Measuring teaching efficiency in higher education: An application of data envelopment analysis to economics graduates from UK Universities 1993

Data envelopment analysis (DEA) is applied to 2547 Economics graduates from UK Universities in 1993 in order to assess teaching efficiency. Following a methodology developed by [Education Economics 10(2) (2002) 183–207], each individual’s efficiency is decomposed into two components: one attributable to the university at which the student studied, and the other attributable to the student himself. From the former component, a measure of each institution’s teaching efficiency is derived and compared to efficiency scores derived from a conventional DEA applied using each Economics department as a decision making unit (DMU). The results suggest that efficiencies derived from DEAs performed at an aggregate level include both institution and individual components, and are therefore misleading. Thus the unit of analysis in a DEA is highly important. Moreover, an analysis at the individual level can give institutions insight into whether it is the students’ own efforts or the institution’s efficiency which are a constraint on increased efficiency. This has implications for the choice of strategy for improving efficiency.     

Widening participation in higher education: student quantitative skills and independent learning as impediments to progression

The UK government's widening participation strategy, and the concomitant development of a mass higher education system, has imposed a variety of pressures on higher education institutions. Not least of these is the changing nature of the student population, and the assumptions that can be made about its skills and knowledge base. It should not be surprising that this rapid expansion of the higher education system has resulted in declining student progression and retention rates. This paper takes a case study approach and attempts to identify the range of factors that might explain the variability of student performance on a first year undergraduate introductory statistics module. The paper concludes that there are no simple predictors of success or failure. However, there is evidence to suggest that any innovations in delivery need to take account of individual student development and that the presumption that students can rapidly become independent learners upon initial entry to higher education is an unrealistic one.     

Do mergers improve hospital productivity?

This paper complements the existing literature on hospital mergers by using data envelopment analysis (DEA) to generate both efficiency and productivity measures to ascertain whether hospital mergers, at least in the short run, result in performance gains. Using data over the period 1996–1998, we apply DEA, both pre-merger and post-merger, to set of hospitals that merged in 1997 as well as to a matching control group of non-merging hospitals over the same timeframe. A comparison of DEA efficiency scores and the Malmquist index values across the case and control hospitals allow us to assess whether any increase in productivity is the result of a merger rather than simply and randomly adding two hospitals' inputs and outputs together.     

Assessing the cost-effectiveness of university academic recruitment and promotion policies,

This paper develops an approach for higher education institutions to assess the economic efficiency of their recruitment and promotion practices concerning academic staff. Research output potential is a key criterion in most academic appointments. Generally, there is a long lead time between the conduct of research and its ultimate value in the form of disseminated knowledge. This means higher education institutions usually reward financially staff on the prospect of research output, albeit on the basis of research outputs achieved up to the point of recruitment or discretionary salary rise (e.g. through promotion). We propose a Data Envelopment Analysis (DEA) model which can be used retrospectively to set salary costs against corresponding research outputs achieved as a measure of the financial efficacy of past recruitment and promotion practices. The analysis can identify potential issues with those practices and lead to improvements for the future.     

Combining the assumptions of variable and constant returns to scale in the efficiency evaluation of secondary schools

Our paper reports on the use of data envelopment analysis (DEA) for the assessment of performance of secondary schools in Malaysia during the implementation of the policy of teaching and learning mathematics and science subjects in the English language (PPSMI). The novelty of our application is that it makes use of the hybrid returns-to-scale (HRS) DEA model. This combines the assumption of constant returns to scale with respect to quantity inputs and outputs (teaching provision and students) and variable returns to scale (VRS) with respect to quality factors (attainment levels on entry and exit) and socio-economic status of student families. We argue that the HRS model is a better-informed model than the conventional VRS model in the described application. Because the HRS technology is larger than the VRS technology, the new model provides a tangibly better discrimination on efficiency than could be obtained by the VRS model. To assess the productivity change of secondary schools over the years surrounding the introduction of the PPSMI policy, we adapt the Malmquist productivity index and its decomposition to the case of HRS model.     

