地理学报, 2023, 78(8): 1955-1968 doi: 10.11821/dlxb202308007

人口与城市研究

中心城市产业结构对腹地城市人口增长及区域城市体系演化的影响

李佳洺,1, 孙东琪1, 姜炎鹏,2

1.中国科学院地理科学与资源研究所 中国科学院区域可持续发展分析与模拟院重点实验室,北京 100101

2.华东师范大学城市与区域科学学院,上海 200062

The influence of the industrial structure of central cities on surrounding cities and regional urban systems

LI Jiaming,1, SUN Dongqi1, JIANG Yanpeng,2

1. Key Laboratory of Regional Sustainable Development Modeling, Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China

2. School of Urban and Regional Science, East China Normal University, Shanghai 200062, China

通讯作者: 姜炎鹏(1984-), 男, 安徽青阳人, 博士, 教授, 研究方向为全球城市与区域发展。E-mail: yanpjiang@163.com

收稿日期: 2022-02-22   修回日期: 2023-04-17  

基金资助: 第二次青藏高原综合考察研究(2019QZKK0406)
国家自然科学基金项目(42171178)

Received: 2022-02-22   Revised: 2023-04-17  

Fund supported: The Second Tibetan Plateau Scientific Expedition and Research(2019QZKK0406)
National Natural Science Foundation of China(42171178)

作者简介 About authors

李佳洺(1984-), 男, 山西阳城人, 副研究员, 主要从事产业区位与区域发展研究。E-mail: lijm@igsnrr.ac.cn

摘要

从产业异质性的角度,实证分析区域中心城市产业结构对周边城市人口增长的作用过程和机制,并进一步探究了产业结构对于区域城市体系的极化和均衡演化的影响。结果表明:① 制造业强化了中心城市的溢出效应,促进了周边城市人口的增长,而服务业则弱化了溢出效应,对周边城市的增长具有抑制作用;② 以制造业为主的区域中心城市由于相对较小的城市规模和较强的溢出效应,更容易形成相对均衡的区域城市体系,而以服务业为主的区域中心城市,更倾向形成相对极化的区域城市体系;③ 产业结构与周边城市人口增长呈现倒“U”型关系,即中心城市制造业或服务业占比过低或过高都不利于周边城市的人口增长。

关键词: 产业区域空间效应; 产业结构; 城市增长; 极化与均衡; 区域城市体系

Abstract

The study of the new economic geography has paid attention to the polarization and balanced development of urban systems. In reality, however, transport accessibility is subject to great uncertainty in explaining the polarization and balance of the urban system. In fact, the polarization or equilibrium of the regional urban system is not only unique in China, but also a general phenomenon in countries with different development outcomes, political systems and degrees of marketization. From the perspective of industrial heterogeneity, this study empirically analyzes the process and mechanism of the industrial structure of regional central cities on the population growth of neighboring cities in China from 1980 to 2020, and examines the influence of industrial structure on the polarization and equilibrium development of the regional urban system. The results show that, firstly, the manufacturing sector strengthens the spillover effect from the core city and promotes the population growth in neighboring cities, while the service sector weakens the spillover effect and hinders the growth in neighboring cities. Secondly, the manufacturing-oriented cities have relatively small city scale, while the service-oriented cities are relatively large. Therefore, the driving effect of manufacturing-oriented central cities on the neighboring cities is larger, which tends to produce a relatively balanced regional city system. Correspondently, the spillover effect of service-oriented central cities on the neighboring cities is smaller, which tends to form a relatively polarized regional city system. Finally, as the share of services in the industrial structure of central cities continues to rise, China's urban system may become more polarized rather than balanced. Certainly, the higher the proportion of manufacturing in the central city, the more balanced the regional urban system is. In fact, the empirical results show that the industrial structure and the spillover effect of the core city have an inverted U-shaped relationship, i.e. a high proportion of manufacturing or services weakens the driving effect of the core city on surrounding cities. The reasons for this vary. The high proportion of manufacturing and the small size of the central city have a limited spillover effect on the neighboring cities and therefore can hardly drive their growth; while the high proportion of service industry and the negative effect of the service industry on the spillover effect lead to the slow development of the neighboring small-and-medium-sized cities. It is worth noting that because of the significant spatial impact of industry on the urban system, the future regional urbanization strategy should be coordinated with the industrial strategy. More importantly, with the gradual liberalization of the household registration system, industry selection can become an effective policy option for macro-control of the regional urban system.

Keywords: industrial structure; urban population growth; polarization and equilibrium; regional urban system

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本文引用格式

李佳洺, 孙东琪, 姜炎鹏. 中心城市产业结构对腹地城市人口增长及区域城市体系演化的影响. 地理学报, 2023, 78(8): 1955-1968 doi:10.11821/dlxb202308007

LI Jiaming, SUN Dongqi, JIANG Yanpeng. The influence of the industrial structure of central cities on surrounding cities and regional urban systems. Acta Geographica Sinica, 2023, 78(8): 1955-1968 doi:10.11821/dlxb202308007

