Piece Rates, Fixed Wages and Incentives: Evidence from a Field Experiment
Bibliographic record
Abstract
Data from a field experiment are used to estimate the gain in productivity that is realized when workers are paid piece rates rather than fixed wages. The experiment was conducted within a tree-planting firm and provides daily observations on individual worker productivity under both copensation systems. Unrestricted statistical methods estimate the productivity gain to be 20%. Since planting conditions potentially affect incentives, structural econometric methods are used to generalize the experimental results to out-of-samples conditions. The structural results suggest that the average productivity gain, outside of the experimental conditions, would be at least 21.7%. Des données expérimentales sont utilisées afin de mesurer le gain en productivité réalisé quand des travailleurs sont payés à la pièce plutôt qu'à taux fixe. L'expérience a été complétée dans une entreprise qui s'occupe de plantation d'arbres et fournit des observations quotidiennes sur la productivité individuelle de chaque travailleur sous les deux systèmes de compensation. Des méthodes statistiques sans restriction mesurent le gain en productivité à 20%. Étant donnée que les conditions de plantation affectent potentiellement la productivité, les méthodes structurelles sont utilisées afin de généraliser les résultats en dehors de l'expérience. Les résultats structurels suggèrent que le gain en productivité, en dehors des conditions expérimentales, sera au moins de 21.7%.
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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 | 0.019 | 0.055 |
| 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 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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".