Admitting Mistakes: Home Country Effect on the Reliability of Restatement Reporting
Bibliographic record
Abstract
ABSTRACT We study the frequency of restatements by foreign firms listed on U.S. exchanges. We find that the restatement rate of U.S.-listed foreign firms is significantly lower than that of comparable U.S. firms and that the difference depends on the firm's home country characteristics. Foreign firms from countries with a weak rule of law are less likely to restate than are firms from strong rule of law countries. While the lower rate of restatements can represent an absence of errors, it can also indicate a lack of detection and disclosure of errors and irregularities. We infer the effect of detection and disclosure by associating the frequency of restatements with the quality of the firm's internal control system. We find that only U.S. firms and foreign firms from strong rule of law countries show a positive association between restatement frequency and internal control weaknesses. Firms from weak rule of law countries show no significant association. We interpret these findings as home country enforcement affecting firms' likelihood of detecting and reporting existing accounting misstatements. This suggests that for U.S.-listed foreign firms, less frequent restatements can be a signal of opportunistic reporting rather than a lack of accounting errors and irregularities.
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 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 | 0.008 | 0.067 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".