1 引言

对于发展大城市还是中小城市的争论由来已久。一些经济学家的研究表明中国对于大城市人口规模的限制,降低了城市运行的经济效率,因此呼吁应积极发展大城市[1-2];而另一些学者从公共服务保障、避免“候鸟式”人口流动以及“城市病”等角度,认为发展中小城市是中国城镇化的现实选择,不应盲目扩大大城市规模[3];还有一些学者则认为进入城市群时代,区域城市体系应该形成大中小城市均衡发展的格局[4-6]

无论是以大城市为主的极化模式,还是以中小城市为主的分散模式,亦或是大中小城市协调发展的均衡模式,都需要厘清区域城市体系城市规模结构演化的驱动机制。尤其是在实现共同富裕战略目标的背景下,城市群建设需要充分发挥大城市溢出效应,带动周边城市发展,实现区域协调发展和均衡发展,探究区域中心城市对于周边城市人口规模的影响过程与机制就显得尤为重要。孙斌栋等以长三角和京津冀等区域为例,证明了上海、北京等大城市对周边城市发展的显著影响[7-8]。对于不同区域中心城市促进或抑制周边城市发展的机制,已有的文献主要从制度文化和运输成本两个角度进行解释[9-11]。由于制度和文化等变化较为缓慢,适用于解释不同区域或国家间的差异,亦或是长历史周期的变化,但并不适用于解释20世纪末以来发展中国家在较短时间内快速城镇化过程中区域城市体系的动态演化。加之,即使在政治制度较为接近、市场机制相对完善的国家间,城市体系的极化程度也存在差异。如Storper的研究表明美国城市体系的极化程度要明显高于欧洲[12]。而运输成本的相关实证研究的结果存在不确定性,有些研究结果甚至完全相反[13]。如有研究表明高铁强化了南昌等中心城市的极化效应,导致高铁沿线城市愈发不均衡[14-15];但是也有研究认为高铁开通扩大了中心城市对周边城市的溢出效应[16-17]

为解释城市的增长,Krugman提出了一个一般性的理论框架,即第一自然和第二自然。第一自然即是自然状态下城镇所在区位的资源禀赋,包括河流、土地等;第二自然是区域和城市间由人类社会经济联系所建构的联系网络和空间结构,即强调城市间紧密的社会经济联系网络对于特定城市发展的影响[18]。这一理论框架被广泛应用于国内外城市发展的实证研究[19-21]。产业联系无疑是城市间社会经济联系的重要内容。学者们对于中国大都市区的研究表明产业联系强度显著影响了中心城市对于周边城市的溢出效应,进而导致不同城市群间大中小城市分布的结构性差异[22]。而且不同行业的产业联系网络存在明显的异质性[23],这很大程度上是由于不同行业产品的实体化程度决定的[24-25]。陆大道在对区域空间结构的分析时,也认为产业类型很大程度上影响了区域空间结构的极化与均衡过程,而区域城市体系中的大中小城市分布特征即是区域空间结构的重要表征[26]

因此,本文将以第一自然和第二自然的理论框架为基础,从产业异质性的角度出发,重点分析中心城市产业结构对周边城市人口增长的影响过程和机制,进而探究产业结构演进对于区域城市体系极化与均衡动态演化过程的影响。

2 研究对象、数据与方法

2.1 研究对象

有研究表明城市间的空间相互作用主要为高等级城市对低等级城市的影响,而同层级中小城市之间的空间关联效应相对微弱[7],因此研究重点关注规模大、等级高的中心城市产业结构对区域城市体系的影响。本文依据2010年住房和城乡建设部发布的《全国城镇体系规划(2010—2020年)》所确定的北京、天津、上海、广州、重庆5大国家中心城市,重点分析以国家中心城市为核心的区域城市体系。区域城市体系范围主要依据中心城市市场潜力确定,即对于一个城市来说,其作为5个中心城市中市场潜力最大城市的腹地城市。由于市场潜力由中心城市人口规模和城市间空间距离决定,而空间距离是固定的,因此区域城市体系的范围会随着不同年份5个国家中心城市人口规模的变化,而有所调整。

就2020年5个中心城市按人口规模划分的影响区范围来看,主要受上海影响的中等及以上规模城市数量最多,其次是北京,广州和重庆相当,天津腹地中等及以上规模城市数量最少(图1表1)。市辖区人口30万人以上的城市主要集中在中东部地区和成渝地区,长三角地区城市最为密集,区域城市体系中大中小城市的分布也较为均衡;其次是珠三角地区,京津冀地区城市密集程度相对较低,以300万以下的Ⅱ型大城市为主,缺少500万~1000万人口的第二等级城市,其周边的山东半岛和河南等则更多为中等规模及小城市,区域城市体系中缺少衔接中心城市与低层级城市、能承转中心城市资源的中间层级城市(图1)。

图1

图1   2020年中国不同规模城市分布特征

注:基于自然资源部标准地图服务网站GS(2016)1554号标准地图绘制,底图边界无修改;数据来源于:United Nations, Department of Economic and Social Affairs, Population Division, World Urbanization Prospects。

Fig. 1   Spatial distribution characteristics of cities of different sizes in China in 2020


表1   2020年5个国家级中心城市影响范围内中等规模及以上城市分布情况

Tab. 1  Distribution of medium-sized cities and above within the influence of the five national central cities in 2020

类型北京广州上海天津重庆
超大型城市数量(个)12111
特大型城市数量(个)32502
I型大城市数量(个)521013
Ⅱ型大城市数量(个)24154538
中等规模城市数量(个)502071129
总计(个)8341132643

