Why Settle for the Status Quo? A Critical Assessment of Pension Liability Measurement Under <scp>IFRS</scp> and <scp>US GAAP</scp>
Bibliographic record
Abstract
Relevance and faithful representation are identified by standard‐setters as fundamental qualitative characteristics for useful accounting information. We critically assess whether current pension measurement guidance under International Financial Reporting Standards (IFRS) and US generally accepted accounting principles (GAAP) results in pension measurement that achieves these characteristics. We argue that: (1) conceptual justification is inconsistent with current guidance; (2) IFRS and US GAAP provide differing justifications; and (3) existing guidance applies inconsistent measurement principles and uses principles that are inconsistent with other standards. We conclude that current guidance does not achieve representational faithfulness. Next, we introduce two alternative approaches to pension liability measurement—going concern and settlement—which use consistent measurement principles and thus are more representationally faithful than current standards. We summarize empirical evidence, suggesting that both alternative measures demonstrate stronger relevance to equity and debt investors than current measurement. We conclude by recommending that standard‐setters: (1) use settlement measurement for pension liabilities; and (2) require disclosures that would enable users to re‐estimate pension liabilities using different parameters that would suit their particular needs and consider the characteristics of the plans themselves. We believe that our recommendations would improve the relevance and faithful representation of pension liabilities.
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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 | 0.121 | 0.275 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.003 | 0.016 |
| Scholarly communication | 0.013 | 0.019 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.002 | 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".