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Networks of directors on Russian boards: the hidden part of the corporate governance iceberg

Félix J. López-Iturriaga [email protected]

Marina A. [email protected]

1. Introduction

The Russian business environment is known for its underdeveloped capital

markets, weak legal institutions, high level of corruption, high concentration of

ownership (the so-called oligarchy), high proportion of government ownership and wide

state involvement in business (Lazareva et al., 2008; Sutela, 2013). Ownership

concentration and governmental control lead to some specific problems of corporate

governance, particularly regarding conflicts between the state and private business.

Enikolopov and Stepanov (2013) have recently highlighted that the government

recognizes these problems and has tried to introduce internationally accepted standards

of corporate governance. Over the last decade, as a result, corporate governance in

Russia has improved among large firms. This trend is reflected in the increasing

disclosure of financial information, the adoption of the International Financial

Reporting Standards and the increasing share of independent directors (Lazareva et al.,

2008).

We claim that the analysis of corporate governance in Russia is usually performed

with a focus on traditional metrics of corporate governance, such as the ownership

structure and the independence of the board of directors. However, Russian businesses

have another feature that has been so far neglected by the existing research: the

importance of relationships. This issue is rarely studied despite its relevance and

potential to shed light not only on the economic efficiency of Russia but also that of

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other emerging countries with relevant business networks. Consequently, social network

analysis acts as a helpful tool in uncovering the true system of Russian corporate

governance and assessing the web of contacts, relationships and their influence on

corporate policy.

With some exceptions (Prem Sankar et al., 2015), most of the existing research

has focused on the most developed countries, analyzing a number of specific topics,

such as the ties between board members and the Chief Executive Officer (Fracassi and

Tate, 2012; Renneboog and Zhao, 2011), the board’s political connections (Hayden et

al., 2013; Kawai and Ko, 2012), the board’s network dynamics (Heemskerk, 2013) and

the performance of connected firms (Andres et al., 2013; Kuhnen, 2009; Schonlau and

Singh, 2009). Given the prominence of relations among corporate top players in

Russian business and the lack of existing research on these social networks, we aim to

fill this gap by analyzing the social networks of the directors of listed firms in Russia.

More specifically, our objective is to provide a descriptive analysis of the networks of

Russian boards of directors (BoD hereinafter) and to analyze some corporate

characteristics related to these networks.

In this study, we analyze a sample of 112 large, listed Russian companies

between 2009 and 2014. Since the unit of observation is a director and each firm

employs up to 22 directors, we have 6,729 director-year observations for analysis.

Traditional corporate governance metrics, such as demographic characteristics,

experience, multiple directorships, etc., confirm a positive trend in Russia towards more

independent and qualified BoDs. We also find a decrease in the centrality of directors

from 2009 to 2014. This result corroborates the decreasing concentration of power in

some directors. The most connected firms are also found to have a specific profile since

they are larger, have lower market valuations and have stronger ties with the

government (both through higher fractions of shares owned by the government and

greater numbers of directors who are former politicians). We also find that the boards of

financial institutions are less connected, whereas political and independent directors are

more centralized.

Our main contribution is the application of social network analysis to the

Russian corporate governance system. Although this approach could be seen as quite

restrictive, the relevance of our research arises not only from the fact that corporate

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governance is a promising field for the application of social networks, but also because

the Russian environment is uniquely characterized by the importance of relations, such

that the social networks approach can be more useful in this environment than in most

developed capital markets on which previous investigations have focused.

The remainder of this paper proceeds as follows. In the next section, we present

the underlying explanations of the importance of networks of directors and the

theoretical foundations of the influence of connections on corporate governance. Then,

in Section 3, we introduce the methodology of network metrics. Section 4 describes the

sample and some characteristics of the studied firms. In Section 5, we report and discuss

the results of the analysis using descriptive statistics and means comparisons. Section 6

concludes and provides a number of future directions for research.

2. Theoretical background

As demonstrated by Chauvet and Chollet (2010), corporate governance is one of

the most active applications of social networks in the business field. Corporate

governance can be defined as a set of relationships between a company’s management,

its board, its shareholders and other stakeholders who set the company’s objectives and

the means of attaining those objectives (OECD, 2015). Among these relationships and

means is the BoD, which emerges as the apex of the system and the focus of

international public authorities’ efforts to improve corporate governance (Jensen, 1993;

OECD, 2015).

Directors play a crucial role in corporate governance, ensuring proper

management of the company and representing investors' interests. The BoD has a

twofold role in large, listed firms: monitoring and supervising managers on behalf of

shareholders and acting as a valuable source of knowledge, finance and other intangible

resources for the firm (Adams et al., 2010; Chen et al., 2013; Fama and Jensen, 1983;

Pugliese et al., 2009).

