地理研究 ›› 2015, Vol. 34 ›› Issue (10): 1943-1956.doi: 10.11821/dlyj201510012

• 研究论文 • 上一篇    下一篇

制造业内部产业关联与空间分布关系的实证研究

陈曦1(), 席强敏2, 李国平1()   

  1. 1. 北京大学政府管理学院,北京 100871
    2. 南开大学经济学院,天津 300071
  • 收稿日期:2015-02-13 修回日期:2015-06-20 出版日期:2015-10-15 发布日期:2015-10-30
  • 作者简介:

    作者简介:陈曦(1987- ),女,吉林长春人,博士研究生,研究方向为区域经济、城市与区域规划。E-mail: chenxi870613@163.com

  • 基金资助:
    国家自然科学基金项目(41171099);国家社会科学基金项目(15CJY055);教育部人文社会科学研究青年基金(14YJC790136)

Industrial linkage and spatial distribution of manufacturing industry

Xi CHEN1(), Qiangmin XI2, Guoping LI1()   

  1. 1.School of Government, Peking University, Beijing 100871, China
    2. School of Economics, Nankai University, Tianjin 300071, China
  • Received:2015-02-13 Revised:2015-06-20 Online:2015-10-15 Published:2015-10-30

摘要:

聚焦于中国制造业内部的产业关联与空间分布,基于《中国投入产出表》中涉及的17个制造业细分行业和中国286个地级市空间单元的统计数据,研究与某一制造业细分行业具有较强产业关联的其他制造业细分行业与其空间分布之间是否存在相关性,并进一步分析产业关联强且具有空间关联的产业组合的主要特征。研究表明,在68个产业关联较强的产业组合中,有39个产业组合具有空间关联;产业关联较强的劳动/劳动密集型制造业和资本/技术密集型制造业更容易呈现空间关联;超过半数的制造业细分行业的产业关联和空间关联的程度之间存在正相关。此外,在产业特征分析基础上,利用GWR模型对39个产业关联强且具有空间关联的产业组合的空间关联度在空间分布上的特征和差异进行分析。结果显示,产业组合的空间关联度较高区域多分布在中等发展水平省区,而在经济发达或欠发达省区分布较少;东北三省区空间关联度较高的产业组合基本一致,产业发展情况较为相似。

关键词: 产业关联, 制造业, 空间分布, 空间计量

Abstract:

The spatial distribution of the manufacturing industry based on industrial linkage has always been an important research topic. Using statistical data of 17 manufacturing industry segments in China (input-output table) and 286 prefecture-level spatial cells, this paper carries out input-output analysis and uses spatial regression models to determine the industrial linkage and spatial distribution of the manufacturing industry in China. Employing ordinary least squares (OLS), spatial lag regression model (SLM) and spatial error regression model (SEM), this paper determines whether there are correlations on spatial distribution between some manufacturing industry segments; it also aims to determine whether the distribution has strong industrial linkage to a specific segment. This paper also examines the characteristics of the industrial combinations with both strong industrial linkage and spatial correlation. Results show that, firstly, in 68 industrial combinations, 39 of these have both strong industrial linkage and spatial correlation, which proves the Marshallian externalities to some extent. Secondly, labor/labor-intensive manufacturing industries and capital/technology-intensive manufacturing industries can easily form such industrial combinations. Third, more than half of the manufacturing industry segments have positive correlations between industrial linkage and spatial correlation. Finally, "pgdp", "city", “kmt", and "zone" have good feedback, indicating that these elements have effects on the spatial distribution of the manufacturing industry in China. Apart from industrial combinations, this paper uses geographically weighted regression model (GWR) to study the spatial distribution of the degree of spatial correlation of 39 industrial combinations. Results show that industrial combinations with higher degree of spatial correlations are generally located in developing provinces (Heilongjiang, Jilin, Liaoning, Inner Mongolia, Shanxi, Hunan and Jiangxi), and not in developed provinces (Beijing, Tianjin, Jiangsu, Shanghai and Zhejiang) or under-developed provinces (Ningxia, Qinghai, Xinjiang and Tibet). Parts of the spatial distribution of the degree of spatial correlation of industrial combinations have regularities. Labor/labor-intensive manufacturing industries and capital/technology-intensive manufacturing industries differ in terms of the spatial distribution of the degree of spatial correlation. Moreover, the industrial combinations with higher degree of spatial correlations are basically the same in Heilongjiang, Jilin, and Liaoning. To ensure the future development of the manufacturing industry, the government should pay more attention to the mutual coordination and spatial correlation between manufacturing industry segments with strong industrial linkage. Formulating corresponding industrial linkages based on different manufacturing industry divisions and geographic spaces shall also play a positive role in the optimization of the spatial layout, further transforming and upgrading the manufacturing industry in China.

Key words: industrial linkage, manufacturing industry, spatial distribution, spatial econometric model