An Empirical Analysis of Employee Responses to Bonuses and Penalties
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
ABSTRACT We examine how employees respond to bonuses and penalties using a proprietary dataset from an electronic chip manufacturer in China. First, we examine the relative effects of bonuses and penalties and observe a stronger effect on subsequent effort and performance for penalties than for bonuses. Second, we find that the marginal sensitivity of penalties diminishes faster than that of bonuses, indicating that the marginal effect of a bonus may eventually exceed that of a penalty as their value increases. Third, we find an undesirable selection effect of penalties: penalties increase employee turnover, especially for skillful and high-quality workers. These results may help inform our understanding of the observed limited use of penalties in practice due to their bounded effectiveness and possible unintended consequences. Data Availability: The confidentiality agreement with the company that provided data for this study precludes the dissemination of detailed data without the company's consent.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 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 it