Does audit partner individualism reduce client earnings comparability? Evidence from the United States
Bibliographic record
Abstract
Abstract We examine whether audit partner individualism reduces earnings comparability in the United States. We argue that individualistic audit partners are more likely to deviate from internal working rules and allow clients more flexibility in making accounting choices, consequently decreasing their clients' earnings comparability. Using a novel partner‐level measure of individualism, we find that within individual Big 4 audit firms, earnings are less comparable between a company audited by an individualistic partner and a company audited by a non‐individualistic partner, relative to a pair of companies that are each audited by a non‐individualistic partner. Our inferences are robust to a changes analysis, a falsification test, and a propensity score matching procedure. We also find that the effect of partner individualism is less salient when the audit firm is under more stringent regulatory monitoring and when clients are more important, but more salient when individualistic partners are more confident about being different. Further analyses suggest that our main inferences are robust to controlling for differences in partners' cultural backgrounds and using client‐pairs audited by the same audit partner. Collectively, our study provides novel evidence on the role of auditor individualism in earnings comparability.
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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 | 0.006 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".