Geographic Clustering of Firms in China

Douglas Hanley, Chengying Luo and Mingqin Wu
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Chengying Luo: University of Pittsburgh
Mingqin Wu: South China Normal University

No 1522, 2019 Meeting Papers from Society for Economic Dynamics

Abstract: The spatial arrangement of firms is known to be a critical factor influencing a variety of firm level outcomes. Numerous existing studies have investigated the importance of firm density and localization at various spatial scales, as well as agglomeration by industry. In this paper, we bring relatively new data and techniques to bear on the issue. Regarding the data, we use a comprehensive census of firms conducted by the National Bureau of Statistics of China (NBS). This covers firms in all industries and localities, and we have waves from both 2004 and 2008 available. Past studies have largely relied on manufacturing firms. This additional data allows us to look more closely at clustering within services, as well as potential spillovers between services and manufacturing. Further, by looking at the case of China, we get a snapshot of a country (especially in the early 2000s) in a period of rapid transition, but one that has already industrialized to a considerable degree. Additionally, this is an environment shaped by far more aggressive industrial policies than those seen in much of Western Europe and North America. In terms of techniques, we take a machine learning approach to understanding firm clustering and agglomeration. Specifically, we use images generated by density maps of firm location data (from the NBS data) as well as linked satellite imagery from the Landsat 7 spacecraft. This allows us to frame the issue as one of prediction. By predicting firm outcomes such as profitability, productivity, and growth using these images, we can understand their relationship to firm clustering. By turning this into a prediction problem using images as inputs, we can tap into the rich and rapidly evolving literature in computer science and machine learning on deep convolutional neural networks (CNNs). Additionally, we can utilize software and hardware tools developed for these purposes.

Date: 2019
New Economics Papers: this item is included in nep-big, nep-cmp, nep-cna, nep-geo, nep-sbm, nep-tra and nep-ure
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