Production frontier estimation to measure efficiency: A critical evaluation in light of data envelopment analysis

The ranking and measurement of efficiency of decision-making units by two methods—data envelopment analysis and frontier production function—may not always lead to identical results. In this framework we attempt here a critical evaluation of the frontier production function theory in terms of theoretical and empirical implications. It is shown that under certain conditions the two approaches to effciency measurement may lead to identical results.     

Slack-adjusted efficiency measures and ranking of efficient units

In non-parametric methods many units are calculated as efficient. The article suggests a method for ranking efficient units, not by their efficiency, but by importance as benchmarks for the inefficient units, in contrast to earlier suggestions in the literature which rank units high if they are specialized. However, the total potentials for improvement frequently remain unrevealed by calculating radial efficiency measures of the Farrell type only. The article therefore first develops efficiency measures that explicitly extend the radial measures to include slacks. The new measures are applied to a typical multidimensional small-sample data set for Norwegian employment offices.     

Analysis on pure e-commerce congestion effect,productivity effect and profitability in China

This paper examines the relationship of e-commerce congestion effect, productivity effect and profit generation in China. The technique of Data Envelopment Analysis (DEA) is used to measure returns to scale and total factor productivity in e-commerce. The results show that e-commerce firms achieve productivity growth but suffer from input congestion. Congestion weakens profitability and leads to negative returns of inputs to outputs. This finding offers a new insight to explain the determinants of profit change. This research enriches production theory of internet companies, and helps managers strengthen their profitability by measuring the existence of congestion and eliminating input congestion resources.     

Dynamic efficiency evaluation of Italian judicial system using DEA based Malmquist productivity indexes

The aim of this study is the empirical analysis of the Italian judicial system, measuring its efficiency and productivity. For this purpose, in details, the Data Envelopment Analysis (DEA) and Malmquist indexes were used, since they are recognized by the current literature as successful techniques to evaluate the performance of decision making units, namely the courts of law in our field of application.The statistical data on the administration of justice in Italy, as reported in Dossier n. 11 May 2013, Senate Research Services, Research Office on Institutional Issues, Justice and Culture, XVII legislature, shows that the efficiency crisis of justice in Italy began in the 1970s of the last century, aggravating during the ‘80s, and reaching its most critical moment during the 1990s. Several studies emphasize the relevance of the effects of inefficiency upon the judicial system on the credit and financial markets.The present analysis, using data that covers a wide time span and is disaggregated at district level, has set the goal of measuring the efficiency of the individual Italian judicial offices while assessing the progress of productivity in its components, by the means of technological progress and scale efficiency. The efficiency analysis that was carried out transcends the aspect of judicial taxation, considering how the passage of time has impacted on judicial efficiency.Considering the judges and judicial administration employed, in the new, pending and finished cases during the years ranging from 2011 to 2016, the results highlighted a distinct heterogeneity among courts, depending on their geographical location. The five-year period that was considered, included the years in which the judicial geographical distribution reform entered into force, so to better comprehend how this complex reform influenced the recovery of efficiency of the judicial offices. Furthermore, by breaking down the index into change in efficiency and change in technology, this work offers a further glimpse into judiciary organization. The application of combined DEA method and Malmquist indexes for evaluating court efficiency in a large time frame showed very interesting and useful results, relevant for judicial administration.     

Productive Efficiency of the Swine Industry in Hawaii: Stochastic Frontier vs. Data Envelopment Analysis

Improving productive efficiency is an increasingly important determinant of the future of the swine industry in Hawaii. This paper examines the productive efficiency of a sample of swine producers in Hawaii by estimating a stochastic frontier production function and the constant returns to scale (CRS) and variable returns to scale (VRS) output-oriented DEA models. The technical efficiency estimates obtained from the two frontier techniques are compared. The scale properties are also examined under the two approaches. The industry's potential for increasing production through improved efficiency is also discussed.     

