Industrial clustering and sectoral growth: a network dynamics approach
Autor(a) principal: | |
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Data de Publicação: | 2011 |
Outros Autores: | , , |
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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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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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 |
_version_ |
1817549539199942656 |