Equity Valuation Effects of the Pension Protection Act of 2006
Bibliographic record
Abstract
We investigate the equity valuation effects of the Pension Protection Act of 2006 (PPA 2006). The PPA 2006 has two main provisions: (1) firms must fully fund their pension plans within seven years (previously allowed 30 years to fund 90 percent of the pension liability) and (2) firms receive a tax deduction for contributions up to 150 percent of the pension liability (previously 100 percent). After controlling for the effects of SFAS 158, growth opportunities, the cost of external funds, and other information released during our sample period, we examine pension firms’ abnormal returns surrounding key dates in the legislative process leading to the adoption of the PPA 2006. First, we find a mean negative abnormal return of −4.20 percent during the period in which the PPA 2006 was first voted on by Congress. The mean (median) firm in our sample experienced a $310 million ($60 million) decline in market capitalization. Second, we find that the valuation effect was more negative for firms with larger unfunded pension liabilities and larger capital expenditure requirements, while firms with higher marginal tax rates experienced a positive effect. Third, we find no evidence of differential valuation effects for firms in different “at risk” categories as defined by the PPA 2006. Finally, we find a significant number of pension freezes occurred during our sample period. Our results are stronger when excluding these firms from our sample.
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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 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".