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Natural Resource Curse, Total Factor Productivity and Regional Disparity in China: Based on Dynamic Panel Data Model Analysis

  • Shuai ChenEmail author
Article

Abstract

There has been the long debate over the relation between natural resource abundance and economic growth. Since the hypothesis of “natural resource curse” advanced by Gelb (1988) and Auty (1994), many scholars have concentrated on the topic, but no agreement has yet been achieved. This paper makes use of panel datasets of China’s 30 provinces from 1998 to 2009, and GMM method of dynamic panel data model, to calculate the Malmquist index of total factor productivity (TFP), test the natural resource curse hypothesis, and estimate the natural resource consumption equation. Firstly, the TFP index shows that China’s high economic growth is less efficient, and interior provinces have large disparities. Moreover, estimation results of capital and labor equations show that, for the whole country, the natural resource sector’s investment and employment promote GDP growth, so no natural resource curse exists in China. But integral three regions show different situation. The eastern region has been constrained by the resource curse effect since 2007, although the middle and west are not troubled by this due to their lower resource consumption. In addition, the natural resource consumption equation indicates that TFP has significantly negative impact on natural resource consumption for the whole country. The three regions’ TFP are all negatively correlated with resource consumption, owing to fast technological progress in resource exploitation and utilization during these years. Based on these findings, the paper reckons that if TFP rise can reduce natural resource consumption remarkably, the country or the region can eliminate the resource curse effect; or else, it has to suffer negative impact of excessive resource reliance. Finally, it makes some policy recommendations.

Key words

natural resource curse total factor productivity (TFP) regional disparity dynamic panel data model 

JEL Classification

O13 O47 Q32 

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Copyright information

© Japan Economic Policy Association (JEPA) 2012

Authors and Affiliations

  1. 1.Business SchoolUniversity of Shanghai for Science and TechnologyShanghaiP.R. China

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