Bulletin of Monetary Economics and Banking, Vol. 22, No. 3 (2019), pp. 287 - 310
CHARACTERISTICS OF BOARD OF DIRECTORS AND PERFORMANCE OF
Yuan Yang*, Yi Pan*a, Bingkun Yang*, Wenli Huang**
*China Academy of Financial Research, Zhejiang University of Finance and Economics, Hangzhou,
China.
*China Academy of Financial Research, Zhejiang University of Finance and Economics, Hangzhou,
China. Email: wlhuangmath@126.com
aCorresponding Author. Email: 18368386103@163.com
ABSTRACT
This paper uses a
Keywords: Venture capital;
JEL Classification: G24.
Article history: |
|
Received |
: July 1, 2019 |
Revised |
: August 23, 2019 |
Accepted |
: September 9, 2019 |
Available online : October 15, 2019
https://doi.org/10.21098/bemp.v22i3.1094
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I. INTRODUCTION
Venture Capital (VC) originated in the United States in the 1930s and has prevailed since the 1970s. It was not until the 1980s that China’s VC industry began to develop. In 1985, the Central Committee of the Communist Party of China approved for the first time the supporting role of VC for
Most VC institutions invest in
The rapid development of VC has caught the eye of scholars at home and abroad. The relation between VC and their invested companies has thus become a research hotspot. Foreign research on the relation between VC and corporate governance is relatively mature (Suchard, 2009), but the findings might not apply to China. Current research in China, however, has the following shortcomings. First, the research mainly focuses on the impact of VC participation on company performance and Initial Public Offering (IPO) underpricing, with most of the research addressing the corporate governance role of VC. Second, most of the research used
Since its establishment in 2009, China’s GEM has faced problems such as a high
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characteristics of the company’s board of directors before and after exit, and then it explores in depth the impact of board characteristics on company performance.
II.LITERATURE REVIEW AND HYPOTHESIS A. VC and Board Characteristics
A1. Board Size
The board of directors is the permanent body responsible for implementing the resolutions of shareholder meetings, and it is composed of directors elected by the shareholders. Board directors have the power to appoint and dismiss managers. Zahra and Pearce (1991) found that, in a startup, the larger the board of directors, the more effectively it can control the executives, and the interests of shareholders can be maintained. Generally, company management will find it difficult to compete with a large board of directors. As the size of the board increases, its authority to supervise managers’ behavior will grow, and resolutions that are not conducive to shareholders’ interests will be difficult to have approved by the board. A larger board is often able to attract more capable board members, with richer knowledge, bringing the board more resources and opportunities. For example, Fama and Jensen (1983) found that the professional management knowledge of a large board can promote the director performance. Because major decisions in a company are usually decided by voting, the larger the board of directors, the more effective the suppression of insider control.
Many scholars have found that VC institutions have an impact on the size and structure of corporate boards (Zhang and Liao, 2011; Zhao and Wen, 2015). After investing in a startup, VC institutions usually send professionals to the company to participate in its business management. These professionals are likely to enter the board of directors of the invested company, thereby gaining more voting rights while expanding the board’s size. Accordingly, we predict that VC institutions will protect their own interests by encouraging the invested companies to establish a larger board of directors to limit the rights of corporate insiders. We thus propose the following hypothesis.
H1: VC participation will increase the size of the board of
A2. CEO Duality
According to the theory of fiduciary responsibility, the management of a company is the fiduciary responsible for the company’s assets. It intends to provide qualified managers and will not deliberately damage the company’s interests. Therefore, CEO duality can provide the company better business management. From the perspective of the company’s
In environmental dependence theory, which synthesizes environmental factors, environmental changes have an important impact on a company’s board structure. If the environment in which the listed company grows changes greatly and is full of uncertainty, the company’s CEO duality structure will improve
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decision making efficiency, and its benefits will be greater than the agency costs. If, however, the company’s environment is stable, given the agency problem, the separation of ownership and management could benefit the company’s development.
In modern management theory, the structure of CEO duality can reduce agency costs, whereas the separation of board of directors and general manager can cause conflicts of interest between the board and the general manager, reducing the efficiency of the company and increasing agency costs. Based on
Because of the imperfect mechanisms of China’s relatively young GEM, the environment of
H2:
A3. Proportion of Independent Directors
The proportion of independent directors, calculated by the ratio of the number of independent directors of the company to the total number of board members, is a measurement of the independence of the board of directors. Independent directors are employed externally, independent of shareholders and managers. Their main role is to supervise the company’s executives and prevent them from harming its interests. Generally, independent directors are professionals in different industries, so that they can properly monitor management’s operations and provide good advice with their professional knowledge. Many scholars believe that
H3: VC participation increases the proportion of independent directors on the boards of
B. Board Characteristics and Company Performance
B1. Board Size
The research on the impact of board size on company performance has produced mixed conclusions: some find that the board’s size is positively correlated with
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company performance (Geoffrey and Gavin, 2003; Li, 2015), whereas others find that it is negatively correlated with company performance (Lipton and Lorsch, 1992; Wang, 2012). For who support the first conclusion, the larger the board of directors, the greater the control and the richer the resources, all of which are conducive to improving company performance; for those who support the second conclusion, a larger board will lead to more serious agency problems and increase agency costs and thus reduce company performance. This paper argues that, for the GEM companies, the impact of board size on firm performance is uncertain. We therefore propose the following two hypotheses.
H4a: The larger the board of directors, the better the company’s performance.
H4b: The larger the board of directors, the worse the company’s performance.
B2. CEO Duality
There are also two different opinions about the impact of CEO duality on company performance. Some believe that CEO duality is conducive to improving company performance (Manigart, 2002; Han, 2015), because this structure reduces the cost of communications between the board chairperson and the general manager, and thus reduces information asymmetry, which can improve company performance. Others find that CEO duality will not benefit company performance (Liu and Chang, 2009), because the chairperson will lose independence under such structure, and the chairperson could hurt the company’s interests. This paper therefore proposes the following hypotheses.
