Production Efficiency and the Pricing of Audit Services*
Bibliographic record
Abstract
Abstract In this paper, we examine the relative efficiency of audit production by one of the then Big 6 public accounting firms for a sample of 247 geographically dispersed audits of U.S. companies performed in 1989. To test the relative efficiency of audit production, we use both stochastic frontier estimation (SFE) and data envelopment analysis (DEA). A feature of our research is that we also test whether any apparent inefficiencies in production, identified using SFE and DEA, are correlated with audit pricing. That is, do apparent inefficiencies cause the public accounting firm to reduce its unit price (billing rate) per hour of labor utilized on an engagement? With respect to results, we do not find any evidence of relative (within‐sample) inefficiencies in the use of partner, manager, senior, or staff labor hours using SFE. This suggests that the SFE model may not be sufficiently powerful to detect inefficiencies, even with our reasonably large sample size. However, we do find apparent inefficiencies using the DEA model. Audits range from about 74 percent to 100 percent relative efficiency in production, while the average audit is produced at about an 88 percent efficiency level, relative to the most efficient audits in the sample. Moreover, the inefficiencies identified using DEA are correlated with the firm's realization rate. That is, average billing rates per hour fall as the amount of inefficiency increases. Our results suggest that there are moderate inefficiencies in the production of many of the subject public accounting firm's audits, and that such inefficiencies are economically costly to the firm.
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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 | 0.006 | 0.053 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".