Three Essays on Environmental, Social, and Governance Transparency
Bibliographic record
Abstract
This dissertation is comprised of three essays on determinants and consequences of Environmental, Social, and Governance (ESG) Transparency. Transparency refers to high quantity of material and value relevant information about ESG issues. In the first essay, we explore the relationship between our two variables of interest (i.e., audit quality and public media exposure) and ESG transparency on a sample of publicly listed Canadian firms in in the S&P/TSX Index of the Toronto Stock Exchange. Results show that audit quality and public media exposure are two main drivers of ESG transparency, hence, commitment to high quality audits and exposure to high public media coverage drive firms to be more transparent about ESG issues. Finally, as a consequence of ESG transparency, we find a negative association between ESG transparency and firm–level investment inefficiency. \n \nThe second essay examine whether the transparency of environmental and social (E&S) information affects financial analysts’ forecast properties that reflect their information set. Focusing on a sample of non-financial and non-utility U.S. firms from the S&P 500 index, results suggest that the level of transparency vis-à-vis both E&S information is negatively related to analysts’ forecast errors as well as forecast dispersion. These negative relationships become more pronounced for firms with low financial reporting quality, low media coverage, and for those with weak governance. Finally, we find that E&S transparency relates with investment efficiency essentially via analysts` information environment, which thus acts as a mediating variable. This finding is consistent with financial analysts also playing a monitoring role in capital markets. \n \nThe third essay, we investigate how a firm’s (E&S) transparency relates with its cash holdings. Focusing on a large sample of S&P 500 firms, results show that a higher level of E&S transparency implies lower firm-level cash holdings. The negative relationship is more pronounced for firms suffering from high information asymmetry, with low financial reporting quality, and for those with weak governance. Further analyses document that the two channels and mechanisms by which E&S transparency affect firm-level cash holdings are the cost of debt and financial constraints. Finally, our findings suggest that E&S transparency increases the market value relevance of an additional dollar in cash holdings.
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".