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[For the entire collection see Zbl 0721.00029.]
Data envelopment analysis (DEA) is a relatively simple procedure designed for measuring efficiencies and identifying inefficiencies in each pertinent input and output of decision making units (DMU) operating with several inputs and outputs. Because the inputs and outputs are usually some approximations the crucial problem is to determine whether the efficiency tests are stable. In the article a result on global stability of these tests is proved. Further, two new important DEA models, which often reduce the number of efficient DMU’s are investigated. Conditions for their stability and a classification of efficient DMU’s as “structurally efficient” and “structurally inefficient” are given.