H5a: The CEO duality structure helps improve company performance.
H5b: The CEO duality structure does not improve company performance.
B3. Proportion of Independent Directors
Most of the research finds a positive correlation between the proportion of independent directors and company performance (Liu, 2011; Xie, 2011; Zhang, 2012). The minority who find the opposite find that independent directors could fail to play their role and thus do not improve company performance. To explore which conclusion holds among China’s GEM companies, this paper proposes the following hypotheses.
H6a: The proportion of independent directors on the board is positively correlated with company performance.
H6b: The proportion of independent directors on the board is negatively correlated with company performance.
III.RESEARCH DESIGN A. Samples and Data
This paper selects companies that were listed on China’s GEM from October 30,
2009, when the GEM was officially launched, to December 31, 2014 (to cover three years after IPOs). Because of the suspension of the GEM in 2013, there are no data for that year. To ensure the objectiveness of our results, we refer to the “Guidelines for the Classification of Listed Companies” promulgated by the China Securities
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Regulatory Commission for industry standards. Our samples were processed as follows: we excluded 1) all ST companies and companies with incomplete data and 2) companies with abnormal data. The financial data and data on board characteristics are from the China Stock Market & Accounting Research database1 and the Wind database.2 Wu’s (2012) method is used to determine VC participation.
The final sample comprises 388 companies listed on the GEM, with 1,164 observations, covering annual data in the IPO year (t) and two and three years after the IPO (t + 2, t + 3). Among these companies, 221 (59.95%) have VC participation, and 167 (43.04%) have no VC participation.
B. Variable Definition
B1. VC and Board Characteristics
(1)Explained Variables. The structure of CEO duality is represented by the dummy variable dual. When a company chooses the CEO duality structure, dual equals one, and zero otherwise. The size of the board of directors (bsize) refers to the number of board members, and the proportion of independent directors (outdire) refers to the ratio of the number of independent directors to the total number of board directors of the company.
(2)Explanatory Variables. The explanatory variables of this paper are the dummy variable vc*exit_before and the characteristic variables of the VC institution. The variable vc*exit_before is determined by the presence (VC = 1) or absence (VC = 0) of VC participation, and whether the VC exits (if it does, exit_before = 0, and otherwise exit_before = 1). If vc*exit_before equals one, this means that the VC institution has not withdrawn from the invested company; that is, there is VC participation; when vc*exit_before equals zero, the VC institution has exited.
(3)Control Variables. This paper uses the company’s growth, financial leverage, size, and performance as control variables. The variable for company growth (Growth) is measured by Tobin’s Q, financial leverage (Lev) is measured by the
B2. Board Characteristics and Company Performance
(1)Explained Variables. This paper uses the return on equity (ROE) to represent the performance of
(2)Explanatory Variables. We include as explanatory variables the dummy variable for CEO duality (dual), board size (bsize), and the independent director ratio (outdire).
(3)Control Variables. We include as control variables company growth (growth), financial leverage (lev), company size (size), an industry dummy variable (IND), and a year dummy variable (YR).
1
2
See http://us.gtadata.com/.
See https://www.wind.com.cn/.
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C. Model Design
C1. VC and Board Characteristics
This paper uses a
(1)
where vc indicates whether it is in the experimental group, and exit_before indicates whether the VC exits.
The traditional model is only applicable in the case in which, for each company i, the VC institutions exit at the same time. However, obviously, VC might not exit at the same time point. Some companies that belong to the control group in period t will be in the experimental group in period t + 1. To resolve this issue, this paper uses the continuous time DID model. According to H1 to H3, the model is as follows.
(2)
(3)
(4)
where exit_before is a dummy variable that measures whether the VC exits, where exit_before = 1 indicates observations of a
C2. Board Characteristics and Company Performance
According to H4 to H6, the following model is proposed:
(5)
(6)
(7)
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where company performance (ROE) is the explanatory variable, the board characteristics (dual, bsize, outdire) are explanatory variables, and the company characteristics are control variables.
D. Descriptive Statistics
Table 1 lists the names and definitions of variables used in this paper. Table 2 shows the descriptive statistics of the main variables of all the samples. There are 1,164 observations, with 388
Table 1.
Variable Definition
In this table, all variables appearing in this paper are defined and explained.
Description |
Variable |
Variable Name |
Definition |
|
Panel A: VC and Board Characteristics |
||
Explained |
CEO Duality |
dual |
dummy variable, dual = 1 if the company |
Variables |
|
|
chooses the CEO duality structure, |
|
|
|
otherwise dual = 0 |
|
Board Size |
bsize |
the number of board members |
|
Proportion of |
outdire |
the number of independent directors / |
|
Independent |
|
the total number of board directors of a |
|
Directors |
|
company |
Explanatory |
vc*exit_before |
dummy variable, determined by |
|
Variables |
|
|
whether there is VC participation and |
|
|
|
whether the VC exit, vc*exit_before = 1 if |
|
|
|
the VC institution has not exited from |
|
|
|
the |
|
|
|
vc*exit_before = 0 |
Control |
Company’s |
growth |
Tobin Q value |
Variables |
Growth |
|
|
|
|
||
|
Financial |
lev |
|
|
Leverage |
|
liabilities |
|
Company |
ROE |
net profit of the year / total equity at the |
|
Performance |
|
end of the year |
|
Company Size |
size |
the natural logarithm of the company’s |
|
|
|
total assets |
|
Year Dummy |
YR |
dummy variable, controlling the year |
|
Variable |
|
effect, set according to the years of |
|
|
|
different companies’ IPO |
|
Industry |
IND |
dummy variable, controlling the industry |
|
Dummy |
|
effect, set according to the industry code |
|
Variable |
|
of the “Industry Classification Guide |
|
|
|
for Listed Companies” published by the |
|
|
|
Shenzhen Stock Exchange, a total of 13 |
|
|
|
codes are used in this paper |
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Table 1.
