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Author
Sakouvogui Kekoura (U.S Census Bureau, Washington D.C., United States), Shaik Saleem (North Dakota State University, United States), Addey Kwame Asiam (North Dakota State University, United States)
Title
Cluster-Adjusted DEA Efficiency in the presence of Heterogeneity: An Application to Banking Sector
Source
Open Economics, 2020, vol. 3, iss. 1, s. 50-69, rys., tab., bibliogr. 45 poz.
Keyword
Bankowość, Analiza skupień, Metoda DEA (analiza obwiedni danych)
Banking, Cluster analysis, Data Envelopment Analysis (DEA)
Note
JEL Classification: A10, C10, C14, C44, G21
summ.
Abstract
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 effciency, 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 differencesin the estimated DEA eciency measures with and without a clustering approach.. Furthermore, using nonparametric tests, the results of Ansari-Bradley, KruskalWallis, andWilcoxon Rank Sum tests suggest that the cluster-adjusted DEA model provides statistically better effciency measures in comparison to the DEA model without a clustering approach. (original abstract)
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Bibliography
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Cited by
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ISSN
2451-3458
Language
eng
URI / DOI
http://dx.doi.org/10.1515/openec-2020-0004
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