混合型基金绩效评估及驱动因素分析——基于DEA-Tobit模型

将随机选出的混合型基金作为研究样本,采用DEA-Tobit二阶段模型进行研究.首先利用数据包络模型对样本进行绩效评估,在此基础上,利用Tobit模型研究相关指标对于基金绩效的影响机制.结果表明,基金本身所面临的风险收益因素以及基金经理团队稳定性对于基金绩效均具显著影响.最后结合实证结果就提升中国基金绩效提出相关的建议.     

广东省县域农业生产效率分析——基于2015-2019年揭阳市5县域DEA模型为例

农业生产效率提升是实现农业现代化的重要手段。本文以《揭阳市统计年鉴(2015-2019)》的农业相关数据,从县域的视角研究揭阳市农业生产效率,运用DEA模型对揭阳市5个县域的3个投入指标和1个产出指标进行实证分析。结果显示,2015-2019年揭阳市农业生产效率均属于DEA无效,总体效率提升并不明显,且各县域呈现发展不平衡的状态,仅惠来县位于生产前沿面上,普宁市的农业生产效率最低,规模报酬属于递减态势。因此,恰逢“十四五”谋划之时,对揭阳市各县域农业生产效率做出分析研究,为现阶段提升揭阳市农业生产效率提出有参考价值的建议,为广东省县域农业生产效率分析提供范本。     

Evaluation of the green technology innovation efficiency of China's manufacturing industries: DEA window analysis with ideal window width

Data Envelopment Analysis (DEA) window analysis with ideal window width is applied to evaluate the green technology innovation efficiency of 28 manufacturing industries in China during 2006–2014. The obtained results are compared with those calculated using the traditional DEA model, and convergence analysis of the efficiency is conducted. Five years is the obtained ideal window width and DEA window analysis with ideal window produces results closer to reality for China's manufacturing industry. The overall efficiency of the green technology innovation in the manufacturing sector is low following a wave-shaped curve – first decreasing, then increasing and decreasing again, with large inter-industrial differences. There are 8 high-, 14 medium- and 6 low-efficiency industries. A convergence trend in the green technology innovation efficiency within the 28 manufacturing industries exists, implying a catch-up effect between them.     

Operating efficiency in Chinese universities: An extended two-stage network DEA approach

The result shows that it accepts the null hypothesis. Namely, there is no significant difference in the operating efficiency of universities in different regions. That is to say, although the efficiency of the central and western universities is slightly better than that of the eastern universities in terms of the average efficiency, there is no significant efficiency difference among the eastern, central, and western regions statistically. Therefore, it shows a balanced development trend for the efficiency of universities in different regions.     

Application of evidential reasoning approach and OWA operator weights in road safety evaluation considering the best and worst practice frontiers

Data envelopment analysis (DEA) has recently become relatively popular with road safety experts. Therefore, various decision-making units (DMUs), such as EU countries, have been assessed in terms of road safety performance (RSP). However, the DEA has been criticized because it evaluates DMUs based only on the concept of self-assessment, and, therefore does not provide a unique ranking for DMUs. Therefore, cross efficiency method (CEM) was developed to overcome this shortcoming. Peer-evaluations in addition to self-evaluation have made the CEM to be recognized as an effective method for ranking DMUs. The traditional CEM is based only on the standard CCR (Charnes, Cooper and Rhodes) model, and it evaluates DMUs according to their position relative to the best practice frontier while neglecting the worst practice frontier. However, the DMUs can also be assessed based on their position relative to the worst practice frontier. In this regard, the present study aims to provide a double-frontier CEM for assessing RSP by taking into account the best and worst frontiers simultaneously. For this purpose, the cross efficiency and cross anti-efficiency matrices are generated.Even though a weighted average method (WAM) is most frequently used for cross efficiency aggregation, the decision maker's (DM) preference structure may not be reflected. For this reason, the present study mainly focuses on the evidential reasoning approach (ERA), as a nonlinear aggregation method, rather than the linear WAM. Equal weights are often used for cross efficiency aggregation; consequently, the effect of the DM's subjective judgments in obtaining the overall efficiency is ignored. In this respect, the minimax entropy approach (MEA) and the maximum disparity approach (MMDA) are applied for determining the ordered weighted averaging (OWA) operator weights for cross efficiency aggregation. The weighted cross efficiencies and cross anti-efficiencies are then aggregated using the ERA. Finally, the proposed method, called DF-CEM-ERA, is used to evaluate the RSP of EU countries as well as Serbian police departments (PDs).     

公司为什么放弃债务的收益呢？

经典资本结构理论认为债务具有税盾效应和财务杠杆效应，因此公司会选择适当的负债以获得债务的税收收益。但是，现实中一些公司的负债很低，甚至为零。公司为什么放弃债务的税收收益呢？本文以2007～2011年中国A股上市公司为研究样本，从行为公司金融的视角分析管理者能力是否导致公司采用“低杠杆”策略。结果表明，中国上市公司存在普遍的低杠杆现象，管理者能力的差异是导致公司采用“低杠杆”策略的重要原因。     

长江经济带生态保护效率与科技服务业发展效率时空关联分析

建立超效率DEA模型并运用视窗分析测算2010-2017年长江经济带11个省市生态保护效率与科技服务业发展效率，通过空间计量模型分析后者对前者的影响。结果表明：科研技术服务业效率对当地及周边地区的生态保护效率具有正向影响；科技资本服务业效率对当地生态保护效率具有正向影响，但对其周边地区有抑制效应；科技宣传服务业未对生态保护效率产生显著影响。据此，提出长江经济带通过提高科技服务业效率改进生态保护效率的对策。     

基于数据包络(DEA)分析的京津冀地区环境绩效评估研究

提高资源环境绩效是区域生态环境治理的核心与关键。在构建环境绩效评估指标体系的基础上，运用数据包络分析模型（DEA）测度并分析2005-2015年京津冀地区静态环境绩效水平。利用Malmquist指数动态分析法将环境绩效分解为技术效率变化指数和技术进步指数，并探讨分解指标对环境绩效的相对贡献。结果显示：京津冀地区环境治理投入与产出水平均呈现逐年上升趋势；北京和天津的总体环境绩效水平要远高于河北，2005-2011年京津冀地区的环境绩效水平排序为北京、天津、河北,2012-2015年京津冀地区的环境绩效水平排序为天津、北京、河北（2013年除外）。河北省环境绩效水平较低的主要制约因素是技术进步缓慢与全要素生产率偏低。Malmquist指数呈现出一定的波性，这主要是由技术变化指标波动所致。最后，就如何提升京津冀地区环境绩效水平，提出相关对策建议。     

Towards a fairer manager performance measure: a DEA application in the retail industry

Abstract

Retailers need to determine the performance of their individual stores and, beyond the stores, the managers in charge of running them. The aim of this paper is to develop performance standards that can be applied to fairly distribute rewards to managers regarding their performances by taking into account store neighbourhood characteristics. In order to propose a fairer measure of store performance, the authors introduce and explore the concepts of organizational justice and store efficiency. They use real data from a French supermarket chain and a geomarketing approach. The two-step Data Envelopment Analysis (DEA) model results are compared with a retailer’s ranking. The retailer tends to favour points of sale having a more important sales area, with more employees and operating in a more densely populated area with a higher buying power. The ranking stemming from the DEA model links store performance to other geo-demographic variables.