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Record W3041180966 · doi:10.1111/1911-3846.12632

Is Audit Committee Equity Compensation Related to Audit Fees?*

2020· article· en· W3041180966 on OpenAlexvenueno aff
Xinming Liu, Gerald J. Lobo, Hung‐Chao Yu

Bibliographic record

VenueContemporary Accounting Research · 2020
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicAuditing, Earnings Management, Governance
Canadian institutionsnot available
FundersFundamental Research Funds for the Central UniversitiesNational Natural Science Foundation of ChinaMinistry of Science and Technology
KeywordsAccountingBusinessEquity (law)AuditAudit committeeJoint auditQuality auditChief audit executiveAuditor independenceAudit evidenceInternal auditLawPolitical science

Abstract

fetched live from OpenAlex

ABSTRACT Section 301 of the Sarbanes‐Oxley Act (SOX) implicitly assumes that audit committees can independently determine audit fees. Critics of section 301 have questioned this assumption in particular, and the efficacy of section 301 more generally. In response, the SEC issued a concept release in 2015 calling for public disclosure of the process that audit committees follow for determining auditor compensation. Motivated by these calls and the widespread use of stocks and options to compensate firms' independent directors, we examine the relation between equity compensation granted to audit committee members and audit fees. Using a sample of 3,685 firm‐year observations during 2007–2015, we find a negative relation between audit committee equity compensation and audit fees, consistent with larger equity pay inducing audit committee members to compromise independence by paying lower audit fees. These findings are robust to controlling for endogeneity, firm size, alternative measures of equity compensation, alternative samples, and an alternative treatment of extreme values. We further show that larger equity compensation is associated with lower earnings quality. We also find that the negative effect of equity compensation on audit fees is stronger when city‐level audit market competition is high. However, this negative relation disappears when (i) firms face high litigation risk, (ii) auditors have stronger bargaining power, (iii) the audit committee includes a high proportion of accounting experts, and (iv) auditors are industry experts. Our results are relevant for regulators and investors.

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)0.004
metaresearch head score (Gemma)0.020
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Scholarly communication, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.479
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.020
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.004
Science and technology studies0.0010.000
Scholarly communication0.0020.003
Open science0.0020.004
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.013

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 head0.088
GPT teacher head0.332
Teacher spread0.245 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
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

Citations44
Published2020
Admission routes1
Has abstractyes

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