A Study on Spatial-temporal Differentiation of Economic Development in the Guangdong-Hong Kong-Macao Greater Bay Area from a Spatial Econometrics Perspective

This study examines the determinants and spatial mechanisms of regional economic development disparities in the Guangdong-Hong Kong-Macao Greater Bay Area using panel data from 11 cities over the period 2008 to 2024. By constructing a spatial econometric model and comparing it with ordinary panel regression approaches, the analysis identifies the key drivers and interactions influencing regional economic outcomes. Following the Hausman test, the fixed effects model was selected, and LM and robust LM tests confirmed that the Spatial Durbin Model (SDM) provides the optimal fit, capturing both direct and indirect spatial effects. The results reveal pronounced spatiotemporal differentiation and increasing spatial correlation in economic development, with the average spatial correlation coefficient (ρ) rising from 0.395 during 2008-2018 to 0.415 in 2014-2024. Core cities play a crucial role in generating spatial spillovers that influence the growth trajectories of neighboring regions. Among the determinants, labor input and transportation infrastructure emerge as significant factors in reducing regional disparities, whereas higher physical capital stock, a concentrated industrial structure, advanced informatization, and elevated fiscal expenditure ratios are associated with widening gaps. Population density appears to have no statistically significant effect. Moreover, spatial lag effects indicate that economic conditions in neighboring cities, particularly fiscal expenditure and human capital accumulation, exert meaningful indirect impacts on local economic development. Overall, the study underscores the importance of accounting for both local and neighboring influences when designing policies aimed at promoting balanced regional growth and mitigating disparities within rapidly urbanizing metropolitan regions.