Redefining the partnership: A study on non‐equity partners
Bibliographic record
Abstract
Abstract Over the past decade, the audit profession has significantly increased its use of non‐equity partners for private (non‐listed) company audits. Such partners lead audit engagements and sign audit reports but do not share in the partnership's profits. Non‐equity partner positions were introduced in response to increasing workloads and to retain talented individuals unsuited to or uninterested in equity partnership, either temporarily or permanently. Using data from Big 4 private company audits during the period 2008–2017, our analyses show that equity incentives affect auditors' reporting behavior and their clients' financial reporting quality. Non‐equity partners are less likely to issue going‐concern opinions to their financially distressed clients, their reporting is less accurate (i.e., more Type II errors), their reporting is less conservative, and their clients' financial reporting is of lower quality (i.e., more frequent reporting of small earnings increases and more tax restatements). We also find that equity incentives mitigate some of the negative effects of fee‐based compensation on auditors' reporting behavior. Moreover, our findings suggest that incentives arising from ownership, rather than partners' innate differences or client differences, drive these associations.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".