Public Company Audits and City‐Specific Labor Characteristics
Bibliographic record
Abstract
Abstract Prior research emphasizes the centrality of audit offices in understanding auditing practices, and documents significant interoffice variation in audit outcomes based on industry expertise and office size. Our study examines how two city‐specific labor characteristics also affect audit offices and local audit markets: the city's average educational attainment, and the number of accountants in a city, which proxy for a city's human capital. Our argument draws on the urban economics literature and predicts that the level of human capital in a city is positively associated with an audit office's ability to conduct high‐quality audits. As expected, there is a positive association between audit quality (quality of audited earnings and accuracy of going‐concern reports) and average education level in the city in which the lead engagement office is located. This association is generally significant for both Big 4 and non‐Big 4 offices, but is relatively stronger for non‐Big 4 firms that are more tied to local labor markets. A company is also more likely to choose a non‐Big 4 auditor in cities with higher educational levels and relatively more accountants, and there is evidence of higher non‐Big 4 audit fees as a city's education level increases. Collectively, these results suggest that local labor characteristics affect audit offices, audit quality, and the ability of non‐Big 4 auditors to compete with Big 4 auditors in the audits of public companies.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".