Different Types of Return to Scale in Dea

Abstract

The format of the efficient frontier is an important measure of technical efficiency; additionally, it determines the type of return to scale verified by the model. The classical Data Envelopment Analysis (DEA) model, CCR (Charnes et al., 1978), assumes constant returns to scale; conversely, the BCC (Banker et al., 1984) model presents a concave downward efficient frontier that presumes variable returns to scale. This study examines how different returns to scale can be revealed in DEA, considering the possibility of the existence of a concave upward efficient frontier. This kind of frontier, not yet explored by the DEA literature, can also represent viable production, seeing that an increase of the inputs causes an increase of the outputs. Considering this, a concave upward efficient frontier presents a variable return to scale, but with different characteristics from those of the concave downward BCC efficient frontier. This proposal is important because it considers the possibility of an efficient frontier that represents different samples of decision-making units (DMUs). An upward curve would better represent DMUs of smaller production scales that have increased marginal productivity but cannot act as efficiently as larger scale units.

Two-Dimensional Representation for Two-Stage Network Dea Models

“…As suggestions for future studies, we propose the analysis of the multiple optimal weights for the relational model and its representations in the graph. Besides that, we propose extending the model presented for different types of return to scale (Benicio and Soares de Mello, 2019) and other Network DEA models, as the additive model (Chen et al, 2009), the relational model (Kao, 2009), multi-stage processes (Despotis et al, 2016b;Zhang et al, 2019;Guo et al, 2017) and other models (Tone and Tsutsui, 2009;Guo and Zhu, 2017;Wang et al, 2019). Another suggestion for future studies is to verify how to determine the target for inefficient DMUs at the efficient frontier, for that, it can be applied the method proposed in Chen et al ( 2010).…”