Industrial clustering and sectoral growth: a network dynamics approach

Detalhes bibliográficos
Autor(a) principal: Lopes, João Carlos Ferreira
Data de Publicação: 2011
Outros Autores: Araújo, Tanya, Dias, João, Amaral, João Ferreira do
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10400.5/29464
Resumo: Cluster analysis has been widely used in an Input-Output framework, with the main objective of uncover the structure of production, in order to better identify which sectors are strongly connected with each other and choose the key sectors of a national or regional economy. There are many empirical studies determining potential clusters from interindustry flows directly, or from their corresponding technical (demand) or market (supply) coefficients, most of them applying multivariate statistical techniques. In this paper we follow a different strategy. Since it is expected that strongly (interindustry) connected sectors share a similar growth and development path, we will try to uncover clusters from sectoral dynamics, by applying a stochastic geometry technique, based on the yearly distances of industry outputs. An application is made, comparing these growth based cluster templates with interindustry based ones, using Portuguese input-output data. Identifying regional clusters and its dynamics can be a useful extension of the methods proposed in this paper.
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spelling Industrial clustering and sectoral growth: a network dynamics approachInput-Output AnalysisClustersSectoral GrowthCluster analysis has been widely used in an Input-Output framework, with the main objective of uncover the structure of production, in order to better identify which sectors are strongly connected with each other and choose the key sectors of a national or regional economy. There are many empirical studies determining potential clusters from interindustry flows directly, or from their corresponding technical (demand) or market (supply) coefficients, most of them applying multivariate statistical techniques. In this paper we follow a different strategy. Since it is expected that strongly (interindustry) connected sectors share a similar growth and development path, we will try to uncover clusters from sectoral dynamics, by applying a stochastic geometry technique, based on the yearly distances of industry outputs. An application is made, comparing these growth based cluster templates with interindustry based ones, using Portuguese input-output data. Identifying regional clusters and its dynamics can be a useful extension of the methods proposed in this paper.European Regional Science Association (ERSA) | ECONSTORRepositório da Universidade de LisboaLopes, João Carlos FerreiraAraújo, TanyaDias, JoãoAmaral, João Ferreira do2023-11-22T10:27:14Z20112011-01-01T00:00:00Zconference objectinfo:eu-repo/semantics/publishedVersionapplication/pdfhttp://hdl.handle.net/10400.5/29464engLopes, João Carlos Ferreira … [et al.] (2011). “Industrial clustering and sectoral growth: a network dynamics approach”. 51st Congress of the European Regional Science Association: "New Challenges for European Regions and Urban Areas in a Globalised World", 30 August - 3 September 2011, Barcelona, Spain, European Regional Science Association (ERSA), Louvain-la-Neuve. At WWW.ECONSTOR.EUinfo:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2024-11-20T19:32:11Zoai:repositorio.ul.pt:10400.5/29464Portal AgregadorONGhttps://www.rcaap.pt/oai/openairemluisa.alvim@gmail.comopendoar:71602024-11-20T19:32:11Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse
dc.title.none.fl_str_mv Industrial clustering and sectoral growth: a network dynamics approach
title Industrial clustering and sectoral growth: a network dynamics approach
spellingShingle Industrial clustering and sectoral growth: a network dynamics approach
Lopes, João Carlos Ferreira
Input-Output Analysis
Clusters
Sectoral Growth
title_short Industrial clustering and sectoral growth: a network dynamics approach
title_full Industrial clustering and sectoral growth: a network dynamics approach
title_fullStr Industrial clustering and sectoral growth: a network dynamics approach
title_full_unstemmed Industrial clustering and sectoral growth: a network dynamics approach
title_sort Industrial clustering and sectoral growth: a network dynamics approach
author Lopes, João Carlos Ferreira
author_facet Lopes, João Carlos Ferreira
Araújo, Tanya
Dias, João
Amaral, João Ferreira do
author_role author
author2 Araújo, Tanya
Dias, João
Amaral, João Ferreira do
author2_role author
author
author
dc.contributor.none.fl_str_mv Repositório da Universidade de Lisboa
dc.contributor.author.fl_str_mv Lopes, João Carlos Ferreira
Araújo, Tanya
Dias, João
Amaral, João Ferreira do
dc.subject.por.fl_str_mv Input-Output Analysis
Clusters
Sectoral Growth
topic Input-Output Analysis
Clusters
Sectoral Growth
description Cluster analysis has been widely used in an Input-Output framework, with the main objective of uncover the structure of production, in order to better identify which sectors are strongly connected with each other and choose the key sectors of a national or regional economy. There are many empirical studies determining potential clusters from interindustry flows directly, or from their corresponding technical (demand) or market (supply) coefficients, most of them applying multivariate statistical techniques. In this paper we follow a different strategy. Since it is expected that strongly (interindustry) connected sectors share a similar growth and development path, we will try to uncover clusters from sectoral dynamics, by applying a stochastic geometry technique, based on the yearly distances of industry outputs. An application is made, comparing these growth based cluster templates with interindustry based ones, using Portuguese input-output data. Identifying regional clusters and its dynamics can be a useful extension of the methods proposed in this paper.
publishDate 2011
dc.date.none.fl_str_mv 2011
2011-01-01T00:00:00Z
2023-11-22T10:27:14Z
dc.type.driver.fl_str_mv conference object
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/10400.5/29464
url http://hdl.handle.net/10400.5/29464
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv Lopes, João Carlos Ferreira … [et al.] (2011). “Industrial clustering and sectoral growth: a network dynamics approach”. 51st Congress of the European Regional Science Association: "New Challenges for European Regions and Urban Areas in a Globalised World", 30 August - 3 September 2011, Barcelona, Spain, European Regional Science Association (ERSA), Louvain-la-Neuve. At WWW.ECONSTOR.EU
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv European Regional Science Association (ERSA) | ECONSTOR
publisher.none.fl_str_mv European Regional Science Association (ERSA) | ECONSTOR
dc.source.none.fl_str_mv reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron:RCAAP
instname_str Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron_str RCAAP
institution RCAAP
reponame_str Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
collection Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
repository.name.fl_str_mv Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
repository.mail.fl_str_mv mluisa.alvim@gmail.com
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