The Economic Consequences of Accounting Standards: Evidence from Risk-Taking in Pension Plans
Bibliographic record
Abstract
ABSTRACT Experts have long conjectured that pension accounting rules, by which pension expense depends on a managerial estimate that is directly tied to the riskiness of plan assets (i.e., the expected rate of return, or ERR, on plan assets), encourage risk-taking with pension investments. The recent passage of IAS 19, Employee Benefits (Revised) (hereafter, IAS 19R) eliminates the ERR and replaces it with a managerial estimate unrelated to plan asset riskiness (the discount rate). We demonstrate that a sample of Canadian firms affected by IAS 19R reduces risk-taking in pension investments post-IAS 19R, compared to a control sample of U.S. firms unaffected by IAS 19R. Therefore, removing firms' ability to recognize immediately in net income the expected higher returns from risk-taking (via a higher ERR) reduces their propensity for that risk-taking—providing some of the first empirical evidence on the economic consequences of eliminating the ERR-based pension accounting model.
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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 | 0.008 | 0.047 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".