Piece Rates, Fixed Wages, and Incentive Effects: Statistical Evidence from Payroll Records
Why this work is in the frame
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Bibliographic record
Abstract
We develop and estimate an agency model of worker behavior under piece rates and fixed wages. The model implies optimal decision rules for the firm's choice of a compensation system as a function of working conditions. Our model also implies an upper and lower bound to the incentive effect (the productivity gain realized by paying workers piece rates rather than fixed wages) that can be estimated using regression methods. Using daily productivity data collected from the payroll records of a British Columbia tree‐planting firm, we estimate these bounds to be an 8.8 and a 60.4 percent increase in productivity. Structural estimation, which accounts for the firm's optimal choice of a compensation system, suggests that incentives caused a 22.6 percent increase in productivity. However, only part of this increase represents valuable output because workers respond to incentives, in part, by reducing quality.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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