper going forward.
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 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 | 0.011 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".