A director can sit on several boards simultaneously (multiple directorships),

giving rise to a network of directors. A social network approach to corporate

governance demonstrates the spillover effect of financial fraud on firms connected via

these board members (Kang, 2008) and how the appointment of directors can be

explained by social networks (Fich and White, 2005). The literature suggests that

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multiple directorships can have very diverse consequences and there is not yet

conclusive evidence on whether it is ultimately beneficial for or detrimental to a given

company. Following Böhler et al. (2010), the reasons for and the consequences of

networks of directors can be explained on the basis of four theoretical approaches: the

attention hypothesis, social class theory, the resource dependency view and reputation

theory:

1. According to the attention hypothesis, serving on a board is time-consuming, such

that serving on several boards can result in less efficient work. Perry and Peyer

mention a paper by Lipton and Lorsch, which suggests that “an individual needs to

devote at least 100 hours a year to fulfill duties as a director” in one company (Perry

and Peyer, 2005). Core et al. (1999) and Fich and Shivdasani (2006) show that

directors serving on multiple boards are overcommitted, which may result in poor

firm performance. Masulis and Mobbs (2011) and Sharma (2011) also find negative

market reaction and lower shareholder compensation when busy directors are

appointed to a firm’s board.

2. The social class theory views directors as part of a corporate elite network with

common values and norms. According to Brass et al. (2004), interorganizational

networks lead to intraorganizational influence. In other words, multiple directorships

render directors more focused on their networks than on managing a given firm.

These networks can be especially relevant given that most independent directors are

executives of other firms, industry association leaders, government officials,

university professors, etc., which reduces their monitoring role (Chen et al., 2014).

3. Within the framework of the resource dependency view, the main role of a director is

to bring to the firm additional, mostly intangible resources, such as specific

knowledge, R&D expertise, easier access to funds, commercial relations, etc. (Chen

et al., 2013). From this perspective, multiple directorships offer a comparative

advantage in making and implementing strategic decisions (Mizruchi, 1996).

Directors can be rewarded for providing these resources with higher compensation or

greater managerial power (Horton et al., 2012). Wong et al. (2015) proved that

directors also tend to imitate compensation packages when they are included on

several boards simultaneously. Directors can use their power and negotiation position

within the firm to extract private benefits – even those that might hurt company

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performance (Larcker et al., 2013; Renneboog and Zhao, 2011). Consequently,

strongly connected boards do not necessarily make for better firm performance.

4. The reputation theory is similar to the resource dependency view but, while the latter

focuses on the board as a whole, the former focuses on individual directors. Firms

hire directors on the basis of their reputational capital (e.g., expertise, previous

managerial experience, knowledge, political connections, etc.). In turn, the more

reputed a director, the more boards to which he belongs (Perry and Peyer, 2005).

This theory implies the existence of a market for directors in which poor past

performance is punished (Ferris et al., 2003).

Given the above-mentioned factors, we follow the conclusion of (Cashman et al.,

2012), who state that it is not easy to determine whether the benefits of hiring well-

connected directors dominate the costs. In fact, the results of research regarding the link

between firm performance and directors’ networks are mixed. The evidence from

developed markets usually support a negative relationship between boards’ networks

and performance. Thus, Devos et al. (2009) conclude that US firms with lower industry-

adjusted performance are more likely to have interlocked directors. According to the

findings of Fich and Shivdasani (2006), Forbes 500 firms in which a majority of outside

directors hold three or more board seats have significantly lower market-to-book ratios

than firms in which a majority of outside directors hold fewer than three board seats.

Fracassi and Tate (2012) report that CEO-director ties reduce firm value and lead firms

to more value-destroying acquisitions. For the German market, Andres et al. (2013) and

Böhler et al. (2010) find that firms with more embedded boards realize worse firm

performance. Evidence consistent with this result has been found by Kawai and Ko

(2012) for Japanese firms. The literature that finds positive effects of multiple

directorships has usually studied future rather than current firm performance (Horton et

al., 2012; Larcker et al., 2013).

Nevertheless, the corporate governance practices and the financial and product

markets in emerging countries are different to those of developed states. Formal and

informal relations are especially important in the emerging countries for several reasons

(Purkayastha et al., 2012). First, the more severe market imperfections found in such

countries can lead to more connected corporate boards since connected directors can

bring resources to the firm that aid in overcoming the frictions of the markets. Second,

more or better-connected directors can provide self-generated institutional support in

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countries with weak institutional infrastructure (Chakrabarti et al., 2007; Yigit and

Behram, 2013). Third, business networks can arise from leveraging bureaucratic

connections in such environments. The empirical evidence on the effect of directors’

networks in emerging countries show that such networks have a positive effect on firm

performance (Qiao et al., 2013; Silva et al., 2006) and on the quality of financial

information (Chen et al., 2014).

