Trade‐offs between Tax and Financial Reporting Benefits: Evidence from Purchase Price Allocations in Taxable Acquisitions
Bibliographic record
Abstract
ABSTRACT Under U.S. GAAP, firms recognize assets acquired in business combinations at fair value. Similarly, in taxable asset acquisitions firms adjust the tax basis of assets to fair value. Managers can increase the present value of future tax savings by allocating a greater portion of the purchase price to shorter‐lived assets than to goodwill or indefinite‐lived intangibles. However, this tax planning strategy imposes a financial reporting cost because it reduces book income following the acquisition; all else equal, allocations to shorter‐lived depreciable assets increase book depreciation expense, whereas allocations to goodwill and indefinite‐lived intangibles do not increase book amortization expense. We exploit the features of taxable asset acquisitions to investigate trade‐offs between tax and financial reporting incentives. We predict and find greater allocations to depreciable versus intangible assets when managers have strong tax incentives and weak financial reporting incentives. However, we also find that strong financial reporting incentives moderate the effects of strong tax incentives. These findings contribute new evidence to the literature on the importance of nontax costs in tax planning decisions
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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 | 0.044 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".