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© Copyright by Sandra Constanza Gaitan Riaño, 2017
All Rights Reserved
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ACKNOWLEDGMENT
I would like to express my sincere gratitude to my committee chair, Professor C.
Edward Fee, for his guidance, wise advice and patience that made this dissertation
possible.
I want to thank my committee members, Professor Venkat Subramaniam and
Professor William Grieser for their valuable comments and suggestions.
I would also like to express my gratitude to the group of professors of the doctoral
studies who gave me knowledge and encouragement I needed during my studies.
I am thankful to Universidad EAFIT for the financial support I received.
I thank my parents Leonor and Enrique for their love, concern and support, as well
as my spouse Carlos Jaime, my son Carlos Andres and my daughter Maria Clara
for always being there for me and for their unlimited patience, love and support.
In addition, I would like to thank my friend Charmane for her unconditional
support.
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How Do Monitoring Boards Perform in Financial Crises?
Abstract
This dissertation tries to examine the link between board type and
performance in financial crises. The topic is of interest because boards of
directors are considered a very important element in the corporate governance
system, and it is important to understand how the makeup of a firm’s board
affects the effectiveness of governance for the firm. Further, while recent
financial regulations have endorsed the presence of more independent members to
serve as monitors, the academic literature to date is not conclusive regards the
effects of performance on monitoring boards vs. advising boards. Thus, I use a
sample of U.S. firms (2004–2012) in the Standard and Poor’s (S&P) 1500 to test
the performance of monitoring boards before and after the subprime crisis. I
analyze this sample using a difference-in-differences approach that relies on the
exogenous variation in firm performance generated by the subprime financial
crisis. The results suggest that monitoring boards appear to have actually
performed worse than advising boards during the subprime crisis. In particular,
there is a decline in Tobin’s Q in monitoring boards compared to advising boards.
In addition, monitoring boards appear to have acted to reduce scale, measured as
the number of employees and capex following the crisis. These results,
suggesting poor performance of monitoring boards during the crisis, have
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important implications for the appropriateness of regulations mandating increased
reliance on independent directors.
v
Table of Contents
List of Tables ..................................................................................................................... vi
List of Figures .....................................................................................................................ix
1. Introduction ................................................................................................................. 1
2. Literature Review and Hypotheses Development ....................................................... 8
2.1. The regulation on board composition ............................................................... 13
2.2. Hypotheses Development ................................................................................. 15
3. Data ........................................................................................................................... 18
3.1 Sample selection ..................................................................................................... 18
3.2 Advisory Board Definition ..................................................................................... 18
3.3 The Definition of Control Variables ....................................................................... 19
3.4 Descriptive Statistics ............................................................................................... 20
4. Results ....................................................................................................................... 23
4.1 Comparison of firm characteristics ......................................................................... 23
4.2 Analysis of MB and Firm Performance .................................................................. 24
4.3 Analysis of MB and Measures of Firm Scale ......................................................... 28
4.4 Additional Robustness Test .................................................................................... 29
4.5 Performance, Investment, Leverage and Monitoring Boards: A Matching
Approach ....................................................................................................................... 30
5. Discussion ................................................................................................................. 35
Appendix ........................................................................................................................... 87
1. Variables Definitions ............................................................................................ 87
References ......................................................................................................................... 90
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List of Tables
Table 1 Descriptive Statistics for the Entire Sample .............................................39
Table 2 Descriptive Statistics before 2008 ............................................................41
Table 3 Descriptive Statistics after 2008 ...............................................................43
Table 4 Monitoring board Analysis .......................................................................45
Table 5 Correlation Matrix ....................................................................................46
Table 6 Comparison of Firm Characteristics by Monitoring Board ......................47
Table 7 Univariate Difference in Difference Analysis of Firm Performance ........49
Table 8 Multivariate Difference in Difference Analysis of Firm Performance .....50
Table 9 Multivariate Difference in Difference Analysis of Tobin’s Q ..................52
Table 10 Multivariate Difference in Difference Analysis of Revenues by
Employee ...............................................................................................................54
Table 11 Multivariate Difference in Difference Analysis of Number of
Employees ..............................................................................................................56
Table 12 Multivariate Difference in Difference Analysis of Change of Number of
Employees ..............................................................................................................58
Table 13 Multivariate Difference in Difference Analysis of Business Segments .60
Table 14 Multivariate Difference in Difference Analysis of Capital investment ..62
Table 15 Multivariate Difference in Difference Analysis for Firms with R&D ....64
Table 16 Multivariate Difference in Difference Analysis of Leverage .................66
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Table 17 Multivariate Difference in Difference Analysis of Firm Performance
before and after 2007 .............................................................................................68
Table 18 Multivariate Difference in Difference Analysis of Tobin’s Q before and
after 2007 ...............................................................................................................70
Table 19 Multivariate Difference in Difference Analysis of Firm Performance
before 2007 and after 2009 ....................................................................................72
Table 20 Multivariate Difference in Difference Analysis of Tobin’s Q before
2007 and after 2009 ...............................................................................................74
Table 21 Probit Model to Predict MB....................................................................76
Table 22 Propensity Score Matching: Balance Diagnostic....................................77
Table 23 Propensity Score Matching Results ........................................................78
Table 24 ROA Analysis Based on Propensity Score Matched Sample .................79
Table 25 Tobin’s Q Analysis Based on Propensity Score Matched Sample .........80
Table 26 Revenues by Employee Analysis Based on Propensity Score Matched
Sample....................................................................................................................81
Table 27 Number of Employees Analysis Based on Propensity Score Matched
Sample....................................................................................................................82
Table 28 Business Segments Analysis Based on Propensity Score Matched
Sample....................................................................................................................83
Table 29 Capital investment Analysis Based on Propensity Score Matched
Sample....................................................................................................................84
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Table 30 R&D Analysis Based on Propensity Score Matched Sample .................85
Table 31 Leverage Analysis Based on Propensity Score Matched Sample...........86
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List of Figures
Figure 1 Frequency of MB switching ....................................................................37
Figure 2 Performance of Monitoring Board vs. Not Monitoring Board ................38
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1. Introduction
Boards of directors are of interest to a large number of institutions, regulators, and
investors. As Jensen (1993, p. 862) states, “The board, at the apex of the internal control
system, has the final responsibility for the functioning of the firm.” Moreover, the
purpose of this internal control mechanism is to gather the relevant information that
affects the equilibrium of the firm (Jensen, 1993). Given the importance of the board,
research into factors that increase or decrease firm performance is warranted. There has
been much debate on whether the role of the board enhances or diminishes firm
performance. This topic is of particular interest to regulators, since boards of directors as
important mechanisms of control within organizations can be affected by regulations and
laws that alter the way in which they function. They are also of interest to shareholders
because the board is responsible for the effective control of the agency problem caused
by the separation of residual risk bearing from decision management (Fama & Jensen,
1983).
The goal of this dissertation is to further our understanding of these issues by using
the 2007 financial crisis1 as a natural experiment to examine the decisions taken by a
monitoring board to maintain performance. As Jensen (1993, p. 862) states “Few boards
1 The 2007 subprime mortgage crisis is considered the worst since the Great Depression.
2
in the past decades have done this job well in the absence of external crises. This is
particularly unfortunate given that the very purpose of the internal control mechanism is
to provide an early warning system to put the organization back on track before
difficulties reach a crisis stage.” Thus, financial crisis provides a natural environment to
help understand board decisions such as assessing management, receiving relevant
information and using it, and looking at risk exposure among others. These board
decisions are particularly important for company sustainability especially when the firms
face economic meltdown.
An effective board’s function includes hiring, firing, compensating, and controlling
the top manager (Fama & Jensen, 1983). However there is no consensus in the literature
related with the role of boards (Adams et al, 2010). For instance, Mace (1971) describes a
board’s function as one of advice and counsel, as an element of discipline that should
take action in unstable periods.
Thus, the literature on boards points out that these are responsible for supervision,
control and reward but they are also there to offer high quality advice (Jensen, 1993). At
this point, although the natural conception of boards is to serve as an element of control
inside organizations, some studies focus on the effectiveness of this role (Jensen, 1986;
Weisbach, 1988; Borokhovich et al., 1996; Boone et al., 2007; Cornelli, et al., 2013) and
in some depth, on that control as an equilibrium solution. However, recent studies have
found negative effects on firm performance when the company has a control board
(Adams & Ferreira, 2007; Faleye et al., 2011; Holmlstrom, 2005).2 This brings us to the
2 For an extensive review of the literature see Hermalin and Weisbach (2003, 2010).
3
question of how the board’s role—monitoring or advising—affects firm performance and
in which circumstances the relationship between the board and the firm reaches an
equilibrium solution.
One of the concerns is the problem of asymmetric information that presents itself in
the interaction between the manager and the board as a result of what company
information the manager decides to communicate to the board. This information that the
CEO provides to the board is very important given that, it is precisely this that dictates
the quality of the counsel given to the CEO; however, it also increases the board’s
monitoring of the CEO.
So, the management and the board face a trade-off: on the one hand, the
management decides to either communicate information or not to the board and, on the
other, the board decides to either monitor or provide advice (Adams, 2007; Schmidt,
2015). One such example of this issue is the case of the Royal Dutch Shell scandal in
2004, in which some of the company’s high executives overstated the information about
the reserves estimates and kept it secret from the board; thus, in this situation, the board
did not take action to overcome the situation and allow the company to accomplish the
regulation.
This information is even more essential when the company runs into problems, so
the boards need to implement strategies to lead the company out of financial difficulties.
At this point, the board’s actions involve extremely important decisions that are critical
for company success.
A monitoring board is one that dedicate most of the time of its duties to monitoring
activities. Following Faleye et al. (2011), I define a monitoring intensive board as one in
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which the majority of the independent directors (outsiders) serve in at least two of the
three principal monitoring committees (audit, compensation and nominating or corporate
governance).
The question of whether monitoring boards act differently to non-monitoring
boards when faced with a crisis has been difficult to test due to endogeneity issues. It is a
well known fact that Board Composition and Firm Performance are endogenous variables
(Hermalin & Weisbach, 1998) because a very well structured board would cause high
economical results, and a high performing firm would structure its board according to its
needs. A primary issue is that the MB status is itself endogenous. One possibility is that
firms with more agency problems have more monitoring boards. Thus, to overcome this
issue, this research uses the subprime crisis as a good experiment to test the link between
board type and firm performance in financial crises, and whether the actions taken by the
boards are different. As mentioned by Holmstron (2005), boards need to both acquire
information from management and use it effectively, so the subprime financial crisis
provides an opportunity to test the ability of the boards to be effective.
I present a test of the effect, in treated firms, of having a monitoring board on firm
performance compared to a control group after the subprime crisis. I use the 2007- 2008
period to control for the financial crisis. Beltratti and Stulz (2012) documented, the
period between July 2007 and December 2008, as the worst since the Great Depression.
The identification strategy is twofold. First, I estimate regressions in which I assess
the impact of the crisis on a specific group of firms by interacting an indicator variable
for these firms with an indicator variable for the crisis (Duchin, et al., 2010). I was able
to use the 2007 financial crisis as a natural experiment to test whether firms with
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monitoring boards are associated with improve performance. I use the difference-in-
difference approach (Ashenfelter and Card, 1985) to test whether monitoring board are
associated with reduced firm performance and compare this to those firms that do not
have a monitoring board. This approach allowed me to overcome endogeneity issues that
can drive the relationship between board characteristics and firm performance
(Hermalin & Weisbach, 1998).
Second, I use a propensity score matching approach to attempt to address the
potential endogeneity mention above. I compare whether the monitoring boards are
related with firm performance of treated firms, with their control group during the
crisis (Almeida et al., 2011). I use this methodology to compare the performance of firms
with monitoring boards with their peers before and after the crisis to confirm that the
results are not driven by characteristics other than the fact of being monitoring boards.
The results suggest that firms with monitoring boards are associated with
reduction on performance measures as Tobin’s Q after a crisis compared to those firms
with advisory boards. I found that monitoring boards compared to advisory boards seems
to be costly to shareholders.
Then I am interesting to test how the investments and the leverage change in firms
with monitoring board compare to firms without monitoring board. For this propose, I
test different measures of investment (number of employees, number of business
segments, capital expenditure, R&D) and leverage affected by manager policies in these
two types of boards.
I found that firms with monitoring boards are associated with reduced the number
of employees and capital expenditure more than advisory boards, and they cut their
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investment in an attempt to reach an optimal state. However, as I mentioned above, they
are not associated with value enhanced.
I also found that such firms are not associated with changes in the number of
business segments, R&D expenditure and leverage following the crisis. This result
suggests that, in general, firms in a market with a specific characteristics of business
environment follow stable debt policy that could be driven by the market discipline.
These results confirm that there is a possibility that the managers adopt a policy
that seem to be related with waste of investment for those firms with monitoring boards.
Overall, the results have shown that firms with monitoring boards appear to be
associated with not optimal equilibrium compared to firms without monitoring boards,
basically because the board’s decisions regarding cutting the number of employees and
making capital investment are not value enhanced.
This dissertation’s main contribution is to shed light on the theoretical debate on
whether monitoring boards are beneficial to the long-term survival of firms. The results
suggest negative effects of the recent policies that recommend firms with monitoring
boards.
This dissertation contributes to the literature on organizations and specifically, to
the theory of internal control. Jensen (1993) argues that the ineffective governance of a
board of directors—the head of the internal control mechanism—is responsible for the
financial disaster. The internal control system seldom acts in the absence of a crisis. One
such example is the case of the General Motors Company that reported losses of $6.5
billion in 1990 and 1991 as a result of decades of inaccurate strategy (Jensen, 1993). In
addition, with the recent automotive industry crisis of 2008-2009, GM filed bankruptcy
7
because it needed the protection be able to reorganize its debts. This research helps us
understand what a monitoring board does to overcome a crisis taking into account the
characteristics of the firms and their boards.
The rest of the dissertation is organized as follows. Section II presents the relevant
literature and develops the hypothesis. Section III describes the sample, measurement of
variables, and descriptive statistics. Section IV presents the results. Section IV concludes.
8
2. Literature Review and Hypotheses Development
This dissertation contributes to the theory of organizations, specifically to the
boards of directors, as a mechanism of internal control. Companies’ allocation of
resources to satisfy managers’ preferences instead of shareholder wealth has been a broad
field of study. Wasted investment, cash in excess, and a lack of effort are some of the
consequences of manager misbehavior that require an effective system of decision
control. Thus, the implementation and monitoring of mechanisms to control manager
behavior are the responsibility of the board of directors (Jensen, 1976; Fama & Jensen,
1983).
Boards of directors play an important role in organizations. They must ensure that
managers do not behave myopically and do not sacrifice long-term return investment for
short term profits. They must therefore, safeguard the adoption of the best corporate
strategy to improve the probability of survival.
Boards face a very important challenge when managers behave myopically and as
such sacrifice long-term benefits to increase short-term profits motivated by market
pressure (Jensen, 1986). Also, when managers decide between external and internal
financing, they face a trade-off between remunerating the shareholder and using external
funding that increase firm monitoring.
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Monitoring by the firm’s internal control body is more important when the firm
yields a large amount of free cash flow. Conflicts of interest arise between the
shareholder and the manager because the managers tend to overinvest resources or use
them in organizational inefficiencies (Jensen, 1986). Thus, the generation of more cash
flow is associated with greater internal control.
Board duties serve as an important mechanism of control inside organizations. The
presence of agency costs is one of the reasons for shareholders to delegate the proper
functioning of mechanisms that ensure the company’s success to the board of directors.
For instance, in 1992, IBM failed to adjust its strategy according to the market
competition, which led to the loss of 65% of its value (Jensen, 1993).
However, Mace (1971) suggests that boards also serve as a counsel; hence, the
importance of knowing strategic information about the firm. Boards have to acquire
strategic information from insiders and use it effectively.
Some papers have found cross-sectional differences in board structure and firm
characteristics (Denis and Sarin, 1999; Boone et al., 2007, Coles, et al., 2008). For
example, Coles et al. (2008) found benefits in R&D firms when the insiders on the board
increase and when the firm’s specific knowledge of insiders is important.
The role of advisors has received less attention, as literature has focused on the
monitoring effects and the role that information plays in the decision process and the
effectiveness of the board duties. For instance, Raheja (2005) develops a model to
account for the ideal size and composition of the board for more effective monitoring. In
this model, strategic information about the company is important and plays a significant
role for the board’s success. She presents the importance of information shared by
10
insiders with outsiders. Similarly, Harris and Raviv (2008) present a model for the
optimal control of boards’ structures, highlighting the importance of information relative
to the agency cost. So, when information is relevant, it is best to have insiders in control
of the board that, in fact, allow outsiders to become informed.
With respect other board characteristics, Fich & Shivdasani (2006) found that the
monitoring function of the board is ineffective when they have busy outside directors,
and Faleye (2007) found that classified boards are costly to shareholders due to their
diminished wealth.
However, to my knowledge, few studies focus on the advisory role of the board.
