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Record W3124788320 · doi:10.1111/1911-3846.12151

Tax Avoidance and the Implications of Weak Internal Controls

2015· article· en· W3124788320 on OpenAlexvenueno aff
Andrew M. Bauer

Bibliographic record

VenueContemporary Accounting Research · 2015
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Taxation and Avoidance
Canadian institutionsnot available
Fundersnot available
KeywordsTax avoidanceAccountingBusinessCorporate taxCorporate governanceCash flowShareholderProxy (statistics)Control (management)Public economicsFinanceEconomicsDouble taxation

Abstract

fetched live from OpenAlex

Abstract I examine whether corporate tax avoidance is associated with internal control weaknesses (ICWs) disclosed under the Sarbanes‐Oxley Act (SOX). ICWs disclosed under SOX are frequently related to a firm's tax function. When pervasive ICWs exist, the likelihood increases that these frequent tax‐related ICWs spill over from financial reporting issues to tax avoidance objectives. Thus, my research helps corporate stakeholders understand the implications of internal controls beyond simply financial reporting objectives. Results indicate that, on average, firms with a tax‐related ICW have a 4 percent higher three‐year cash effective tax rate relative to firms without any such weaknesses. Further estimates reveal that this negative relation stems from pervasive, company‐level tax ICWs. Analysis of remediation suggests a causal link. I find that after remediating tax‐related ICWs, firms report higher levels of tax avoidance in the future. Broadly, these findings support that internal control quality represents a proxy for internal governance, and thus the strength of alignment between managers and shareholders. Furthermore, tax‐related internal controls represent an important underlying determinant of tax avoidance with significant cash flow effects, and implications beyond financial reporting.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.017
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.004
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.017
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0020.001
Open science0.0000.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.098
GPT teacher head0.325
Teacher spread0.227 · 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 source (direct Gemma or distilled Codex), 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

Citations187
Published2015
Admission routes1
Has abstractyes

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