机构投资者抱团与高管超额薪酬

摘要/Abstract

摘要：

基于社会网络理论,本文借助2013~2017年我国沪、深A股主板上市公司数据,运用Louvain算法,通过构建机构投资者共同持股网络并识别网络团体,从高管超额薪酬角度考察机构投资者抱团行为的治理效果。研究发现:机构抱团后倾向于扮演“合谋者”角色,具体表现为机构投资者抱团规模越大,高管超额薪酬现象越严重;进一步从企业产权性质和高管权力两个方面探讨机构投资者抱团的治理行为差异,发现相对于其他企业,在国有企业和高管权力较大的企业中,机构投资者抱团对高管超额薪酬的加剧作用更加明显。上述研究从网络社团结构视角重新审视了机构投资者抱团的治理行为,为证监会深化认识机构投资者治理角色,引导完善机构投资者制度设计、避免非理性的过度行为协同提供参考。

关键词: 机构投资者, 网络团体, 高管超额薪酬, 企业性质, 高管权力

Abstract:

Under the influence of “Inchworm Effect”, executive excess compensation not only fails to alleviate the agency problem, but also becomes a tool for managers to seek personal gains, which hinders the development of enterprises and damages the fairness of social distribution. Institutional investors, as an important force in the capital market, usually are considered capable of rational participation in corporate governance according to their own preferences, which is an important mechanism for monitoring executive excess compensation. However, in reality, institutional investors frequently choose to act together, and their governance behavior will inevitably be affected by the social network in which they are embedded. Therefore, taking the phenomenon of executive excess compensation as the starting point, this study re-examines the governance effect of institutional investors’ clique behavior from the perspective of internal member interaction.
On the basis of social network theory, taking A-share main-board listed companies in Shanghai and Shenzhen from 2013 to 2017 as the sample and adopting Louvain algorithm to construct the shareholding network of institutional investors and identify the network cliques, this paper applies the fixed effect regression model to analyze the influence of institutional investors’ cliques on executive excess compensation. Then the instrumental variable method is utilized to test the robustness of the research results and the difference of governance effect of institutional investors’ cliques under different circumstances is discussed.
The results are as follows: The negative governance effect of institutional investors’ cliques is dominant. It means that the larger the scale of institutional investors’ cliques is, the more likely they are to form a “strategic alliance” with senior executives to obtain more internal information. Institutional investors play the role of “conspirator” in the enterprise to increase the excess compensation of senior executives. The institutional investors’ cliques governance effect is distinct in different situations. From two aspects of the nature of property rights and executive control, this study finds that compared with other companies, institutional investors’ cliques are more inclined to “conspire” with executives who are in large state-owned enterprises and more powerful, which causes the phenomenon of executives excess compensation more obvious in these enterprises.
The contributions of this study are as follows: First, from the perspective of network community structure, it investigates the influence of institutional investors’ cliques on executive excess compensation, which enriches the first type of agency problem research on institutional investors’ participation in the governance. Second, it explores the heterogeneous influence of institutional investors’ cliques on executive excess compensation under different situations, which provides a basis for further understanding of institutional investors’ cliques. This paper helps to clarify the corporate governance role and the effect of institutional investors. In addition, this study can provide a deep insight of institutiond investors’ clique behavior CSRC, improve and guide institutional investors’ system design, and avoid the coordination of irrational excessive behaviors.

Key words: Institutional Investors, Network Clique, Executives’ Excess Compensation, Ownership, Executive Power

中图分类号:

F272.9

刘新民, 沙一凡, 王垒, 康旺霖. 机构投资者抱团与高管超额薪酬[J]. 财经论丛, 2021, 37(7): 90-100.

LIU Xinmin, SHA Yifan, WANG Lei, KANG Wanglin. Institutional Investors Clique and Executives’ Excess Compensation[J]. Collected Essays on Finance and Economics, 2021, 37(7): 90-100.

图/表 5

参考文献 35

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变量	1	2	3	4	5	6	7	8	9	10	11	12	13
1.Pay_exc	1
2.Clique_ownership	0.144^***	1
3.Clique_number	0.225^***	0.489^***	1
4.Clique_owntop₁	0.074^***	0.796^***	0.249^***	1
5.Size	0.213^***	0.164^***	0.577^***	0.050^***	1
6.Lev	0.016	-0.015	0.045^***	-0.031^**	0.444^***	1
7.Asturn	0.089^***	0.062^***	0.051^***	0.013	-0.011	0.070^***	1
8.Growth	-0.018	0.004	-0.021	0.010	-0.012	0.014	-0.020	1
9.Top1	-0.027^*	-0.053^***	0.166^***	-0.077^***	0.288^***	0.054^***	0.053^***	0.006	1
10.Balance	0.098^***	0.117^***	-0.031^**	0.167^***	-0.042^***	-0.078^***	-0.058^***	-0.001	-0.620^***	1
11.Separate	0.066^***	0.006	0.009	-0.003	-0.004	0.023	0.041^***	0.006	0.087^***	-0.068^***	1
12.Indep	0.004	-0.007	-0.044^***	-0.003	-0.014	0.004	0.006	-0.001	-0.024	0.022	0.011	1
13.Market	0.434^***	0.045^***	0.079^***	0.005	0.088^***	-0.005	0.040^***	-0.007	0.002	0.042^***	0.021	-0.002	1
Mean	0.020	0.070	2.673	0.028	22.681	0.497	0.635	1.349	0.364	0.562	0.051	0.371	8.294
S.D.	0.656	0.069	0.836	0.030	1.330	0.202	0.529	35.511	0.157	0.558	0.080	0.054	1.864

