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Record W4407405134 · doi:10.3390/jrfm18020094

The PCAOB’s 2006 Tax Service Restrictions and Earnings Management

2025· article· en· W4407405134 on OpenAlexvenueno aff
Matthew Notbohm, Xiaoli Guo, Adrian Valencia

Bibliographic record

VenueJournal of risk and financial management · 2025
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Taxation and Avoidance
Canadian institutionsnot available
Fundersnot available
KeywordsBusinessAccountingEarnings managementAuditService (business)EarningsMarketing

Abstract

fetched live from OpenAlex

In 2006, the PCAOB implemented new restrictions on the auditor provision of some tax and contingent fee services provided to issuer audit clients. These restrictions were implemented to reduce auditor conflicts of interest inherent when the auditor provides any of these specific services and a financial statement audit. Subsequent research found that these tax service restrictions did not impact audit quality, measured as the probabilities of going concern opinions or financial statement restatements. We reexamine this research question in the context of the regulation’s earnings management effects. Our investigation of this question uses a difference-in-difference regression approach and 20,043 issuer company fiscal year observations from 2002 to 2009, consistent with that used in prior studies, and four measures of earnings management (discretionary accruals, abnormal working capital accruals, current accruals, and the likelihood of meeting or slightly beating the zero earnings change benchmark) to proxy for audit quality. We find, consistent with findings in prior studies, no detectable effects of the 2006 PCAOB tax service restrictions. These null results persist through a series of robustness tests that include re-estimating our primary regressions on a Big 4 subsample, adding multiple alternative treatment variable definitions, generating a propensity-score-matched sample, and adding a control for internal control weakness. These findings raise further doubt about the need for these non-audit service restrictions.

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)0.004
metaresearch head score (Gemma)0.026
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 score0.248
Threshold uncertainty score0.494

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.026
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0020.001
Scholarly communication0.0040.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0050.001

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.

Opus teacher head0.006
GPT teacher head0.196
Teacher spread0.190 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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