Spatial infrastructure disparities: Impacts on economic efficiency across diverse urban–rural geographic regions
Keywords:
Spatial Infrastructure, Economic Efficiency, Urban–Rural Disparities, Spatial Econometrics, Regression AnalysisAbstract
Spatial infrastructure gaps in roads, energy, digital connectivity, and public services increase regional inequalities and reduce economic efficiency in both urban and rural areas. Research examines how the imbalance in infrastructure affects economic efficiency on 2150 observations in 2018-2025. Road density, transport connectivity, electricity coverage, broadband penetration, and public service accessibility are the independent variables, whereas economic efficiency (EE) (per capita income, industrial output and employment rate) is the dependent variable. One of the control variables is population density. Research utilizes descriptive statistical analysis through the International Business Machines Statistical Package for the Social Sciences (IBM SPSS), Version 26.0, to come up with spatial variation and regional patterns, which are analyzed through correlation analysis to determine the initial association between the variables. To examine the influence of infrastructure inequalities on economic efficiency, panel regression equations, such as Ordinary Least Squares (OLS) and fixed-effects regression, are used. Given the spatial dependence of regions, spatial econometric models, such as the Spatial Lag Model (SLM) and Geographically Weighted Regression (GWR), can be used to represent local spatial heterogeneity in infrastructure-economy relationships.
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