Abstract:Motivated by the traditional path of “competition for growth”, local governments in pursuit of maximizing economic benefits within their jurisdictions usually employ competitive strategies. These strategies, such as local protectionism and market segmentation, impede the flow of capital and other production factors across administrative borders, resulting in capital misallocation and hindering the profound progress of regional economic integration. Therefore, inter-governmental cooperation emerges as a critical catalyst for advancing regional economic integration by facilitating efficient and equitable resource allocation among cities.Despite its significance, empirical research on how inter-governmental cooperation can improve capital misallocation remains sparse. The prevailing discourse on inter-governmental cooperation tends to focus on the existence or number of “relationships”, with little consensus on definitive conclusions. To fill this research gap, this paper introduces the theoretical framework of network embeddedness, examining the effects of inter-governmental cooperation on capital misallocation from two dimensions of network embeddedness: relational embeddedness and structural embeddedness. Relational embeddedness captures the number of cooperative relationships a city involves within the network, while structural embeddedness reflects the city’s network position.Utilizing cooperation news spanning from 2009 to 2020, this paper constructs an inter-governmental cooperative network encompassing 27 central cities in the Yangtze River Delta (YRD), which is one of the most dynamic and integrated regions in China. Employing social network analysis metrics, relative degree centrality and eigenvector centrality, this paper gauges the levels of relational embeddedness and structural embeddedness of cities in the inter-governmental cooperative network. Subsequently, a panel threshold model is formulated to explore the nonlinear relationship between these forms of embeddedness and urban capital misallocation. The main findings of the panel threshold model are as follows.Firstly, relational embeddedness in cooperative network has a negative impact on capital misallocation, highlighting that robust and frequent cooperation bolsters the efficiency of capital allocation. Secondly, this effect diminishes as structural embeddedness surpasses a certain threshold, suggesting that once a city’s position in the network reaches a critical point, the positive impact of relational embeddedness becomes constrained. Thirdly, mechanism analysis reveals that while an increase in structural embeddedness can elevate a city to a more advantageous network position, it simultaneously casts a “cluster shadow”, implying higher transaction costs and cooperation risks that impede the ongoing enhancement of urban industrial efficiency and, by extension, the rectification of capital misallocation.The above findings deepen our understanding of the complex dynamics between relational and structural embeddedness and local government cooperation strategies. Notably, we emphasize the potential risks associated with specific cooperation strategies that prioritize securing “elite” partners and improving network position. Such strategies have the unintended consequence of distorting factor resource prices, which is not conducive to the continuous improvement of urban capital allocation efficiency. In essence, a city’s advantageous position within the inter-governmental cooperative network does not inherently ensure its long-term competitive viability. Therefore, to promote high-quality integration of regional economy, it is crucial for policymakers to achieve incentive compatibility between orderly competition and beneficial cooperation among local governments, prioritizing cooperation strategies that foster regional equality.
麻玉琦, 张蔚文. 府际合作能否改善城市资本错配?基于网络嵌入视角[J]. 浙江大学学报(人文社会科学版), 2024, 54(10): 81-96.
Ma Yuqi, Zhang Weiwen. Can Inter-governmental Cooperation Improve Urban Capital Misallocation? From the Perspective of Network Embeddedness. JOURNAL OF ZHEJIANG UNIVERSITY, 2024, 54(10): 81-96.
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