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

Cainelli, Giulio, Ganau, Roberto and Jiang, Yuting (2020) Detecting space–time agglomeration processes over the Great Recession using firm-level micro-geographic data. Journal of Geographical Systems, 22 (4). 419 - 445. ISSN 1435-5930

Text (Detecting space–time agglomeration processes over the Great Recession using firm-level micro-geographic data) - Published Version Available under License Creative Commons Attribution. Download (1MB)

• Scopus publication

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.

Item Type:	Article
Official URL:	https://www.springer.com/journal/10109
Additional Information:	© 2020 The Authors
Divisions:	Geography & Environment
Subjects:	G Geography. Anthropology. Recreation > G Geography (General)
Date Deposited:	29 Jul 2020 15:54
Last Modified:	17 Oct 2024 16:36
URI:	http://eprints.lse.ac.uk/id/eprint/105822

Actions (login required)

View Item