地理科学进展, 2022, 41(5): 770-784 doi: 10.18306/dlkxjz.2022.05.003

研究论文

区域产业相关多样化和非相关多样化对中国出口市场多样化的影响

蒋晟,1,2, 贺灿飞,1,2,*

1.北京大学城市与环境学院,北京100871

2.北京大学—林肯研究院城市发展与土地政策研究中心,北京100871

Effects of related variety and unrelated variety on China’s export market diversification

JIANG Sheng,1,2, HE Canfei,1,2,*

1. School of Urban and Environmental Sciences, Peking University, Beijing 100871, China

2. Peking University-Lincoln Institute Center for Urban Development and Land Policy, Beijing 100871, China

通讯作者: *贺灿飞(1972— ),男,江西永新人,教授,博士生导师,主要从事经济地理、产业与区域经济研究。E-mail: hecanfei@urban.pku.edu.cn

收稿日期: 2021-07-26   修回日期: 2021-11-26  

基金资助: 国家自然科学基金重点项目(41731278)

Received: 2021-07-26   Revised: 2021-11-26  

Fund supported: Major Program of National Natural Science Foundation of China(41731278)

作者简介 About authors

蒋晟(1994— ),男,浙江金华人,博士生,研究方向为贸易地理和跨国公司。E-mail: 1901111739@pku.edu.cn

摘要

在出口环境下行的背景下,出口市场多样化成为应对贸易冲击的重要策略,也是中央的重要政策方向。近年来,经济地理学界开始关注产业多样化对于产业出口拓展的影响。基于以上背景,论文利用2002—2016年中国海关数据,构建产业相关多样化和非相关多样化指标,从HS四位数产业尺度分析区域产业多样化对于区域出口市场多样化的影响,并进一步构建中介变量来分析其中的影响机制。此外,考虑产业多样化对于不同产业影响的差异,将产业分为劳动密集型、资本密集型和技术密集型3类进行分析。结果表明:① 产业多样化显著促进了区域出口市场多样化的提升;② 产业多样化主要通过降低企业的出口沉没成本来促进区域出口市场多样化,且产业相关多样化的促进作用更强;③ 相反,产业多样化会抑制企业生产效率和产品创新能力的提升,进而抑制产业的出口市场多样化;④ 产业多样化的影响存在明显的产业异质性,其中,劳动密集型产业能从两类产业多样化中获益,而资本密集型和技术密集型产业仅能从产业相关多样化中获益。研究一方面为区域贸易政策的制定提供了新思路,即通过提升本地产业多样化来强化区域的市场拓展能力;另一方面也为经济地理学理解产业多样化对于产业出口拓展的影响提供了新的证据和思考。

关键词: 相关多样化; 非相关多样化; 出口市场多样化; 产业创新; 产业出口拓展; 演化经济地理; 中国

Abstract

Under the background of export depression, export market diversification is not only a useful strategy to resist external shocks, but also an important policy agenda. Recently, effects of industrial diversification on export extension have received plenty of attention from academic researchers. This study examined the effects of related and unrelated variety on regional export market diversification at HS 4-digit industry level based on China Customs data during 2002-2016 and explored the mechanisms of industrial diversification. In addition, heterogeneous effects of related and unrelated variety across different industries were empirically examined. Based on entropy proxy, this study constructed the proxies of export market diversification, related variety, and unrelated variety and found that both related and unrelated variety can enhance export market diversification. This study also constructed three mediating variables to capture the mechanisms of industry diversification and found that both related and unrelated variety can promote export market expansion by reducing firms' export sunk costs, but impede export market expansion by damaging firms' productivities and innovation capabilities. This study classified all the 4-digit industries into three types, including labor intensive industries, capital intensive industries, and knowledge intensive industries and found that labor intensive industries benefit from both related and unrelated variety; capital intensive industries benefit from related variety, but are negatively affected by unrelated variety; and knowledge intensive industries benefit from related variety, but not affected by unrelated variety. The results of this study not only afford a new perspective for trade policy making, but also provide new evidence and analysis for the research of industry diversification and export market diversification.

Keywords: related variety; unrelated variety; export market diversification; industrial innovation; export expansion; evolutionary economic geography; China

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本文引用格式

蒋晟, 贺灿飞. 区域产业相关多样化和非相关多样化对中国出口市场多样化的影响[J]. 地理科学进展, 2022, 41(5): 770-784 doi:10.18306/dlkxjz.2022.05.003

JIANG Sheng, HE Canfei. Effects of related variety and unrelated variety on China’s export market diversification[J]. Progress in Geography, 2022, 41(5): 770-784 doi:10.18306/dlkxjz.2022.05.003

改革开放以来,中国对外贸易快速增长,成为推动经济发展的主要力量。近年来,由于全球经济下行和他国贸易保护政策的兴起,中国的出口贸易受到冲击。出口市场多样化有助于提升贸易韧性,是应对贸易冲击的有效策略[1-2]。2019年,中央出台关于推进贸易高质量发展的指导意见,提出要优化贸易市场结构,在深耕传统发达经济体市场的同时,积极拓展与亚非拉等新兴经济体的合作[3]。在这一背景下,有必要关注如何推动区域出口市场的多样化。而区域产业多样化作为区域经济发展的重要特征,其对于中国出口市场多样化的影响是一个重要的问题。

经济地理学对于产业多样化的讨论由来已久。早期研究关注产业专精化和产业多样化对于区域经济发展影响的差异[4-6]。其中,一类学者强调产业专精化,即马歇尔外部性对于经济发展的促进作用,认为企业能从本地相同产业的集聚中获益。另一类学者则强调了产业多样化,即雅各布斯外部性的积极作用,认为不同产业的共聚可以弥补企业的能力短板,促进企业创新[7]。演化经济地理学者Frenken等[5]在2007年扩展了产业多样化概念,创造性地提出相关多样化和非相关多样化概念,并由此引发了学界对于两类产业多样化影响的探讨。

早期文献关注产业多样化对于区域经济增长的影响。主流文献基于Frenken等[5]所提出的熵指标法,研究相关多样化和非相关多样化对于区域经济发展的影响。Frenken等[5]基于荷兰就业数据发现,产业相关多样化有利于本地就业增长,而非相关多样化不利于本地就业增长,但能降低失业率[5]。Boschma等[8]基于意大利贸易数据发现,产业相关多样化显著促进了本地就业、产品附加值和劳动生产率的增长。孙晓华等[9]基于中国城市统计年鉴数据,发现产业相关多样化有助于区域经济发展,而产业非相关多样化则有助于降低区域失业率。彭荣熙等[10]则基于中国工业企业数据,讨论了产业相关多样化和非相关多样化对于区域短期和长期经济韧性的影响。也有文献在传统熵指标法的基础上,结合Hidalgo等[11]所提出的共现法,通过计算产业间的邻近度来识别相关和非相关产业。Boschma等[12]基于产业分类编码和产业邻近度构建了两套产业相关和非相关多样化指标,并发现相关多样化对于西班牙产业附加值增长的显著促进作用。Firgo等[13]借鉴Boschma等[12]的做法,基于奥地利劳动力数据同样构建了两套产业相关和非相关多样化指标,发现了产业相关多样化能够促进劳动力增长,而非相关多样化只在非都市地区才有显著的促进作用。

