Some Models for Estimating Technical and Scale Inefficiencies in Data Envelopment Analysis

TLDR

Data Envelopment Analysis uses mathematical programming to evaluate the relative efficiency of managerial decisions, capturing both technical and scale inefficiencies without pre‑specified weights or functional forms. The paper aims to separate technical and scale efficiencies in DEA and introduces a new variable to identify increasing, constant, or decreasing returns to scale in multi‑input, multi‑output settings. The authors develop a method that retains the standard DEA framework while adding a separate variable to distinguish technical from scale inefficiencies and to classify returns to scale. The results demonstrate that the proposed approach aligns with both classical single‑output economics and modern contestable market theories.

Abstract

In management contexts, mathematical programming is usually used to evaluate a collection of possible alternative courses of action en route to selecting one which is best. In this capacity, mathematical programming serves as a planning aid to management. Data Envelopment Analysis reverses this role and employs mathematical programming to obtain ex post facto evaluations of the relative efficiency of management accomplishments, however they may have been planned or executed. Mathematical programming is thereby extended for use as a tool for control and evaluation of past accomplishments as well as a tool to aid in planning future activities. The CCR ratio form introduced by Charnes, Cooper and Rhodes, as part of their Data Envelopment Analysis approach, comprehends both technical and scale inefficiencies via the optimal value of the ratio form, as obtained directly from the data without requiring a priori specification of weights and/or explicit delineation of assumed functional forms of relations between inputs and outputs. A separation into technical and scale efficiencies is accomplished by the methods developed in this paper without altering the latter conditions for use of DEA directly on observational data. Technical inefficiencies are identified with failures to achieve best possible output levels and/or usage of excessive amounts of inputs. Methods for identifying and correcting the magnitudes of these inefficiencies, as supplied in prior work, are illustrated. In the present paper, a new separate variable is introduced which makes it possible to determine whether operations were conducted in regions of increasing, constant or decreasing returns to scale (in multiple input and multiple output situations). The results are discussed and related not only to classical (single output) economics but also to more modern versions of economics which are identified with “contestable market theories.”