Is There a Brain Drain in Auditing? The Determinants and Consequences of Auditors Leaving Public Accounting*
Bibliographic record
Abstract
ABSTRACT This study investigates why auditors leave public accounting and the consequences of auditor departures, both of which have been of great concern for audit firms and regulators worldwide. Using data from China and controlling for demographics, we find that audit partners and managers, auditors who generate more revenue, and auditors who provide higher audit quality have a lower likelihood of departure, while non–Big 4 auditors have a higher likelihood of departing public accounting. We also find that an audit firm is likely to lose clients when an auditor departs. Clients who stay with the same firm pay lower audit fees but with no drop‐off in audit quality after a signing auditor departs. In supplementary analyses, we also demonstrate that the determinants and consequences of auditor departures are different for auditors who leave the firm but not the profession (i.e., auditor turnover). Specifically, high audit quality is associated with a lower likelihood of auditor departure, but it increases auditor turnover. We also observe that audit quality is reduced for the clients who stay with an audit firm after highly skilled auditors depart for high‐visibility corporate positions. Our study provides insights that should be of interest to the audit profession, audit firms, and regulators.
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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 | 0.022 |
| 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 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".