Fraud Firms' Non‐Implicated <scp>CFOs</scp>: An Investigation of Reputational Contagion and Subsequent Employment Outcomes*
Bibliographic record
Abstract
ABSTRACT We investigate labor market consequences for CFOs employed by fraud firms, focusing on reputational contagion for those who are not implicated. These individuals provide an opportunity to understand reputational contagion and the nuanced meaning of “guilt” because the labor market may suspect complicity or infer negligence regardless of whether that is truly the case. We compare these CFOs to a matched sample of non‐fraud CFOs and track both turnover and subsequent employment positions. Non‐implicated CFOs are more likely to experience turnover compared to non‐fraud CFOs, driven in particular by the public revelation of fraud to the labor market. We further find that non‐implicated CFOs are more likely to obtain comparable subsequent employment than non‐fraud CFOs before the fraud is publicly revealed, but not after. In supplementary analyses, we find that turnover rates are highest for non‐implicated CFOs who started their employment with the firm before the fraud began as compared to non‐implicated CFOs who started their employment after the fraud began. These results highlight the labor market significance of the public revelation of fraud and imply that the labor market does not fully distinguish between fraud firm association and general firm performance when making executive hiring decisions.
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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 | 0.004 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".