Bibliographic record
Abstract
In this study, I investigate whether auditors are a good external control mechanism to restrain management from intentionally increasing CEO compensation through earnings management. To examine this, I regress variations of CEO cash and total compensation on changes in auditors after controlling financial, stock market, industry and year variables. There are two sample periods. In the period 1993–2004, I examine the impacts of overall auditor changes and auditor changes that are classified by the audit failure or auditor brand names. In the period 2000–2004, I check the influences of auditor resignations and of auditor dismissals due to accounting disagreements. The empirical results show that changes in cash compensation is positively related to the existence of auditor changes and this relationship actually comes from auditor changes from big 5 auditors to non-big 5 auditors. Meanwhile, changes in total compensation is negatively associated with the appearance of auditor switches from non-big 5 auditors to big 5 auditors. These findings are consistent with the notion that big 5 auditors are more active at discouraging earnings management. Overall speaking, the empirical evidences support my belief that auditors do not function very well to monitor managers’ opportunistic behaviors.
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 | 0.003 | 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 | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 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".