Government Intelligent Transformation and Mixed-Ownership Reform: Evidence From China
Ruiqing Cao
School of Management, Shandong University, Jinan, China
Search for more papers by this authorCorresponding Author
Chenchen Shi
School of Management, Shandong University, Jinan, China
Correspondence: Chenchen Shi ([email protected])
Search for more papers by this authorJing Zhao
School of Economics and Management, Changji University, Changji Hui Autonomous Prefecture, China
Search for more papers by this authorRuiqing Cao
School of Management, Shandong University, Jinan, China
Search for more papers by this authorCorresponding Author
Chenchen Shi
School of Management, Shandong University, Jinan, China
Correspondence: Chenchen Shi ([email protected])
Search for more papers by this authorJing Zhao
School of Economics and Management, Changji University, Changji Hui Autonomous Prefecture, China
Search for more papers by this authorABSTRACT
Mixed-ownership reform (the Reform) is crucial for the construction of a modern market economy. Through a proprietary data set of 32 provinces in China, we find that government intelligent transformation (GIT) can attract private firms acquiring local state-owned enterprises (SOEs), and such link is stronger among target firms in central and western regions, and acquirers with lower competitive positions and higher levels of digital transformation. Additionally, we also find this effect exists both in local and nonlocal private firms. Further tests suggest that GIT can regulate government behaviors, reduce relational transactions, and enhance local policy environment quality and the level of local market legal governance, thereby promoting private firms to participate in the Reform. Moreover, in the provinces with higher levels of GIT, there is a greater likelihood that private firms will gain control rights over SOEs and obtain more resources following the Reform. Our findings may have policy implications for the world's largest emerging market.
Open Research
Data Availability Statement
Research data are not shared.
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