Hidden costs of government-guided funds: Evidence from executive-employee pay gaps
Bibliographic record
Abstract
• GGFs investment is associated with a larger increase in pay gaps in recipient firms relative to non-recipient firms. • The larger post-GGFs pay gap is due to disproportionate growth in executive compensation compared with employee salaries. • Cash holdings serve as a key mechanism through which GGFs influence the pay gap. • Results reveal an unintended agency cost of GGFs investment. While government support programs are often effective at helping firms achieve their objectives, these programs may have unintended consequences. Motivated by this, this study empirically examines the impacts of receiving investments from government-guided funds (GGFs) on executive-employee pay gaps in Chinese public firms. The results from difference-in-differences analyses show that GGFs investments lead to a significantly greater widening of the pay disparities between executives and employees in recipient firms than in other firms after funding is granted. This widening gap is driven by a larger increase in executive compensation relative to employee wages. The mechanism analysis suggests that increased cash holdings associated with receiving GGFs create financial slack that facilitates managerial opportunism, ultimately resulting in greater pay gaps. These findings reveal an unintended consequence of GGFs, highlighting an overlooked agency cost of government financial support programs.
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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 | 0.002 |
| 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 | 0.000 |
| Open science | 0.002 | 0.001 |
| 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".