3. Methodology

The literature usually employs three main measures for the analysis of multiple

directorships: the number of directorships, the interlocks and the social networks. The

first measure accounts only for the number of boards on which a particular director

serves (Chen et al., 2013; Ferris et al., 2003; Fich and Shivdasani, 2006; Perry and

Peyer, 2005) and the term busyness has been coined to describe the situation in which a

director is included on several boards – usually more than three (Andres et al., 2013;

Core et al., 1999). Directors’ interlock happens “when Firm A has Director 1 serving as

an insider and Director 2 serving as an outsider, whereas Firm B has Director 1 serving

as an outsider and Director 2 serving as an insider” (Bohman, 2012; Fennema and

Schijf, 1978; Koskinen and Edling, 2012). Some researchers analyze more specific

cases, such as reciprocal interlocks of CEOs (Fich and White, 2005), ownership-

directors interlocks (Bohman, 2012; Comet and Pizarro, 2011) or corporate linear

triples, whereby two corporations share directors with the board of the third firm

(Hayden et al., 2013). The third set of metrics borrows the social network analysis

technique and uses four main indicators (degree centrality, betweenness centrality,

closeness centrality and eigenvector centrality) to assess involvement in a network and

the relative importance of a given node (Bonacich and Lloyd, 2015; Brandes et al.,

2016; Carrington et al., 2005; Chuluun et al., 2014; Freeman, 1978; Malinick et al.,

2013; Nikolaev et al., 2015; Schonlau and Singh, 2009). This approach is quite new in

corporate governance analysis and outperforms other techniques since it uniquely

captures several dimensions of connections, such as the quality of the connections and

the position of each director's connections within the network (Barnea and Guedj,

2007). Consequently, in this study we use network analysis to gain a more

comprehensive view of the relations among directors in the identified Russian listed

firms.

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Directors can be linked to one another either formally or informally. In order to

measure the importance of a director in a network we identify how directors interact

with one another via formal contacts (i.e., board meetings). This metric has the

advantage of not being affected by personal biases. Following the arguments of Andres

et al. (2013) and Hwang and Kim (2009), we can understand formal contacts to be a

good proxy for their informal counterparts. Hwang and Kim (2009) posit that, whether

consciously or subconsciously, people choose to connect with similar people more

frequently than dissimilar people. Further, directors, who are well-connected according

to formally determined ties, can also be well-connected according to informal contacts

outside the boardroom (Andres et al., 2013).

We choose four metrics of network centrality: degree centrality, betweenness

centrality, eigenvector centrality and closeness centrality. While it is not possible to

choose an overall best metric of centrality, we use all four together to gain a more

comprehensive view of the networks of directors.

4. Sample

The database used in this research includes large, listed Russian companies. A

number of different sources are used to collect the data. Financial information is

collected using the Ruslana database provided by Bureau van Dijk. Information

regarding board compositions is collected from the companies’ websites and annual

reports. Personal data on board members are hand-collected from annual reports and

other open sources on the Internet. The sample consists of 628 firm-year observations

from 112 firms between 2009 and 20141. The Russian stock market is usually regarded

as underdeveloped and there are not very many companies with actively traded shares

(McCarthy and Puffer, 2013; Michailova et al., 2013). As a result, our sample is very

representative of the whole population of listed firms since it refers to approximately

80% of the total market capitalization of Russian companies. Since the unit of

observation is a director and each firm employs up to 22 directors, we have 6,729

director-year observations.

1 Some observations were excluded because the company was founded later than 2009 or was delisted before 2014.

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The structure of a sample reflects the Russian financial market’s structure, which

is characterized by the predominance of manufacturing, oil and gas sectors (Lazareva et

al., 2008). Thus, the potential for sample selection bias can be ruled out.

Table 1 outlines the descriptive statistics of the firms included in the sample. We

analyze the main financial variables2 after excluding outliers. We also report

government ownership, as it is an important factor for financial management and

corporate governance in Russian companies. Descriptive statistics of the variables by

year are provided in Appendix 1.

<Insert Table 1 here>

The median firm in the sample has less debt than equity, a size of 2.184 bln.

euro, around 8% return on invested capital (ROIC) and an equity market price almost

equal to its book value. The low levels of leverage and market-to-book value found in

Russian firms in comparison to European or US companies can be explained by

reference to the underdevelopment of Russian financial markets and their idiosyncratic

corporate finance. Debt is usually considered to be the most risky and expensive source

of funds. Thus, Russian firms rely primarily on retained earnings and do not incur debt

until they have depleted their internal funds. Twenty-six companies have more than one

governmental owner, while 53 companies do not have any governmental owners. The

mean government fraction of shares is 4.59%.

5. Empirical results

5.1. Basic statistics

We start with the descriptive statistics of directors included in the sample, before

turning to the analysis of network metrics. Panel A in the Table 2 represents

demographic information regarding directors and their education.


The average director in the network is 49 years old and male. Two electric

power industry companies, TGK-5 and the Interregional Distribution Grid Company of

2 Size was measured as a logarithm of book value. Leverage was calculated as the ratio of debt to equity book value. Return on invested capital (ROIC) was calculated as the ratio of net operating profit after taxes (NOPAT) to the invested capital of the previous period.

