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Article(id=1322906740127314361, tenantId=1146029695717560320, journalId=1235980733773295621, issueId=1322887634511314961, articleNumber=null, orderNo=null, doi=null, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=null, receivedDateStr=null, revisedDate=null, revisedDateStr=null, acceptedDate=1617033600000, acceptedDateStr=2021-03-30, onlineDate=1793335145264, onlineDateStr=2026-10-30, pubDate=1632931200000, pubDateStr=2021-09-30, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1793335145264, onlineIssueDateStr=2026-10-30, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1793335145264, creator=13701087609, updateTime=1793335145264, updator=13701087609, issue=Issue{id=1322887634511314961, tenantId=1146029695717560320, journalId=1235980733773295621, year='2021', volume='30', issue='18', pageStart='1633', pageEnd='1728', issueExtLink='null', onlineDate='null', pubDate='1632931200000', pubDateStr='2021-09-30', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1793330590131, creator='13701087609', updateTime=1793337355310, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1322916009916629692, tenantId=1146029695717560320, journalId=1235980733773295621, issueId=1322887634511314961, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1322916009916629693, tenantId=1146029695717560320, journalId=1235980733773295621, issueId=1322887634511314961, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=1633, endPage=1637, ext={EN=ArticleExt(id=1322906740320252346, articleId=1322906740127314361, tenantId=1146029695717560320, journalId=1235980733773295621, language=EN, title=Research on innovation efficiency of Chinese pharmaceutical industry, columnId=null, journalTitle=Chinese Journal of New Drugs, columnName=null, runingTitle=null, highlight=null, articleAbstract=
Objective:

To provide reference for improving the innovation efficiency of China's pharmaceutical industry and promoting high-quality development of China's pharmaceutical industry.

Methods:

Data Envelopment Analysis (DEA) was used to measure the innovation (Technology) efficiency under constant scale (CRS) and variable scale (VRS) of pharmaceutical industry. The input and output data of 28 provinces (autonomous regions and municipalities) in China from 2014 to 2018 were selected from China Statistical Yearbook and China High-tech Industry Statistical Yearbook. On this basis, further comparative analysis was conducted from the perspective of industrial cluster to explore the change law of innovation efficiency of China's pharmaceutical industry, and corresponding suggestions were given.

Results and Conclusion:

There were great differences in the development of technological innovation efficiency among different regions in China; the innovation input and output of pharmaceutical industry was unbalanced and not directly proportional to the development level of the industry; the innovation efficiency in China was mainly affected by pure technical efficiency. through the analysis, it was found that there is a deviation between the evaluation of innovation efficiency of pharmaceutical industry by using DEA model alone and the actual situation of various regions. We conjectured that the deviation may be caused by the particularities of pharmaceutical industry and the impact of innovation spillover. It is suggested that when using DEA to evaluate the innovation efficiency of pharmaceutical industry, we should consider the factors such as the weak periodicity of pharmaceutical industry, the long output cycle of innovation achievements, and the existence of innovation efficiency spillover, so as to make the results more realistic.

, authors=Lin-lin BU1, 2, Qi-qi ZHANG1, 2, Hong SU2, Bin ZHOU1, 3, authorsList=Lin-lin BU, Qi-qi ZHANG, Hong SU, Bin ZHOU, authorCompany=null, correspAuthors=Hong SU, Bin ZHOU, authorNote=null, correspAuthorsNote=null, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=null, pdfFileSize=null, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, fund=null), CN=ArticleExt(id=1322906740978758078, articleId=1322906740127314361, tenantId=1146029695717560320, journalId=1235980733773295621, language=CN, title=我国医药制造业创新效率研究, columnId=1236331162679955859, journalTitle=中国新药杂志, columnName=新药述评与论坛, runingTitle=null, highlight=null, articleAbstract=
目的：

为提升我国医药产业创新效率、促进我国医药产业高质量发展提供参考。

方法：

通过数据包络分析法（DEA）对医药制造业不变规模（CRS）以及可变规模（VRS）下的创新（技术）效率进行测算，从《中国统计年鉴》和《中国高技术产业统计年鉴》中选取2014—2018年我国28个省（自治区、直辖市）的投入、产出等相关指标数据，在此基础上基于产业集群视角进一步对比分析，探讨我国医药产业创新效率变化规律并给出相应建议。

结果和结论：

我国各地区技术创新效率发展差异大；医药制造业创新投入与产出之间不平衡，且与行业发展水平不成正比；我国创新效率主要受纯技术效率影响。通过分析发现，单纯使用DEA模型进行医药制造业创新效率评价与各地区实际情况会存在偏差，造成这种偏差可能是由于医药制造业本身的特殊性以及受到创新溢出的影响导致的。建议在用DEA评价医药制造业的创新效率时，需要结合医药制造业周期性不强、创新成果产出周期长、存在创新效率溢出等因素综合考虑，让结果更贴合实际。

, authors=卜琳麟1, 2, 张琪绮1, 2, 苏红2, 周斌1, 3, authorsList=卜琳麟, 张琪绮, 苏红, 周斌, authorCompany=null, correspAuthors=苏红, 周斌, authorNote=

卜琳麟，女，硕士研究生，研究方向：医药产业研究。E-mail：。

, correspAuthorsNote=
苏红，女，副研究员，硕士生导师，研究方向：医药产业研究。E-mail：。
周斌，男，研究员，博士生导师，研究方向：社会与管理药学。E-mail：。
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卜琳麟，女，硕士研究生，研究方向：医药产业研究。E-mail：。

