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Article(id=1322132578228883949, tenantId=1146029695717560320, journalId=1146120045056339983, issueId=1322132553822228690, articleNumber=null, orderNo=null, doi=10.12404/j.issn.1002-980X.J25120109, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1764518400000, receivedDateStr=2025-12-01, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1793150570685, onlineDateStr=2026-10-28, pubDate=1774368000000, pubDateStr=2026-03-25, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1793150570685, onlineIssueDateStr=2026-10-28, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1793150570685, creator=13701087609, updateTime=1793150570685, updator=13701087609, issue=Issue{id=1322132553822228690, tenantId=1146029695717560320, journalId=1146120045056339983, year='2026', volume='45', issue='3', pageStart='1', pageEnd='170', issueExtLink='null', onlineDate='null', pubDate='1774368000000', pubDateStr='2026-03-25', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=1, specialIssue=null, createTime=1793150564865, creator='13701087609', updateTime=1793150672274, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1322133004726678037, tenantId=1146029695717560320, journalId=1146120045056339983, issueId=1322132553822228690, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1322133004726678038, tenantId=1146029695717560320, journalId=1146120045056339983, issueId=1322132553822228690, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=113, endPage=124, ext={EN=ArticleExt(id=1322132578484736494, articleId=1322132578228883949, tenantId=1146029695717560320, journalId=1146120045056339983, language=EN, title=How Do Data Elements Affect Firm Performance: Quasi-Experimental Evidence from Public Data Openness in China, columnId=1149284495636906656, journalTitle=Journal of Technology Economics, columnName=Technology Economics Evaluation, runingTitle=null, highlight=null, articleAbstract=

As a pivotal institutional tool for digital government construction and the market-oriented reform of production factors, the economic impact of public data openness on micro-level corporate behavior was systematically evaluated. Based on a sample of Chinese A-share listed companies from 2009 to 2023, the causal effect of public data openness on corporate performance and its underlying mechanisms were identified by employing a multi-period difference-in-differences (DID) model integrated with city-level pilot information. A significant positive effect of public data openness on corporate performance was revealed through benchmark regression. The reliability of this conclusion was confirmed by a series of robustness tests, including parallel trend tests, placebo tests, PSM-DID estimation, and the exclusion of contemporaneous policy interferences such as the "Broadband China" strategy and National Big Data Comprehensive Pilot Zones. Mechanism analysis indicates that corporate performance is enhanced through two primary transmission pathways: the alleviation of financing constraints and the promotion of corporate innovation. By serving as an authoritative signaling mechanism, public data openness reduces information asymmetry in credit markets and lowers credit acquisition costs. Simultaneously, it provides low-cost knowledge reserves that reduce R&D trial-and-error costs and stimulate patent output. Heterogeneity analysis further demonstrates that the performance-enhancing effects are more pronounced among enterprises in eastern regions, small-scale enterprises, non-state-owned enterprises, and non-high-tech firms. These findings provide empirical evidence and policy orientation for optimizing public data supply quality, mitigating the digital divide, and fostering the efficient utilization of data elements.

, authors=Yunjiang Yu1, 2, Chenwei Fan1, 2, Yetong Liu1, 2, Xuelu Xu1, 2, authorsList=Yunjiang Yu, Chenwei Fan, Yetong Liu, Xuelu Xu, authorCompany=null, correspAuthors=Xuelu Xu, 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=1322132578736394735, articleId=1322132578228883949, tenantId=1146029695717560320, journalId=1146120045056339983, language=CN, title=数据要素如何影响企业绩效：来自中国公共数据开放的准实验证据, columnId=1148001301952192921, journalTitle=技术经济, columnName=技术经济评价, runingTitle=null, highlight=null, articleAbstract=

