Does a Female Director in the Boardroom Affect Sustainability Reporting in the U.S. Healthcare Industry?
Bibliographic record
Abstract
In this in-depth study, we explored the nuanced dynamics of boardroom gender diversity and its consequential impact on sustainability reporting within the U.S. Healthcare sector. Leveraging a comprehensive dataset from Refinitiv Eikon, our analysis spanned a spectrum of 646 observations across 57 healthcare entities listed in the S&P 500, covering the period from 2010 to 2021. Our methodology combined various empirical techniques to dissect correlations, unravel heterogeneity, and account for potentially omitted variables. Central to our findings is the discovery that various metrics of board gender diversity, such as the proportion of female directors and the Blau and Shannon diversity indices, exhibit a robust and positive correlation with the intensity and quality of sustainability reporting. This correlation persists even when controlling for a multitude of factors, including elements of corporate governance (such as board size, independence, and meeting attendance), as well as intrinsic firm characteristics (such as size, profitability, growth potential, and leverage). The presence of female directors appears to not only bolster the breadth and depth of sustainability reporting but also align with a broader perspective that their inclusion in boardrooms significantly influences corporate reporting practices. These insights extend beyond academic discourse by offering tangible and actionable intelligence for policymakers and corporate decision-makers. By elucidating the intrinsic value of gender diversity in governance, our study contributes a compelling argument for bolstering female representation in leadership roles as a catalyst for enhanced corporate responsibility and stakeholder engagement.
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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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".