Audit-Firm Profitability: Determinants and Implications for Audit Outcomes
Bibliographic record
Abstract
We use a novel dataset that links audit-firm and client-firm financial statement information from the U.K.’s largest audit firms to examine drivers of audit-firm profitability and its implications for audit outcomes. We first explore the determinants of audit-firm profitability and conclude that Big-4 and non-Big-4 audit firms have fundamentally different profitability structures. Big-4 firms have higher profit margins than non-Big-4 firms. Furthermore, Big-4 profitability increases with client size and complexity, while non-Big-4 profitability is higher for smaller, private-firm clients. Next, we examine the relation between audit-firm profitability and audit outcomes. Using a battery of alternative outcome measures, we find that more profitable audit firms deliver higher audit quality. In supplemental analyses we show that the positive relation between audit-firm profitability and audit outcomes is generally stronger for more influential and illiquid clients (i.e. when auditors are exposed to more litigation risk). Our inferences are robust to several endogeneity controls, such as using an instrumental variables approach, controlling for client-firm and audit-firm fixed effects, employing lead-lag and changes specifications, and assessing bias from correlated omitted variables. Our study contributes to the literature by being the first to provide insights into audit-firm profitability and examine in detail its implications for audit quality.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".