变量	1	2	3	4	5	6	7	8	9	10	11	12	13
1.Pay_exc	1
2.Clique_ownership	0.144^***	1
3.Clique_number	0.225^***	0.489^***	1
4.Clique_owntop₁	0.074^***	0.796^***	0.249^***	1
5.Size	0.213^***	0.164^***	0.577^***	0.050^***	1
6.Lev	0.016	-0.015	0.045^***	-0.031^**	0.444^***	1
7.Asturn	0.089^***	0.062^***	0.051^***	0.013	-0.011	0.070^***	1
8.Growth	-0.018	0.004	-0.021	0.010	-0.012	0.014	-0.020	1
9.Top1	-0.027^*	-0.053^***	0.166^***	-0.077^***	0.288^***	0.054^***	0.053^***	0.006	1
10.Balance	0.098^***	0.117^***	-0.031^**	0.167^***	-0.042^***	-0.078^***	-0.058^***	-0.001	-0.620^***	1
11.Separate	0.066^***	0.006	0.009	-0.003	-0.004	0.023	0.041^***	0.006	0.087^***	-0.068^***	1
12.Indep	0.004	-0.007	-0.044^***	-0.003	-0.014	0.004	0.006	-0.001	-0.024	0.022	0.011	1
13.Market	0.434^***	0.045^***	0.079^***	0.005	0.088^***	-0.005	0.040^***	-0.007	0.002	0.042^***	0.021	-0.002	1
Mean	0.020	0.070	2.673	0.028	22.681	0.497	0.635	1.349	0.364	0.562	0.051	0.371	8.294
S.D.	0.656	0.069	0.836	0.030	1.330	0.202	0.529	35.511	0.157	0.558	0.080	0.054	1.864

变量	Pay_exc(N=4673)				Pay_dum(N=4671)
变量	(1)	(2)	(3)		(4)		(5)		(6)
Clique_ownership	0.808^*** (6.18)			2.180^*** (4.21)
Clique_number		0.105^*** (7.44)				0.348^*** (6.39)
Clique_owntop₁			1.145^*** (4.04)					3.293^*** (2.83)
Controls/Yea/Ind	控制	控制	控制	控制		控制		控制
constant	-3.476^*** (-18.07)	-2.941^*** (-13.56)	-3.645^*** (-19.23)	-10.084^*** (-13.22)		-8.147^*** (-9.90)		-10.500^*** (-13.89)
Adj-R²/Pseudo R²	0.254	0.257	0.250	0.145		0.149		0.144
F/Wald chi2	42.13^***	42.08^***	41.12^***	595.26^***		609.68^***		590.27^***

变量	Pay_exc(N=4673)				Pay_dum(N=4671)
变量	(1)	(2)	(3)		(4)		(5)		(6)
Clique_ownership	0.808^*** (6.18)			2.180^*** (4.21)
Clique_number		0.105^*** (7.44)				0.348^*** (6.39)
Clique_owntop₁			1.145^*** (4.04)					3.293^*** (2.83)
Controls/Yea/Ind	控制	控制	控制	控制		控制		控制
constant	-3.476^*** (-18.07)	-2.941^*** (-13.56)	-3.645^*** (-19.23)	-10.084^*** (-13.22)		-8.147^*** (-9.90)		-10.500^*** (-13.89)
Adj-R²/Pseudo R²	0.254	0.257	0.250	0.145		0.149		0.144
F/Wald chi2	42.13^***	42.08^***	41.12^***	595.26^***		609.68^***		590.27^***

变量	Pay_exc(N=2816)				Pay_dum(N=2814)
变量	(1)	(2)	(3)		(4)		(5)		(6)
Clique_ownership	1.031^*** (3.36)			2.699^*** (3.62)
Clique_number		0.136^*** (4.44)				0.284^*** (4.01)
Clique_owntop₁			1.983^** (2.05)					5.146^** (2.06)
变量	Pay_exc(N=2816)				Pay_dum(N=2814)
变量	(1)	(2)	(3)		(4)		(5)		(6)
Controls/Yea/Ind	控制	控制	控制	控制		控制		控制
constant	-3.304^*** (-12.73)	-2.667^*** (-8.20)	-3.497^*** (-14.14)	-5.825^*** (-9.34)		-4.599^*** (-6.06)		-6.301^*** (-10.45)
Adj-R²/Pseudo R²	0.242	0.246	0.238	—		—		—
F/Wald chi2	26.22^***	25.99^***	25.50^***	512.44^***		521.06^***		509.12^***