中国的区域关联与经济增长的空间溢出效应

发布时间：2016-11-13 07:58

本文关键词：中国的区域关联与经济增长的空间溢出效应，由笔耕文化传播整理发布。

PanWenqing135；tobefoundinthetraditiona；(2)Localestimationanalys；Table1showstheresultsofo；Table2FactorsContributin；betweencapital；d<10001000<d<15；914.9(0.000)(0.059)(0.00；T

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to be found in the traditional panel data model. This again proves that the traditional model that leaves out spatial correlations has a biased model speci? cation and gives rise to biased estimation results.

(2) Local estimation analysis

Table 1 shows the results of our global estimation, re? ecting the average conditions of the economic development of China’s 31 provinces. As mentioned above, the spatial spillovers of regional economic development are very likely to be closely related to the spatial distance between regions: the shorter the distance, the clearer the spillover effects. Therefore, in specifying the variable of a region’s market potential, we take the weighted average of the GDP of other regions with the inverse distance across regions as the weight. To investigate in depth the in? uence of distance on spatial spillovers, we conduct further spatial panel data analysis below based on the linear distance between capital cities.

Table 2 Factors Contributing to Regional Economic Growth in China: A Distance-based Investigation

between capital

d<10001000<d<15001500<d<20002000<d<25002500<d<3000(0.000)(0.077)(0.000)(0.000)0.698

914.9(0.000)(0.059)(0.000)(0.000)0.698914.3(0.000)(0.053)(0.000)(0.000)0.695914.4(0.000)(0.049)(0.000)(0.000)0.692910.8(0.000)(0.040)(0.000)(0.001)0.691909.5d>3000(0.000)(0.026)(0.000)(0.529))0.688905.5ln(Krt/Kr,t–1)DKLrtDLrtln(MPrt/MPr,t–1)λAdiusted R2log-likelihood

Table 2 presents the results of the ML ? xed effects estimation of the spatial error panel data model excluding the time dummy variable, with market potential calculated on the basis of the linear distance between capital cities. As shown in Table 2, the estimated market potential parameter decreases as the inter-regional spatial distance increases. It is when linear distance is less than 1,000 kilometers that market potential has the greatest effect on regional economic

development, with a parameter estimate as high as 0.399. When the distance is between 2,500 and 3,000 kilometers, the estimate falls to 0.275. And when the distance is over 3,000 kilometers, the parameter estimate is 0.076, but it fails to pass the test at the 10 percent signi? cance level in terms of concomitant probability. This means that one region’s economic growth has no substantive direct spillovers on other regions 3,000 or more kilometers away;

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or to put it differently, there are no direct spatial spillovers between regions more than 3,000 kilometers from each other. Unlike the inter-regional direct spatial spillover effect represented by market potential, the values λ of the indirect inter-regional effects represented by random error do not decrease as spatial distance increases, and they all pass the statistical test at the 1 percent significance level. This implies that through random error effects, one region’s economic growth indirectly affects the economic growth of other regions, and these effects are not subject to the in? uence of inter-regional spatial distance.

V.Conclusions and Implications

This paper uses an exploratory spatial data analysis tool to analyze the spatial distribution patterns and characteristics of the GDP of China’s 32 provinces during 1998-2009. The results show, on the one hand, a positive global spatial autocorrelation whose intensity decreases as inter-regional spatial distance increases. At the same time, from the temporal perspective, positive global spatial autocorrelation becomes stronger over time. On the other hand, in terms of local spatial correlation, the number of regions signi? cantly correlated with neighboring provinces increases over time.

We then employ a new economic geography model that signals the effect of market potential on regional economic growth and reveals how a region’s economic growth is affected by that of its neighbors. Econometric analysis shows that, on one hand, the growth mechanism represented by classical growth model and new growth theory still dominate the fundamentals of China’s regional economic growth, and factor accumulation is indispensable. On the other, when the effects of factor input are controlled for, we ? nd that market potential has highly signi? cant positive effects on a region’s economic growth. In terms of elasticity, the effect of market potential on a region’s GDP per capita outperforms that of ? xed assets investment. Our spatial error model further reveals that a region’s economic growth is closely related to random shocks from the economic growth of its neighbors. In other words, other factors contributing to a region’s economic growth also have an indirect diffusion effect on the economic growth of its neighbors. This has immediate policy implications: China should further lift inter-regional market barriers and encourage the marketization pro cess across

the country in order to create more space for spatial spillover effects in regional economic growth. This issue should be given great importance in policy making for regional economic development in China.

Another important question raised in this paper is whether inter-regional spatial spillover effect will decrease as spatial distance increases. Our econometric analysis shows that spillover effects do decrease as distance increases, thus again clearly con? rming the famous hypothesis of the ? rst law of geography. One policy implication is as follows: the government should encourage the spatial agglomeration of economic activities and population. In other words, given trans-regional industrial agglomeration, consideration could be given to having

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part of population of western regions migrate to southeastern coastal areas and form some world-class metropolises there, in order to give full play to agglomeration and diffusion effects. This could facilitate the economic growth of central and western regions and make the achievements of economic development accessible to more people.

Finally, it should be noted that due to the different speci? cations of variables in this study, we cannot directly compare the results of our econometric analysis of human capital and labor input with market potential and capital input. However, the estimation results indicate that fixed assets investment and labor input remain the most important drivers of regional economic growth, and that input of labor and physical capital is indispensable for current regional economic growth. Of course, the quite considerable potential of human capital and inter-regional spatial spillover effects also reveals two major areas we need to work at in promoting future regional economic growth. One is further increasing human capital investment and raise the proportion of the high-quality population in the population as a whole, in order to give full play to the important role of human capital in the middle and later stages of China’s industrialization; the other is continuing to lift inter-regional market barriers and accelerating the construction of a nationwide integrated market, so as to give full play to the spatial spillover effects of regional economic growth. These two areas seem extraordinarily important against the background of the future transformation of China’s mode of economic growth and the ? agging of its demographic dividend (especially the labor dividend).

Notes on Contributor

Pan Wenqing, Ph.D. in Economics, is Associate Professor at the School of Economics and Management, Tsinghua University. His research mainly focuses on regional economics, macroeconomics and quantitative economics. Some of his articles are: “Divergence and Convergence in China’s Regional Economy” (中国区域经济差异与收敛，，Zhongguo Shehui Kexue [中国社会科学], 2010, no. 1); “Inter-industry Technology Spillover Effects in China: Evidence from 35 Industrial Sectors” (中国产业间的技术溢出效应：基于35个工业部门的经济研究, Economic Research Journal [经济研究], 2011, no. 7); “Feedback and Spillover Effects between Coastal and Noncoastal Regions of China” (中国沿海与内陆间经济影响的反馈与溢出效应, Economic Research Journal, 2007, no. 5); “A Study of the Correlation between Capital Allocation Ef? ciency and Financial Development” (中国资本配置效率与金融发展相关性研究, Management World [管理世界], 2003, no. 8), and Econometrics (计量经济学, 3rd Edition, Beijing: Higher Education Press, 2000). E-mail: panwq@sem.tsinghua.edu.cn.

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—Translated by Li Cunna from

Economic Research Journal (经济研究), 2012, no. 1

Revised by Sally Borthwick

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