Dealing with endogeneity in data envelopment analysis applications

Although the presence of the endogeneity is frequently observed in economic production processes, it tends to be overlooked when practitioners apply data envelopment analysis (DEA). In this paper we deal with this issue in two ways. First, we provide a simple statistical heuristic procedure that ena...

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Detalles Bibliográficos
Autores: Santín González, Daniel, Sicilia, Gabriela
Tipo de recurso: artículo
Fecha de publicación:2017
País:España
Institución:Universidad Complutense de Madrid (UCM)
Repositorio:Docta Complutense
Idioma:inglés
OAI Identifier:oai:docta.ucm.es:20.500.14352/18905
Acceso en línea:https://hdl.handle.net/20.500.14352/18905
Access Level:acceso abierto
Palabra clave:Data envelopment analysis (DEA)
Endogeneity
Simulation
Education.
Econometría (Economía)
Educación
5302 Econometría
58 Pedagogía
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spelling Dealing with endogeneity in data envelopment analysis applicationsSantín González, DanielSicilia, GabrielaData envelopment analysis (DEA)EndogeneitySimulationEducation.Econometría (Economía)Educación5302 Econometría58 PedagogíaAlthough the presence of the endogeneity is frequently observed in economic production processes, it tends to be overlooked when practitioners apply data envelopment analysis (DEA). In this paper we deal with this issue in two ways. First, we provide a simple statistical heuristic procedure that enables practitioners to identify the presence of endogeneity in an empirical application. Second, we propose the use of an instrumental input DEA (II-DEA) as a potential tool to address this problem and thus improve DEA estimations. A Monte Carlo experiment confirms that the proposed II-DEA approach outperforms standard DEA in finite samples under the presence of high positive endogeneity. To illustrate our theoretical findings, we perform an empirical application on the education sector.ElsevierUniversidad Complutense de Madrid20172017-01-0120172017-01-01journal articlehttp://purl.org/coar/resource_type/c_6501info:eu-repo/semantics/articleapplication/pdfhttps://hdl.handle.net/20.500.14352/18905reponame:Docta Complutenseinstname:Universidad Complutense de Madrid (UCM)Inglésengopen accesshttp://purl.org/coar/access_right/c_abf2info:eu-repo/semantics/openAccessoai:docta.ucm.es:20.500.14352/189052026-06-02T12:44:21Z
dc.title.none.fl_str_mv Dealing with endogeneity in data envelopment analysis applications
title Dealing with endogeneity in data envelopment analysis applications
spellingShingle Dealing with endogeneity in data envelopment analysis applications
Santín González, Daniel
Data envelopment analysis (DEA)
Endogeneity
Simulation
Education.
Econometría (Economía)
Educación
5302 Econometría
58 Pedagogía
title_short Dealing with endogeneity in data envelopment analysis applications
title_full Dealing with endogeneity in data envelopment analysis applications
title_fullStr Dealing with endogeneity in data envelopment analysis applications
title_full_unstemmed Dealing with endogeneity in data envelopment analysis applications
title_sort Dealing with endogeneity in data envelopment analysis applications
dc.creator.none.fl_str_mv Santín González, Daniel
Sicilia, Gabriela
author Santín González, Daniel
author_facet Santín González, Daniel
Sicilia, Gabriela
author_role author
author2 Sicilia, Gabriela
author2_role author
dc.contributor.none.fl_str_mv Universidad Complutense de Madrid
dc.subject.none.fl_str_mv Data envelopment analysis (DEA)
Endogeneity
Simulation
Education.
Econometría (Economía)
Educación
5302 Econometría
58 Pedagogía
topic Data envelopment analysis (DEA)
Endogeneity
Simulation
Education.
Econometría (Economía)
Educación
5302 Econometría
58 Pedagogía
description Although the presence of the endogeneity is frequently observed in economic production processes, it tends to be overlooked when practitioners apply data envelopment analysis (DEA). In this paper we deal with this issue in two ways. First, we provide a simple statistical heuristic procedure that enables practitioners to identify the presence of endogeneity in an empirical application. Second, we propose the use of an instrumental input DEA (II-DEA) as a potential tool to address this problem and thus improve DEA estimations. A Monte Carlo experiment confirms that the proposed II-DEA approach outperforms standard DEA in finite samples under the presence of high positive endogeneity. To illustrate our theoretical findings, we perform an empirical application on the education sector.
publishDate 2017
dc.date.none.fl_str_mv 2017
2017-01-01
2017
2017-01-01
dc.type.none.fl_str_mv journal article
http://purl.org/coar/resource_type/c_6501
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://hdl.handle.net/20.500.14352/18905
url https://hdl.handle.net/20.500.14352/18905
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
dc.source.none.fl_str_mv reponame:Docta Complutense
instname:Universidad Complutense de Madrid (UCM)
instname_str Universidad Complutense de Madrid (UCM)
reponame_str Docta Complutense
collection Docta Complutense
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,300724

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