Audit quality and COVID-19 restrictions
Bibliographic record
Abstract
Purpose This study aims to examine declines in audit quality after the COVID-19 travel restrictions/stay-at-home orders were issued in the USA in early 2020. Design/methodology/approach Taking advantage of variation in the dates of stay-at-home orders issued by different US states, this study identifies engagements that were significantly affected by the lock down orders. Findings The results suggest that engagements affected by the restrictions produced lower audit quality, as measured through restatements and discretionary accruals, relative to those completed before COVID-19 travel restrictions/stay-at-home orders. Further analysis reveals that this decrease in audit quality was attributable to firms with high inventory relative to assets, high R&D expenses relative to assets and non-Big 4 auditors. Practical implications This study finds that the restrictions on physical and on-site interaction caused auditors to universally struggle with resource/judgment-intensive accounts such as inventory and R&D expenditures. The results suggest that while Big 4 auditors managed to maintain their status quo level of audit quality following COVID-19 restrictions, non-Big 4 auditors were unable to overcome the challenges of an online work environment and their audit quality declined. Originality/value To the best of the authors’ knowledge, this paper is the first to empirically examine changes in audit quality as a response to a substantial change in auditors’ working environment due to the global health crisis. As work-from-home becomes more prevalent in audit firms, the results suggest that, on average, this move does diminish 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 | 0.006 | 0.045 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".