互联网对中国城市内部就业分布的动态影响

doi:10.11821/dlxb202206010

地理学报 ›› 2022, Vol. 77 ›› Issue (6): 1446-1460.doi: 10.11821/dlxb202206010

互联网对中国城市内部就业分布的动态影响

吴思栩¹^,²^,³^,⁴(), 孙斌栋¹^,²^,³^,⁴(), 张婷麟¹^,²^,³^,⁴

1.华东师范大学中国行政区划研究中心,上海200241
2.崇明生态研究院,上海 202162
3.华东师范大学城市与区域科学学院,上海 200241
4.华东师范大学未来城市实验室,上海 200241

收稿日期:2021-03-03 修回日期:2022-01-06 出版日期:2022-06-25 发布日期:2022-08-19
通讯作者: 孙斌栋(1970-), 男, 河北阜平人, 博士, 教授, 博士生导师, 研究方向为城市地理与经济地理。E-mail: bdsun@re.ecnu.edu.cn
作者简介:吴思栩(1996-), 女, 湖北黄冈人, 博士生, 研究方向为数字经济与城市发展。E-mail: wusixuecnu@163.com
基金资助:
国家社会科学基金重大项目(17ZDA068);国家自然科学基金项目(42001183);国家自然科学基金项目(41901184);国家自然科学基金项目(42071210);中国博士后科学基金项目(2020M671044);中央高校基本科研业务费专项资金资助项目(YBNLTS2022-001)

The dynamic impact of the Internet on intra-urban employment distribution in Chinese cities

WU Sixu¹^,²^,³^,⁴(), SUN Bindong¹^,²^,³^,⁴(), ZHANG Tinglin¹^,²^,³^,⁴

1. Research Center for China Administrative Division, East China Normal University, Shanghai 200241, China
2. Institute of Eco-Chongming, Shanghai 202162, China
3. School of Urban and Regional Science, East China Normal University, Shanghai 200241, China
4. Future City Lab, East China Normal University, Shanghai 200241, China

Received:2021-03-03 Revised:2022-01-06 Published:2022-06-25 Online:2022-08-19
Supported by:
Major Program of National Social Science Foundation of China(17ZDA068);National Natural Science Foundation of China(42001183);National Natural Science Foundation of China(41901184);National Natural Science Foundation of China(42071210);China Postdoctoral Science Foundation(2020M671044);Fundamental Research Funds for the Central Universities(YBNLTS2022-001)

摘要/Abstract

摘要：

本文旨在揭示互联网影响城市内部就业分布的净效应及其动态变化。首先,将互联网分散力与集聚力的相对变化纳入互联网动态影响的分析框架并提出相应理论假说;其次,利用中国2004年、2008年、2013年3次经济普查中289个地级市的2247万个企业的微观数据,以“邮区”为基本空间单元构建了城市内部就业集聚指数;最后,构造工具变量以缓解内生性问题。结果显示,平均而言,互联网能显著促进城市内部的就业集聚,但随互联网渗透率的提高,其促进作用呈现“边际递减”趋势,而城市就业的空间多中心性不断提升,即就业向远离城市主中心的次中心集聚;对于高发展水平的城市,互联网渗透率的提高促进了就业先短暂分散,而后“边际递增”式集聚。本文研究发现对于预判未来城市中经济活动的空间分布和指导空间规划具有重要意义。

关键词: 互联网, 分散, 集聚, 城市内部就业分布, 动态影响, 中国

Abstract:

