The Effects of the Tax Cuts and Jobs Act of 2017 on Defined Benefit Pension Contributions*
Bibliographic record
Abstract
ABSTRACT This study examines the effect of the Tax Cuts and Jobs Act of 2017 (TCJA) on corporate defined benefit pension contributions. The TCJA decreases the corporate tax rate from 35 percent in 2017 to 21 percent in 2018 and thereafter. This change incentivizes firms to increase 2017 pension contributions to take advantage of tax deductions at a higher rate. Consistent with this incentive, we find firms increase defined benefit pension contributions by an average of 25 to 31 percent in 2017 compared with earlier years. We also find that taxpaying firms are the primary contributors. Further, taxpaying firms with high levels of pension‐related deferred tax assets contribute over three times as much as taxpaying firms with low levels of pension‐related deferred tax assets. We also find firms that increase pension contributions in 2017 reduce 2018 contributions, consistent with intertemporal income shifting rather than a permanent change in pension funding strategy.
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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 | 0.002 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".