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Geography of productivity: evidence from China’s manufacturing industries

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Abstract

Empirical evidence has confirmed that the locational agglomeration of firms is associated with productivity benefits. Several competing explanations have been proposed for this association: agglomeration externalities effect, sorting effect and selection effect. This work builds on these studies and seeks to make four contributions by using a firm-level dataset on China’s manufacturing industries over the 1999–2007 period. First, it points out that this stand of literature pays insufficient attention to the possibility of adverse sorting. Second, agglomeration externalities have been decomposed to two constituting parts: inter- and intra-industry, which play different roles in the process of sorting and agglomeration. Third, this paper also points out the need to pay attention to firm heterogeneity, in terms of market orientation and ownership type. Finally, the complex role of government policies has been also examined. Econometric results support our hypotheses.

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Notes

  1. We follow Neffke et al. (2011) and calculate the relatedness indicator based on data in one country. First, China is a large country with a high level of regional disparity, which means calculation based on data in such a big economy should be sufficient. Second, calculating the relatedness indicator based on Chinese data rather than world data may allow us to better control China’s unique, nation-wide political and economic environments.

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Acknowledgements

This work was supported by the Natural Science Foundation of China [Grant Nos. 41425001, 41701115, 41731278].

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Authors and Affiliations

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

    Shengjun Zhu, Canfei He & Xinming Xia

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  1. Shengjun Zhu
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  2. Canfei He
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  3. Xinming Xia
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Correspondence to Canfei He.

Appendix

Appendix

See Tables 3, 4, 5 and 6.

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Zhu, S., He, C. & Xia, X. Geography of productivity: evidence from China’s manufacturing industries. Ann Reg Sci 62, 141–168 (2019). https://doi.org/10.1007/s00168-018-0890-5

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  • DOI: https://doi.org/10.1007/s00168-018-0890-5

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