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卜琳麟，女，硕士研究生，研究方向：医药产业研究。E-mail：。

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我国医药制造业创新效率研究
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卜琳麟 1, 2 , 张琪绮 1, 2 , 苏红 2 , 周斌 1, 3
中国新药杂志 | 新药述评与论坛 2021,30(18): 1633-1637
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中国新药杂志 |新药述评与论坛 2021 , 30 (18) : 1633 -1637
我国医药制造业创新效率研究
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卜琳麟，女，硕士研究生，研究方向：医药产业研究。E-mail：。

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卜琳麟，女，硕士研究生，研究方向：医药产业研究。E-mail：。

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卜琳麟1, 2 , 张琪绮1, 2, 苏红2 , 周斌1, 3
作者信息
  • 1中国医药工业研究总院，上海　201203
  • 2中国医药工业信息中心，上海　200040
  • 3上海现代制药股份有限公司，上海　200040
通讯作者:
苏红，女，副研究员，硕士生导师，研究方向：医药产业研究。E-mail：。
周斌，男，研究员，博士生导师，研究方向：社会与管理药学。E-mail：。
作者简介:

卜琳麟，女，硕士研究生，研究方向：医药产业研究。E-mail：。

Research on innovation efficiency of Chinese pharmaceutical industry
Lin-lin BU1, 2 , Qi-qi ZHANG1, 2, Hong SU2 , Bin ZHOU1, 3
Affiliations
  • 1China State Institute of Pharmaceutical Industry, Shanghai 201203, China
  • 2China National Pharmaceutical Industry Information Center, Shanghai 200040, China
  • 3Shanghai Modern Pharmaceutical Co., Ltd., Shanghai 200040, China
出版时间: 2021-09-30
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摘要
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目的：

为提升我国医药产业创新效率、促进我国医药产业高质量发展提供参考。

方法：

通过数据包络分析法（DEA）对医药制造业不变规模（CRS）以及可变规模（VRS）下的创新（技术）效率进行测算，从《中国统计年鉴》和《中国高技术产业统计年鉴》中选取2014—2018年我国28个省（自治区、直辖市）的投入、产出等相关指标数据，在此基础上基于产业集群视角进一步对比分析，探讨我国医药产业创新效率变化规律并给出相应建议。

结果和结论：

我国各地区技术创新效率发展差异大；医药制造业创新投入与产出之间不平衡，且与行业发展水平不成正比；我国创新效率主要受纯技术效率影响。通过分析发现，单纯使用DEA模型进行医药制造业创新效率评价与各地区实际情况会存在偏差，造成这种偏差可能是由于医药制造业本身的特殊性以及受到创新溢出的影响导致的。建议在用DEA评价医药制造业的创新效率时，需要结合医药制造业周期性不强、创新成果产出周期长、存在创新效率溢出等因素综合考虑，让结果更贴合实际。

DEA模型  /  医药制造  /  技术创新效率
Objective:

To provide reference for improving the innovation efficiency of China's pharmaceutical industry and promoting high-quality development of China's pharmaceutical industry.

Methods:

Data Envelopment Analysis (DEA) was used to measure the innovation (Technology) efficiency under constant scale (CRS) and variable scale (VRS) of pharmaceutical industry. The input and output data of 28 provinces (autonomous regions and municipalities) in China from 2014 to 2018 were selected from China Statistical Yearbook and China High-tech Industry Statistical Yearbook. On this basis, further comparative analysis was conducted from the perspective of industrial cluster to explore the change law of innovation efficiency of China's pharmaceutical industry, and corresponding suggestions were given.

Results and Conclusion:

There were great differences in the development of technological innovation efficiency among different regions in China; the innovation input and output of pharmaceutical industry was unbalanced and not directly proportional to the development level of the industry; the innovation efficiency in China was mainly affected by pure technical efficiency. through the analysis, it was found that there is a deviation between the evaluation of innovation efficiency of pharmaceutical industry by using DEA model alone and the actual situation of various regions. We conjectured that the deviation may be caused by the particularities of pharmaceutical industry and the impact of innovation spillover. It is suggested that when using DEA to evaluate the innovation efficiency of pharmaceutical industry, we should consider the factors such as the weak periodicity of pharmaceutical industry, the long output cycle of innovation achievements, and the existence of innovation efficiency spillover, so as to make the results more realistic.

DEA model  /  pharmaceutical industrial  /  technological innovation efficiency
卜琳麟, 张琪绮, 苏红, 周斌. 我国医药制造业创新效率研究. 中国新药杂志, 2021 , 30 (18) : 1633 -1637 .
Lin-lin BU, Qi-qi ZHANG, Hong SU, Bin ZHOU. Research on innovation efficiency of Chinese pharmaceutical industry[J]. Chinese Journal of New Drugs, 2021 , 30 (18) : 1633 -1637 .

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2021年第30卷第18期
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  • 首发时间：2026-10-30
  • 出版时间：2021-09-30
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  • 录用日期：2021-03-30
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作者信息
    1中国医药工业研究总院，上海　201203
    2中国医药工业信息中心，上海　200040
    3上海现代制药股份有限公司，上海　200040

通讯作者:

苏红，女，副研究员，硕士生导师，研究方向：医药产业研究。E-mail：。
周斌，男，研究员，博士生导师，研究方向：社会与管理药学。E-mail：。
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鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
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