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2.2 研究数据

研究尽管以中国城市体系为例,但是核心是探求产业结构影响区域城市体系空间结构的普遍性规律和机制,因此与其他国家具有可比性非常重要。研究采用联合国经济和社会部(UN DESA)的城市群(Urban Agglomeration)人口数据,数据集涵盖自1950年以来的人口规模在30万级以上的392个城市(包括直辖市、地级市、县级市),每5年的人口数据。同时,由于伊宁和玉树两个城市控制变量等相关数据不可得,研究仅包括390个城市样本。5个国家中心城市的产业结构数据来源于各中心城市的统计年鉴。

除核心变量外,研究数据还包括从业人员、平均工资、万人医生数等控制变量相关数据,这些数据均来自于1981年以来的城市统计年鉴和各省统计年鉴,缺失数据采用所在省平均增长率或两个年份平均值的方法进行补充完善。

研究聚焦1980年以来的城市人口变化,一方面由于研究重点关注在市场经济条件下的极化和均衡规律,中国市场机制自1978年改革开放以后才逐步发挥作用;另一方面是由于改革开放前的城市统计数据缺失较多,从业人员、工资、医生等控制变量数据可得性较差。

2.3 研究方法

为了直观反映产业结构对区域城市体系极化与均衡过程的影响,本文采用基尼系数测度区域新增城镇人口在各城市分布的情况,具体公式如下:

Gi=Ti/2Si(n-1)      (i=1, 2,, n)

式中:n表示区域城市系统中的城市数量;S表示整个城市体系的新增人口总规模;T是城市体系中各城市之间的新增人口规模之差的绝对值总和;Gi反映区域城市体系i中新增人口在少数城市中的集中程度。基尼指数的取值范围在0~1之间,基尼指数越接近0,表明新增人口在区域各城市中分布相对平均,将促进区域城市体系更加均衡;越接近1,表明新增人口倾向于向少数城市集中,可能导致区域城市体系更加极化[27]

2.4 计量模型构建

除第一自然和第二自然的理论框架外,从业人员规模、工资水平、公共服务水平、到中心城市空间距离等也被证明对城市增长由显著影响[28-30]。因此,本文以第一自然和第二自然理论框架为基础,加入从业人员规模等控制变量,重点聚焦产业结构对城市人口增长的影响。具体模型如下:

POPit=α+βfirst naturei+γsecond natureit-1+industria effectjt-1+θlocationalit-1+δcontrolit-1+ε

式中:POPitt-1到t时期内的城市i人口增长规模;first naturei为城市i在1980年的人口规模(Pop 1980),1980年是改革开放初期,能够表征自然状态下城市的资源禀赋特征;second natureit-1t-1时期城市i的所有城市市场潜力;industria effectjt-1t-1时期国家中心城市j产业结构的影响,包括国家中心城市市场潜力、产业结构、产业结构二次项以及市场潜力与产业结构的交叉项;locational测度除北京、天津、上海、重庆、广州5个全国层面中心城市外的第二级和第三级中心城市对城市i的影响;controlit-1包括t-1时期从业人员规模、工资水平、公共服务水平3个控制变量(表2表3)。

表2   自变量列表

Tab. 2  Independent variables

变量名称指标描述及计算过程
1980年人口规模(Pop 1980)1980年的人口规模
全部城市市场潜力(MPAC)j=1,jiNPOPtj/distanceij
中心城市市场潜力(MPCC)POPtk/distanceik
中心城市产业结构(IM)IMmm为工业占比,IMs为服务业占比,IM为工业与服务业比值
市场潜力与产业结构交叉项(MP×IM)中心城市市场潜力与工业/服务业占比交叉项(MPCC×IM)
从业人员数(Employment)城市第二和第三产业从业人员数
工资水平(Wage)职工平均工资
公共服务水平(Urban Amenity)万人医生数
到第二层级中心城市距离
(Distance 2ndRC)
距离城市i最近的第三层级中心城市到最近的第二层级中心城市的距离,即增量距离
到第三层级中心城市距离
(Distance 3rdRC)
城市i到最近的第三层级中心城市距离,如距离第二层级中心更近,则该值为零

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表3   主要变量统计描述

Tab. 3  Statistical description

1980年
人口规模
(千人)
从业
人员数
(万人)
工资
水平
(元)
万人
医生数(个)
全部城市市场
潜力
中心城市市场
潜力
到第二层级中心城市
距离(km)
到第三层级中心城市
距离(km)
中心城市工业占比(%)中心城市服务业
占比(%)
均值229.5420.7217815.9328.87388.0632.74198.27100.3340.5849.76
标准差354.6130.322620.5116.02216.1842.71331.23123.9312.7415.36
最小值12.000.4420.151.5932.981.4919.630.0016.1222.58
最大值3418536.06576545146.301299.84569.092741.12911.2266.6679.65

