Examination and Interpretation of the Quantitative Validity in China’s Corporate-based Urban Network Analysis

As a matter of expediency, most existing corporate-based urban networks can only be quantitatively measured by either counting the number of linkages or calculating the product of estimated service values. However, the impreciseness arising due to the limits of quantitative analysis may prove fatal to studies about non-market economies like China. Employing the capital investment dataset as an example, we build a capital-weighted intervention network as well as an unweighted control network to carry out an examination of the quantitative validity in China’s corporate-based urban network analysis. Both the overall spatial pattern and top city-dyads within the capital-weighted network witness Beijing, as the most dominant city, overshadow the performance of the others, and the unweighted network shows multilateral interactions between China’s top cities including Beijing, Shanghai, Shenzhen, and Guangzhou. To further interpret the noticeable differences, we divide the overall network into two subnetworks, inferred by focusing on state-owned enterprises(SOEs) and private enterprises. The results show that the public and private sectors have separately created vastly different subnetworks in China and that SOEs play a much more significant role in terms of capital. Besides fresh insights into China’s urban network, this study provides a cautionary tale reminding researchers of the essentiality and complexity when making a quantitative distinction between different linkages.

Examination and Interpretation of the Quantitative Validity in China's Corporate-based Urban Network Analysis

As a matter of expediency, most existing corporate-based urban networks can only be quantitatively measured by either counting the number of linkages or calculating the product of estimated service values. However, the impreciseness arising due to the limits of quantitative analysis may prove fatal to studies about non-market economies like China. Employing the capital investment dataset as an example, we build a capital-weighted intervention network as well as an unweighted control network to carry out an examination of the quantitative validity in China's corporate-based urban network analysis. Both the overall spatial pattern and top city-dyads within the capital-weighted network witness Beijing, as the most dominant city, overshadow the performance of the others, and the unweighted network shows multilateral interactions between China's top cities including Beijing, Shanghai, Shenzhen, and Guangzhou. To further interpret the noticeable differences, we divide the overall network into two subnetworks, inferred by focusing on state-owned enterprises(SOEs) and private enterprises. The results show that the public and private sectors have separately created vastly different subnetworks in China and that SOEs play a much more significant role in terms of capital. Besides fresh insights into China's urban network, this study provides a cautionary tale reminding researchers of the essentiality and complexity when making a quantitative distinction between different linkages.