Statistical precision of DEA and Malmquist indices: A bootstrap application to Norwegian grain producers

Previous applications of data envelopment analysis (DEA) and its subsequent Malmquist indices to efficiency and productivity measurements have been criticised for not providing statistical inferences regarding the significance of observed results. In this paper, DEA and a Malmquist index are combined with a bootstrap method in order to provide succinct statistical inferences that determine the performance of grain producers in Eastern Norway. The data cover the period between 1987 and 1997. Results reveal: (i) a significant degree of inefficiency (approximately 11%) and an average productivity progress of 38% over the period considered; (ii) the formidable productivity progress observed is primarily explained by technical efficiency changes that enabled producers to catch up with front runners; and (iii) environmental factors, such as weather conditions, impact both efficiency and productivity. Finally, the analysis reveals that using bootstrapping to make statistical inferences suggests that researchers should be careful in making performance comparisons based on conventional DEA methods, as any discovered differences may not be significant.     

Changes in productivity of Spanish university libraries

This paper analyzes productivity growth, technical progress, and efficiency change in a sample of 34 Spanish university libraries between 2003 and 2007. Data envelopment analysis and a Malmquist index are combined with a bootstrap method to provide statistical inference estimators of individual productivity, technical progress, pure efficiency, and scale efficiency scores. To calculate productivity, a three-stage service model has been developed, examining productivity changes in the relationships between the libraries' basic inputs, intermediate outputs, and final outputs. The results indicate a growth in the productivity of the libraries (relationship between basic inputs and intermediate outputs) and in the productivity of the service (relationship between basic inputs and final outputs). The growth in productivity in both relationships is due to technical progress. If the variable representing the use of electronic information resources is removed from the final output, the result is a significant reduction in productivity.     

Taiwan quality indicator project and hospital productivity growth

The Taiwan Quality Indicator Project (TQIP) is a quality management program that measures and monitors the healthcare quality of hospitals in Taiwan. This paper examines the impact of TQIP participation on hospital productivity growth with the application of the Malmquist productivity change index based on data envelopment analysis (DEA). Analyzing operations data from 31 TQIP regional hospitals over the period 1998–2004, we find that TQIP hospitals improved their productivity in the post-TQIP period. This improvement is attributable to quality change and relative efficiency progress. The simultaneous enhancement in both quality and relative efficiency coincides with the philosophy of total quality management (TQM) spirit, and confirms the efficiency improvement and quality assurance functions of TQIP.     

基于双前沿面数据包络分析的循环全局Malmquist指数

利用全部时期的技术构成的前沿面为参照,将双前沿面数据包络分析下的Malmquist指数拓展为双前沿面数据包络分析下的全局Malmquist指数。该全局Malmquist指数不仅较好地综合了乐观前沿面和悲观前沿面两部分信息,而且满足循环性检验;不仅可以避免由于前沿面选择的不同而导致的差异,还可以避免因时期选择的任意性而导致的差异。将其用于福建省工业行业2001-2008年的全要素生产率分析,研究结果较好地说明了该方法的有效性和实用性。     

Malmquist productivity index based on common-weights DEA: The case of Taiwan forests after reorganization

The performance of a decision making unit (DMU) can be evaluated in either a cross-sectional or a time-series manner, and data envelopment analysis (DEA) is a useful method for both types of evaluation. In order to eliminate the inconsistency caused by using different frontier facets to calculate efficiency, common-weights DEA models have been developed, under which a group of DMUs can be ranked for a specific period. This study proposes a common-weights DEA model for time-series evaluations to calculate the global Malmquist productivity index (MPI) so that the productivity changes of all DMUs have a common basis for comparison. The common-weights global MPI not only has sound properties, but also produces reliable results. The case of Taiwan forests after reorganization shows that the MPIs calculated from the conventional DEA model produce misleading results. The common-weights global MPI approach, on the other hand, correctly identifies districts with unsatisfactory performance before the reorganization and those with unsatisfactory productivity improvement after the reorganization.     

