An Economic Analysis of Audit and Nonaudit Services: The Trade‐off between Competition Crossovers and Knowledge Spillovers*
Bibliographic record
Abstract
Abstract In this paper, I present a model in which both markets for audit services and nonaudit services (NAS) are oligopolistic. Accounting firms providing both audit services and NAS will employ oligopolistic competition in each of these markets. In addition to auditors' gaining “knowledge spillovers” from auditing to consulting or vice versa, oligopolistic competition in one market will influence the counterpart in the other market ‐ what I call “competition crossovers”. Although scope economies due to knowledge spillovers (for example, cost savings) are always beneficial to auditors, such benefits can entice accounting firms to adopt strategies (for example, price reductions) to compete aggressively in the audit market so that some, or all, firms become worse off. A trade‐off arises between these two economic forces in the two oligopolistic markets. Given the trade‐off between competition crossovers and knowledge spillovers, accounting firms may not reduce their audit prices, even though supplying NAS enables firms to decrease auditing costs — a nontrivial impact of oligopolistic competition in two markets on audit pricing. The empirical implication of my results is that because of competition‐crossover effects between the auditing and consulting service markets, finding empirical evidence for knowledge‐spillover benefits is likely to be difficult. Control variables for “audit‐market concentration” concerned with competition‐crossover effects and “auditor expertise” concerned with knowledge‐spillover benefits should be included in audit‐fee regressions to increase the power of empirical tests. With regard to policy implications, my analyses help explain the impact of the Sarbanes‐Oxley Act on “market segmentation” and, hence, the profitability of accounting firms.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".