Vertical Pay Dispersion, Peer Observability, and Misreporting in a Participative Budgeting Setting
Bibliographic record
Abstract
ABSTRACT In this study, we examine the joint effect of vertical pay dispersion and peer observability on subordinates' misreporting choices. We adopt a participative budgeting setting in which two subordinates report to one superior, and we manipulate vertical pay dispersion (low/high) and peer observability (absent/present). Subordinates have private information about actual project costs and can over‐report project costs to the superior without detection and thus create budgetary slack. When a peer's reporting choices are observable, we predict and find that peer reporting choices have an asymmetric influence on the focal subordinates' reporting choices, and this asymmetric influence depends on the level of vertical pay dispersion. Specifically, we find that when vertical pay dispersion is low, subordinates who observe peer reports containing low slack misreport less, whereas observing peer reports that contain high slack has no significant effect. However, when vertical pay dispersion is high, subordinates who observe peer reports containing high slack misreport more, whereas observing peer reports that contain low slack has no significant effect. Driven by these asymmetric effects, subordinates misreport less in the presence of peer observability than in its absence when vertical pay dispersion is low and misreport more in the presence of peer observability than in its absence when vertical pay dispersion is high. Overall, our findings suggest that when a firm has a more egalitarian pay structure (i.e., low vertical pay dispersion), an open information policy is conducive to a more honest reporting environment, whereas under a more hierarchical pay structure (i.e., high vertical pay dispersion), open information policies can compromise the honesty of subordinates' reports.
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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 | 0.032 | 0.076 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".