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需要说明的是产业效应(industrial effect)的变量集。市场潜力(MP)和产业结构(IM)是研究的核心变量,产业结构的二次项主要测度产业结构的非线性影响,市场潜力和产业结构的交叉项测度5个全国层面的中心城市产业结构对市场潜力的影响。其中,产业结构分别以第二产业中的工业在GDP中的占比、服务业(第三产业)占比以及工业与服务业的比值进行测度,前两个更多表征工业或服务业自身特征,而工业与服务业比值测度两大产业的比例关系,反映产业结构的动态演进;产业效应变量集中的市场潜力关注5个国家中心城市产生的市场潜力(MPCC)。同时,研究还考虑了所有城市对特定城市产生的市场潜力(MPAC)。本质上所有城市的市场潜力反映了其他城市对特定城市的影响,而对5个中心城市的关注即是进一步聚焦中心城市对周边城市发展的影响。本文借鉴Ioannides等的测度方法[19],5个全国层面中心城市市场潜力(MPCC)和所有城市市场潜力(MPAC)的计算公式如下:

MPCCit-1k=POPt-1k/distanceikMPACit-1=j=1, jiNPOPt-1j/distanceij

式中:MPCCit-1k为城市i所对应的核心城市kt-1时期的市场潜力;POPt-1kt-1时期核心城市k的人口规模;distanceik为城市i和核心城市k的距离;MPACit-1为所有其他城市对城市i产生的市场潜力;POPt-1jt-1时期除城市i外的其他城市j的人口规模,distanceij为城市i和城市j的距离。

第二层级和第三层级中心城市的确定主要是依据服务业从业人员规模,5个全国层面中心城市服务业从业人员规模都在100万以上,第二层级中心城市为50万~100万,第三层级为20万~50万人的省会城市。第二层级中心城市包括武汉、西安、杭州、南京、沈阳、成都、济南、哈尔滨等区域中心城市,第三层级城市主要是其他省会城市,不同时期第二和第三层级城市略有变化。

3 中心城市产业结构对周边城市人口增长的影响过程与机制

3.1 中心城市产业结构影响周边城市人口增长的实证分析

为了解决可能的变量内生性的问题,模型所有自变量采用滞后一期变量,同时采用selection ratio对遗漏变量进行检验,结果表明selection ratio的值均大于1。已有的研究表明当selection ratio的值大于1时,即表明模型存在遗漏变量的可能性很小[31-32]。豪斯曼检验的结果表明样本数据更适用于固定效应模型。

计量模型的结果显示,工业占比与中心城市市场潜力的交叉项为正,而服务业占比与中心城市市场潜力交叉项则为负,表明制造业强化了中心城市对周围城市的溢出效应,促进周边城市的人口增长,而服务业则弱化了中心城市的溢出效应,不利于周边城市的增长。同时,表4表5显示,无论是工业占比还是服务业占比的二次项均为负,且均通过显著性检验,表明中心城市产业结构对于周边城市发展的影响呈现倒“U”型的特征。换句话说,中心城市制造业或服务业占比过低或过高都不利于周边城市的人口增长。

表4   工业影响下的回归结果

Tab. 4  Regression results (manufacturing and mining)

固定效应(FE)随机效应(RE)
模型1模型2模型3模型1模型2模型3
1980年人口规模
(Pop 1980)
0.2767***
(0.000)
0.2799***
(0.000)
0.2742***
(0.000)
全部城市市场潜力
(MPAC)
-0.0163
(0.511)
-0.0416
(0.197)
-0.0538
(0.115)
0.0704***
(0.001)
-0.0058
(0.824)
-0.0136
(0.598)
中心城市市场潜力
(MPCC)
0.2065
(0.221)
0.0130
(0.958)
0.5872***
(0.000)
-0.1643
(0.498)
工业占比
(IMmm)
2.4347*
(0.017)
2.3598*
(0.021)
1.8534
(0.099)
4.0999***
(0.000)
3.8838***
(0.000)
2.7550***
(0.010)
工业占比二次项
(IMmm2)
-0.0479***
(0.000)
-0.0484***
(0.000)
-0.0452***
(0.000)
-0.0519***
(0.000)
-0.0533***
(0.000)
-0.0462***
(0.000)
市场潜力与工业占比交叉项
(MP×IMmm)
0.0099*
(0.028)
0.0246***
(0.000)
到第二层级中心城市距离
(Distance 2ndRC)
-0.0558*
(0.011)
-0.0553*
(0.012)
-0.0545*
(0.013)
-0.0120
(0.346)
-0.0083
(0.511)
-0.0040
(0.745)
到第三层级中心城市距离
(Distance 3rdRC)
-0.0744*
(0.032)
-0.0735*
(0.034)
-0.0739*
(0.033)
-0.0494
(0.076)
-0.0401
(0.148)
-0.0340
(0.218)
从业人员数
(Employment)
0.0814
(0.473)
0.0608
(0.596)
0.0909
(0.441)
0.3765***
(0.001)
0.3204**
(0.006)
0.4330**
(0.000)
工资水平
(Wage)
0.0004**
(0.006)
0.0005**
(0.004)
0.0005**
(0.004)
0.0002
(0.151)
0.0003
(0.056)
0.0003
(0.057)
万人医生数
(# of Doctors per 10000 Population)
-0.6359***
(0.001)
-0.6372***
(0.001)
-0.6355***
(0.001)
-0.4259**
(0.019)
-0.4410**
(0.015)
-0.4249**
(0.019)
R20.11520.12030.13050.35700.37400.3793
样本数量269526952695269526952695
F/Wald Test17.1715.4314.01634.98673.67714.17
0.00000.00000.00000.00000.00000.0000