Variable Definition (Continued)
Description |
Variable |
Variable Name |
Definition |
|
Panel B: Board Characteristics and Company Performance |
||
Explained |
Company |
ROE |
net profit of the year / total equity at the |
Variables |
Performance |
|
end of the year |
Explanatory |
CEO Duality |
dual |
dummy variable, dual = 1 if the company |
Variables |
|
|
chooses the CEO duality structure, |
|
|
|
otherwise dual = 0 |
|
Board Size |
bsize |
the number of board members |
|
Proportion of |
outdire |
the number of independent directors / |
|
Independent |
|
the total number of board directors of a |
|
Directors |
|
company |
|
Company’s |
growth |
Tobin Q value |
|
Growth |
|
|
|
|
|
|
|
Financial |
lev |
|
|
Leverage |
|
liabilities |
|
Company Size |
size |
the natural logarithm of the company’s |
|
|
|
total assets |
Control |
Year Dummy |
YR |
dummy variable, controlling the year |
Variable |
|
effect, set according to the years of |
|
Variables |
|
||
|
|
different companies’ IPO |
|
|
|
|
|
|
Industry |
IND |
dummy variable, controlling the industry |
|
Dummy |
|
effect, set according to the industry code |
|
Variable |
|
of the “Industry Classification Guide |
|
|
|
for Listed Companies” published by the |
|
|
|
Shenzhen Stock Exchange, a total of 13 |
|
|
|
codes are used in this paper |
Table 2.
Descriptive Statistics of All Samples
This table has descriptive statistics of the data for the entire sample. Each variable noted in column 1 is explained in Table 1.
Variables |
Obs. |
Mean |
Std. Dev. |
Min. |
Max. |
Dual |
1164 |
0.4898 |
0.5001 |
0.0000 |
1.0000 |
Bsize |
1164 |
8.1946 |
1.3604 |
4.0000 |
14.0000 |
Outdire |
1164 |
0.3784 |
0.0588 |
0.1818 |
0.8333 |
ROE |
1164 |
0.0999 |
0.0530 |
0.3423 |
|
Lev |
1164 |
0.2054 |
0.1377 |
0.0110 |
0.7695 |
Size |
1164 |
20.7065 |
0.5486 |
19.4910 |
23.8933 |
Growth |
1164 |
4.7549 |
3.4363 |
1.0876 |
29.5610 |
Tables 3 and 4 show the descriptive statistics of the samples with and without VC participation, respectively. In Table 3, the average board size of enterprises with VC participation is 8.3251 members, with an average ratio of independent directors of 0.3890, whereas the figures for companies without VC participation (Table 4) are 7.99 members and 0.3748, respectively. Comparing Tables 3 and 4,
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we find that, in companies with VC participation, the board size is larger, and the proportion of independent directors is higher.
Table 3.
Descriptive Statistics of Samples of
This table has descriptive statistics of the data for the sample of
Variables |
Obs. |
Mean |
Std. Dev. |
Min. |
Max. |
Dual |
663 |
0.5169 |
0.5001 |
0.0000 |
1.0000 |
Bsize |
663 |
8.3251 |
1.3278 |
4.0000 |
14.0000 |
Outdire |
663 |
0.3890 |
0.0827 |
0.0000 |
0.8333 |
ROE |
663 |
0.0971 |
0.0531 |
0.3423 |
|
Lev |
663 |
0.2170 |
0.1430 |
0.0158 |
0.7695 |
Size |
663 |
20.7617 |
0.5333 |
19.4910 |
22.5661 |
Growth |
663 |
4.9940 |
3.7460 |
1.0876 |
29.5610 |
Table 4.
Descriptive Statistics of Samples of
This table has descriptive statistics of the data for the sample of
Variables |
Obs. |
Mean |
Std. Dev. |
Min. |
Max. |
Dual |
501 |
0.4488 |
0.4980 |
0.0000 |
1.0000 |
Bsize |
501 |
7.9977 |
1.3867 |
4.0000 |
12.0000 |
Outdire |
501 |
0.3748 |
0.0653 |
0.1818 |
0.6000 |
ROE |
501 |
0.1042 |
0.0525 |
0.3060 |
|
Lev |
501 |
0.1879 |
0.1879 |
0.0110 |
0.6951 |
Size |
501 |
20.6228 |
0.5615 |
19.5314 |
23.8933 |
Growth |
501 |
4.3924 |
2.8707 |
1.3354 |
18.4001 |
IV. EMPIRICAL RESULTS AND ANALYSIS
A. VC and Board Characteristics
A1. Experimental and Control Groups in the Absence of VC Participation
When choosing an investment target, VC institutions prefer companies with a better governance structure and higher performance. Therefore, endogenous problems can arise during the research process, such as
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Table 5.
CEO duality after VC Exits
This table reports results of CEO duality and VC exists. The statistics in () is the
respectively. Finally, for variable definitions, please refer to Table 1.
