The Strength of Performance Incentives, Pay Dispersion, and Lower-Paid Employee Effort
Bibliographic record
Abstract
ABSTRACT The strength of performance incentives differs for employees within an organization. We describe how differences in incentive strength can lead to pay dispersion because employees facing stronger incentives work harder and earn more pay than those facing weaker incentives. We then conduct four experiments examining how the lower-paid employees respond to such pay dispersion. Consistent with our hypothesis derived from referent cognitions theory, we find that such pay dispersion decreases the lower-paid employees' perceived fairness and thus their effort. These results hold whether the employees are assigned to or self-select into the job with weaker incentives and whether they have more explicit or less explicit information about the economic rationale for the difference in incentive strength. Our findings are inconsistent with conventional economic reasoning and refine the conclusions from prior pay dispersion studies. The robustness of our results demonstrates their generalizability to a range of actual employment settings. Data Availability: Data and experimental instruments are available upon request. JEL Classifications: M41; M52; M55.
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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 | 0.004 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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 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".