Assessing performance in Greek bauxite mining by means of frontier estimation methodologies

Tsolas, IE

dc.contributor.author	Tsolas, IE	en
dc.date.accessioned	2014-03-01T01:32:52Z
dc.date.available	2014-03-01T01:32:52Z
dc.date.issued	2010	en
dc.identifier.issn	1471-678X	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/20232
dc.subject	Bauxite mining	en
dc.subject	Bootstrapping	en
dc.subject	Data envelopment analysis	en
dc.subject	Greece	en
dc.subject	Stochastic frontier analysis	en
dc.subject.other	Bauxite mining	en
dc.subject.other	Data sets	en
dc.subject.other	Frontier estimation	en
dc.subject.other	Independence assumption	en
dc.subject.other	Mining industry	en
dc.subject.other	Performance evaluation	en
dc.subject.other	Performance measurements	en
dc.subject.other	Point estimate	en
dc.subject.other	Productivity measure	en
dc.subject.other	Return to scale	en
dc.subject.other	Stochastic frontier analysis	en
dc.subject.other	Bauxite deposits	en
dc.subject.other	Data handling	en
dc.subject.other	Linear programming	en
dc.subject.other	Stochastic systems	en
dc.subject.other	Time series	en
dc.subject.other	Data envelopment analysis	en
dc.title	Assessing performance in Greek bauxite mining by means of frontier estimation methodologies	en
heal.type	journalArticle	en
heal.identifier.primary	10.1093/imaman/dpp018	en
heal.identifier.secondary	http://dx.doi.org/10.1093/imaman/dpp018	en
heal.language	English	en
heal.publicationDate	2010	en
heal.abstract	This paper employs for the first time data envelopment analysis (DEA) and stochastic frontier analysis (SFA) as two performance measurement competing approaches to assess efficiency in the Greek mining industry. These two frontier estimation methodologies overcome the limitations of the partial productivity measures by explicitly considering two inputs and one output in the measurement of efficiency for the period 1970-1996. The paper is also innovative in utilizing a bootstrapping approach in DEA to aggregated industry (time series) data as an alternative to the more common DEA point estimates. In particular, the bootstrapping approach used relies on the homogeneity assumption that the distribution of the efficiency scores is independently distributed over the sample; the results from DEA and SFA are more comparable under this assumption as it corresponds to the independence assumption regarding the distribution of the inefficiency term in SFA. The two different approaches to performance evaluation, as used here, do not provide confirmation of each other's findings since they are based on different principles and treat the data in different ways. Although the joint use made here of DEA and SFA provides results that are consistent with points of view that have regarded these two approaches as mutually exclusive alternatives, this paper demonstrates that from a policy perspective DEA and SFA can be utilized in tandem on a common data set to assess the efficiency and investigate the return to scale patterns at the sectoral level. © 2009 The authors. Published by Oxford University Press on behalf of the Institute of Mathematics and its Applications. All rights reserved.	en
heal.publisher	OXFORD UNIV PRESS	en
heal.journalName	IMA Journal Management Mathematics	en
dc.identifier.doi	10.1093/imaman/dpp018	en
dc.identifier.isi	ISI:000279776700003	en
dc.identifier.volume	21	en
dc.identifier.issue	3	en
dc.identifier.spage	253	en
dc.identifier.epage	265	en