近年来,学界拓展了对产业多样化问题的讨论,将其与区域创新、创业和国际化等议题联系起来。其中,产业出口拓展(包括出口市场多样化、出口额增长和企业出口进入等现象)作为当今全球化背景下产业发展的重要手段,受到学界关注。Antonietti等[14]基于意大利制造业数据讨论两类产业多样化(产业相关多样化和非相关多样化)与企业出口绩效间的关联性,但并未发现两者间显著的联系。Farole等[15]基于低收入国家企业数据发现,产业多样化显著抑制了企业的出口进入决策。Cainelli等[16]基于意大利人力市场数据发现,相关多样化并未对本地产业出口额增长产生显著影响。而Naldi等[17]基于瑞典初创企业案例发现,产业相关多样化和非相关多样化促进企业的出口市场进入。贺灿飞等[18]基于中国海关数据发现,相关产业的存在显著促进了区域出口市场的拓展。

综上所述,现有研究并未明确两类产业多样化对于产业出口拓展的影响。这表明,现有研究对于上述问题的讨论仍然存在不足。首先,主流研究语境仍是发达国家[14,16-17],而针对发展中国家的研究往往未对两类产业多样化展开系统性讨论[15,18]。事实上,相比于发达国家,发展中国家的企业以加工和中低端制造为主,处于全球价值链的下游,技术能力不足[19]。而企业技术能力的差异可能造成发达国家和发展中国家出口市场拓展模式的差异。例如,产业多样化可通过促进发达国家企业的产品创新推动出口市场的拓展,但这对于发展中国家企业而言,可能并非易事[17]。其次,主流研究较少讨论产业多样化对于产业出口拓展影响的机制。而Content等[20]指出,理解产业多样化的作用机制是了解产业多样化影响的重要前提。最后,相关研究主要从企业或城市尺度讨论产业多样化对于产业出口拓展的影响,却忽略了产业差异所带来的异质性。事实上,研究发现产业多样化对于区域经济增长的影响存在明显的产业异质性[21]

因此,本文基于中国海关数据讨论相关多样化和非相关多样化对于区域出口市场多样化的影响,主要贡献体现在3个方面:① 为产业多样化与产业出口拓展的讨论提供了来自中国的证据;② 尝试讨论产业多样化对于产业出口市场多样化的影响机制,完善对于产业多样化影响机制的理解;③ 关注产业差异所造成的异质性,丰富对于产业多样化影响的认识。

1 理论框架

1.1 出口市场多样化的动因

经典的国际贸易理论认为,当企业能够承担市场拓展所需的出口沉没成本,就会向新市场拓展[22]。具体而言,在经典的异质性企业贸易模型中,企业生产效率和出口沉没成本是决定企业是否进行市场拓展的两个关键因素[22]。其中,出口沉没成本指代企业为适应新市场与母国在需求、制度、文化等方面的差异而在营销环节上的成本投入[23];而生产效率则指代企业投入单位成本所能生产的产品数量[24]。当出口沉没成本较低时,企业能够负担出口沉没成本,进而选择向新市场拓展;而当企业生产效率较高时,企业的盈利情况更好,进而更有能力承担向新市场拓展所需的出口沉没成本[22]。国际贸易研究较少讨论企业产品创新对于出口市场拓展的影响。然而,国际商务学者基于发达国家案例指出,产品创新也是企业出口市场拓展的重要驱动力[25-28]。特别是在新产品面向市场的早期,出口不仅能收获更大的市场,也能够避免竞争对手获得这些创新[29]。当然,技术创新是一种高风险的策略,容易陷入技术锁定陷阱。因此,能通过产品创新的方式进行出口市场拓展的企业,往往在研发、设备、人力等环节上大量投入,即具有较高的技术水平[30]

1.2 区域产业多样化与市场多样化

对于产业多样化的讨论源于Jacobs[31]所提出的雅各布斯外部性。Jacobs[31]认为,本地经济增长源于产业创新,而创新的本质则是预先存在的各种知识和产品以新的方式重新组合。Frenken等[5]认为知识溢出是产业多样化的主要作用。他们将多样化分为相关多样化和非相关多样化两种,并指出由于知识交流的前提是不同知识间存在认知邻近。因此,知识溢出往往发生在相似且不同的产业之间,即相关多样化,而非相关多样化则更多体现了投资组合效应,即通过多种非关联产业的组合以分散风险。尽管Frenken等[20]的观点在部分实证研究中得到证实,但也有文献发现了与Frenken等预测不符的结果。如Bishop等[21]和Caragliu等[32]分别基于欧盟和英国产业数据发现,相关多样化对于本地就业增长的影响并不显著,而非相关多样化的正面影响更为显著。这表明,产业多样化的影响机制可能较为复杂。对此,Castaldi等[33]指出,不相关多样化之间也可能存在知识的溢出,并且不相关知识的交流可能促进更为激进的创新产生。

总而言之,尽管学界对于产品多样化具体的作用机制仍未有定论,但需肯定的是,产业多样化作为集聚经济的一个表现形式,主要通过促进知识溢出的方式影响区域的产业发展。就出口市场多样化而言,结合上文对于市场多样化动力机制的讨论,本文认为产品多样化可能通过以下3种方式施加影响。

机制1:产业多样化通过促进海外市场相关知识的溢出,降低企业的出口沉没成本,推动企业向新市场拓展。Cainelli等[16]指出,海外市场相关的知识可能会在本地不同行业间流动。而McCann[34]指出,相较于同行业企业,不同行业企业的出口市场可能存在差异。这使得不同行业企业所掌握的市场知识不同。而通过学习差异化的市场知识,企业得以完善其对于海外市场认知的短板,进而实现新市场的拓展。Koenig等[35]基于法国出口数据,在假定企业生产效率不变后发现,本地其他产业的存在对于企业的出口市场进入仍存在显著正向影响。

机制2:产业多样化通过促进产业技术知识的流动,提升企业的生产效率,进而推动企业的出口市场拓展。企业并非是全知全能的,可能在技术上存在短板。而产品多样化则为企业提供了一个相对完善的知识库。通过学习其他产业的技术知识,企业得以补强其技术短板,提升生产效率[36]。另外,Coe等[37]基于对全球生产网络的研究指出,产业并非是独立发展的。在产业日益复杂化的今天,任何一个产业的发展有赖于其他产业的支撑。Aarstad等[38]指出,企业间的密切合作是企业生产效率提升的重要保障。这意味着,企业不仅需要了解其所在产业相关的知识,还需要对其他行业的知识有一定的了解,以保障其与供应链内其他企业沟通的顺畅。而通过学习其他产业的技术知识,企业得以更好地与其他企业合作,进而通过提升生产效率拓展出口市场。Quatraro[39]基于意大利企业数据也发现,产业多样化显著促进了企业生产效率的提升。

机制3:产业多样化还通过促进产业技术知识的流动,降低企业的创新沉没成本,推动企业以产品创新的方式进行出口市场拓展。Schilling等[40]指出,成功的创新往往是从不同领域借用知识的结果,而不能集中关注单一领域。产业多样化则为产品技术创新提供了重要的外部知识溢出。身处于多样化的产业集群中,企业可通过学习多元的知识,得以发现更多的创新路径,进而避免陷入创新路径锁定的陷阱中,造成不必要的成本浪费。Naldi等[17]基于瑞典企业数据发现,本地产业多样化有助于企业通过产品创新的方式进入海外市场。

需要注意的是,对于上述3类机制,两类产业多样化的作用可能存在差异。相比于产品创新,企业出口沉没成本的降低和生产效率的提升更可能依赖于渐进式的知识学习模式,即企业围绕自身知识体系,对已有体系的短板进行补强[35,36]。此时,由于认知距离的存在,与企业已有知识更为接近的知识往往更容易被企业吸收利用[41]。因此,相较于非相关多样化,相关多样化的促进作用可能更强。与前两类模式不同,产品创新可能依赖更为激进的知识学习模式,即将不同类型知识通过全新方式重新组合[34]。因此,当企业进行产品创新时,可能需要打破其已有的认知模式,将自身知识与本地其他知识通过新的方式进行组合。此时,企业不仅需要与自身知识相关的知识,可能还需要大量与自身知识高度不相关的知识,以碰撞出更大的创新火花。因此,产业非相关多样化在企业的产品创新过程中,可能也发挥了重要作用[33]