A network DEA approach for series multi-stage processes

We present in this paper a general network DEA approach to deal with efficiency assessments in multi-stage processes. Our approach complies with the composition paradigm, where the efficiencies of the stages are estimated first and the overall efficiency of the system is obtained ex post. We use multi-objective programming as modeling framework. This provides us the means to assess unique and unbiased efficiency scores and, if required, to drive the efficiency assessments effectively in line with specific priorities given to the stages. A direct comparison with the multiplicative decomposition approach on data drawn from the literature brings into light the advantages of our method and some critical points that one should be concerned about when using the multiplicative efficiency decomposition.     

Governance Structures,Multidimensional Efficiency and Firm Profitability

Using a panel data set of 361 German corporations for the period 1991–1996 we test the hypothesis that firms with more efficient governance structures have higher profitability. To determine efficiency we compare firms with respect to ownership concentration, the identity of owners, capital structure, investment and firm growth by a multi-input/multi-output Data Envelopment Analysis (DEA). This non-parametric linear programming technique considers both multiple in- and out- puts. Based on the concept of pareto efficiency, it computes an efficiency score where the associated weights of the inputs and outputs are determined endogenously. The DEA efficiency scores are then used as explanatory variables in panel data regressions of profitability. Our main finding is that the efficiency scores indeed contribute significantly to explaining profitability differences between firms, even after controlling for industry effects and unobserved systematic firm effects.     

Efficiency and productivity of the cement industry: Pakistani experience of deregulation and privatisation

This study evaluates the impact of privatisation on efficiency and productivity of the Pakistani cement industry. To address some of the serious concerns about the problem of dimension and outlier, we use a newly developed unconditional hyperbolic α-quantile estimator of Wheelock and Wilson to estimate efficiency (Wheelock DC, Wilson PW. Non-parametric, unconditional quantile estimation for efficiency analysis with an application to Federal Reserve check processing operations. Journal of Econometrics; 2008: 209–25). Subsequently, we use these efficiency estimates to calculate the Malmquist productivity growth and its components. The results show that deregulation and privatisation had the desired positive effect on productivity growth due to technological progress. We conclude that this improvement in the post reform period could be linked to political stability, improved economic conditions and a competitive industry.     

Common benchmarking and ranking of units with DEA

This paper develops a common framework for benchmarking and ranking units with DEA. In many DEA applications, decision making units (DMUs) experience similar circumstances, so benchmarking analyses in those situations should identify common best practices in their management plans. We propose a DEA-based approach for the benchmarking to be used when there is no need (nor wish) to allow for individual circumstances of the DMUs. This approach identifies a common best practice frontier as the facet of the DEA efficient frontier spanned by the technically efficient DMUs in a common reference group. The common reference group is selected as that which provides the closest targets. A model is developed which allows us to deal not only with the setting of targets but also with the measurement of efficiency, because we can define efficiency scores of the DMUs by using the common set of weights (CSW) it provides. Since these weights are common to all the DMUs, the resulting efficiency scores can be used to derive a ranking of units. We discuss the existence of alternative optimal solutions for the CSW and find the range of possible rankings for each DMU which would result from considering all these alternate optima. These ranking ranges allow us to gain insight into the robustness of the rankings.     

Research fronts in data envelopment analysis

Research activities relating to data envelopment analysis (DEA) have grown at a fast rate recently. Exactly what activities have been carrying the research momentum forward is a question of particular interest to the research community. The purpose of this study is to find these research activities, or research fronts, in DEA. A research front refers to a coherent topic or issue addressed by a group of research articles in recent years. The large amount of DEA literature makes it difficult to use any traditional qualitative methodology to sort out the matter. Thus, this study applies a network clustering method to group the literature through a citation network established from the DEA literature over the period 2000 to 2014. The keywords of the articles in each discovered group help pinpoint its research focus. The four research fronts identified are “bootstrapping and two-stage analysis”, “undesirable factors”, “cross-efficiency and ranking”, and “network DEA, dynamic DEA, and SBM”. Each research front is then examined with key-route main path analysis to uncover the elements in its core. In addition to presenting the research fronts, this study also updates the main paths and author statistics of DEA development since its inception and compares them with those reported in a previous study.     