For example, Adams and Ferreira (2007) model the consequences of the board’s two
roles: advisor and monitor. They suggest that an ideal board is one that, in Adams and
Ferreira’s words, is a friendly board, meaning that it plays the role of not monitoring too
much and implying that the board has more insiders. This dissertation is similar to that of
Adams and Ferreira’s. I examine the idea of the benefits of a board that does not monitor
intensively when firms are suffering financial pressure.
Similarly, Faleye et al. (2011) suggest that monitoring that is too intense leads to
lower firm value. They defined a board as monitoring intensive when the majority of the
independent directors serve at least two of the three principal monitoring committees
(audit, compensation and nominating or corporate governance). I extend this finding by
providing evidence of the importance of the advisory role when it is needed more. For
instance, Schmidt (2015) examines the cost and benefits to shareholders of having more
friendly boards—a measure of social connection—in a sample of M&A. This finding
11
supports the view that boards that monitor more are not always the most efficient. And
Linck et al. (2008) found empirical results that support the hypothesis that a firm’s
structure their board based on the costs and benefits of monitoring and advising. This is
particularly important because the regulations recommend more independent board;
however, there is no clear theory of whether an equilibrium model for boards works for
any aspect of the firm.
Additionally, Schwartz and Weisbach (2013), in a study for eleven business
companies in which the Israeli government is involved, categorized models of boards as
managerial models—when the boards play a role in managing the firm or a supervisory
model—when the boards monitor top management. They found that in general, boards
are more active as monitors.
Baldenius et al. (2014) model the interaction between the CEO and the board of
directors of a firm. They investigate the effect of CEO power and monitoring in the
information flow for the board. They show that shareholders choose a board that tends
more towards being advisory with entrenchment issues.
The financial crisis of 2007 - 2009, initially originated in the United States and is
considered the worst since the Great Depression of 1929 to 1932 (Bekaert et al., 2014). It
therefore provides an opportunity to study a number of firm characteristics that influence
the way in which companies face crises. It is imperative to understand, both during a
crisis and after it, how firms recover from crises and the effect of the board of directors in
leading the firms to overcome the crunch. For instance, it is important to study how the
policy choices change by the managers in terms of cash, dividends, capex, etc. during and
after a crisis that allow some firms to perform better than others.
12
Regarding corporate behavior during a crisis, Campello et al. (2010) study the
effect of companies’ plans, financial policies and company spending in firms that are
financially constrained. They found differences between decisions made by constrained
firms when compared to unconstrained firms in terms of company policies. The
management team is responsible for making such decisions. Thus, I argue that the board
of directors influences company behavior. For instance, companies’ plans are approved
by the board so the final decision to accomplish these plans depends on the information
shared by the CEO with the board in order to take the risk and either execute the
company strategy or not. So, my contribution is to add to the literature on corporate
policy debate in order to understand how the directors influence firm practice following a
crisis.
I have found scarce literature related with financial crises and corporate
governance. Particularly in the family context, Lins, Volpin and Wagner (2013) study the
effect of family control during the financial crisis. They found that the cost of family
controlled firms outweighs the benefits. However, firms with low expected agency costs
do not underperform in relation to other firms. In the context of the board, Beltratti and
Stulz (2012) investigated the performance of banks during crises. They found evidence
that banks with shareholder-friendly boards3 were associated with poor performance.
Also, Lemmon and Lins (2003) study the effect of ownership structure during the
financial crisis in East Asian countries. They found evidence that supported the view that
controlling shareholders expropriate minority shareholders during crises.
3 They construct and index to indicate whether the board is shareholder-friendly. Conventional wisdom considers such firms to be better governed.
13
It is important to understand whether the regulation related with the board could
potentially affect the study window and therefore the results. Then, in the next section I
present the major rules that could affect the variable of this study.
2.1. The regulation on board composition
Corporate governance rules have been focused on attention since the recent
financial crises that have affected the economic arena throughout the world. Institutions
like the NYSE and NASDAQ and the U.S. Government have adopted rules in order to
ensure high standards of conduct to protect investors wealth. In this section, I discuses
corporate governance rules regarding the board of directors that would impact the
composition of the board.
The Sarbanes-Oxley Act of 2002 (SOX) is a federal law enacted to respond to the
financial scandals of the beginning of the 21st century to protect financial investors. Title
I of this law is dedicated to establishing all the rules that account for the functioning of
the board. Section 101 (e) establishes that there must be 5 members to the board, and that
these members will work for the company on a full-time basis and adopt independent
standards and rules for the protection of all the investors.
Title 3 is related with corporate responsibility, and point 3 in Section 301
establishes that listed firms must have audit committees made up by independent
members of the board.4
4 According with SOX an independent member is one that may not “accept any consulting, advisory,
or other compensatory fee from the issuer”; or may not “be an affiliated person of the issuer or any subsidiary thereof.”
14
Also, national stock exchanges such as NYSE, NASDAQ and AMEX adopted
corporate governance standards for listed companies in order to ensure market efficiency
and investor protection. The corporate governance rules for the New York Stock
Exchange (2003) required listed companies to have a majority of independent directors,
to establish nominating/corporate governance committees, a compensations committee,
and an audit committee made up entirely of independent directors.
The Dodd-Frank Act of 2010 is the financial regulatory reform law that protects the
investor. Title IX of the Investor Protection and Securities Reform Act of 2010,
establishes the main principle as increasing investor protection and empowering
shareholder participation in the firm. Section 951 establishes that all listed companies
must have compensation committees made up by independent members of the board.
After reviewing the rules that could potentially affect the results of the main
variable of this study, which is monitoring intensive boards that account for those firms
where the majority of directors are classified as intensive directors (a director who serves
in at least two of the three main committees: audit, compensation and
nominating/corporate governance), meaning those firms where the majority of directors
are devoted to monitoring activities. It is clear that the regulations discussed above do
affect the inclusion of independent directors and the establishment of committees
however during the window of this study there were no significance changes that would
affect the results of the main variable.
15
2.2. Hypotheses Development
In addition to the fact that the previously mentioned institutions such as the New
York Stock Exchange and Nasdaq, and regulations such as the Sarbanes-Oxley Act of
2002 require the majority of independent directors to serve on the board. There is no clear
information in the literature on whether too much control exercised by the independent
directors benefits shareholder wealth (Adams and Ferreira 2007; Faleye et al. 2011;
Holmlstrom 2005). Moreover, the board’s duty to advise is one of the most important
function in setting up the right firm strategy (Mace 1971; Jensen 1993). Thus, the
interaction between the manager and the board would potentially be different when the
firms have a monitoring board compared with those firms with advisory boards for the
following reasons: first, the quality of the information provided by the manager to the
board would be lower, and second, the decisions made by the board to accomplish the
firm strategy plan would not be effective. On the other hand, firms with advisory boards
would enjoy some benefits such as receiving the relevant information from the manager
and adjusting the firm strategy plan as needed according to the economic environment.
Thus, one would expect a different performance from those firms with monitoring boards
compared to those without them, which would give rise to underperformance when the
firms face unstable periods. The above lead to the following hypothesis.
Hypothesis 1 Firms with a monitoring board of directors perform differently to firms
with non-monitoring boards.
16
There is scarce literature that directly analyzes the boards’ role and its effect on
corporate policies such investment and leverage, as well as how those firms respond to
economic downturn. In the board literature, Coles, Daniel and Naveen (2009), examine
the relationship between board structure and some firm characteristics; for example, they
study the optimal board structure of complex firms -firms that diversify their operations,
larger firms and high leverage firms-. Regarding corporate policies Almeida, Campello,
Laranjeira and Weisbenner (2011) use the 2007 credit crisis to analyse the implications of
debt maturity for firms’ financial policies. For firms with monitoring boards, I would
expect that the board would decide to cut investments measures as the number of
employees and capital investment as soon as the economic slowdown begins because the
directors would take action to control the administrators to a greater degree and as such,
guarantee the firm’s survival. On the other hand, different authors have shown that
different firms need to have a diverse board made up by a group of independent directors
with expertise and experience in different types of businesses (Coles et. al. 2009). Thus, I
expect that firms with monitoring boards would operate in more business segments. With
respect R&D investment, I consider that firms with monitoring boards would have a
significant effect on R&D investment mainly because CEOs would expend on risky
projects and, on the other hand, the advice and counsel provided by the board would not
be effective. The above discussion leads me to propose that firms with monitoring boards
of directors adopt different decisions regarding investment when compared to firms with
non-monitoring boards that affect performance. Therefore, I would expect change in
investment measures as the number of employees, number of business segments, capital
investment, and R&D expenditure following the crisis for those type of firms.
17
Hypothesis 2 Firms with a monitoring board change their investment when compared to
firms with non-monitoring boards.
Regarding leverage, the financial theory states that more leverage firms are less likely to
suffer from agency problems because of the market discipline so that firms with
monitoring boards would be no different to those firms with non-monitoring boards.
Hypothesis 3 Firms with a monitoring board do not change the leverage when compared
to firms with non-monitoring boards.
18
3. Data
3.1 Sample selection
To construct a sample for my analysis, I use data on board characteristics from the
Institutional Shareholder Services (ISS) database (formerly Risk Metrics) of 2,541 firms
between 2004 and 2012 that trade in the Standard and Poor’s (S&P) 1500. The
accounting data is from Compustat and the market data is from The Center for Research
in Security Prices (CRSP). I then exclude financial firms and utilities5 mainly because of
the differences in regulation. I also, exclude firms with assets of less than $ 10 million,
revenues of less than $5 million, return on assets (ROA) and equity of less than zero, and
stock prices of less than one dollar.6
3.2 Advisory Board Definition
The measure I use for advisory boards is as follows: A board is considered advisory
when it does not engage in intensive board monitoring. This measure requires
information about the committees that each director serves. Thus, I use an ISS database to
collect the number of directors that serve in the main monitoring committee—audit,
5 I use the Standard Industrial classification (SIC), SIC Code between 6000 to 6999 from the finance sector (3723 observations deleted) and SIC code 49 from the utilities sector (1342 observations deleted) are excluded. 6 By Assets < $ 10 million (I dropped 55 observations), Equity < $ 0 (315 observations), Price < $1 (22 observation), Revenues < $ 5 million (23 observations), ROA < 0 (698 observations).
19
compensation and nomination—for each firm in the study window. I then verify
odd data manually using an EDGAR database from SEC. I use the definition by Faleye et
al. (2011) for a monitoring intensive board, whereby most of the directors are monitoring
intensive. A director is considered monitoring intensive when he serves in at least two of
the three main committees. This measure provides a proxy to understand the devotion of
the director to play the role of monitor. Then, at board level, a monitoring intensive board
is one in which most of the members engage in monitoring duties.
Thus a board that is not devoted to excessive amounts of monitoring is one that I
define as providing counsel; i.e., an advisory board. Thus, an advisory board faces low
information asymmetry costs because for the CEO the cost of providing information to
the board is lower than not sharing information. Thus, an advisory board would provide
better advice for the CEO’s actions.
This proxy allowed me to identify the determinants of the companies with advisory
boards compared to those firms with monitoring boards.
3.3 The Definition of Control Variables
Following the Board literature, I control for a vector of firm and industry
characteristics that may affect firm performance. All variables are estimated for firm i
over its fiscal year t. The control variables include Firm Size, measured by the natural log
of market value, Stock Return is the annual stock price return, Board size measured by
the natural log of the total directors, and Board Ownership measured by the percentage of
ownership of the board members. In Appendix, I present a detailed definition of the
variables.
20
3.4 Descriptive Statistics
To minimize the effect of outliers, I winsorize all variables at the 1st and 99th
percentiles. Panel A of Table 1 provides descriptive statistics for board characteristics for
the entire sample. The unit of observation is the firm-year. On average, the board has 9
members of which 3 are monitoring intensive directors who sit on at least two of the three
main committees (audit, compensation, or nominating). About 48% of firms has a
monitoring intensive board in which the majority of the members are monitoring
intensive directors.
Panel B of Table 1 presents summary statistics of the firm characteristics. In our
sample, an average firm has a market value of $8,943 million, return on assets of 15%,
Capex scaled by sales of 7%, leverage of 16%, Tobin’s Q of 1.8, and number of business
segments of 2.5.
Table 2 provides descriptive statistics for the sample before 2008. The unit of
observation is the firm-year. On average, the board has 9 members of which 3 are
monitoring intensive directors who sit on at least two of the three main committees
(audit, compensation, or nominating). About 47% of firms has a monitoring intensive
board in which the majority of the members are monitoring intensive director.
Panel B of Table 2 presents summary statistics of the firm characteristics before
2008. In this sample, an average firm has a greater market value of $9,239 million,
greater return on assets of 16%, equal capex scaled by sales of 7%, equal leverage of
16%, greater Tobin’s Q of 1.95, and similar average of number of business segments of
2.5.
21
Table 3 provides descriptive statistics for the sample after 2008. The unit of
observation is the firm-year. On average, the board has 9 members of which 3.8 are
monitoring intensive directors who sit on at least two of the three main committees
(audit, compensation, or nominating). About 49% of firms has a monitoring intensive
board in which the majority of the members are monitoring intensive director.
Panel B of Table 3 presents summary statistics of the firm characteristics after
2008. In this sample, compared to the firm characteristics before 2008, an average firm
has a lower market value of $9,128 million, equal returns on assets of 15%, lower capex
scaled by sales of 6%, equal leverage of 16%, lower Tobin’s Q of 1.72, and similar
average of number of business segments of 2.49.
Table 4 presents the monitoring board analysis. In Panel A I show the average of
monitoring board by year for a different definition of the MB variable. On average, 53%
of the firms where identified with MB however only the 22% of the firms never changed
their MB status. Similarly, in the year of 2009 44% of the firms where MB but only 18%
of the firms never changed their MB status.
In panel B of Table 4, I present the results of a Probit model that estimate the
determinant of monitoring board switching. Firm Size is negative and economically
significant at 1%, Tobin’s Q base on average pre-crisis values is negative and significant
and Capex divided by sales pre-crisis values is negative and significant when I control for
industry and year characteristics.
Figure 1 shows the average frequency of firm that change its monitoring board
status during the study window. The frequency of switching is estimates as a dummy
22
variable that take the value of one if the firm change its monitoring board status zero
otherwise. On average 6% of the firms switch MB status after the crisis.
Table 5 presents the correlation matrix showing the relationship between the
variables used in this study. An MB board correlates highly with board size, board
composition and firm size and correlated only slightly with number of business segments
and board ownership. However, these last two variables are important to consider
because previous works have shown that different firms need a group of diverse
independent directors from a wide variety of industries and board ownership would allow
greater control over the CEO.
Variables such as Leverage, Capex/Sales, Firm Size, R&D/Total Assets and
Board Size correlate with ROA as it documented in the literature. In addition, for Tobin’s
Q, the variables used in the regression show the relationship between each one and the
dependent variable.
Since the regression between performance and MB could be endogenous, for
example, one can argue that an MB board would reduce performance, however it is
possible that low-value firms adopt more independent directors that devote most of the
time to monitoring activities. Then for addressing endogeneity issues encountered in the
relation between performance and MB, I use a difference-in-differences model and a
matching approach.
Figure 2 shows the average performance measure as ROA and Tobin’s Q of firms
with monitoring board and non-monitoring firm.
23
4. Results
4.1 Comparison of firm characteristics
Table 6 presents the comparison of means between firms with MB and advisory
boards. The results suggest that there is statistical significance difference at 5% or lower
between monitoring boards and advisory boards regarding firm characteristics such as
Tobin’s Q, ROA, Board Composition, Board Size, Leverage, Firm Size, Number of
Business Segments, and R&D divided by assets when the MB variable is defined based
on the last year prior to the crisis. These results suggest that MB and advisory boards
behave differently in terms of these characteristics that determine how firms decide to
implement their strategies. Thus, I am interested in understanding the influence of an MB
on a firm.
The results are similar, when the MB variable is defined based on the first sample
year for the firm except for ROA and R&D/Assets variable. Also when I defined the MB
variable based on a window of 3 years before the crisis when the MB status is in the three
years I find similar results with an exception in R&D/Assets variable.
In addition to the mention above, Panel D of Table 6 presents the comparison of
firm characteristics when the MB variable is defined base on a window of three years
before the crisis when the MB status is in the two of the three years and in Panel E I test
the mean difference when the MB status is in at least in one of the three years. Overall
the results remain similar with an exception of the following variables: Leverage, Firm
24
Size and R&D divided by Assets for the results in Panel D and Board Size, # of Business
Segment, Leverage and R&D divided by Assets for Panel E.
4.2 Analysis of MB and Firm Performance
In this section, I use the difference-in-differences (DID) approach to determine the
effect of MB on firm performance following a crisis. This methodology compares firm
performance output before and after the financial crisis of 2008 that caused a natural
shock to monitoring board versus advisory board. The DID methodology is convenient in
some aspects. First, the DID methodology excludes omitted trends that are
correlated with MB and firm performance in both the treatment and control groups.
Second, the DID approach helps establish causality as the experiment is conducted
surrounding financial crises that cause exogenous variation in the change in MB (the
main independent variable). And finally, as with the inclusion of firm fixed effects and
industry-year fixed effects in the OLS specifications, the DID approach controls for
constant unobserved differences between the treatment group and the control group.