基于上述论述,本文提出如图1所示的理论模型和4点假设:

图1

图1   理论模型

Fig.1   Theoretical model


假设1:区域产业的相关多样化和非相关多样化均能促进区域产业的出口市场多样化。

假设2:区域产业的相关多样化和非相关多样化通过降低企业的出口沉没成本,推动区域产业出口市场多样化。相比于非相关多样化,相关多样化的促进作用可能更强。

假设3:区域产业的相关多样化和非相关多样化通过提升企业的生产效率,推动区域产业出口市场多样化。相比于非相关多样化,相关多样化的促进作用可能更强。

假设4:区域产业的相关多样化和非相关多样化通过提升企业产品创新能力,推动区域产业出口市场多样化。并且,非相关多样化的促进作用可能并不弱于相关多样化。

2 研究数据与方法

2.1 数据来源与处理

本文研究的主要数据来源于2002—2016年中国海关数据库的出口数据。该数据包含了出口企业编码、出口额、产品HS编码,所在城市及出口目的地、贸易类型、企业所有制等信息。基于研究需求,本文将数据进行必要的处理:① 将各年份产品的HS六位数编码,按世界银行提供的方法,统一转化为HS2002六位数编码规则;② 基于Ahn等[42]和Manova等[43]的方法将数据中的贸易公司剔除,以排除贸易公司对于最终结果的影响;③ 将加工贸易类企业排除;④ 将城市出口指标与城市统计年鉴数据匹配,剔除数据缺失项。处理后数据为2002—2016年的非平衡面板数据,包含1007175条样本、1240种四位数产业、237个国家/地区和283个城市。

2.2 核心指标构建

本文基于Frenken等[5]所提出的熵值法构建HS 四位数产业的市场多样化指标。选择四位数产业尺度的原因有2个。首先,需要排除产业多样化和市场多样化之间互为因果所造成的内生性问题。例如,某一城市的5种产业分别向5个国家/地区出口,此时若基于城市尺度构建产业多样化和市场多样化指标,那么很明显,这2个指标指代的是同一个现象。而基于四位数产业尺度构建市场多样化指标则能避免这一情况。其次,本文还要分析产业多样化影响的产业异质性,选择四位数产业尺度可以增加数据量,尽可能多地保留产业特征。相应的计算方法如下:

VARIETYs,i=c=1Nps,i,cln1ps,i,c

式中:ps,i,c指代s城市的四位数产业ic国的出口额占该城市产业i总出口额的比重;VARIETYs,i则指代产业i出口市场的熵值,熵值越高,表明产业i的出口市场多样化程度越高。

在现有研究中,产业多样化指标的构建方法主要有2类:一类基于Frenken等[5]的方法,先通过产业编码识别相关和非相关产业,再通过熵值法计算产业相关和非相关多样化指数;另一类则先用Hidalgo等所提出的共现法,通过计算产业邻近度区分相关和非相关产业,再基于熵值法计算产业相关和非相关多样化指数[11-12]。其中,前者更关注产业在技术上的相似性,而后者则将制度关联、组织关联等多方面因素纳入考虑[12]

本文首先基于产业编码构建产业多样化指标。由于本文基于四位数产业尺度分析,因此,Frenken等[5]所提出的城市产业的整体相关多样化指标并不适用于本文。对此,本文借鉴Bishop等[21]的做法,将与四位数产业i同属于二位数产业组g的其他四位数产业视为产业i的相关产业,通过熵值法计算产业i的相关多样化指数。计算方法如下:

RELVs,i=i=1gps,iln1ps,i

式中:ps,is城市的四位数产业i出口额占二位数产业组g出口额的比重;RELVs,i则指s城市产业i所属的二位数产业组g出口产品的熵值,熵值越高,表明产业i周边有较多的相关产业,即有较高的产业相关多样化水平。

而非相关多样化指标则与Frenken等[5]的相同。相应的计算方法如下:

UNRELVs=g=1GPs,gln1Ps,g

式中:Ps,gs城市的二位数产业组g出口额占该城市总体出口额的比重;UNRELVs则指s城市出口二位数产业的熵值,熵值越高,表明城市s有较高的产业非相关多样化水平。

接着,本文基于产品邻近度构建产业多样化指标。本文通过Hidalgo等[11]的方法测度了HS四位数产业间的邻近度,并借鉴Boschma等[12]的方法,将邻近度高于0.33(前15%)的产业定义为相关产业,其余产业则为非相关产业。相关多样化指标计算方式如下:

RELVs,i=j=1gps,i,jln1ps,i,j

式中:ps,i,js城市与四位数产业i相关的产业j出口额占产业组g(产业i的相关产业集合)出口额的比重;RELVs,i则指s城市产业i的相关多样化熵值,熵值越高,表明产业i周边有较多的相关产业,即有较高的产业相关多样化水平。

同理,非相关多样化指标计算方式如下:

UNRELVs,i=q=1zps,i,qln1ps,i,q

式中:ps,i,qs城市与四位数产业i非相关的产业q的出口额占产业组z(产业i的非相关产业集合)出口额的比重;UNRELVs,is城市产业i的非相关多样化熵值,熵值越高,表明产业i周边有较多的非相关产业。

针对上文所述的3类产业多样化影响机制,本文分别构建了3个中介变量。首先,选取s城市从事四位数产业i下属四位数旧产品,即上一年已存在产品的企业的当年平均出口额(SUPOLDs,i),用以捕捉产业多样化对于企业生产效率的影响。选取该指标的原因在于,从国际贸易理论和中外实证研究结果来看,企业的出口额主要由企业的生产效率决定,而与出口沉没成本关系不大,因此能较好地反映产业内企业的生产效率情况[44-46]。其次,以s城市的四位数产业i是否出现六位数新产品(SUPNEWs,i)用以衡量产业多样化对于企业产品创新的影响。为保证新产品确实为本地知识创新所得,而非模仿其他城市的已有产品,本文规定,新出现的六位数产品的区位商至少为全国前5%水平,即具有本地特色。最后,由于没有指标能直接反映出口沉没成本的变化,本文选取s城市的四位数产业i下属的六位数旧产品当年的平均出口额(OLDVs,i),如式(6)所示,将其与SUPOLDs,i回归,取残差(DEMs,i)用以衡量产业多样化对于出口沉没成本的影响。其理由在于,根据国际贸易理论,产业的整体出口额受企业生产效率和出口沉没成本的影响,因此,取其与企业出口额回归的残差能剔除企业生产效率的影响,仅保留出口沉没成本的影响[47]

OLDVs,i,t=α0+β1SUPOLDs,i,t+β2Control+μs,i+DEMs,i,t

式中:Control为控制变量(表1);α0为常数;β1β2为系数;下标中t为时间;μs,i为城市s产业i的固定效应。

表1   控制变量基本信息

Tab.1  Control variables

变量类型变量名称变量描述数据来源
集聚外部性相关变量LQs,i,t-1s城市t-1年i产业的区位商,用以衡量产业专精度海关数据库
HHIs,i,t-1s城市t-1年i产业企业出口额的赫芬达尔指数的倒数,用以衡量同行业企业竞争海关数据库
Denses,t-1s城市t-1年的人口密度(人/km2),用以衡量人口集聚所带来的城市外部性中国城市统计年鉴
其他变量GDPPs,t-1s城市t-1年的人均GDP(元/人),用以衡量城市经济发展水平中国城市统计年鉴
COUs,t-1s城市t-1年城市整体出口市场多样化水平,基于熵值计算,与式(1)相似海关数据库