Variables |
|
|
Dual |
|
|
|
(1) |
(2) |
(3) |
(4) |
(5) |
||
|
||||||
Vc |
0.0395 |
0.0395 |
0.0707 |
0.0673 |
0.0664 |
|
Lev |
(0.0913) |
(0.0913) |
(0.0931) |
(0.0933) |
(0.0933) |
|
|
0.234 |
0.282 |
0.306 |
|||
Size |
|
(0.289) |
(0.321) |
(0.323) |
(0.326) |
|
|
|
|||||
Growth |
|
|
(0.0815) |
(0.0816) |
(0.0817) |
|
|
|
|
0.0269** |
0.0286** |
||
ROE |
|
|
|
(0.0143) |
(0.0146) |
|
|
|
|
|
0.0505 |
||
|
|
|
|
|
(0.0960) |
|
Constant |
2.730 |
2.636 |
2.622 |
|||
IND |
(0.0690) |
(0.102) |
(1.671) |
(1.675) |
(1.676) |
|
controlled |
controlled |
controlled |
controlled |
controlled |
||
YR |
controlled |
controlled |
controlled |
controlled |
controlled |
|
Observations |
775 |
775 |
775 |
775 |
775 |
|
Sample Size |
388 |
388 |
388 |
388 |
388 |
Table 6.
Board Size after VC Exits
This table reports results of board size and VC exists. The statistics in () is the
respectively. Finally, for variable definitions, please refer to Table 1.
Variables |
|
|
Bsize |
|
|
|
(1) |
(2) |
(3) |
(4) |
(5) |
||
|
||||||
Vc |
0.149 |
0.101 |
0.0978 |
0.0929 |
0.0752 |
|
Size |
(0.128) |
(0.128) |
(0.127) |
(0.127) |
(0.127) |
|
|
0.219*** |
0.247*** |
0.255*** |
0.326*** |
||
Growth |
|
(0.0778) |
(0.0786) |
(0.0788) |
(0.0858) |
|
|
|
|||||
ROE |
|
|
(0.0117) |
(0.0119) |
(0.0119) |
|
|
|
|
0.0678 |
0.0491 |
||
Lev |
|
|
|
(0.0517) |
(0.0521) |
|
|
|
|
|
|||
|
|
|
|
|
(0.355) |
|
Constant |
8.047*** |
3.468** |
2.982* |
2.802* |
1.503 |
|
IND |
(0.0973) |
(1.630) |
(1.640) |
(1.645) |
(1.755) |
|
controlled |
controlled |
controlled |
controlled |
controlled |
||
YR |
controlled |
controlled |
controlled |
controlled |
controlled |
|
Observations |
776 |
776 |
776 |
776 |
776 |
|
Sample Size |
388 |
388 |
388 |
388 |
388 |
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Table 7.
Proportion of Independent Directors after VC Exits
This table reports results of proportion of independent directors after VC exists. The statistics in () is the
Variables |
|
|
Outdire |
|
|
|
|
|
|
|
|
||
(1) |
(2) |
(3) |
(4) |
(5) |
||
|
||||||
|
|
|
|
|
|
|
Vc |
0.0026 |
0.0041 |
0.0050 |
0.0052 |
0.0051 |
|
|
(0.0057) |
(0.0058) |
(0.0058) |
(0.0058) |
(0.0058) |
|
Size |
|
|||||
|
|
(0.0037) |
(0.0040) |
(0.0040) |
(0.0041) |
|
Lev |
|
|
0.0343** |
0.0315* |
0.0321* |
|
|
|
|
(0.0165) |
(0.0167) |
(0.0167) |
|
ROE |
|
|
|
|||
|
|
|
|
(0.0026) |
(0.0026) |
|
Growth |
|
|
|
|
0.0004 |
|
|
|
|
|
|
(0.0005) |
|
Constant |
0.375*** |
0.504*** |
0.568*** |
0.572*** |
0.578*** |
|
|
(0.0043) |
(0.0785) |
(0.0841) |
(0.0842) |
(0.0847) |
|
IND |
controlled |
controlled |
controlled |
controlled |
controlled |
|
YR |
controlled |
controlled |
controlled |
controlled |
controlled |
|
Observations |
776 |
776 |
776 |
776 |
776 |
|
Sample Size |
388 |
388 |
388 |
388 |
388 |
|
|
|
|
|
|
|
A2. Randomness of VCs Choosing Target Companies
This paper uses probit regression in a binary selection model to determine the relation between VC screening factors and board characteristics. The regression results are shown in Tables 8 to 10. The coefficient of board characteristics is not significant, indicating no obvious influence of board characteristics on VCs’ selection of target companies.
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Table 8.
Relationship between VC’s Screening Factors and CEO Duality
This table reports results of the relation between VC’s screening factors and CEO duality. The statistics in () is the
Variables |
|
|
Vc |
|
|
|
(1) |
(2) |
(3) |
(4) |
(5) |
||
|
||||||
Dual |
0.0395 |
0.0616 |
0.0680 |
0.0673 |
0.0670 |
|
|
(0.0912) |
(0.0921) |
(0.0924) |
(0.0924) |
(0.0924) |
|
Size |
|
0.359*** |
0.456*** |
0.456*** |
0.455*** |
|
|
|
(0.0747) |
(0.0833) |
(0.0834) |
(0.0834) |
|
Lev |
|
|
||||
|
|
|
(0.326) |
(0.327) |
(0.331) |
|
Growth |
|
|
|
0.00481 |
0.00615 |
|
|
|
|
|
(0.0146) |
(0.0149) |
|
ROE |
|
|
|
|
0.0413 |
|
|
|
|
|
|
(0.0982) |
|
Constant |
0.157*** |
|||||
|
(0.0604) |
(1.574) |
(1.720) |
(1.722) |
(1.722) |
|
IND |
controlled |
controlled |
controlled |
controlled |
controlled |
|
YR |
controlled |
controlled |
controlled |
controlled |
controlled |
|
Observations |
755 |
755 |
775 |
775 |
775 |
Table 9.
