Technical Inefficiency, Allocative Inefficiency, and Audit Pricing
Bibliographic record
Abstract
The critical global role of audit firms, combined with the scarcity of qualified staff and downward pressure on fees, has increased the importance of understanding efficiency in this industry. This article examines the technical and allocative inefficiencies of audit firm staffing using data from 165 audit engagements performed by a Big 4 international certified public accountant (CPA) firm. Prior research has shown that the technical inefficiency of audit engagements leads to lower billing realization rates on audit engagements. We complement and extend this research by examining whether there are inefficiencies in allocating staff for audit engagements in addition to technical inefficiency, and whether each of these inefficiencies leads to lower billing realization rates. We find that there are differences in both technical and allocative inefficiencies across audit engagements, and that both inefficiencies lead to lower billing realization rates after controlling for other characteristics that could affect the realization rates of the audit engagements.
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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 | 0.003 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".