Auditor Responses to and Prevention of Non-Income-Increasing Misreporting: Evidence from Audit Fees and Restatements
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
SUMMARY We document that non-income-increasing (NII) misreporting is important to investors and then investigate whether auditors respond to and prevent different types of NII misstatements. We categorize NII misstatements into those whose correction adversely (i.e., unfavorably) affect financial reports and those whose correction positively (i.e., favorably) affect financial reports. We find that audit fees are positively associated with unfavorable NII misstatements, but not with favorable NII misstatements. We examine whether auditors prevent quarterly misstatements from resulting in annual report misstatements and find that auditors are less likely to prevent both favorable and unfavorable NII quarterly report misstatements from resulting in annual report misstatements, with the audit efficacy being lower for favorable NII misstatements. In sum, our research indicates that auditors do attempt to constrain NII misreporting with greater effort expended on unfavorable NII misstatements. Data Availability: Data used in this study are available from public sources. JEL Classifications: M49.
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.
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 | 0.008 | 0.247 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.004 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 it