A New Metric for Scale Elasticity in Data Envelopment Analysis
نویسندگان
چکیده
Robust measurement of scale elasticity (SE) in data envelopment analysis (DEA) models remains elusive, primarily reflecting the computational challenges brought about by the piecewise linear nature of the DEA technology. SE is meaningfully defined only at frontier points or at the projection of interior points to the frontier but not for the inefficient unit itself. A long held issue of concern is that returns to scale (RTS) are not uniquely determined for efficient units since they may be located on vertices or on ridges of the efficient fronier. Thus the multiplier of the convexity constraint defining scale elasticity can take on multiple values with existing methods providing different ways to estimate intervals for SE values in which RTS determination may be ambiguous. In this paper, we propose a linear programming (LP) model providing a unique measure of SE based on a simple proposition of closeness to most productive scale size (MPSS). The model is non-oriented, and the result not only provides the SE measure but also allows RTS to be classified. Furthermore, the model yields feasible consistent results using different solvers, while the same can not be said for the results of other methods. The model was tested using a data set of 95 banks across 11 years and compared with the interval approaches commonly used in the literature.
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