Archival Evidence on the Audit Process: Determinants and Consequences of Interim Effort*
Bibliographic record
Abstract
ABSTRACT Using proprietary data from a global accounting firm, we investigate the determinants of auditors' interim effort as well as the impact of interim effort on audit quality, client disclosure timeliness, audit hours, and audit fees. Public statements from accounting firms and regulators suggest various benefits from accelerating auditor effort, but these claims remain largely untested. We find that interim effort is higher for large, complex clients that require integrated audits of both financial statements and internal control over financial reporting. With respect to consequences, we find that allocating relatively more work to the interim period is associated with a reduced likelihood of late 10‐K filings, decreased total audit hours, and higher fees. Although increasing interim period effort is not, on average, associated with a reduced likelihood of misstatement, we do find that current period material weaknesses are less likely with greater interim work. Thus, greater interim effort appears to facilitate the remediation of internal control deficiencies before year‐end. We also show that the benefits of increased interim period effort allocation are much stronger—including improved audit quality—when manager and partner interim involvement is high. Overall, our study provides important new insights on audit production and highlights benefits of reduced hours for auditors, earlier identification of control deficiencies for clients, and more timely financial reports for investors.
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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 | 0.018 | 0.071 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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".