2012
DOI: 10.1177/0042098012466601
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Making Location Quotients More Relevant as a Policy Aid in Regional Spatial Analysis

Abstract: Location Quotients (LQs) remain an important tool for geographical analysis, particularly in terms of assessing industrial specialisation and clustering. LQs as decision aids are typically understood through the use of arbitrarily set cut-off values. However, LQs are rarely accompanied by an associated level of variance that can be connected with the estimated data used to calculate them. This paper reveals the importance of understanding this variance and shows how confidence intervals can be estimated for em… Show more

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Cited by 58 publications
(48 citation statements)
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“…The question of which LQ value sectoral Specialization begins in the regional unit is the most important debate issue (Crawley et al, 2013). Bergman and Feser (1999) argue that the first evidence of sectoral Specialization in the region is formed by the coefficient of 1.25.…”
Section: Methodsmentioning
confidence: 99%
“…The question of which LQ value sectoral Specialization begins in the regional unit is the most important debate issue (Crawley et al, 2013). Bergman and Feser (1999) argue that the first evidence of sectoral Specialization in the region is formed by the coefficient of 1.25.…”
Section: Methodsmentioning
confidence: 99%
“…In terms of public policy, studies have also used LQ to identify industrial clusters, which might become the focus of government support (Crawley et al. ).…”
Section: Datamentioning
confidence: 99%
“…Crawley et al. () and Martin and Sudley () both note that there are no commonly agreed or theoretical LQ cut‐off values for defining what a high LQ value is or what LQ value defines a cluster. Guided by recent research on the appropriate LQ cut‐off value to define regional industrial clusters (see Crawley et al.…”
Section: Datamentioning
confidence: 99%
“…The most common tool to identify clusters and, more specifically, industrial specialization/concentration in a region is the location quotient (LQ) (Delgado et al, 2014(Delgado et al, , p. 1791Crawley et al, 2013Crawley et al, , pp. 1854Crawley et al, -1855.…”
Section: Identification Of Clusters In the Field Of The "Precision Enmentioning
confidence: 99%