Can individual auditors' career advancements predict audit partner quality?
Bibliographic record
Abstract
Abstract This mixed‐methods study investigates whether individual auditors' career advancements to more prestigious audit firms can predict their audit quality. Using hand‐collected data on more than 2,000 audit partners from professional networking website profiles, I identify audit partners with advancements from less to more prestigious audit firms and empirically test whether these upward trajectories predict audit partner quality. I find that these audit partners provide higher‐quality audits, as evidenced by discretionary accruals and going‐concern opinions. These results are robust to audit partner changes, entropy balancing, and other sensitivity analyses. Moreover, clients of these partners report more conservative financial statements. The qualitative results from 10 semistructured audit partner interviews indicate that audit partners enter the auditing labor market at less prestigious firms due to both internal factors (e.g., late entry into the job market, location preferences) and external factors (e.g., poor market conditions/recessions or lack of Big N recruitment). In fact, their choice to make upward advancements results from both work considerations, such as limited growth opportunities, feeling unchallenged in their previous roles, and the desire to specialize, alongside nonwork considerations, such as audit firm culture and reducing commute/travel time for client work. Taken together, the evidence suggests that these significant upward career transitions represent market corrections of initial auditing labor markets and that such transitions may be of interest to investors, regulators, audit committees, and academics.
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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 | 0.009 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".