Using DEA to improve the management of congestion in Chinese industries (1981–1997)

Congestion is said to be present when increases in inputs result in output reductions. An “iron rice bowl” policy instituted in China shortly after the revolution led by Mao Tze Tung resulted in congestion that ultimately led to bankruptcy in the textile industry, and near bankruptcy in other industries. A major policy shift away from the “iron rice bowl policy” in 1990 led to massive layoffs and increasing social tensions. Were these massive layoffs necessary? Extensions of data envelopment analysis models effected in the present paper identified inefficiencies in the management of congestion. Using textiles and automobiles for illustration, it is shown how elimination of such managerial inefficiencies could have led to output augmentation without reducing employment. Thus, even in the presence of congestion, it proved to be possible to identify additional (managerial) inefficiencies that provided opportunities for improvement. In the heavily congested textile industry, these output augmentations could have been accompanied by reductions in the amounts of capital used (as an added bonus). In any case, we show how to identify and evaluate new types of efficiency—viz., the efficiency with which needed (or desired) inefficiencies are managed.     

A review of DEA methods to identify and measure congestion

Congestion is an economic phenomenon of overinvestment that occurs when excessive inputs decrease the maximally possible outputs. Although decision-makers are unlikely to decrease outputs by increasing inputs, congestion is widespread in reality. Identifying and measuring congestion can help decision-makers detect the problem of overinvestment. This paper reviews the development of the concept of congestion in the framework of data envelopment analysis (DEA), which is a widely accepted method for identifying and measuring congestion. In this paper, six main congestion identification and measurement methods are analysed through several numerical examples. We investigate the ideas of these methods, the contributions compared with the previous methods, and the existing shortcomings. Based on our analysis, we conclude that existing congestion identification and measurement methods are still inadequate. Three problems are anticipated for further study: maintaining the consistency between congestion and overinvestment, considering joint weak disposability assumption between desirable outputs and undesirable outputs, and quantifying the degree of congestion.     

Estimating capacity utilization of Chinese manufacturing industries

This study investigates the capacity utilization (CU) of Chinese manufacturing industries, using a CU indicator based on data envelopment analysis and directional distance functions (DDFs). The inputs are separated into variable inputs and a quasi-fixed input to measure the gap of DDFs, which indicated either under-utilization of inputs or overcapacity. Moreover, we define an indicator for CU change over time and introduced the corresponding decomposition. We note that, during the study time period (2007–2010), the CU of Chinese manufacturing industries improved, which implies that Chinese manufacturing industries expanded their production and got closer to their capacity during the examined period. The driving force of this improvement is technical changes. The higher average CU values of light manufacturing industries than that of the heavy industries and the extremely high CU values of two light industries reveal a severe overcapacity problem in the light industries. We also provided the methods and conduct analysis on determining optimal variable inputs and the type of the overcapacity on specific DMUs. The bootstrap regression procedures are employed to test the influence of environmental variables on CU values. Finally, we provide policy implications and suggestions for policymakers who oversee the development of Chinese manufacturing industries.     

Classification in automobile insurance using a DEA and discriminant analysis hybrid

In this article, we adopt an efficiency approach to the two-group linear programming method of discriminant analysis (DA), using principles taken from data envelopment analysis (DEA), to predict group membership in an insurance underwriting scheme. Using an empirical insurance data base we illustrate the effectiveness of our model as a decision-making tool to distinguish among automobile insurance applicants by contrasting our hybrid model with both statistical and LP methods of discriminant analysis. We find for this insurance application that our hybrid model significantly outperforms the more traditional methods in separation and misclassification outcomes.     

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.     

A new method for measuring congestion in data envelopment analysis

In data envelopment analysis (DEA), there are two principal methods for identifying and measuring congestion: Those of Färe et al. [Färe R, Grosskopf S. When can slacks be used to identify congestion. An answer to W. W. Cooper, L. Seiford, J. Zhu. Socio-Economic Planning Sciences 2001;35:1–10] and Cooper et al. [Cooper WW, Deng H, Huang ZM, Li SX. A one-model approach to congestion in data envelopment analysis. Socio-Economic Planning Sciences 2002;36:231–8]. In the present paper, we focus on the latter work in proposing a new method that requires considerably less computation. Then, by proving a selected theorem, we show that our proposed methodology is indeed equivalent to that of Cooper et al.     

