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An Exploratory Spatial Data Analysis of Manufacturing Concentration Patterns in Italy

Ercole, Roberto (2013) An Exploratory Spatial Data Analysis of Manufacturing Concentration Patterns in Italy. Working Paper. Emerging Markets Research Group University of Huddersfield, Huddersfield, UK. (Unpublished)

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Abstract

This research aims to increase the academic debate on which indicators and approaches
capture more appropriately the phenomenon of economic clustering (see, for instance, Arbia
& Piras, 2007). A composite measure is proposed combining discrete and continuous space
indicators in order to identify spatial localization more effectively as proposed by several
authors such as Arbia (2001), Guillain and Le Gallo (2007), and Sohn (2004). The paper is
devoted to unfolding the localization patterns of employment within three-digit
manufacturing industries by Italian provinces in 2007 employing the location quotient, the
locational Gini, the global Moran’s I, and the local indicator of spatial associations since they
provide complementary information (see, for instance, Guillain & Le Gallo, 2007). Evidence
shows that the manufacture of military fighting vehicles has the highest localization with
almost full concentration; whereas the industry of other general-purpose machinery holds the
highest global Moran’s I. Moreover, the five three-digit industries within the two-digit
manufacture of machinery and equipment n.e.c. show a relatively high global autocorrelation
and concentration. It emerged that these five three-digit medium-high technology intensity
industries showed numerous hot-spots clusters in the North of Italy, whereas the South of
Italy and Islands were characterized by low-low values associationsa mong Italian provinces
in 2007.

Item Type: Monograph (Working Paper)
Additional Information: EMERGE Working Paper 13/02
Subjects: H Social Sciences > H Social Sciences (General)
H Social Sciences > HD Industries. Land use. Labor
Schools: The Business School
The Business School > Emerging Markets Research Group
The Business School > Quantitative Analysis Research Group
Depositing User: Sharon Beastall
Date Deposited: 12 Nov 2013 09:10
Last Modified: 05 Dec 2016 18:28
URI: http://eprints.hud.ac.uk/id/eprint/19107

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