Using Malmquist Indexes to measure changes in the productivity and efficiency of US accounting firms before and after the Sarbanes–Oxley Act

There have been many criticisms of the Sarbanes–Oxley (SOX) Act passed in July of 2002 to correct business accountability and performance practices. The act has a major emphasis on accounting and its practices. This paper attempts a response to these criticisms by investigating changes in productive efficiency for 62 of the largest US public accounting firms between the periods (2000–2001) and (2003–2004)—the periods before and after enactment of SOX in July of 2002. DEA is used to calculate Malmquist indexes of productivity and efficiency changes. This index is used because it can distinguish between changes in technical efficiency, which limit the possibilities, and changes in the performance efficiencies for each firm. Contrary to many of the criticisms, results indicate that accounting firms have exhibited significant post SOX growth in productive efficiency which is better than pre-SOX performances.     

Turbofan aero-engine efficiency evaluation: An integrated approach using VSBM two-stage network DEA

The application of Data Envelopment Analysis (DEA) has been wide, especially for the purpose of evaluating efficiency among similar production processes within enterprises belonging to particular industries. Although research pertinent to DEA has primarily focused on efficiency of production systems or corporate entities/organizations (e.g., terminals, hospitals, universities/schools, banks), fairly little attention has been given to efficiency evaluation among engineering systems featuring common configurations (e.g., automobiles, power plants). Furthermore, the limited previous literature involving efficiency evaluation of engineering systems has implemented DEA methodologies with limited discriminative power, i.e. there is a quite increased portion of efficient Decision Making Units (DMUs). In the current paper, a methodological framework deploying Variable intermediate measures Slacks-Based Measure (VSBM) Two-Stage Network DEA is implemented, in order to evaluate the efficiency of turbofan aero-engines, currently utilized by active-duty commercial and military aircraft. Apart from exploring the positive correlation of DEA efficiency with engineering efficiency, we also develop a methodology evaluating the features of near-future turbofan designs in terms of DEA efficiency, thus comprising a potential tool for efficiency assessment of any turbofan aero-engine being in the conceptual or preliminary design stage.     

基于DEA技术的华东地区建筑业生产效率研究

利用DEA方法对2007年华东地区的建筑业生产效率进行详细分析发现,江西、安徽及山东均处于规模递增阶段,特别是安徽和山东,存在较大的投入冗余及产出不足;进一步分析安徽的生产效率增长情况发现,其经历了由高速增长到低速增长的转变,而这种生产效率增长仅来自技术进步,未获得任何规模经济;最后,得出安徽及山东建筑业发展方向主要为如何实现现有资源的整合及优化,通过鼓励企业资产兼并与重组,提高单个企业的资源利用效率,最终实现建筑业的规模经济。     

Evaluating USAF Vehicle Maintenance Productivity Over Time: An Application of Data Envelopment Analysis

In response to increasing costs and reductions in manpower, the Tactical Air Command (TAC) of the USAF experimented with a specialized productivity measurement model known as data envelopment analysis (DEA). A medium-sized application of DEA was employed by TAC to evaluate the productivity of its seventeen subordinate vehicle maintenance sections over a four-year period. The application reports gains in productivity and the reactions of the field managers to the use of DEA.     