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Volga, hired 23-year-old directors in 2011 and 2013 respectively. The oldest director,

Alfred Fenoti, who was born in 1926, was appointed in 2008 as a chair of the board of

Yandex, Russia’s largest Internet company. Around 30-35% of directors have at least

one degree. This is another unique feature of Russian business culture: a degree is

regarded as an informal requirement, something fashionable for people in top

management or politics. Eleven percent of directors have MBA degrees, but only a few

of these are from foreign universities.

Descriptive statistics regarding the age, education and gender of directors by

year are provided in Appendix 2. The results show that there are no dynamics according

to the level of directors’ education. Each year the average age of directors increases

slightly, probably because the analyzed sample is quite stable. We have also analyzed

the education dynamics according to particular industries (results are available on

request). The ratio of directors with MBA degrees decreases in construction, increases

in trade and presents an inverted U-shape in the services industry. There are no clear

trends regarding MBAs in the other industries.

Panel B in the Table 2 reports statistics on directors’ experience levels. A high

proportion of directors (around 75%) have experience of working within the industry in

which their company operates. Further, almost half of the analyzed directors have been

a CEO at some point. We can see that international experience increases by year, while

the share of politically connected directors decreases. This may be evidence of the

enhancement of corporate governance in large Russian companies, given that people

with international experience may have a broader view and implement knowledge that

differs from Russian practices. At the same time, the decrease in governmental

experience share means that political connections become less important for the

management of large companies.

We have also analyzed the dynamics of industry experience in the various

industries (results are available upon request). During the analyzed period, within the

manufacturing, energy and trade industries, the percentage of directors with experience

in these industries increased. Other industries do not show clear trends.

Panel C in the Table 2 contains descriptive statistics of some other board

features that characterize corporate governance: size of the board, number of additional

boards to which a director belongs, percentage of busy directors on a board and share of

independent directors. Following Andres et al. (2013), we identify a busy director as a

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person who is included on three or more boards. The number of directorships held by a

particular director was determined according to a company’s annual reports, so this

number includes information about companies external to the sample.

The average board of large, listed Russian companies includes approximately 10

directors, consistently with the results of Muravyev et al. (2014). Around 40-45% of

directors are busy, in that they hold three or more board appointments. These results are

similar to the average board size (13 directors) and percentage of busy directors

(52.44%) identified by Andres et al. (2013) for a sample of large German companies.

On average, Russian directors seem to have too many board appointments (7.06 in

2009, decreasing to 5.92 in 2014) as compared to other countries. Given the limited

possibility of effectively performing one’s duties on so many boards, the observed

decrease in the number of multiple directorships is likely to improve corporate

governance. The share of independent directors has risen each year, with the exception

of 2014, which is also a sign of developing corporate governance practices.

Taken together, these results show the improvement of corporate governance

practices in large Russian companies over the observed period: both international

experience and the proportion of independent directors have increased, whereas the

percentage of politically connected directors and excessive multiple directorships have

decreased. The size of the boards and the proportion of busy directors are consistent

with large European companies. In any case, these data only provide a partial picture –

in the next section we analyze another perspective of corporate governance and

networks, gaining a broader understanding of Russian corporate governance.

5.2. Dynamics of network measures

In order to calculate network metrics we use the following two steps:

1. The data collected regarding boards are used to identify all existing pairs of

directors. We complete the table in Microsoft Access® using a separate n*n network

matrix for each year. We assign 1 to a cell a ij if director i and director j are included

on the same board, and 0 otherwise. The matrix is thus symmetric. As a result, we

have 98,456 pairs.

2. We calculate the chosen network metrics using NodeXL software. It is a free, open-

source template for Microsoft Excel® that allows for the analysis of social networks

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using the matrix in Microsoft Access®. Although the results following the

calculations are not normalized, the number of directors in the network hardly

changes between the analyzed periods, so it is possible to use non-normalized

values.

Table 3 reports the degree, betweenness, closeness and eigenvector centrality of

the sample directors in dynamics and the diversity of these metrics within a particular

company. Diversity is calculated as a standard deviation of a network metric within a

company in a particular year.


The most consistent results are the decreasing trends of degree and eigenvector

centrality, both in terms of centrality and dispersion. The degree centrality indicates the

number of direct relationships of each director. The second metric, eigenvector

centrality, describes the relative importance of a director’s network in comparison to his

neighbors’ networks. It means that, within the network of Russian directors, the average

importance of each director becomes lower. This evolution can be affected by the size

of the boards. Nevertheless, since the average board size, according to Table 2, remains

more or less the same throughout the studied period, the decrease of both degree and

eigenvector centrality could be attributed to the fewer connections and lower

importance of connected directors. For a better assessment of the dynamics of the

network, in Figures 1 and 2 we report the evolution of degree and eigenvector (both

centrality and diversity).

<Insert Figure 1 here>

<Insert Figure 2 here>

Note that the relationship between closeness and the other metrics of centrality is

negative and significant (results are available on request). Normally, networks’ metrics

are positively correlated (Li et al., 2015; Valente et al., 2008). The negative correlation

between closeness and the other metrics of centrality could be due to the evolution of

the Russian BoD network: whereas the size of the networks has not changed

significantly, the centrality of directors has decreased. Thus, the average distance of

each director from the others has increased. Another possible explanation is that there

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can be one or several clusters of directors located far from the rest of the network,

which is consistent with the idea of a more decentralized network.