This paper aims to reveal the net effect and its dynamic changes of the Internet on intra-urban employment distribution. Firstly, we incorporate the relative changes of the Internet's dispersion and agglomeration force into the analytical framework of the dynamic impact of the Internet on intra-urban employment distribution, and propose corresponding theoretical hypothesis. Secondly, we use the micro-data of 22.47 million enterprises in 289 prefecture-level cities in China's Economic Census in 2004, 2008 and 2013, and take postal districts as the basic spatial unit to construct the employment agglomeration index. To alleviate endogeneity, we construct instrumental variables with the "8-horizontal and 8-longitudinal" optical cable trunk network and the functional form of the exogenous logistic curve, and re-estimate the results. The results show that, on average, the Internet can significantly promote intra-urban employment agglomeration, but with the increase of Internet penetration rate, its promotion effect is characterized as "marginal diminishing", while the spatial multi-centricity of urban employment continues to increase. For cities with a high economic development level, the increase in Internet penetration rate promotes temporary dispersion of intra-urban employment and then "marginal increasing" agglomeration. Thus, this paper concludes that along with the continuous penetration of the Internet and the development of cities, the increase of the agglomeration force of the Internet will exceed the increase of the dispersion force, thereby reinforcing the city's strengths as a center of production and exchange to be a facilitator of urban employment agglomeration, while driving the evolution of urban employment towards a polycentric structure. This paper contributes to the existing literature by revealing, for the first time, the changing characteristics of the Internet's impact on the spatial structure of urban employment with the continuous penetration of the Internet, as well as the heterogeneity of its manifestations in cities at different levels of development, and offers a new explanation for the inability of existing studies to reach consistent conclusions on the diversified impact of the Internet with a rigorous analytical paradigm and a dynamic perspective. The findings of this paper are of great significance for predicting the spatial distribution of economic activities in future cities and guiding spatial planning.

Key words: Internet, dispersion, agglomeration, intra-urban employment distribution, dynamic impact, China

吴思栩, 孙斌栋, 张婷麟. 互联网对中国城市内部就业分布的动态影响[J]. 地理学报, 2022, 77(6): 1446-1460.

WU Sixu, SUN Bindong, ZHANG Tinglin. The dynamic impact of the Internet on intra-urban employment distribution in Chinese cities[J]. Acta Geographica Sinica, 2022, 77(6): 1446-1460.

图/表 6

表1

表2

表3

互联网渗透率对城市内部就业集聚的非线性影响：工具变量结果

变量	德尔塔指数		基尼指数		多中心指数
变量	(1)	(2)	(3)	(4)	(5)	(6)
A：2SLS
渗透率(ln)	0.217^***	0.445^***	0.143^***	0.292^***	0.808^**	0.677^**
	(0.067)	(0.071)	(0.050)	(0.060)	(0.318)	(0.284)
渗透率二次项(ln)		-0.041^***		-0.027^***		0.024
		(0.008)		(0.007)		(0.028)
B：第一阶段回归
初始年份固定电话( $β 2 ″$ )	3.721^***	3.721^***	3.721^***	3.721^***	3.721^***	3.721^***
	(0.121)	(0.121)	(0.121)	(0.121)	(0.121)	(0.121)
扩散速度(α)	0.181^***	0.181^***	0.181^***	0.181^***	0.181^***	0.181^***
	(0.007)	(0.007)	(0.007)	(0.007)	(0.007)	(0.007)
R²	0.773	0.773	0.773	0.773	0.773	0.773
F值	2514	2514	2514	2514	2514	2514
观测值	747	747	744	744	740	740
R²	0.208	0.253	0.180	0.213	0.114	0.115

表3

图1

表4

图2

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变量	含义	观测值	均值	标准差	最小值	最大值
邮区面积	邮区面积(km²)	91668	154.231	152.881	0.740	1431.892
德尔塔指数	城市内部就业集聚程度	842	0.668	0.104	0.332	0.971
基尼指数	城市内部就业集聚程度	839	0.801	0.094	0.337	0.996
渗透率	每百人互联网宽带接入用户数	826	9.198	10.172	0.089	92.067
道路面积	年末人均铺装道路面积(m²)	832	10.139	16. 617	0.790	442.950
人口规模	常住人口数量(10⁴人)	834	426.087	295.882	0.006	3358.010
人口密度	常住人口数量/行政区域面积(10⁴人/km²)	833	0.043	0.038	0.001	0.445
人均实际GDP	城市人均实际GDP(元)	766	23914.5	23892.7	674.034	294592
制造业比重	本地制造业人数/本地总从业人数(%)	842	25.221	13.746	1.476	81.296
职工工资	城镇职工平均工资(元)	768	21344	10350	1208	68530
政府干预程度	政府非公共财政支出/全国均值(%)	834	0.999	0.829	0.076	17.983
生产性服务业占比	城市生产性服务业从业人数/全国生产性服务业从业人数(%)	835	0.352	0.866	0.011	12.877
人力资本	平均受教育年限(年)	834	8.857	0.822	7.135	12.734
城市创新水平	城市创新能力指数	842	4.813	27.579	0	543.080
初始年份固定电话	2000年每百人固定电话数(部/百人)	842	12.466	6.420	2.915	48.110

