How Does the National New Generation AI Innovation and Development Pilot Zone Impact Corporate Labor Investment Efficiency? Evidence From China
Year: 2025
Type: article
Abstract: Improving corporate labor investment efficiency (LABEFF) is necessary for achieving high-quality economic development. The rapid advancement of artificial intelligence (AI) has profoundly impacted the labor force. However, there remains a lack of theoretical discussion and empirical analysis regarding how AI influences corporate LABEFF. Using a multi-period difference-in-differences (DID) model and data from Chinese A-share-listed companies from 2010 to 2023, this study examines the impact and mechanism of China’s National New Generation Artificial Intelligence Innovation and Development Pilot Zone (NAIPZ) policy on LABEFF. The results indicate that the NAIPZ policy enhances corporate LABEFF within pilot zones. The validity of these findings was confirmed through several rigorous tests. The mechanism analysis indicates that the NAIPZ policy improves corporate LABEFF within pilot zones by optimizing human capital structure, alleviating financial constraints, and reducing agency problems. Heterogeneity analysis suggests that the NAIPZ policy significantly improves corporate LABEFF within pilot zones in the eastern region, highly competitive industries, and high-tech enterprises. Further analysis reveals that the NAIPZ policy primarily alleviates labor under-investment, with a less pronounced effect on suppressing labor over-investment. Additionally, it improves corporate productivity within pilot zones by enhancing LABEFF. Therefore, it is imperative to implement and expand the NAIPZ policy, promote AI adoption across enterprises, and foster its synergistic integration with optimized labor resource allocation. Such efforts will facilitate the deep convergence of AI and the real economy, advancing enterprises toward high-quality and sustainable development.
Source: SAGE Open
Authors: Zhi Wang, Mei Xue, Yunlei Luo
Institution: Changsha University of Science and Technology
Language: English
Domain: Social Sciences
Open Access status: gold
Estimated APC paid: $1,600
OpenAlex ID: W7117547124