Relationship between VC’s Screening Factors and Board Size
This table reports results of the relation between VC’s screening factors and board size. The statistics in () is the
Variables |
|
|
Vc |
|
|
|
(1) |
(2) |
(3) |
(4) |
(5) |
||
|
||||||
Bsize |
0.0475 |
0.0199 |
0.0151 |
0.0165 |
0.0161 |
|
|
(0.0349) |
(0.0357) |
(0.0358) |
(0.0360) |
(0.0360) |
|
Size |
|
0.353*** |
0.450*** |
0.449*** |
0.449*** |
|
|
|
(0.0755) |
(0.0842) |
(0.0842) |
(0.0842) |
|
Lev |
|
|
||||
|
|
|
(0.326) |
(0.328) |
(0.331) |
|
Growth |
|
|
|
0.00556 |
0.00682 |
|
|
|
|
|
(0.0146) |
(0.0149) |
|
ROE |
|
|
|
|
0.0398 |
|
|
|
|
|
|
(0.0982) |
|
Constant |
||||||
|
(0.285) |
(1.566) |
(1.710) |
(1.713) |
(1.713) |
|
IND |
controlled |
controlled |
controlled |
controlled |
controlled |
|
YR |
controlled |
controlled |
controlled |
controlled |
controlled |
|
Observations |
776 |
776 |
776 |
776 |
776 |
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Bulletin of Monetary Economics and Banking, Volume 22, Number 3, 2019 |
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|
Table 10.
Relationship between VC’s Screening Factors and Proportion of
Independent Directors
This table reports results of the relation between VC’s screening factors and proportion of independent directors. The statistics in () is the
Variables |
|
|
VC |
|
|
|
|
|
|
|
|
||
(1) |
(2) |
(3) |
(4) |
(5) |
||
|
||||||
|
|
|
|
|
|
|
Outdire |
0.203 |
0.215 |
0.191 |
0.193 |
0.193 |
|
|
(0.742) |
(0.746) |
(0.750) |
(0.750) |
(0.750) |
|
Size |
0.361*** |
0.457*** |
0.457*** |
0.456*** |
0.456*** |
|
|
(0.0745) |
(0.0830) |
(0.0831) |
(0.0831) |
(0.0831) |
|
Lev |
|
|||||
|
|
(0.326) |
(0.327) |
(0.330) |
(0.330) |
|
Growth |
|
|
0.00447 |
0.00579 |
0.00579 |
|
|
|
|
(0.0146) |
(0.0149) |
(0.0149) |
|
ROE |
|
|
|
0.0412 |
0.0412 |
|
|
|
|
|
(0.0983) |
(0.0983) |
|
Constant |
||||||
|
(1.599) |
(1.742) |
(1.742) |
(1.743) |
(1.743) |
|
IND |
controlled |
controlled |
controlled |
controlled |
controlled |
|
YR |
controlled |
controlled |
controlled |
controlled |
controlled |
|
Observations |
776 |
776 |
776 |
776 |
776 |
|
|
|
|
|
|
|
A3. VC and Board Characteristics
Table 11 shows the empirical results of Model 1. It can be seen that the coefficient of vc*exit_before is always positive at the 1% level of significance, even after all the control variables are added, indicating that, compared with
Characteristics of Board of Directors and Performance of |
301 |
The Perspective of Venture Capital Participation |
Table 11.
The Impact of VC on Board Characteristics (Model 1, CEO Duality)
This table reports results of the impact of VC on board characteristics (CEO duality). The statistics in () is the
Variables |
|
|
Dual |
|
|
|
(1) |
(2) |
(3) |
(4) |
(5) |
||
|
||||||
Vc*exit_before |
0.751*** |
0.778*** |
1.495*** |
1.175*** |
1.152*** |
|
(2.95) |
(3.01) |
(2.70) |
(3.21) |
(3.13) |
||
|
||||||
Growth |
|
|||||
|
||||||
|
|
|||||
Size |
|
|
1.103** |
1.364*** |
1.370*** |
|
|
|
(2.46) |
(2.78) |
(2.77) |
||
|
|
|
||||
Lev |
|
|
|
|||
|
|
|
||||
|
|
|
|
|||
ROE |
|
|
|
|
||
|
|
|
|
|||
|
|
|
|
|
||
IND |
controlled |
controlled |
controlled |
controlled |
controlled |
|
YR |
controlled |
controlled |
controlled |
controlled |
controlled |
Table 12 illustrates the empirical results of Model 2, where the coefficient of vc*exit_before is also significantly positive. The results verify H2, that is, companies with VC participation have a larger board of directors. This could be due to the fact that VC institutions always send professionals to the invested company’s board of directors to participate in the company’s operations and management.
Table 12.
The Impact of VC on Board Characteristics (Model 2, Board Size)
This table reports results of the impact of VC on board characteristics (board size). The statistics in () is the
Variables |
|
|
bsize |
|
|
|
(1) |
(2) |
(3) |
(4) |
(5) |
||
|
||||||
Vc*exit_before |
0.207***# |
0.206*** |
0.214*** |
0.213*** |
0.203*** |
|
(4.12) |
(4.20) |
(5.02) |
(4.94) |
(4.76) |
||
|
||||||
Growth |
|
0.0153 |
0.0132 |
0.0124 |
0.0126 |
|
|
(0.47) |
(0.41) |
(0.40) |
(0.41) |
||
|
|
|||||
Size |
|
|
0.317*** |
0.403*** |
0.406*** |
|
|
|
(4.72) |
(5.93) |
(5.88) |
||
|
|
|
||||
Lev |
|
|
|
|||
|
|
|
||||
|
|
|
|
|||
ROE |
|
|
|
|
||
|
|
|
|
|||
|
|
|
|
|
||
IND |
controlled |
controlled |
controlled |
controlled |
controlled |
|
YR |
controlled |
controlled |
controlled |
controlled |
controlled |
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|
Table 13 shows the empirical results of Model 3. The coefficient of vc*exit_before is significantly positive at the 5% level, indicating that VC participation is always accompanied by a higher proportion of independent directors on the invested company’s board, and verifying H3. The previous analysis (in Section II A3) has shown that independent directors supervise corporate executives to prevent them from harming the company’s interests. After VC invests in the company, to reduce the risk of
Table 13.