人力资本不平等对全要素生产力增长的影响研究

本文通过理论和实证分析研究了人力资本不平等对全要素生产力增长的影响。文章首先对人力资本不平等影响全要素生产力增长率的作用进行了数理分析。在实证研究部分,采用DEA包络分析法计算了1995-2005年30个省的全要素生产力增长率及其两个组成部分——技术进步率和效率变化率的曼奎斯特指数,利用30个省的面板数据分别用三个计量经济模型对人力资本水平和人力资本不平等与全要素生产力增长率、技术进步率和技术效率变化率之间的关系进行了实证检验。通过理论与实证分析提出本文的政策建议。     

Pitfalls of decomposition weights in the additive multi-stage DEA model

This paper examines limitations of the multi-stage DEA (data envelopment analysis) model in the literature. We focus on the DEA model with additive efficiency decomposition. We create taxonomy for the multi-stage DEA models and show when the decomposition weights can be non-increasing. When the decomposition weight for a stage is deemed reflective of the stage׳s relative importance, this property then implies that upstream stages (regardless the stage efficiency scores) in the model will obtain higher priority in efficiency decomposition. We also find that the non-increasing weights can affect the evaluation of overall and stage efficiency scores. We illustrate our findings through an empirical data set.     

An expanded decomposition of the Luenberger productivity indicator with an application to the Chinese healthcare sector

The objective of this research is to combine productivity analysis at the “firm-level” and the “industry-level” so that a novel, fuller decomposition of the sources of productivity change can be undertaken. Productivity change is decomposed into technological progress, technical, mix, and scale efficiency effects. Specifically, our decomposition allows us to capture changes in productivity due to the reallocation of inputs or outputs across productive units. In practice, such reallocation might take place across plants operated by the same firm, across regions within a country, or via mergers and acquisitions. The new decomposition of the aggregate Luenberger productivity indicator is illustrated using data at both the provincial and regional levels for China's healthcare sector over the period 2009–2014. Our results indicate that the growth of the aggregate Luenberger productivity indicator varied across both time and regions. We find that China's regional productivity growth in healthcare was primarily driven by technological progress, while the contributions of the other components of productivity change were smaller and more varied across regions.     

Enhancing standard performance practices with DEA

Data envelopment analysis (DEA) can be interpreted as a natural generalization of elemental practices for efficiency and performance evaluation used in diverse areas, some of which have evolved independently of each other and of the traditional efficiency framework of Charnes et al. [Measuring the efficiency of decision making units. European Journal of Operational Research 1978;2(6):429–44]. The paper aims to show that some standard “real-world” procedures employed by practitioners in the fields of retailing, finance, and social science can be interpreted as unconscious applications of DEA, and that such analyses could be enhanced by the explicit use of DEA. A supplementary aim is to demonstrate that DEA can be derived from concrete examples and first principles. The choice of the examples covers the two typical returns to scale environments of constant returns to scale (CRS) and variable returns to scale (VRS). Motivating and deriving DEA as a generalization of established performance evaluation practices will help to explain and disseminate this methodology to practitioners and to those who find their methods familiar.     

矩阵型网络DEA模型及其实证检验

针对传统DEA模型无法有效的评价矩阵型网络系统的效率,本文构建了矩阵型网络决策单元的生产可能集,建立了矩阵型网络DEA模型。在此基础上证明了决策单元在矩阵型网络DEA模型下为弱DEA有效的充分必要条件为其每个子系统均为弱DEA有效。最后,选用美国的十个电力公司作为决策单元对模型进行实证检验,得出结论:矩阵型网络DEA模型弥补了传统DEA模型无法反映内部有效性从而可能得到错误结果的缺陷,并能精确地计算出各个子过程的效率,辨识出具体需要改进的子过程。同时新模型为评价复杂系统的效率提供了新的思路。     

存在保证域的模糊非径向偏好DEA模型——基于中科院24个研究所的实证分析

数据包络分析（DEA）是一种非参数化的方法,用于评价具有类似输入和输出的决策单元的效率。传统的非径向DEA模型假设输入和输出数据均为准确值,且对权重变量不加以限制,本文构建了存在保证域的模糊非径向偏好DEA模型,并给出了一种基于模糊数截集的模型求解方法,有效地解决了输入和输出全部或部分为模糊数的决策单元评价问题。最后给出了一个中科院研究所效率评价的实例说明了方法的有效性。