The Importance of Proximity to the Audit Firm’s National Office to Practice Office Growth and Audit Quality
Bibliographic record
Abstract
We examine the role that an audit firm’s national office plays in shaping its practice offices’ economic outcomes. Given that national offices provide superior support, monitoring, and advising to physically closer offices, we expect an increase in proximity to improve practice offices’ capacity and expertise. Exploiting the introduction of new airline routes that results in a decrease in travel time between national and practice offices as an exogenous shock, we find that treated offices enjoy significant growth in their market share and their clients exhibit a significant improvement in financial reporting quality relative to pre-treatment and untreated offices. Cross-sectional tests reveal that these effects are magnified in non-Big 4 and remote Big 4 offices, offices that are more distant from the nearest SEC office, and offices that experience large travel time reductions. Moreover, we show that, after becoming more proximate to the national office, treated offices are more likely to accept inherently riskier engagements, implying that growth among treated offices is partly driven by an increase in treated offices’ ability to take on riskier engagements given their improved expertise. Collectively, these results shed light on the importance of auditors’ national offices to their practice offices.
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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 | 0.002 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".