{"title":"衡量国家创新体系的效率--DEA 方法评述","authors":"M. Kotsemir","doi":"10.2139/ssrn.2304735","DOIUrl":null,"url":null,"abstract":"The paper reviews the application of the data envelopment analysis (DEA) method for measuring the efficiency of national innovation systems (NIS). The paper firstly visualizes the logic of DEA method and briefly summarizes the key advantages and main limitations of the DEA method. Further, this paper provides a comprehensive review of 11 empirical studies on cross-country analysis of NIS efficiency with DEA technique. In its main part the paper analyses the specifications of DEA models used in the reviewed studies, the content of the country samples, sets of input and output variables used and the resulting lists of efficient countries. The review detects general trends and differences in the sets of variables and the content of country samples. Moreover, this paper highlights the problem of “small countries bias” in the reviewed studies: situation when “small” (in terms of national innovation system scope and the level of development) countries (like Venezuela, Kyrgyzstan etc.) are included in the country sample, these “small” countries become the efficient ones. In general, empirical studies on cross-country analysis of national innovation systems efficiency using DEA method pay little attention to profound analysis of previous relevant studies. Therefore, this paper is among the first papers with deep review of such empirical studies","PeriodicalId":346559,"journal":{"name":"Innovation Measurement & Indicators eJournal","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2013-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"38","resultStr":"{\"title\":\"Measuring National Innovation Systems Efficiency – A Review of DEA Approach\",\"authors\":\"M. Kotsemir\",\"doi\":\"10.2139/ssrn.2304735\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The paper reviews the application of the data envelopment analysis (DEA) method for measuring the efficiency of national innovation systems (NIS). The paper firstly visualizes the logic of DEA method and briefly summarizes the key advantages and main limitations of the DEA method. Further, this paper provides a comprehensive review of 11 empirical studies on cross-country analysis of NIS efficiency with DEA technique. In its main part the paper analyses the specifications of DEA models used in the reviewed studies, the content of the country samples, sets of input and output variables used and the resulting lists of efficient countries. The review detects general trends and differences in the sets of variables and the content of country samples. Moreover, this paper highlights the problem of “small countries bias” in the reviewed studies: situation when “small” (in terms of national innovation system scope and the level of development) countries (like Venezuela, Kyrgyzstan etc.) are included in the country sample, these “small” countries become the efficient ones. In general, empirical studies on cross-country analysis of national innovation systems efficiency using DEA method pay little attention to profound analysis of previous relevant studies. Therefore, this paper is among the first papers with deep review of such empirical studies\",\"PeriodicalId\":346559,\"journal\":{\"name\":\"Innovation Measurement & Indicators eJournal\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2013-08-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"38\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Innovation Measurement & Indicators eJournal\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.2139/ssrn.2304735\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Innovation Measurement & Indicators eJournal","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.2139/ssrn.2304735","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 38
摘要
本文回顾了数据包络分析法(DEA)在衡量国家创新体系(NIS)效率方面的应用。本文首先直观地介绍了 DEA 方法的逻辑,并简要总结了 DEA 方法的主要优势和局限性。此外,本文还全面回顾了利用 DEA 技术对国家创新体系效率进行跨国分析的 11 项实证研究。本文的主要部分分析了所综述研究中使用的 DEA 模型的规格、国家样本的内容、所使用的投入和产出变量集以及由此得出的高效率国家名单。审查发现了变量集和国家样本内容的一般趋势和差异。此外,本文还强调了综述研究中的 "小国偏差 "问题:当 "小 "国家(就国家创新体系范围和发展水平而言)(如委内瑞拉、吉尔吉斯斯坦等)被纳入国家样本时,这些 "小 "国家就会成为高效国家。一般来说,利用 DEA 方法对国家创新系统效率进行跨国分析的实证研究很少关注对以往相关研究的深刻分析。因此,本文是首批对此类实证研究进行深入评述的论文之一。
Measuring National Innovation Systems Efficiency – A Review of DEA Approach
The paper reviews the application of the data envelopment analysis (DEA) method for measuring the efficiency of national innovation systems (NIS). The paper firstly visualizes the logic of DEA method and briefly summarizes the key advantages and main limitations of the DEA method. Further, this paper provides a comprehensive review of 11 empirical studies on cross-country analysis of NIS efficiency with DEA technique. In its main part the paper analyses the specifications of DEA models used in the reviewed studies, the content of the country samples, sets of input and output variables used and the resulting lists of efficient countries. The review detects general trends and differences in the sets of variables and the content of country samples. Moreover, this paper highlights the problem of “small countries bias” in the reviewed studies: situation when “small” (in terms of national innovation system scope and the level of development) countries (like Venezuela, Kyrgyzstan etc.) are included in the country sample, these “small” countries become the efficient ones. In general, empirical studies on cross-country analysis of national innovation systems efficiency using DEA method pay little attention to profound analysis of previous relevant studies. Therefore, this paper is among the first papers with deep review of such empirical studies