Location patterns of service industries in France: A distance-based approach

Barlet, Muriel; Briant, Anthony; Crusson, L.

doi:10.1016/j.regsciurbeco.2012.08.004

Cited by 38 publications

(29 citation statements)

References 21 publications

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“…Their aim is to detect phenomena of attraction (also called aggregation, agglomeration, localization), repulsion (dispersion) or independence between those entities in a territory. Industries that are spatially concentrated or dispersed are sometimes referred as diverging industries (Barlet et al, 2013). Another field of research analyzes the relations between entities belonging not to one, but to two different groups (co-localization phenomenon).…”

Section: Basic Principlesmentioning

confidence: 99%

“…In a later study, Duranton and Overman (2008) paved the way for future research by studying various pertinent economic questions on location patterns: plant entries and exits, affiliated and nonaffiliated plants, domestic and foreign plants etc. Then their methodology has been applied to various countries (see Klier and McMillen, 2008, among others) and extended to empirical studies in services (Nakajima et al, 2012;Barlet et al, 2013;Koh and Riedel, 2014). Behrens and Bougna (2015) Three recent interesting contributions have been made.…”

Section: 6mentioning

confidence: 99%

“…This global test has been justified theoretically by Myllymäki et al (2015) who called it the "rank envelope test". Barlet et al (2013) showed empirically that it is still not restrictive enough when the number of simulations is too small relatively to the number of points: its confidence level is overestimated so K d detects localization where there may not be any. The solution is to increase the number of simulations.…”

Section: Last Step: Null Hypothesismentioning

confidence: 99%

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A typology of distance-based measures of spatial concentration

Marcon

Puech

2017

Regional Science and Urban Economics

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To cite this version:Eric Marcon, Florence Puech. A typology of distance-based measures of spatial concentration. 2012. halshs-00679993v6Hal 00679993v6 A Typology of Distance-Based Measures of Spatial ConcentrationEric Marcon 1 , Florence Puech 2 * Abstract Over the last decade, distance-based methods have been introduced and then improved in the field of spatial economics to gauge the geographic concentration of activities. There is a growing literature on this theme including new tools, discussions on their specific properties and various applications. However, there is currently no typology of distance-based methods. This paper fills that gap. The proposed classification helps understand all the properties of distance-based methods and proves that they are variations on the same framework.JEL Classification: C10, C60, R12

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Section: Basic Principlesmentioning

confidence: 99%

Section: 6mentioning

confidence: 99%

Section: Last Step: Null Hypothesismentioning

confidence: 99%

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A typology of distance-based measures of spatial concentration

Marcon

Puech

2017

Regional Science and Urban Economics

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“…This benchmark may be reasonable at the country level. Thus Barlet et al (2013) adopted the same benchmark for the analysis of the industrial spatial distribution in France. However, the benchmark set by Duranton and Overman is obvious problematic at the city level.…”

Section: Choice Of a Reference Distributionmentioning

confidence: 99%

“…Specifically, whether a sector is concentrated or not is defined by comparing the distribution of that sector to the whole industry. Barlet et al (2013) also employed this approach in an empirical study on France. The study suggests that for an analysis of the location patterns of services, a distance-based approach is better suited than the traditional EG cluster-based index of spatial agglomeration.…”

Section: Introductionmentioning

confidence: 99%

Industrial spatial agglomeration using distance-based approach in Beijing, China

Zhang

et al. 2015

Chin. Geogr. Sci.

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To study the difference of industrial location among different industries, this article is to test the spatial agglomeration across industries and firm sizes at the city level. Our research bases on a unique plant-level data set of Beijing and employs a distance-based approach, which considers space as continuous. Unlike previous studies, we set two sets of references for service and manufacturing industries respectively to adapt to the investigation in the intra-urban area. Comparing among eight types of industries and different firm sizes, we find that: 1) producer service, high-tech industries and labor-intensive manufacturing industries are more likely to cluster, whereas personal service and capital-intensive industries tend to be randomly dispersed in Beijing; 2) the spillover of the co-location of firms is more important to knowledge-intensive industries and has more significant impact on their allocation than business-oriented services in the intra-urban area; 3) the spatial agglomeration of service industries are driven by larger establishments, whereas manufacturing industries are mixed.

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