Effort d'audit et taille de l'entreprise : barème réglementaire et économies d'échelle dans le commissariat aux comptes des PME-PMI
Bibliographic record
Abstract
(VF)Les commissaires aux comptes appliquent un barème réglementaire pour déterminer le volume horaire de la mission générale. La présente étude teste la cohérence de ce barème, par rapport à l’effort d’audit requis. L’échantillon comprend 92 PME-PMI. Les modèles soulignent le pouvoir explicatif majeur de la taille. Ils corroborent également le phénomène d’économies d’échelle et permettent d’en chiffrer l’ampleur. Enfin, ils montrent que le barème, en plus de la taille, affecte positivement l’effort d’audit mobilisé.(VA) Statutory auditors in France must rely on a compulsory scale when setting the number of working hours to be devoted to the audit engagement. This paper tests whether this scale is consistent with the requested audit effort. Our sample is composed of 92 small-and-medium-size companies. Regression analysis emphasizes the large explanatory power of client company size. Our results also corroborate the presence of economies of scale, and provide an estimate of the extent of such economies. Finally, we suggest that the compulsory scale positively affects the realized audit effort, independently of the client company’s size.
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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 | 0.005 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".