Allfinanz,bancassurance and the future of banking

The purpose with this paper is first to analyse the strategies of Allfinanz (Bancassurance) in Germany and then to investigate if the German banks have become more efficient during the period since Allfinanz was introduced. Two methods are used to analyse efficiency: ‘financial ratio analysis’ (FRA) and ‘data envelopment analysis’ (DEA). FRA is useful in studying the change in productivity. DEA generates, efficiency scores and alternative and more efficient combinations of banks.     

Measuring dynamic efficiency: Theories and an integrated methodology

Almost all dynamic production systems are subject to lagged productive effects, which are an often-ignored latent source of interference in the efficiency measuring process. Existing data envelopment analysis (DEA) approaches rely on a static production environment. They can easily lead to biased evaluation results due to the erroneous assumption. To tackle this issue, this paper develops a dynamic DEA model that allows intertemporal effects in efficiency measuring. Specifically, the dynamic DEA model incorporates dynamic factors via a linear parametric formulation. Our model can be applied in place of static DEA models to a wide range of applications, such as analyzing longitudinal firm performance and productivity changes. As for the empirical efficiencies, we demonstrate how the lag parameters in the dynamic model can be estimated by the panel vector autoregressive model (PVAR). We use our methodology to evaluate advertising efficiencies of several major automobile and pharmaceutical firms in North America. The result shows that using static DEA in dynamic production can lead to both rank reversals and changes in efficiency scores.     

The law of one price in data envelopment analysis: Restricting weight flexibility across firms

The Law of One Price (LoOP) states that all firms face the same prices for their inputs and outputs under market equilibrium. Taken here as a normative condition for ‘efficiency prices’, this law has powerful implications for productive efficiency analysis, which have remained unexploited thus far. This paper shows how LoOP-based weight restrictions can be incorporated in Data Envelopment Analysis (DEA). Utilizing the relation between industry-level and firm-level cost efficiency measures, we propose to apply a set of input prices that is common for all firms and that maximizes the cost efficiency of the industry. Our framework allows for firm-specific output weights and for variable returns-to-scale, and preserves the linear programming structure of the standard DEA. We apply the proposed methodology to the evaluation of the research efficiency of economics departments of Dutch Universities. This application shows that the methodology is computationally tractable for practical efficiency analysis, and that it helps in deepening the DEA analysis.     

基于平行DEA模型的我国小学教育资源配置效率分析

小学教育是教育阶段的基础,也是我国强制教育的一部分。但如何在各省份性别比例相差较大的情况下,更加有效的提高各省份小学教育资源配置效率是一个值得思考的问题。为了避免性别带来的影响,可以通过平行结构DEA模型根据学生性别分为两个平行子系统,来衡量我国2012~2018年各省份小学教育的资源配置情况。研究发现大部分省份小学教育资源配置的总系统效率未达到有效边界,仍有需要改进的地方。男学生在小学教育阶段的效率通常优于女学生在小学教育阶段的效率,但随着近些年我国教育体制的日益完善,女学生在小学教育阶段的效率呈上升趋势。此外,我国西部地区的小学教育资源配置效率在一些年份普遍优于东部和中部地区,而中部地区和东部地区小学教育总系统效率相对稳定,西部地区小学教育总系统效率波动较大。     

An improvement on the Tracy and Chen model ‘A generalized model for weight restrictions in DEA’

Recently Tracy and Chen presented a parametric DEA model (PDEA) to assess relative efficiency in the presence of a generalized form of linear weight restrictions. This paper proposes a modification to the PDEA model that avoids the need to resort to searching algorithms to estimate efficiency, and assures that the correct efficiency scores are obtained in a single stage using mathematical programming solvers. The results of this model and the results of Tracy and Chen's PDEA model are compared using the examples reported in their paper. The results confirm the superiority of the model proposed in this paper.     