注:括号内为p值;*p < 0.05,**p < 0.01,***p < 0.001。

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表5   服务业影响下的回归结果

Tab. 5  Regression results (service sector)

固定效应(FE)随机效应(RE)
模型1模型2模型3模型1模型2模型3
1980年人口规模
(Pop 1980)
0.2742***
(0.000)
0.2760***
(0.000)
0.2693***
(0.000)
全部城市市场潜力
(MPAC)
-0.0482
(0.080)
-0.0811*
(0.023)
-0.0984
(0.007)
0.0519*
(0.028)
-0.0374
(0.201)
-0.0386
(0.183)
中心城市市场潜力
(MPCC)
0.246
(0.148)
1.6532*
(0.013)
0.6194***
0.000)
2.5873***
0.000)
服务业占比
(IMs)
7.2876***
(0.000)
7.5254***
(0.000)
6.9504***
(0.000)
6.3217***
(0.000)
7.0160***
(0.000)
6.2005***
(0.000)
服务业占比二次项
(IMs2)
-0.0563***
(0.000)
-0.0571***
(0.000)
-0.0486***
(0.000)
-0.0595***
(0.000)
-0.0623***
(0.000)
-0.0492***
(0.000)
市场潜力与服务业占比交叉项
(MP×IMs)
-0.0179*
(0.029)
-0.0304***
(0.000)
到第二层级中心城市距离
(Distance 2ndRC)
-0.0533*
(0.015)
-0.0529*
(0.016)
-0.0505*
(0.021)
-0.0128
(0.313)
-0.0097
(0.441)
-0.0024
(0.848)
到第三层级中心城市距离
(Distance 3rdRC)
-0.0684*
(0.048)
-0.0674*
(0.051)
-0.0684*
(0.048)
-0.0497
(0.075)
-0.0419
(0.130)
-0.0308
(0.263)
从业人员数
(Employment)
0.1525
(0.183)
0.13332
(0.248)
0.1954
(0.100)
0.4076***
(0.000)
0.3662**
(0.002)
0.5229***
(0.000)
工资水平
(Wage)
0.0004**
(0.009)
0.0004**
(0.007)
0.0004**
(0.009)
0.0003
(0.065)
0.0004*
(0.027)
0.0003*
(0.030)
万人医生数
(# of Doctors per 10,000 Population)
-0.5836**
(0.002)
-0.5829**
(0.002)
-0.5877**
(0.002)
-0.3910*
(0.031)
-0.3393*
(0.027)
-0.3883*
(0.031)
R20.12640.13480.15950.36020.37790.3881
样本数量269526952695269526952695
F/Wald Test18.8216.9715.77649.03695.04746.91
0.00000.00000.00000.00000.00000.0000

注:括号内为p值;*p < 0.05,**p < 0.01,***p < 0.001。

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就市场潜力而言,国家中心城市对周边城市发展具有正向带动作用,而其他城市整体上呈现负向影响或没有显著影响。结果显示在不考虑国家中心城市影响(MPCC)时,所有城市所产生的市场潜力(MPAC)总体影响并不显著,但是加入国家中心城市影响后,表5显示所有城市产生的市场潜力显著为负,而国家中心城市的影响则显著为正。这很大程度上表明国家中心城市对于周边城市的人口增长具有正向影响,但除国家中心城市外,其他所有城市整体上对于特定城市可能不产生影响或影响为负。同时,当考虑工业与国家中心城市市场潜力交叉项后,国家中心城市的正向影响也不再显著,表明国家中心城市市场潜力的正向效应,很大程度上来自中心城市与周边城市间的工业经济联系。

此外,1980年的人口规模、平均工资两个变量呈现显著的正向作用,到第二和第三等级中心城市距离影响为负,这些结果均符合预期。1980年的人口规模表征第一自然,表明城市的初始自然条件和资源禀赋对城市的人口增长依然有显著正向作用;城市平均工资越高,越有利于吸引外来人口;到第二和第三等级中心城市距离影响为负,表明距离区域中心城市越远,越不利于城市人口增长。随机效应模型的结果表明从业人员数对于城市人口增长也具有正向作用,这一定程度上说明较多的就业岗位对于城市的人口迁入也有正向作用。

与预期差异明显的是表征公共服务水平的万人医生数,该变量为显著负向影响,即公共服务水平越高的城市,其城市人口增长越慢。这可能与中国的户口制度有关,公共服务资源有限配置在城市的行政等级较高的城市,而户口制度的放松是从行政等级较低的小城市开始探索的[1,33]。因此,公共服务资源集中的大城市,人口增量反而越低。

随着城市的发展,主导产业将从第二产业向第三产业演进,制造业占比将降低,而服务业则增加。未来明确产业结构演进对区域城市体系演化的影响,进一步分析了中心城市工业与服务业的比例关系对周边城市人口增长的影响。以上研究表明工业和服务业占比与周边城市人口增长都呈现非线性关系,因此预期工业与服务业之比与周边城市人口同样是非线性的关系。从工业与服务业之比的二次项和三次项结果来看,中心城市的工业与制造业之比和周边城市人口增长同样是负二次项关系,即倒“U”型关系(表6)。豪斯曼检验结果表明固定效应更为适用,因此根据固定效应的结果,国家中心城市市场潜力按照其均值32.74计算,工业占比达到服务业92.04%时,即工业与服务业之比为47.93∶52.07左右,国家中心城市对周边城市发展的促进作用最大。

