THE MEASUREMENT OF HIERARCHICALLY SPATIAL INDUSTRIAL KNOWLEDGE SPILLOVER EFFECTS

Qianting Ye

doi:10.29121/granthaalayah.v6.i6.2018.1335

Abstract

Based on the “year–region–industry” three - dimensional unbalanced industrial production panel data of Guangdong Province in China from 2005-2013, the relationship between knowledge spillovers and industrial structure is investigated by hierarchically spatial lagged with spatial autoregressive error (HSARAR) model. The empirical results indicate that the impacts of MAR, Jacobs, and Porter spillover on Guangdong's industry economic growth is positive and statistically significant. The industrial HSARAR model considers the hierarchical structure and spatial effect simultaneously, which has a better description on economic reality than the pooled model and SARAR model.

Highlights

Knowledge spillover is one of the important concepts to explain agglomeration, innovation and regional growth in endogenous growth theory, new economic geography and other theoretical economic branches
The pooled regression model and SARAR panel data model in our empirical research are used to compare to HSARAR model
For SARAR model, we use the GMM estimators for the spatial autoregressive error coefficient and standard deviation of the error term, the regression parameters are estimated by IV estimation

Summary

Introduction

Knowledge spillover is one of the important concepts to explain agglomeration, innovation and regional growth in endogenous growth theory, new economic geography and other theoretical economic branches. Knowledge spillover becomes the important factor that affects the regional growth and innovation. It has become an entry point for explaining spatial interaction and is one of the important reasons for industrial agglomeration. Baltagi et al (2014) applied the IV-2SLS method to estimate a hierarchically spatial lag (HSLAG) panel data model. While He and Lin (2015) extended the framework of Baltagi et al (2014) to a SARAR model with both the spatially lagged dependent variable and the spatial autoregressive error, using MLE for estimation. This article intends to use the HSARAR model to analyze the hierarchical effect, spatial correlation, and knowledge spillover effect

The Theoretical Model and Variables

Estimation Results of the HSARAR Model

Conclusions

Methods