A demotion in disguise? The real effects of relocating pension smoothing from operating income to non-operating income
Bibliographic record
Abstract
Although operating income is a pervasively used performance metric, the FASB has never defined operating income. ASU 2017-07 moves toward defining operating income for the first time in the FASB’s history by specifying the inclusion and exclusion of certain income components in operating income. We examine the real effects of a mandated relocation of the income-smoothing mechanisms for defined benefit pensions from “above the line” to “below the line” of operating income. For over 30 years, the income-smoothing mechanisms from SFAS 87 (1985) have created financial reporting incentives for employers to invest in higher-risk pension assets. Consistent with ASU 2017-07 reducing the financial reporting incentives for risk-taking, we predict and find that a sample of US firms subject to this mandate reduces investment in riskier pension assets following the change, relative to a control sample of Canadian firms not subject to the change. In cross-sectional tests, we find that the reduction in risk-taking is more pronounced in (1) firms where the financial reporting benefits to risk-taking were stronger in the pre-period, and (2) firms where the regulatory change particularly reduced those financial reporting benefits. Our findings provide the first direct evidence that smoothing induces US pension sponsors to tilt toward riskier pension investments; they also indicate that financial statement presentation has real economic consequences.
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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 | 0.004 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".