Does Distance Matter? An Investigation of Partners Who Audit Distant Clients and the Effects on Audit Quality†
Bibliographic record
Abstract
ABSTRACT We examine how audit partners' geographic proximity to clients affects audit quality. We use hand‐collected data to show that approximately half of audit partners are assigned to clients headquartered more than 100 km away from the partners' home locations. Few of these partners relocate after receiving their assignments and, as a result, more than one‐third of clients are audited by partners who must commute long distances to visit the client in person. We explore this phenomenon by first modeling how distance affects partner‐client matching. We find that partners' geographic proximity to a prospective client is an important matching criterion, but also that trade‐offs are made when other partner characteristics such as industry specialization are more likely to be important. Next, consistent with our prediction, we show that audit quality is lower when partners reside farther from their clients. We corroborate our primary findings by showing that the association between partner distance and audit quality is mitigated when partners have access to direct flights to their clients' headquarters and when clients are geographically dispersed. Our paper should be informative for regulators, practicing auditors, and academics interested in how partner‐client matching affects audit outcomes.
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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 | 0.004 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| 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".