Bibliographic record
Abstract
This paper examines how partners in an audit firm can use profit-sharing rules to induce optimal partner behavior from the firm's point of view, taking into account the strategic competition of firms in an auditing oligopoly. We use a linear contracting framework to investigate the effects of profit-sharing rules on individual partners' various decisions, including their pricing strategies and effort choices.We assume that efficient audits of different types of clients require different effort profiles with respect to degree of partner cooperation. For example, the audit of a complex company requires different amounts of partner collaboration than does the audit of a simple company. Moreover, since it is too costly for an enforcement party, such as the head office of an audit firm or a court, to verify each client's type in order to resolve compensation disputes among the firm's partners, it is reasonable to assume that client type cannot be contracted upon for partner compensation purposes. Given this assumption, we derive conditions under which there exists an equilibrium in which audit firms strategically choose different profit-sharing rules to specialize in different types of clients, thereby earning positive economic profits. Our analysis provides insights into the strategic competition among the big audit firms, and helps to explain the observed differences in the compensation plans of these firms and in the nature of their client portfolios.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".