The Impact of VC on Board Characteristics
(Model 3, Proportion of Independent Directors)
This table reports results of the impact of VC on board characteristics (proportion of independent directors). The statistics in () is the
Variables |
|
|
outdire |
|
|
|
|
|
|
|
|
||
(1) |
(2) |
(3) |
(4) |
(5) |
||
|
||||||
|
|
|
|
|
|
|
Vc*exit_before |
0.0031** |
0.0031** |
0.0030** |
0.0030** |
0.0034** |
|
(2.65) |
(2.75) |
(2.34) |
(2.66) |
(2.92) |
||
|
||||||
Growth |
|
|||||
|
||||||
|
|
|||||
Size |
|
|
||||
|
|
|||||
|
|
|
||||
Lev |
|
|
|
0.0469*** |
0.0473*** |
|
|
|
|
(4.35) |
(4.38) |
||
|
|
|
|
|||
ROE |
|
|
|
|
0.0023 |
|
|
|
|
|
(0.80) |
||
|
|
|
|
|
||
IND |
controlled |
controlled |
controlled |
controlled |
controlled |
|
YR |
controlled |
controlled |
controlled |
controlled |
controlled |
|
|
|
|
|
|
|
B. Board Characteristics and Company Performance
Table 14 shows the regression results for company performance and the CEO duality structure. The coefficient of dual is significantly positive at the 1% level, indicating that CEO duality is beneficial to company performance, supporting H5a.
Characteristics of Board of Directors and Performance of |
303 |
The Perspective of Venture Capital Participation |
Table 14.
Relationship between Company Performance and CEO Duality
This table reports results of relation between company performance and CEO duality. The statistics in () is the
Variables |
|
|
ROE |
|
|
|
|
|
|
||
(1) |
(2) |
(3) |
(4) |
||
|
|||||
|
|
|
|
|
|
Dual |
0.102*** |
0.102*** |
0.101*** |
0.100*** |
|
|
(0.0104) |
(0.0099) |
(0.0099) |
(0.0098) |
|
Size |
|
0.0384*** |
0.0570** |
0.0564** |
|
|
|
(0.0113) |
(0.0254) |
(0.0243) |
|
Lev |
|
|
|||
|
|
|
(0.111) |
(0.108) |
|
Growth |
|
|
|
0.0012 |
|
|
|
|
|
(0.0024) |
|
IND |
controlled |
controlled |
controlled |
controlled |
|
YR |
controlled |
controlled |
controlled |
controlled |
|
Constant |
0.0652 |
||||
|
(0.0448) |
(0.262) |
(0.535) |
(0.524) |
|
Observations |
1,163 |
1,163 |
1,163 |
1,163 |
|
Sample Size |
388 |
388 |
388 |
388 |
|
0.651 |
0.652 |
0.652 |
0.652 |
||
|
|
|
|
|
Table 15 shows the regression results for company performance and board size. The variable bsize is also positively correlated with ROE at the 1% level, indicating that the larger the board size, the higher the company’s performance, and H4a is verified. This paper concludes that a company with a large board of directors can have more resources and opportunities, which have a positive effect on the company’s performance.
Table 16 shows the regression results for company performance and the proportion of independent directors. The coefficient of outdire is positive at the 1% significance level, indicating that the higher the proportion of independent directors, the better the company’s performance. Thus, H6a is verified. Many of the company’s independent directors are celebrities or scholars of high prestige or professional knowledge in a certain field. Not only can they bring different resources to the company, but also they can provide consulting services and thus make a positive contribution to the company’s performance.
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Table 15.
Relationship between Company Performance and Board Size
This table reports results of the relationship between company performance and board size. The statistics in () is the
Variables |
|
|
ROE |
|
|
|
|
|
|
||
(1) |
(2) |
(3) |
(4) |
||
|
|||||
|
|
|
|
|
|
Bsize |
0.0529*** |
0.0522*** |
0.0514*** |
0.0512*** |
|
|
(0.0096) |
(0.0096) |
(0.0101) |
(0.0105) |
|
Size |
|
0.0220* |
0.0362* |
0.0358* |
|
|
|
(0.0117) |
(0.0199) |
(0.0193) |
|
Lev |
|
|
|||
|
|
|
(0.0937) |
(0.0916) |
|
Growth |
|
|
|
0.0011 |
|
|
|
|
|
(0.0025) |
|
Constant |
|||||
|
(0.0950) |
(0.264) |
(0.378) |
(0.374) |
|
IND |
controlled |
controlled |
controlled |
controlled |
|
TR |
controlled |
controlled |
controlled |
controlled |
|
Observations |
1,164 |
1,164 |
1,164 |
1,164 |
|
Sample Size |
388 |
388 |
388 |
388 |
|
0.655 |
0.655 |
0.655 |
0.655 |
||
|
|
|
|
|
Table 16.
Relation between Company Performance and the Proportion of
Independent Directors
This table reports results of the relation between company performance and the proportion of independent directors. The statistics in () is the
(4)show the results that contain additional control variables than the previous column. Coefficients significant at the 10%, 5% and 1% level is indicated with *, **, ***, respectively. Finally, for variable definitions, please refer to Table 1.
Variables |
|
|
ROE |
|
|
|
|
|
|
||
(1) |
(2) |
(3) |
(4) |
||
|
|||||
|
|
|
|
|
|
Outdire |
0.729*** |
0.741*** |
0.760*** |
0.774*** |
|
|
(0.173) |
(0.177) |
(0.158) |
(0.146) |
|
Size |
|
0.0457*** |
0.0709*** |
0.0691*** |
|
|
|
(0.00941) |
(0.0197) |
(0.0177) |
|
|
|
|
|
|
Characteristics of Board of Directors and Performance of |
305 |
The Perspective of Venture Capital Participation |
Table 16.
