A graphically based machine learning approach to predict secondary schools performance in Tunisia

The main purpose of this paper is to identify the key factors that impact schools' academic performance and to explore their relationships through a two-stage analysis based on a sample of Tunisian secondary schools. In the first stage, we use the Directional Distance Function approach (DDF) to deal with undesirable outputs. The DDF is estimated using Data Envelopment Analysis method (DEA). In the second stage we apply machine-learning approaches (regression trees and random forests) to identify and visualize variables that are associated with a high school performance. The data is extracted from the Program for International Student Assessment (PISA) 2012 survey. The first stage analysis shows that almost 22% of Tunisian schools are efficient and that they could improve their students’ educational performance by 15.6% while using the same level of resources. Regression trees findings indicate that the most important factors associated with higher performance are school size, competition, class size, parental pressure and proportion of girls. Only, school location appears with no impact on school efficiency. Random forests algorithm outcomes display that proportion of girls at school and school size have the most powerful impact on the predictive accuracy of our model and hence could more influence school efficiency. The findings disclose also the high non-linearity of the relationships between these key factors and school performance and reveal the importance of modeling their interactions in influencing efficiency scores.     

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.     

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 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.     

基于DEA的区域高等院校协作效率评价

本文应用DEA方法从定量的角度研究通过区域内和区域之间的合作带来的益处。通过构建评价高等院校协作效率的DEA模型,对某地14所院校的区域内和区域间的高等院校协作效率进行评价。计算结果表明,通过区域内合作,每一个区域内都有部分院校的效率有较大的提高,最高的超过30％。     

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.

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.     

A novel method for computing single output for DEA with application in hospital efficiency

There are two main methods for measuring the efficiency of decision-making units (DMUs): data envelopment analysis (DEA) and stochastic frontier analysis (SFA). Each of these methods has advantages and disadvantages. DEA is more popular in the literature due to its simplicity, as it does not require any pre-assumption and can be used for measuring the efficiency of DMUs with multiple inputs and multiple outputs, whereas SFA is a parametric approach that is applicable to multiple inputs and a single output. Since many applied studies feature multiple output variables, SFA cannot be used in such cases. In this research, a unique method to transform multiple outputs to a virtual single output is proposed. We are thus able to obtain efficiency scores from calculated virtual single output by the proposed method that are close (or even the same depending on targeted parameters at the expense of computation time and resources) to the efficiency scores obtained from multiple outputs of DEA. This will enable us to use SFA with a virtual single output. The proposed method is validated using a simulation study, and its usefulness is demonstrated with real application by using a hospital dataset from Turkey.     

Causes of inefficiency in Japanese railways: Application of DEA for managers and policymakers

Although railway services have been suffering financially due to modal shifts and aging populations, they have been, and will continue to be, an essential component of nations' basic social infrastructures. Since railway firms generate positive externalities, and are required to operate in pre-determined licensed areas, governmental intervention/support may, in some cases, be justified. Indeed, many types of subsidies are created and offered for railway operations in Japan; while some are meant to cover large investments, others are used as compensation for regional disparities. However, thus far, no attempt has been made to analyze the reasons for the underperformance of Japanese railway services. In other words, it is unclear whether this underperformance can be attributed to exogenous and uncontrollable causes, or endogenous phenomena and, hence, capable of being handled by managers. The optimal degree of intervention is thus not sufficiently known. In the current paper, we propose a method based on data envelopment analysis (DEA) to analyze the causes of inefficiency in Japanese railway operations, and, further, to calculate optimal subsidy levels. The latter are designed to compensate for railways' lack of complete discretion in changing location of their operations and/or increasing/decreasing these operations since they are a regulated service. Our proposed method was applied to 53 Japanese railway operators. In so doing, we identified several key characteristics related to their inefficiencies, and developed optimal subsidies designed to improve performance.     

Research efficiency analysis of China's university faculty members: A modified meta-frontier DEA approach

Traditionally, data envelopment analysis (DEA) requires all decision-making units (DMUs) to have similar characteristics and experiences within the same external conditions. In many cases, this assumption fails to hold, and thus, difficulties will be encountered to some extent when measuring efficiency with a standard DEA model. Ideally, the performance of DMUs with different characteristics could be examined using the DEA meta-frontier framework. However, some of these DMUs are mixed-type DMUs that may affiliate with more than one group. Furthermore, the total number of observations of these mixed-type DMUs is limited. This is one of the common problems when studies focus on faculty research performance in higher education institutions. In general, a faculty member is affiliated with a certain department, and if the departmental assessment policy is not suitable for faculty members who are involved in interdisciplinary research, their performance could be underestimated. Therefore, the proposed model is an extension of the DEA meta-frontier framework that can assess the performance of mixed-type DMUs by constructing the reference set without the same type of DMUs. In this paper, the scientific research efficiency of faculty members at the Inner Mongolia University is used as an example to provide a better understanding of the proposed model. The proposed model is intended to provide a fair and balanced performance assessment method that reflects actual performance, especially for mixed-type DMUs.     

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.     

