Calculating scale elasticity in DEA models

In the data envelopment analysis (DEA) efficiency literature, qualitative characterizations of returns to scale (increasing, constant, or decreasing) are most common. In economics it is standard to use the scale elasticity as a quantification of scale properties for a production function representing efficient operations. Our contributions are to review DEA practices, apply the concept of scale elasticity from economic multi-output production theory to DEA piecewise linear frontier production functions, and develop formulas for scale elasticity for radial projections of inefficient observations in the relative interior of fully dimensional facets. The formulas are applied to both constructed and real data and show the differences between scale elasticities for the two valid projections (input and output orientations). Instead of getting qualitative measures of returns to scale only as was done earlier in the DEA literature, we now get a quantitative range of scale elasticity values providing more information to policy-makers.     

企业技术创新能力评价新方法的研究

企业发展的核心竞争力是技术创新能力,公正客观的评价企业的技术创新能力十分必要,研究新型的企业技术创新能力评价方法对企业的成长和可持续发展有所帮助。本文较为全面的分析了企业技术创新能力的概念、内涵与特征,并对现有的评价方法进行了调研和对比分析。提出企业技术创新能力评价分析时应将企业持续创新能力和经济效益作为评价的重要内容,研究并构建了包括创新支撑和创新主体两个二级指标在内的新的企业技术创新能力评价指标体系,并对指标的重要性进行了划分,以期为企业评价技术创新能力提供参考。     

基于生产结构视角的高新技术产业技术扩散影响研究

基于奥地利学派生产结构视角探讨高新技术产业外部技术扩散的影响路径,提出技术扩散通过生产结构的迂回性作用于技术创新,进而影响产出绩效。基于2005~2014年我国四个高新技术产业的面板数据,利用stata14软件进行数据分析,并对所提假设进行检验。结果表明:外部技术扩散对生产结构迂回性具有正向影响,但迂回性负向影响企业技术自主创新能力。研究结论对企业提升产业竞争力、增强技术创新和创新扩散效率具有重要的理论和实践启示。     

A directional distance based super-efficiency DEA model handling negative data

This paper develops a new radial super-efficiency data envelopment analysis (DEA) model, which allows input–output variables to take both negative and positive values. Compared with existing DEA models capable of dealing with negative data, the proposed model can rank the efficient DMUs and is feasible no matter whether the input–output data are non-negative or not. It successfully addresses the infeasibility issue of both the conventional radial super-efficiency DEA model and the Nerlove–Luenberger super-efficiency DEA model under the assumption of variable returns to scale. Moreover, it can project each DMU onto the super-efficiency frontier along a suitable direction and never leads to worse target inputs or outputs than the original ones for inefficient DMUs. Additional advantages of the proposed model include monotonicity, units invariance and output translation invariance. Two numerical examples demonstrate the practicality and superiority of the new model.     

Measuring returns to scale in DEA models when the firm is regulated

In the standard framework of data envelopment analysis (DEA) models, the returns to scale are fully characterized using the multiplier on the convexity constraint of inefficient decision making units (DMU) using the projection of the input–output vector on the frontier. In this note, we investigate how the returns to scale measurements in DEA models are affected by the presence of regulatory constraints. These additional constraints change the role played by the convexity constraint. In order to avoid biased estimation of the returns to scale, we show that the interaction between the regulatory and the convexity constraints has to be taken into account.     

基于主成分分析的新能源储能企业技术创新能力研究

基于科技创新投入、产出、支撑和转化等四个影响因素,构建新能源储能企业科技创新能力评价模型,并运用主成分分析法和聚类分析法,对我国主要新能源储能企业的科技创新能力进行分析和评价．分析表明,我国新能源储能企业科技创新的能力主要取决于经费投入和科研能力两大因素．新能源企业科技创新的研发经费投入、科研人员配备、科研成果等方面,与国外企业相比差距大,科技创新能力亟待提高．     

DEA方法进行规模收益分析的几点注记

自从 Banker等人 ( 1 984)用 DEA方法进行规模收益分析 ,已有越来越多的学者也在进行相关的研究 .研究主要从两个方面进行 :基于 DEA输入模型的方法和基于 DEA输出模型的方法 .这两类方法所应用的模型、条件、结论都有所不同 ,但在应用中有些文章将它们混淆在一起 .本文针对应用中容易出现的错误 ,给出反例和正确的判别规模收益状况的充分必要条件 .并由规模收益的定义 ,建议使用 DEA—输出模型进行规模收益分析 .     

Linear programming solutions and distance functions under a constant returns to scale technology

In this paper, we investigate the various relationships among the linear programming solutions of data envelopment analysis (DEA) models under a constant returns to scale technology. We derive the analytical relationships among the efficiency measures and the activity variables for four separate models: the input-based, the output-based, the hyperbolic, and the proportional distance functions. We apply our results in order to derive a test of consistency that can be used in assessing the returns to scale among differing DEA models.     

A new non-oriented model for classifying flexible measures in DEA

In conventional data envelopment analysis (DEA), measures are classified as either input or output. However, in some real cases there are variables which act as both input and output and are known as flexible measures. Most of the previous suggested models for determining the status of flexible measures are oriented. One important issue of these models is that unlike standard DEA, even under constant returns to scale the input- and output-oriented model may produce different efficiency scores. Also, can be expected a flexible measure is selected as an input variable in one model but an output variable in the other model. In addition, in all of the previous studies did not point to variable returns to scale (VRS), but the VRS assumption is prevailed on many real applications. To deal with these issues, this study proposes a new non-oriented model that not only selects the status of each flexible measure as an input or output but also determines returns to scale status. Then, the aggregate model and an extension with the negative data related to the proposed approach are presented.     

Measuring super-efficiency in DEA in the presence of infeasibility

Super-efficiency data envelopment analysis (DEA) model is obtained when a decision making unit (DMU) under evaluation is excluded from the reference set. Because of the possible infeasibility of super-efficiency DEA model, the use of super-efficiency DEA model has been restricted to the situations where constant returns to scale (CRS) are assumed. It is shown that one of the input-oriented and output-oriented super-efficiency DEA models must be feasible for a any efficient DMU under evaluation if the variable returns to scale (VRS) frontier consists of increasing, constant, and decreasing returns to scale DMUs. We use both input- and output-oriented super-efficiency models to fully characterize the super-efficiency. When super-efficiency is used as an efficiency stability measure, infeasibility means the highest super-efficiency (stability). If super-efficiency is interpreted as input saving or output surplus achieved by a specific efficient DMU, infeasibility does not necessary mean the highest super-efficiency.