Institutional efficiency and budget constraints: a Directional Distance Function approach to lead a key policy reform

Working Paper Ircres-CNR 06/2021. This manuscript focuses on the Italian judicial system and on how to shape a policy reform aimed at increasing court efficiency, taking the financial negative externalities generated by this production process into account. On the one hand, the authors identify the benchmarks and main drivers of judicial inefficiency, while, on the other hand, they show how incorrect model definition may mislead policy makers tackling this reform process, based on an analysis of the Directional Distance Function with and without bad outputs. According to the results, incorrect model definition causes a type I error equal to 10.37% and a type II error equal to 3.66%. Policy implications concern the opportunity to adopt the proposed model and the collected benchmarks to reform the judicial system, improving its technical efficiency and maintaining the public budget under control

A novel network data envelopment analysis model for performance measurement of Turkish electric distribution companies

Electric distribution companies have a significant role for both households and industries. Benchmarking of the electric distribution companies in the energy sector has become a subject that is studied widely nowadays due to the effect of privatization policies for developing countries. Since there are multiple production stages regarding the generation and supply procedures of electric power, Network DEA technique is used. Directional Distance Function is also integrated into Network DEA technique. Electric distribution companies are organizations that are aiming at maximizing profit while minimizing the expenses. The main problem is how the profit idea can be integrated into the evaluation process. The aim of the proposed model is to evaluate profit efficiency of electric distribution companies while taking into account expansion cost for additional energy supply. This two stage approach is applied to Turkish electric distribution companies. Results are presented based on radial and profit efficiency measures. The proposed model is demonstrates realistic results by considering the expenses and incomes of distribution companies

A novel network data envelopment analysis model for performance measurement of Turkish electric distribution companies

Electric distribution companies have a significant role for both households and industries. Benchmarking of the electric distribution companies in the energy sector has become a subject that is studied widely nowadays due to the effect of privatization policies for developing countries. Since there are multiple production stages regarding the generation and supply procedures of electric power, Network DEA technique is used. Directional Distance Function is also integrated into Network DEA technique. Electric distribution companies are organizations that are aiming at maximizing profit while minimizing the expenses. The main problem is how the profit idea can be integrated into the evaluation process. The aim of the proposed model is to evaluate profit efficiency of electric distribution companies while taking into account expansion cost for additional energy supply. This two stage approach is applied to Turkish electric distribution companies. Results are presented based on radial and profit efficiency measures. The proposed model is demonstrates realistic results by considering the expenses and incomes of distribution companies

Technical Efficiency of Automobiles – A Nonparametric Approach Incorporating Carbon Dioxide Emissions

We conduct an empirical analysis of the technical efficiency of cars sold in Germany in 2010. The analysis is performed using traditional data envelopment analysis (DEA) as well as directional distance functions (DDF). The approach of DDF allows incorporating the reduction of carbon dioxide emissions as an environmental goal in the efficiency analysis. A frontier separation approach is used to gain deeper insight for different car classes and regions of origin. Natural gas driven cars and sports-utility-vehicles are also treated as different groups. The results show that the efficiency measurement is significantly influenced by the incorporation of carbon dioxide emissions. Moreover, we find that there is indeed a trade-off between technological performance and environmental performance.nonparametric efficiency measurement, directional distance function, automobiles, air pollution

Two-Phase Network Data Envelopment Analysis: An Example of Bank Performance Assessment

Data envelopment analysis (DEA) models assess decision-making units (DMUs), which directly convert multiple inputs into multiple outputs. Network DEA models have been studied extensively. However, the performance indices that link the two stages are assumed to be fixed or non-discretionary; their values are not adjustable. These models only assumed that the reductions on the inputs and additions on the outputs would improve the overall efficiency. But in the real world, the link is always adjustable. “Free links” means that the intermediate items are adjustable or discretionary, and each DMU can be increased or decreased from the observed one. The current chapter introduces a two-phase procedure with free links to assess system performance, Phase-I is a proposed slack-based measurement (SBM) model to partition the links into two sets: as-input and as-output. Phase-II is a modified SBM model to determine the slack of each input, as-input link, output and as-output link. This proposed model counts the slacks associated with the intermediate items in the efficiency scores and determines the entire system performance by the directional distance function. It is validated using network procedure and assesses the performance of supply chain management system

A Data Envelopment Analysis Toolbox for MATLAB

The Data Envelopment Analysis Toolbox is a new package for MATLAB that includes functions to calculate the main data envelopment analysis models. The package includes code for the standard radial input, output and additive measures, allowing for constant and variable returns to scale, as well as recent developments related to the directional distance function, and including both desirable and undesirable outputs when measuring efficiency and productivity; i.e., Malmquist and Malmquist-Luenberger indices. Bootstrapping to perform statistical analysis is also included. This paper describes the methodology and implementation of the functions, and reports numerical results using a reliable productivity database on US agriculture to illustrate their use

The measurement of profit, profitability, cost and revenue efficiency through data envelopment analysis: A comparison of models using BenchmarkingEconomicEfficiency.jl

We undertake a systematic comparison of existing models measuring and decomposing the economic efficiency of organizations. For this purpose we introduce the package BenchmarkingEconomicEfficiency.jl for the open-source Julia language including a set of functions to be used by scholars and professionals working in the fields of economics, management science, engineering, and operations research. Using mathematical programming methods known as Data Envelopment Analysis, the software develops code to decompose economic efficiency considering alternative definitions: profit, profitability, cost and revenue. Economic efficiency can be decomposed, multiplicative or additively, into a technical (productive) efficiency term and a residual term representing allocative (or price) efficiency. We include traditional decompositions like the radial efficiency measures associated with the input (cost) and output (revenue) approaches, as well as new ones corresponding to the Russell measures, the directional distance function, DDF (including novel extensions like the reverse DDF, modified DDF, or generalizations based on Hölder norms), the generalized distance function, and additive measures like the slack based measure, their weighted variants, etc. Moreover, regardless the underlying economic efficiency model, many of these technical inefficiency measures are available for calculation in a computer software for the first time. This article details the theoretical methods and the empirical implementation of the functions, comparing the obtained results using a common dataset on Taiwanese BanksJosé L. Zofío thanks the grant PID2019-105952 GB-I00 funded by Ministerìo de Ciencia e Innovación/ Agencia Estatal de Investigación /10.13039/50110001103

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

CO2 emissions reduction of Chinese light manufacturing industries:a novel RAM-based global Malmquist-Luenberger productivity index

Climate change has become one of the most challenging issues facing the world. Chinese government has realized the importance of energy conservation and prevention of the climate changes for sustainable development of China's economy and set targets for CO2 emissions reduction in China. In China industry contributes 84.2% of the total CO2 emissions, especially manufacturing industries. Data envelopment analysis (DEA) and Malmquist productivity (MP) index are the widely used mathematical techniques to address the relative efficiency and productivity of a group of homogenous decision making units, e.g. industries or countries. However, in many real applications, especially those related to energy efficiency, there are often undesirable outputs, e.g. the pollutions, waste and CO2 emissions, which are produced inevitably with desirable outputs in the production. This paper introduces a novel Malmquist-Luenberger productivity (MLP) index based on directional distance function (DDF) to address the issue of productivity evolution of DMUs in the presence of undesirable outputs. The new RAM (Range-adjusted measure)-based global MLP index has been applied to evaluate CO2 emissions reduction in Chinese light manufacturing industries. Recommendations for policy makers have been discussed