A new slacks-based measure of Malmquist-Luenberger index in the presence of undesirable outputs

In the majority of production processes, noticeable amounts of bad byproducts or bad outputs are produced. The negative effects of the bad outputs on efficiency cannot be handled by the standard Malmquist index to measure productivity change over time. Toward this end, the Malmquist-Luenberger index (MLI) has been introduced, when undesirable outputs are present. In this paper, we introduce a Data Envelopment Analysis (DEA) model as well as an algorithm, which can successfully eliminate a common infeasibility problem encountered in MLI mixed period problems. This model incorporates the best endogenous direction amongst all other possible directions to increase desirable output and decrease the undesirable outputs at the same time. A simple example used to illustrate the new algorithm and a real application of steam power plants is used to show the applicability of the proposed model

A generalized directional distance function in data envelopment analysis and its application to a cross-country measurement of health efficiency

Economic activity produces not only desirable outputs but also undesirable outputs that are usually called negative externalities in economic theory. Negative externalities are usually omitted from efficiency assessments (i.e., applications of Data Envelopment Analysis) which fail to express the true production process. In the present paper we develop a generalized directional distance function method for handling asymmetrically both desirable and undesirable outputs in the assessment process. Unlike the existing directional distance function-based approaches, the proposed method is units-invariant even in case assumptions for the direction vectors are relaxed. The new method is applied to data from national health systems of 160 countries. Desirable and undesirable outputs are incorporated to obtain a clear view of the efficiency status of the national health systems

A generalized directional distance function in data envelopment analysis and its application to a cross-country measurement of health efficiency

Economic activity produces not only desirable outputs but also undesirable outputs that are usually called negative externalities in economic theory. Negative externalities are usually omitted from efficiency assessments (i.e., applications of Data Envelopment Analysis) which fail to express the true production process. In the present paper we develop a generalized directional distance function method for handling asymmetrically both desirable and undesirable outputs in the assessment process. Unlike the existing directional distance function-based approaches, the proposed method is units-invariant even in case assumptions for the direction vectors are relaxed. The new method is applied to data from national health systems of 160 countries. Desirable and undesirable outputs are incorporated to obtain a clear view of the efficiency status of the national health systems

A unified approach to radial, hyperbolic, and directional distance models in Data Envelopment Analysis

The paper analyzes properties of a large class of "path-based" Data Envelopment Analysis models through a unifying general scheme, which includes as standard the well-known oriented radial models, the hyperbolic distance measure model, and the directional distance measure models. The scheme also accommodates variants of standard models over negative data. Path-based models are analyzed from the point of view of nine desired properties that a well-designed model should satisfy. The paper develops mathematical tools that allow systematic investigation of these properties in the general scheme including, but not limited to, the standard path-based models. Among other results, the analysis confirms the generally accepted view that path-based models need not generate Pareto--Koopmans efficient projections, one-to-one identification, or strict monotonicity

The Impact of Port Technical Efficiency on Mediterranean Container Port Competitiveness

