Incentive Effects of Subjective Allocations of Rewards 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
We examine the incentive effects of subjectivity in allocating tournament-based rewards and punishments. We use data from a company where reward and punishment decisions are based on a combination of objective metrics and subjective performance assessments. Rankings based on the objective metrics and the ultimate payoff allocations are disclosed to all members of the organization. This information allows employees to observe whether and how managers subjectively override the objective rankings. Consistent with expectancy theory, we predict and find that subjective rewards and punishments manifesting as favorable (unfavorable) deviations from formula-based payoff expectations are associated with subsequent performance improvements (declines). These performance responses are incremental to the effects of receiving a reward or punishment per se. Our results suggest that managers can benefit from using subjective rewards, but using subjective punishments can be very costly in the absence of sufficiently strong ex ante incentive effects associated with the prospect of subjective penalties. Our findings contribute to the literature on subjectivity in performance evaluations and have important practical implications for designing incentive systems. This paper was accepted by Brian Bushee, accounting. Funding: The authors appreciate the Harvard Business School for financial support during the development of this study. Supplemental Material: Data are available at https://doi.org/10.1287/mnsc.2022.4501 .
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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