I start by identifying the date of the financial crisis, and use 2007 to 2008 to control
for the financial crisis date. Beltratti and Stulz (2012) document the period from July
2007 to December 2008, the worst since the Great Depression.7
7 I carried out several tests in periods near to the Financial Crisis and I found similar results. The results are presented in the Robustness section.
25
For my purposes, “treatment” firms are those that have monitoring board (firms in which
most of the board’s members are monitoring intensive directors8), while “control” firms
are those that have an advisory board (firms that do not have a monitoring board). I
estimate the following model:
(1)
The dependent variable is either ROA, firms i’s operating performance in a given
year, or Tobin’s Q, firms i’s Q in a given year. MB is a dummy that equals one if a firm-
year observation has intense board monitoring and zero otherwise. Post Crisis is a
dummy that equals one if the year is greater than 2008, and zero otherwise. is a
vector of firms with board and industry characteristics that could affect firm performance.
I include industry-year fixed effects to account for industry and year variations that
may affect the relationship between firm performance and monitoring boards, and firm
fixed effects to control for omitted firm characteristics that are constant over time. I
cluster standard errors by firm to avoid inflated t-statistics (Petersen, 2009).
Table 7 presents the univariate DID analysis of firm performance and monitoring
intensive boards. In Panel A, the MB variable is defined based on the last year prior to
the crisis. In Columns (1) and (3), I use Tobin’s Q is estimated as the book assets minus
book equity plus market value of equity minus deferred taxes all divided by book assets.
The effect of market performance measured as the Tobin’s Q of a monitoring intensive
board compared to an advisory board is negative but not significant even when I control
for industry-year characteristics after the crisis.
8 A monitoring intensive director is a director that serves in at least two of the three main committees (audit, compensation and nominated).
0 1 2 3 + MB + MB +X + + + it it it it it it st i itPerform PostCrisis PostCrisis
X it
st
i
26
In Columns (2) and (4), the dependent variable is the operative performance estimated as
the operational income before depreciation divided by total assets “ROA”. The results
suggest that advisory board firms, on average, increase operating performance compared
with monitoring board firms after the crisis. However, after controlling by industry year
fixed effects the results remain negative but are not significant. To check the validity of
the study window and the sensitivity of the results, I test this model using different time
windows surrounding the crisis as the analysis and the results are similar.9
However, in panel B when the MB variable is defined based on firms which never
changed MB status at all over the sample period, Tobin’s Q became significant and
remain negative.
The greater increase in operative performance with an advisory board for some
firms reflects differences beyond being managed by an MB, such as differences in firm
size, capital structure, capital expenditure, or industry specific factors. Prior literature has
shown that board structure is defined by some observed firm characteristics (Boone et al.,
2007; Coles et al., 2007). Moreover, unobserved firm-level heterogeneity could account
for firms’ differential performance in crises. If firm size allows firms to respond better to
crisis, then MB might not be the only factor that drives the increase in firm performance
in advisory board firms. For this reason, I use a multivariate analysis to test MB and
advisory firms’ response to the shock of the financial crises.
I estimate a panel regression with firm fixed effects and industry-year fixed effects
to relate firm performance with monitoring intensive boards after the subprime crisis. The
dependent variable in each regression is the operating performance “ROA” estimated as
9In addition, I use the periods from 2007 to 2008 and 2007 to 2009 for the crisis time window, obtaining similar results.
27
the operational income before depreciation divided by total assets. In the regressions, the
main explanatory variable of interest is Monitoring Board (MB), a dummy equal to 1 if
most of the board’s members are monitoring intensive directors. I interact MB with Post
Crisis, a dummy variable equal to 1 for observations later than 2008. These interaction
terms capture the effect of MB firms after the subprime crisis on firm performance. I
account for time-invariant firm characteristics by including firm fixed effects in each
regression regardless of whether they are unobserved and of their correlations with the
variables of study. The regressions also include control variables for Firm Size, Board
Size and Board Ownership. Robust standard errors are clustered at firm level.
Table 8 reports the results of the fixed-effects regressions. The effect of intense
board monitoring of firm performance after the crisis compared to the control group is
negative and economically significant at 10% when the MB variable is defined based on
a window of 3 years before crisis when the MB status is in the three years. Thus,
consistent with the Hypothesis, the results suggest that firms with monitoring boards, on
average, reduce operative performance following the crisis when compared to advisory
board firms.
In Columns (3) and (4) of Panel B, I use industry fixed effects to account for
industry heterogeneity characteristics. The overall results are similar in economical and
statistical significance.
Table 9, reports the analysis of Tobin’s Q, the variables of interest are economic
and statistical significance. Thus, on average, Tobin’s Q of monitoring boards is lower
after crises than that of advisory board.
28
Table 10 shows the results of a multivariate analysis of revenue by employee. I
include firm fixed effects and industry-year fixed effects. The dependent variable is
Revenue by Employee. Our main variable of interest is “MIB x Post Crisis” which is
negative but no significant. This result suggests that monitoring boards do not change
revenue by employee after the crisis compared to advisory boards even after controlling
for industry year fixed effects. There is no difference in the change of Revenue by
employees among this two types of boards.
4.3 Analysis of MB and Measures of Firm Scale
Next, I present multivariate regressions to analyze how a monitoring board acts in
terms of scale. Thus, in Table 11 to Table 16, I examine different choices such as the
number of employees, number of business segments, capital investment, R&D and
leverage. This allowed me to examine the effect of scale in firms with MIB compared to
advisory boards before and after the crisis. For this effect, I estimate the following
baseline difference-in-differences specification:
(2)
The dependent variable Firm scale is either number of employees, number of
business segments, capital investment, R&D or leverage in a firm i in a given year. MB is
a dummy that equals one if a firm-year observation has a monitoring intensive board, and
zero otherwise. Post Crisis is a dummy that equals one if the year is greater than 2008,
and zero otherwise. is a vector of firm, board, and industry characteristics that could
affect firm performance. I include industry-year fixed effects to account for industry
and year variation that may affect the relation between firm performance and monitoring
0 1 2 3_ + MB + MB +X + + + it it it it it it st i itFirm scale PostCrisis PostCrisis
X it
st
29
board, and firm fixed effects to control for omitted firm characteristics that are
constant over time. Robust standard errors are clustered by firms.
The results in Table 11, Panel A and B suggest that firms with monitoring boards
tend to decrease the number of employees after a crisis. These results are consistent with
the point of view that when firms are in financial difficulties and need to reduce
expenses, one of the mechanisms is to reduce the number of employees. However, when I
test the change of number of employees I do not find any difference between this two
types of boards.
Then in Table 13, I study the number of business segments, another measure of
scale, so the results suggest that MBs do not change the number of business segments.
Regarding capital investment, Table 14 suggests that MBs tend to have less capital
expenditure divided by sales.
Then Table 15 suggests that MB firms do not differ in their R&D divided by sales when
compared to non MB firms following the crisis.
And finally, Table 16 presents that MBs, on average, do not differ in their leverage
decision when compared to advisory boards after the crisis.
Overall, the above results are consistent in terms of scale. On average, MBs reduce
scale, number of employees and capital expenditure. However, MB firms do not differ in
number of business segments, R&D expenditure and leverage.
4.4 Additional Robustness Test
I use different time windows to study and confirm the main hypothesis. First, I
analyze the sensitivity of MB firms to the definition of the financial crisis period. Table
17 to Table 20 present the results of the regressions. Table 17 and Table 18 present the
i
30
results when the control group is made up of firms before 2007, and the treatment group
is made up of firms after 2007.
Table 19 and Table 20 show the results when the treatment group is made up of
those firms reporting information after 2009 and the control group is made up of those
firms reporting information before 2007. Overall, the results for Tobin’s Q remain
economically similar and significant.
4.5 Performance, Investment, Leverage and Monitoring Boards: A Matching
Approach
This section addresses the issue that the results could be due to selection bias. Thus,
I consider several alternative hypotheses to explain the underperformance of monitoring
board firms, which could weaken the conclusion that a monitoring board is costly to
shareholders.
Adams and Ferreira (2007), and Faleye, Hoitash and Hoitash (2011) argue that
information provided by the CEO to the board is important to the performance of board
duties. However, when the board monitors the CEO too much, he does not share strategy
information with the board that could allow them to take better decisions about the
strategy and its implementation, therefore leading to extra costs for the investors. My
findings support this argument.
However, a second alternative is that monitoring board firms may be different from
other firms and such differences could make monitoring board firms more vulnerable to
financial breakdown. I found that monitoring board firms have different corporate
policies.
Another possibility is that firms with more agency problems have more monitoring
boards. In this case, the results might indicate how firms with agency problems
31
performed following the crises. The propensity score matching approach is an attempt to
address this endogeneity.
Now to test whether the results are driven by firm characteristics, other than being a
monitoring board firm, I use propensity score matching to generate samples of firms
without monitoring boards that have the same observables characteristics as monitoring
board firms. I follow Rosenbaum and Rubin (1983) to conduct a propensity score
matching method to account for the bias in the estimation of treatment effects.
The effect of a treatment on firm , , is estimated as the difference between
potential outcomes with a monitoring board and without a monitoring board:
Where states 0 account for non-treatment (advisory board) and states 1 correspond
to firms with the treatment effect (monitoring board).
The average treatment effect (ATE) is measured by , where is
the performance (or firm scale) in firm when the firm has a monitoring board and
when it does not. ATE measures the expected effect on the monitoring board firm had the
population been assigned randomly as treated firms (monitoring board firm).
ATE includes effects of firms for which the treatment (monitoring board status)
was never intended. I am interested in the average effect on the treated (ATT) which
estimates the expected outcome on those for whom the treatment (monitoring board
status) is intended. The average treatment effect on the treated is defined as ATT=
. Where refers to the treatment. The counterfactual mean
is not observed so that I need to define a substitute measure to estimate
the value. The use of the mean outcome of untreated (advisory board) is
ii
iii yy 01
)]0()1([ ii yyE )1(iy
i )0(iy
]1|)0()1([ DyyE ii1D
]1|)0([ DyE i
]0|)0([ DyE i
32
subject to a selection bias as the outcome (firm performance or firm scale) of the treated
(monitoring board firms) and untreated firms (advisory board firms) could differ even in
the absence of treatment (monitoring board status) as the covariates that influence the
treatment decision could also influence the outcome variable. The following equation
explains the estimation bias.
The selection bias is present if when the
performance or firm scale in monitoring board firm could differ from that of advisory
board firm even in the absence of treatment (monitoring board status) due to both
observable and unobservable factors. This bias could be eliminated if firms randomly
assign the monitoring board and not monitoring board status, so the propensity score
matching method using the nearest neighbor matching method minimizes the effect of the
selection bias. Regardless of the treatment status, observations with the same propensity
score must have the same distribution of observable and unobservable characteristics
therefore the exposure to the treatment can be considered random (Rosenbaum and
Rubin, 1983).
The propensity score based analysis is estimated following Dehejia and Wahba
(2002). I use a probit regression with a binary variable as a dependent variable that takes
the value of one if the firm has a monitoring board, and zero otherwise. The set of
pretreatment characteristics include the pre-crisis mean values of the variables Board
Size, Board Composition, Firm Size, and Revenue by Employees. I include industry and
]0|)0([]1|)1([ DyEDyE ii
]0|)0([]1|)0([]1|)0([]1|)1([ DyEDyEDyEDyE iiii
0]0|)0([]1|)0([ DyEDyE ii
33
year dummies to account for any heterogeneity between industry and time. The results
are shown in Table 21.
An MB has a negative and significant relationship at 1% with the board size. Board
Composition is negative and economic significance is at 1%. All those variables suggest
the importance of scale when the firm has an MB.
I present the propensity score matching in Table 22. I match each monitoring board
firm with a set of control firms that have comparable characteristics to the monitoring
board firm but they are not monitoring board firms. Monitoring board firms and matched
non-monitoring board firms are not significantly different across all the matching
dimensions for the matched sample. Additional, almost all the variables have a well-
balanced matched sample with a bias of less than 5% after matching.
Table 23 presents the average difference in the variable of this study between
treated (Monitoring Board Firms) and matched control firms (Advisory Board Firms). I
estimate the nearest available Mahalanobis metric matching within calipers defined by
the propensity score. I use dummies to account for heterogeneity between industry. The
results show than, overall, the monitoring board firms have lower Tobin’s Q than
matching control firms.
Then, I perform a regression analysis using the propensity score matched sample.
The results are shown in Table 24 to Table 31. I confirm the hypothesis that monitoring
board firms underperform measure as Tobin’s Q following the crisis period when
compared to the matched sample that suggest compelling results. However, in terms of
ROA, Revenues by Employees, the number of employees, Business Segment, Capital
34
Investment, R&D, and Leverage there is not difference in the changes of MB compare to
non MB firms.
35
5. Discussion
The board of directors as an organism responsible for the internal control of firms
has constituted a broad field of study. However, mixed results have not provided a
conclusive answer. This dissertation provides new evidence on the value of advisory
boards by studying their effect on performance and corporate policy after the financial
crisis of 2007-2008.
The dissertation tries to examine the link between board type and performance
following the crisis. I find evidence that MB is detrimental to shareholder. However,
there is endogeneity concerns that could weak this conclusion, for example one
possibility is that firm with more agency problems have more monitoring boards. For that
I try to address this issue with propensity score matching methodology. This paper
provides some unique an important evidence on how different types of firms performed
following the crisis.
The decisions made by the firm to overcome financial crisis shocks affect the
benefits and costs of the firms in having a monitoring or advisory board. The results show
that advisory board focuses in long term consequences while monitoring board discipline
more. For instance, a monitoring board may be detrimental to performance measure such
as Tobin’s Q, even after controlling for different periods of time and different tests
compared to advisory boards. On the other hand, monitoring boards reduced the number
36
of employees and capital expenditure following the crisis. MB firms tend to not differ of
the number of business segments and research and development expenditure.
Comparative to firms with monitoring boards, the actions taken by advisory boards
are in the interest of the shareholder. I found that in terms of leverage, there is no
statistical difference in the firm policy made between an MB and advisory board.
Notice that one of the mechanism when the companies expect low performance is
to reduce number of employees; however, this action does not reflect a better
performance in any of the variables proposed when compared to non-monitoring firm.
There is a cost associated with monitoring board firms that do not allow the
strategy and its implementation to be affective when the company faces financial failure.
This may, among other reasons, be due to the fact that so much oversight reduces the
flow of information shared by the managers with the board, so the board cannot execute
its function properly.
37
Figure 1 Frequency of MB switching
This figure shows the average frequency of firm that change its monitoring board status from three years
before the subprime financial crisis to three years after it. The frequency of switching is estimates as a
dummy variable that take the value of one if the firm change its monitoring board status during the study
window.
0,00
0,02
0,04
0,06
0,08
0,10
0,12
0,14
0,16
0,18
2004 2005 2006 2007 2008 2009 2010 2011 2012
From Advisory Board to MB
From MB to Advisory Board
38
Figure 2 Performance of Monitoring Board vs. Not Monitoring Board
This figure shows the average performance estimated by the mean ROA and Tobin’s Q for treatment and
control firms, from four years before the subprime financial crisis to four years after it. The vertical lines
from each of the annual nodes represent two standard errors. The MB variable is defined based on a
window of three years before crisis when the MB status is in the three years.
0,1
0,11
0,12
0,13
0,14
0,15
0,16
0,17
0,18
2003 2004 2005 2006 2007 2008 2009 2010 2011 2012
RO
A
Year
Performance
MB
Non MB
1,20000
1,40000
1,60000
1,80000
2,00000
2,20000
2,40000
2003 2004 2005 2006 2007 2008 2009 2010 2011 2012
Q T
ob
in
Year
Performance
MB
Non MB
39
Table 1 Descriptive Statistics for the Entire Sample
The sample consists of 2,541 firms between 2004 and 2012. The number of Dependent Directors (DD) is
the number of inside directors. The number of Linked Directors (LD) is the number of affiliate external
directors. The number of Independent Directors (ID) represents the directors with no material connection to
the company. The Total Directors (TD) represent board size. The number of Independent Directors M.
Committees (IDC) is the sum of the number of members of the audit committee plus the number of
members of the compensation committee, and plus the number of members of the nominating or the
corporate governance committees. Monitoring Intensive Director (MID) is a dummy variable that takes the
value of one if a director serves in at least two of the three monitoring committees (audit, compensation and
nominating or corporate governance), and 0 otherwise. Monitoring Board (MB) is a dummy variable that
takes the value of one if most of the board’s members are monitoring intensive directors. Board
Composition refers to the number of independent directors divided by the number of the total directors.
Tobin’s Q is estimated as the book assets minus book equity plus market value of equity minus deferred
taxes, all divided by book assets. ROA is estimated as the operational income before depreciation divided
by total assets. Leverage is the long-term debt divided by total assets. Rev/Employ is the total revenue
divided by number of employees. Capex represents the capital expenditure in millions of dollars. Market
Value is in millions of dollars. R&D represents research and development. The number of Business
Segments is the number of segments. All continuous variables are winsorized at the 1st and 99th percentile
values.