Methods of critical value reduction for type-2 fuzzy variables and their applications

A type-2 fuzzy variable is a map from a fuzzy possibility space to the real number space; it is an appropriate tool for describing type-2 fuzziness. This paper first presents three kinds of critical values (CVs) for a regular fuzzy variable (RFV), and proposes three novel methods of reduction for a type-2 fuzzy variable. Secondly, this paper applies the reduction methods to data envelopment analysis (DEA) models with type-2 fuzzy inputs and outputs, and develops a new class of generalized credibility DEA models. According to the properties of generalized credibility, when the inputs and outputs are mutually independent type-2 triangular fuzzy variables, we can turn the proposed fuzzy DEA model into its equivalent parametric programming problem, in which the parameters can be used to characterize the degree of uncertainty about type-2 fuzziness. For any given parameters, the parametric programming model becomes a linear programming one that can be solved using standard optimization solvers. Finally, one numerical example is provided to illustrate the modeling idea and the efficiency of the proposed DEA model.     

基于复合DEA和Malmquist指数的科技投入产出效率评价

本文利用非参数方法DEA对2004~2008年福建省9个地市的科技投入产出效率进行评价,并应用复合DEA方法和Malmquist指数来分析科技投入产出效率非DEA有效地区的形成原因及其生产力的动态变化。实证结果显示,福建省各市的科技效率总体呈上升趋势,但各地区科技的投入产出规模以及效率还存在着较大的差距,而其生产力的提高主要是技术效率所带来的贡献,技术进步则呈现大起大落的状态,技术进步对生产力的拉动仍需得到进一步加强与巩固,应加大科技研发投入和强化科技的引进创新,并注重消化吸收创新。     

Integrating DEA into Six Sigma methodology for measuring health service efficiency

As a linear programming technique that is used to assess performance efficiency among organizational units, data envelopment analysis (DEA) has the practical value for planners to identify the most efficient units and thus project a best possible course of actions. This is also the fundamental goal of Six Sigma methodology for process improvement. Therefore, it is natural to integrate the DEA method into the framework of Six Sigma to enhance both the usefulness of Six Sigma and the effectiveness of DEA for assessing and improving health service efficiency. In this paper, the implementation of DEA is well incorporated into the Define, Measure, Analyze, Improve and Control process. The procedure can be well fitted into the Blackbelts training curriculum, which can also serve as a roadmap for Six Sigma practitioners to implement the DEA-enhanced projects. To illustrate how to integrate DEA into the Six Sigma framework, we present a Six Sigma project implemented to measure physician productivity in a clinical department.     

Evaluating higher education teaching performance using combined analytic hierarchy process and data envelopment analysis

Evaluating higher education teaching performance is complex as it involves consideration of both objective and subjective criteria. The student evaluation of teaching (SET) is used to improve higher education quality. However, the traditional approaches to considering students’ responses to SET questionnaires for improving teaching quality have several shortcomings. This study proposes an integrated approach to higher education teaching evaluation that combines the analytical hierarchy process (AHP) and data envelopment analysis (DEA). The AHP allows consideration of the varying importance of each criterion of teaching performance, while DEA enables the comparison of tutors on teaching as perceived by students with a view to identifying the scope for improvement by each tutor. The proposed teaching evaluation method is illustrated using data from a higher education institution in Greece.     

商业银行分支机构效率评价的参数与非参数方法比较研究

本文根据某商业银行反映效率的真实数据,分别采用因子分析、聚类和线性回归、DEA分析三种方法,计算了银行分支机构的效率,并对效率评价的参数与非参数方法进行了差异比较。进而,本文用历史数据对效率影响因素分析方法的有效性进行了实证检验。     

Data Envelopment Analysis Applied to Electricity Distribution Districts

In this paper Data Envelopment Analysis (DEA) is used to evaluate efficiency measures for the 45 distribution districts of the Greek Public Power Corporation (PPC). Results are derived under different sets of assumptions and are compared with simple productivity indices used by PPC and with efficiency measures produced by econometric methods. DEA scores appear to be more reliable than simple productivity indices. Comparison of the different cases explains the reason for the low efficiencies, which can be due to the management of controllable inputs, the design of the supply system or other environmental factors.     

