数据要素市场建设如何治理企业产能过剩：基于数据交易平台的准自然实验

Abstract
Figure/Table
References
Related Citation (1)

Download: PDF (704 KB) HTML (1 KB)
Export: BibTeX | EndNote (RIS)

Abstract The development of a data factor market presents a crucial pathway to unlocking the productive potential of data. Particularly in contexts of widespread overcapacity, such market development can enhance resource allocation efficiency, avert redundant production and resource wastage, and thereby offer a more effective solution for mitigating corporate overcapacity. Accordingly, this study constructs a quasi-natural experiment, leveraging the establishment of regional data factor trading platforms as a policy shock, to investigate the mitigating effect of data factor market development on corporate overcapacity. The empirical findings reveal that the development of a data factor market significantly reduces the level of corporate overcapacity. This mitigating effect is more pronounced for non-state-owned, resource-based, and eastern-region enterprises. Further channel analysis indicates that the key mechanisms include alleviating information asymmetry, strengthening innovation impetus, and curbing inefficient investment. Moreover, the development of the data factor market, in synergy with technology transfer policies, forms a novel technology-data hybrid paradigm for capacity governance. This combined approach yields a more potent impact, better addressing the structural overcapacity that arises from fragmented production planning. These findings suggest a need to move beyond traditional policy frameworks. It is imperative to develop innovative, data-factor-based solutions and to fortify synergistic mechanisms with technology innovation policies, thereby more effectively addressing overcapacity and enhancing corporate productivity and quality.

Key words： data factor market overcapacity technology transfer policy synergy governance effect

Received: 20 July 2025

F49

	Service

	E-mail this article
	Add to my bookshelf
	Add to citation manager
	E-mail Alert
	RSS
	Articles by authors
	SHI Zhen

Cite this article:

SHI Zhen. How to Address Corporate Overcapacity in Enterprises through Data Factor Market Development: A Quasi-Natural Experiment Based on Data Trading Platforms[J]. , 2026, 43(1): 74-86.

URL:

https://xb.szu.edu.cn/EN/ OR https://xb.szu.edu.cn/EN/Y2026/V43/I1/74

[1] Gambatese J, Hallowell M R.Factors That Influence the Development and Diffusion of Technical Innovations in the Construction Industry[J].Construction Management and Ec-onomics, 2011,(29):507-517.
[2] 宋冬林,孙尚斌,范欣.数据成为现代生产要素的政治经济学分析[J].经济学家,2021,(7):35-44.
[3] 刘涛雄,张亚迪,戎珂等.数据要素成为中国经济增长新动能的机制探析[J].经济研究,2024,59(10):19-36.
[4] 朱安东,张宏博.科学认识当前我国产能过剩[J].上海经济研究,2023,(12):25-36+62.
[5] Brynjolfsson E., McElheran K. The Rapid Adoption of Data-driven Decision-making[J].American Economic Re-view,2016,106(5):133-139.
[6] Porter M.E., Heppelmann J. E. How Smart, Connected Products Are Transforming Competition[J].Harvard Busin-ess Review,2014,92(11):64-88.
[7] Gandomi A., Haider M.Beyond the Hype:Big Data Conce-pts, Methods, and Analytics[J].International Journal of In-formation Management,2015,35(2):137-144.
[8] 余靖雯,韩秀华,李一可.政府补贴与企业产能过剩[J].产业经济评论,2022,(5):130-153.
[9] 付强. 规制型政府竞争框架下结构性减速的形成机制分析:基于重工业产能过剩的视角[J].中国软科学,2023, (10):190-204.
[10] 赵放,蒋国梁,马婉莹.数据要素市场赋能数字产业创新——来自准自然实验的证据[J].经济评论,2024,(3):109-125.
[11] Farboodi M, Mihet R, Philippon T, et al.Big Data and Firm Dynamics[J].AEA Papers and Proceedings,2019,(109): 38-42.
[12] Mikalef P., Boura M., Lekakos G., et al.Big Data Analytics Capabilities and Innovation:The Mediating Ro-le of Dynamic Capabilities and Moderating Effect of the Environment[J].British Journal of Management,2019,30(2):272-298.
[13] 胡继晔,付炜炜.数据要素价值化助力培育新质生产力[J].财经问题研究,2024,(9):48-60.
[14] Gupta M., George J.F.Toward the Development of A Big Data Analytics Capability[J].Information & Management,2016,53(8):1049-1064.
[15] C rte-Real N., Oliveira T., Ruivo P.Assessing Business Value of Big Data Analytics in European Firms[J].Jour-nal of Business Research,2017,(70):379-390.
[16] 张益豪,郭晓辉.大数据发展与企业全要素生产率——基于国家级大数据综合试验区的实证分析[J].产业经济研究,2023,123(2):69-82.
[17] Wamba S.F., Gunasekaran A., Akter S., et al. Big Data Analytics and Firm Performance:Effects of Dynamic Ca-pabilities[J].Journal of Business Research,2017,(70):356-365.
[18] 蔡跃洲,马文君.数据要素对高质量发展影响与数据流动制约[J].数量经济技术经济研究,2021,38(3):64-83.
[19] 邱子迅,周亚虹.数字经济发展与地区全要素生产率——基于国家级大数据综合试验区的分析[J].财经研究,2021,47(7):4-17.
[20] 吴非. 取向一致还是合成谬误:“数字化-绿色化”政策协同对企业新质生产力的影响研究[J].西北工业大学学报(社会科学版),2025,(3):144-153.
[21] 陈劲,阳镇.新发展格局下的产业技术政策:理论逻辑、突出问题与优化[J].经济学家,2021,(2):33-42.
[22] Akter S., Wamba S.F. Big Data Analytics in E-commerce:A Systematic Review and Agenda for Future Research[J].Electronic Markets,2016,26(2):173-194.
[23] 杨俊,李小明,黄守军.大数据、技术进步与经济增长 ——大数据作为生产要素的一个内生增长理论[J].经济研究,2022,57(4):103-119.
[24] Aretz K., Pope P.F. Real Options Models of the Firm, Capacity Overhang, and the Cross Section of Stock Returns[J].The Journal of Finance,2018,73(3):1363-1415.
[25] 郑国强,张馨元,赵新宇.数据要素市场化如何驱动企业数字化转型?[J].产业经济研究,2023,(2):56-68.
[26] 田小平. 双碳背景下企业金融化与绿色发展的“多言寡行”——基于金融改革政策的治理效应评估[J].金融经济学研究,2024,39(3):93-109.
[27] 史丹,郑玉.数据要素的赋能机制与企业全要素生产率提升——来自国家级大数据综合试验区的证据[J].改革,2024,(11):1-16.
[28] 王晓丹,石玉堂,刘达.公共数据开放能促进数字经济与实体经济融合吗?——来自政府数据平台上线的准自然实验[J].南方经济,2024,(9):25-44.
[29] 胡熙. 政府治理数字化转型与家庭创业行为——来自“信息惠民国家试点”政策的证据[J].贵州财经大学学报,2024,(6):10-17.
[30] 顾乃华,廖桂铭,胡哲力.供应商数字化转型如何影响客户稳定性[J].广东财经大学学报,2024,39(6):17-31.
[31] Richardson S.Over-investment of Free Cash Flow[J].Re-view of Accounting Studies,2006,(11):159-189.
[32] 郑曼妮,黎文靖,谭有超.技术转移与企业高质量创新[J].世界经济,2024,47(3):66-93.