Spatial Econometrics of Economic Growth Dynamics

Spatial econometrics integrates geographical context into the analysis of economic growth, recognising that regions do not evolve in isolation but are influenced by their neighbours through economic, social and technological linkages. By accounting for spatial dependence and spillover effects, researchers can obtain unbiased estimates of growth determinants and capture the diffusion of prosperity or stagnation across space. Dynamic spatial panel models extend this framework by incorporating temporal evolution, enabling the assessment of how past growth in one region affects current performance both locally and in adjacent areas.

This field has advanced through the development of diversified spatial weight matrices that formalise proximity—whether based on physical distance, economic similarity or network connections—and through sophisticated estimators that address endogeneity and unobserved heterogeneity. Core applications include testing convergence hypotheses, evaluating the geographic reach of policy interventions and measuring technological or financial externalities that propagate across borders. Practical outcomes range from informing regional development strategies to guiding infrastructure investment, with relevance to urban agglomerations, innovation hubs and peripheral regions alike.

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Spatial Econometrics of Economic Growth Dynamics publication trend

The graph below shows the total number of articles in spatial econometrics of economic growth dynamics across all publications each year (not limited to Nature Index journals).

Technical terms

Spatial dependence: The phenomenon where economic outcomes in one location are statistically related to those in neighbouring areas.

Spatial weight matrix: A matrix defining the strength of connections between regions, based on criteria such as distance, contiguity or economic similarity.

Spatial autoregressive model (SAR): A regression framework that includes a term for the weighted average of the dependent variable in neighbouring units.

Spatial error model (SEM): A specification accounting for spatial correlation in the error term, capturing unobserved influences that cluster geographically.

Spatial panel data: Data structure combining cross‐sectional units observed over time, used to model both spatial and temporal dynamics.

Spillover effects: Indirect influences that economic activities in one region exert on the performance of other regions through spatial linkages.

References

1. Estimation of Economic Spillover Effects under the Hierarchical Structure of Urban Agglomeration Based on Time-Series Night-Time Lights: A Case Study of the Pearl River Delta, China. Remote Sensing (2024).
2. Growth Models and Influencing Mechanisms of Total Factor Productivity in China’s National High-Tech Zones. Sustainability (2024).
3. Rural financial development, spatial spillover, and poverty reduction: evidence from China. Economic Research-Ekonomska Istraživanja (2021).