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The impact of joint provision of audit and tax services on the advice of tax professionals

2025· article· en· W4407834656 on OpenAlexaff
Devan Mescall, Regan N. Schmidt

Bibliographic record

VenueJournal of Accounting and Public Policy · 2025
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicTaxation and Compliance Studies
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsAuditAccountingAdvice (programming)BusinessJoint (building)Computer science

Abstract

fetched live from OpenAlex

Prior public policy research has questioned the impact of joint provision of audit and non-audit services by examining auditor behavior and audit quality. This study contributes to the public policy debate by examining the behavior of the non-audit service provider, specifically tax professionals. The results of an experiment provide the first evidence that joint provision of audit and tax services impacts the judgments of the tax professional and reduces the aggressiveness of tax advice provided by experienced tax professionals, consistent with ingroup behavioral theory. In addition, tax professionals’ assessments of uncertainty—the basis for financial statement reserve recognition—are relatively greater when their firm is providing joint audit and tax services. Tax uncertainty assessments mediate the relationship between service provision and tax aggressive advice. Collectively, this study informs the public policy debate by demonstrating that joint provision of audit and non-audit services impacts the non-audit service provided by the same firm and provides clarity on how public policy may decrease tax aggressive advice.

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)0.009
metaresearch head score (Gemma)0.101
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 score0.032
Threshold uncertainty score0.064

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.101
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.002
Scholarly communication0.0030.001
Open science0.0010.002
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0070.000

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 head0.027
GPT teacher head0.294
Teacher spread0.268 · 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

Citations2
Published2025
Admission routes1
Has abstractyes

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