公共数据开放作为数字政府建设与要素市场化改革的关键工具，其对微观企业行为的经济影响仍缺乏系统实证评估。以2009—2023年中国A股上市公司为研究对象，结合城市层面公共数据开放试点信息，构建多时点双重差分(DID)模型，深入识别公共数据开放对企业绩效的因果效应及其内在机制。基准研究表明，公共数据开放对企业绩效产生显著的正向促进作用。该结论在经历平行趋势检验、安慰剂检验、PSM-DID估计及排除同期政策干扰等系列稳健性检验后依然成立。机制分析揭示，政策通过两条核心路径赋能微观主体：一是发挥信号传递功能，降低信贷市场信息不对称，有效缓解企业融资约束；二是提供低成本知识储备，降低研发试错成本，显著促进企业创新产出。异质性分析发现，由于区域基础设施与企业吸收能力的差异，公共数据开放对东部地区企业、小规模企业、非国有企业及非高技术企业的绩效提升作用更为显著。本文研究不仅拓宽了数据要素微观经济效应的研究视角，也为政府深化企业数据应用、提升供给质量、缩小数字鸿沟并推动公共数据高效利用提供了实证依据与政策启示。

, authors=余运江1, 2, 范宸玮1, 2, 刘叶彤1, 2, 许薛璐1, 2, authorsList=余运江, 范宸玮, 刘叶彤, 许薛璐, authorCompany=null, correspAuthors=许薛璐, authorNote=

余运江（1988—），博士，上海师范大学商学院教授，研究方向：数字经济

范宸玮（2001—），硕士，上海师范大学数字经济研究中心助理，研究方向：数字经济

刘叶彤（2001—），硕士，上海师范大学数字经济研究中心助理，研究方向：数字经济

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许薛璐（1990—），博士，上海师范大学商学院讲师，研究方向：数字经济。
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余运江（1988—），博士，上海师范大学商学院教授，研究方向：数字经济

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余运江（1988—），博士，上海师范大学商学院教授，研究方向：数字经济

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范宸玮（2001—），硕士，上海师范大学数字经济研究中心助理，研究方向：数字经济

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范宸玮（2001—），硕士，上海师范大学数字经济研究中心助理，研究方向：数字经济

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刘叶彤（2001—），硕士，上海师范大学数字经济研究中心助理，研究方向：数字经济

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刘叶彤（2001—），硕士，上海师范大学数字经济研究中心助理，研究方向：数字经济

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数据要素如何影响企业绩效：来自中国公共数据开放的准实验证据
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余运江 1, 2 , 范宸玮 1, 2 , 刘叶彤 1, 2 , 许薛璐 1, 2
技术经济 | 技术经济评价 2026,45(3): 113-124
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技术经济 |技术经济评价 2026 , 45 (3) : 113 -124
数据要素如何影响企业绩效：来自中国公共数据开放的准实验证据
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2．上海师范大学数字经济研究中心，上海 200234, bio={"content":"

余运江（1988—），博士，上海师范大学商学院教授，研究方向：数字经济

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余运江1, 2, 范宸玮1, 2, 刘叶彤1, 2, 许薛璐1, 2
作者信息
  • 1．上海师范大学商学院，上海 200234
  • 2．上海师范大学数字经济研究中心，上海 200234
通讯作者:
许薛璐（1990—），博士，上海师范大学商学院讲师，研究方向：数字经济。
作者简介:

