The Effects of Openness of Internal Reporting and Shared Interest with an Employee on Managerial Collusion and Subsequent Cooperation*
Bibliographic record
Abstract
ABSTRACT Collusion between managers who share private information represents a significant control concern for firms. Prior research suggests that mutual monitoring contracts that incentivize honest reporting do not prevent all collusion, making it important to understand how elements of the control environment may facilitate collusion, as well as how control choices—and collusion itself—affect subsequent behavior within the firm. Results of two experiments show that the frequency of collusion between managers in a repeated‐interaction setting is greatest when one can view the other's reports before making their own (“open internal reporting”) and slack obtained from misreporting is shared with a non‐reporting employee (“shared interest”). Moreover, although open (versus closed) internal reporting increases collusion in a single‐shot setting with or without a residual claimant to managers' budget reports being present, neither openness alone nor shared interest alone increases collusion in a repeated‐interaction setting. Results further suggest that collusion improves managers' perceptions of autonomy and group identification, resulting in greater cooperation on a subsequent task and potentially reducing the costs of some collusion to the extent such cooperation benefits the firm. Finally, open internal reporting worsens managers' perceptions of autonomy and group identification which, for managers who did not previously engage in collusion, leads to less cooperation on a subsequent task. In total, these results highlight to firms that the costs of collusion in repeated‐interaction settings frequently found in practice may be greatest when the common control choices of open internal reporting and shared interest are made in tandem, and that open internal reporting may carry another unintended cost in the form of lower cooperation on other tasks.
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How this classification was reachedexpand
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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".