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Record W2946180686 · doi:10.1111/1911-3846.12528

Are Audit Firms' Compensation Policies Associated with Audit Quality?

2019· article· en· W2946180686 on OpenAlexvenueno aff
Jürgen Ernstberger, Christopher Koch, Eva Maria Schreiber, Greg Trompeter

Bibliographic record

VenueContemporary Accounting Research · 2019
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicAuditing, Earnings Management, Governance
Canadian institutionsnot available
Fundersnot available
KeywordsAuditBusinessQuality auditProfit sharingProfit (economics)AccountingCompensation (psychology)IncentiveExecutive compensationFinanceEconomicsMicroeconomicsCorporate governance

Abstract

fetched live from OpenAlex

ABSTRACT We examine how compensation policies of audit firms are associated with audit quality. Specifically, we investigate the effects of the ratio of variable to fixed compensation and the size of the basis for profit sharing (i.e., whether partners share profits in a small or in a large profit pool). For our analyses, we use detailed mandatory disclosure of the compensation policies in German audit firms. We document that compensation policies vary considerably across audit firms. We find that profit sharing in a small profit pool and high variable compensation are two characteristics of auditor compensation associated with lower audit quality. We also find some evidence suggesting that audit quality may be most at risk in cases in which partners rely more heavily on variable compensation to divide a relatively small profit pool. In additional analyses, we find that these associations are more pronounced in medium‐sized audit firms. We argue that this finding may result from these firms being too large for audit partners to directly monitor each other effectively, yet simultaneously too small to have sophisticated centralized monitoring systems in place. Finally, we find that integrating partner‐specific, nonprofit‐related performance metrics into the compensation structure mitigates the adverse effects of small profit pools and high variable compensation.

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)0.006
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 categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.212
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.020
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0010.004
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.004

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 head0.062
GPT teacher head0.313
Teacher spread0.251 · 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.

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

Citations58
Published2019
Admission routes1
Has abstractyes

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