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Record W3145047218 · doi:10.1111/1911-3846.12679

Do Financing Constraints Lead to Incremental Tax Planning? Evidence from the Pension Protection Act of 2006*

2021· article· en· W3145047218 on OpenAlexvenueno aff
John L. Campbell, Nathan C. Goldman, Bin Li

Bibliographic record

VenueContemporary Accounting Research · 2021
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Taxation and Avoidance
Canadian institutionsnot available
Fundersnot available
KeywordsPensionFinanceCashBusinessExternal financingInvestment (military)Cash flowShock (circulatory)Tax planningEconomicsMonetary economicsTax avoidanceDouble taxationDebt

Abstract

fetched live from OpenAlex

ABSTRACT Over the past three decades, academic research has sought to understand how cash shortfalls impact a firm's ability to take all available value‐increasing investment projects. We investigate whether firms facing greater financing constraints turn to tax strategies that generate lower cash effective tax rates (ETRs) to mitigate the adverse effect of these financing constraints. We use the Pension Protection Act of 2006 (PPA 2006) as an exogenous shock to financing constraints for pension firms, but not for other firms. Using a difference‐in‐differences research design, we predict and find that pension firms experience a decrease in their cash ETRs by 1.8%–2.4% after the PPA 2006, relative to other firms. These cash tax savings mitigate the investment shortfall brought about by financing constraints by 19%. We also predict and find that the decline in cash ETRs is greater among firms more adversely affected by the PPA 2006. Our paper sheds light on the direction, causality, and economic magnitude of the association between financing constraints and tax planning activities. We also provide insight into the role of tax planning activities within firms' broader corporate business strategies in responding to financing constraints.

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)0.003
metaresearch head score (Gemma)0.004
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 score0.258
Threshold uncertainty score0.642

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0010.000
Scholarly communication0.0010.002
Open science0.0000.001
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 head0.164
GPT teacher head0.342
Teacher spread0.178 · 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

Citations40
Published2021
Admission routes1
Has abstractyes

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