5.3. Characteristics of the most connected firms

In the next stage of our descriptive analysis we study whether the most connected

firms differ from the rest of the sample. First, we calculate the mean centrality metrics

for each company. Second, we define the most connected firms as those in the first

quartile of each metric. Third, we report some firm-level characteristics (size, valuation,

ownership structure, etc.) of both groups of firms. Fourth, we implement t-tests to check

whether there are significant differences between the means of the most connected firms

and the rest of the sample. Given the correlation between degree and betweenness, we

analyze both metrics simultaneously. However, we analyze degree and betweenness

together because of their high correlation. The results for all comparisons are presented

in Table 4.


As shown in both Panel A and C, the most connected firms according to degree,

betweenness and eigenvector are larger and less priced, have higher governmental

ownership, and their directors have less international experience but greater

governmental experience. However, the closeness metric offers another result. Given

the negative correlation between closeness and the other metrics, we consider that the

most relevant results are those in Panel A and C. They are coherent with the Russian

corporate system, with some large and well-connected firms in which the state plays an

active role (both in terms of ownership and presence on the BoD), which does not

necessarily translate to higher performance.

5.4. Characteristics of the most isolated firms

We then turn to study the characteristics of the most isolated firms. Again, first we

define isolated firms according to networks’ metrics distributions. We take into account

the lowest 25% of distribution for each variable. Degree and betweenness are analyzed

together for the above-mentioned reason. The results of t-tests are presented in Table 5.


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As expected, the results in Panel A and C show that firms with fewer connections

are smaller, have high market valuation and lower government ownership, and their

directors have less international and governmental experience. Once again, the

closeness centrality differs from the other metrics.

5.5. Networks of financial institutions

The next stage of the research analyzes financial firms, i.e., banks, insurance firms

and other financial institutions. The underlying intuition is that such firms should have

better connections because they serve as mediators. We compare the means not only for

networks’ metrics but also for the number of outside directorships. The results of t-tests

are presented in the Panel A of the Table 6.


The results show that, contrary to our expectations, financial firms enjoy lower

centrality in terms of betweenness and eigenvector. This means that directors of

financial companies are not usually important mediators in the network, both in absolute

terms and relative to their neighbors. The number of other directorships for directors of

such firms is also significantly smaller. This result can be explained by reference to

some Russian companies’ features: the choice of financial institution is rarely a matter

of the directors’ decision and is instead determined by some other factors, for example,

the owner’s decisions and relationships. That is why it is not so important for these

firms to hire especially central directors. Interestingly, directors of financial companies

are more diverse, which could be the result of a conscious decision on the part of the

firms, with the intention of getting in touch with different companies and industries as a

way to expand business.

5.6. Centrality of independent directors

At this stage of the research, we analyze how central the independent directors

are. These directors are supposed to bring experience and resources to the board, so they

should be more central and have better connections. The results of the relevant t-tests

are presented in in the Panel B of the Table 6.

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In accordance with our expectations, independent directors have more direct

connections (higher degree) and higher relative importance (higher eigenvector) within

their networks. They are characterized by lower closeness, which could indicate that

these directors offer valuable connections but do not necessarily represent the shortest

distance between different directors. We also see that independent directors more

frequently work in companies with higher diversity, coherent with their role as resource

providers, such that firms use diverse directors to bring diverse resources. Despite these

results regarding centrality, we can see that, in general, independent directors sit on

fewer other boards.

5.7. Centrality of political directors

Since political connections are very important for Russian business, Russian

companies, especially large ones, are likely to hire directors with political backgrounds.

These directors raise the firm’s bargaining power, and simplify and accelerate the

solving of important questions. This is a reason for their inclusion on several boards.

Thus, the centrality of political directors should be higher than that of the rest of the

network. The results of the relevant t-tests are presented in the Panel C of the Table 6.

As expected, politically connected directors enjoy greater centrality. They have

more direct connections than their non-political counterparts, more often bridge

subnetworks and have connections with more connected directors. However, despite

high results for the other centrality metrics, we observe a lower value of closeness for

political directors. Thus, the average shortest distance between these and others

directors in the sample is higher. Again, the average number of other boards on which

political directors serve is not statistically different than that for non-political directors.

Diversity is also higher in companies with political directors, which coheres with the

role of these directors as enhancers of diverse relations.

6. Conclusion

This study uses the instruments of social network analysis to investigate a

number of issues related to the BoDs of large Russian companies. The BoD is one of the

cornerstones of corporate governance and plays a dual role: managerial oversight and

the provision of strategic resources. Existing research underlines recent improvements

in this field in Russia according to internationally accepted standards, since there are

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increasing numbers of independent and internationally experienced directors on the

boards of listed Russian firms. Nevertheless, an important characteristic of the Russian

corporate system is the relevance of personal relations for business. Consequently, we

use social network analysis to verify the consistency of this trend.