Description Obs. Mean Median Min. 25th
%ile
75th
%ile
Max. SD.
Panel A
Board characteristics
# Dependent Director (DD) 8,409 1.480 1 0 1 2 7 0.780
# Linked Director (LD) 8,409 0.670 0 0 0 1 8 0.970
# Independent Director (ID) 8,409 6.890 7 0 5 8 18 2.120
Total Director (TD) 8,409 9.040 9 4 7 10 23 2.130
# Audit Committee Member 8,409 3.610 3 0 3 4 9 1.010
# Compensation Committee Member 8,409 3.500 3 0 3 4 9 1.130
# Nom./Corp.Gov Committee Member 8,409 3.460 3 0 3 4 13 1.570
# Independent Director M. Committees (IDC) 8,409 10.57 10 0 9 12 24 3.100
Monitoring Intensive Director (MID) 8,409 3.600 3 0 3 5 11 1.710
Monitoring Board (MB) 8,409 0.480 0 0 0 1 1 0.500
Board Composition 8,409 0.760 0.780 0 0.670 0.880 1 0.130
40
Table 1, continued
Description Obs. Mean Median Min. 25th %ile. 75th %ile Max. SD.
Panel B
Firm characteristics
Tobin’s Q 8,409 1.790 1.540 0 1.140 2.170 7.690 1.150
ROA 8,409 0.150 0.140 0 0.100 0.190 0.410 0.0800
Leverage 8,409 0.160 0.150 0 0.0100 0.260 0.600 0.140
Rev./Employ 8,409 423.4 276.0 24.73 184.8 442.7 3016 494.0
Capex/Sales 8,409 0.0700 0.0300 0 0.0200 0.0600 0.800 0.110
Market Value 8,409 8,943 2,007 86.25 801.2 6,240 159,298 22,309
Capex 8,409 366.1 63.47 0.730 20.62 221.8 7061 975.2
R&D/Total Assets 8,409 0.0300 0 0 0 0.0400 0.200 0.0400
# Business Segment 8,409 2.510 2 1 1 4 9 1.840
41
Table 2 Descriptive Statistics before 2008
The sample consists of 2,536 firms between 2004 and 2007. The number of Dependent Directors (DD) is
the number of inside directors. The number of Linked Directors (LD) is the number of affiliate external
directors. The number of Independent Directors (ID) represents the directors with no material connection to
the company. The Total Directors (TD) represent board size. The number of Independent Directors M.
Committees (IDC) is the sum of the number of members of the audit committee plus the number of
members of the compensation committee, and plus the number of members of the nominating or the
corporate governance committees. Monitoring Intensive Director (MID) is a dummy variable that takes the
value of one if a director serves in at least two of the three monitoring committees (audit, compensation and
nominating or corporate governance), and 0 otherwise. Monitoring Board (MB) is a dummy variable that
takes the value of one if most of the board’s members are monitoring intensive directors. Board
Composition refers to the number of independent directors divided by the number of the total directors.
Tobin’s Q is estimated as the book assets minus book equity plus market value of equity minus deferred
taxes, all divided by book assets. ROA is estimated as the operational income before depreciation divided
by total assets. Leverage is the long-term debt divided by total assets. Rev/Employ is the total revenue
divided by number of employees. Capex represents the capital expenditure in millions of dollars. Market
Value is in millions of dollars. R&D represents research and development. The number of Business
Segments is the number of segments. All continuous variables are winsorized at the 1st and 99th percentile
values.
Description Obs. Mean Median Min. 25th
%ile
75th
%ile
Max. SD.
Board characteristics
# dependent director (DD) 3526 1.560 1 0 1 2 7 0.810
# Linked Director (LD) 3526 0.920 1 0 0 1 8 1.150
# Independent Director (ID) 3526 6.540 6 0 5 8 16 2.140
Total Director (TD) 3526 9.020 9 4 7 10 18 2.190
# Audit Committee Member 3526 3.470 3 0 3 4 9 1.060
# Compensation Committee Member 3526 3.300 3 0 3 4 9 1.170
# Nom./Corp.Gov Committee Member 3526 3.110 3 0 2 4 13 1.650
# Independent Director M. Committees (IDC) 3526 9.880 9 0 8 12 23 3.220
Monitoring Intensive Director (MID) 3526 3.330 3 0 2 4 11 1.710
Monitoring Board (MB) 3526 0.470 0 0 0 1 1 0.500
Board Composition 3526 0.720 0.750 0 0.630 0.830 1 0.140
42
Table 2, continued
Description Obs. Mean Median Min. 25th %ile 75th %ile Max. SD.
Firm characteristics
Tobin’s Q 3,526 1.950 1.690 0 1.270 2.390 7.690 1.230
ROA 3,526 0.160 0.150 0 0.100 0.200 0.410 0.0800
Leverage 3,526 0.160 0.140 0 0.0100 0.250 0.600 0.140
Rev/Employ 3,526 392.0 253.7 24.73 172.3 404.5 3016 467.4
Capex/Sales 3,526 0.0700 0.0400 0 0.0200 0.0700 0.800 0.110
Market Value 3,526 9,239 2,164 86.25 896.4 6,629 159,298 22,363
Capex 3,526 353.1 65.41 0.730 21 224.3 7061 937.3
R&D/Total Assets 3,526 0.0300 0.0100 0 0 0.0400 0.200 0.0400
# Business Segment 3,526 2.540 2 1 1 4 9 1.850
43
Table 3 Descriptive Statistics after 2008
The sample consists of 2,541 firms between 2009 and 2012.The number of Dependent Directors (DD) is
the number of inside directors. The number of Linked Directors (LD) is the number of affiliate external
directors. The number of Independent Directors (ID) represents the directors with no material connection to
the company. The Total Directors (TD) represent board size. The number of Independent Directors M.
Committees (IDC) is the sum of the number of members of the audit committee plus the number of
members of the compensation committee, and plus the number of members of the nominating or the
corporate governance committees. Monitoring Intensive Director (MID) is a dummy variable that takes the
value of one if a director serves in at least two of the three monitoring committees (audit, compensation and
nominating or corporate governance), and 0 otherwise. Monitoring Board (MB) is a dummy variable that
takes the value of one if most of the board’s members are monitoring intensive directors. Board
Composition refers to the number of independent directors divided by the number of the total directors.
Tobin’s Q is estimated as the book assets minus book equity plus market value of equity minus deferred
taxes, all divided by book assets. ROA is estimated as the operational income before depreciation divided
by total assets. Leverage is the long-term debt divided by total assets. Rev/Employ is the total revenue
divided by number of employees. Capex represents the capital expenditure in millions of dollars. Market
Value is in millions of dollars. R&D represents research and development. The number of Business
Segments represents the number of segments. All continuous variables are winsorized at the 1st and 99th
percentile values.
Description Obs. Mean Median Min. 25th
%ile
75th
%ile
Max. SD.
Panel A
Board characteristics
# Dependent Director (DD) 3,964 1.420 1 0 1 2 6 0.760
# Linked Director (LD) 3,964 0.470 0 0 0 1 7 0.760
# Independent Director (ID) 3,964 7.160 7 0 6 9 18 2.070
Total Director (TD) 3,964 9.050 9 4 8 10 23 2.090
# Audit Committee Member 3,964 3.720 4 0 3 4 9 0.960
# Compensation Committee Member 3,964 3.650 3 0 3 4 9 1.050
# Nom./Corp.Gov. Committee Member 3,964 3.710 3 0 3 4 11 1.450
# Independent Director M. Committees (IDC) 3,964 11.09 11 0 9 13 24 2.900
Monitoring Intensive Director (MID) 3,964 3.810 4 0 3 5 11 1.670
Monitoring Board (MB) 3,964 0.490 0 0 0 1 1 0.500
Board Composition (ID/TD) 3,964 0.790 0.800 0 0.710 0.880 1 0.110
44
Table 3, continued
Description Obs. Mean Median Min. 25th %ile 75th %ile Max. SD.
Panel B
Firm characteristics
Tobin’s Q 3,964 1.720 1.480 0 1.110 2.050 7.690 1.100
ROA 3,964 0.150 0.140 0 0.100 0.190 0.410 0.0700
Leverage 3,964 0.160 0.150 0 0.0100 0.260 0.600 0.150
Rev/Employ 3,964 448 294.6 24.73 196.0 473.1 3016 510.2
Capex/Sales 3,964 0.0600 0.0300 0 0.0200 0.0600 0.800 0.120
Market Value 3,964 9,128 2,029 86.25 795.4 6,687 159,298 22,773
Capex 3,964 370.3 61.11 0.730 19.59 215.2 7061 1000
R&D/Total Assets 3,964 0.0300 0 0 0 0.0400 0.200 0.0400
# Business Segment 3,964 2.490 2 1 1 4 9 1.830
45
Table 4 Monitoring board Analysis
Panel A shows the average of MB by year. Panel B presents a Probit model to estimate the determinants of
MB switching. The sample consists of 2,396 firms between 2004 and 2012. The number of Independent
Directors (ID) represents the directors with no material connection to the company. The Total Directors
(TD) represent board size. The number of Independent Directors M. Committees (IDC) is the sum of the
number of members of the audit committee plus the number of members of the compensation committee,
and plus the number of members of the nominating or the corporate governance committees. Monitoring
Intensive Directors (MID) is a dummy variable that takes the value of one if a director serves in at least two
of the three monitoring committees (audit, compensation and nominating or corporate governance), and 0
otherwise. Monitoring Board (MB) is a dummy variable that takes the value of one if most of the board’s
members are monitoring intensive directors. Pre-crisis Monitoring Board (PC MB) is a dummy variable
that takes the value of one if the firm never changed the MB status before crisis. All Sample Monitoring
Board (AS MB) is a dummy variable that takes the value of one if most of the board’s members are
monitoring intensive directors of those firms which never changed MB status at all over the sample period.
After crisis Monitoring Board (AC MB) is a dummy variable that takes the value of one if most of the
board’s members are monitoring intensive directors after crisis.
Panel A Annual distribution of monitoring board
Year Observations MB MID/ID IDC/ID MID/TD PC MB
2004-2006
AS MB
2004-2012
AC MB
2009-2012
2004 872 0.538 0.585 1.644 0.396 0.308 0.227 -
2005 873 0.496 0.574 1.630 0.400 0.299 0.211 -
2006 876 0.332 0.442 1.398 0.310 0.271 0.183 -
2007 798 0.484 0.571 1.630 0.432 - 0.192 -
2008 750 0.465 0.555 1.607 0.426 - 0.179 -
2009 719 0.471 0.566 1.623 0.435 - 0.182 0.339
2010 738 0.463 0.558 1.605 0.437 - 0.171 0.332
2011 689 0.438 0.554 1.590 0.436 - 0.163 0.313
2012 689 0.440 0.544 1.569 0.430 - 0.164 0.325
Panel B Determinants of MB switching
(1) (2) (3)
Variables
Firm Size -0.0826*** -0.0852*** -0.0879***
(0.0269) (0.0283) (0.0289)
Tobin’s Q pre-crisis -0.1032*** -0.0818** -0.0832**
(0.0360) (0.0354) (0.0365)
Capex/Sales pre-crisis -0.6541 -0.9397* -0.9773*
(0.4155) (0.5023) (0.5110)
Constant -0.0155 0.0288 -0.2138
(0.2332) (0.6029) (0.4739)
Observations 1,599 1,573 1,484
Number of firms 862 849 845
Time dummies No Yes No
Year Dummies No Yes No
Industry Year dummies No No Yes
46
Table 5 Correlation Matrix
The sample consists of 2,541 firms between 2004 and 2012. Monitoring Board (MB) is a dummy variable that takes the value of one if most of the board’s
members are monitoring intensive directors. Board Size is the natural log of the total directors. Board Composition refers to the number of independent directors
divided by the number of the total directors. Busy Board is a dummy variable that take the value of one when the majority of independent directors serve in 3 or
more boards. Board Ownership is the percentage of ownership of the board members. Tobin’s Q is estimated as the book assets minus book equity plus market
value of equity minus deferred taxes, all divided by book assets. ROA is estimated as the operational income before depreciation divided by total assets.
Leverage is the long-term debt divided by total assets. Revenue/Employ is the total revenue divided by the number of employees. Capex is the capital
expenditure in millions of dollars. Firm Size is the natural log of the Market Value. R&D refers to research and development. # Business segments represents the
number of segments. All continuous variables are winsorized at the 1st and 99th percentile values. The asterisk (*) next to the coefficients indicates a p-value is
0.05 or lower.
MB Board
size
Board
Composition
Busy
Board
Board
Ownership
Tobin’s Q ROA Leverage Revenue
Employ
Capex/
Sale
Firm Size R&D/
Total Assets
MB 1 Board size -0.3878* 1 Board Composition -0.2239* 0.1608* 1 Busy Board 0.0156 0.0156 -0.0413* 1 Board ownership 0.0919* -0.0829* -0.3783* -0.0141 1 Tobin’s Q 0.0459* -0.1498* -0.0859* 0.0084 0.0407* 1 ROA 0.0234* -0.0288* -0.0377* 0.0295* 0.0264* 0.5597* 1 Leverage -0.0790* 0.2252* 0.0986* -0.0105 -0.0695* -0.2731* -0.1723* 1 Revenue/Employ 0.0258* 0.0453* 0.0793* -0.0059 -0.0918* -0.0588* 0.0476* 0.0304* 1 Capex/Sale 0.0557* -0.0318* -0.00960 -0.0190 -0.0549* -0.0591* 0.0566* 0.1589* 0.3221* 1 Firm size -0.2245* 0.5071* 0.1597* 0.0584* -0.1915* 0.1459* 0.2137* 0.0972* 0.2050* 0.0798* 1 R&D/Total Assets -0.0231* -0.1805* 0.0405* -0.0125 -0.0697* 0.1625* -0.0581* -0.2412* -0.0602* -0.1114* -0.0258* 1
# Business Segment -0.0554* 0.1895* 0.0795* 0.0232* -0.0990* -0.1169* -0.1275* 0.0766* -0.0682* -0.0872* 0.1885* -0.0107
47
Table 6 Comparison of Firm Characteristics by Monitoring Board
This table presents the comparisons of means between firms with Monitoring Boards (MB) with those without.
Tobin’s Q is estimated as the book assets minus book equity plus market value of equity minus deferred taxes, all
divided by book assets. ROA is estimated as the operational income before depreciation divided by total assets.
Board Composition refers to the number of independent directors divided by the number of the total directors.
Leverage is the long-term debt divided by total assets. Firm Size is the natural log of market value in millions of
dollars. The number of Business Segments represents the number of segments. R&D refers to research and
development.
Panel A: The MB variable is defined based on the first sample year for the firm
Advisory MB Diff. t p-value
Q 1.6962 1.7893 -0.0931 -3.3730 0.0007
ROA 0.1519 0.1551 -0.0032 -1.7069 0.0879
Board Composition 0.7826 0.7447 0.0379 11.7534 0.0000
Board size 2.2731 2.1447 0.1285 22.4904 0.0000
Leverage 0.1787 0.1554 0.0234 6.6243 0.0000
Firm Size 8.2613 7.8028 0.4585 11.9469 0.0000
# Business Segment 2.6835 2.5719 0.1115 2.3130 0.0208
R&D/Assets 0.0274 0.0296 -0.0021 -1.8944 0.0582
Panel B: The MB variable is defined based on the last year prior to the crisis
Advisory MB Diff. t p-value
Q 1.6502 1.7438 -0.09354 -2.9942 0.0028
ROA 0.1508 0.1557 -0.0048 -2.2541 0.0242
Board Composition 0.7947 0.7485 0.7792 13.4586 0.0000
Board size 2.2556 2.1051 0.1505 22.9045 0.0000
Leverage 0.1781 0.1489 0.0292 6.9295 0.0000
Firm Size 8.1609 7.6161 0.5448 12.0156 0.0000
# Business Segment 2.6691 2.5316 0.1375 2.4723 0.0135
R&D/Assets 0.0293 0.0245 0.0048 3.6865 0.0002
Panel C: The MB variable is defined based on a window of 3 years before crisis when the MB status is in
the three years Advisory MB Diff. t p-value
Q 1.7087 1.8724 -0.1637 -5.5151 0.0000
ROA 0.1507 0.1585 -0.0078 -3.9698 0.0001
Board Composition 0.7727 0.7258 0.0468 13.8143 0.0000
Board size 2.2379 2.0854 0.1525 25.3846 0.0000
Leverage 0.1727 0.1429 0.0298 7.9687 0.0000
Firm Size 8.0641 7.6015 0.4626 11.5086 0.0000
# Business Segment 2.6357 2.4452 0.1905 3.8553 0.0001
R&D/Assets 0.0289 0.0274 0.0016 1.3472 0.1780
48
Panel D: The MB variable is defined based on a window of 3 years before crisis when the MB status is in
two of the three years Advisory MB Diff. t p-value
Q 1.7775 1.6375 0.1400 3.8084 0.0001
ROA 0.1536 0.1495 0.0041 1.6747 0.0940
Board Composition 0.7608 0.7513 0.0095 2.2283 0.0259
Board size 2.1971 2.1804 0.0166 2.1381 0.0325
Leverage 0.1647 0.1614 0.0033 0.7020 0.4827
Firm Size 7.9440 7.8644 0.0806 1.6058 0.1084
# Business Segment 2.5608 2.6911 -0.1303 -2.1281 0.0334
R&D/Assets 0.0283 0.0294 -0.0011 -0.7756 0.4380
Panel E: The MB variable is defined based on a window of 3 years before crisis when the MB status is in
one of the three years Advisory MB Diff. t p-value
Q 1.7739 1.6552 0.1187 3.2067 0.0013
ROA 0.1541 0.1466 0.0075 3.0832 0.0021
Board Composition 0.7572 0.7707 -0.0135 3.1608 0.0016
Board size 2.1956 2.1879 0.0077 0.9821 0.3261
Leverage 0.1629 0.1714 -0.0085 -1.8272 0.0677
Firm Size 7.9855 7.6436 0.3419 6.7897 0.0000
# Business Segment 2.5728 2.6279 -0.0551 -0.8939 0.3714
R&D/Assets 0.0289 0.0263 0.0026 1.7705 0.0767
49
Table 7 Univariate Difference in Difference Analysis of Firm Performance
This table presents the result from the diff in diff model. The sample consists of unbalanced panel data of 876 firms
between 2006 and 2012. In Columns (1) and (3) the dependent variables are Tobin’s Q estimates of book assets
minus book equity plus market value of equity, all divided by book assets. In Columns (2) and (4) the dependent
variable is the ROA estimated as the operational income before depreciation divided by total assets. MB is a dummy
variable that takes the value of one if most of the board’s members are monitoring intensive directors, and zero
otherwise. Post Crisis is a dummy variable that takes the value of 1 if the year is after 2008, and zero if year is
before than 2008. Robust standard errors are in parentheses *** p<0.01, ** p<0.05, * p<0.1.
