Cross-Listing Audit Fee Premiums: Theory and Evidence
Why this work is in the frame
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Bibliographic record
Abstract
ABSTRACT: We study the effects of cross-listings on audit fees. We first develop a model in which legal environments play a crucial role in determining the auditor's legal liability. Our model and analysis predict that auditors charge higher fees for firms that are cross-listed in countries with stronger legal regimes than they do for non-cross-listed firms and that the cross-listing audit fee premium increases with the difference in the strength of legal regimes between the cross-listed foreign country and the home country. We then empirically test these predictions. The results of our cross-country regressions strongly support our predictions. In addition, we find no significant cross-listing fee premium for firms that are cross-listed in countries whose legal regimes are. no stronger than those of their home countries. This suggests that cross-listing audit fee premiums are associated with increased legal liability and not with increased audit complexity per se. Our findings help explain why cross-listing premiums occur and what determines their magnitude.
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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 | 0.007 | 0.047 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it