Technical efficiency of Norwegian banks: The non-parametric approach to efficiency measurement

The nonparametric frontier methodology is applied to a sample of banks, where output levels are measured either by the number of accounts and their average size, or by the total balances of the accounts. The efficiency rankings of individual banks are found to depend substantially on our choice of output metric, whereas the estimated size of potential productivity improvements in the banking sector are less affected. The results on economies of scale are also largely unchanged.The refereeing process of this paper was handled through S. Grosskopf.     

Measuring efficiency in education: The influence of imprecision and variability in data on DEA estimates

Many studies devoted to efficiency performance evaluation in the education sector are based on measures of central tendency at school level as, for example, the average values of students belonging to the same school. Although this is a common and accepted way of summarizing data from the original observations (students), it is not less true that this approach neglects the existing dispersion of data, which may become a serious problem if variability across schools is high. Additionally, imprecision may arise when experts on each evaluated subject select the battery of questions, with different levels of difficulty, which will be the base for the final questionnaires completed by students. This paper uses data from US students and schools participating in PISA (Programme for International Student Assessment) 2015 to illustrate that schools' efficiency measures based on aggregate data and imprecision may reflect an inaccurate picture of their performance if they are compared to measures estimated accounting for broader information provided by all students of the same school. In order to operationalize our approach, we resort to Fuzzy Data Envelopment Analysis. This methodology allows us to deal with the notion of fuzziness in some variables such as the socio-economic status of students or test scores. Our results indicate that the estimated measures of performance obtained with the fuzzy DEA approach are highly correlated with those calculated with traditional DEA models. However, we find some relevant divergences in the identification of efficient units when we account for data dispersion and vagueness.     

Innovation and productivity: insights from Malaysia's logistics industry

Efficient logistics is critical to a country's trade competitiveness. This paper uses the Data Envelopment Analysis Malmquist Index that includes an innovation component comprising three dimensions (input, process, and output), to assess the impact of innovation by third-party logistics (3PL). We treat the panel data of asset-based 3PL in Malaysia from 2007 to 2012. Three findings hold. First, innovation is not always accompanied by productivity gains. Second, innovation is still lacking in the logistics industry in Malaysia. Third, 3PL need to invest more in technology and stay financially healthy to accelerate the diffusion of innovation under a more competitive cost structure. This paper has two contributions. First, we offer an enhanced methodology for total factor productivity by including an innovation component and apply it for the first time to the logistics industry. Second, we augment the performance measurement literature specific to the logistics industry and our research suggests that the antecedents of productivity in logistics companies are investment and financial strength. These productivity–innovation findings provide insights into Malaysia's logistics industry and may be helpful for economies similar to Malaysia.     

Efficiency vectors, efficiency and performance measures: new methods for ranking efficient organizational units

We present and discuss measures for analyzing productivity in deterministic frontier models. A new efficiency measure is introduced allowing for discrimination among efficient organizational units. In addition, a new performance measure for analyzing productivity of organizational units is presented. This measure fulfills various properties of efficiency measures but relaxes the indicator property. Both new approaches are based on the development of efficiency vectors which is a new vector measure for measuring efficiency. The vector components are efficiency measures related to subsets of a production possibility set. The new approaches are applied in the context of data envelopment analysis.

Decomposing economic inefficiency in base-load power plants

Over the last decade, improving the performance of infrastructure industries has been the main focus of Australia's ongoing microeconomic reform program. The development and application of performance measures has been central to the reform process. In the electricity industry, a number of studies have quantified differences in economic efficiency using the multilateral total factor productivity and data envelopment analysis techniques. While both techniques aim to measure the same phenomena, rarely have the results obtained using the two techniques been compared using an identical database. A principal objective of this article is to compare the results from the two techniques using a database made up of observations from the Australian Loy Yang A base-load power plant and a range of similar plants in North America. The article also illustrates the use of international benchmarking as a tool for productivity enhancement. International benchmarking is playing a key role in the reform of Australia's infrastructure industries by promoting indirect or yardstick competition, by high-lighting the need for further reform, and by building a consensus for implementing necessary changes.     