Port efficiency is a significant element that stimulates port competitiveness and enhances regional development. With increasing international maritime traffic and changing technology in the maritime transport sector, containerisation and enhanced logistic activities, infrastructure might be one of the main determining factors of port competition (Merk & Dang, 2012). Due to the increasing container traffic and the high quality of service required by the shipping lines, Mediterranean container ports are being compelled to enhance port efficiency to improve comparative advantages that will increase cargo traffic and satisfy the customers’ requirements. The Mediterranean Sea is a link point between Europe, Africa and Asia. This research aims to examine the impact of ports' technical efficiency on the improvement of Mediterranean container ports’ competitiveness. The research analyses the competitiveness and the relative efficiency of the top 22 container ports in the Mediterranean basin using a cross-section, panel data and window analysis application of data envelopment analysis (DEA) for the period between 1998 and 2012. The selected 15 year period enables the analysis of Mediterranean container port market dynamics and the benchmarking of the technical efficiency of the selected ports for three consecutive market cycles. This research can be classified as quantitative analytical research. The research follows the concept of the Industrial Organization (IO) and the Structuralism (Harvard school) methodology that analyses the market Structures, Conduct and Performance (SCP) of market players. The study conducts a simultaneous three-stage procedure: in the first stage, the competitiveness of the main container ports in the Mediterranean is analysed through the study of market structure and conduct. Market structure is assessed through measuring and analysing market concentration by using four different methods. These methods are: the K-Firm concentration ratio (K-CR), Hirshman-Herfindahl Index (HHI), the Gini coefficient (GC) and the generalized entropy index. Boston Consultant Group (BCG) matrix is also used to visualize the dynamics between ports in the defined market and assess the ports' competitive position. Market conduct is analysed using shift-share analysis (SSA) to get a thorough understanding of the issue of port traffic development. In the second stage, market performance is analysed through the use of the non-parametric models of Data Envelopment Analysis (DEA) which estimates the relative efficiency scores and ranking seaports according to their efficiency. Five DEA models are adopted for comparative purpose, the DEA- CCR, DEA-BCC, the Super-Efficiency (A&P, 1993), the sensitivity analysis and slack variable analysis models. In the third stage, to examine the impact of port efficiency on port competitiveness, a number of hypotheses are examined through the use of parametric correlation coefficients (Spearman’s rank order) and Simar and Wilson (2007) procedure to bootstrap the DEA scores with a truncated regression. Using this approach enables more reliable evidence compared to previous studies analysing the efficiency of seaports. The main findings demonstrate that the recent deconcentration tendency of the Mediterranean container port market is due to the increased number of market players which will in turn reshape the market structure, change the container port hierarchy and intensify the competition between ports as the market shifts from oligopoly to pure competition. The research findings also reveal the existence of inefficiency pertaining to the management of container ports in the region, since the total technical efficiency is found to be below 50% on average. This relatively limited technical efficiency of the Mediterranean container ports indicates the need for appropriate capital investments for ports’ infra/superstructure. In particular, those ports whose efficiency is not favoured by some factors such as size, geographical position and socio-economic conditions of the region in which they are located, must adopt suitable reform strategies to promptly improve their efficiency and competitive position. What differentiates this work from previous studies on the subject is that both cross-sectional and panel data have been collected and analysed at the level of individual container ports in the Mediterranean. The study is based on a wide range of methodologies, both parametric and non-parametric, that have ensured the validity of the empirical examination that has been undertaken and the results obtained. The research analysed the Mediterranean container ports competitiveness, benchmarked and ranked their efficiency by considering the Mediterranean in its totality, including South Europe, Middle East and North Africa. The study puts forward a way to assess container port efficiency based on simple, yet validated and meaningful physical efficiency measures

Operational efficiency vs clinical safety, care appropriateness, timeliness, and access to health care

Health care systems face resource scarcity that may jeopardise their financial sustainability as well as the quality of delivered health care. In view of that, the association between technical efficiency, access, and quality of services should be investigated, despite some past attempts that led to mixed, unclear, and perhaps biased results. We use a dataset composed of financial resources, hospital services, appropriateness and timeliness of care, patients’ clinical safety, access to health care services, demographics, and epidemiology variables to study the aforementioned link regarding the Portuguese public hospitals (operating between 2013 and 2016). Quality and access data are aggregated into three main composite indicators, through Grey Relational Analysis (GRA). Bias- and environmentally corrected efficiency scores are estimated via bootstrap-based directional Data Envelopment Analysis. A double bootstrap algorithm is employed, using GRA-based quality indicators as predictors of technical efficiency. Evidence suggests that (1) Portuguese public hospitals exhibit low performance in terms of quality, while the different indicators present considerable correlation among them and with hospital size and patients’ complexity characteristics; (2) patients’ clinical safety, appropriateness and timeliness, as well as access to health care services are consistent and significant predictors of technical efficiency; and (3) the association between efficiency, quality, and access depends on the interaction between appropriateness, timeliness, and access. Therefore, quality and access can be improved with no efficiency sacrifice and vice versa.info:eu-repo/semantics/publishedVersio