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2.3 模型设定

本文构建固定效应模型对上述3个假设进行检验。式(7)为基础模型,主要测度两类产业多样化对于出口市场多样化的影响。为了捕捉上文所述的3类影响机制,本文分别在式(8)、(9)和(10)中加入DEMs,i、SUPOLDs,i和SUPNEWs,i三个中介变量。而在式(11)、(12)和(13)中,DEMs,i、SUPOLDs,i和SUPNEWs,i被分别加入,以验证中介效应的存在与否。

VARIETYs,i,t=α0+β1RELVs,i,t-1+β2UNRELVs,t-1+β3Control+μs,i+ϵs,i,t
DEMs,i,t=α0+β1RELVsit-1+β2UNRELVst-1+β3Control+μs,i+ϵs,i,t
SUPOLDs,i,t=α0+β1RELVs,i,t-1+β2UNRELVst-1+β3Control+μs,i+ϵs,i,t
SUPNEWs,i,t=α0+β1RELVs,i,t-1+β2UNRELVs,t-1+β3Control+μs,i+ϵs,i,t
VARIETYs,i,t=α0+β1RELVs,i,t-1+β2UNRELVs,t-1+β3DEMs,i,t+β4Control+μs,i+ϵs,i,t
VARIETYs,i,t=α0+β1RELVs,i,t-1+β2UNRELVs,t-1+β3SUPOLDs,i,t+β4Control+μs,i+ϵs,i,t
VARIETYs,i,t=α0+β1RELVs,i,t-1+β2UNRELVs,t-1+β3SUPNEWs,i,t+β4Control+μs,i+ϵs,i,t

式中:β1β2β3β4为系数;ϵs,i,t为模型残差。

为了排除其他区域特征对于结果的干扰,本文还对其他变量进行控制。首先,假定其他集聚外部性指标不变,包括产业专精化、企业间竞争强度以及人口密度;其次,还假定城市经济发展水平、城市市场多样化水平以及年份特征不变。

3 结果与分析

3.1 中国出口市场与产品多样化概况

本文初步探究市场多样化和产业多样化之间的关联性。基于各城市四位数产业每年的市场多样化数值,本文将高于该产业当年全国平均值的产业定为高市场多样化产业,反之则为低市场多样化产业。接着,按上文所述的2种方法刻画了两类产业所在城市前一年产业相关多样化和非相关多样化的核密度分布。可以看出(图2),高市场多样化组的核密度曲线均明显位于低市场多样化组的右侧,显示高市场多样化产业所在城市的两类产业多样化均比低市场多样化产业所在城市的高。这表明,市场多样化和两类产业多样化之间可能存在正向关系。并且,ANOVA检验显示,高、低市场多样化组的相关多样化与非相关多样化分布差异在统计上显著。

图2

图2   高市场多样化、低市场多样化产业的相关多样化和非相关多样化核密度分布

Fig.2   Kernel density distribution of related variety and unrelated variety


3.2 基准回归分析

本文首先基于产业编码构建产业多样化指标,其回归结果如表2所示。并且,本文也检验了加入中介变量前后两类产业多样化系数是否在统计学上存在显著差异,以验证中介效应的存在与否。结果均通过检验。其中,模型1显示本地产业相关多样化和非相关多样化对于四位数产业市场多样化的影响,而模型2~3、模型4~5和模型6~7则分别表示产业多样化对于出口沉没成本、企业生产效率和企业创新能力的中介作用。与此同时,为了比较两类产业多样化影响的强弱,将RELV和UNRELV两个变量进行标准化处理。如模型1所示,两类产业多样化均显著促进了本地产业的出口市场多样化。这一结果支持了假设1,表明在中国,产业多样化所带来的知识溢出是显著存在的,并且产业相关多样化的促进作用更强。

表2   产业相关多样化和非相关多样化对于出口市场多样化的影响(基于产业编码计算)

Tab.2  Effects of related variety and unrelated variety on export market diversification (based on industry code)

变量VARIETYDEMVARIETYSUPOLDVARIETYSUPNEWVARIETY
(模型1)(模型2)(模型3)(模型4)(模型5)(模型6)(模型7)
RELV0.158***0.949***0.086***-0.016***0.158***-0.005***0.158***
UNRELV0.014***0.255***-0.005***-0.008***0.014***-0.005***0.014***
LQ0.015***0.011*0.014***0.012***0.014***0.0003**0.015***
HHI0.089***-0.038***0.092***0.001**0.089***-0.001***0.089***
GDPP0.079***-0.171***0.092***0.020***0.078***-0.004***0.078***
Dense0.008***-0.012***0.009***0.003***0.008***-0.002***0.008***
COU0.184***-0.626***0.231***0.008***0.184***-0.018***0.185***
DEM0.076***
SUPOLD0.036***
SUPNEW0.044***
年份
常数项0.046***1.918***-0.099***-0.075***0.048***0.091***0.042***
样本数1007175100717510071751007175100717510071751007175
拟合度0.2550.0400.3940.0010.2560.0170.255
系数差异检验差异显著差异显著差异显著

注:本文通过检验中介效应来验证产业多样化的影响机制。首先检测RELV和UNRELV两个变量是否对中介变量DEM造成影响,再检验DEM这一中介变量是否对最终的VARIETY产生影响,最终判断RELV和UNRELV对于VARIETY的影响,因此,模型2、4和6以DEM、SUPOLD和SUPNEW三个中介变量为因变量,用以检验RELV和UNRELV对于中介变量的影响。*、**、***分别表示通过10%、5%、1%的统计显著性水平检验,“是”表示假定该变量不变。下同。

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模型2的结果显示,两类产业多样化均显著促进了本地已有六位数产品的出口额残差DEM的增长,其中,产业相关多样化的促进作用明显强于非相关多样化。而模型3的结果显示DEM指标与本地四位数产业的出口市场多样化呈显著的正向关系。这一结果符合本文的假设2,即本地产业多样化通过降低企业出口沉没成本,推动本地产业的出口市场多样化,且相比于产业非相关多样化,产业相关多样化影响更大。模型4的结果显示,两类产业多样化的系数均显著为负,表明其抑制了本地企业生产效率的提升,其中相关多样化的抑制作用强于非相关多样化。而模型5的结果显示,SUPOLD的系数显著为正,表明本地企业生产效率与产业出口市场多样化之间存在显著的正向关系。综合来看,结果不符合假设3的预期。在产品创新方面,模型6的结果显示,两类产业多样化的系数显著为负,表明其抑制了企业的产品创新,并且两类产业多样化的抑制强度相似。而模型7中,SUPNEW的系数显著为正,表明企业的产品创新与产业出口市场多样化之间存在显著的正向关系。因此,假设4的预期也未得到证实。综上所述,在出口沉没成本方面,两类产业多样化的系数符合假设预期。而对于另外两类机制,两类产业多样化的系数均与预期相反。对此,本文认为,可能与中国企业的技术吸收能力不足有关。与发达国家不同,中国产业以中小企业为主,且位于全球价值链中下游,技术水平较低,不仅无法有效吸收本地其他产业的技术溢出,反而可能由于不同产业间的资源竞争摊薄了企业的生产资源,降低了企业的生产效率和产品创新能力[48-49]

