科研机构科技资源投入与产出的多阶段时滞效应研究

›› 2018, Vol. 30 ›› Issue (8): 69-78.

科研机构科技资源投入与产出的多阶段时滞效应研究

高军¹, 索玮岚²

1. 中国科学技术大学公共事务学院, 合肥 230026;
2. 中国科学院科技战略咨询研究院, 北京 100190

收稿日期:2017-03-06 出版日期:2018-08-28 发布日期:2018-08-31
通讯作者: 索玮岚(通讯作者),中国科学院科技战略咨询研究院副研究员,博士
作者简介:高军,中国科学技术大学公共事务学院博士研究生。
基金资助:
国家自然科学基金青年项目（71301159）；国家自然科学基金面上项目（71673267）。

Study on Multi-phases Time-lag Effect between Inputs and Outputs of S&T Resource for Scientific Research Institutes

Gao Jun¹, Suo Weilan²

1. School of Public Affairs, University of Science and Technology of China, Hefei 230026;
2. Institute of Science and Development, Chinese Academy of Sciences, Beijing 100190

Received:2017-03-06 Online:2018-08-28 Published:2018-08-31

摘要/Abstract

摘要：

考虑到科研机构科技资源投入与产出的多阶段性和各阶段内不同形式产出的滞后差异性，本文首先设计了研究方案，明确了变量选取的原则、过程和结果，并分别提出了多阶段时滞效应分析方法与处理方法进行各阶段内不同形式产出的滞后期测算以及各产出的数值修正。然后，利用1992-2014年的统计数据，对中国科学院及其30家代表性研究院所开展了实证研究，并以中国科学院的测算结果为基准，分类比对了各研究院所多阶段时滞效应的差异，发现高效型研究院所约占63.33%、低效型约占6.67%、失衡型占30%，进而结合致因分析给出了相应的改进建议。研究成果可以为科研机构明晰科技创新规律、保障科技资源高效配置提供有价值的决策参考。

关键词: 科技资源, 多阶段时滞效应, 投入产出, 分类比对, 改进建议

Abstract:

For scientific research institutes, the process from inputs to outputs of S&T resource is phased obviously. Meanwhile, there exists a lag difference among various outputs from each phase. In view of this, a research scheme is designed in this paper firstly. The principle, process and result of variable selection is determined. Also, an analysis method as well as a processing method on multi-phases time-lag effect are proposed to calculate the time lags of various outputs from each phase and revise the values of each output, respectively. Then, an empirical study on Chinese Academy of Sciences (CAS) and its 30 typical institutes is conducted using statistical data from 1992 to 2014. Furthermore, the differences on multi-phases time-lag effect of these institutes are classified and compared using the measurement result of CAS as a benchmark. The results show that about 63.33% institutes have a high efficiency, 6.67% ones have a low efficiency, and 30% ones are unbalanced. Finally, several improvement suggestions are presented according to a cause analysis. This study can provide a valuable decision reference for scientific research institutes to clarify the rules of S&T innovation and ensure the high efficient allocation of S&T resource.

Key words: S&T resource, multi-phases time-lag effect, inputs and outputs, classification comparison, improvement suggestions

高军, 索玮岚. 科研机构科技资源投入与产出的多阶段时滞效应研究[J]. , 2018, 30(8): 69-78.

Gao Jun, Suo Weilan. Study on Multi-phases Time-lag Effect between Inputs and Outputs of S&T Resource for Scientific Research Institutes[J]. , 2018, 30(8): 69-78.

参考文献

[1] 孙续华,陈诗波,王艳芬,等. 我国科技资源研发效率监测及影响因素分析[J]. 管理评论, 2012,24(9):83-93
[2] Boeing P. The Allocation and Effectiveness of China's R&D Subsidies——Evidence from Listed Firms[J]. Research Policy, 2016,45(9):1774-1789
[3] 万劲波. 按科技创新规律提高资源配置效率[N]. 光明日报, 2016-8-5
[4] 索玮岚,高军,陈锐. 科研机构科技资源使用效益评估研究——基于时滞效应和关联效应视角[J]. 科学学研究, 2015,33(2):234-241
[5] 索玮岚,陆桂昌,陈锐. 高校科技资源配置效率测度研究——基于共享投入关联网络DEA模型[J]. 科研管理, 2015,36(11):155-161
[6] Wang N., Hagedoorn J. The Lag Structure of the Relationship between Patenting and Internal R&D Revisited[J]. Research Policy, 2014,43(8):1275-1285
[7] Moralles H. F., Rebelatto D. A. N. The Effects and Time Lags of R&D Spillovers in Brazil[J]. Technology in Society, 2016,47(1):148-155
[8] Lee T. H., Zhang Y. S., Jeong B. H. A Multi-period Output DEA Model with Consistent Time Lag Effects[J]. Computer & Industrial Engineering, 2016,93(1):267-274
[9] 周忠宝,孙亮,刘德斌,等. 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[J]. 中国管理科学, 2014,22(2):75-84
[10] 陈洪转,羊震,刘思峰,等. 基于滞后DEA的我国高校科研经费使用效率评价[J]. 管理评论, 2011,23(8):72-77
[11] 倪渊. 基于滞后非径向超效率DEA的高校科研效率评价研究[J]. 管理评论, 2016,28(11):85-94
[12] 俞立平. 不同科研经费投入与产出互动关系的实证研究——基于面板数据及面板VAR模型的估计[J]. 科研管理, 2013,34(10):94-102
[13] Zbay N., Kaçiranlar S. The Almon Two Parameter Estimator for the Distributed Lag Models[J]. Journal of Statistical Computation and Simulation, 2017,87(4):834-843
[14] Lee S. H., Lee H. K. Measuring and Comparing the R&D Performance of Government Research Institutes:A Bottom-up Data Envelopment Analysis Approach[J]. Journal of Informetrics, 2015,9(1):942-953
[15] Neusser K. Interpretation and Identification of VAR Models[M]. Neusser K. Time Series Econometrics. Switzerland:Springer International Publishing, 2016
[16] 杨留星,田贵良,王珏. 基于VAR模型的海港对腹地影响实证研究:以连云港港为例[J]. 管理评论, 2016,28(9):250-259
[17] 范德成,李昊,刘赟. 基于改进DEA——以复相关系数为基准的滞后期的我国产业结构演化效率评价[J]. 运筹与管理, 2016,25(6):195-203