Research on the spatial correlation structure of urban agglomeration economic growth and its threshold effects in Northeast China

Introduction This study investigates the mechanisms through which the internal network structures of urban agglomerations influence economic growth. Methods Based on panel data from 50 cities in Northeast China, spanning the period 2000 to 2019, a gravity model is employed to quantify economic spatial correlations between cities. From this model and Social Network Analysis, three core network structure indicators are derived, namely degree centrality, closeness centrality, and betweenness centrality. By then incorporating network centrality as a threshold variable in panel regression models, this study examines the nonlinear relationship between network structure and economic growth. Results and Discussion The results show that: First, urban agglomerations exhibit a core-periphery structure; only a few nodes possess high centrality. Second, all centrality measures display significant single or multiple threshold effects. When betweenness centrality crosses the first threshold, both degree and closeness centrality exert strong positive effects. This enhances industrial linkages and resource integration without bearing the full costs of core-city congestion. Third, optimizing the spatial network structure and removing barriers to factor mobility (especially human capital and technology) are two key pathways for promoting coordinated development between the Harbin-Changchun and central and southern Liaoning urban agglomerations and reducing inter-city disparities.