Detecting space–time agglomeration processes over the Great Recession using firm-level micro-geographic data

Giulio Cainelli (), Roberto Ganau and Yuting Jiang ()
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Giulio Cainelli: University of Padova
Yuting Jiang: Guangdong University of Finance

Journal of Geographical Systems, 2020, vol. 22, issue 4, No 1, 419-445

Abstract: Abstract We analyze the spatio-temporal agglomeration dynamics that occurred in the Italian manufacturing industry during the recent period of the Great Recession. To study this phenomenon, we employ three different statistical methods—namely, Ellison and Glaeser’s index of industrial geographic concentration, the spatial K-function, and the space–time K-function—, and rely on a large sample of geo-referenced, single-plant manufacturing firms observed over the period 2007–2012. First, we demonstrate that different statistical techniques can lead to (very) different results. Second, we find that most Italian manufacturing sectors experienced spatial dispersion processes during the period of the Great Recession. Finally, we show that space–time dispersion processes occurred at small spatial distances and short time horizon, although we do not detect statistically significant space–time interactions.

Keywords: Agglomeration dynamics; Spatial methods; Manufacturing industry; Italy (search for similar items in EconPapers)
JEL-codes: C23 D22 L60 R12 (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (1)

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DOI: 10.1007/s10109-020-00332-4

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