Place Dependence, Industrial Diversification, and Economic Performance of Chinese Cities

Yibo Qiao, Yingcheng Li and Ron Boschma

No 2531, Papers in Evolutionary Economic Geography (PEEG) from Utrecht University, Department of Human Geography and Spatial Planning, Group Economic Geography

Abstract: Place dependence is a widely recognized concept but has rarely been quantified in existing research. Employing the Wasserstein Distance algorithm from machine learning literature and China’s Annual Survey of Industrial Firms dataset, this paper introduces a novel method to measure the place dependence of industrial dynamics in Chinese cities, and explore its impact on urban economic performance. Our empirical findings confirm the presence of place dependence in Chinese cities, and show that cities diversifying into more related and complex industries tend to exhibit higher levels of place dependence. Moreover, place dependence appears to complement the effects of relatedness and complexity in enhancing urban economic performance. These findings offer important insights for regional industrial development and urban planning practices.

Keywords: Place dependence; path dependence; knowledge complexity; industrial dynamics; economic performance; China (search for similar items in EconPapers)
Date: 2025-10, Revised 2025-10
New Economics Papers: this item is included in nep-cna, nep-sbm and nep-tid
References: Add references at CitEc
Citations:

Downloads: (external link)
http://econ.geo.uu.nl/peeg/peeg2531.pdf Version October 2025 (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:egu:wpaper:2531

Access Statistics for this paper

More papers in Papers in Evolutionary Economic Geography (PEEG) from Utrecht University, Department of Human Geography and Spatial Planning, Group Economic Geography Contact information at EDIRC.
Bibliographic data for series maintained by ( this e-mail address is bad, please contact ).