本文也基于产业邻近度构建产业多样化指标,其回归结果如表3所示。3类机制的中介效应均通过检验。其中,模型8显示本地产业相关多样化和非相关多样化对于四位数产业市场多样化的影响,而模型9~10、模型11~12和模型13~14分别表示产业多样化对于出口沉没成本、企业生产效率和产品创新的中介作用。如模型8所示,两类产业多样化均显著促进了本地产业的出口市场多样化,与假设1相符。但与模型1结果不同,模型8的相关多样化的系数为0.071,而非相关多样化的系数为0.086,反而大于前者,表明非相关多样化所带来的知识溢出似乎高于相关多样化所带来的知识溢出。

表3   产业相关多样化和非相关多样化对于出口市场多样化的影响(基于产业邻近度计算)

Tab.3  Effects of related variety and unrelated variety on export market diversification (based on industry proximity)

变量VARIETYDEMVARIETYSUPOLDVARIETYSUPNEWVARIETY
(模型8)(模型9)(模型10)(模型11)(模型12)(模型13)(模型14)
RELV0.071***0.506***0.032***-0.027***0.072***-0.002***0.071***
UNRELV0.086***0.474***0.049***0.007***0.086***-0.008***0.086***
LQ0.016***0.019***0.014***0.012***0.016***0.0002*0.016***
HHI0.091***-0.025***0.093***0.001**0.091***-0.001***0.091***
GDPP0.072***-0.266***0.093***0.020***0.071***-0.002***0.072***
Dense0.001***-0.007***0.010***0.003***0.010***-0.002***0.072***
COU0.135***-0.718***0.191***-0.0020.135***-0.015***0.136***
DEM0.077***
SUPOLD0.035***
SUPNEW0.036***
年份
常数项0.077***1.447***-0.034***-0.0060.077***0.087***0.074***
样本数1007175100717510071751007175100717510071751007175
拟合度0.2430.0200.3910.0010.2450.0170.243
系数差异检验差异显著差异显著差异显著

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模型9~10的结果与表2中模型2~3的结果一致,表明两类产业多样化均通过降低企业的出口沉没成本推动产业出口市场多样化,且产业相关多样化的促进作用更强。模型11~12显示,产业相关多样化会抑制本地企业的生产效率提升,进而阻碍产业的出口市场多样化,而产业非相关多样化则会促进本地企业的生产效率提升,进而促进产业的出口市场多样化。这一结果不仅与模型4~5的结果存在出入,也不符合假设3对于两类产业多样化的预测。而模型13~14的结果显示,两类产业多样化均通过抑制企业的产品创新间接阻碍产业的出口市场多样化,其中,非相关多样化的抑制作用更强。这一结果与模型6/7基本一致,也不符合假设4的预测。

综上所述,2种方法构建的产业多样化指标的回归结果差异不大,证明了实证结果的可靠性。从结果来看,两类产业多样化主要通过降低企业的出口沉没成本,推动产业的出口市场多样化。对于表2表3结果存在出入之处,本文借鉴Boshcma等[12]和Firgo等[13]的评判标准,认为表2的结果可能更为可信。理由如下:① 上述结果显示,降低出口沉没成本是产业多样化的主要作用机制。那么,由于认知距离的限制,产业非相关多样化的促进作用应弱于产业相关多样化。因此,表2中模型1的结果更符合理论预期。② 从模型拟合度来看,表2中模型的拟合度也普遍高于表3中模型的拟合度。

3.3 产业异质性分析

本文经验性地探究产业多样化对于本地不同产业出口市场多样化影响的异质性。具体而言,本文参考蒋海兵等[50]的产业分类标准,在排除了“其他制品(Hs90-97)”产业后,基于HS二位数代码,将产业分为如表4所示的3类。基于上文对于产业多样化指标构建方法的评估,此处通过产业编码构建两类产业多样化指标。本文首先对3类产业进行分组回归分析,结果由表5展示。从模型15、16和17的结果来看,产业多样化的影响确实存在显著的产业异质性。其中,相关多样化的系数都显著为正,分别为0.154、0.203和0.141;而非相关多样化的异质性较高,劳动密集型产业的系数显著为正(0.038),资本密集型产业的系数则显著为负(-0.008),而技术密集型产业的系数则不显著。

表4   产业分类

Tab.4  Industry classification

产业分类所属细分产业
劳动密集型产业动物制品(HS01-05),植物制品(HS06-14),食品制品(HS15-24),皮革制品(HS41-43),木材制品(HS44-49),纺织材料(HS50-55),纺织服装、鞋帽制造业(HS56-67)
资本密集型产业矿产品(HS25-27)、化工产品(HS28-38)、塑料制品(HS39-40)、石料、陶瓷玻璃类制品(HS68-70)、金属制品(HS71-83)
技术密集型产业机械电子产品(HS84-85)、交通运输产品(HS86-89)

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表5   产业相关多样化和非相关多样化影响的产业异质性

Tab.5  Heterogeneous effects of related variety and unrelated variety

变量VARIETY
劳动密集型资本密集型技术密集型
(模型15)(模型16)(模型17)
RELV0.154***0.203***0.141***
UNRELV0.038***-0.008***0.001
LQ0.007***0.059***0.023***
HHI0.071***0.099***0.131***
GDPP0.061***0.059***0.125***
Dense0.005**0.011***0.009***
COU0.153***0.161***0.261***
年份
常数项0.189***0.167***0.034
样本数324591395200196991
拟合度0.2430.2440.343

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本文发现产业多样化对于3类产业出口市场多样化的影响机制也存在差异。如表6所示,两类产业多样化均通过降低出口沉没成本推动产业出口市场的多样化,且相关多样化的促进作用均强于非相关多样化。其中,如模型18~20所示,产业相关多样化对于劳动密集型产业的促进作用最强,对资本密集型产业的促进作用次之,对于技术密集型产业的促进作用最弱;非相关多样化对于劳动密集型产业的促进作用也最强,对技术密集型产业的促进作用次之,而对资本密集型产业的促进作用最弱。与此同时,模型21~23显示,出口沉没成本指标(DEM)与3类产业的出口市场多样化显著正相关,但系数差距不大。

表6   机制1的产业异质性(影响出口沉没成本)

Tab.6  Heterogeneous effect mechanism of industrial related variety and unrelated variety (affecting export sunk cost)

变量DEMVARIETY
劳动密集型资本密集型技术密集型劳动密集型资本密集型技术密集型
(模型18)(模型19)(模型20)(模型21)(模型22)(模型23)
RELV1.354***1.148***0.750***0.064***0.114***0.086***
UNRELV0.275***0.139***0.228***0.019***-0.018***-0.016***
LQ-0.068***0.410***0.112***0.012***0.027***0.014***
HHI-0.112***-0.011***0.085***0.078***0.100***0.125***
GDPP-0.347***-0.257***0.076***0.084***0.079***0.119***
Dense-0.039***-0.008***-0.004***0.008***0.011***0.009***
COU-0.718***-0.717***-0.326***0.201***0.217***0.285***
DEM0.067***0.078***0.073***
年份
常数项3.365***1.947***3.157***-0.036***0.016-0.198***
样本数324591395200196991324591395200196991
系数差异检验差异显著差异显著差异显著

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表7所示,产业相关多样化显著抑制了3类产业生产效率提升,进而阻碍产业的出口市场多样化,而产业非相关多样化在抑制资本密集型产业和技术密集型产业生产效率提升的同时,却显著促进了劳动密集型产业的生产效率提升。具体而言,如模型24~26所示,相关多样化和非相关多样化均表现为:对于资本密集型产业生产效率(SUPOLD)的抑制作用最强,对技术密集型产业的抑制作用次之,对劳动密集型产业的抑制作用最弱。与此同时,模型27~29显示,生产效率指标(SUPOLD)与3类产业的出口市场多样化显著正相关,其中,劳动密集型产业的系数远高于其余两类产业。

