地理学报 ›› 2022, Vol. 77 ›› Issue (8): 1953-1970.doi: 10.11821/dlxb202208008

• 经济地理与区域发展 • 上一篇    下一篇

基于企业大数据的京津冀制造业集聚的影响因素

黄宇金1,2(), 盛科荣3, 孙威1,2()   

  1. 1.中国科学院地理科学与资源研究所,北京 100101
    2.中国科学院大学资源与环境学院,北京 100049
    3.山东理工大学经济学院,淄博 255012
  • 收稿日期:2021-07-26 修回日期:2022-06-27 出版日期:2022-08-25 发布日期:2022-10-25
  • 通讯作者: 孙威(1975-), 男, 河南开封人, 副研究员, 中国科学院大学岗位教授, 研究方向为经济地理与区域发展。E-mail: sunw@igsnrr.ac.cn
  • 作者简介:黄宇金(1996-), 男, 江苏南通人, 硕士生, 研究方向为经济地理与区域发展。E-mail: huangyj.19s@igsnrr.ac.cn
  • 基金资助:
    国家自然科学基金项目(41871117);国家自然科学基金项目(41771173)

Influencing factors of manufacturing agglomeration in the Beijing-Tianjin-Hebei region based on enterprise big data

HUANG Yujin1,2(), SHENG Kerong3, SUN Wei1,2()   

  1. 1. Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China
    2. College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China
    3. School of Economics, Shandong University of Technology, Zibo 255012, Shandong, China
  • Received:2021-07-26 Revised:2022-06-27 Published:2022-08-25 Online:2022-10-25
  • Supported by:
    National Natural Science Foundation of China(41871117);National Natural Science Foundation of China(41771173)

摘要:

产业集聚是经济活动最突出的地理特征之一,也是经济地理学重要的研究对象,然而对产业集聚的机制解释往往由于没有很好的区分产业空间分布形态和产业集聚阶段而出现偏差。本文基于3次全国经济普查企业微观数据,利用DO指数方法计算了2004—2013年京津冀地区三位数制造业行业的空间分布形态,利用Hurdle模型定量解释了集聚形成和集聚提升两个阶段的影响因素及其差异性。结果表明:① 2004年、2008年和2013年京津冀地区分别有124个、127个、129个三位数行业集聚,技术密集型和劳动密集型制造业集聚强度较高,但整体集聚强度出现下降,从0.332下降至0.261。② 制造业集聚存在两阶段且主导因素存在差异。在集聚形成阶段,企业主要考虑基础条件,农业资源和交通运输有负向作用,劳动力池和外商投资有正向作用;在集聚提升阶段,企业更侧重于集聚经济和政策等因素,产业内部关联和产业外部关联有正向作用且前者作用更强,开发区主导产业政策和电力燃气水资源起负向作用。③ 影响因素对产业集聚的作用具有尺度效应,均随距离扩大呈现减弱趋势,但不同因素对距离的反应存在差异。

关键词: 制造业集聚, Hurdle模型, DO指数, 京津冀地区

Abstract:

Industrial agglomeration is a highly prominent geographical feature of economic activities, and it is an important research topic in economic geography. However, mechanism-based explanations of industrial agglomeration often differ due to a failure to distinguish properly between the spatial distributions of industries and the stages of industrial agglomeration. Based on micro data from three national economic censuses, this study uses the Duranton-Overman (DO) index method to calculate the spatial distribution of manufacturing industries (three-digit classifications) in the Beijing-Tianjin-Hebei region (BTH region hereafter) from 2004 to 2013 as well as the Hurdle model to explain quantitatively the influencing factors and differences in the two stages of agglomeration formation and agglomeration development. Our research results show the following: (1) In 2004, 2008, and 2013, there were 124, 127, and 129 agglomerations of three-digit industry types in the BTH region, respectively. Technology-intensive and labor-intensive manufacturing industries had high agglomeration intensity, but overall agglomeration intensity declined during the study period, from 0.332 to 0.261. (2) There are two stages of manufacturing agglomeration, with different dominant factors. During the agglomeration formation stage, the main locational considerations of enterprises are basic conditions. Agricultural resources and transportation have negative effects on agglomeration formation, while labor pool and foreign investment have positive effects. In the agglomeration development stage, enterprises focus more on factors such as agglomeration economies and policies. Both internal and external industry linkages have a positive effect, with the former having a stronger effect, while development zone policies and electricity, gas, and water resources have a negative effect. (3) Influencing factors on industrial agglomeration have a scale effect, and they all show a weakening trend as distance increases, but different factors respond differently to distance.

Key words: manufacturing agglomeration, Hurdle model, DO index, Beijing-Tianjin-Hebei region