Panel A: The MB variable is defined based on the last year prior to the crisis
(1) (2) (3) (4)
Variables Q ROA Q ROA
Post Crisis (0/1) -0.2820*** -0.0109*** -0.1657 -0.0230
(0.0324) (0.0023) (0.7551) (0.0517)
MB x Post Crisis -0.0516 -0.0082* -0.0450 -0.0018
(0.0564) (0.0045) (0.0390) (0.0027)
Constant 1.9097*** 0.1604*** 1.7885** 0.1707***
(0.0165) (0.0012) (0.7533) (0.0516)
Observations 4,386 4,386 4,386 4,386
R-squared 0.0736 0.0334 0.8182 0.8139
Firm Fixed Effects Yes Yes Yes Yes
Industry Year Fixed Effects No No Yes Yes
Panel B: The MB variable is defined based on firms that never changed MB status at all over the sample period
(1) (2) (3) (4)
Variables Q ROA Q ROA
Post Crisis (0/1) -0.3356*** -0.0124*** -0.1657 -0.0230
(0.0315) (0.0021) (0.8867) (0.0551)
MB x Post Crisis -0.0995 -0.0031 -0.1041** 0.0010
(0.0792) (0.0053) (0.0462) (0.0029)
Constant 1.9550*** 0.1585*** 1.7829** 0.1685***
(0.0131) (0.0009) (0.8842) (0.0549)
Observations 6,254 6,254 6,254 6,254
R-squared 0.0756 0.0256 0.7658 0.7872
Firm Fixed Effects Yes Yes Yes Yes
Industry Fixed Effects No No Yes Yes
Year Fixed Effects No No Yes Yes
50
Table 8 Multivariate Difference in Difference Analysis of Firm Performance
This table presents the result from the diff in diff model. The sample consists of unbalanced panel data of 1004 firms
between 2006 and 2012. The dependent variable is the ROA estimated as the operational income before depreciation
divided by total assets. MB is a dummy variable that takes the value of one if most of the board’s members are
monitoring intensive directors, and zero otherwise. Post Crisis is a dummy variable that takes the value of 1 if the
year is after 2008, and zero if year is before than 2008. Firm Size is the natural log of market value in millions of
dollars. Board Size is the natural log of the total directors. Board Ownership is the percentage of ownership of the
board members. Robust standard errors clustered by firms are in parentheses *** p<0.01, ** p<0.05, * p<0.1.
Panel A: The MB variable is defined based on the last year prior to the crisis
(1) (2) (3) (4)
Variables
Post Crisis (0/1) -0.0076*** -0.0077*** -0.0230 -0.0230
(0.0022) (0.0022) (0.0487) (0.0487)
MB x Post Crisis -0.0094** -0.0094** -0.0033 -0.0033
(0.0041) (0.0041) (0.0025) (0.0025)
Firm Size 0.0384*** 0.0383*** 0.0382*** 0.0383***
(0.0026) (0.0026) (0.0019) (0.0019)
Board size -0.0181** -0.0176** -0.0239*** -0.0241***
(0.0087) (0.0087) (0.0063) (0.0063)
Board Ownership -0.0092 0.0032
(0.0144) (0.0124)
Constant -0.1108*** -0.1099*** -0.0864 -0.0867*
(0.0291) (0.0292) (0.0526) (0.0526)
Observations 4,386 4,386 4,386 4,386
R-squared 0.1422 0.1423 0.8350 0.8350
Number of id 876 876 876 876
Firm Fixed Effects Yes Yes Yes Yes
Industry Year Fixed Effects No No Yes Yes
Panel B: The MB variable is defined based on a window of 3 years before crisis when the MB status is in the
three years
(1) (2) (3) (4)
Variables
Post Crisis (0/1) -0.0105*** -0.0106*** -0.0230 -0.0230
(0.0021) (0.0020) (0.0516) (0.0516)
MB x Post Crisis -0.0082* -0.0083* -0.0050** -0.0050**
(0.0042) (0.0042) (0.0023) (0.0023)
Firm Size 0.0382*** 0.0382*** 0.0392*** 0.0393***
(0.0023) (0.0024) (0.0015) (0.0015)
Board size -0.0179** -0.0177** -0.0214*** -0.0216***
(0.0071) (0.0072) (0.0052) (0.0052)
Board Ownership -0.0029 0.0052
(0.0148) (0.0102)
Constant -0.1073*** -0.1069*** -0.0994* -0.1000*
(0.0239) (0.0240) (0.0539) (0.0539)
Observations 6,254 6,254 6,254 6,254
R-squared 0.1419 0.1419 0.8132 0.8132
Firm Fixed Effects Yes Yes Yes Yes
Industry Year Fixed Effects No No Yes Yes
51
Panel C: The MB variable is defined based on firms, which never changed MB status at all over the sample
period
(1) (2) (3) (4)
Variables
Post Crisis (0/1) -0.0116*** -0.0117*** -0.0230 -0.0230
(0.0020) (0.0019) (0.0516) (0.0516)
MB x Post Crisis -0.0069 -0.0069 -0.0028 -0.0027
(0.0050) (0.0050) (0.0027) (0.0027)
Firm Size 0.0383*** 0.0382*** 0.0392*** 0.0393***
(0.0024) (0.0024) (0.0015) (0.0015)
Board size -0.0190*** -0.0189*** -0.0222*** -0.0224***
(0.0071) (0.0072) (0.0051) (0.0052)
Board Ownership -0.0030 0.0053
(0.0148) (0.0102)
Constant -0.1051*** -0.1047*** -0.0984* -0.0990*
(0.0239) (0.0240) (0.0539) (0.0539)
Observations 6,254 6,254 6,254 6,254
R-squared 0.1409 0.1409 0.8131 0.8131
Firm Fixed Effects Yes Yes Yes Yes
Industry Year Fixed Effects No No Yes Yes
52
Table 9 Multivariate Difference in Difference Analysis of Tobin’s Q
This table presents the result from the diff in diff model. The sample consists of unbalanced panel data of 991 firms
between 2006 and 2012. The dependent variable is Tobin’s Q estimated as the book assets minus book equity plus
market value of equity, all divided by book assets. MB is a dummy variable that takes the value of one if most of the
board’s members are monitoring intensive directors, and zero otherwise. Post Crisis is a dummy variable that takes
the value of 1 if the year is after 2008, and zero if year is before than 2008. Firm Size is the natural log of market
value in millions of dollars. Stock Return is the annual stock price return. Board Size is the natural log of the total
directors. Board Ownership is the percentage of ownership of the board members. Robust standard errors clustered
by firms are in parentheses *** p<0.01, ** p<0.05, * p<0.1.
Panel A: The MB variable is defined based on the last year prior to the crisis
(1) (2) (3) (4)
VARIABLES
Post Crisis (0/1) -0.2619*** -0.2584*** -0.2097 -0.2099
(0.0316) (0.0315) (0.6784) (0.6782)
MB x Post Crisis -0.0768 -0.0689 -0.0846** -0.0793**
(0.0537) (0.0536) (0.0358) (0.0359)
Firm Size 0.5387*** 0.5453*** 0.5331*** 0.5351***
(0.0468) (0.0477) (0.0283) (0.0286)
Stock Return 0.2472*** 0.2436*** 0.2396*** 0.2384***
(0.0291) (0.0289) (0.0267) (0.0267)
Board Size -0.2402** -0.1699*
(0.1147) (0.0898)
Board Ownership 0.0695 -0.1073
(0.2216) (0.1829)
Constant -2.4786*** -2.0095*** -2.5314*** -2.1646***
(0.3779) (0.4299) (0.7149) (0.7377)
Observations 4,260 4,260 4,260 4,260
R-squared 0.2289 0.2306 0.8505 0.8507
Number of firms 868 868 868 868
Firm Fixed Effect Yes Yes Yes Yes
Industry Year Fixed Effects No No Yes Yes
Panel B: The MB variable is defined based on a window of 3 years before crisis when the MB status is in the
three years
(1) (2) (3) (4)
VARIABLES
Post Crisis (0/1) -0.3308*** -0.3219*** -0.2114 -0.2112
(0.0332) (0.0325) (0.8030) (0.8011)
MB x Post Crisis -0.1437** -0.1270** -0.1256*** -0.1122***
(0.0644) (0.0644) (0.0367) (0.0368)
Firm Size 0.5359*** 0.5510*** 0.5731*** 0.5814***
(0.0459) (0.0470) (0.0253) (0.0256)
Stock Return 0.2684*** 0.2606*** 0.2490*** 0.2453***
(0.0269) (0.0268) (0.0258) (0.0259)
Board Size -0.4419*** -0.3887***
(0.1274) (0.0832)
Board Ownership 0.1132 -0.1161
(0.2085) (0.1681)
Constant -2.3730*** -1.5358*** -2.8427*** -2.0447**
(0.3660) (0.4557) (0.8265) (0.8413)
53
Observations 5,969 5,969 5,969 5,969
R-squared 0.2025 0.2072 0.8015 0.8024
Number of firms 991 991 991 991
Firm Fixed Effect Yes Yes Yes Yes
Industry Year Fixed Effects No No Yes Yes
Panel C: The MB variable is defined based on firms, which never changed MB status at all over the sample
period
(1) (2) (3) (4)
VARIABLES
Post Crisis (0/1) -0.3465*** -0.3340*** -0.2112 -0.2110
(0.0315) (0.0306) (0.8029) (0.8010)
MB x Post Crisis -0.1385* -0.1300* -0.1501*** -0.1444***
(0.0762) (0.0759) (0.0437) (0.0436)
Firm Size 0.5370*** 0.5527*** 0.5753*** 0.5840***
(0.0458) (0.0469) (0.0253) (0.0256)
Stock Return 0.2680*** 0.2600*** 0.2477*** 0.2439***
(0.0270) (0.0268) (0.0258) (0.0258)
Board Size -0.4578*** -0.4008***
(0.1271) (0.0830)
Board Ownership 0.1122 -0.1177
(0.2091) (0.1681)
Constant -2.3822*** -1.5146*** -2.8697*** -2.0458**
(0.3654) (0.4561) (0.8265) (0.8411)
Observations 5,969 5,969 5,969 5,969
R-squared 0.2016 0.2067 0.8015 0.8025
Number of firms 991 991 991 991
Firm Fixed Effect Yes Yes Yes Yes
Industry Year Fixed Effects No No Yes Yes
54
Table 10 Multivariate Difference in Difference Analysis of Revenues by Employee
This table presents the result from the diff in diff model. The sample consists of unbalanced panel data between
2004 and 2012. The dependent variable is Revenue/Employee. MB is a dummy variable that takes the value of one
if most of the board’s members are monitoring intensive directors, and zero otherwise. Post Crisis is a dummy
variable that takes the value of 1 if the year is after 2008, and zero if year is before than 2008. Firm Size is the
natural log of market value in millions of dollars. Board Size is the natural log of the total directors. Board
Ownership is the percentage of ownership of the board members. Robust standard errors clustered by firms are in
parentheses *** p<0.01, ** p<0.05, * p<0.1.
Panel A: The MB variable is defined based on the last year prior to the crisis
(1) (2) (3) (4)
VARIABLES
Post Crisis (0/1) 35.0419*** 34.9501*** -44.2774 -44.3731
(6.4282) (6.4188) (136.8059) (136.8179)
MB x Post Crisis -7.4042 -7.4444 -0.8258 -0.9036
(10.8218) (10.8279) (7.0881) (7.0897)
Firm Size 36.9036*** 36.6316*** 29.4333*** 29.1064***
(8.6379) (8.6492) (5.3393) (5.3626)
Board size -11.4191 -10.5957 -5.3903 -4.4262
(22.3242) (22.3067) (17.7262) (17.7877)
Board Ownership -18.3030 -23.0726
(29.7352) (34.9261)
Constant 140.0376* 141.7115* 276.7373* 278.8289*
(77.8253) (77.9366) (147.7274) (147.7743)
Observations 4,386 4,386 4,386 4,386
R-squared 0.0364 0.0364 0.9721 0.9721
Number of firms 876 876 876 876
Firm Fixed Effect Yes Yes Yes Yes
Industry Year Fixed Effects No No Yes Yes
Panel B: The MB variable is defined based on a window of 3 years before crisis when the MB status is in the
three years
(1) (2) (3) (4)
VARIABLES
Post Crisis (0/1) 46.0224*** 45.6502*** -44.2774 -44.3650
(7.2038) (7.2278) (158.6379) (158.6469)
MB x Post Crisis -7.8185 -7.9174 2.3474 2.2825
(12.4519) (12.4622) (6.9863) (6.9873)
Firm Size 44.8541*** 44.1720*** 35.2619*** 34.9069***
(9.4659) (9.5274) (4.6544) (4.6841)
Board size -1.2000 0.2154 19.4009 20.2258
(24.4679) (24.5984) (15.8643) (15.9121)
Board Ownership -36.3876 -21.1329
(35.9799) (31.2580)
Constant 38.9903 44.1348 169.4169 171.9033
(92.5608) (92.7463) (165.6097) (165.6598)
Observations 6,254 6,254 6,254 6,254
R-squared 0.0538 0.0540 0.9584 0.9584
Number of firms 1,004 1,004 1,004 1,004
Firm Fixed Effect Yes Yes Yes Yes
55
Industry Year Fixed Effects No No Yes Yes
Panel C: The MB variable is defined based on firms, which never changed MB status at all over the sample
period
(1) (2) (3) (4)
VARIABLES
Post Crisis (0/1) 47.4390*** 47.0590*** -44.2774 -44.3677
(6.7046) (6.7305) (158.6231) (158.6316)
MB x Post Crisis -20.8165 -20.9925 -8.3605 -8.4511
(13.3932) (13.4091) (8.2843) (8.2858)
Firm Size 45.2868*** 44.5894*** 35.5869*** 35.2225***
(9.4760) (9.5336) (4.6584) (4.6880)
Board size -1.3675 0.0774 20.3345 21.1775
(24.3792) (24.5075) (15.8183) (15.8654)
Board Ownership -37.3598 -21.7738
(36.0630) (31.2559)
Constant 35.8854 41.1614 166.7455 169.3088
(93.2470) (93.4090) (165.5889) (165.6385)
Observations 6,254 6,254 6,254 6,254
R-squared 0.0547 0.0549 0.9584 0.9584
Number of firms 1,004 1,004 1,004 1,004
Firm Fixed Effect Yes Yes Yes Yes
Industry Year Fixed Effects No No Yes Yes
56
Table 11 Multivariate Difference in Difference Analysis of Number of Employees
This table presents the result from the diff in diff model. The sample consists of unbalanced panel data between
2004 and 2012. The dependent variable is number of Employees. MB is a dummy variable that takes the value of
one if most of the board’s members are monitoring intensive directors, and zero otherwise. Post Crisis is a dummy
variable that takes the value of 1 if the year is after 2008, and zero if year is before than 2008. Firm Size is the
natural log of market value in millions of dollars. Board Size is the natural log of the total directors. Board
Ownership is the percentage of ownership of the board members. Busy Board is a dummy variable that take the
value of one when the majority of independent directors serve in 3 or more boards. Robust standard errors clustered
by firms are in parentheses *** p<0.01, ** p<0.05, * p<0.1.
