Honor Among Thieves: Open Internal Reporting and Managerial Collusion
Bibliographic record
Abstract
Abstract Firms have increasingly adopted open work environments. Although openness is thought to have benefits, it could also expose firms to an unanticipated cost. An open (closed) internal reporting environment makes it more (less) likely that managers will observe a colleague's communications with senior executives. This increase in what one manager knows about another manager's communication to senior executives could facilitate employee collusion to extract resources from the firm. To test whether internal reporting openness results in more collusion, we conduct an experiment in which two managers each make separate reports to the firm about cost information they know in common but that remains unknown by the firm. Because both managers face the same truth‐inducing contract, conventional economic theory predicts that they will not collude to misreport costs regardless of reporting openness. However, using behavioral theory involving trust and reciprocity, we predict and find that managers honor their nonbinding collusive agreements and successfully collude more often in an open versus closed internal reporting environment, leading to lower firm welfare in the open environment. These results suggest that firms should consider how the cost of collusion compares to the benefits of openness.
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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 | 0.015 | 0.089 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".