Measuring Cost Efficiency in European Banking: A Comparison of Frontier Techniques

This paper investigates the consistency of efficiency frontier methods on European banking samples. We measure the cost efficiency of banks from five European countries (France, Germany, Italy, Spain, Switzerland) with three approaches: stochastic frontier approach, distribution-free approach, and data envelopment analysis. We compare means, correlation coefficients, two public policy issues, and the correlation with standard measures of performance. In general, we conclude in favor of the lack of robustness between approaches, even if there are some similarities in particular between parametric approaches. We do, however, observe some correlation between all frontier approaches and standard measures of performance.     

A multi-component enhanced Russell measure of efficiency: With application to water supply plans

Public water providers aim at developing a water supply plan (WSP) that not only provides a reliable and satisfactory level of service but also is efficient in terms of performance. This paper deals with evaluating the performance of WSPs within the framework of multi-component data envelopment analysis. Specifically, we consider the overall performance of each WSP as a decision making unit (DMU) so that economic, social, hygienic, technological, managerial and environmental performances of the WSP make up independent components of the defined DMU. To assess the performance of a set of WPSs, we propose a multi-component enhanced Russell measure of efficiency that takes all sources of inefficiency into account. We show that the proposed measure can be decomposed into individual efficiency measures at component level. This decomposition much enhances the efficiency of computing the proposed measure, noting the fact that this measure is obtained by solving a single linear program. It also guarantees the proposed measure to inherit two important—unit invariance and strong monotonicity—properties of the conventional enhanced Russell measure. In our empirical study, we apply our model to evaluate the efficiency of 10 urban WSPs in Qom city of Iran. In line with experts’ practical opinions, our findings reveal that the (relatively) most efficient WSP is to construct a potable water network and separation of non-drinking water network for urban usage.     

DEA, DFA and SFA: A comparison

The nonparametric data envelopment analysis (DEA) model has become increasingly popular in the analysis of productive efficiency, and the number of empirical applications is now very large. Recent theoretical and mathematical research has also contributed to a deeper understanding of the seemingly simple but inherently complex DEA model. Less effort has, however, been directed toward comparisons between DEA and other competing efficiency analysis models. This paper undertakes a comparison of the DEA, the deterministic parametric (DFA), and the stochastic frontier (SFA) models. Efficiency comparisons across models in the above categories are done based on 15 Colombian cement plants observed during 1968–1988.     

Localized Innovation and Productivity Growth in Asia: An Intertemporal DEA Approach

Recent contributions to growth theory stress the importance of localized innovation for the performance of more backward countries. In earlier papers, analyses by means of DEA techniques confirmed this intuition. In this paper, we extend this type of analysis by relaxing the macroeconomic viewpoint adopted until now. New databases on output, labor and capital input in the agricultural and manufacturing sectors are developed for 40 countries. Using intertemporal DEA, it is found that changes in the global production frontier are localized at high levels of capital intensity. This result is stronger in agriculture than in manufacturing. Further, a decomposition of labor productivity growth in eight Asian countries for the period 1975–1992 into the effects of capital intensification, learning and innovation is made. The results suggest that there is a particular development path in which increases in capital intensity appear to be a prerequisite to benefit from international technology spillovers.JEL Classification: O14, O30, O40, O47     

Eco-efficiency changes of the electricity and gas sectors across 28 European countries: A value-based data envelopment analysis productivity approach

This paper assesses the relative eco-efficiency changes in the electricity and gas sector (E&G)' production and consumption chains in 28 European countries. We propose a novel robustness assessment for the productivity index, specifically adjusted to value-based data envelopment analysis. Overall, results indicate that total factor productivity gains have been mainly driven by the catch-up effect across all chains of the E&G sector. When a more demanding perspective concerning negative environmental externalities is adopted, we find that the number of European countries that achieved productivity gains across all chains decreases. Besides, results depict the existence of lower productivity gains for the direct production chain when compared with the direct and indirect supply chains of the E&G sector. Germany, Luxembourg, and Belgium were consistently viewed as innovators across all chains, according to the environmental perspective. Several Eastern Europe countries usually viewed as policy laggards that resisted adopting the ambitious European decarbonization targets, showed total factor productivity gains in the supply chain of the E&G sector under a more environmental demanding perspective. Czechia was the only country with productivity losses across all chains, due to increasing coal-fired electricity generation in the time horizon assessed. The current partial return to coal as a source of electricity, due to the geopolitical tensions between Russia and Europe, brings additional challenges to the enhancement of the eco-efficiency of the European E&G sector.     

