Abstract
The Guangdong-Hong Kong-Macao Greater Bay Area is witnessing a surge in economic development, showcasing robust resilience in the co-agglomeration of producer services and manufacturing industries, along with the increasingly evident impact generated by such co-agglomeration. Based on the panel data collected from ten cities in the Guangdong-Hong Kong-Macao Greater Bay Area (excluding Zhaoqing) spanning from 2010 to 2020, we examined the formation mechanism for industrial co-agglomeration in the area from three dimensions: industrial correlation, spatial correlation, and policy guidance. Our analysis was based on a vertical correlation model and was validated using the seemingly unrelated regressions (SUR) method. The findings are as follows: (a) There currently exists no spatial correlation between the producer services and manufacturing sectors in the area, and the correlation between the two remains limited to the former providing specialized services to the latter. (b) Policy guidance has a positive impact on the location selection of industries within the area. By implementing a comprehensive range of favorable policies, local governments can provide effective and scientifically guided recommendations regarding the location of producer services and manufacturing sectors. Therefore, it is crucial to foster the collaboration between producer services and manufacturing sectors and further enhance the co-agglomeration between the two sectors. In addition, the government authorities should also strive to enhance its economic governance capacity, thereby facilitating the formation of such co-agglomeration
DOI
I: http://dx.doi.org/10.19873/j.cnki.2096-0212.2023.05.003
Recommended Citation
Ping, Jiao; Feibin, Huang; and Susu, Li
(2023)
"Research on the Formation Mechanism for the Co-agglomeration of Producer Services and Manufacturing in the Guangdong-Hong Kong-Macao Greater Bay Area,"
Contemporary Social Sciences:
No.
5, Article 3.
DOI: I: http://dx.doi.org/10.19873/j.cnki.2096-0212.2023.05.003
Available at:
https://css.researchcommons.org/journal/vol2023/iss5/3