表7   机制2的产业异质性(影响企业生产效率)

Tab.7  Heterogeneous effect mechanism of industrial related variety and unrelated variety (affecting firm productivity)

变量SUPOLDVARIETY
劳动密集型资本密集型产业技术密集型产业劳动密集型资本密集型产业技术密集型产业
(模型24)(模型25)(模型26)(模型27)(模型28)(模型29)
RELV-0.007***-0.031***-0.019***0.156***0.204***0.141***
UNRELV0.002***-0.013***-0.011**0.037***-0.007***0.001
LQ0.004***0.052***0.038***0.006***0.057***0.022***
HHI0.001***0.001-0.0000.070***0.099***0.131***
GDPP0.004***0.025***0.044***0.059***0.058***0.124***
Dense0.001***0.0030.012***0.005***0.011***0.009***
COU0.0010.018***0.0120.152***0.160***0.261***
SUPOLD0.265***0.036***0.022***
年份
常数项-0.069***-0.118***-0.0080.208***0.172***0.034
样本数324591395200196991324591395200196991
系数差异检验差异显著差异显著差异显著

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表8所示,两类产业多样化均显著抑制劳动密集型和资本密集型产业的产品创新,进而阻碍产业的出口市场多样化,但这种影响对于技术密集型产业并不显著。具体而言,模型30~32显示,产业相关多样化对于3类产业产品创新(SUPNEW)的抑制作用差异不大,而非相关多样化对劳动密集型产业产品创新(SUPNEW)的抑制作用最强,对资本密集型产业的抑制作用次之,对技术密集型产业的抑制作用最弱。与此同时,模型33~35显示,产品创新(SUPNEW)与劳动密集型和资本密集型产业的出口市场多样化显著正相关,但系数差距不大,而与技术密集型产业的出口市场多样化无显著关联。

表8   机制3的产业异质性(影响企业创新能力)

Tab.8  Heterogeneous effect mechanism of industrial related variety and unrelated variety (affecting firm’s innovation ability)

变量SUPNEWVARIETY
劳动密集型资本密集型技术密集型劳动密集型资本密集型技术密集型
(模型30)(模型31)(模型32)(模型33)(模型34)(模型35)
RELV-0.006***-0.007***-0.007***0.155***0.141***0.203***
UNRELV-0.005***-0.004***-0.001***0.038***0.001-0.007***
LQ0.004***-0.001***0.005***0.007***0.059***0.023***
HHI-0.005***-0.001***-0.001***0.071***0.131***0.099***
GDPP-0.004***-0.003***-0.003***0.061***0.059***0.125***
Dense-0.002***-0.002***-0.002***0.005***0.011***0.009***
COU-0.018***-0.017***-0.019***0.154***0.261***0.162***
SUPNEW0.071***0.074***-0.010
年份
常数项0.094***0.087***0.072***0.183***0.161***0.035
样本数324591395200196991324591395200196991
系数差异检验差异显著差异显著差异不显著

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表5模型结果来看,劳动密集型产业能从两类产业多样化中获益,而资本密集型和技术密集型产业则主要受产业相关多样化的促进作用。而从表6~8来看,降低出口沉没成本是产业多样化的主要作用机制。其中,劳动密集型产业从该机制中的获益最大,资本密集型产业次之,技术密集型产业最弱。本文初步分析,不同产业出口模式的差异可能是造成上述异质性的主要原因。以纺织服装鞋帽制造、纺织业、木材制品等为代表的劳动密集型产业本身技术门槛较低,竞争激烈[51]。对此,企业需采取针对性的营销策略获得竞争优势。并且其中的纺织业和纺织服装鞋帽制造业本身就是出口导向的,对市场知识较为敏感[52]。因此,劳动密集型企业更注重吸收产业多样化所带来的市场知识溢出,降低出口沉没成本,获得竞争优势。相对地,以金属制品、化工制品等为代表的资本密集型产业,则具有一定的技术门槛。因此,企业更有可能通过自身的技术特色获得出口竞争优势[53]。并且其中的金属制品和化工制品本身并非出口导向。因此,资本密集型企业对于市场知识溢出的敏感性较低[54]。而以机械电子产品和交通运输产品为主的技术密集型产业本身也是出口导向,对市场知识敏感。但由于技术密集型产品本身并没有明确的产品标准,很难通过产品基本信息判断产品优劣。因此,这类产品的出口更多依靠企业自身的市场资源,如企业间长期合作所建立的信任关系以及企业的品牌声望,而对市场知识溢出的敏感性较低[55]

3.4 稳健性检验

本文对上述结果的稳健性进行检验。在调整了1%的异常值之后,本文也放宽了产品创新的界定标准,将区位商高于全国前10%水平的六位数新产品定为创新性产品。表9表10分别为对应上文表2表3的检验结果。稳健性检验结果与基准回归结果差异不大。这表明模型结果比较稳健,不过多赘述。

表9   稳健性检验(基于产业编码)

Tab.9  Robustness test (based on industry code)

变量VARIETYDEMVARIETYSUPOLDVARIETYSUPNEWVARIETY
(模型1)(模型2)(模型3)(模型4)(模型5)(模型6)(模型7)
RELV0.158***0.949***0.086***-0.016***0.158***-0.005***0.158***
UNRELV0.014***0.255***-0.005***-0.008***0.014***-0.005***0.014***
DEM0.075***
SUPOLD1.061***
SUPNEW0.026***
控制变量表2表2表2表2表2表2表2
年份
常数项0.058***1.722***-0.070***-0.074***0.136***0.125***0.055***
样本数1007175100717510071751007175100717510071751007175
拟合度0.2520.0410.3840.0110.2820.0210.252
系数差异检验差异显著差异显著差异显著

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表10   稳健性检验(基于产业邻近度)

Tab.10  Robustness test (based on industry proximity)

变量VARIETYDEMVARIETYSUPOLDVARIETYSUPNEWVARIETY
(模型8)(模型9)(模型10)(模型11)(模型12)(模型13)(模型14)
RELV0.070***0.478***0.034***-0.009***0.080***-0.001***0.070***
UNRELV0.090***0.460***0.055***0.002***0.088***-0.014***0.090***
DEM0.076***
SUPOLD1.077***
SUPNEW0.016***
控制变量表3表3表3表3表3表3表3
年份
常数项0.092***1.303***-0.007-0.062***0.159***0.112***0.090***
样本数1007175100717510071751007175100717510071751007175
拟合度0.2400.0220.3810.0140.2710.0210.240
系数差异检验差异显著差异显著差异显著

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4 结论与讨论

自改革开放以来,中国逐渐融入全球经济体系。与此同时,国际贸易也成为推动中国区域产业发展的重要引擎。在当前国际贸易环境下行的大背景下,出口市场多样化是区域贸易发展的重要手段,也是当前重要的政策方向。近年来,经济地理学界开始关注本地产业多样化对于产业出口拓展的影响。然而,由于产业多样化影响的复杂性,至今研究尚未得出明确结论。具体而言,当前研究仍有3个方面的不足:① 研究重点仍是发达国家,对于发展中国家包括中国的讨论仍然不足;② 对于产业多样化如何影响产业出口拓展的机制讨论仍然缺乏;③ 研究主要从企业或城市尺度展开讨论,而对产业异质性的影响缺乏关注。本文基于2002—2016年间中国海关数据,讨论中国城市产业相关多样化和非相关多样化对于出口市场多样化的影响;针对当前研究的不足,分析了产业多样化对于出口市场多样化的3类影响机制,并且经验性地讨论了产业多样化对于劳动密集型、资本密集型和技术密集型3类产业出口市场多样化影响的异质性。主要结论如下:

