Reporting Peers’ Wrongdoing: Evidence on the Effect of Incentives on Morally Controversial Behavior
Bibliographic record
Abstract
Abstract I show that offering monetary rewards to whistleblowers can backfire as a moral aversion to being paid for harming others can reverse the effect of financial incentives. I run a field experiment with employees of the Afghan Ministry of Education, who are asked to confidentially report on their colleagues’ attendance. I use a two-by-two design, randomizing whether or not reporting absence carries a monetary incentive as well as the perceived consequentiality of the reports. In the consequential treatment arm, where employees are given examples of the penalties that might be imposed on absentees, 15% of participants choose to denounce their peers when reports are not incentivized. In this consequential group, rewards backfire: Only 10% of employees report when denunciations are incentivized. In the non-consequential group, where participants are guaranteed that their reports will not be forwarded to the government, only 6% of employees denounce absence without rewards. However, when moral concerns of harming others are limited through the guarantee of non-consequentiality, rewards do not backfire: The incentivized reporting rate is 12%.
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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 | 0.028 | 0.167 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.015 | 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".