On the accuracy of bootstrap confidence intervals for efficiency levels in stochastic frontier models with panel data

We study the construction of confidence intervals for efficiency levels of individual firms in stochastic frontier models with panel data. The focus is on bootstrapping and related methods. We start with a survey of various versions of the bootstrap. We also propose a simple parametric alternative in which one acts as if the␣identity of the best firm is known. Monte Carlo simulations indicate that the parametric method works better than the␣percentile bootstrap, but not as well as bootstrap methods that make bias corrections. All of these methods are valid␣only for large time-series sample size (T), and correspondingly none of the methods yields very accurate confidence intervals except when T is large enough that the identity of the best firm is clear. We also present empirical results for two well-known data sets.      

A combined machine learning algorithms and DEA method for measuring and predicting the efficiency of Chinese manufacturing listed companies

Data Envelopment Analysis (DEA) is a linear programming methodology for measuring the efficiency of Decision Making Units (DMUs) to improve organizational performance in the private and public sectors. However, if a new DMU needs to be known its efficiency score, the DEA analysis would have to be re-conducted, especially nowadays, datasets from many fields have been growing rapidly in the real world, which will need a huge amount of computation. Following the previous studies, this paper aims to establish a linkage between the DEA method and machine learning (ML) algorithms, and proposes an alternative way that combines DEA with ML (ML-DEA) algorithms to measure and predict the DEA efficiency of DMUs. Four ML-DEA algorithms are discussed, namely DEA-CCR model combined with back-propagation neural network (BPNN-DEA), with genetic algorithm (GA) integrated with back-propagation neural network (GANN-DEA), with support vector machines (SVM-DEA), and with improved support vector machines (ISVM-DEA), respectively. To illustrate the applicability of above models, the performance of Chinese manufacturing listed companies in 2016 is measured, predicted and compared with the DEA efficiency scores obtained by the DEA-CCR model. The empirical results show that the average accuracy of the predicted efficiency of DMUs is about 94%, and the comprehensive performance order of four ML-DEA algorithms ranked from good to poor is GANN-DEA, BPNN-DEA, ISVM-DEA, and SVM-DEA.     

Sensitivity and stability of efficiency classifications in Data Envelopment Analysis

A new technique for assessing the sensitivity and stability of efficiency classifications in Data Envelopment Analysis (DEA) is presented. Here developed for the ratio (CCR) model, this technique extends easily to other DEA variants. An organization's input-outut vector serves as the center for a cell within which the organization's classification remains unchanged under perturbations of the data. For the l ₁, l and generalized l norms, the radius of the maximal cell can be computed using linear programming formulations. This radius can be interpreted as a measure of the classification's stability, especially with respect to errors in the data.Abraham Charnes passed away December 19, 1992.     

Comparing mean efficiency and productivity scores from small samples: A bootstrap methodology

This paper provides a bootstrap methodology for constructing confidence intervals for means of DEA and econometrically estimated efficiency scores, Malmquist productivity indices, and other similar measures in small samples. The procedure is nonparametric since no distributional assumptions are required. An empirical example is provided.This research was performed while Wilson was under contract with the Management Science Group, US Department of Veterans Affairs, Bedford, MA 01730.     

On the efficiency of national innovation systems

This paper presents a non-parametric approach to the disentanglement of the related effects of efficiency and productivity of a country's technological effort. The ultimate purpose is to identify the extent to which the alleged decrease in the productivity growth of many countries can be explained by differences in efficiency and by differences in its components, namely scale and congestion. The role of moderators in explaining productivity fluctuations if also assessed. The database consists of the countries included in the World Competitiveness Report.