Panel A: The MB variable is defined based on the last year prior to the crisis
(1) (2) (3) (4)
VARIABLES
Post Crisis (0/1) 1.629*** 1.635*** 0 0.0192
(0.552) (0.553) (10.14) (10.14)
MB x Post Crisis -1.555* -1.544* -1.626*** -1.600***
(0.799) (0.798) (0.526) (0.526)
Firm Size 4.328*** 4.390*** 3.872*** 3.964***
(0.648) (0.659) (0.396) (0.398)
Board Size 4.121** 3.943** 4.131*** 3.861***
(1.779) (1.773) (1.318) (1.324)
Board Ownership 2.227 4.619*
(2.971) (2.604)
Busy Board -2.816 -2.314
(2.847) (1.774)
Constant -17.99** -18.24** -11.81 -12.27
(7.387) (7.413) (10.95) (10.95)
Observations 4,373 4,373 4,373 4,373
R-squared 0.048 0.049 0.985 0.985
Number of firms 874 874 874 874
Firm Fixed Effect Yes Yes Yes Yes
Industry Year Fixed Effects No No Yes Yes
Panel B: The MB variable is defined based on a window of 3 years before crisis when the MB status is in the
three years
(1) (2) (3) (4)
VARIABLES
Post Crisis (0/1) 2.278*** 2.267*** -0.000 0.0132
(0.641) (0.640) (12.33) (12.32)
MB x Post Crisis -1.591 -1.601 -1.685*** -1.683***
(1.015) (1.016) (0.543) (0.543)
Firm Size 4.534*** 4.579*** 4.412*** 4.484***
(0.676) (0.692) (0.362) (0.365)
Board Size 4.968** 4.816** 4.420*** 4.227***
(1.977) (1.953) (1.236) (1.240)
Board Ownership 0.998 3.173
(3.487) (2.433)
Busy Board -4.459 -3.080**
(2.906) (1.492)
Constant -22.20*** -22.25*** -16.85 -17.21
57
(7.722) (7.759) (12.87) (12.87)
Observations 6,225 6,225 6,225 6,225
R-squared 0.051 0.053 0.976 0.976
Number of firms 1,002 1,002 1,002 1,002
Firm Fixed Effect Yes Yes Yes Yes
Industry Year Fixed Effects No No Yes Yes
Panel C: The MB variable is defined based on firms, which never changed MB status at all over the sample
period
(1) (2) (3) (4)
VARIABLES
Post Crisis (0/1) 2.012*** 1.989*** -0 0.0134
(0.583) (0.581) (12.34) (12.33)
MB x Post Crisis -0.999 -0.942 -0.811 -0.762
(1.185) (1.183) (0.645) (0.645)
Firm Size 4.533*** 4.576*** 4.398*** 4.469***
(0.671) (0.687) (0.363) (0.365)
Board Size 4.724** 4.567** 4.157*** 3.961***
(1.984) (1.958) (1.234) (1.237)
Board Ownership 1.023 3.236
(3.505) (2.435)
Busy Board -4.361 -2.996**
(2.937) (1.494)
Constant -21.65*** -21.69*** -16.48 -16.84
(7.750) (7.782) (12.88) (12.88)
Observations 6,225 6,225 6,225 6,225
R-squared 0.050 0.052 0.976 0.976
Number of firms 1,002 1,002 1,002 1,002
Firm Fixed Effect Yes Yes Yes Yes
Industry Year Fixed Effects No No Yes Yes
58
Table 12 Multivariate Difference in Difference Analysis of Change of Number of
Employees
This table presents the result from the diff in diff model. The sample consists of unbalanced panel data between
2004 and 2012. The dependent variable is number of Employees. MB is a dummy variable that takes the value of
one if most of the board’s members are monitoring intensive directors, and zero otherwise. Post Crisis is a dummy
variable that takes the value of 1 if the year is after 2008, and zero if year is before than 2008. Firm Size is the
natural log of market value in millions of dollars. Board Size is the natural log of the total directors. Board
Ownership is the percentage of ownership of the board members. Busy Board is a dummy variable that take the
value of one when the majority of independent directors serve in 3 or more boards. Robust standard errors clustered
by firms are in parentheses *** p<0.01, ** p<0.05, * p<0.1.
Panel A: The MB variable is defined based on the last year prior to the crisis
(1) (2) (3) (4)
VARIABLES
Post Crisis (0/1) -1.946*** -1.948*** -1.179 -1.175
(0.742) (0.743) (17.54) (17.54)
MB x Post Crisis -0.824 -0.831 0.0557 0.0672
(1.256) (1.256) (1.196) (1.197)
Firm Size 10.70*** 10.67*** 8.386*** 8.424***
(0.959) (0.956) (0.884) (0.888)
Board Size -1.329 -1.223 -0.578 -0.717
(2.973) (3.010) (2.814) (2.837)
Board Ownership -1.992 0.996
(6.119) (5.596)
Busy Board -0.376 -2.442
(2.439) (3.543)
Constant -77.82*** -77.72*** -60.89*** -60.96***
(9.962) (9.962) (19.81) (19.82)
Observations 3,399 3,399 3,399 3,399
R-squared 0.072 0.072 0.467 0.467
Number of firms 801 801 801 801
Firm Fixed Effect Yes Yes Yes Yes
Industry Year Fixed Effects No No Yes Yes
Panel B: The MB variable is defined based on a window of 3 years before crisis when the MB status is in the
three years
(1) (2) (3) (4)
VARIABLES
Post Crisis (0/1) -3.462*** -3.440*** -1.179 -1.153
(0.539) (0.546) (19.62) (19.62)
MB x Post Crisis -1.184 -1.172 -0.333 -0.306
(0.954) (0.953) (0.979) (0.979)
Firm Size 7.276*** 7.343*** 5.553*** 5.662***
(0.794) (0.807) (0.699) (0.703)
Board Size -3.398 -3.553 -2.907 -3.181
(2.467) (2.475) (2.317) (2.324)
Board Ownership 3.495 6.180
(6.492) (4.612)
Busy Board -1.178 -1.790
(1.669) (2.985)
Constant -43.63*** -44.07*** -31.91 -32.60
59
(8.619) (8.685) (20.91) (20.92)
Observations 5,061 5,061 5,061 5,061
R-squared 0.050 0.050 0.399 0.399
Number of firms 938 938 938 938
Firm Fixed Effect Yes Yes Yes Yes
Industry Year Fixed Effects No No Yes Yes
Panel C: The MB variable is defined based on firms, which never changed MB status at all over the sample
period
(1) (2) (3) (4)
VARIABLES
Post Crisis (0/1) -3.423*** -3.403*** -1.179 -1.154
(0.490) (0.498) (19.62) (19.61)
MB x Post Crisis -2.106* -2.076* -1.335 -1.289
(1.165) (1.163) (1.157) (1.158)
Firm Size 7.322*** 7.386*** 5.597*** 5.703***
(0.795) (0.807) (0.700) (0.704)
Board Size -3.509 -3.656 -2.890 -3.156
(2.441) (2.450) (2.308) (2.315)
Board Ownership 3.418 6.118
(6.482) (4.611)
Busy Board -0.988 -1.690
(1.673) (2.986)
Constant -43.75*** -44.19*** -32.18 -32.86
(8.556) (8.622) (20.90) (20.91)
Observations 5,061 5,061 5,061 5,061
R-squared 0.051 0.051 0.399 0.399
Number of firms 938 938 938 938
Firm Fixed Effect Yes Yes Yes Yes
Industry Year Fixed Effects No No Yes Yes
60
Table 13 Multivariate Difference in Difference Analysis of Business Segments
This table presents the result from the diff in diff model. The sample consists of unbalanced panel data between
2004 and 2012. The dependent variable is the number of Business Segments. MB is a dummy variable that takes the
value of one if most of the board’s members are monitoring intensive directors, and zero otherwise. Post Crisis is a
dummy variable that takes the value of 1 if the year is after 2008, and zero if year is before than 2008. Firm Size is
the natural log of market value in millions of dollars. Board Size is the natural log of the total directors. Board
Ownership is the percentage of ownership of the board members. Busy Board is a dummy variable that take the
value of one when the majority of independent directors serve in 3 or more boards. Robust standard errors clustered
by firms are in parentheses *** p<0.01, ** p<0.05, * p<0.1.
Panel A: The MB variable is defined based on the last year prior to the crisis
(1) (2) (3) (4)
VARIABLES
Post Crisis (0/1) -0.00818 -0.0114 -0 -0.00324
(0.0337) (0.0338) (1.188) (1.187)
MB x Post Crisis -0.0269 -0.0281 -0.0108 -0.0130
(0.0582) (0.0581) (0.0615) (0.0615)
Firm Size 0.00752 -0.000714 0.0321 0.0227
(0.0413) (0.0415) (0.0464) (0.0466)
Board Size 0.453*** 0.478*** 0.337** 0.365**
(0.148) (0.148) (0.154) (0.154)
Board Ownership -0.618** -0.780**
(0.289) (0.303)
Busy Board -0.0934 -0.147
(0.201) (0.208)
Constant 1.575*** 1.630*** 1.637 1.704
(0.450) (0.450) (1.282) (1.282)
Observations 4,386 4,386 4,386 4,386
R-squared 0.003 0.004 0.853 0.853
Number of firms 876 876 876 876
Firm Fixed Effect Yes Yes Yes Yes
Industry Year Fixed Effects No No Yes Yes
Panel B: The MB variable is defined based on a window of 3 years before crisis when the MB status is in the
three years
(1) (2) (3) (4)
VARIABLES
Post Crisis (0/1) -0.0190 -0.0250 -0 -0.00303
(0.0275) (0.0276) (1.226) (1.225)
MB x Post Crisis -0.00194 -0.00382 0.000353 -0.00244
(0.0522) (0.0522) (0.0540) (0.0540)
Firm Size -0.00263 -0.0111 0.00469 -0.00651
(0.0323) (0.0326) (0.0360) (0.0362)
Board Size 0.371*** 0.387*** 0.253** 0.278**
(0.119) (0.119) (0.123) (0.123)
Board Ownership -0.501** -0.730***
(0.232) (0.241)
Busy Board -0.164 -0.185
(0.144) (0.148)
Constant 1.796*** 1.870*** 1.997 2.083
(0.346) (0.347) (1.280) (1.279)
61
Observations 6,254 6,254 6,254 6,254
R-squared 0.002 0.003 0.833 0.834
Number of firms 1,004 1,004 1,004 1,004
Firm Fixed Effect Yes Yes Yes Yes
Industry Year Fixed Effects No No Yes Yes
Panel C: The MB variable is defined based on firms, which never changed MB status at all over the sample
period
(1) (2) (3) (4)
VARIABLES
Post Crisis (0/1) -0.0311 -2.980** -0.00301 -0.00301
(0.0258) (1.225) (1.225) (1.225)
MB x Post Crisis 0.0672 0.0641 0.0641 0.0641
(0.0617) (0.0640) (0.0640) (0.0640)
Firm Size -0.00452 -0.00854 -0.00854 -0.00854
(0.0323) (0.0362) (0.0362) (0.0362)
Board Size 0.366*** 0.273** 0.273** 0.273**
(0.118) (0.123) (0.123) (0.123)
Board Ownership -0.726*** -0.726*** -0.726***
(0.241) (0.241) (0.241)
Busy Board -0.189 -0.189 -0.189
(0.148) (0.148) (0.148)
Constant 1.822*** 4.368*** 2.098 2.098
(0.345) (1.014) (1.279) (1.279)
Observations 6,254 6,254 6,254 6,254
R-squared 0.002 0.092 0.834 0.834
Number of firms 1,004 1,004 1,004 1,004
Firm Fixed Effect Yes Yes Yes Yes
Industry Year Fixed Effects No No Yes Yes
62
Table 14 Multivariate Difference in Difference Analysis of Capital investment
This table presents the result from the diff in diff model. The sample consists of unbalanced panel data between
2004 and 2012. The dependent variable is Capex/Sales. MB is a dummy variable that takes the value of one if most
of the board’s members are monitoring intensive directors, and zero otherwise. Post Crisis is a dummy variable that
takes the value of 1 if the year is after 2008, and zero if year is before than 2008. Firm Size is the natural log of
market value in millions of dollars. Board Size is the natural log of the total directors. Board Ownership is the
percentage of ownership of the board members. Busy Board is a dummy variable that take the value of one when the
majority of independent directors serve in 3 or more boards. Robust standard errors clustered by firms are in
parentheses *** p<0.01, ** p<0.05, * p<0.1.
Panel A: The MB variable is defined based on the last year prior to the crisis
(1) (2) (3) (4)
VARIABLES
Post Crisis (0/1) -0.00708*** -0.00715*** -0.000827 -0.000863
(0.00204) (0.00204) (0.0541) (0.0541)
MB x Post Crisis -0.00350 -0.00352 -0.00484* -0.00485*
(0.00445) (0.00445) (0.00280) (0.00280)
Firm Size 0.00888*** 0.00871*** 0.00613*** 0.00608***
(0.00279) (0.00279) (0.00211) (0.00212)
Board Size 0.00130 0.00182 0.00132 0.00151
(0.00952) (0.00940) (0.00700) (0.00704)
Board Ownership -0.0139 -0.00865
(0.0146) (0.0138)
Busy Board -0.00417 -0.00589
(0.00291) (0.00946)
Constant -0.00397 -0.00277 0.0186 0.0192
(0.0324) (0.0325) (0.0584) (0.0584)
Observations 4,386 4,386 4,386 4,386
R-squared 0.019 0.020 0.918 0.918
Number of firms 876 876 876 876
Firm Fixed Effect Yes Yes Yes Yes
Industry Year Fixed Effects No No Yes Yes
Panel B: The MB variable is defined based on a window of 3 years before crisis when the MB status is in the
three years
(1) (2) (3) (4)
VARIABLES
Post Crisis (0/1) -0.00464** -0.00470** -0.000827 -0.000845
(0.00210) (0.00210) (0.0554) (0.0554)
MB x Post Crisis -0.00380 -0.00382 -0.00645*** -0.00649***
(0.00463) (0.00463) (0.00244) (0.00244)
Firm Size 0.0125*** 0.0124*** 0.00634*** 0.00630***
(0.00255) (0.00257) (0.00163) (0.00164)
Board Size -0.00541 -0.00527 -0.00190 -0.00188
(0.00777) (0.00776) (0.00554) (0.00556)
Board Ownership -0.00496 -0.00420
(0.0132) (0.0109)
Busy Board -0.00206 -0.00669
(0.00293) (0.00671)
Constant -0.0205 -0.0197 0.0233 0.0239
(0.0272) (0.0274) (0.0578) (0.0579)
63
Observations 6,254 6,254 6,254 6,254
R-squared 0.017 0.017 0.901 0.901
Number of firms 1,004 1,004 1,004 1,004
Firm Fixed Effect Yes Yes Yes Yes
Industry Year Fixed Effects No No Yes Yes
Panel C: The MB variable is defined based on firms, which never changed MB status at all over the sample
period
(1) (2) (3) (4)
VARIABLES
Post Crisis (0/1) -0.00488** -0.00495** -0.000827 -0.000846
(0.00196) (0.00196) (0.0554) (0.0554)
MB x Post Crisis -0.00469 -0.00469 -0.0104*** -0.0103***
(0.00611) (0.00612) (0.00289) (0.00289)
Firm Size 0.0126*** 0.0125*** 0.00651*** 0.00647***
(0.00257) (0.00260) (0.00163) (0.00164)
Board Size -0.00585 -0.00570 -0.00246 -0.00243
(0.00767) (0.00766) (0.00552) (0.00554)
Board Ownership -0.00508 -0.00444
(0.0132) (0.0109)
Busy Board -0.00166 -0.00590
(0.00290) (0.00671)
Constant -0.0200 -0.0193 0.0231 0.0237
(0.0270) (0.0272) (0.0578) (0.0578)
Observations 6,254 6,254 6,254 6,254
R-squared 0.017 0.017 0.901 0.901
Number of firms 1,004 1,004 1,004 1,004
Firm Fixed Effect Yes Yes Yes Yes
Industry Year Fixed Effects No No Yes Yes
64
Table 15 Multivariate Difference in Difference Analysis for Firms with R&D
This table presents the result from the diff in diff model. The sample consists of unbalanced panel data between
2004 and 2012. The dependent variable is R_D/Sales. MB is a dummy variable that takes the value of one if most of
the board’s members are monitoring intensive directors, and zero otherwise. Post Crisis is a dummy variable that
takes the value of 1 if the year is after 2008, and zero if year is before than 2008. Firm Size is the natural log of
market value in millions of dollars. Board Size is the natural log of the total directors. Board Ownership is the
percentage of ownership of the board members. Busy Board is a dummy variable that take the value of one when the
majority of independent directors serve in 3 or more boards. Robust standard errors clustered by firms are in
parentheses *** p<0.01, ** p<0.05, * p<0.1.
