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Towards Inclusive Growth: Perspective of Regional Spatial Correlation Network in China

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  • Suwan Lu

    (School of Statistics and Applied Mathematics, Anhui University of Finance and Economics, Bengbu 233030, China)

  • Guobin Fang

    (School of Statistics and Applied Mathematics, Anhui University of Finance and Economics, Bengbu 233030, China)

  • Mingtao Zhao

    (School of Statistics and Applied Mathematics, Anhui University of Finance and Economics, Bengbu 233030, China)

Abstract

China’s economic growth has been impressive, but the persistent income inequality poses a threat to its sustainability. To address this issue, we use the complex network analysis method for the first time to explore the structural characteristics of the regional spatial correlation network of inclusive growth (RSCN) of 26 provinces (autonomous regions and municipalities) in China from 1999 to 2020. We use exponential random graph models to explore the internal mechanisms and driving factors that shape this network. Our results show that inclusive growth dependencies between regions are widespread and increasing. Beijing, Shanghai, Jiangsu, and Zhejiang serve as benchmark regions, while provinces in the middle reach of the Yangtze River play an increasingly important bridging role. The northwestern region mainly acts as a receiving region. Our study identifies transitivity, reciprocity, and high interaction tendency as critical microstructures. Furthermore, we find that infrastructure, digital economy development, financial marketization, fiscal expenditure linkages, and inter-provincial trade linkages are crucial factors in shaping network relationships. Our study provides theoretical support for the development of China’s regional coordinated development strategy and sustainable economic growth policies.

Suggested Citation

  • Suwan Lu & Guobin Fang & Mingtao Zhao, 2023. "Towards Inclusive Growth: Perspective of Regional Spatial Correlation Network in China," Sustainability, MDPI, vol. 15(7), pages 1-19, March.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:7:p:5725-:d:1106682
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