Growing Pains: Audit Quality and Office Growth
Bibliographic record
Abstract
Abstract This study provides evidence on how local office growth affects audit quality. We predict that significant recent growth will temporarily stress office resources, leading to a negative relation between office‐level growth and audit quality. To test this prediction, we examine a sample of 17,062 firm‐year observations from 2005 to 2010. Results indicate a consistent negative relation between changes in volume of audit work and audit quality. Specifically, clients of offices that experience increases in workload over the prior year have greater absolute discretionary accruals as well as an increased likelihood of restatement. Our tests also indicate that the effect of office growth is transient and vanishes after one year. We find limited evidence that the size of the auditor's national network of offices partially mitigates the negative effects of office growth on audit quality. We further show that proxies for audit quality are negatively related to office‐level growth from new and existing clients. These findings are robust to controls for client and auditor characteristics as well as alternative specifications of growth. Taken together, evidence indicates that while larger offices provide higher audit quality, the benefits of office size are not realized immediately and rapid growth temporarily impairs audit quality. These results are informative to regulators concerned with audit quality and to practitioners charged with adjusting to office growth.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.014 | 0.058 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".