Efficiency evaluation of Chinese regional industrial systems using a dynamic two-stage DEA approach

Industrial air pollution control recently becomes a major policy issue in China. Performnce evaluation can examine policy effectiveness and provide decesion support for industrial development. A regional industrial system in China contains production and abatement stages. Within this structure, the capacity of industrial waste gas treatment can be treated as a carry-over variable. More precisely, it is a desirable output of the abatement stage in the previous period but an input to the abatement stage in the current period. Using this framework, this study establishes a dynamic two-stage data envelopment analysis model to explore the efficiencies of regional industrial systems in China. This model provides measures of the overall, period, stage, and period stage efficiencies. Based on empirical data from 2007 to 2015, it is concluded that (1) the carry-over variable capacity of industrial waste gas treatment has significant influences on the operational efficiency scores of regional industrial systems, especially in the abatement stage; (2) there exist distinct geographic characteristics of inefficiencies in the regional industrial systems; (3) the average period efficiency of the abatement stage was lower than that of the production stage during China's 11th Five-Year Plan period (2006–2010); however, the reverse happens during the 12th Five-Year Plan period (2011–2015); and 4) industrial structure and economic development level are the key influencing factors of regional industrial efficiencies. This study entails important implications for policy makers in terms of industrial waste gas treatment and revelant contextual factors.     

The interplay between regulation and efficiency: Evidence from the Austrian hospital inpatient sector

The Austrian health policy aims to maintain and even expand high-quality health care. The implementation of an integrated master plan, which contains input and output target values for the overall health care system, is an important regulatory intervention. To support the regulatory authorities, efficiency studies that simultaneously reveal input and output improvement potential can be promising. To serve this purpose, we computed bootstrapped non-oriented super-efficiency measures to identify any improvement potential in inputs and outputs for the Austrian hospital inpatient sector for four years. Variations in hospital efficiency were investigated using non-parametric post-efficiency analyses.     

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.     

Exploring the performances and determinants of public service provision in 35 major cities in China from the perspectives of efficiency and effectiveness

The measurement of public service provision (PSP) performance and identification of its influencing variables are of great importance for the government to efficiently utilize public resources and provide more effective public services. However, existing studies have mainly focused on the efficiency to provide public services and ignored the effectiveness to improve public subjective well-being. To fill this gap, this study constructs a two-stage PSP conceptual model from the perspective of service production and consumption and develops a corresponding PSP performance evaluation indicator system to evaluate PSP efficiency, effectiveness, and comprehensive performance. The PSP performances of 35 major cities in China during 2011–2018 are calculated using the super efficiency network slack-based measure model. Moreover, the influencing factors of these performances are identified through the truncated regression model. Results indicate that the comprehensive performance of these cities is relatively low and shows a trend of decrease that is predominantly caused by provision efficiency. Owing to the absolute advantage in effectiveness and the catch-up effect in efficiency, cities in the western region perform the best in comprehensive performance, followed by the eastern region and then the central region. In addition, only seven cities achieve a balance between efficiency and effectiveness. Factors from economy, society, government, and public aspects have different impacts on PSP performances. On the basis of these findings, several policy suggestions are proposed. This study proposes a relatively comprehensive and accurate measurement framework of PSP performance that considers both efficiency and effectiveness and can help policymakers implement appropriate strategies promoting PSP in an efficient, effective, and sustainable manner.     

The evolution and determinants of Chinese property insurance companies’ profitability: A DEA-based perspective

The property insurance industry grows fast in China and it is necessary to further investigate the profitability of the Chinese property insurance industry. This study investigates the evolution and determinants of the profitability of 53 Chinese property insurers during the year 2013–2017. Profitability is measured by profit ratio efficiency by data envelopment analysis (DEA) methodology and a profit ratio change index is applied to compare the performance of these insurers over different periods. Tobit regression models are used to investigate several influencing factors of profitability. The empirical results show the importance of proper arrangement of costs and revenues for an insurer and help to better understand the effect of firm size, age, and product specification on profitability. Some policy implications and suggestions are also proposed.     

Adjusting for the environment in DEA: A comparison of alternative models based on empirical data

Due to the existence of free software and pedagogical guides, the use of Data Envelopment Analysis (DEA) has been further democratized in recent years. Nowadays, it is quite usual for practitioners and decision makers with no or little knowledge in operational research to run their own efficiency analysis. Within DEA, several alternative models allow for an environmental adjustment. Four alternative models, each user-friendly and easily accessible to practitioners and decision makers, are performed using empirical data of 90 primary schools in the State of Geneva, Switzerland. Results show that the majority of alternative models deliver divergent results. From a political and a managerial standpoint, these diverging results could lead to potentially ineffective decisions. As no consensus emerges on the best model to use, practitioners and decision makers may be tempted to select the model that is right for them, in other words, the model that best reflects their own preferences. Further studies should investigate how an appropriate multi-criteria decision analysis method could help decision makers to select the right model.     

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.

A quantitative study of financing efficiency of low‐carbon companies: A three‐stage data envelopment analysis

This study set out to evaluate the financing efficiency of low‐carbon companies. Applying a three‐stage data envelopment analysis with the data from 85 listed companies in China's low‐carbon industries over the period 2011 to 2017, this study has found that the overall financing efficiency of low‐carbon companies was relatively high, and the pure technical efficiency was quite steady over the period. The overall financing efficiency of these low‐carbon companies on average tended to change with the scale efficiency. This study has also shown that the scale efficiency was the main constraint influencing the financing efficiency of low‐carbon companies in China over the period. Our results are robust and have significant implications for policy makers and corporate managers.     

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.