How does the executive pay gap influence audit fees? The roles of R&D investment and institutional ownership
Bibliographic record
Abstract
Abstract Using a sample of US firms from 2003–2014, this study examines how the executive pay gap affects audit fees for firms with different levels of R&D investment and institutional ownership. Consistent with managerial power theory, we find that the executive pay gap is positively associated with audit fees, and that the positive association is attenuated by intense R&D investment and higher institutional ownership. We also find that the executive pay gap more strongly affects audit fees after the passage of the 2010 Dodd–Frank Act and the PCAOB's 2012 call to identify the audit risk related to executive incentive compensation. Additional analyses show that the moderating effects of R&D investment and institutional ownership on the pay gap–audit fees association are not conditional on auditor tenure, but the moderating effect of institutional ownership is stronger for firms hiring specialist auditors. Collectively, our findings suggest that auditors consider the business context, such as innovation initiative and external monitoring, when assessing audit risk related to the executive pay gap.
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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 | 0.003 | 0.022 |
| 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 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".