The Impact of a Change in Regulation on Environmental Disclosure: SAB92 and the US Chemical Industry
Bibliographic record
Abstract
This study investigates environmental disclosure in the annual reports of US public companies in the chemical industry during a time when there was a substantial change in reporting regulation. This change concerned contingent environmental liabilities. We draw on signal theory and the economic cost perspective to generate predictions about environmental disclosure strategies. We find evidence that managers use disclosure to distinguish their companies from other companies: first by disclosing environmental liabilities that many other companies did not reveal; and later by disclosing other future-oriented financial information. We assumed initially, that this behaviour was indicative of signaling strategy. We find, however, that the companies which we initially thought were signaling have higher levels of pollutant emissions (per dollar of assets) than non- signaling companies. This evidence does not support our earlier assumption. We argue that public concern about this industry, and the fact that emissions levels are open to public scrutiny, lowers the disclosure-cost threshold for high emission companies, leading managers to disclose information they previously withheld.
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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 | 0.005 | 0.036 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".