Component Auditor and Corporate Tax Aggressiveness
Bibliographic record
Abstract
The PCAOB has expressed continuous concerns on the use of component auditors by U.S. multinational corporations. Prior studies mainly focus on the potential negative impact of component auditors on audit quality, but the literature has thus far overlooked the benefits brought about by their superior local knowledge. This study utilizes the new Form AP filings on disclosure of component auditor involvement in group audits to investigate whether and how component auditors contribute their local tax expertise to the lead auditors. Specifically, our analysis focuses on the relations between component auditor use and corporate tax aggressiveness. Using a sample of companies with auditor-provided tax service, we find that component auditor use is negatively related to corporate tax aggressiveness. We further find that this negative relation is more pronounced when the component and lead auditors share the same network, when the corporate income tax systems in the component auditors’ jurisdictions are more complex, and when the component auditors exhibit higher competence. Lastly, we also find the association between component auditor use and corporate tax aggressiveness varies significantly with the institutional environment facing component auditors. Overall, our study demonstrates the positive effect of tax knowledge spillover from the component to the lead auditors when auditors jointly provide both audit and tax services. We provide novel evidence in support of the bright side of component auditor use; component auditors aid the lead auditors in deterring their clients’ aggressive tax planning.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".