表6   产业结构(工业与服务业之比)的回归结果

Tab. 6  Regression results (industrial structure)

固定效应(FE)随机效应(RE)
模型1模型2模型1模型2
1980年人口规模
(Pop 1980)
0.2662***
(0.000)
0.2659***
(0.000)
全部城市市场潜力
(MPAC)
-0.0840*
(0.017)
-0.0826*
(0.020)
0.0378
(0.164)
0.0380
(0.165)
中心城市市场潜力
(MPCC)
-0.3508
(0.128)
-0.2417
(0.475)
-0.4579*
(0.023)
-0.5130
(0.105)
工业/服务业
(IM)
-0.4406***
(0.000)
-0.4394***
(0.000)
-0.2966***
(0.000)
-0.2957***
(0.000)
工业/服务业与市场潜力交叉项
(IM×MPCC)
0.01380***
(0.001)
0.0100
(0.288)
0.0256***
(0.000)
0.0276**
(0.004)
工业/服务业的二次项与市场潜力交叉项
((IM)2×MPCC)
-0.00006***
(0.000)
-0.00002
(0.788)
-0.00005***
(0.000)
-0.00001
(0.237)
工业/服务业的三次项与市场潜力交叉项
((IM)3×MPCC)
-0.0000001
(0.659
-0.00000001
(0.830)
到第二层级中心城市距离
(Distance 2ndRC)
-0.0507*
(0.019)
-0.0506*
(0.019)
-0.0052
(0.676)
-0.0051
(0.680)
到第三层级中心城市距离
(Distance 3rdRC)
-0.0698*
(0.041)
-0.0696*
(0.042)
-0.0353
(0.197)
-0.0352
(0.198)
从业人员数
(Employment)
0.1747
(0.132)
0.1710
(0.141)
0.5427***
(0.000)
0.5483***
(0.000)
工资水平
(Wage)
0.0005***
(0.001)
0.0005***
(0.001)
0.0003*
(0.044)
0.0003*
(0.045)
万人医生数
(# of Doctors per 10,000 Population)
-0.6217***
(0.001)
-0.6223***
(0.001)
-0.4300*
(0.017)
-0.4274*
(0.018)
R20.14220.14150.38570.3861
样本数量2695269526952695
F/Wald Test18.4016.74777.06782.77
0.0000.0000.0000.000

注:括号内为p值;*p < 0.05,**p < 0.01,***p < 0.001。

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3.2 中心城市产业结构对于周边城市人口增长的作用过程与机制

实证研究的结果表明以制造业为主的工业经济能够增强中心城市对周边城市的溢出效应,进而促进了周边城市人口的增长,而服务业则降低了中心城市的溢出效应,从而阻碍周边城市人口的增长。制造业和服务业对于周边城市人口增长产生完全相反作用,其原因是制造业的产品多为中间品,且产品实体化的特点,考虑到货物运输的成本,因此更倾向于在中心城市附近区域形成上下游产业分工和区域性生产网络,强化中心城市与周边中小城市联系,增强溢出效应,从而促进周边城市人口增长;而服务业的产品是多为最终产品,需要寻求更大的市场,加之产品的非实体化特点,使得中心城市的服务业更倾向于与距离较远但市场规模大的城市产生联系,而弱化与周边城市联系,降低中心城市的溢出效应[25]

尽管有学者认为服务业与制造业之间紧密的产业联系促使了上海周边中小城市的快速发展[34],但是这很难解释服务业同样发达的北京周边中小城市发展的困境。事实上,服务业与制造业间的紧密联系并不会改变服务业倾向于与空间距离较远但市场规模较大的城市联系的特点,即使是上海,其服务业与北京等中心城市的联系强度也远大于周边的昆山、嘉善等以制造业为主的中小城市[24,35]

4 中心城市产业结构演进对区域城市体系极化与均衡演化的影响

中心城市产业结构不仅影响了周边城市人口的增长,已有的研究表明产业结构对于城市自身的规模也有显著影响。Au等的研究表明在中国城市体系中,以服务业为主的城市达到效率最大化时的最优城市规模要远大于制造业[36],同时Hong等对美国城市规模与产业类型的研究也表明,以服务业为主的城市其规模要明显大于制造业城市[37]。综合考虑产业结构对于周边城市增长和城市自身规模的影响,发现以制造业为主的中心城市,其城市规模相对较小,同时强化对于周边城市的溢出效应,因此以该中心城市为核心的区域城市体系更容易形成大中小城市较为均衡的格局;而以服务业为主的中心城市,其城市规模相对较大,而对于周边城市的溢出效应较弱,因而更容易形成中心城市一城独大的极化型区域城市体系(图2)。

图2

图2   中心城市产业结构对区域城市体系极化与均衡演化的影响过程示意图

Fig. 2   The influence of the industrial mix of core cities on the evolution of the regional urban system


