高铁对县域经济增长溢出效应的影响研究——以福建省为例

doi:10.13249/j.cnki.sgs.2018.02.009

Abstract

Abstract:

Based on theoretical and empirical perspectives, this article demonstrates the impact of high-speed rail construction on the economy growth spillovers of Fujian County. At first, it theoretically proves that high-speed rail construction will bring the direct and indirect effects of economic growth. Then, we collect 2001-2014 microscopic panel data of 58 county-level cities in Fujian Province. Firstly, using two mechanisms of spatial panel Durbin Model, taking into account two kinds of spatial weights matrix, the space adjacent weight matrix and area reaching time -distance weight matrix, to test the high-speed railway spillover effect on regional economic growth. It shows that: First, the economic growth of Fujian County has significant positive spatial correlation between 2001 and 2014. The economic growth of a county is not spatially random distributed. There is significant spatial dependence and the combined effect of Fujian County economy. That is to say, economic growth of one area will generate positive spillover effects on that of adjacent area. Economic growth spillovers which result from shorten time accessibility between different cities because of high-speed rail than that of spatial correlation results from simple adjacent areas. Second, compared with the results of the traditional panel model, the direct impact of the high-speed rail on the regional economic growth is also very significant before and after the opening of the high-speed rail, which is resulted from the significant change in the contribution of labor and capital to economic growth. That is to say, the output elasticity of labor has become smaller, the output elasticity of capital becomes larger. It indicates that the opening of high-speed rail brings convenience to flow of the labor and the capital, and contribution of capital on economic growth will be more and more important. Third, the output elasticity of capital and labor is positive, and the former is smaller than the latter. It means that economic growth in Fujian is mainly driven by labor-based, and contribution of the capital is not yet fully realized. Forth, the spillover effect of the economic growth spillover effect based on the accessibility distance is larger than that under the simple spatial adjacency weight. Regional economic growth spillover effects are significant different before and after opening of high-speed rail, and the spillover effect after the opening of high-speed rail is smaller than that of before. This is conducive to balance the regional economic development. Finally, the article summarizes the relevant conclusions and inspiration, and puts forward policy recommendations.

Key words: high-speed railway, economic growth, spatial spillover, two mechanisms of spatial panel Durbin Model

CLC Number:

F290

Xinguang L, Anmin Huang. Spillovers Effect of the High-Speed Railway on Counties’Economic Growth:Taking Fujian Province as An Example[J].SCIENTIA GEOGRAPHICA SINICA, 2018, 38(2): 233-241.

Figures/Tables 6

Fig.1

Fig.2

Table 1

The Moran’s I of lnGDP, lnK and lnL in major years"

	$W 1$				$W 2$
年份	$lnGDP$		$lnK$	$ln L$	$ln GDP$	$ln K$	$ln L$
2001	0.512***	0.078	0.266***		0.200***	-0.002	0.122***
2004	0.504***	0.254***	0.408***		0.196***	0.071***	0.171***
2008	0.498***	0.29***	0.426***		0.197***	0.09***	0.173***
2010	0.500***	0.303***	0.418***		0.196***	0.095***	0.172***
2012	0.502***	0.333***	0.467***		0.198***	0.118***	0.178***
2014	0.499***	0.312***	0.525***		0.199***	0.116***	0.201***

Table 1

Table 2

Estimates of the direct impact of high-speed rail on regional economic growth"

	固定效应模型	随机效应模型
$lnK$	0.37870***	0.38435***
$lnL$	0.05756**	0.17102***
$dum_lnL$	-0.02695	-0.04809**
$dum_lnK$	0.04271***	0.05601***
常数项	7.70249***	6.49675***
样本数总计	812	812
组内R²	0.91520	0.91187
F/Wald(P值)	2.02e+03（0.0000）	6882.77（0.0000）

Table 2

Table 3

Estimation results of single-mechanism space Dubin model"

	模型1： $W 1$	模型2： $W 2$
$lnL$	0.599***	0.511***
$lnK$	0.375***	0.385***
$W * lnL$	-0.005	0.422***
$W * lnK$	-0.386***	-0.575***
$W * dep . var$	0.369***	0.684***
$R 2$	0.904	0.9084
$sigm a 2$	0.096	0.0902
$logL$	-5164.077	-186.4882
样本数总计	812	812
Wald检验（P值）	SDM和SAR：11200（0.001） SDM和SEM：56.768（0.000）^①^（ ① 检验是基于时间效应下的SDM模型,下同。^）	SDM和SAR：11.0738（0.001） SDM和SEM：8.787（0.012）

Table 3

Table 4

Estimation results of two-mechanism space Dubin model"

	模型3： $W 1$	模型4： $W 2$
常数项	-0.019	-1.597**
$lnL$	0.603***	0.565***
$lnK$	0.561***	0.38***
$W*lnL$	-0.103**	-0.161**
$W*lnK$	-0.273***	-0.314***
$rho 1$	0.339***	0.625***
$rho 2$	0.345***	0.751***
$R 2$	0.8835	0.8988
$sigm a 2$	0.1146	0.0996
$logL$	-285.445	-225.222
总计	812	812
$rho 1 - rho 2$ (t值)	-0.0063(-0.7998)	-0.1252(-2.5690)

Table 4

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Spillovers Effect of the High-Speed Railway on Counties’Economic Growth:Taking Fujian Province as An Example

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