导语

  

内容提要

  

    DEA（data envelopment analysis）方法及其模型主要应用于投入产出效率评价、全要素生产率分析、生产函数构建以及组织标杆设定等领域。本书主要汇编了DEA框架中与生产规模相关的三个重要经济学概念［规模收益（RTS）、阻塞（Congestion）和产能利用率（CU）］相关的理论研究方法及相应的实践应用和思考。本书主要供经济学、运筹学和统计学相关研究人员、宏观管理人员以及相关领域研究生阅读使用。

    杨国梁，博士，中国科学院科技战略咨询研究院研究员。
    2013年于中国科学院大学获得管理科学与工程博土士学位，并于2014～2016年在德国DAAD-王宽诚研究基金、国家留学基金管理委员会和英国牛顿基金的资助下分别出访德国弗劳恩霍夫协会系统与创新研究所、英国曼彻斯特大学商学院和英国阿斯顿大学商学院。
    长期从事科技规划与管理、智库理论与方法、决策理论与方法研究。承担多项中国科学院发展规划局委托的各类与科技规划、科技管理相关的应用研究任务，主持过30余项英国皇家工程院、德意志学术交流中心、教育部、科学技术部、农业农村部、国家自然科学基金委员会、国家电网等机构的委托任务与竞争性项目课题，取得了一批有影响的规划相关决策咨询成果和理论方法研究成果。在国内外学术期刊和会议上发表学术论文120余篇。

Chapter 1  Estimating Directional Returns to Scale in DEA
  1.1  Introduction
  1.2  Classical RTS in DEA framework
  1.3  Directional SE and directional RTS
  1.4  Measurement of directional RTS
  1.5  A case study
  1.6  Conclusions
Chapter 2  Data Envelopment Analysis in the Absence of Convexity: Specifying Efficiency Status and Estimating Returns to Scale
  2.1  Introduction
  2.2  Preliminaries and literature review
  2.3  Methodology
  2.4  Most productive scale size
  2.5  Illustrative examples
  2.6  Conclusions and future extensions
Chapter 3  Institutional Change and Optimal Size of Universities
  3.1  Introduction
  3.2  Background and theory
  3.3  Data and identification strategy
  3.4  Results
  3.5  Discussions and conclusions
Chapter 4  A Study on Directional Returns to Scale
  4.1  Introduction
  4.2  Methodology
  4.3  Analysis of directional RTS and directional congestion effect
  4.4  Conclusions and discussions
Chapter 5  Directional Congestion in the Framework of Data Envelopment Analysis
  5.1  Introduction
  5.2  Primary approaches to congestion measurement
  5.3  Definitions of directional congestion
  5.4  Measurement of directional congestion
  5.5  A case study
  5.6  Conclusions
Chapter 6  Integer Data in DEA: Illustrating the Drawbacks and Recognizing Congestion
  6.1  Introduction
  6.2  Classical congestion
  6.3  Karimi et al.'s (2016) congestion approach
  6.4  The drawbacks of the PEIC
  6.5  Recognizing congestion with both negative and/or non-negative continuous and integer data
  6.6  Graphical illustration of our proposed approach
  6.7  Numerical example
  6.8  Empirical application
  6.9  Concluding remarks and possible extensions
Chapter 7  Negative Data in DEA: Recognizing Congestion and Specifying the Least and the Most Congested Decision-Making Units
  7.1  Introduction
  7.2  Implications of congestion and negative data in DEA
  7.3  The proposed congestion approach
  7.4  Specifying the strongly and weakly most congested DMUs in the presence of negative data
  7.5  Ranking of the congested DMUs in the presence of negative data
  7.6  Numerical example
  7.7  Empirical application
  7.8  Conclusions and future extensions
Chapter 8  Estimating Capacity Utilization of Chinese Manufacturing Industries
  8.1  Introduction
  8.2  Literature review
  8.3  Methodology and indicators
  8.4  Empirical results
  8.5  Conclusions and discussions
Chapter 9  Measuring the Chinese Regional Production Potential Using A Generalized Capacity Utilization Indicator
  9.1  Introduction
  9.2  Literature review
  9.3  Generalized capacity utilization indicator
  9.4  Empirical study:Chinese regions
  9.5  Conclusions and discussions
Chapter 10  Estimating Capacity Utilization of Chinese State Farms
  10.1  Introduction
  10.2  Literature review
  10.3  Methodology
  10.4  Results and policy implication
  10.5  Conclusions
Chapter 11  Measuring the Capacity Utilization of the 48 Largest Iron and Steel Enterprises in China
  11.1  Introduction
  11.2  Literature review
  11.3  Notation and models
  11.4  Dataset and input and output variables
  11.5  Empirical results
  11.6  Conclusions and discussions