An Analysis of Cross-Sectional Differences in Big and Non-Big Public Accounting Firms' Audit Programs
Bibliographic record
Abstract
A significant body of prior research has shown that audits by the Big 5 (now Big 4) public accounting firms are quality differentiated relative to non-Big 5 audits. This result can be derived analytically by assuming that Big 5 and non-Big 5 firms face different loss functions for “audit failures” and is consistent with a variety of empirical evidence from studies of audit fees, auditor changes, and the stock price reaction to audited earnings. However, there is no existing evidence (of which we are aware) concerning the underlying production differences between Big 5 and non-Big 5 audits. As a result, existing empirical evidence cannot distinguish between the possibility that Big 5 audits are simply perceived to be different (e.g., by investors) or actually differ in how they are produced. Our research objective is to identify the production characteristics of audit engagements that may explain the differences in expected audit quality between Big 5 and non-Big 5 firms. In this archival study, we examine the total audit effort and the allocation of effort to four audit phases—planning, (control) risk assessment, substantive testing, and completion—for a cross-section sample of 113 audits of Dutch companies in 1998/99 by 14 public accounting firms. We find that, after controlling for client characteristics: (1) both types of auditors exert about the same amount of total audit effort; (2) Big 5 auditors allocate relatively more effort to planning and (control) risk assessment, and relatively less to substantive testing and completion; and (3) client size, use of the business-risk-based audit approach, and reliance on client internal controls affect audit hours differently for the two auditor types. We conclude that the Big 5 firms actually produce a higher audit quality level, and that this quality difference is related to how audit hours are deployed in a more contextual and less procedural audit approach.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".