Do White-Collar Employee Incentives Improve Firm Profitability?
Why this work is in the frame
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Bibliographic record
Abstract
ABSTRACT We use proprietary archival compensation panel data from Finnish white-collar employees (WCEs) over the period of 2002 to 2011 in order to examine the relationship between performance-based incentives for WCEs and the future profitability of the firm as well as to determine whether this association is moderated by task complexity. While many studies examine the determinants and performance effects of CEO compensation, virtually no evidence has been presented to indicate that explicit financial incentives for WCEs improve the profitability of the firm. Our empirical results show that performance-based incentives for WCEs are significantly positively related to the future return-on-assets, return-on-equity, and profit margin ratios of the firm. We also find that this effect comes from the performance-based incentives for low-level WCEs, corroborating the importance of implementing performance-based incentives also to low-task complexity jobs. JEL Classifications: M40.
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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 | 0.007 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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