(1) 在中国,产业相关多样化和非相关多样化主要通过降低企业的出口沉没成本来推动本地产业的出口市场多样化。并且,相较于产业非相关多样化,产业相关多样化的促进作用更强。而相反地,两类产业多样化不仅显著抑制了企业生产效率的提升,也并非如西方研究所说的那样,能够提升企业的产品创新能力[17,30]。本文认为,造成这一反常现象的原因在于,相比发达国家,中国企业的技术吸收能力较弱,无法有效地从技术知识溢出中获益。

(2) 产业多样化对于出口市场多样化的影响存在明显的产业异质性。从综合效应来看,劳动密集型产业能从两类产业多样化中获益,而资本密集型产业和技术密集型产业仅能从产业相关多样化中获益。从作用机制来看,出口沉没成本的降低是3类产业出口市场多样化的主要推动力。其中,劳动密集型产业的获益更大,这可能与劳动密集型产业本身技术门槛低,且是出口导向有关;资本密集型产业的获益次之,这可能与该产业本身具有一定的技术门槛,且主要服务国内市场,对市场知识溢出的敏感性较低有关;技术密集型产业的获益最少,这可能与该产业的出口更依赖企业自身的市场资源有关。

对于政策而言,本文认为需要鼓励本地产业的多样化发展,这有利于本地产业的出口市场拓展。当然,也需要注重中小企业知识吸收能力的提升,以提升这些企业从产业多样化中的获益。最后,还需要注意产业出口模式差异所带来的异质性,针对不同产业的出口特点,制定针对性的出口扶持政策。当然,本文的研究相对初级,后续还有一些完善的地方:① 需关注产业多样化指标的构建方式。由于相关和非相关产业的界定并没有明确的标准,这导致产业多样化指标的精度可能存在问题。在未来的研究中,可能还需采用更多的产业分类方式,尽可能多地还原本地产业的多样性特征。② 深化对产业异质性的讨论。出于文章篇幅的考虑,本文初步讨论了产业多样化影响的产业异质性,而在后续的研究中,可以对不同产业的异质性特征进行深入讨论。③ 需要关注本地其他因素对于产业多样化影响的调节作用,如本地的社会、制度和文化特征差异所导致的异质性。④ 还需关注产业多样化对于出口市场多样化影响机制的复杂性,找出更多的影响机制,进而更为全面地理解产业多样化的影响。

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DOI:10.11821/dlyj020200486      [本文引用: 1]

经济韧性的强弱决定着城市在面对冲击时可以快速度过危机还是陷入长期经济发展停滞。本文将城市经济韧性区分为长期经济韧性和短期经济韧性,分析中国东部沿海地级及以上城市的经济韧性和产业结构的时空分异特征,探讨产业结构在不同时期对城市经济韧性的影响。研究发现,东部沿海地区城市不同时期的短期经济韧性及长期经济韧性存在明显的空间差异;长期经济韧性和短期经济韧性的影响因素不同,表征产业整体技术含量的经济复杂度和表征产业关联程度的相关多样化指数能显著提升城市长期经济韧性,短期经济韧性的影响因素则因时期不同而存在差别。延长产业价值链、构建地方产业集群、提升产业层次水平,有助于城市提高经济韧性。

[ Peng Rongxi, Liu Tao, Cao Guangzhong.

Spatial pattern of urban economic resilience in eastern coastal China and industrial explanation

Geographical Research, 2021, 40(6): 1732-1748. ]

DOI:10.11821/dlyj020200486      [本文引用: 1]

Since the global financial crisis in 2008, regional economic resilience has been attracting increasing attention across the world. When facing economic shocks, some regions suffer less and could manage to get through crisis in a short period, while some might be mired in economic stagnation, which mainly depends on the economic resilience of the country. Existing research usually classifies economic resilience into resistance and recovery resilience based on the analysis of a specific economic shock. It is simple and operable in empirical works though, which aims to unravel the economic resilience in a relatively short period and neglects the impacts of longstanding “slow burn” in the urban economy. Thus, this paper divided urban economic resilience into long-term and short-term economic resilience, and further analyzed the features of spatiotemporal distributions of industrial structures (including economic complexity and industrial variety) and urban economic resilience, and explored the impacts of industrial structures on urban economic resilience in different economic development stages with eastern coastal China as a study case. The conclusions are as follows. (1) The economic complexity in the study area is higher in the south and lower in the north, and the Yangtze River Delta and Pearl River Delta are the most prominent areas. The distribution of industrial variety is more balanced, while the related variety of center cities is generally higher than that of surrounding cities. Distribution patterns of short-term economic resilience in different periods show great differences, and the long-term economic resilience of the Yangtze River Delta is higher than that in other areas. (2) The elevation of economic complexity and related industrial variety could improve urban long-term economic resilience significantly, while the unrelated variety has no evident impacts, which verifies the importance of knowledge spillover and technology links in the promotion of urban long-term economic resilience. (3) Factors influencing short-term economic resilience vary in different periods. Cities with enormous financial industries were vulnerable to economic shocks in 2008. Comparatively, cities predominated by heavy industries had the lowest economic resilience in the structural adjustment period after 2011. (4) Factors influencing the resistance and recovery resilience on the same shock are different. A higher level of related variety could help cities resist the crisis in 2008, but have no distinct impacts on their recovery, while an elevated level of unrelated variety might harm the recovery from the crisis of 2008. Thus, extending the industrial value chain, establishing local industrial clusters, and upgrading the industrial level are possible ways to raise urban economic resilience.

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在国际贸易摩擦不断升级的背景下,厘清贸易保护对中国区域经济发展的影响是中国当前面临的重要议题。既有演化经济地理学框架主要从供给视角出发,认为出口产品路径演化过程呈现依赖技术知识溢出的特征,忽略了需求视角的影响。基于中国海关库2002—2016年的数据,以中国出口产品进入新市场的演化路径为研究对象,本文将供需视角纳入同一解释框架,补充需求视角下的外部市场关联以及需求市场的贸易壁垒措施作为外部力量,试图探究贸易保护、出口溢出效应如何作用于中国出口市场拓展过程。研究发现:① 在出口产品结构升级的同时,中国出口目的地结构也在不断地向新兴市场国家和地区拓展。其中,东部与中部地区是出口拓展的主力区域,技术与资本密集型产品是出口拓展的主要产品类型。② 中国出口市场的拓展进程呈现出典型的路径依赖特征,包括供给视角下基于本地技术知识溢出的路径依赖与需求视角下基于目的地市场信息溢出的路径依赖。其中后者被长期忽视,但在中国出口市场拓展进程中扮演着十分重要的角色。③ 贸易保护作为一种外部冲击,可有效地削弱基于本地技术知识溢出和外部市场信息溢出的路径依赖。采用投资、区域合作等方式绕开贸易壁垒,实现出口产品与市场结构的多元化,是应对外部冲击实现可持续发展的长久之计。

[ He Canfei, Yu Changda, Jin Lulu.