Relation between Company Performance and the Proportion of
Independent Directors (Continued)
Variables |
|
|
ROE |
|
|
|
|
|
|
||
(1) |
(2) |
(3) |
(4) |
||
|
|||||
|
|
|
|
|
|
Lev |
|
|
|||
|
|
|
(0.113) |
(0.107) |
|
Growth |
|
|
|
0.00436 |
|
|
|
|
|
(0.00483) |
|
IND |
controlled |
controlled |
controlled |
controlled |
|
TR |
controlled |
controlled |
controlled |
controlled |
|
Observations |
1,164 |
1,164 |
1,164 |
1,164 |
|
Sample size |
388 |
388 |
388 |
388 |
|
0.652 |
0.653 |
0.654 |
0.654 |
||
|
|
|
|
|
C. Robustness Tests
C1. Fictional Treatment Group
In the previous analysis, t refers to the year a company goes public, with VC still participating in the company’s operations, whereas years t + 2 and t + 3 refer to two and three years, respectively, after the IPO, when VC has exited. The empirical results above show that VC participation has a significant impact on board characteristics. To test the robustness of this conclusion, this paper further sets t + 2 as the year in which a company still has VC participation, that is, years t and t + 2 are VC participation years, and year t + 3 is the VC exit year.
The regression results are shown in Tables 17 to 19. When we set years t and t + 2 as having VC participation, and year t + 3 as the year VC exits, the coefficient of vc*exit_before is no longer significant. This means that the original empirical results are stable.
Table 17.
VC and CEO Duality (t+2 as the years with VC participation)
This table reports results of the VC and CEO duality (t+2 as the years with VC participation). The statistics in () is the
Variables |
|
|
dual |
|
|
|
(1) |
(2) |
(3) |
(4) |
(5) |
||
|
||||||
Vc*exit_before |
0.0283 |
0.0281 |
0.0305 |
0.0310 |
0.0390 |
|
|
(0.0202) |
(0.0226) |
(0.0253) |
(0.0251) |
(0.0209) |
|
Growth |
|
0.0153*** |
0.0148** |
0.0149** |
0.0140** |
|
|
|
(0.0047) |
(0.0048) |
(0.0049) |
(0.0057) |
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Bulletin of Monetary Economics and Banking, Volume 22, Number 3, 2019 |
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|
Table 17.
VC and CEO Duality (t+2 as the years with VC participation) (Continued)
Variables |
|
|
dual |
|
|
|
(1) |
(2) |
(3) |
(4) |
(5) |
||
|
||||||
Lev |
|
|
0.260 |
0.260 |
0.229** |
|
|
|
|
(0.147) |
(0.148) |
(0.0979) |
|
ROE |
|
|
|
0.0095 |
||
|
|
|
|
(0.0221) |
(0.0174) |
|
Size |
|
|
|
|
||
|
|
|
|
|
(0.0369) |
|
IND |
controlled |
controlled |
controlled |
controlled |
controlled |
|
YR |
controlled |
controlled |
controlled |
controlled |
controlled |
|
Observations |
1,163 |
1,163 |
1,163 |
1,163 |
1,163 |
|
Sample Size |
388 |
388 |
388 |
388 |
388 |
|
0.046 |
0.055 |
0.061 |
0.061 |
0.105 |
Table 18.
VC and Board Size (t+2 as the years with VC participation)
This table reports results of the relation between VC and board size (t+2 as the years with VC participation). The statistics in () is the
Variables |
|
|
bsize |
|
|
|
(1) |
(2) |
(3) |
(4) |
(5) |
||
|
||||||
Vc*exit_before |
||||||
Size |
(0.0428) |
(0.0484) |
(0.0451) |
(0.0470) |
(0.0470) |
|
|
0.314*** |
0.399*** |
0.406*** |
0.400*** |
||
Lev |
|
(0.0686) |
(0.0754) |
(0.0772) |
(0.0690) |
|
|
|
|||||
ROE |
|
|
(0.184) |
(0.185) |
(0.193) |
|
|
|
|
||||
Growth |
|
|
|
(0.0327) |
(0.0333) |
|
|
|
|
|
0.0134 |
||
IND |
|
|
|
|
(0.0308) |
|
controlled |
controlled |
controlled |
controlled |
controlled |
||
YR |
controlled |
controlled |
controlled |
controlled |
controlled |
|
Observations |
1,163 |
1,163 |
1,163 |
1,163 |
1,163 |
|
Sample Size |
388 |
388 |
388 |
388 |
388 |
|
0.056 |
0.067 |
0.072 |
0.073 |
0.074 |
Characteristics of Board of Directors and Performance of |
307 |
The Perspective of Venture Capital Participation |
Table 19.
VC and Proportion of Independent Directors
(t+2 as the years with VC participation)
This table reports results of the VC and proportion of independent directors (t+2 as the years with VC participation). The statistics in () is the
(4)show the results that contain additional control variables than the previous column. Coefficients significant at the 10%, 5% and 1% level is indicated with *, **, ***, respectively. Finally, for variable definitions, please refer to Table 1.