余运江（1988—），博士，上海师范大学商学院教授，研究方向：数字经济

范宸玮（2001—），硕士，上海师范大学数字经济研究中心助理，研究方向：数字经济

刘叶彤（2001—），硕士，上海师范大学数字经济研究中心助理，研究方向：数字经济

How Do Data Elements Affect Firm Performance: Quasi-Experimental Evidence from Public Data Openness in China
Yunjiang Yu1, 2, Chenwei Fan1, 2, Yetong Liu1, 2, Xuelu Xu1, 2
Affiliations
  • 1.School of Finance and Business, Shanghai Normal University, Shanghai 200234, China
  • 2.Digital Economy Research Center, Shanghai Normal University, Shanghai 200234, China
出版时间: 2026-03-25 doi: 10.12404/j.issn.1002-980X.J25120109
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公共数据开放作为数字政府建设与要素市场化改革的关键工具，其对微观企业行为的经济影响仍缺乏系统实证评估。以2009—2023年中国A股上市公司为研究对象，结合城市层面公共数据开放试点信息，构建多时点双重差分(DID)模型，深入识别公共数据开放对企业绩效的因果效应及其内在机制。基准研究表明，公共数据开放对企业绩效产生显著的正向促进作用。该结论在经历平行趋势检验、安慰剂检验、PSM-DID估计及排除同期政策干扰等系列稳健性检验后依然成立。机制分析揭示，政策通过两条核心路径赋能微观主体：一是发挥信号传递功能，降低信贷市场信息不对称，有效缓解企业融资约束；二是提供低成本知识储备，降低研发试错成本，显著促进企业创新产出。异质性分析发现，由于区域基础设施与企业吸收能力的差异，公共数据开放对东部地区企业、小规模企业、非国有企业及非高技术企业的绩效提升作用更为显著。本文研究不仅拓宽了数据要素微观经济效应的研究视角，也为政府深化企业数据应用、提升供给质量、缩小数字鸿沟并推动公共数据高效利用提供了实证依据与政策启示。

数据要素  /  公共数据开放  /  企业绩效  /  融资约束  /  企业创新  /  双重差分

As a pivotal institutional tool for digital government construction and the market-oriented reform of production factors, the economic impact of public data openness on micro-level corporate behavior was systematically evaluated. Based on a sample of Chinese A-share listed companies from 2009 to 2023, the causal effect of public data openness on corporate performance and its underlying mechanisms were identified by employing a multi-period difference-in-differences (DID) model integrated with city-level pilot information. A significant positive effect of public data openness on corporate performance was revealed through benchmark regression. The reliability of this conclusion was confirmed by a series of robustness tests, including parallel trend tests, placebo tests, PSM-DID estimation, and the exclusion of contemporaneous policy interferences such as the "Broadband China" strategy and National Big Data Comprehensive Pilot Zones. Mechanism analysis indicates that corporate performance is enhanced through two primary transmission pathways: the alleviation of financing constraints and the promotion of corporate innovation. By serving as an authoritative signaling mechanism, public data openness reduces information asymmetry in credit markets and lowers credit acquisition costs. Simultaneously, it provides low-cost knowledge reserves that reduce R&D trial-and-error costs and stimulate patent output. Heterogeneity analysis further demonstrates that the performance-enhancing effects are more pronounced among enterprises in eastern regions, small-scale enterprises, non-state-owned enterprises, and non-high-tech firms. These findings provide empirical evidence and policy orientation for optimizing public data supply quality, mitigating the digital divide, and fostering the efficient utilization of data elements.

data elements  /  open public data  /  enterprise performance  /  financing constraints  /  corporate innovation  /  DID
余运江, 范宸玮, 刘叶彤, 许薛璐. 数据要素如何影响企业绩效：来自中国公共数据开放的准实验证据. 技术经济, 2026 , 45 (3) : 113 -124 . DOI: 10.12404/j.issn.1002-980X.J25120109
Yunjiang Yu, Chenwei Fan, Yetong Liu, Xuelu Xu. How Do Data Elements Affect Firm Performance: Quasi-Experimental Evidence from Public Data Openness in China[J]. Journal of Technology Economics, 2026 , 45 (3) : 113 -124 . DOI: 10.12404/j.issn.1002-980X.J25120109

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doi: 10.12404/j.issn.1002-980X.J25120109
  • 接收时间：2025-12-01
  • 首发时间：2026-10-28
  • 出版时间：2026-03-25
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  • 收稿日期：2025-12-01
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    1．上海师范大学商学院，上海 200234
    2．上海师范大学数字经济研究中心，上海 200234

通讯作者:

许薛璐（1990—），博士，上海师范大学商学院讲师，研究方向：数字经济。
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2种不同金属材料的力学参数
科
Family		 属数
Number of
genus		 种数
Number of
species		 占总种数比例
Percentage of
total species (%)		 属
Genus		 种数
Number of
species		 占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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