Panel A: The MB variable is defined based on the last year prior to the crisis
(1) (2) (3) (4)
VARIABLES
Post Crisis (0/1) -0.000921 -0.000914 0.0135 0.0135
(0.000877) (0.000876) (0.0191) (0.0191)
MB x Post Crisis -0.000185 -0.000183 -0.000412 -0.000422
(0.00120) (0.00120) (0.000991) (0.000991)
Firm Size -0.00347*** -0.00346*** -0.00336*** -0.00339***
(0.00102) (0.00103) (0.000746) (0.000751)
Board Size 0.00374 0.00370 0.00356 0.00366
(0.00331) (0.00332) (0.00248) (0.00249)
Board Ownership 0.00131 -0.00107
(0.00353) (0.00489)
Busy Board 0.000652 0.00184
(0.00114) (0.00335)
Constant 0.0590*** 0.0588*** 0.0459** 0.0461**
(0.0104) (0.0104) (0.0206) (0.0207)
Observations 4,386 4,386 4,386 4,386
R-squared 0.009 0.009 0.969 0.969
Number of firms 876 876 876 876
Firm Fixed Effect Yes Yes Yes Yes
Industry Year Fixed Effects No No Yes Yes
Panel B: The MB variable is defined based on a window of 3 years before crisis when the MB status is in the
three years
(1) (2) (3) (4)
VARIABLES
Post Crisis (0/1) -0.00121 -0.00118 0.0135 0.0135
(0.00104) (0.00103) (0.0339) (0.0339)
MB x Post Crisis 0.000981 0.000985 0.00106 0.00106
(0.00148) (0.00148) (0.00149) (0.00149)
Firm Size -0.00401*** -0.00392** -0.00431*** -0.00427***
(0.00153) (0.00157) (0.000995) (0.00100)
Board Size 0.00555 0.00533 0.00546 0.00534
(0.00383) (0.00389) (0.00339) (0.00340)
Board Ownership 0.00419 0.00199
(0.00466) (0.00669)
Busy Board -0.00242 -0.00198
(0.00309) (0.00411)
Constant 0.0616*** 0.0611*** 0.0515 0.0512
(0.0135) (0.0137) (0.0354) (0.0354)
65
Observations 6,254 6,254 6,254 6,254
R-squared 0.005 0.005 0.920 0.920
Number of firms 1,004 1,004 1,004 1,004
Firm Fixed Effect Yes Yes Yes Yes
Industry Year Fixed Effects No No Yes Yes
Panel C: The MB variable is defined based on firms, which never changed MB status at all over the sample
period
(1) (2) (3) (4)
VARIABLES
Post Crisis (0/1) -0.00102 -0.000995 0.0135 0.0135
(0.000917) (0.000912) (0.0339) (0.0339)
MB x Post Crisis 0.000420 0.000469 0.000241 0.000272
(0.00164) (0.00165) (0.00177) (0.00177)
Firm Size -0.00400*** -0.00391** -0.00430*** -0.00425***
(0.00153) (0.00157) (0.000996) (0.00100)
Board Size 0.00572 0.00549 0.00564* 0.00552
(0.00377) (0.00383) (0.00338) (0.00340)
Board Ownership 0.00417 0.00195
(0.00466) (0.00669)
Busy Board -0.00247 -0.00202
(0.00311) (0.00411)
Constant 0.0612*** 0.0607*** 0.0512 0.0509
(0.0136) (0.0137) (0.0354) (0.0354)
Observations 6,254 6,254 6,254 6,254
R-squared 0.005 0.005 0.920 0.920
Number of firms 1,004 1,004 1,004 1,004
Firm Fixed Effect Yes Yes Yes Yes
Industry Year Fixed Effects No No Yes Yes
66
Table 16 Multivariate Difference in Difference Analysis of Leverage
This table presents the result from the diff in diff model. The sample consists of unbalanced panel data of 1334 firms
between 2004 and 2012. The dependent variable is leverage. MB is a dummy variable that takes the value of one if
most of the board’s members are monitoring intensive directors, and zero otherwise. Post Crisis is a dummy variable
that takes the value of 1 if the year is after 2008, and zero if year is before than 2008. Firm Size is the natural log of
market value in millions of dollars. Board Size is the natural log of the total directors. Board Ownership is the
percentage of ownership of the board members. Busy Board is a dummy variable that take the value of one when the
majority of independent directors serve in 3 or more boards. Robust standard errors clustered by firms are in
parentheses *** p<0.01, ** p<0.05, * p<0.1.
Panel A: The MB variable is defined based on the last year prior to the crisis
(1) (2) (3) (4)
VARIABLES
Post Crisis (0/1) 0.0128*** 0.0128*** -2.43e-05 1.27e-05
(0.00399) (0.00400) (0.0924) (0.0924)
MB x Post Crisis -0.00507 -0.00505 -0.00689 -0.00686
(0.00651) (0.00651) (0.00479) (0.00479)
Firm Size -0.0281*** -0.0279*** -0.0318*** -0.0316***
(0.00493) (0.00498) (0.00361) (0.00363)
Board Size 0.0245 0.0241 0.0313*** 0.0310**
(0.0168) (0.0171) (0.0120) (0.0120)
Board Ownership 0.00763 0.00893
(0.0304) (0.0236)
Busy Board -0.00211 -0.000133
(0.00825) (0.0162)
Constant 0.332*** 0.331*** 0.375*** 0.374***
(0.0490) (0.0490) (0.0998) (0.0999)
Observations 4,386 4,386 4,386 4,386
R-squared 0.032 0.032 0.845 0.845
Number of firms 876 876 876 876
Firm Fixed Effect Yes Yes Yes Yes
Industry Year Fixed Effects No No Yes Yes
Panel B: The MB variable is defined based on a window of 3 years before crisis when the MB status is in the
three years
(1) (2) (3) (4)
VARIABLES
Post Crisis (0/1) 0.0189*** 0.0189*** -2.43e-05 5.19e-05
(0.00388) (0.00388) (0.102) (0.102)
MB x Post Crisis -0.00932 -0.00937 -0.0123*** -0.0124***
(0.00733) (0.00733) (0.00448) (0.00448)
Firm Size -0.0298*** -0.0293*** -0.0329*** -0.0325***
(0.00465) (0.00470) (0.00299) (0.00300)
Board Size 0.0296* 0.0282* 0.0354*** 0.0341***
(0.0155) (0.0156) (0.0102) (0.0102)
Board Ownership 0.0155 0.0184
(0.0262) (0.0201)
Busy Board -0.0281*** -0.0266**
(0.00919) (0.0123)
Constant 0.328*** 0.326*** 0.376*** 0.374***
(0.0443) (0.0443) (0.106) (0.106)
67
Observations 6,254 6,254 6,254 6,254
R-squared 0.038 0.039 0.799 0.799
Number of firms 1,004 1,004 1,004 1,004
Firm Fixed Effect Yes Yes Yes Yes
Industry Year Fixed Effects No No Yes Yes
Panel C: The MB variable is defined based on firms, which never changed MB status at all over the sample
period
(1) (2) (3) (4)
VARIABLES
Post Crisis (0/1) 0.0168*** 0.0167*** -2.43e-05 5.44e-05
(0.00359) (0.00360) (0.102) (0.102)
MB x Post Crisis -0.00262 -0.00222 -0.00298 -0.00259
(0.00925) (0.00926) (0.00532) (0.00532)
Firm Size -0.0299*** -0.0294*** -0.0332*** -0.0327***
(0.00465) (0.00470) (0.00299) (0.00301)
Board Size 0.0279* 0.0266* 0.0333*** 0.0320***
(0.0157) (0.0158) (0.0102) (0.0102)
Board Ownership 0.0158 0.0190
(0.0259) (0.0201)
Busy Board -0.0277*** -0.0262**
(0.00924) (0.0123)
Constant 0.332*** 0.331*** 0.379*** 0.377***
(0.0441) (0.0442) (0.106) (0.106)
Observations 6,254 6,254 6,254 6,254
R-squared 0.037 0.038 0.798 0.799
Number of firms 1,004 1,004 1,004 1,004
Firm Fixed Effect Yes Yes Yes Yes
Industry Year Fixed Effects No No Yes Yes
68
Table 17 Multivariate Difference in Difference Analysis of Firm Performance before and
after 2007
This table presents the result from the diff in diff model. The sample consists of unbalanced panel data between
2004 and 2012. The dependent variable is ROA estimated as the operational income before depreciation divided by
total assets. MB is a dummy variable that takes the value of one if most of the board’s members are monitoring
intensive directors, and zero otherwise. Post Crisis is a dummy variable that takes the value of 1 if year is after 2007,
and zero if it is before 2007. Firm Size is the natural log of market value in millions of dollars. Board Size is the
natural log of the total directors. Board Ownership is the percentage of ownership of the board members. Robust
standard errors clustered by firms are in parentheses *** p<0.01, ** p<0.05, * p<0.1.
Panel A: The MB variable is defined based on the last year prior to the crisis
(1) (2) (3) (4)
Variables
Post Crisis (0/1) -0.0066*** -0.0067*** -0.0204 -0.0204
(0.0024) (0.0024) (0.0484) (0.0484)
MB x Post Crisis -0.0080* -0.0080* -0.0039 -0.0039
(0.0043) (0.0043) (0.0031) (0.0031)
Firm Size 0.0256*** 0.0255*** 0.0362*** 0.0362***
(0.0022) (0.0022) (0.0018) (0.0018)
Board size -0.0179** -0.0175** -0.0213*** -0.0213***
(0.0086) (0.0086) (0.0065) (0.0065)
Board Ownership -0.0104 0.0006
(0.0151) (0.0130)
Constant -0.0059 -0.0051 -0.0784 -0.0785
(0.0271) (0.0272) (0.0524) (0.0524)
Observations 4,337 4,337 4,337 4,337
R-squared 0.0867 0.0868 0.8368 0.8368
Number firms 876 876 876 876
Firm Fixed Effects Yes Yes Yes Yes
Industry Year Fixed Effects No No Yes Yes
Panel B: The MB variable is defined based on a window of 3 years before crisis when the MB status is in the
three years
(1) (2) (3) (4)
Variables
Post Crisis (0/1) -0.0076*** -0.0076*** -0.0204 -0.0204
(0.0021) (0.0021) (0.0520) (0.0520)
MB x Post Crisis -0.0065 -0.0065 -0.0050** -0.0050**
(0.0042) (0.0042) (0.0023) (0.0023)
Firm Size 0.0301*** 0.0300*** 0.0374*** 0.0375***
(0.0021) (0.0022) (0.0015) (0.0015)
Board size -0.0161** -0.0159** -0.0197*** -0.0199***
(0.0072) (0.0072) (0.0053) (0.0053)
Board Ownership -0.0033 0.0071
(0.0157) (0.0103)
Constant -0.0450* -0.0446* -0.0914* -0.0921*
(0.0231) (0.0232) (0.0542) (0.0542)
Observations 6,206 6,206 6,206 6,206
R-squared 0.1044 0.1044 0.8102 0.8102
Number firms 1,004 1,004 1,004 1,004
Firm Fixed Effects Yes Yes Yes Yes
Industry Year Fixed Effects No No Yes Yes
69
Panel C: The MB variable is defined based on firms which never changed MB status at all over the sample period
(1) (2) (3) (4)
Variables
Post Crisis (0/1) -0.0088*** -0.0088*** -0.0204 -0.0204
(0.0020) (0.0020) (0.0520) (0.0520)
MB x Post Crisis -0.0034 -0.0035 -0.0020 -0.0020
(0.0049) (0.0049) (0.0028) (0.0028)
Firm Size 0.0300*** 0.0300*** 0.0373*** 0.0374***
(0.0021) (0.0022) (0.0015) (0.0015)
Board size -0.0171** -0.0170** -0.0205*** -0.0207***
(0.0072) (0.0072) (0.0053) (0.0053)
Board Ownership -0.0033 0.0072
(0.0157) (0.0103)
Constant -0.0424* -0.0421* -0.0903* -0.0910*
(0.0230) (0.0232) (0.0542) (0.0543)
Observations 6,206 6,206 6,206 6,206
R-squared 0.1034 0.1034 0.8100 0.8100
Number firms 1,004 1,004 1,004 1,004
Firm Fixed Effects Yes Yes Yes Yes
Industry Year Fixed Effects No No Yes Yes
70
Table 18 Multivariate Difference in Difference Analysis of Tobin’s Q before and after 2007
This table presents the result from the diff in diff model. The sample consists of unbalanced panel data between
2004 and 2012. The dependent variable is Tobin’s Q estimated as the book assets minus book equity plus market
value of equity, all divided by book assets. MB is a dummy variable that takes the value of one if most of the
board’s members are monitoring intensive directors, and zero otherwise. Post Crisis is a dummy variable that takes
the value of 1 if the year is after 2007, and zero if it is before 2007. Firm Size is the natural log of market value in
millions of dollars. Stock Return is the annual stock price return. Board Size is the natural log of the total directors.
Board Ownership is the percentage of ownership of the board members. Robust standard errors clustered by firms
are in parentheses *** p<0.01, ** p<0.05, * p<0.1.
Panel A: The MB variable is defined based on the last year prior to the crisis
(1) (2) (3) (4)
VARIABLES
Post Crisis (0/1) -0.2406*** -0.2402*** -0.2476 -0.2418
(0.0352) (0.0352) (0.6556) (0.6556)
MB x Post Crisis -0.1056* -0.0984* -0.0960** -0.0922**
(0.0593) (0.0592) (0.0432) (0.0434)
Firm Size 0.4941*** 0.4972*** 0.5164*** 0.5165***
(0.0416) (0.0420) (0.0277) (0.0278)
Stock Return 0.1280*** 0.1263*** 0.1649*** 0.1651***
(0.0241) (0.0242) (0.0270) (0.0270)
Board Size -0.1699 -0.0920
(0.1073) (0.0904)
Board Ownership -0.0403 -0.1666
(0.1633) (0.1839)
Constant -2.0905*** -1.7392*** -2.3427*** -2.1359***
(0.3367) (0.3765) (0.6917) (0.7142)
Observations 4,196 4,196 4,196 4,196
R-squared 0.2332 0.2341 0.8472 0.8473
Number of firms 861 861 861 861
Firm Fixed Effect Yes Yes Yes Yes
Industry Year Fixed Effects No No Yes Yes
Panel B: The MB variable is defined based on a window of 3 years before crisis when the MB status is in the
three years
(1) (2) (3) (4)
VARIABLES
Post Crisis (0/1) -0.3311*** -0.3237*** -0.2521 -0.2270
(0.0367) (0.0361) (0.8026) (0.8008)
MB x Post Crisis -0.1507** -0.1339* -0.1183*** -0.1051***
(0.0701) (0.0699) (0.0380) (0.0381)
Firm Size 0.4880*** 0.4998*** 0.5261*** 0.5327***
(0.0441) (0.0446) (0.0249) (0.0250)
Stock Return 0.1677*** 0.1618*** 0.2023*** 0.1994***
(0.0246) (0.0247) (0.0263) (0.0264)
Board Size -0.4475*** -0.3853***
(0.1317) (0.0846)
Board Ownership -0.0058 -0.1673
(0.1770) (0.1689)
Constant -1.9469*** -1.0628** -2.4143*** -1.6318*
(0.3498) (0.4466) (0.8253) (0.8399)
71
Observations 5,918 5,918 5,918 5,918
R-squared 0.2141 0.2188 0.7918 0.7928
Number of firms 986 986 986 986
Firm Fixed Effect Yes Yes Yes Yes
Industry Year Fixed Effects No No Yes Yes
Panel C: The MB variable is defined based on firms which never changed MB status at all over the sample period
(1) (2) (3) (4)
VARIABLES
Post Crisis (0/1) -0.3493*** -0.3384*** -0.2520 -0.2262
(0.0344) (0.0336) (0.8027) (0.8008)
MB x Post Crisis -0.1343 -0.1257 -0.1297*** -0.1243***
(0.0829) (0.0823) (0.0450) (0.0449)
Firm Size 0.4883*** 0.5007*** 0.5272*** 0.5342***
(0.0441) (0.0445) (0.0249) (0.0251)
Stock Return 0.1676*** 0.1614*** 0.2016*** 0.1986***
(0.0246) (0.0246) (0.0263) (0.0264)
Board Size -0.4636*** -0.3965***
(0.1314) (0.0844)
Board Ownership -0.0089 -0.1694
(0.1767) (0.1689)
Constant -1.9500*** -1.0345** -2.4343*** -1.6280*
(0.3499) (0.4475) (0.8254) (0.8398)
Observations 5,918 5,918 5,918 5,918
R-squared 0.2130 0.2181 0.7917 0.7928
Number of firms 986 986 986 986
Firm Fixed Effect Yes Yes Yes Yes
Industry Year Fixed Effects No No Yes Yes
72
Table 19 Multivariate Difference in Difference Analysis of Firm Performance before 2007
and after 2009
This table presents the result from the diff in diff model. I use a study window of before 2007 to 2008. The sample
consists of unbalanced panel data of 946 firms between 2004 and 2012. The dependent variable is ROA estimated as
the operational income before depreciation divided by total assets. MB is a dummy variable that takes the value of
one if most of the board’s members are monitoring intensive directors, and zero otherwise. Post Crisis is a dummy
variable that takes the value of 1 if the year is after 2009, and zero if it is before 2007. Firm Size is the natural log of
market value in millions of dollars. Board Size is the natural log of the total directors. Board Ownership is the
percentage of ownership of the board members. Robust standard errors clustered by firms are in parentheses ***
p<0.01, ** p<0.05, * p<0.1.
