Please use this identifier to cite or link to this item: http://bura.brunel.ac.uk/handle/2438/25632
Title: Geographic clusters, regional productivity and resource reallocation across firms: Evidence from China
Authors: Guo, D
Jiang, K
Xu, C
Yang, X
Keywords: industrial cluster;productivity growth;resource reallocation;competition
Issue Date: 7-Dec-2022
Publisher: Elsevier
Citation: Guo, D. et al. (2023) 'Geographic clusters, regional productivity and resource reallocation across firms: Evidence from China', Research Policy, 52 (2). 104691, pp. 1 - 24. doi: 10.1016/j.respol.2022.104691.
Abstract: Copyright © 2022 The Authors. Published by Elsevier B.V.. We link industrial clusters, regional productivity and resource reallocation efficiency with geographical and sectoral disaggregated data. Based on a county-industry level panel from 1998 to 2007 in China, we find that industrial clusters significantly increase local industries' productivity by lifting the average firm productivity and reallocating resources from less to more productive firms. Moreover, we find major mechanisms through which resource reallocation is improved within clusters: (i) clusters are associated with a higher firm turnover with increased entry and exit rates simultaneously; and (ii) within clusters' environment, the dispersion of individual firm's markup is significantly reduced, indicating intensified local competition within clusters. Such results suggest that industrial clusters in China help improve regional productivity and resource allocation efficiency with intensified competition and accelerated firm dynamics. The identification issues are carefully addressed by two-stage estimations with instrumental variables and other robustness checks.
Description: Data availability: Data will be made available on request.
URI: https://bura.brunel.ac.uk/handle/2438/25632
DOI: https://doi.org/10.1016/j.respol.2022.104691
ISSN: 0048-7333
Other Identifiers: ORCID iD: Di Guo https://orcid.org/0000-0002-2757-1220
104691
Appears in Collections:Brunel Business School Research Papers

Files in This Item:
File Description SizeFormat 
FullText.pdfCopyright © 2022 The Authors. Published by Elsevier B.V. under a Creative Commons license (https://creativecommons.org/licenses/by/4.0/).1.09 MBAdobe PDFView/Open


This item is licensed under a Creative Commons License Creative Commons