Psychological Incentives, Financial Incentives, and Risk Attitudes in Tournaments: An Artefactual Field Experiment
Bibliographic record
Abstract
Tournaments are widely used to assign bonuses and determine promotions because of the link between relative performance and rewards. However, performing relatively well (poorly) may also yield psychological benefits (pain). This may also stimulate effort. Through a real-effort artefactual field experiment with factory workers and university students as a comparison group in China, we examine how both psychological and financial incentives, together with attitudes toward risk, may influence motivation and performance. We provided performance-ranking information both privately and publicly, with and without rank-based financial incentives. Our results show that performance-ranking information had a significant motivational effect on average performance for students, but not for that of workers. Adding financial incentives based on rank provided little evidence of further improvement. Much of the difference between workers and students can be explained by differences in attitudes toward risk. Indeed, for both groups financial and psychological incentive effects are both inversely related to individual levels of risk aversion, and are positive and significant both for workers and for students who are sufficiently risk-tolerant.
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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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".