Assessing productive efficiency of banks using integrated Fuzzy-DEA and bootstrapping:a case of Mozambican banks

Performance analysis has become a vital part of the management practices in the banking industry. There are numerous applications using DEA models to estimate efficiency in banking, and most of them assume that inputs and outputs are known with absolute precision. Here, we propose new Fuzzy-DEA α-level models to assess underlying uncertainty. Further, bootstrap truncated regressions with fixed factors are used to measure the impact of each model on the efficiency scores and to identify the most relevant contextual variables on efficiency. The proposed models have been demonstrated using an application in Mozambican banks to handle the underlying uncertainty. Findings reveal that fuzziness is predominant over randomness in interpreting the results. In addition, fuzziness can be used by decision-makers to identify missing variables to help in interpreting the results. Price of labor, price of capital, and market-share were found to be the significant factors in measuring bank efficiency. Managerial implications are addressed

An evaluation of cross-efficiency methods: With an application to warehouse performance

Cross-efficiency measurement is an extension of Data Envelopment Analysis that allows for tie-breaking ranking of the Decision Making Units (DMUs) using all the peer evaluations. In this article we examine the theory of cross-efficiency measurement by comparing a selection of methods popular in the literature. These methods are applied to performance measurement of European warehouses. We develop a cross-efficiency method based on a rank-order DEA model to accommodate the ordinal nature of some key variables characterizing warehouse performance. This is one of the first comparisons of methods on a real-life dataset and the first time that a model allowing for qualitative variables is included in such a comparison. Our results show that the choice of model matters, as one obtains statistically different rankings from each one of them. This holds in particular for the multiplicative and game-theoretic methods whose results diverge from the classic method. From a managerial perspective, focused on the applicability of the methods, we evaluate them through a multidimensional metric which considers their capability to rank DMUs, their ease of implementation, and their robustness to sensitivity analyses. We conclude that standard weight-restriction methods, as initiated by Sexton et al. [48], perform as well as recently introduced, more sophisticated alternativesSpanish Ministry of Science and Innovation (Ministerio de Ciencia e Innovación), the State Research Agency (Agencia Estatal de Investigación) and the European Regional Development Fund (Fondo Europeo de Desarrollo Regional) under grants EIN2020-11226

AN ASSESSMENT OF THE IMPACT OF UNDESIRABLE OUTPUTS ON THE PRODUCTIVITY OF UNITED STATES MOTOR CARRIERS

The U.S. economy depends heavily on the trucking industry as it moves 70% of the entire nation's freight. With the inclusion of $295 billion in truck trade with Canada and$195.6 billion in truck trade with Mexico in 2007, it is apparent that any disruption in truck traffic will lead to rapid economic instability (ATA Releases: American Trucking Trends 2008 - 2009, 2008). Yet, the critical nature of the trucking industry comes at a societal price. Indeed, undesirable outputs, e.g., truck crashes and associated injuries and fatalities, have very significant economic and human consequences. This dissertation uses Data Envelopment Analysis (DEA) to investigate the impact of undesirable outputs on the productivity of the motor carrier industry during the years 1999-2003. Previous DEA studies at the firm level have focused on the relationship between inputs and desirable outputs. The proposed approach in this dissertation simultaneously considers both the positive and negative outputs. This dissertation addresses two key problems with the DEA analysis technique previously identified by Yang and Pollit (2009): i.e., failure to take into consideration undesirable outputs and the failure to assess the impact of exogenous variables on the DEA scores of individual firms. As a result, this study will provide a new perspective into the productivity of U.S. motor carriers by incorporating both of these considerations into a more comprehensive DEA analysis. It will also provide opportunities to evaluate how individual firms might change their mix of inputs in order to simultaneously maximize desirable outputs and minimize undesirable ones