Juan Aparicio

On the inconsistency of the Malmquist–Luenberger index

Apart from the well-known weaknesses of the standard Malmquist productivity index related to infeasibility and not accounting for slacks, already addressed in the literature, we identify a new and significant drawback of the Malmquist–Luenberger index decomposition that questions its validity as an empirical tool for environmental productivity measurement associated with the production of bad outputs. In particular, we show that the usual interpretation of the technical change component in terms of production frontier shifts can be inconsistent with its numerical value, thereby resulting in an erroneous interpretation of this component that passes on to the index itself. We illustrate this issue with a simple numerical example. Finally, we propose a solution for this inconsistency issue based on incorporating a new postulate for the technology related to the production of bad outputs.

Decomposing profit inefficiency in DEA through the weighted additive model

An issue that has received little attention in the Data Envelopment Analysis literature is the decomposition of profit inefficiency by means of measures that account all sources of technical inefficiency. In this paper we introduce a new way to measure and decompose profit inefficiency through weighted additive models. All our results are derived from a new Fenchel-Mahler inequality using duality theory.

On how to properly calculate the Euclidean distance-based measure in DEA

Recently, in a data envelopment analysis framework, Amirteimoori and Kordrostami [A Euclidean distance-based measure of efficiency in data envelopment analysis, Optimization 59 (2010), pp. 985–996] introduced a Euclidean distance-based measure of efficiency in order to obtain the shortest path to the strongly efficient frontier from an assessed unit. In this article, we show a drawback of the aforementioned approach and two examples of its consequences. In order to overcome this weakness, we slightly modify a model introduced by Aparicio et al. [Closest targets and minimum distance to the Pareto-efficient frontier in DEA, J. Prod. Anal. 28 (2007), pp. 209–218].

Accounting for slacks to measure and decompose revenue efficiency in the Spanish Designation of Origin wines with DEA

In this paper, we show how Data Envelopment Analysis (DEA) may be used to measure and decompose revenue inefficiency, taking into account all sources of technical waste in the context of an application to assess the Spanish quality wine sector, in particular Designation of Origin (DO) wines. We try to go beyond the standard approaches, which use Shephard distance functions or directional distance functions, to provide decomposition that incorporates slacks as a source of technical inefficiency. To accomplish this, we will base our analysis on a recent approach introduced in Cooper et al. (2011a). In particular, we show how an output-oriented version of the Weighted Additive model can be used to properly identify revenue, technical, and allocative inefficiencies in Spanish DOs. In the application, we conclude that the main source of revenue inefficiency in this sector is technical waste, and that Cava can be highlighted as the DO that performs as a benchmark for more numbers of units.

An overall measure of technical inefficiency at the firm and at the industry level: The ‘lost profit on outlay’

As a measure of overall technical inefficiency, the Directional Distance Function (DDF) introduced by Chambers, Chung, and Fare ties the potential output expansion and input contraction together through a single parameter. By duality, the DDF is related to a measure of profit inefficiency, which is calculated as the normalized deviation between optimal and actual profit at market prices. As we show, in the most usual case, the associated normalization represents the sum of the actual revenue and the actual cost of the assessed firm. Consequently, the corresponding profit inefficiency measure associated with the DDF has no obvious economic interpretation. In contrast, in this paper we allow outputs to expand and inputs to contract by different proportions. This results in a modified DDF that retains most of the properties of the original DDF. The corresponding dual problem has a much simpler interpretation as the lost profit on (average) outlay that can be decomposed into a technical and an allocative inefficiency component. In addition, an overall measure of technical inefficiency at the industry level is introduced resorting to the direction corresponding to the average input–output bundle.

Decomposing technical inefficiency using the principle of least action

In for-profit organizations, profit efficiency decomposition is considered important since estimates on profit drivers are of practical use to managers in their decision making. Profit efficiency is traditionally due to two sources – technical efficiency and allocative efficiency. The contribution of this paper is a novel decomposition of technical efficiency that could be more practical to use if the firm under evaluation really wants to achieve technical efficiency as soon as possible. For this purpose, we show how a new version of the Measure of Inefficiency Proportions (MIP), which seeks the minimization of the total technical effort by the assessed firm, is a lower bound of the value of technical inefficiency associated with the directional distance function. The targets provided by the new MIP could be beneficial for firms since it specifies how firms may become technically efficient simply by decreasing one input or increasing one output, suggesting that each firm should focus its effort on a specific dimension (input or output). This approach is operationalized in a data envelopment analysis framework and applied to a dataset of airlines.

How to properly decompose economic efficiency using technical and allocative criteria with non-homothetic DEA technologies

We discuss how to properly decompose economic efficiency when the underlying technology is non-homothetic using alternative allocative and technical efficiency criteria. We first show that only under the production of one output and assuming the particular case of constant returns to scale homotheticity, we may claim that the standard radial models correctly measure pure technical efficiency. Otherwise, when non-homotheticity is assumed, we then show that these traditional estimations would measure an undetermined mix of technical and allocative efficiency. To restore a consistent measure of technical efficiency in the non-homothetic case we introduce a new methodology that takes as reference for the economic efficiency decomposition the preservation of the allocative efficiency of firms producing in the interior of the technology. This builds upon the so-called reversed approach recently introduced by Bogetoft et al. (2006) that allows estimating allocative efficiency without presuming that technical efficiency has been already accomplished. We illustrate our methodology within the Data Envelopment Analysis framework adopting the most simple non-homothetic BCC model and a numerical application. In this application we show that there are significant differences in the allocative and technical efficiency scores depending on the approach.

Comparing school ownership performance using a pseudo-panel database: A Malmquist-type index approach

In this paper, we propose a different way of using the Malmquist index that allows us to further analyze the relative performance divergences between two groups of decision-making units (DMUs) over time when only a pseudo-panel database is available. To do this, we extend the Camanho and Dyson (2006) one-period Malmquist-type index (CDMI) for a pseudo-panel database with a new pseudo-panel Malmquist index (PPMI). To illustrate the methodology, we apply it to examine how the performance gap between public and private government-dependent secondary schools in the Basque Country (Spain) performed across three PISA waves (2006, 2009 and 2012). The results suggest that performance is persistently and significantly higher for private government-dependent schools than for public schools.

Benchmarking and Data Envelopment Analysis. An Approach based on Metaheuristics.

Abstract Data Envelopment Analysis (DEA) is a non-parametric technique for estimating technical efficiency of a set of units. DEA also provides information on benchmarking. In this paper, we study DEA models based on closest efficient targets, which are associated with the least distance and allow inefficient units to find the easiest way to achieve the efficient frontier. In the literature these models have been solved through unsatisfactory methods related to combinatorial NP-hard problems. In this paper, the problem is approached by metaheuristic techniques. Due to the high number of restrictions of the problem, finding solutions to be used in the metaheuristic algorithm is a difficult problem. Thus, this paper analyzes and compares some heuristic algorithms to obtain solutions of the problem. Each restriction determines the design of these heuristics. Thus, the problem is considered by adding constraints one by one. In this paper, the problem is presented and studied taking into account 9 of the 14 constraints, and the solution to this new problem is an upper bound of the optimal value of the original problem.

The weighted additive distance function

Distance functions in production theory are mathematical structures that characterize the belonging to the reference technology through a numerical value, behave as technical efficiency measures when the focus is analyzing an observed input–output vector within its production possibility set and present a dual relationship with some support function (profit, revenue, cost function). In this paper, we endow the well-known weighted additive models in Data Envelopment Analysis with a distance function structure, introducing the Weighted Additive Distance Function and showing its main properties.