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Empirical Analysis of the Impact of Regional Comprehensive Development Factors on Human Capital Accumulation

In: Proceedings of 2024 4th International Conference on Public Management and Big Data Analysis (PMBDA 2024)

Author

Listed:
  • Bin Wang

    (San Sebastian College-Recoletos
    Anhui Business and Technology College)

  • Jin Wang

    (Anhui Business and Technology College)

Abstract

This study explores the relationship between regional development factors and human capital using Principal Component Analysis (PCA), focusing on the impact of regional economic activities and industrial innovation characteristics on human capital quality and labor market dynamics. The results show that regional economic activities (PC_X1) have a significant positive effect on human capital quality (PC_Y1), while industrial innovation characteristics (PC_X2) negatively impact human capital quality. Further panel regression analysis indicates that regional economic activities enhance labor force quality by promoting the expansion of educational resources, skill training, and employment opportunities. In contrast, industrial innovation may increase the demand for high-skilled labor, leaving low-skilled labor underserved, thus leading to labor market segmentation. The analysis of individual effects emphasizes the impact of long-term regional fixed factors such as educational resource allocation, infrastructure development, and policy environment on human capital disparities. This study provides theoretical foundations and practical recommendations for regional policymakers in promoting balanced economic development and optimizing human capital allocation.

Suggested Citation

  • Bin Wang & Jin Wang, 2025. "Empirical Analysis of the Impact of Regional Comprehensive Development Factors on Human Capital Accumulation," Advances in Economics, Business and Management Research, in: Soon M. Chung & Fairouz Kamareddine & Azah Kamilah Draman & Sim Kwan Yong (ed.), Proceedings of 2024 4th International Conference on Public Management and Big Data Analysis (PMBDA 2024), pages 395-404, Springer.
    • Handle: RePEc:spr:advbcp:978-94-6463-656-7_38
    DOI: 10.2991/978-94-6463-656-7_38
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