Reward Taxation, Reward Type, and Employee Effort
Bibliographic record
Abstract
ABSTRACT Performance-contingent cash and tangible rewards are commonly used to motivate employees, and the taxation of such rewards is unavoidable. We use two experiments to examine how the effect of reward taxation on employee effort varies by reward type. In Experiment 1, we find reward taxation decreases positive affect, increases negative affect, and decreases reward attractiveness for employees when rewards are tangible, but not when rewards are cash. In Experiment 2, we find reward taxation reduces employee effort when rewards are tangible, but not when rewards are cash. Collectively, this evidence advances knowledge at the intersection of tax and management accounting by explaining why reward type alters the effect of reward taxation on employee effort. Moreover, our experimental results inform managers of a potential downside to using tangible rewards to motivate employees. Data Availability: Authors will make data available on request. JEL Classifications: J41; M11; M52; M55.
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How this classification was reachedexpand
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 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".