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作者

来源

INTERNATIONAL REGIONAL SCIENCE REVIEW,Vol.41,Issue4,P.410-447

语言

英文

关键字

economic growth convergence; urban and county economy; spatial econometrics model; China; DYNAMIC PANEL ANALYSIS; POST-MAO CHINA; REGIONAL INEQUALITY; INCOME CONVERGENCE; PROVINCIAL CHINA; JIANGSU PROVINCE; GIS ENVIRONMENT; UNITED-STATES; GLOBALIZATION; D

作者单位

[Li, Guangdong; Fang, Chuanglin] Chinese Acad Sci, IGSNRR, Key Lab Reg Sustainable Dev Modeling, 11A Datun Rd, Beijing 100101, Peoples R China. Fang, CL (reprint author), Chinese Acad Sci, IGSNRR, Key Lab Reg Sustainable Dev Modeling, 11A Datun Rd, Beijing 100101, Peoples R China. E-Mail: fangcl@igsnrr.ac.cn

摘要

Economic growth convergence, one of the classical assumption in regional economic growth, has been perplexing. There are many empirical studies trying to test if there is regional convergence in China. In this article, we bring new information of the finer spatial scale to the existing literature by using neoclassical convergence analysis, cross-sectional specifications, panel data models, and spatial econometric techniques to test the convergence hypothesis across 2,286 cities and counties in China. Empirical findings from cross-sectional data and spatial panel data show that significant absolute and conditional convergence are present in gross domestic product per capita after controlling for investment return rate, human capital, savings rate, population growth, technology advancement, capital depreciation rate, and initial technology level. We also find spatial agglomeration in urban and county economic growth is strong, and spatial effects are significant. Urban and county economic growth convergence rates for 1992-2010 show a gradually accelerated development trend. We present significant evidence that levels of investment, human capital, and initial technology impose significant facilitating effects on city and county economic growth, while savings and population growth have significant negative effects. And city and county economic growth differ in terms of convergence levels and influential factors.