Audit Fee Differential, Audit Effort, and Litigation Risk: An Examination of <scp>ADR</scp> Firms
Bibliographic record
Abstract
Abstract Prior studies find that audit fees are higher for cross‐listed firms, and these studies primarily attribute the incremental fees to added litigation costs. In this study, we investigate whether the higher audit fees that foreign firms cross‐listed in the United States pay are also attributable to incremental audit effort associated with U.S. disclosure requirements and a more stringent U.S. auditing environment. By comparing audit fees of foreign cross‐listed firms to U.S. domiciled firms and to non‐cross‐listed foreign firms, we are able to decompose incremental audit fees into portions attributable to added audit effort and to added litigation costs. We find that, on average, foreign firms cross‐listed in the United States pay significantly higher fees than domestic U.S. firms and foreign firms that do not cross‐list. Furthermore, we find that audit effort is almost as important as litigation costs in explaining the higher fees associated with foreign cross‐listed firms; our estimates suggest that between 29 percent and 48 percent of the incremental fees are attributable to incremental audit effort. In addition, the total cross‐listing premium is increasing in the difference between the U.S. auditing regulatory environment and that of the home country of the cross‐listed firm. Our study improves our understanding of the role of audit effort in explaining the added fees charged by auditors when foreign firms cross‐list in the United States.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".