Public data openness and corporate total factor productivity

被引:2
作者
Qian, Yifan [1 ]
机构
[1] China Agr Univ, Coll Econ & Management, Beijing 100083, Peoples R China
关键词
Public data openness; Total factor productivity; Information asymmetry; Data utilization capability; Regional market fairness; MARKET COMPETITION; IDENTIFICATION; RESILIENCE; MANAGEMENT; INNOVATION; BENEFITS; ADOPTION; DEBT; RISK;
D O I
10.1016/j.eap.2024.12.036
中图分类号
F [经济];
学科分类号
02 ;
摘要
Public data openness is a crucial initiative in advancing the development of digital government and the digital economy strategy. This study utilizes data from China's A-share listed companies from 2010 to 2022 and employs a multi-period difference-in-differences (DID) model to analyze the impact of public data openness on corporate total factor productivity (TFP). The conclusions are as follows: (1) Public data openness can enhance corporate total factor productivity. This conclusion remains robust after considering the heterogeneous treatment effects and addressing endogeneity issues. (2)Public data openness improves corporate total factor productivity by reducing information asymmetry, enhancing corporate operational capabilities, and optimizing the market environment. (3)Heterogeneity analysis reveals that public data openness has a more significant impact on improving TFP for companies in eastern regions and non-manufacturing industries. Moreover, this improvement is more pronounced in firms with higher market positions and greater resilience. Based on these findings, this research provides empirical evidence and policy insights for advancing public data openness, improving corporate productivity, and strengthening the stability and resilience of the socio-economic system.
引用
收藏
页码:733 / 753
页数:21
相关论文
共 89 条
[1]   IDENTIFICATION PROPERTIES OF RECENT PRODUCTION FUNCTION ESTIMATORS [J].
Ackerberg, Daniel A. ;
Caves, Kevin ;
Frazer, Garth .
ECONOMETRICA, 2015, 83 (06) :2411-2451
[2]  
Agrawal A, 2019, The economics of artificial intelligence: an agenda, DOI DOI 10.7208/CHICAGO/9780226613475.001.0001
[3]   Motivations for open data adoption: An institutional theory perspective [J].
Altayar, Mohammed Saleh .
GOVERNMENT INFORMATION QUARTERLY, 2018, 35 (04) :633-643
[4]   A systematic review of open government data initiatives [J].
Attard, Judie ;
Orlandi, Fabrizio ;
Scerri, Simon ;
Auer, Soeren .
GOVERNMENT INFORMATION QUARTERLY, 2015, 32 (04) :399-418
[5]   Mineral policy dynamics and their impact on equity market volatility in the global south: A multi-country analysis [J].
Awijen, Haithem ;
Ben Jabeur, Sami ;
Ballouk, Houssein .
Resources Policy, 2024, 99
[6]   How much should we trust staggered difference-in-differences estimates? * [J].
Baker, Andrew C. ;
Larcker, David F. ;
Wang, Charles C. Y. .
JOURNAL OF FINANCIAL ECONOMICS, 2022, 144 (02) :370-395
[7]   Forecasting default with the Merton distance to default model [J].
Bharath, Sreedhar T. ;
Shumway, Tyler .
REVIEW OF FINANCIAL STUDIES, 2008, 21 (03) :1339-1369
[8]   The systemic risk of European banks during the financial and sovereign debt crises [J].
Black, Lamont ;
Correa, Ricardo ;
Huang, Xin ;
Zhou, Hao .
JOURNAL OF BANKING & FINANCE, 2016, 63 :107-125
[9]  
Borusyak K., 2021, Working Paper No. 28957
[10]   Quasi-Experimental Shift-Share Research Designs [J].
Borusyak, Kirill ;
Hull, Peter ;
Jaravel, Xavier .
REVIEW OF ECONOMIC STUDIES, 2022, 89 (01) :181-213