Our study analyzes a sample of 112 listed Russian companies between 2009 and

2014. Given the relative underdevelopment of Russian capital markets, our sample

includes only the most actively traded large firms. Since the unit of observation is a

director and each firm employs up to 22 directors, we have 6,729 director-year

observations. Our preliminary analysis confirms the above-mentioned improvement in

the BoDs: independence and international experience have become more important,

while the percentage of politically connected directors has decreased. Along with these

initial issues, social network analysis draws a complementary picture.

We have chosen four quite popular network analysis metrics, which have

already been implemented for boards analysis elsewhere: degree centrality, betweenness

centrality, closeness centrality and eigenvector centrality. Since each of these metrics

provide information about different network characteristics, they are analyzed together.

Thus, degree is regarded as a metric of popularity, betweenness is the ability to bridge

subnetworks, closeness is the average shortest distance from one director to others and

eigenvector provides information on how connected the nodes of the network are to a

particular director.

First, we study the evolution of these networks metrics. The most consistent

result is the decrease in centrality from 2009 to 2014 (both in terms of degree and

eigenvector centrality). Since the average size of the boards has not changed much,

these results confirm a trend away from concentration of power in the hands of a few

directors. The boards have also become more homogeneous, given the decrease in

degree and eigenvector diversity. Second, we analyze the characteristics of the most

connected and the most isolated boards according to the network metrics, and make

some mean comparisons with the rest of the sample. Our results show that the most

connected firms are larger, have lower market valuations and have stronger ties with the

government (both through higher fractions of shares owned by the government and

greater numbers of directors who are former politicians). Third, we make some mean

comparisons for several specific cases: the connections of the boards of financial

institutions, the connections of political directors and the connections of independent

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directors. Contrary to our expectations, the boards of financial institutions are less

central. In fact, both political and independent directors play a more central role, which

suggests that these directors are hired to bring valuable resources and connections.

Our paper marks a step forward in the use of the social networks approach to the

study of corporate governance. There are several indicated directions for future

research. An interesting subject of analysis would be the networks of the directors of

financial institutions. Since such Russian boards are not as central as expected, further

research should shed some light on the role of bankers in the corporate governance

system and its relationship to how firms raise funds. Relatedly, another possible

direction for further investigation is the impact of the networks of directors on other

firm-level issues, such as firm profitability, the quality of financial information and

corporate finance, or other strategic decisions. In any case, social networks analysis

shows itself to be a powerful tool to complement classical corporate governance

analyses.

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Table 1. Descriptive statistics of sample

Variable Mean Median Std. Dev. Min Max

Leverage 1.09 0.67 1.05 0.01 5.76

Age 20.03 13 22.42 1 126

Size 8.03 7.69 1.43 4.76 12.61

ROIC 0.10 0.08 0.10 -0.14 0.48

Price-to-book value 1.37 0.97 1.21 0.06 6.88

Number of governmental owners 0.83 1 1.03 0.00 5.00

Share of Government ownership (%) 4.59 0 16.26 0.00 100.00

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Table 2. Directors characteristics of the sample The Panel A gives the number of observations, mean, standard deviation, minimum and maximum of the directors background and demographic statistics. We report only mean for the dummy variables “Share of women in a sample” and “MBA” equals to a proportion of women in the sample and directors that have MBA degree respectively. The Panel B gives the number of observations and mean of the experience variables. The variables “International experience”, “Governmental experience”, “Industry experience” and “CEO experience” equals to a proportion of directors in the sample that have a certain type of working experience. The Panel C gives the number of observations, mean, standard deviation, minimum and maximum of the corporate governance metrics. We report variables “Size of a board”, “Share of busy directors”, “Share of independent directors” for a company as a whole. The variable “Number of another boards for busy directors” is reported for busy directors only.

Panel A: directors background and demographic statistics

Variable Observations Mean Std. Dev. Min Max

Age 5119 48.91 10.91 23.00 88.00Number of degrees3 4933 0.45 0.70 0.00 2.00MBA degree 4942 0.11 - - -Share of women in a particular board

671 0.10 0.15 0 1

Share of women in the sample 6719 0.09 - - -

Panel B: directors experience by year

VariableInternational

experienceIndustry

experienceCEO

experienceGovernmental

experience

YearObservations Mean Observatio

nsMea

nObservatio

nsMea

nObservatio

nsMea

n2009 1063 0.22 1093 0.76 1069 0.51 1099 0.252010 1083 0.21 1110 0.75 1088 0.49 1124 0.262011 1079 0.24 1113 0.76 1084 0.50 1124 0.252012 1105 0.26 1140 0.75 1108 0.51 1149 0.212013 1082 0.28 1112 0.76 1084 0.48 1124 0.212014 1067 0.28 1088 0.77 1067 0.45 1094 0.21

