Leadership ability: Labor market outcomes, organizational benefits, and talent management in the auditing profession
Bibliographic record
Abstract
Abstract Leadership is considered a core competency for auditors. This study examines how auditors' leadership ability affects their labor market outcomes and audit firm performance. Using Swedish military data on qualified auditors (CPAs), we first show that auditors' leadership ability, measured at around age 18, is a strong predictor of their income and career success. However, our results also suggest that the audit labor market compensates for auditors' leadership ability at a much later stage than the general labor market. Second, we examine whether the value of leadership ability derives from higher‐quality auditing, commercial performance, or both. We find strong evidence that leadership ability enhances auditors' commercial performance and some evidence on leadership ability being associated with higher audit quality. Third, at the audit firm level, we find that auditors' leadership ability significantly benefits audit firm performance measured as client portfolio size and audit firm profitability. Finally, we investigate leadership talent attraction and retention in the auditing profession. We find that the auditing profession attracts better leadership talent than the general labor market. Although nearly a quarter of CPAs leave the profession over the sample period, there is no significant difference in leadership ability between those who stay and those who leave. Overall, our results have important practical implications for audit firms' talent management.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".