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Record W3125278592 · doi:10.1111/1911-3846.12484

Trade‐offs between Tax and Financial Reporting Benefits: Evidence from Purchase Price Allocations in Taxable Acquisitions

2019· article· en· W3125278592 on OpenAlexvenueno aff
Daniel P. Lynch, Miles A. Romney, Bridget Stomberg, Daniel Wangerin, John R. Robinson

Bibliographic record

VenueContemporary Accounting Research · 2019
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Taxation and Avoidance
Canadian institutionsnot available
Fundersnot available
KeywordsTaxable incomeBusinessIncentiveGoodwillBook valueTax incentiveDeferred taxAmortizationFinanceAsset (computer security)Value (mathematics)Monetary economicsAccountingEconomicsDebtState income taxTax reformPublic economicsMicroeconomicsEarningsGross income

Abstract

fetched live from OpenAlex

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

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.006
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.051
Threshold uncertainty score0.994

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0010.004
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.121
GPT teacher head0.326
Teacher spread0.206 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations43
Published2019
Admission routes1
Has abstractyes

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