Closest targets and strong monotonicity on the strongly efficient frontier in DEA |
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| Institution: | 1. Decision Science Institute, School of Economics & Management, Fuzhou University, Fuzhou 350116, PR China;2. Key Laboratory of Spatial Data Mining & Information Sharing of Ministry of Education, Fuzhou University, Fuzhou 350116, PR China;1. Schulich School of Business, York University, Toronto, Ontario M3J 1P3, Canada;2. Centro de Investigación Operativa, Universidad Miguel Hernández, Avd. de la Universidad, s/n, 03202-Elche (Alicante), Spain;3. School of Business, Worcester Polytechnic Institute, Worcester, MA 01609, USA |
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| Abstract: | The determination of closest efficient targets has attracted increasing interest of researchers in recent Data Envelopment Analysis (DEA) literature. Several methods have been introduced in this respect. However, only a few attempts exist that analyze the implications of using closest targets on the technical inefficiency measurement. In particular, least distance measures based on Hölder norms satisfy neither weak nor strong monotonicity on the strongly efficient frontier. In this paper, we study Hölder distance functions and show why strong monotonicity fails. Along this line, we provide a solution for output-oriented models that allows assuring strong monotonicity on the strongly efficient frontier. Our approach may also be extended to the most general case, i.e. non-oriented models, under some conditions of regularity. |
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| Keywords: | Data envelopment analysis Closest targets Hölder norms Strong monotonicity |
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