最终,中心城市产业结构不仅影响了周边城市人口的增长,同时对于区域城市体系极化与均衡特征及其演化产生重要影响。考虑到后工业社会,区域中心城市产业逐步由制造业向高端服务业演进,特别是北京等国家中心城市将纽约等以高端服务业为核心的世界城市作为发展目标,在没有外部干预的情况下,中国的区域城市体系更可能向以超大城市和大城市为主的相对极化的方向演化。

近年来新增人口在中国区域城市体系中整体是明显的极化过程,即新增人口主要向少数城市集中(表7)。而将区域城市体系中新增人口的基尼系数与国家中心城市产业结构进行统计分析,结果表明新增人口的基尼系数与滞后一期中心城市的服务业在GDP中的占比成正相关关系,而工业占比则为负相关(图3)。核心城市人口增长与腹地城市人口增长中位数差值与滞后一期中心城市产业结构也有相同相关关系。即服务业占比越高,区域城市体系新增城镇人口越集中,而工业占比越高,则新增人口在各城市中越区域均衡分布。统计分析的结果也印证了中心城市产业结构对区域城市体系极化与均衡演化有重要影响。

表7   1980—2020年受5个国家级中心城市影响的主要城市新增人口增长分布的基尼系数测度

Tab. 7  Gini coefficient measures of population growth in major cities affected by five central cities from 1980 to 2020

1980—19851985—19901990—19951995—20002000—20052005—20102010—20152015—2020
北京0.52250.54700.50560.49010.58420.61770.63510.5911
广州0.49980.61130.64070.67260.64440.65180.61440.6431
上海0.51220.52990.43900.48340.54080.56270.56800.5318
天津0.56870.60840.59160.57470.47700.48180.48320.4854
重庆0.55860.51950.57470.56260.59850.60250.60740.5914

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图3

图3   新增人口的基尼系数与国家中心城市工业和服务业占比的相关性分析

注:工业或服务业占比数据来源于各城市统计年鉴

Fig. 3   Correlation analysis of the Gini coefficient of t population growth and the proportion of service in the central city


此外,结合Henderson等对于产业结构影响城市自身规模的研究结果[10],能够很好地解释了中心城市产业结构与周边城市人口增长的倒“U”型关系。北京等国家中心城市市辖区以非农产业为主,制造业和服务业是此消彼长的关系。城市中制造业占比过高,则服务业占比低,由于以制造业为主的城市其最优规模相对较小,正如孙斌栋等所指出的较小的城市规模对周边城市的影响有限[7],因而制造业城市对周边城市的溢出效应也小,难以带动周边城市人口增长;当城市服务业占据城市经济的绝对主导地位时,尽管其最优城市规模较大,但是由于服务业自身对于周边城市的负向作用,因而减缓了周边的中小城市人口增长。因此,随着城市由工业社会向后工业社会转变,较高的制造业占比开始降低,而服务业占比则增加,当在达到服务业负向作用超过制造业正向作用的临界点之前,制造业的正向溢出随着服务业和城市规模的增大而不断强化,从而促进周边中小城市人口增长,临界点之后由于服务业的负向作用,正向溢出逐渐转变为负向的虹吸效应,对周边中小城市人口增长的促进作用开始降低,最终导致中心城市产业结构与周边城市人口增长呈现倒“U”型关系。正如表6所示只有当制造业和服务业相对平衡时,城市不因制造业占比过高而规模太小,也不因服务业占比过高而呈现负向作用,中心城市对周边中小城市的溢出效应才能达到最大。

5 结论

本文从产业异质性的角度,实证分析了中国5个国家中心城市产业结构对周边城市人口增长的影响,并结合产业类型与城市自身规模的相关研究,进一步探究了产业结构对于区域城市体系极化与均衡演化的影响。结果表明:

(1)制造业强化了区域中心城市对于周边城市的溢出效应,促进周边中小城市人口规模的增长,而服务业则弱化了区域中心城市的溢出效应,进而阻碍了中小城市规模扩张。制造业和服务业表现出的差异化的产业区域空间效应,很好的解释了长三角、珠三角发达的制造业所形成的大中小城市相对均衡的区域城市体系,而北京、纽约等城市发达的高端服务业则使得区域呈现显著的极化特征。同时,生产性服务业和制造业在城市间分别呈现点状集聚和面状集聚的差异化特征,也进一步印证了产业对于区域城市体系极化与均衡的影响[38]

(2)结合Henderson等产业类型对城市自身规模影响的相关研究[10],发现区域中心城市产业结构很大程度上影响了区域城市体系极化与均衡的特征及其演化过程。以制造业为主的区域中心城市更倾向于形成较为均衡的区域城市体系,而以服务业为主的区域中心城市倾向于导致较为极化的区域城市体系,因此中心城市产业很大程度上影响了区域的城镇化路径。随着国家中心城市以向纽约、伦敦等国际化大都市为目标,进一步聚焦发展高端服务业,中国区域城市体系将可能更加极化。同时,区域整体效率最优很可能与中心城市自身效率最优时的城市规模并不一致,因此城市的最佳规模需要平衡自身利益与区域整体利益,这常常被城市经济学家所忽略[2,36]

