Impact of Core Employee Equity Incentive on Enterprise Performance
Bibliographic record
Abstract
Considering China’s A-share listed enterprises that completed equity incentive from 2010 to 2017 as the research sample, this paper empirically analyses the impact of core employee equity incentive on enterprise performance. Controlling key enterprises’ variables such as size, type, financial leverage, book to market ratio in a time series regression, return on equity and earnings per share are explained by incentive of core employees and importance of core employees’ ratio. The results suggest that, increasing the intensity of equity incentive of core employees has an incentive effect on corporate performance and also, the higher the enterprise attaches importance to employees in equity incentive, the more obvious the incentive effect on performance. The empirical results of this paper have important implications for the design of equity incentive schemes for listed companies, which will serve as a guide for investors’ decisions. Therefore, in order to solve the agency cost problem, the shareholders of listed enterprises should increase the incentive intensity and importance of the core employees who are the cornerstone of the enterprise, in the design of the equity incentive scheme.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".