A Study on the Influence of Institutional Investor Heterogeneity on the Executive Pay Stickiness——Based on the Perspective of Industrial Factor Intensity
Bibliographic record
Abstract
Based on the data of Shanghai and Shenzhen A-share listed companies from 2012-2016, this paper empirically studies the influence of heterogeneous institutional investors on executive compensation stickiness of listed companies by using the method of multiple regression. The results show that the pay stickiness is very common in the listed companies. The overall institutional investor’s shareholding is promoting the executive compensation stickiness. The empirical results show that the institutional investors are divided into the pressure resistance institutional investors and the pressure sensitive institutional investors, according to whether the institutional investors have the commercial relationship with the listed companies. The empirical results show that they are compared to the pressure. Sensitive institutions, pressure resistance institutional investors can significantly inhibit the stickiness of executive compensation. However, different types of institutional investors have different preferences for the types of listed companies, and the enthusiasm of participating in corporate governance is different, and the pressure resistance institutional investors pay more attention to labor out of social responsibility. The long-term performance of a force intensive enterprise has a significant inhibitory effect on the stickiness of the executive compensation, while the pressure sensitive institutional investors actively manage and supervise the production and operation of the technology intensive enterprises for the consideration of the investment income, which has a restraining effect on the pay stickiness of the technology intensive enterprises.
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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 | 0.000 | 0.001 |
| 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 | 0.000 | 0.000 |
| 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".