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Identity of multiple large shareholders and corporate governance: are state-owned entities efficient MLS?

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Abstract

This paper empirically investigates how the identity of multiple large shareholders (MLS) affects principal-agent and principal–principal conflicts of interests in Chinese listed privately controlled firms during 2006–2017, by distinguishing between state-owned and non-state-owned MLS. We find that the presence of non-state-owned MLS significantly mitigates the principal-agent conflict of interests as manifested in a lower selling, general, and administrative expenses scaled by total sales (SG&A ratio) of Chinese listed privately controlled firms. However, this effect is not observed when state-owned entities serve as MLS. Although we do not observe a strong impact of non-state-owned MLS in reducing principal–principal conflict of interests, i.e., a lower ratio of related-party transactions (RPT), the presence of financial non-state-owned MLS helps to alleviate RPT in Chinese listed privately controlled firms. Conversely, state-owned MLS do not mitigate principal–principal conflict of interests but worsen it, as evidenced by a higher ratio of RPT. Additionally, the presence of state-owned MLS is associated with a large magnitude of overinvestment by and increased government subsidies to Chinese listed privately controlled firms. Finally, the entry of non-state-owned MLS enhances the performance of these firms, while the presence of state-owned MLS does not engender a performance-enhancement effect.

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Notes

  1. For example, Maury and Pajuste (2005) analytically suggest that it is less likely for financial institutions to collude with family controlling shareholder because the cost of getting caught for private benefit extraction is extremely high for financial institutions, such as the heavy loss of reputation and the strict ex ante responsibility they have.

  2. As an example, Attig et al. (2013) show that the presence of the state as the second largest shareholder is not associated with an effective monitoring of the controlling shareholder, which aims to enhance the valuation of cash holding. They attribute this finding to outside investors’ perception of the potential misuse of excess cash when the government is one of the large shareholders; however, they but do not conduct an in-depth investigation of this matter.

  3. In September 2015, the Central Committee of the Communist Party of China and the State Council issued the Opinions on Deepening the Reform of State-Owned Enterprises, which proposed “promoting the reform of mixed ownership to amplify the function of state-owned capital and to improve the efficiency of state-owned assets.” This reform not only encourages state-owned enterprises to introduce non-state-owned capital but also pushes state-owned capital to invest in privately controlled firms.

  4. Cheung et al. (2010) classify related-party transactions into three categories: (1) transactions that are a priori likely to result in the expropriation of the listed firm’s minority shareholders; (2) transactions likely to benefit the listed firm’s minority shareholders; 3) transactions that could have strategic rationales and perhaps are not expropriation.

  5. The outcomes of all additional tests that are discussed but not shown in the paper can be obtained from the authors upon request.

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Acknowledgements

We would like to thank the editor and the anonymous reviewers for suggestions that substantially improved the article. We also would like to thank Shaoqing Kang, Zheng Qiao, Wenzhou Qu, Zhe Shen, Shinong Wu, Yuhui Wu, Yujia Yi, Yevgeny Mugerman, Fang Wan, Gady Jacoby, and participants of the 2019 cross country perspectives in finance conference for their suggestions and comments on an earlier draft of this article. Lihong Wang acknowledges the Chinese National Funding of Social Sciences (19BGL075), Humanities and Social Science Fund of Ministry of Education of China (18YJC630181), and the National Natural Science Foundation of China (71502150) for financial support. Sen Lin acknowledges the National Natural Science Foundation of China (NSFC-71790601, 71532012) for financial support.

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Correspondence to Lihong Wang.

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Appendix

Appendix

 

Pre-match

Post-match

Panel A: Probit regression used to calculate the propensity score

Intercept

0.683

0.632

 

(1.07)

(0.86)

Size

− 0.144***

− 0.120***

 

(− 5.36)

(− 3.91)

EBIT

− 1.238***

− 0.429

 

(− 2.67)

(− 0.70)

Top1

0.366***

0.144

 

(2.75)

(0.94)

Lev

0.847***

0.521***

 

(6.50)

(3.07)

Sales growth

− 0.005

− 0.063

 

(− 0.09)

(− 0.92)

Tangi

− 0.115

0.321

 

(− 0.42)

(0.91)

MB

0.056

0.071

 

(1.49)

(1.57)

SOE

− 0.097**

0.047

 

(− 2.13)

(0.84)

Pseudo R2

0.071

0.055

N

17,246

3583

Variable

Mean value of treated firm

Mean value of benchmark firm

Mean-diff

Panel B: Test of the effectiveness of PSM

Size

   

 Pre-match

21.930

21.913

0.017

 Post-match

21.930

21.930

− 0.000

EBIT

   

 Pre-match

0.050

0.059

− 0.010***

 Post-match

0.050

0.052

− 0.003

Top1

   

 Pre-match

0.401

0.409

− 0.008*

 Post-match

0.401

0.431

− 0.030***

Lev

   

 Pre-match

0.471

0.449

0.021***

 Post-match

0.471

0.485

− 0.014**

Sales growth

   

 Pre-match

0.216

0.195

0.022**

 Post-match

0.216

0.198

0.019

Tangi

   

 Pre-match

0.929

0.939

− 0.011***

 Post-match

0.929

0.939

− 0.011***

MB

   

 Pre-match

0.984

0.943

0.041*

 Post-match

0.984

0.939

0.044

SOE

   

 Pre-match

0.407

0.490

− 0.084***

 Post-match

0.407

0.421

− 0.015

  1. This table presents the procedure to develop propensity-score-matched (PSM) benchmark firms. The PSM approach involves pairing treated and control firms based on similar observable characteristics. Specifically, we implement this procedure by first running a Probit regression to estimate the probability of being a treated firm using the data in year t − 1, i.e., the year before the entry of multiple large shareholders. Next, we match each treated firm to the control firms with the same year and industry using the nearest neighbor matching technique without replacement. In Panel A, the first column reports the estimation results of the Probit model. The dependent variable is a dummy variable indicating treated firms. Size is the natural logarithm of the book value of total assets. EBIT is earnings before interest and tax scaled by total assets. Top1 is the shareholding by the controlling shareholder relative to total shares outstanding. Lev is the ratio of total liabilities to total assets. Sales growth is the rate of sales growth. Tangi is the ratio of tangible assets to total assets. BM is the book value of total assets divided by the market value of equity. SOE is a dummy variable, equal to one if a firm’s ultimate controller belongs to the government entity, and zero otherwise. Besides, in Panel B, we compare the univariate distribution of control variables used to estimate the propensity score. Regression models include industry and year fixed effects, clustering standard errors at the firm level
  2. t-statistics are reported between parentheses underneath coefficients. Significance levels 0.1, 0.05, and 0.01 are denoted by *, **, and ***, respectively

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Lin, S., Chen, F. & Wang, L. Identity of multiple large shareholders and corporate governance: are state-owned entities efficient MLS?. Rev Quant Finan Acc 55, 1305–1340 (2020). https://doi.org/10.1007/s11156-020-00875-z

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