Are ESG Female? The Hidden Benefits of Female Presence on Sustainable Finance
Bibliographic record
Abstract
Though gender equality has been at the centre of debate over the last decades, a number of benefits concerning the impact of female directors on corporate performance are still overlooked. Particularly, the link that seems to exist between female directors and sustainable finance has received limited attention. We investigate the impact of an enhancement in female presence, meant as women in decision-making positions, on a firm’s performance both in financial and sustainability terms. The goal is to contribute to the literature streams on gender economics and on sustainable finance. Most research on sustainable finance and its impact on corporate governance rely only on aggregate ESG ratings for their results. Such scores are typically a black-box, with financial providers supplying little information about their methodology. Our analysis not only develops disaggregate scores for each dimension, but also provides motivation for the measurement of gender equality by means of specific indicators, such as the number of female directors, going beyond the bare (S) or (G) rating. ESG ratings and specific indicators of gender equality were retrieved from the well-known Bloomberg provider. Relying on a dataset concerning European companies, we empirically show that an increase in gender equality has a positive effect on a firm’s financial performance and on its share of sustainable investments.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".