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.     

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.     

Competition and public high school performance

Increasing the level of school competition has been suggested as a way to improve school performance. This study examines one of the most extreme examples of such reform using data from New Zealand public high schools. In the 1990s school zoning was abolished in New Zealand and public schools competed for students, not just with private schools, but also with each other. A categorical Data Envelopment Analysis model using data on school resources and student academic performance, stratified using student socio-economic characteristics, is used to calculate efficiency scores for schools. A regression model is then used to analyse differences in these efficiency scores and their relationship to different levels of competition.     

我国城市交通拥堵成因与治理对策

论文从我国城市交通发展现状、拥堵成因及危害等方面进行分析,认为迅速增长的交通需求与有限的交通供给之间的矛盾是导致交通拥堵的内在原因;城市空间布局不合理与交通管理水平较低是交通拥堵的重要原因.在借鉴国外先进经验的基础上,提出了治理我国城市交通拥堵的对策建议.     

Target and technical efficiency in DEA: controlling for environmental characteristics

In this paper we propose a target efficiency DEA model that allows for the inclusion of environmental variables in a one stage model while maintaining a high degree of discrimination power. The model estimates the impact of managerial and environmental factors on efficiency simultaneously. A decomposition of the overall technical efficiency into two components, target efficiency and environmental efficiency, is derived. Estimation of target efficiency scores requires the solution of a single large non-linear optimization problem and provides both a joint estimation of target efficiency scores from all DMUs and an estimation of a common scalar expressing the environmental impact on efficiency for each environmental factor. We argue that if the indices on environmental conditions are constructed as the percentage of output with certain attributes present, then it is reasonable to let all reference DMUs characterized by a composed fraction lower than the fraction of output possessing the attribute of the evaluated DMU enter as potential dominators. It is shown that this requirement transforms the cone-ratio constraints on intensity variables in the BM-model (Banker and Morey 1986) into endogenous handicap functions on outputs. Furthermore, a priori information or general agreements on allowable handicap values can be incorporated into the model along the same lines as specifications of assurance regions in standard DEA.

Research performance evaluation of Chinese university: A non-homogeneous network DEA approach

Performance evaluation for universities or research institutions has become a hot topic in recent years. However, the previous works rarely investigate the multiple departments’ performance of a university, and especially, none of them consider the non-homogeneity among the universities’ departments. In this paper, we develop data envelopment analysis (DEA) models to evaluate the performance of general non-homogeneous decision making units (DMUs) with two-stage network structures and then apply them to a university in China. Specifically, the first stage is faculty research process, and the second stage is student research process. We first spit each DMU (i.e. department) into a combination of several mutually exclusive maximal input subgroups and output subgroups in terms of their homogeneity in both stages. Then an additive DEA model is proposed to evaluate the performance of the overall efficiency of the non-homogeneous DMUs with two-stage network structure. By analyzing the empirical results, some implications are provided to support the university to promote the research performance of each department as well as the whole university.     

Using data envelopment analysis to quantify ILO objectives and identify policies conducive to decent work in a globalizing world

The International Labor Office, an arm of the UN based in Geneva, has as its goal the promotion of opportunities for women and men to obtain decent and productive work, in conditions of freedom, equity, security and human dignity. Since 1999, the ILO has conducted a series of studies of decent work. In 2001, the organization posed the global challenge of reducing the decent work deficit as measured by an employment gap, rights gap, a social protection gap, and a social dialogue gap. Using standard economic terms, “decent work” may be seen as an efficiency point along a generalized input-output function, dependent upon variables of both economic performance and economic and social policy. The decent work deficit of a given country (if any) is then obtained as the difference between an observed point and its projection on the efficiency frontier. Using data envelopment analysis (DEA), we fit a piecewise linear frontier to observations for 61 countries from all continents. Importantly, 27 of these countries lie on the decent work frontier; the remaining ones reveal conditions of decent work deficit. The possibilities of reducing such deficits by appropriate control of policy variables are discussed.     

