The Effects of Compensation Schemes on Self-Selection and Work Productivity: An Experimental Investigation
Bibliographic record
Abstract
This research examines experimentally the impact of productivity-based versus lump-sum compensation schemes on self-selection and work performance. We find that as predicted by Jensen (2003), participants in a laboratory experiment with salient incentives self-select themselves into preferred compensation schemes based at least partially on performance. However, the compensation scheme selected is also based on individual attitudes toward risk. Individuals demonstrating a higher degree of riskaversion in a lottery-selection task exhibit a higher probability of selecting the risk-free lump-sum compensation scheme, while less risk-averse individuals are more likely to select the productivity-based scheme. A laboratory firm offering linear compensation achieved significantly higher productivity than an identical firm offering flat compensation for two reasons: first, more highly skilled workers selected it, and second, workers on average, regardless of their self-selections, were more productive under the linear scheme.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".