The informational contribution of social and environmental disclosures for investors
Bibliographic record
Abstract
Purpose The aim of the paper is to investigate whether social disclosure and environmental disclosure have a substituting or a complementing effect in reducing information asymmetry between managers and stock market participants Design/methodology/approach This study attempts to provide a comprehensive analysis of a firm's social and environmental disclosure strategy. The authors posit that this strategy simultaneously affects information asymmetry and disclosure. Findings Findings suggest that social disclosure and environmental disclosure substitute each other in reducing stock market asymmetry. Research limitations/implications The measurement of social and environmental disclosure is based upon a coding instrument that makes some explicit assumptions about the value and relevance of information. Moreover, information asymmetry cannot be directly measured and is inferred from the behaviour of proxy variables such as share price volatility and bid‐ask spread. Practical implications Results suggest that social disclosure reinforces the informativeness of environmental disclosure for stock markets, even substituting for it under certain conditions. Stakeholders must assess and retain an increasing flow of information: a more efficient disclosure strategy becomes critical if firms want to convey the right picture of their CSR performance. Originality/value To the best of the authors' knowledge, this is the first study to explore the joint effect of social disclosure and environmental disclosure in reducing information asymmetry.
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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 | 0.010 | 0.081 |
| 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 | 0.003 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".