Panel C: Size of boards, busyness and independence of directors by year

Variable Year Observations Mean Std. Dev. Min Max

Size of a board

2009 112 9,79 2,86 1,00 17,00

2010 112 10,05 2,95 1,00 22,002011 112 10,04 2,64 1,00 19,002012 112 10,26 2,57 6,00 19,00

2013 111 10,14 3,00 1,00 19,00

2014 112 9,72 3,15 1,00 18,00

Number of other boards for busy directors

2009 295 7,06 5,24 3,00 37,00

2010 295 6,81 5,32 3,00 37,00

2011 312 5,93 3,95 3,00 37,00

3 Number of degrees refers to Russian system of education. Degree equals 1 if a director has a degree of Candidate of sciences (Russian analogue of PhD degree); equals 2 if a director has a degree of “Doctor nauk” which is a higher doctoral degree which may be earned after the Candidate of sciences; equals 0 otherwise.

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2012 322 5,99 3,60 3,00 28,00

2013 275 6,36 4,14 3,00 29,00

2014 306 5,92 3,67 3,00 29,00

Share of busy directors

2009 112 0,44 0,36 0,00 1,00

2010 112 0,43 0,35 0,00 1,00

2011 112 0,43 0,34 0,00 1,00

2012 112 0,42 0,33 0,00 1,002013 111 0,40 0,35 0,00 1,002014 112 0,45 0,36 0,00 1,00

Share of independent directors

2009 97 0,25 0,26 0,00 1,002010 99 0,27 0,27 0,00 1,002011 99 0,29 0,28 0,00 1,00

2012 100 0,30 0,28 0,00 1,00

2013 98 0,32 0,27 0,00 1,002014 97 0,28 0,25 0,00 1,00

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Table 3. Centrality metrics and their diversity within a company The Table gives the number of observations, mean and standard deviation of the relevant variables. Panel A reports the information of networks metrics measured for a particular director. Panel B reports the information for a networks metrics diversity and the unit of an observation is a company.

Panel A: centrality metricsDegree Betweenness Closeness Eigenvector

Year Obs. Mean Std. Dev. Mean Std.

Dev. Mean Std. Dev. Mean Std.

Dev.

2009 1105 15,543 7,298 1823,09 1964,21 0,030 0,056 0,0022

0,0033

2010 1127 15,139 6,382 2061,37 2345,90 0,028 0,056 0,0017

0,0031

2011 1126 14,084 5,688 2088,85 2694,66 0,031 0,057 0,0017

0,0028

2012 1149 13,846 5,296 2347,20 2580,56 0,030 0,056 0,0015

0,0022

2013 1129 13,529 4,918 1913,04 2633,67 0,031 0,055 0,0011

0,0045

2014 1094 12,936 4,749 1518,67 2105,98 0,039 0,061 0,0015

0,0025

Total 6730 14,180 5,854 1962,54 2419,15 0,031 0,057 0,0016

0,0032

Panel B: centrality metrics diversityDegree

diversityBetweenness

diversityClosenessdiversity

Eigenvectordiversity

Year Obs. Mean Std. Dev. Mean Std.

Dev. Mean Std. Dev. Mean Std.

Dev.

2009 110 7,226 6,612 3176,53 3396,31 0,0008 0,0042 0,0013 0,0019

2010 110 7,059 5,850 3683,83 3900,06 0,0008 0,0049 0,0010 0,0016

2011 111 5,671 4,709 3497,24 3927,64 0,0006 0,0041 0,0011 0,0015

2012 112 5,640 5,027 3939,51 3837,42 0,0003 0,0015 0,0008 0,0011

2013 109 5,232 4,757 3507,58 4885,75 0,0008 0,0024 0,0004 0,0013

2014 108 4,511 4,308 2522,02 3539,87 0,0011 0,0033 0,0007 0,0012

Total 660 5,894 5,339 3392,07 3953,06 0,0007 0,0036 0,0009 0,001

5

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Table 4. Test of mean comparisons of the most connected firmsThe Table reports the number of observations, mean and standard deviation of the relevant variables. In Panel A, we divide the sample according to degree and betweenness; in Panel B – according to closeness; in Panel C – according to eigenvector. t-tests are conducted to check the differences between the means.*** 99% confidence level; ** 95% confidence level; * 90% confidence level.

Panel A: Definition of the most connected firms according to degree and betweennessMost connected firms Rest of the firms

Obs. Mean Std.Dev

. Obs. Mean Std.Dev.

