网络关联、空间溢出效应与中国区域经济增长——基于腾讯位置大数据的研究

doi:10.13249/j.cnki.sgs.2019.09.002

Abstract

Abstract:

Based on population mobility data between all prefecture-level cities in China acquired from Tencent mobile phone locations, this article examines the evolution of the spatial correlation model of China's prefecture-level economic growth under the population mobility network. And the spatial spillover effect of economic growth among these cities is also measured in this article by utilizing the network analysis method. The main conclusions obtained in this article are: 1) The construction of the spatial weight matrix should be based on the data that characterizes the interaction and degree between regions. The spatial weight matrix that conforms to reality should be asymmetric and change with the modifying of interaction. 2) Factors such as population mobility networks that interact with prefecture-level cities play an important role in the spillover of economic growth. China's prefecture-level cities show a more obvious ‘center-periphery’ structure. Five central cities: Beijing, Shanghai, Guangzhou, Shenzhen and Chengdu form five fulcrums from north to south and from east to west. The prefecture-level cities with which these five cities have population relationships are concentrated in the eastern and central regions in terms of spatial distribution, and slightly less in the west and northeast. Meanwhile, sub-central prefecture-level cities have formed a hexagonal population mobility network pattern from northeast to southwest. Changchun, Lanzhou, Hangzhou, Dongguan, Nanning and Kunming are vertices of this network. Population mobility is very frequent in this hexagon network and is relatively infrequent outside this hexagon. 3) Network growth effect is an important factor in the economic growth of prefecture-level cities. Using the population mobility network, the population factor has a negative network spillover effect, while foreign direct investment has a positive spillover effect on economic growth. The population factor also has a negative spillover effect on local economic growth, while the fixed asset investment and total retail sales of consumer goods have a significant positive spillover effect, if the geographical proximity network and geographical distance network are used. Finally, according to the research in this article, the policy recommendations are proposed to narrow the economic differences between prefecture-level cities and to achieve coordinated development.

Key words: population mobility network, spatial correlation, spatial spillover effect, network analysis, prefecture-level cities in China, big data

CLC Number:

F129.9

Zhang Weili, Ye Xinyue, Li Dong, Fu Jibin, Wu Menghe. Network Association, Spillover Effect and China's Regional Economic Growth Based on Tencent's Location Big Data[J].SCIENTIA GEOGRAPHICA SINICA, 2019, 39(9): 1371-1377.

Figures/Tables 2

Table 1

Table 2

Results of spatial spillover effects of economic growth in prefecture-level and above cities in China under different network structures"

变量	基准模型	$W pop$		$W bun$		$W dis$
变量	基准模型	局部加总	局部平均	局部加总	局部平均	局部加总	局部平均
$ln pop$	0.152^***（0.021）	0.237^***（0.021）	0.159^***（0.021）	0.187^***（0.021）	0.197^***（0.022）	0.220^***（0.022）	0.157^***（0.021）
$ln inv$	0.241^***（0.016）	0.214^***（0.016）	0.231^***（0.018）	0.161^***（0.019）	0.157^***（0.019）	0.124^***（0.019）	0.229^***（0.018）
$ln fdi$	0.00003（0.001）	0.00006（0.001）	0.0001（0.001）	0.0001（0.001）	0.00002（0.001）	-0.001（0.001）	-0.0004（0.001）
$ln con$	0.277^***（0.023）	0.255^***（0.022）	0.255^***（0.024）	0.171^***（0.026）	0.176^***（0.024）	0.163^***（0.026）	0.251^***（0.027）
$ln gov$	0.092^***（0.018）	0.080^***（0.017）	0.100^***（0.018）	0.096^***（0.017）	0.093^***（0.017）	0.088^***（0.016）	0.105^***（0.018）
$W × ln pop$		-0.342^***（0.073）	-0.066^***0.029）	-0.189^***（0.054）	-0.127^***0.035）	-0.355^***（0.068）	-0.002（0.005）
$W × ln inv$		0.143（0.121）	0.014（0.013）	0.232^***（0.031）	0.173^***（0.023）	0.410^***（0.052）	0.004（0.004）
$W × ln fdi$		0.022^*（0.015）	-0.003^*（0.002）	-0.00005（0.003）	0.0006（0.002）	0.006（0.007）	-0.002^***（0.000）
$W × ln con$		-0.026（0.107）	0.031^***（0.013）	0.298^***（0.037）	0.209^***（0.026）	0.437^***（0.067）	0.008^***（0.003）
$W × ln gov$		0.020（0.244）	-0.023（0.023）	-0.041（0.048）	-0.012（0.021）	-0.072（0.160）	-0.011^***（0.005）
常数项	5.989^***（0.603）	6.377^***（0.568）	6.395^***（0.637）	4.687^***（0.721）	3.782^***（0.738）	3.177^***（1.476）	6.372^***（0.704）
个体效应	控制	控制	控制	控制	控制	控制	控制
时间效应	控制	控制	控制	控制	控制	控制	控制
R²	0.999	0.999	0.999	0.999	0.999	0.999	0.999
J		0.740	0.000^***	0.475	0.798	0.684	0.000^***

Table 2

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[1]	盛广耀 . 区域经济增长的多重关联效应及其实证检验[J].经济学家, 2018(4):34-41.

排名	地级市	得分	排名	地级市	得分
1	北京市	1	11	杭州市	0.1948449
2	上海市	0.5565135	12	郑州市	0.1922852
3	广州市	0.5523373	13	沈阳市	0.1730208
4	深圳市	0.4947236	14	哈尔滨市	0.1633661
5	成都市	0.4614487	15	东莞市	0.1587858
6	重庆市	0.2882033	16	长沙市	0.1551933
7	武汉市	0.2267726	17	兰州市	0.1396111
8	西安市	0.2152768	18	南宁市	0.1312138
9	乌鲁木齐市	0.2000090	19	苏州市	0.129687
10	昆明市	0.1958328	20	南京市	0.1261395

Network Association, Spillover Effect and China's Regional Economic Growth Based on Tencent's Location Big Data

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