High-Powered Performance Pay and Crowding Out of Nonmonetary Motives
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
A previous literature cautions that paying workers for performance might crowd out nonmonetary motives to work hard. Empirical evidence from the field, however, has been based on between-subjects designs that are best suited for detecting crowding out due to low-powered incentives. High-powered incentives in the workplace tend to increase output, but it is unknown whether this masks crowding out. This paper uses a within-subject experimental design and finds evidence that crowding out also extends to high-powered incentives in a real work setting with paid workers. There is individual heterogeneity, however, with a minority of workers reporting crowding in of motivation. Thus, the impact of performance pay might depend on the mix of worker types. Data and the online appendix are available at https://doi.org/10.1287/mnsc.2017.2846 . This paper was accepted by Uri Gneezy, behavioral economics.
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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 | 0.002 | 0.003 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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 it