Size 595 8.42 0.95 3047 7.99*** 1.48

Price-to-book value 595 0.96 0.76 3047 1.36*** 1.17

Share of governmental owners 595 22.25 35.35 304

7 6.17*** 17.3

International experience 595 0.13 0.12 3047 0.25*** 0.23

Governmental experience 595 0.28 0.13 3047 0.20*** 0.16

Panel B: Definition of the most connected firms according to closenessMost connected firms Rest of the firms

Obs. Mean Std.Dev


Size 742 7.33 1.38 2900 8.25*** 1.37



011.05*** 24.33



Panel C: Definition of the most connected firms according to eigenvectorMost connected firms Rest of the firms

Obs. Mean Std.Dev


Size 749 8.02 1.06 2893 8.07 1.50



3 8.03*** 20.50



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Table 5. Test of mean comparisons of the isolated and more connected firmsThe Table reports the number of observations, mean and standard deviation of the relevant variables. In Panel A, we divide the sample according to degree and betweenness; in Panel B – according to closeness; in Panel C – according to eigenvector. t-tests are conducted to check the differences between the means.

Panel A: Definition of the isolated firms according to degree and betweennessIsolated firms Rest of the firms

Obs. Mean Std.Dev


Size 421 7.91 1.46 3221 8.08** 1.41Price-to-book value 421 2.03 1.76 3221 1.20*** 0.97Share of governmental owners 421 0.00 0.00 3221 9.95*** 23.32

International experience 421 0.19 0.20 3221 0.24*** 0.22Governmental experience 421 0.13 0.13 3221 0.22*** 0.16

Panel B: Definition of the isolated firms according to closenessIsolated firms Rest of the firms

Obs. Mean Std.Dev


Size 857 7.82 1.16 2785 8.14*** 1.48


Share of governmental owners 857 5.58 14.66 2785 9.7

9*** 23.92

International experience 857 0.22 0.22 2785 0.24** 0.22


Panel C: Definition of the isolated firms according to eigenvectorIsolated firms Rest of the firms

Obs. Mean Std.Dev


Size 751 8.06 1.40 2891 8.06 1.43Price-to-book value 751 1.52 1.23 2891 1.24*** 1.09Share of governmental owners 751 2.80 11.50 2891 10.35*** 23.93

International experience 751 0.27 0.21 2891 0.23*** 0.22Governmental experience 751 0.16 0.16 2891 0.22*** 0.16

*** 99% confidence level; ** 95% confidence level; * 90% confidence level.

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Table 6. Tests of mean comparisons: networks analysisThe Table reports the number of observations, mean and standard deviation of the relevant variables. In Panel A, we divide the sample by financial and non-financial firms; in Panel B we divide directors by their independency; in Panel C – according to directors political connections. t-tests are conducted to check the differences between the means. The variable “Number of another boards” is reported for directors who sit in the other boards only.

Panel A: mean comparisons between financial and non-financial firmsFinancial firms Non-financial firms

Obs. Mean Std.Dev. Obs. Mean Std.Dev

.Degree 579 13.78 6.26 6138 14.23 10.11

Betweenness 579 1193.81 3586.71 6138 2041.21*** 5835.78

Closeness 579 0.04 0.05 6138 0.03** 0.06Eigenvector 579 0.0013 0.0045 6138 0.0016** 0.0035Degree diversity 579 3.65 3.13 6122 6.62*** 5.40

Betweenness diversity 579 2332.53 2492.01 6122 3825.72*** 4019.61

Closeness diversity 579 0.0008 0.0035 6122 0.0007 0.0037Eigenvector diversity 579 0.0005 0.0016 6122 0.0011*** 0.0015Number of the other boards 62 1.26 2.05 3077 2.45** 3.91

Panel B: mean comparisons between independent and non-independent directorsIndependent directors Non-independent directors


.

Degree 1709 14.54 9.44 413

2 13.79*** 9.86

Betweenness 1709 1982.99 5450.73 413

2 2061.30 6072.57

Closeness 1709 0.0258 0.0529 413

2 0.0350*** 0.0643

Eigenvector 1709 0.0019 0.0038 413

2 0.0015*** 0.0037

Degree diversity 1709 6.75 5.09 413

2 6.06*** 5.24

Betweenness diversity 1709 3921.13 3893.18 413

2 3775.20 4160.42

Closeness diversity 1709 0.0008 0.0032 413

2 0.0005*** 0.0021

Eigenvector diversity 1709 0.0012 0.0017 413

2 0.0009*** 0.0015

Number of the other boards 840 2.13 3.03 1928 2.45** 3.96

Panel C: mean comparisons between political and non-political directorsPolitical directors Non-political directors


.Degree 147 17.50 12.47 5010 13.34*** 8.89

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1

Betweenness 1471 3192.11 6473.53 5010 1697.9

6*** 5504.22

Closeness 1471 0.0182 0.0470 5010 0.0357*** 0.0632

Eigenvector 1471 0.0027 0.0050 5010 0.0013*** 0.0030

Degree diversity 1471 7.63 5.05 5010 6.07*** 5.33

Betweenness diversity 1471 4636.90 3857.08 5010 3502.3

0*** 3956.06

Closeness diversity 1471 0.0007 0.0043 5010 0.0007 0.0032

Eigenvector diversity 1471 0.0014 0.0018 5010 0.0009*** 0.0015

Number of the other boards 647 2.32 3.00 2468 2.47 3.86*** 99% confidence level; ** 95% confidence level; * 90% confidence level.

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