Fuzzy approach to discrete data reduction: an application in economics for assessing the skill premium
Autor(a) principal: | |
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Data de Publicação: | 2016 |
Outros Autores: | , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://hdl.handle.net/10071/12131 |
Resumo: | Measures of stock of skills alternative to human capital have raised fresh difficulties, especially in data managing. We propose to empirically compare the efficiency of a hierarchical cluster analysis and a fuzzy clustering in reducing discrete skill data. The outcomes of both methods are subsequently used to measure the impact of skills on earnings in addition to human capital. The proposed methodological comparison was made using an original dataset of retail bankers’ skills assessed by supervisors. Empirical evidence shows that the fuzzy approach is more efficient than the hierarchical clustering: the resulting clusters are fewer and easier to interpret. Furthermore, the earnings equation enriched with skill variables allowed us to correct the education premium, and provides information on monetary incentives related to individual skills. Our paper attempts to raise researchers’ and practitioners’ awareness of data reducing methods, and their implications for wage determinants. |
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Fuzzy approach to discrete data reduction: an application in economics for assessing the skill premiumHuman capitalSkillsEarningsData reductionHierarchical cluster analysisFuzzy setsGrade of membership modelMeasures of stock of skills alternative to human capital have raised fresh difficulties, especially in data managing. We propose to empirically compare the efficiency of a hierarchical cluster analysis and a fuzzy clustering in reducing discrete skill data. The outcomes of both methods are subsequently used to measure the impact of skills on earnings in addition to human capital. The proposed methodological comparison was made using an original dataset of retail bankers’ skills assessed by supervisors. Empirical evidence shows that the fuzzy approach is more efficient than the hierarchical clustering: the resulting clusters are fewer and easier to interpret. Furthermore, the earnings equation enriched with skill variables allowed us to correct the education premium, and provides information on monetary incentives related to individual skills. Our paper attempts to raise researchers’ and practitioners’ awareness of data reducing methods, and their implications for wage determinants.Vilnius Gediminas Technical University2016-12-02T17:03:08Z2016-01-01T00:00:00Z20162019-02-20T16:05:42Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10071/12131eng1611-169910.3846/16111699.2014.978361Suleman, A.Suleman, F.Reis, E.info: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:RCAAP2023-11-09T18:02:01Zoai:repositorio.iscte-iul.pt:10071/12131Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T22:33:20.360985Repositório 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 |
Fuzzy approach to discrete data reduction: an application in economics for assessing the skill premium |
title |
Fuzzy approach to discrete data reduction: an application in economics for assessing the skill premium |
spellingShingle |
Fuzzy approach to discrete data reduction: an application in economics for assessing the skill premium Suleman, A. Human capital Skills Earnings Data reduction Hierarchical cluster analysis Fuzzy sets Grade of membership model |
title_short |
Fuzzy approach to discrete data reduction: an application in economics for assessing the skill premium |
title_full |
Fuzzy approach to discrete data reduction: an application in economics for assessing the skill premium |
title_fullStr |
Fuzzy approach to discrete data reduction: an application in economics for assessing the skill premium |
title_full_unstemmed |
Fuzzy approach to discrete data reduction: an application in economics for assessing the skill premium |
title_sort |
Fuzzy approach to discrete data reduction: an application in economics for assessing the skill premium |
author |
Suleman, A. |
author_facet |
Suleman, A. Suleman, F. Reis, E. |
author_role |
author |
author2 |
Suleman, F. Reis, E. |
author2_role |
author author |
dc.contributor.author.fl_str_mv |
Suleman, A. Suleman, F. Reis, E. |
dc.subject.por.fl_str_mv |
Human capital Skills Earnings Data reduction Hierarchical cluster analysis Fuzzy sets Grade of membership model |
topic |
Human capital Skills Earnings Data reduction Hierarchical cluster analysis Fuzzy sets Grade of membership model |
description |
Measures of stock of skills alternative to human capital have raised fresh difficulties, especially in data managing. We propose to empirically compare the efficiency of a hierarchical cluster analysis and a fuzzy clustering in reducing discrete skill data. The outcomes of both methods are subsequently used to measure the impact of skills on earnings in addition to human capital. The proposed methodological comparison was made using an original dataset of retail bankers’ skills assessed by supervisors. Empirical evidence shows that the fuzzy approach is more efficient than the hierarchical clustering: the resulting clusters are fewer and easier to interpret. Furthermore, the earnings equation enriched with skill variables allowed us to correct the education premium, and provides information on monetary incentives related to individual skills. Our paper attempts to raise researchers’ and practitioners’ awareness of data reducing methods, and their implications for wage determinants. |
publishDate |
2016 |
dc.date.none.fl_str_mv |
2016-12-02T17:03:08Z 2016-01-01T00:00:00Z 2016 2019-02-20T16:05:42Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/10071/12131 |
url |
http://hdl.handle.net/10071/12131 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
1611-1699 10.3846/16111699.2014.978361 |
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 |
Vilnius Gediminas Technical University |
publisher.none.fl_str_mv |
Vilnius Gediminas Technical University |
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 |
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1799134894758035456 |