基于DEA的经济新区投资绩效评价——以天津滨海新区为例

本文利用数据包络分析(DEA)方法,对经济新区投资绩效进行了综合分析,指出各个非DEA有效决策单元中存在的投入冗余与产出不足并提出了相关结论与建议。     

Understanding resource deployment efficiency for ESG and financial performance: A DEA approach

COVID-19 has stimulated additional research interest on economic sustainability and ESG in both academia and industry. This study adopts a DEA approach to examine the efficiency of achieving ESG targets and their relationships with financial performance. Using MSCI ESG data from 2015 to 2019 on 1108 Chinese firms, we examine the ESG proportional and pillar mix efficiencies. The dominant strategies for our sampled firms are to improve overall ESG performance by enhancing the E and S pillars through sacrificing G’s performance. The second result shows a positive relationship between proportional efficiency and financial performance while a mixed relationship between pillar mix efficiency and financial performance. However, for the technology sector, there exists some trade-offs between ESG performance and financial performance. Specifically, relative to non-technology firms, improving proportional and pillar mix efficiencies for technology firms could result in some sacrifice in stock valuation.     

基于DEA的我国股份制商业银行竞争力分析

入世后,我国商业银行面临严峻的竞争局面,要求我们对商业银行竞争力以及与改善竞争力相关的因素的研究必须全面而准确。区别于传统的使用财务比率的方法,应用DEA(Data Envelopm ent Analysis,数据包络分析)方法,对于我国的股份制商业银行进行了竞争力分析。     

技术特征不同行业中FDI技术外溢效果的考察——基于非参数分析方法的研究

本文通过非参数性方法得到全要素生产率,考察了FDI对我国工业行业技术进步的影响。发现FDI外溢效果在科技含量高的行业中较为明显,在低科技行业中效果较差。并且通过进一步观察发现在外溢效果显著的行业中,技术外溢效果的显著性排名与行业中内资部门的技术进步排名高度吻合。因此我们应调整引资结构,吸引更多外资流入高科技行业以促进我国技术进步。     

我国部分省市区域创新系统效率评价

发展经济学认为经济的持续增长是技术进步的作用。而区域创新对技术进步具有重要的推动作用。分两阶段利用DEA方法对我国24省市的区域创新效率进行了评价，旨在为提高我国各地区的区域创新系统的效率提供基础性资料。本文在指标体系设立时考虑大学和中介机构两因素在区域创新中的关键作用，所以将两指标纳入到指标体系中，以使问题考察的更加全面。     

基于DEA模型的城市科技活动效率评价

将DEA理论应用于广东21城市科技活动的分析中，建立评价城市科技活动效率的数据包络分析模型，谊模型评价了各城市的总体效率和技术效率的状况，进而对非DEA有效的城市提出达到DEA有效的可行措施，并分析了城市科技活动效率和资源配置之间的关系。     

零售业上市公司业绩的DEA有效性分析

本文应用DEA模型对零售业上市公司的业绩进行了实证研究,通过DEA有效分析和规模收益分析对八家上市公司的业绩进行了效率衡量,为投资者理性投资提供了参考依据,也为上市公司提高效率提供了数据借鉴。     

The Application of Mathematical Programming Approaches to Estimating Container Port Production Efficiency

Container terminal production is both an important and complicated element in the contemporary global economy. This paper aims to evaluate the efficiency of the world’s most important container ports and terminals using the two alternative techniques of Data Envelopment Analysis (DEA) and the Free Disposal Hull (FDH) model. The results give an insight into the current efficiency ranking of the world’s major container ports and terminals. They also confirm expectations that the available mathematical programming methodologies lead to different results and that appropriate variable definition of input and output factors is a crucial element in meaningful applications of DEA and FDH. It is also concluded that the availability of panel data, rather than cross-sectional data would greatly improve the validity of the efficiency estimates derived from all the mathematical programming techniques applied.JEL Classification:C61, D24, E23, L23, L25, L92     

高等学校内部各学院办学效益的相对有效性评价

本文运用DEA方法,以国内某高校的7个学院为研究对象,对学院办学效率相对有效性进行了评价。研究结果表明,该校非DEA有效的学院存在投入过剩,产出不足等问题,从而对学院的办学规模、科研政策等方面提出了改进的意见。     

Chance Constrained Programming Formulations for Stochastic Characterizations of Efficiency and Dominance in DEA

Pareto-Koopmans efficiency in Data Envelopment Analysis (DEA) is extended to stochastic inputs and outputs via probabilistic input-output vector comparisons in a given empirical production (possibility) set. In contrast to other approaches which have used Chance Constrained Programming formulations in DEA, the emphasis here is on joint chance constraints. An assumption of arbitrary but known probability distributions leads to the P-Model of chance constrained programming. A necessary condition for a DMU to be stochastically efficient and a sufficient condition for a DMU to be non-stochastically efficient are provided. Deterministic equivalents using the zero order decision rules of chance constrained programming and multivariate normal distributions take the form of an extended version of the additive model of DEA. Contacts are also maintained with all of the other presently available deterministic DEA models in the form of easily identified extensions which can be used to formalize the treatment of efficiency when stochastic elements are present.