Audit pricing, legal liability regimes, and big 4 premiums: Theory and cross-country evidence
Bibliographic record
Abstract
In this paper, we first develop a model in which national legal environments play a crucial role in determining auditor effort and audit fees. Our model predicts that (a) audit fees increase monotonically with the strength or strictness of a country's legal liability regime; (b) given a legal liability regime, Big 4 auditors charge higher audit fees than non-Big 4 auditors; and (c) the Big 4 fee premium is lower in countries with strong legal regimes than in countries with weaker legal regimes. We then test the model's predictions using a large sample of audit clients from 15 countries with different legal regimes where audit fee data are publicly available. The results of our cross-country regressions are consistent with the above three predictions and are robust to a variety of sensitivity checks. In addition, our hypotheses are all consistent with the pattern of auditor effort (measured by labor hours) observed in proprietary data sets from four countries whose legal regimes vary. Finally, we find that the effects of a legal regime on audit pricing and the Big 4 premium are more salient for the small client segment than for the large client segment. Overall, our results indicate that a country's legal environment plays an important role in determining auditor effort, audit fees, and the fee spread between Big 4 and non-Big 4 auditors. © CAAA.
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".