Slacks-based inefficiency approach for general networks with bad outputs: An application to the banking sector
Sebastián Lozano
Omega, 2016, vol. 60, issue C, 73-84
Abstract:
In this paper the efficiency assessment of general networks of processes that produce both desirable and undesirable outputs is addressed. This problem arises in many contexts (e.g. transportation, energy generation, etc.). A general networks slacks-based inefficiency (GNSBI) measure can be computed using a simple linear program that takes into account the weak disposability of the bad outputs. The slacks-based inefficiency (SBI) of each process is also calculated. Target values for all inputs, outputs (both desirable and undesirable) and even intermediate products are also provided. The proposed approach is rather general and can accommodate many different network topologies and returns to scale assumptions. Two applications to the banking sector are presented: one to assess banks efficiencies and another to assess bank branches.
Keywords: Efficiency; Network DEA; Undesirable outputs; Slacks-based inefficiency; Banks; Bank branches (search for similar items in EconPapers)
Date: 2016
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (38)
Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0305048315000511
Full text for ScienceDirect subscribers only
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:eee:jomega:v:60:y:2016:i:c:p:73-84
Ordering information: This journal article can be ordered from
http://www.elsevier.com/wps/find/supportfaq.cws_home/regional
https://shop.elsevie ... _01_ooc_1&version=01
DOI: 10.1016/j.omega.2015.02.012
Access Statistics for this article
Omega is currently edited by B. Lev
More articles in Omega from Elsevier
Bibliographic data for series maintained by Catherine Liu ().