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Acta Geographica Sinica, 2020, 75(4): 665-680. ]

DOI:10.11821/dlxb202004001      [本文引用: 2]

In the context of globalization, the interests of countries are intertwined and complicated, thus leads to multiple types of trade frictions. In order to protect domestic industries, some countries frequently set up trade protection barriers to restrict Chinese exports, which has a significant impact on China's economic development. Therefore, it is crucial for us to clarify the impact of trade protection barriers on regional export expanding processes in China. Based on the data of the China Customs Database from 2002 to 2016, this article focuses on the expanding path of Chinese export products by integrating the supply and demand perspectives into the same framework. In addition to the introduction to trade barriers set in the demand market as external forces, we also take the external market relatedness effect into consideration, which enriches traditional export spillover mechanisms. The main findings are as follows: (1) With the process of product upgrading, Chinese export destination structure is gradually leaning to emerging economies. Eastern and Central China, which expand their exporting portfolio by exporting more technology-intensive and capital-intensive products, are main contributors to this process. (2) The evolution path of Chinese export products presents typical path dependence characteristics composed of market linkages and technological linkages. In comparison, market linkages, which have been formerly neglected, are more vital to the expanding process of Chinese export market. (3) Multiple trade barriers will inhibit export expanding by cutting off channels for products entry, thereby breaking the region's original export expanding routes. (4) External shocks have a common effect on expanding mechanisms of regional export products by weakening the path dependence of technological linkages and market linkages. In conclusion, absorbing foreign investment, regional economic cooperation and diversification of export product and destination structure are effective countermeasures against external shocks like trade barriers.

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京津冀地区产业转移升级、协同发展和先进制造业深度融合发展战略对制造业产业集群的空间布局提出更高要求。论文基于2000—2013年京津冀地区规模以上工业企业微观数据,运用核密度分析法和面板数据回归模型等方法,探究京津冀地区制造业空间格局演化特征及其驱动因素。结果表明:① 京津冀地区全部制造业总体格局相对稳定,高值区集聚于京津唐地区。资本密集型产业区域联动发展势头明显;技术密集型产业则日趋集中于少数区县,且与周边区县空间自相关程度整体弱化;劳动密集型产业区县邻近扩张与疏散转移发展交替出现。区域联动发展促进各地制造业均衡增长,缩小了区域制造业发展差距。② 京津冀地区制造业呈现出明显专业化地域分工趋势。劳动密集型产业日益向中心城市城区外围及中南部县区集中扩散;资本密集型产业集聚于环渤海西岸产业带,京津冀外围地区产业产值大幅度增加;技术密集型产业扎堆于京津高科技产业带。③ 3类制造业的关键驱动因素有所差异。劳动力密集型产业受投资和交通可达性影响;资本密集型产业对本地市场规模和投资依赖性强,受交通可达性影响弱;技术密集型产业主要受制于交通可达性与工资水平。3类制造业均明显受到地方财政支出作用影响。研究可为城市群先进制造业产业空间优化提供参考依据。

[ Jiang Haibing, Li Yejin.

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Progress in Geography, 2021, 40(5): 721-735. ]

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The development strategy of industrial transfer and upgrading, coordinated development, and in-depth integration of advanced manufacturing in the Beijing-Tianjin-Hebei region put forward higher requirements for the spatial layout of manufacturing industrial clusters. Research on the change of manufacturing industry spatial pattern can provide a reference for the optimization of urban agglomerations' advanced manufacturing industries. Based on the micro-level data of industrial enterprises above designated size in the Beijing-Tianjin-Hebei region from 2000 to 2013, this study used kernal density analysis and panel data regression models to explore the characteristics and driving factors of the change of the manufacturing industry spatial pattern in the region. The results of this empirical research show that: 1) The overall spatial pattern of all manufacturing industries in the Beijing-Tianjin-Hebei region is relatively stable, and high-value areas are concentrated in the Beijing-Tianjin-Tangshan area. The regional linked development of capital-intensive industries is gaining momentum; technology-intensive industries are increasingly concentrated in a few districts and counties, and the degree of spatial autocorrelation with surrounding districts and counties has weakened as a whole; spatial expansion into nearby districts and counties and spatial transfer of labor-intensive industries appeared alternately; and the regional linked development promotes the balanced growth of manufacturing industries in various regions and narrows the development gap. 2) The manufacturing industry in the Beijing-Tianjin-Hebei region shows a clear trend of specialization and regional division of labor, and labor-intensive industries are increasingly spreading to the periphery of the central cities and the counties in the central and southern areas of the region. Capital-intensive industries are concentrated in the industrial belt on the west coast of the Bohai Sea, the industrial output value of the peripheral areas of the region has increased significantly, and technology-intensive industries are gathered in the Beijing-Tianjin high-tech industrial belt. 3) The key driving factors of the three types of manufacturing industries are different. Labor-intensive industries are affected by investment and transportation accessibility. Capital-intensive industries are highly dependent on local market size and investment, and are insensitive to transportation accessibility. Technology-intensive industries are mainly constrained by transportation accessibility and wage levels. The three types of manufacturing industries are obviously affected by local fiscal expenditures.

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新企业是产业空间重构的重要驱动力。采用1998-2007年中国工业企业数据库,以金属制品业为例,发现金属制品业新企业成立的活跃地区由东部沿海转向中西部地区,特别是中部地区。随后建立“全球链接、区域竞争和地方环境”的分析框架,通过城市层面的面板Tobit模型,考察新企业成立空间差异的影响因素,并突出企业效率在这些因素作用过程中的影响。研究发现:① 外向型城市有利于新企业成立;低效率的新企业只是追求劳动力的低成本,但是高效率的新企业还能兼顾劳动力的高质量。② 区域市场潜力大的城市有利于新企业成立。③ 市场化环境好的城市有利于新企业成立,地方化经济对新企业成立无显著影响,而适度的相关多样化能够促进高效率新企业成立。

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Geographical Research, 2018, 37(7): 1282-1296. ]

DOI:10.11821/dlyj201807003      [本文引用: 1]

New firm formation is one of the driving forces of spatial restructuring of industries because it represents entrepreneurship in a regional context. Its spatial variation has attracted growing attention among economic geographers, regional analysts as well as policy makers. Existing research seems to overemphasize local factors (e.g. market demand, labor supply, agglomeration economies and the level of economic freedom) at the expense of regional, national, and global factors. Using the Annual Survey of Industrial Firms from 1998 to 2007 in China, this paper takes the metal product industry as an example to explore the determinants of the spatial variation of new firm formation. By calculating the rate of new firm formation using the ecological approach at the city level, it reveals that the hot spot of start-ups in the metal product industry shifted from coastal to inland China, especially towards the central region. It then establishes an analytical framework which not only encompasses multi-scalar factors, namely 'global linkage, regional competition, and local environment', but also takes firm heterogeneity into consideration. The random effect panel Tobit model at city level suggests three major findings. (1) To the extent of global linkage, new firms were disposed to locate in export-oriented cities endowed with cheap labor; the more the city was closely linked to the world market, the greater the attraction of cheap labor was. Low-efficient start-ups would run after cheap labor, but their high-efficient counterparts were able to strike a balance between labor cost and labor quality. (2) To the extent of regional competition, cities with large market potential would foster new firm formation. (3) To the extent of local environment, new firms were attracted to cities with a higher level of marketization. Localization economies have no significant effect on new firm formation whereas related variety only fosters high-efficient start-ups. This paper enriches the empirical research on the spatial variation and its determinants of new firm formation in China by providing evidence for the metal product industry, which is labor intensive, and domestic market oriented with a moderate level of technology. It also provides reference to local governments on policy incentives for start-ups, which is particularly relevant against the backdrop of 'mass entrepreneurship and innovation' campaign recently in China.

Gosens J, Lu Y L.

Prospects for global market expansion of China's wind turbine manufacturing industry

[J]. Energy Policy, 2014, 67: 301-318.

DOI:10.1016/j.enpol.2013.12.055      URL     [本文引用: 1]

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