Recognition and Disclosure Reliability: Evidence from SFAS No. 106*
Bibliographic record
Abstract
Abstract This paper examines a fundamental question of interest to researchers and regulators: Does the market treat disclosed financial statement information as if it is less reliable than information recognized in the body of the financial statements? Specifically, we compare the perceived reliability of liabilities for retiree benefits other than pensions (PRBs) disclosed prior to adoption of Statement of Financial Accounting Standards No. 106 (SFAS No. 106) with the perceived reliability of PRB liabilities subsequently recognized under SFAS No. 106. Overall, the evidence is consistent with the market treating disclosed PRB liabilities as less reliable than recognized PRB liabilities and pension liabilities. However, once PRB liabilities are recognized, they do not appear to be any less reliable than pension liabilities. These findings are inconsistent with the Choi, Collins, and Johnson 1997 conclusion that PRB liabilities are inherently less reliable than pension liabilities. The paper also investigates factors that may have contributed to the lower perceived disclosure reliability. Our results suggest that the market perceived PRB liability disclosures to be less reliable when firms provided range disclosures, had higher probabilities of reducing plan benefits, or had lower ratios of retiree to total PRB obligations. These findings suggest that reliability may have been enhanced if more supporting details had been provided in Staff Accounting Bulletin No. 74 disclosures.
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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 | 0.024 | 0.161 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".