Can high ESG disclosure quality boost the impact of ESG investing: international evidence
Bibliographic record
Abstract
By analyzing an international dataset, we find that ESG disclosure quality significantly moderates the impact of institutional investors in promoting improvements in firms’ environmental and social (E&S) performance. These effects are particularly pronounced in firms with greater potential for improvement in E&S performance. Additionally, we observe that higher ESG disclosure quality directly incentivizes firms to enhance their E&S performance, with this effect being stronger in firms that already have higher average ESG disclosure quality. Our findings suggest that high-quality ESG disclosure can play a key role in strengthening the effects of ESG investing and mitigating potential negative impacts of common institutional ownership. This research underscores the need for specific regulatory measures to enhance ESG disclosure standards, ensuring that firms are more effectively aligned with sustainability goals. Policymakers should prioritize establishing standardized and transparent ESG reporting frameworks to strengthen corporate accountability and drive sustainable development across industries.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".