Pitfalls in estimating the X-factor: The case of energy transmission regulation in Brazil

In Brazil, which has one of the biggest energy systems in the world, the National Electricity Regulatory Agency (Agência Nacional de Energia Elétrica – ANEEL) regulates the generation, transmission, distribution and commercialization of electricity. As a key step in running the incentive regulation regime, the regulator applies a DEA-based Malmquist index for estimating the X-factor, which reflects productivity gains that can be expected across all companies involved in the industry. This paper investigates the way the X-factor was estimated in 2015 by ANEEL in the transmission sector concerning the review period 2009–2014. The regulator applies a modified version of the Malmquist index developed by Ray and Desli [1] and also adapts the bootstrapping method of Simar and Wilson [2] to generate confidence intervals for an estimate of the X-factor. Analyzing ANEEL's approach, we have discovered a number of serious analytical and computational shortcomings, which we demonstrate in the paper. On this basis, we address the effect of the estimated X-factor on social welfare, having the final customers to pay a higher price for electricity than they were supposed to.     

Projections Onto Efficient Frontiers: Theoretical and Computational Extensions to DEA

Data Envelopment Analysis (DEA) has been widely studied in the literature since its inception in 1978. The methodology behind the classical DEA, the oriented method, is to hold inputs (outputs) constant and to determine how much of an improvement in the output (input) dimensions is necessary in order to become efficient. This paper extends this methodology in two substantive ways. First, a method is developed that determines the least-norm projection from an inefficient DMU to the efficient frontier in both the input and output space simultaneously, and second, introduces the notion of the observable frontier and its subsequent projection. The observable frontier is the portion of the frontier that has been experienced by other DMUs (or convex combinations of such) and thus, the projection onto this portion of the frontier guarantees a recommendation that has already been demonstrated by an existing DMU or a convex combination of existing DMUs. A numerical example is used to illustrate the importance of these two methodological extensions.     

Measuring efficiency in Australian Schools: A preliminary analysis

In this paper, we apply a public sector Data Envelopment Analysis model to estimate the efficiency of Australian primary and secondary schools. Standard microeconomic production theory showing the transformation of inputs into outputs is extended to allow nondiscretionary environmental variables characteristic of educational production. Failure to properly control for the socioeconomic environment leads to inappropriate comparisons and biased efficiency estimates. We employ a conditional estimator that does not allow a school with a better environment to serve as a benchmark for a school with a worse environment. The results suggest that Australian schools are moderately inefficient and that efficiency increases for the quintile of schools with the most favorable environment. Further, efficiency gains are realized with increasing enrollment.     

Aggregating the DEA prospect cross-efficiency with an application to state key laboratories in China

With growing investments in the state key laboratories (SKLs) of China, it is essential to assess the efficiency of the administrative departments regarding managing the SKLs of China. However, few studies have been conducted about the evaluation of the performance of Chinese administrative departments in managing SKLs. To fill this gap, this paper investigates the performance within 22 SKL administrative departments in China, based on a data envelopment analysis cross-efficiency prospect aggregation approach incorporating the risk preference of decision maker. The empirical results show the major findings: (a) serious imbalance exists regarding the investment of 22 SKL administrative departments; (b) there are great differences among the performance of 22 SKL administrative departments; (c) some SKL administrative departments face an insufficient situation regarding the outputs; (d) different risk attitudes of decision maker have the significant impact on the evaluation results of the 22 SKL administrative departments. Based on these findings, we provide several policy suggestions for the development of SKLs in China.     

The Effect of Sample Size on the Mean Efficiency in DEA with an Application to Electricity Distribution in Australia,Sweden and New Zealand

This study examines the effect of sample size on the mean productive efficiency of firms when the efficiency is evaluated using the non-parametric approach of Data Envelopment Analysis. By employing Monte Carlo simulation, we show how the mean efficiency is related to the sample size. The paper discusses the implications for international comparisons. As an application, we investigate the efficiency of the electricity distribution industries in Australia, Sweden and New Zealand.     

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.     

Directional congestion in the framework of data envelopment analysis

This study first proposes a definition for directional congestion in certain input and output directions within the framework of data envelopment analysis. Second, two methods from different viewpoints are proposed to estimate the directional congestion. Third, we address the relationship between directional congestion and classic (strong or weak) congestion. Finally, we present a case study investigating the analysis performed by the research institutes of the Chinese Academy of Sciences to demonstrate the applicability and usefulness of the methods developed in this study.