To Benefit Oneself and the Company in Earnings Management: The Effects of Codes of Ethics and Ethical Beliefs
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
ABSTRACT Earnings management (EM) is permissible when it complies with GAAP, but managers’ perceptions of its ethicality vary. We theorize that codes of ethics and ethical beliefs shape managers’ view of the ethicality of EM and their use of justification. We then investigate how both ethical frameworks interact with justification to influence EM. We examine a setting where managers can justify that the company will also benefit from EM. We find that instead of constraining EM, rules-based codes accentuate it when the company also benefits. The effect is not observed for consequences-based codes. We also find that managers whose ethical beliefs predispose them to seek justification when they see EM as dishonest are more likely to manage earnings when the company also benefits. Implications for the design of codes and manager training and selection are discussed. Data Availability: Available on request. JEL Classifications: M40; M41.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.048 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it