The Impact of R&D Intensity on Demand for Specialist Auditor Services*
Bibliographic record
Abstract
Abstract The audit fee research literature argues that auditors' costs of developing brand name reputations, including top‐tier designation and recognition for industry specialization, are compensated through audit fee premiums. Audited firms reduce agency costs by engaging high‐quality auditors who monitor the levels and reporting of discretionary expenditures and accruals. In this study we examine whether specialist auditor choice is associated with a particular discretionary expenditure ‐ research and development (R&D). For a large sample of U.S. companies from a range of industries, we find strong evidence that R&D intensity is positively associated with firms' choices of auditors who specialize in auditing R&D contracts. Additionally, we find that R&D intensive firms tend to appoint top‐tier auditors. We use simultaneous equations to control for interrelationships between dependent variables in addition to single‐equation ordinary least squares (OLS) and logistic regression models. Our results are particularly strong in tests using samples of small firms whose auditor choice is not constrained by the need to appoint a top‐tier auditor to ensure the auditor's financial independence from the client.
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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 | 0.002 | 0.024 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
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".