Variables |
|
|
Outdire |
|
|
|
|
|
|
|
|
||
(1) |
(2) |
(3) |
(4) |
(5) |
||
|
||||||
|
|
|
|
|
|
|
Vc*exit_before |
||||||
|
(0.0024) |
(0.0026) |
(0.0027) |
(0.0025) |
(0.0029) |
|
Size |
|
|||||
|
|
(0.0018) |
(0.0018) |
(0.0023) |
(0.0022) |
|
Growth |
|
|
||||
|
|
|
(0.0003) |
(0.0002) |
(0.0002) |
|
Lev |
|
|
|
0.0467*** |
0.0470*** |
|
|
|
|
|
(0.0108) |
(0.0107) |
|
ROE |
|
|
|
|
0.0016 |
|
|
|
|
|
|
(0.0034) |
|
IND |
controlled |
controlled |
controlled |
controlled |
controlled |
|
YR |
controlled |
controlled |
controlled |
controlled |
controlled |
|
Observations |
1,164 |
1,164 |
1,164 |
1,164 |
1,164 |
|
Sample Size |
388 |
388 |
388 |
388 |
388 |
|
0.144 |
0.146 |
0.149 |
0.154 |
0.154 |
||
|
|
|
|
|
|
C2. Regression Results for the Manufacturing Industry
Manufacturing companies account for more than 60% of the entire sample in this paper. To further verify the robustness of the previous empirical results, this paper uses the DID model to conduct a regression analysis of the manufacturing industry subsamples. The regression results shown in Tables 20 to 22 indicate that H1 to H3 still hold when only manufacturing companies are considered. The original regression results are therefore stable.
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Bulletin of Monetary Economics and Banking, Volume 22, Number 3, 2019 |
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Table 20.
VC and CEO Duality (Manufacturing Industry)
This table reports results of the VC and CEO duality relation (manufacturing industry). The statistics in () is the
Variables |
|
|
Dual |
|
|
|
(1) |
(2) |
(3) |
(4) |
(5) |
||
|
||||||
Vc*exit_before |
1.026* |
1.191** |
1.754*** |
1.874** |
1.897** |
|
|
(0.608) |
(0.471) |
(0.626) |
(0.740) |
(0.789) |
|
Growth |
|
|||||
|
|
(0.222) |
(0.147) |
(0.177) |
(0.173) |
|
Size |
|
|
||||
|
|
|
(0.568) |
(0.719) |
(0.697) |
|
Lev |
|
|
|
|||
|
|
|
|
(2.769) |
(2.785) |
|
ROE |
|
|
|
|
0.390 |
|
|
|
|
|
|
(0.772) |
|
YR |
controlled |
controlled |
controlled |
controlled |
controlled |
|
Observations |
779 |
779 |
779 |
779 |
779 |
|
Sample Size |
260 |
260 |
260 |
260 |
260 |
Table 21.
VC and Board Size (Manufacturing Industry)
This table reports results of the VC and board size relation (manufacturing industry). The statistics in () is the
Variables |
|
|
bsize |
|
|
|
(1) |
(2) |
(3) |
(4) |
(5) |
||
|
||||||
Vc*exit_before |
0.199** |
0.191** |
0.201** |
0.201** |
0.188** |
|
|
(0.0920) |
(0.0918) |
(0.0911) |
(0.0910) |
(0.0931) |
|
Growth |
|
0.0444* |
0.0538** |
0.0512** |
0.0518** |
|
|
|
(0.0233) |
(0.0232) |
(0.0234) |
(0.0234) |
|
Size |
|
|
0.416*** |
0.459*** |
0.465*** |
|
|
|
|
(0.0986) |
(0.108) |
(0.109) |
|
Lev |
|
|
|
|||
|
|
|
|
(0.400) |
(0.401) |
|
ROE |
|
|
|
|
||
|
|
|
|
|
(0.106) |
|
YR |
controlled |
controlled |
controlled |
controlled |
controlled |
|
Observations |
780 |
780 |
780 |
780 |
780 |
|
Sample Size |
260 |
260 |
260 |
260 |
260 |
Characteristics of Board of Directors and Performance of |
309 |
The Perspective of Venture Capital Participation |
Table 22.
VC and Proportion of Independent Directors (Manufacturing Industry)
This table reports results of the VC and proportion of independent directors relation (manufacturing industry). The statistics in () is the
Variables |
|
|
outdire |
|
|
|
(1) |
(2) |
(3) |
(4) |
(5) |
||
|
||||||
Vc*exit_before |
0.0135** |
0.0131** |
0.0134** |
0.0134** |
0.0117** |
|
|
(0.0053) |
(0.0053) |
(0.0053) |
(0.0053) |
(0.0054) |
|
Size |
|
|||||
|
|
(0.0046) |
(0.0047) |
(0.0051) |
(0.0051) |
|
Growth |
|
|
||||
|
|
|
(0.0013) |
(0.0013) |
(0.0013) |
|
Lev |
|
|
|
0.0235 |
0.0221 |
|
|
|
|
|
(0.0199) |
(0.0198) |
|
ROE |
|
|
|
|
||
|
|
|
|
|
(0.0062) |
|
YR |
controlled |
controlled |
controlled |
controlled |
controlled |
|
Observations |
780 |
780 |
780 |
780 |
780 |
|
Sample Size |
261 |
261 |
261 |
261 |
261 |
V. CONCLUSION AND POLICY SUGGESTIONS
By using a DID model, multiple linear regression, probit regression, logit regression, and other methods, this paper explores the impact of VC participation on the board characteristics and performance of
Based on the analysis above, this paper suggests the following. First, VC institutions in China should continuously improve their professional level, enrich their social resources, and increase the introduction and training of professional talent, thus enhancing the professional level of the entire industry. Moreover, government departments should actively guide the development of the VC industry, for example, by instituting laws and regulations, and prevent VC institutions from harming the development of startups for their own interests. Last but not the least, the state should create a sound multilevel capital market to broaden the exit channels of VC institutions, allowing VCs to exit more smoothly, and encourage VC institutions to invest in more enterprises.
310 |
Bulletin of Monetary Economics and Banking, Volume 22, Number 3, 2019 |
|
|
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