How to generate regularly behaved production data? A Monte Carlo experimentation on DEA scale efficiency measurement

Monte Carlo experimentation is a well-known approach used to test the performance of alternative methodologies under different hypotheses. In the frontier analysis framework, whatever the parametric or non-parametric methods tested, experiments to date have been developed assuming single output multi-input production functions. The data generated have mostly assumed a Cobb–Douglas technology. Among other drawbacks, this simple framework does not allow the evaluation of DEA performance on scale efficiency measurement. The aim of this paper is twofold. On the one hand, we show how reliable two-output two-input production data can be generated using a parametric output distance function approach. A variable returns to scale translog technology satisfying regularity conditions is used for this purpose. On the other hand, we evaluate the accuracy of DEA technical and scale efficiency measurement when sample size and output ratios vary. Our Monte Carlo experiment shows that the correlation between true and estimated scale efficiency is dramatically low when DEA analysis is performed with small samples and wide output ratio variations.     

A DEA analysis of risk,cost, and revenues in insurance

Insurance companies have to take risk and cost into account when pricing car insurance policies that cover the risk of private use of cars. In this paper we use data from 80?000 car insurance policies in order to assess, once risk and cost have been taken into account, the combinations of risk that generate the highest returns for the company under existing pricing practices. We use data envelopment analysis (DEA) and frame the study within an analysis of experiments context. The results of DEA are interpreted in a multivariate statistical analysis context using factor analysis, and property fitting techniques. The impact of risk factors in the efficiency is explored by means of regression analysis with dummy variables. There are consequences for the pricing policy of the company.     

Exploring the use of DEA for formative evaluation in primary diabetes care: An application to compare English practices

Primary care is currently at the heart of the UK National Health Service policy. Primary care trusts have the crucial role of improving the efficiency and equity in primary care delivery. However, few studies have focused on performance assessment in primary care provision. In this paper, we examine the role of data envelopment analysis (DEA) in helping decision makers to understand and improve the performance of primary care practices. We discuss the results from a study of 14 practices in England in terms of their delivery of diabetes services. In order to take into account the multiple values that underlie public services provision, we have considered several different perspectives for evaluation. These were: technical, allocative and cost efficiency, clinical and patient-focused effectiveness, and equity. The approach adopted involved a deep engagement with the practices. The purpose was to understand the structures and processes supporting effective practice using DEA in a formative role rather than the more usual summative role with a large data set. This approach is in the tradition of the developmental engagement approach of operational research.     

The training institution efficiency of the semiconductor institute programme in Taiwan—application of spatiotemporal ICA with DEA approach

Despite the fact that Taiwan’s high-tech industry has gradually secured a leading position in the world, enterprises in Taiwan have striven to strengthen their technical advancement by providing employees with various internal or external training programmes. These institutional training programmes are designed to sustain competitive advantage, enhance the quality of manpower and improve operational efficiency. Much literature assesses the efficiency of an internal training programme that is initiated by a firm, but only a little literature studies the efficiency of an external training programme that is led by a government. Various efficiency measurement tools, such as conventional statistical methods and non-parametric methods, have been successfully developed in the literature. Among these tools, the data envelopment analysis (DEA) approach is one of the most widely discussed. However, the DEA's capability to discriminate efficient decision-making units from inefficient decision-making units requires much improvement (Adler and Yazhemsky). In this paper, a two-stage approach of integrating spatiotemporal independent component analysis (stICA) and DEA is developed for efficiency measurement. stICA is used to search for latent source signals where no relevant signal mixture mechanisms are available; and DEA is used to measure the relative efficiencies of decision-making units (DMUs). We suggest using stICA first to extract the input variables for generating independent components (IC), then selecting the ICs representing the independent sources of input variables, and finally inputting the selected ICs as new variables in the DEA model. To find the effects of environmental variables on the estimated efficiency scores, the Tobit–Bayes (censored) regression is applied. A simulated dataset and the training institution dataset provided by the Semiconductor Institute in Taiwan is used for analysis. The empirical result shows that the proposed method can not only separate performance differences between the training institutions but also improve the discriminatory capability of the DEA's efficiency measurement. The study results can serve as a reference for training institutions wishing to enhance their training efficiency.