The Efficiency of Non-Homogeneity Security Firms in Taiwan

Yu-Chuan Chen (), Pin Ruei Lee () and Yung-Ho Chiu

Panoeconomicus, 2017, vol. 64, issue 3, 353-370

Abstract: Data envelopment analysis (DEA) is a nonparametric technique for determining the efficiency of a homogeneous set of decision-making units (DMUs). There are two common problems with traditional DEA. First, traditional DEA fails to adequately distinguish the efficiency DMUs. Second, the DMUs within the same industry are non-homogeneous. This study aims to develop a system-ranking-efficiency model to solve the problems of non-homogeneity and efficiency ranking for DMUs in the same group. The proposed system-ranking-efficiency model is based on the concept of boundary change and considers the efficiency DMUs with the greatest influence on the boundary as the most important and, thus, as the ones that should have the highest efficiency ranking. The model is applied in the Taiwan securities industry, in which it was found to successfully rank all the DMUs. Key words: Data envelopment analysis, Non-homogeneity, System-DEA, Ranking. JEL: D24, G23. Obuhvatna analiza podataka (DEA) je neparametarska tehnika za određivanje efikasnosti homogenog skupa jedinica odlučivanja (DMU). Postoje dva osnovna problema sa tradicionalnom DEA. Prvo, tradicionalna DEA ne razlikuje adekvatno efikasne DMU. Drugo, DMU u okviru iste grane su ne-homogene. Ovo istraživanje ima za cilj da razvije model za efikasno rangiranje sistema kako bi se rešio problem ne-homogenosti i efikasnosti rangiranja unutar iste grupe. Predloženi model za efikasno rangiranje sistema se zasniva na konceptu granične promene i smatra da su DMU sa najvećim uticajem na graničnu vrednost najvažnije i, samim tim, treba da imaju najviši rang efikasnosti. Model se primenjuje u industriji osiguranja u Tajvanu, u kojoj je utvrđeno da system uspešn rangira sve DMU. Ključne reči: Obuhvatna analiza podataka, Nehomogenost, Sistem-DEA, Rangiranje.

Keywords: Data envelopment analysis; Non-homogeneity; System-DEA; Ranking (search for similar items in EconPapers)
Date: 2017
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