(3)中心城市产业结构与周边城市人口增长存在明显的倒“U”型关系,即过高的制造业或服务业占比都不利于中心城市对周边城市的溢出效应。过高的制造业占比,导致最优城市规模较小,因而减弱城市的溢出效应;而过高的服务业占比,则是由于服务业对溢出效应的抑制作用,因而同样不利于周边城市的人口增长。本文的研究结果不仅验证了Krugman提出的第一自然和第二自然的一般性理论框架,而且从产业异质性的角度对其进行了拓展。同时,制造业和服务业内部不同行业间的差异依然十分明显,因此对于不同细分行业之间的差异化影响值得进一步深入分析。

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This article introduces the reaction degree coefficient and the influence coefficient, constructs the industrial contact intensity model, and analyzes the spatial network characteristics of industries in the Changjiang River Delta and Beijing-Tianjin-Hebei (BTH) metropolitan region through the model of industry contact intensity. It focuses on the difference between the two regions and research the formation of metropolis shadow. The results show: 1) the better the industries contacts among the cities in region, the higher the economy level of region; 2) the better the industries between central city and other cities in region, he higher the economy level of region; 3) from the perspective of industries spatial contacts, the weak industry contact between central city and outlying areas is the key reason for the metropolitan shadow’s formation.

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基于产业空间联系的“大都市阴影区”形成机制解析: 长三角城市群与京津冀城市群的比较研究

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通过引入感应度系数和影响力系数,构建产业联系强度测度模型,探讨了当前长江三角洲与京津冀城市群产业空间联系特征,研究了两大城市群空间经济差异,基于产业空间联系视野对&#x0201c;大都市阴影区&#x0201d;的形成进行了实证研究。研究表明:① 城市群内部各城市间产业联系强度越高,城市群整体经济发展水平就越好;② 城市群的中心城市与其他各城市产业联系强度越高,城市群整体经济发展水平就越好、越趋于均衡;③从产业空间联系来看,中心城市与其邻近的外围地区的产业联系强度弱化是造成 &#x0201c;大都市阴影区&#x0201d;形成的关键因素。

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We analyse the geographies of urban networks created by leading producer services (PS) firms in China. Because of the national regulation of the Chinese state-led economy and the location strategies of global advanced producer services (APS) firms, the geography of global APS in China as examined by the Globalization and World Cities Research Network (GaWC) cannot be studied as a subnetwork of GaWC’s global network, but needs an empirical study based on a wide range of leading PS in the Chinese market. We explore the spatial differentiation in the connectivity of Chinese cities based on the location strategies of 323 APS firms in 287 Chinese cities. Beijing, Shanghai, Guangzhou and Shenzhen emerge as the primary nodes. The spatial distribution of banking, securities, and insurance services networks appears to be more even than those of non-financial PS firms. Regional disparity exists in terms of polycentric urban development in coastal China, as well as in the centralisation model in central and western areas. We suggest that owing to the continued tight regulation of China’s state-led economy and the nature of the location strategies of ‘globalised’ PS firms, the urban networks created by Chinese PS firms are not only an extension of urban networks at a global scale but also an embodiment of economic activities at other scales.

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In the past decade, rapid output growth and employment in the producer services of China have interested the academic sphere in spatial cluster and elaboration of locational theory concerning producer services' activities. The article examines spatial clusters and location characteristics of producer services in the Chinese urban system in the years 2000, 2005 and 2010. This study indicates that the location of cities of a large number of employments of service sectors is dispersed in China, comparing to geographical concentration of cities of developed manufacturing industry. Most of producer services experienced a spatially-centralization process in the period 2000-2010. However, there are different cluster modes in various service sectors. According to characteristics of industry agglomeration, these industries are classified into three modes, namely Primate City cluster mode, Rank-Size distribution mode and Balanced distribution mode. Primate City cluster mode means the largest city of industry have overwhelming employment scales, cluster characteristics of information service industry and business service industry. The number of employment of technology and science service industry as well as real estate in the largest cities is slightly more than other classes of cities. Thus these two industries are classified as Rank-Size distribution mode. Although the number of employment of finance and insurance service has increased in the first class cities, the largest city has no advantage of urban system and the number of employ is relatively balanced in urban system. Therefore, this industry is categorized as Balanced distribution mode. Moreover, the analysis of location quotient and scale of employment evidences there is positive correlation between amount of employment and the number of superiority functions. This means that distribution of producer services in China is in accordance with central place theory. Finally, the study also shows there are complementarities and division of labor among of cities in the same provinces and developed metropolitan regions.

[李佳洺, 孙铁山, 张文忠.

中国生产性服务业空间集聚特征与模式研究: 基于地级市的实证分析

地理科学, 2014, 34(4): 385-393.]

DOI:10.13249/j.cnki.sgs.2014.04.385      [本文引用: 1]

2000年以后,中国生产性服务业高速发展,产业格局逐渐形成。利用2000年、2005年和2010年3个年份地级市行业从业人员的数据,通过区位基尼系数和空间自相关性分析,发现中国生产性服务业在地理空间中整体呈现点状集中的模式,进而对不同行业的集聚特点进行比较分析,认为信息服务业和商务服务业是首位城市集聚模式,科研技术服务业和房地产业是位序规模分布的模式,金融业则是均衡分布的模式。最后,结合主要城市行业的相对优势度分析,得出随着从业人员的增加,承担的生产性服务功能更加综合,符合中心地理论的特点,在省域和城市群内各城市具有较好的职能分工和互补性。

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