Salary perception and career prospects in audit firms
Bibliographic record
Abstract
Purpose This paper aims to examine the role that auditor’s salary perception has on audit quality and delay. The findings contribute to a greater understanding of the audit employee-level factors that influence audit work outcomes. Design/methodology/approach The authors use Big 6 employee reviews, salary data and audit and financial data from 2007 to 2017 to measure how to audit employees’ pay satisfaction affects audit quality (small profits and going concern opinions) and audit delay. The authors use a regression approach to analyze this relationship. In subsequent tests, the authors split the sample on high career opportunities to investigate how this moderates the relationship between salary perception and audit quality. Findings The authors document a discrepancy between pay perception and reality. It is explained, though not completely, by salary level, comparisons to peers and superiors, firm-wide attitudes, cost of living and human capital in the area, work–life balance and perceived career prospects. Surprisingly, the unexplained pay dissatisfaction relates positively to audit quality and audit efficiency (audit delay), after controlling for salary level. Further tests show that an audit employee’s expectation of career opportunities moderates this result. Originality/value This is the first paper that empirically tests the relationship between pay satisfaction and job performance in the context of audit employees in public accounting. The authors contribute to an emerging literature that investigates audit employee-level characteristics and attitudes in relation to audit quality.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".