变量	德尔塔指数		基尼指数
变量	(1)	(2)	(3)	(4)
渗透率(ln)	0.023^*	0.044^**	0.012	0.026^**
	(0.012)	(0.017)	(0.009)	(0.011)
渗透率二次项(ln)		-0.012^***		-0.007^***
		(0.004)		(0.003)
道路面积(ln)	0.008	0.002	0.005	0.002
	(0.018)	(0.019)	(0.014)	(0.014)
人口规模(ln)	-0.247^***	-0.303^***	-0.150^***	-0.184^***
	(0.071)	(0.080)	(0.057)	(0.064)
人口密度(ln)	-0.057^***	-0.062^***	-0.043^***	-0.047^***
	(0.019)	(0.020)	(0.015)	(0.016)
人均实际GDP(ln)	-0.222^***	-0.242^***	-0.152^***	-0.164^***
	(0.060)	(0.063)	(0.046)	(0.047)
制造业比重(ln)	0.052^***	0.045^**	0.045^**	0.040^**
	(0.019)	(0.019)	(0.019)	(0.018)
职工工资(ln)	-0.035	-0.052	-0.010	-0.021
	(0.050)	(0.050)	(0.035)	(0.036)
政府干预程度(ln)	-0.003	-0.013	-0.010	-0.016
	(0.018)	(0.018)	(0.018)	(0.018)
观测值	759	759	756	756
R²	0.211	0.227	0.181	0.191

变量	德尔塔指数				基尼指数
变量	经济发展水平	生产性服务业	人力资本	创新水平	经济发展水平	生产性服务业	人力资本	创新水平
渗透率(ln)	1.449^***	0.280^**	1.515^*	0.315^***	0.844^***	0.185^**	1.033^*	0.231^**
	(0.360)	(0.116)	(0.841)	(0.112)	(0.276)	(0.091)	(0.614)	(0.092)
渗透率二次项(ln)	-0.325^***	-0.011	-0.142	-0.032^*	-0.196^***	-0.008	-0.121	-0.029^**
	(0.086)	(0.019)	(0.197)	(0.018)	(0.060)	(0.014)	(0.149)	(0.014)
渗透率(ln)×调节变量(ln)	-0.110^**	-0.085^**	-0.546	-0.056^**	-0.062^***	-0.053^*	-0.370	-0.031^*
	(0.044)	(0.043)	(0.401)	(0.025)	(0.013)	(0.031)	(0.296)	(0.018)
渗透率二次项(ln)×调节变量(ln)	0.029^***	0.018^**	0.058	0.013^***	0.012^***	0.014^**	0.050	0.008^**
	(0.009)	(0.009)	(0.091)	(0.004)	(0.002)	(0.006)	(0.069)	(0.003)
调节变量(ln)	-0.132	0.118^*	0.751	0.066^*	-0.049	0.061	0.397	0.034
	(0.097)	(0.062)	(0.514)	(0.036)	(0.049)	(0.048)	(0.394)	(0.027)
观测值	747	744	747	744	746	744	747	743
R²	0.280	0.239	0.279	0.230	0.269	0.233	0.267	0.223

互联网对中国城市内部就业分布的动态影响

The dynamic impact of the Internet on intra-urban employment distribution in Chinese cities

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