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Executive Compensation Stickiness and Value Creation: Evidence from Chinese State-Owned Listed Enterprises

In: Proceedings of the 2026 2nd International Conference on Data Mining and Project Management (DMPM 2026)

Author

Listed:
  • Bingshi Zhang

    (State Grid Energy Research Institute CO., LTD.)

  • Ting Chen

    (State Grid Energy Research Institute CO., LTD.)

Abstract

The phenomenon of executive compensation stickiness in state-owned enterprises reflects a lack of contract effectiveness and has a significant impact on firms’ value creation capacity. This study constructs a panel dataset covering state-controlled A-shares listed companies in China from 2010 to 2022, and empirically examines the effect of compensation stickiness on corporate value creation. Based on pay-performance sensitivity and information asymmetry theory, a compensation stickiness index is developed. The baseline regression is conducted using fixed effects models, while robustness is tested through alternative measurements, propensity score matching (PSM), and instrumental variable (IV) approaches to mitigate endogeneity. Results show that higher levels of compensation stickiness significantly hinder corporate value creation. Mechanism analysis further reveals that the negative impact operates mainly through reduced resource allocation efficiency and constrained growth potential. Heterogeneity tests suggest that this effect is more pronounced in local SOEs, non-high-tech firms, and firms in low-competition or weak legal environments. These findings provide empirical support for strengthening incentive and constraint mechanisms in SOEs and offer policy implications for enhancing internal governance and advancing reform.

Suggested Citation

  • Bingshi Zhang & Ting Chen, 2026. "Executive Compensation Stickiness and Value Creation: Evidence from Chinese State-Owned Listed Enterprises," Advances in Economics, Business and Management Research, in: Ljiljana Trajkovic & José Alfredo F. Costa & Zaher Al Aghbari & Nor Azman Ismail & Dariusz Jacek Jak (ed.), Proceedings of the 2026 2nd International Conference on Data Mining and Project Management (DMPM 2026), pages 36-48, Springer.
    • Handle: RePEc:spr:advbcp:978-94-6239-689-0_5
    DOI: 10.2991/978-94-6239-689-0_5
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