Calculating the scale elasticity in DEA models.

In economics scale properties of a production function is charcterised by the value of the scale elasticity. In the field of efficiency studies this is also a valid approach for the frontier production function. It has no good meaning to talk about scale properties of inefficient observations. In the DEA literature a qualitative characterisation is most common. The contribution of the paper is to apply the concept of scale elasticity from multi output production theory in economics to the piecewise linear frontier production function, and to develop formulas for calculating values of the scale elasticity for radial projections of inefficient observations. Illustrations also on real data are provided, showing the differences between scale elasticity values for the input- and output oriented projections and the range of values for efficient observations.Scale elasticity; DEA, production theory; Farrell efficiency measures

Marginal values and returns to scale for nonparametric production frontiers

We present a unifying linear programming approach to the calculation of various directional derivatives for a very large class of production frontiers of data envelopment analysis (DEA). Special cases of this include different marginal rates, the scale elasticity and a spectrum of partial and mixed elasticity measures. Our development applies to any polyhedral production technology including, to name a few, the conventional variable and constant returns-to-scale DEA technologies, their extensions with weight restrictions, technolo gies with weakly disposable undesirable outputs and network DEA models. Furthermore, our development provides a general method for the characterization of returns to scale (RTS) in any polyhedral technology. The new approach effectively removes the need to develop bespoke models for the RTS characterization and calculation of marginal rates and elasticity measures for each particular technology

Using Total Factor Productivity and Data Envelopment Analysis for Performance Comparisons Among Government Enterprises: Concepts and Issues

The purpose of this paper is to review and comment on a number of popular methods of performance measurement, in particular total factor productivity and data envelopment analysis; and to draw attention to potential pitfalls or misinterpretations which can arise in using these techniques. Potential users may not be aware that there are a number of different formulations and interpretations of these concepts, and that numerical measures of performance can vary considerably even when a consistent performance measure is being used

A Brief History of Production Functions

This paper gives an outline of evolution of the concept and econometrics of production function, which was one of the central apparatus of neo-classical economics. It shows how the famous Cobb-Douglas production function was indeed invented by von Thunen and Wicksell, how the CES production function was formulated, how the elasticity of substitution was made a variable and finally how Sato’s function incorporated biased technical changes. It covers almost all specifications proposed during 1950-1975, and further the LINEX production functions and incorporation of energy as an input. The paper in divided into (1) single product functions, (2) joint product functions, and (3) aggregate production functions. It also discusses the ‘capital controversy’ and its impacts.Production function; Cobb-Douglas; CES; Transcendental; translog; Zellner-Revankar; VES; Bruno; Kadiyala; Diewert; Kummel; Mundlak; Engineering production function; Multi-output; joint product; Data Envelopment; Household production function; Humbug production function; capital controversy; Cambridge controversy

Returns to scale in convex production technologies

The notion of returns to scale (RTS) is well-established in data envelopment analysis (DEA). In the variable returns-to-scale production technology, the RTS characterization is closely related to other scale characteristics, such as the scale elasticity, most productive scale size (MPSS), and global RTS types indicative of the direction to MPSS. In recent years, a number of alternative production technologies have been developed in the DEA literature. Most of these technologies are polyhedral, and hence are closed and convex sets. Examples include technologies with weakly disposable undesirable outputs, models with weight restrictions and production trade-offs, technologies that include several component production processes, and network DEA models. For most of these technologies, the relationship between RTS and other scale characteristics has remained unexplored. The theoretical results obtained in this paper establish such relationships for a very large class of closed convex technologies, of which polyhedral technologies are an important example

Total Factor Productivity and the Efficiency of Rice Farms in Bangladesh: a Farm Level Panel Data Comparison of the Pre- and Post-Market Reform Period

The market reform policy in agriculture and the trade liberalization during the early 1990s has led to structural changes in the agricultural sector of Bangladesh. The question of whether market reform policies in Bangladesh facilitated rice production is examined in this paper. This paper uses stochastic frontier production function to measure total factor productivity (TFP), technical change, and technical efficiency change covering the period of pre-market reform (1987) and post-market reform (2000 and 2004). To fulfill the objective, the study used panel data of 73 same farm households from a field survey of 1987–1988, 1999-2000 and 2003-04. It is evident from the study results that over time period (1987-2004), the TFP increased (31.76%) only due to upward shift in the technology. Technological change increased 59.99% in post reform period. However, although TFP increased substantial inefficiencies remain in Bangladesh rice sector. Technical efficiency change (-34.46%) developed negatively over the years of study at farm level. Market reform policy has negative impact on technical efficiency change but positive in technical change and TFP change although all are declining over the time period. Therefore, government policies need for further reform of domestic market and trade policies focusing on institutional changes, tariff and nontariff barriers in order to develop a competitive environment in rice sector.Farm Management,

Measurement of Returns-to-Scale using Interval Data Envelopment Analysis Models

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI linkThe economic concept of Returns-to-Scale (RTS) has been intensively studied in the context of Data Envelopment Analysis (DEA). The conventional DEA models that are used for RTS classification require well-defined and accurate data whereas in reality observations gathered from production systems may be characterized by intervals. For instance, the heat losses of the combined production of heat and power (CHP) systems may be within a certain range, hinging on a wide variety of factors such as external temperature and real-time energy demand. Enriching the current literature independently tackling the two problems; interval data and RTS estimation; we develop an overarching evaluation process for estimating RTS of Decision Making Units (DMUs) in Imprecise DEA (IDEA) where the input and output data lie within bounded intervals. In the presence of interval data, we introduce six types of RTS involving increasing, decreasing, constant, non-increasing, non-decreasing and variable RTS. The situation for non-increasing (non-decreasing) RTS is then divided into two partitions; constant or decreasing (constant or increasing) RTS using sensitivity analysis. Additionally, the situation for variable RTS is split into three partitions consisting of constant, decreasing and increasing RTS using sensitivity analysis. Besides, we present the stability region of an observation while preserving its current RTS classification using the optimal values of a set of proposed DEA-based models. The applicability and efficacy of the developed approach is finally studied through two numerical examples and a case study