R&D Investment, Skill-Based Wage Gap, and Firm Innovation Performance: Evidence from Chinese Listed Companies
Bibliographic record
Abstract
Against China’s innovation-driven strategy, this study explores the impact of R&D investment on firm innovation performance and the mediating role of the wage gap between high- and low-skilled labor (HLWG) using data from Chinese A-share non-financial listed firms spanning 2010–2022. Employing static panel regression, Bootstrap test, and instrumental variables (R&D investment deduction, college enrollment expansion), the study finds three key results. First, R&D investment positively affects both firm innovation performance and HLWG. Second, HLWG exerts a positive impact on firm innovation performance. Third, HLWG plays a partial mediating role in the relationship between R&D investment and firm innovation performance. Robustness tests and instrumental variable regression confirm the stability of these conclusions. This finding enriches the theoretical understanding of the R&D-innovation transmission mechanism, offers insights into enterprises to coordinate R&D investment and wage structure optimization, and provides policy references for refining innovation incentives and labor market policies.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".