Cluster-adjusted DEA efficiency in the presence of heterogeneity: An application to banking sector

Artikel

This paper improves on the issues of extreme data points and heterogeneity found in the linear programming data envelopment analysis (DEA) by presenting a cluster-adjusted DEA model (DEA with cluster approach). This analysis, based on efficiency, determines the number of clusters via Gap statistic and Elbow methods. We use the December quarterly panel data consisting of 122 U.S agricultural banks across 37 states from 2000 to 2017 to estimate the cluster-adjusted DEA model. Empirical results show differences in the estimated DEA efficiency measures with and without a clustering approach. Furthermore, using non-parametric tests, the results of Ansari-Bradley, Kruskal Wallis, and Wilcoxon Rank Sum tests suggest that the cluster-adjusted DEA model provides statistically better efficiency measures in comparison to the DEA model without a clustering approach.

Angaben zum Objekt

• Sprache: Englisch

Verweise und Beziehungen

• Erschienen in: Journal: Open Economics ; ISSN: 2451-3458 ; Volume: 3 ; Year: 2020 ; Issue: 1 ; Pages: 50-69 ; Warsaw: De Gruyter

Klassifikation und Themen

• Klassifikation: Wirtschaft ; General Economics: General ; Econometric and Statistical Methods and Methodology: General ; Semiparametric and Nonparametric Methods: General ; Operations Research; Statistical Decision Theory ; Banks; Depository Institutions; Micro Finance Institutions; Mortgages
• Thema: Banking ; Cluster analysis ; Efficiency Analysis ; Nonparametric tests

Beteiligte, Orts- und Zeitangaben

• Ereignis: Geistige Schöpfung
• (wer): Sakouvogui, Kekoura ; Shaik, Saleem ; Addey, Kwame Asiam
• Ereignis: Veröffentlichung
• (wer): De Gruyter
• (wo): Warsaw
• (wann): 2020

Weitere Informationen

• DOI: doi:10.1515/openec-2020-0004
• Handle: http://hdl.handle.net/10419/236605
• Letzte Aktualisierung: 10.03.2025, 11:42 MEZ