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State ownership and firm performance: Empirical evidence from Chinese listed companies

Open access
Yu, Mei·2013·China Journal of Accounting Research

Abstract

While the relationship between state ownership and firm performance has been widely researched, the empirical evidence has provided mixed results. This study applies panel data regression techniques to 10,639 firm-year observations of non-financial Chinese listed firms during 2003–2010 to examine the relationship between state ownership and firm performance. The results show that state ownership has a U-shaped relationship with firm performance. The Split Share Structure Reform in 2005–2006 played a positive role in enhancing the relationship between state ownership and firm profitability ratios. Although state ownership decreased significantly after 2006, it remains high in strategically important industry sectors such as the oil, natural gas and mining sector and the publishing, broadcasting and media sector. The findings reveal that a higher level of state ownership is superior to a dispersed ownership structure due to the benefits of government support and political connections. The Split Share Structure Reform made previously non-tradable shares legally tradable, improving corporate governance and reducing the negative effect of non-tradable state shares.

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Metadata

Authors
Yu, M.
Venue
China Journal of Accounting Research
Publisher
Elsevier BV
Published
2013-06
Volume
6
Issue
2
Pages
75-87
ISSN
1755-3091 (ISSN-L)
OpenAlex
W2043019907
Cited by
193
References
2
FWCI
50.77
Type
journal article
Language
en
Open access
gold

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CoChat. (2026). Citation check: State ownership and firm performance: Empirical evidence from Chinese listed companies. https://app.cochat.ai/papers/10.1016/j.cjar.2013.03.003
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Yu, M. (2013). State ownership and firm performance: Empirical evidence from Chinese listed companies. China Journal of Accounting Research. https://doi.org/10.1016/j.cjar.2013.03.003

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.