Panel A: The MB variable is defined based on the last year prior to the crisis
(1) (2) (3) (4)
Variables
Post Crisis (0/1) -0.0075*** -0.0075*** -0.0204 -0.0204
(0.0026) (0.0026) (0.0487) (0.0487)
MB x Post Crisis -0.0092* -0.0092* -0.0027 -0.0027
(0.0048) (0.0048) (0.0034) (0.0034)
Firm Size 0.0359*** 0.0359*** 0.0384*** 0.0386***
(0.0030) (0.0031) (0.0024) (0.0024)
Board size -0.0195* -0.0194* -0.0257*** -0.0261***
(0.0112) (0.0112) (0.0083) (0.0084)
Board Ownership -0.0050 0.0125
(0.0195) (0.0172)
Constant -0.0858** -0.0852** -0.0869 -0.0883
(0.0358) (0.0361) (0.0550) (0.0550)
Observations 2,919 2,919 2,919 2,919
R-squared 0.1271 0.1271 0.8580 0.8581
Number firms 876 876 876 876
Firm Fixed Effects Yes Yes Yes Yes
Industry Year Fixed Effects No No Yes Yes
Panel B: The MB variable is defined based on a window of 3 years before crisis when the MB status is in the
three years
(1) (2) (3) (4)
Variables
Post Crisis (0/1) -0.0102*** -0.0102*** -0.0204 -0.0204
(0.0024) (0.0023) (0.0519) (0.0519)
MB x Post Crisis -0.0074 -0.0074 -0.0044 -0.0043
(0.0047) (0.0047) (0.0027) (0.0027)
Firm Size 0.0371*** 0.0371*** 0.0388*** 0.0390***
(0.0027) (0.0027) (0.0017) (0.0017)
Board size -0.0191** -0.0191** -0.0238*** -0.0244***
(0.0083) (0.0083) (0.0061) (0.0061)
Board Ownership 0.0009 0.0131
(0.0191) (0.0123)
Constant -0.0941*** -0.0942*** -0.0937* -0.0952*
(0.0274) (0.0276) (0.0549) (0.0549)
Observations 4,737 4,737 4,737 4,737
R-squared 0.1324 0.1324 0.8253 0.8254
Number firms 1,004 1,004 1,004 1,004
Firm Fixed Effects Yes Yes Yes Yes
73
Industry Year Fixed Effects No No Yes Yes
Panel C: The MB variable is defined based on firms which never changed MB status at all over the sample period
(1) (2) (3) (4)
Variables
Post Crisis (0/1) -0.0113*** -0.0113*** -0.0204 -0.0204
(0.0022) (0.0022) (0.0519) (0.0519)
MB x Post Crisis -0.0053 -0.0053 -0.0018 -0.0018
(0.0056) (0.0057) (0.0032) (0.0032)
Firm Size 0.0371*** 0.0371*** 0.0387*** 0.0390***
(0.0027) (0.0028) (0.0017) (0.0017)
Board size -0.0202** -0.0202** -0.0245*** -0.0251***
(0.0082) (0.0083) (0.0061) (0.0061)
Board Ownership 0.0009 0.0132
(0.0191) (0.0123)
Constant -0.0918*** -0.0920*** -0.0927* -0.0942*
(0.0274) (0.0276) (0.0549) (0.0549)
Observations 4,737 4,737 4,737 4,737
R-squared 0.1313 0.1313 0.8252 0.8252
Number firms 1,004 1,004 1,004 1,004
Firm Fixed Effects Yes Yes Yes Yes
Industry Year Fixed Effects No No Yes Yes
74
Table 20 Multivariate Difference in Difference Analysis of Tobin’s Q before 2007 and after
2009
This table presents the result from the diff in diff model. The sample consists of unbalanced panel data between
2004 and 2012. The dependent variable is Tobin’s Q estimated as the book assets minus book equity plus market
value of equity minus deferred taxes, all divided by book assets. MB is a dummy variable that takes the value of one
if most of the board’s members are monitoring intensive directors, and zero otherwise. Post Crisis is a dummy
variable that takes the value of 1 if the year is after 2009, and zero if it is before 2007. Firm Size is the natural log of
market value in millions of dollars. Stock Return is the annual stock price return. Board Size is the natural log of the
total directors. Board Ownership is the percentage of ownership of the board members. Robust standard errors
clustered by firms are in parentheses *** p<0.01, ** p<0.05, * p<0.1.
Panel A: The MB variable is defined based on the last year prior to the crisis
(1) (2) (3) (4)
VARIABLES
Post Crisis (0/1) -0.2682*** -0.2683*** -0.2556 -0.2563
(0.0391) (0.0391) (0.7203) (0.7206)
MB x Post Crisis -0.0871 -0.0844 -0.0828 -0.0836
(0.0664) (0.0663) (0.0515) (0.0518)
Firm Size 0.4394*** 0.4399*** 0.4625*** 0.4575***
(0.0561) (0.0575) (0.0382) (0.0387)
Stock Return 0.2624*** 0.2615*** 0.2310*** 0.2344***
(0.0429) (0.0432) (0.0429) (0.0431)
Board Size -0.0670 0.0022
(0.1504) (0.1268)
Board Ownership -0.0619 -0.2627
(0.2367) (0.2723)
Constant -1.6639*** -1.5162*** -1.9076** -1.8553**
(0.4537) (0.5240) (0.7829) (0.8217)
Observations 2,812 2,812 2,812 2,812
R-squared 0.1844 0.1845 0.8520 0.8521
Number of firms 853 853 853 853
Firm Fixed Effect Yes Yes Yes Yes
Industry Year Fixed Effects No No Yes Yes
Panel B: The MB variable is defined based on a window of 3 years before crisis when the MB status is in the
three years
(1) (2) (3) (4)
VARIABLES
Post Crisis (0/1) -0.3478*** -0.3418*** -0.2591 -0.2354
(0.0406) (0.0398) (0.8492) (0.8476)
MB x Post Crisis -0.1613** -0.1465* -0.1315*** -0.1202***
(0.0783) (0.0784) (0.0460) (0.0461)
Firm Size 0.4889*** 0.5007*** 0.5353*** 0.5399***
(0.0530) (0.0544) (0.0305) (0.0309)
Stock Return 0.2872*** 0.2789*** 0.2608*** 0.2590***
(0.0367) (0.0372) (0.0355) (0.0356)
Board Size -0.4131*** -0.3666***
(0.1569) (0.1043)
Board Ownership -0.0485 -0.2693
(0.2227) (0.2150)
Constant -1.9697*** -1.1560** -2.4885*** -1.7231*
75
(0.4210) (0.5305) (0.8819) (0.9029)
Observations 4,492 4,492 4,492 4,492
R-squared 0.1822 0.1861 0.7973 0.7982
Number of firms 979 979 979 979
Firm Fixed Effect Yes Yes Yes Yes
Industry Year Fixed Effects No No Yes Yes
Panel C: The MB variable is defined based on firms which never changed MB status at all over the sample period
(1) (2) (3) (4)
VARIABLES
Post Crisis (0/1) -0.3669*** -0.3577*** -0.2588 -0.2344
(0.0382) (0.0370) (0.8492) (0.8474)
MB x Post Crisis -0.1499 -0.1422 -0.1605*** -0.1568***
(0.0940) (0.0937) (0.0555) (0.0554)
Firm Size 0.4905*** 0.5027*** 0.5378*** 0.5427***
(0.0530) (0.0543) (0.0305) (0.0310)
Stock Return 0.2863*** 0.2777*** 0.2583*** 0.2564***
(0.0366) (0.0370) (0.0355) (0.0356)
Board Size -0.4306*** -0.3781***
(0.1563) (0.1040)
Board Ownership -0.0545 -0.2768
(0.2228) (0.2150)
Constant -1.9821*** -1.1338** -2.5178*** -1.7275*
(0.4211) (0.5318) (0.8819) (0.9027)
Observations 4,492 4,492 4,492 4,492
R-squared 0.1809 0.1853 0.7973 0.7983
Number of firms 979 979 979 979
Firm Fixed Effect Yes Yes Yes Yes
Industry Year Fixed Effects No No Yes Yes
76
Table 21 Probit Model to Predict MB
This table presents a Probit model to predict the monitoring board. The sample consists of unbalanced panel data
between 2004 and 2012. The dependent variable is Monitoring (MB), a dummy variable that takes the value of one
if most of the board’s members are monitoring intensive directors, and zero otherwise. In column (1) and (2) the MB
variable is defined based on a window of 3 years before crisis when the MB status is in the three years. In column
(3) and (4) the MB variable is defined based on firms, which never changed MB status at all over the sample period.
Board Size is the pre-crisis mean value of the natural log of the total directors. Board Composition refers to the pre-
crisis mean value of the number of independent directors divided by the number of the total directors. Rev/Employ
is the pre-crisis mean value of the total revenue divided by number of employees. Robust standard errors are in
parentheses *** p<0.01, ** p<0.05, * p<0.1.
(1) (2) (3) (4)
Variables
Board Size -1.6476*** -1.7459*** -1.7067*** -1.9325***
(0.2071) (0.2339) (0.2215) (0.2525)
Board Composition -1.4060*** -1.4097*** -1.2354*** -1.1922***
(0.3380) (0.3591) (0.3624) (0.3860)
Rev/Employ 0.0001 0.0000 0.0002* 0.0000
(0.0001) (0.0001) (0.0001) (0.0001)
Constant 3.8803*** 4.2001*** 3.6101*** 4.2122***
(0.4863) (0.6364) (0.5147) (0.6773)
Observations 926 897 926 888
Industry Dummies No Yes No Yes
77
Table 22 Propensity Score Matching: Balance Diagnostic
This table presents the balance diagnostics for comparing the distribution of baseline covariates between treatment
groups in propensity score matched samples when the MB variable is defined based on firms which never changed
MB status at all over the sample period.
Treated Control %bias % reduction
In bias
t-stat p-value
(1) (2) (3) (4) (5) (6)
Board Size Unmatched
Matched
2.0524
2.0764
2.1989
2.0756
-66.0
0.4
99.5
-8.08
0.03
0.000
0.977
Board Composition Unmatched
Matched
0.6729
0.6822
0.7179
0.6839
-34.0
-1.3
96.2
-4.09
-0.10
0.000
0.917
Rev/Employ Unmatched
Matched
400.43
391.20
383
387.2
3.8
0.9
77.0
0.47
0.07
0.638
0.943
78
Table 23 Propensity Score Matching Results
This table presents the difference between monitoring board firm (treated firm) and matched non-monitoring board
firm (control firm) for each of the variables of interest. The matching is performed using nearest available
Mahalanobis metric matching within calipers defined by the propensity score.
Treated Control Difference t-stat
(1) (2) (3) (4)
ROAw Unmatched 0.1508 0.1541 -0.0033 -0.55
ATT 0.1398 0.1593 -0.0194 -1.83
Qw Unmatched
ATT
2.0306
1.8992
1.9561
2.2669
0.0745
-0.3677
0.76
-2.14
Rev/Employ Unmatched 400.4261 383.0015 17.4247 0.47
ATT 391.2048 387.1993 4.0055 0.06
Number of Employees Unmatched 13.1843 23.2037 -10.0194 -2.78
ATT 14.6285 15.8533 -1.2248 -0.24
Business Segment Unmatched 2.2857 2.5684 -0.2827 -1.91
ATT 2.4280 2.4067 0.0213 0.09
Capital investment Unmatched 0.0773 0.0598 0.0175 2.18
ATT 0.0716 0.0613 0.0103 0.69
R&D/Total Assets Unmatched 0.0503 0.0527 -0.0024 -0.33
ATT 0.0617 0.0688 -0.0072 -0.66
Leverage Unmatched 0.1402 0.1553 -0.0152 -1.40
ATT 0.1343 0.1265 0.0078 0.43
79
Table 24 ROA Analysis Based on Propensity Score Matched Sample
The sample includes propensity score matched sample of monitoring board firms and non-monitoring board firms of
888 firms between 2004 and 2012. The dependent variable is ROA estimated as the operational income before
depreciation divided by total assets. MB is a dummy variable that takes the value of one if most of the board’s members
are monitoring intensive directors, and zero otherwise. The MB variable is defined based on a window of 3 years
before crisis when the MB status is in the three years. Post Crisis is a dummy variable that takes the value of 1 if the
year is after 2008, and zero if it is before 2007. Firm Size is the natural log of market value in millions of dollars.
Board Size is the natural log of the total directors. Board Ownership is the percentage of ownership of the board
members. Robust standard errors clustered by firms are in parentheses *** p<0.01, ** p<0.05, * p<0.1.
(1) (2) (3) (4)
Variables
Post Crisis (0/1) -0.0118*** -0.0117*** 0.0191 0.0198
(0.0021) (0.0021) (0.0157) (0.0157)
MB x Post Crisis -0.0041 -0.0041 -0.0004 -0.0003
(0.0056) (0.0056) (0.0029) (0.0029)
Firm Size 0.0375*** 0.0376*** 0.0380*** 0.0382***
(0.0026) (0.0026) (0.0016) (0.0016)
Board Size -0.0223*** -0.0225*** -0.0251*** -0.0256***
(0.0078) (0.0079) (0.0055) (0.0055)
Board Ownership 0.0046 0.0139
(0.0155) (0.0106)
Constant -0.0926*** -0.0933*** -0.1227*** -0.1250***
(0.0260) (0.0260) (0.0223) (0.0224)
Observations 5,515 5,515 5,515 5,515
R-squared 0.1295 0.1295 0.8080 0.8081
Firm Fixed Effects Yes Yes Yes Yes
Industry Year Fixed Effects No No Yes Yes
80
Table 25 Tobin’s Q Analysis Based on Propensity Score Matched Sample
The sample includes propensity score matched sample of monitoring board firms and non-monitoring board firms of
876 firms between 2004 and 2012. The dependent variable is Tobin’s Q estimated as the book assets minus book
equity plus market value of equity, all divided by book assets. MB is a dummy variable that takes the value of one if
most of the board’s members are monitoring intensive directors, and zero otherwise. The MB variable is defined
based on a window of 3 years before crisis when the MB status is in the three years. Post Crisis is a dummy variable
that takes the value of 1 if the year is after 2008, and zero if it is before 2007. Firm Size is the natural log of market
value in millions of dollars. Stock Return is the annual stock price return. Board Size is the natural log of the total
directors. Board Ownership is the percentage of ownership of the board members. Robust standard errors clustered
by firms are in parentheses *** p<0.01, ** p<0.05, * p<0.1.
(1) (2) (3) (4)
VARIABLES
Post Crisis (0/1) -0.3595*** -0.3467*** -0.5801** -0.5461**
(0.0340) (0.0329) (0.2487) (0.2485)
MB x Post Crisis -0.1055 -0.1013 -0.0922* -0.0913*
(0.0842) (0.0844) (0.0472) (0.0471)
Firm Size 0.5316*** 0.5486*** 0.5661*** 0.5751***
(0.0507) (0.0519) (0.0268) (0.0271)
Stock Return 0.2806*** 0.2710*** 0.2552*** 0.2500***
(0.0292) (0.0291) (0.0277) (0.0277)
Board Size -0.4790*** -0.3778***
(0.1369) (0.0884)
Board Ownership 0.1272 -0.1008
(0.2217) (0.1757)
Constant -2.3370*** -1.4339*** -2.4359*** -1.6966***
(0.4047) (0.5000) (0.3223) (0.3635)
Observations 5,276 5,276 5,276 5,276
R-squared 0.1930 0.1985 0.8001 0.8010
Firm Fixed Effect Yes Yes Yes Yes
Industry Year Fixed Effects No No Yes Yes
81
Table 26 Revenues by Employee Analysis Based on Propensity Score Matched Sample
The sample includes propensity score matched sample of monitoring board firms and non-monitoring board firms of
888 firms between 2004 and 2012. The dependent variable is Revenue/Employee. MB is a dummy variable that
takes the value of one if most of the board’s members are monitoring intensive directors, and zero otherwise. The
MB variable is defined based on a window of 3 years before crisis when the MB status is in the three years. Post
Crisis is a dummy variable that takes the value of 1 if the year is after 2008, and zero if it is before 2007. Firm Size
is the natural log of market value in millions of dollars. Board Size is the natural log of the total directors. Board
Ownership is the percentage of ownership of the board members. Robust standard errors clustered by firms are in
parentheses *** p<0.01, ** p<0.05, * p<0.1.
(1) (2) (3) (4)
VARIABLES
Post Crisis (0/1) 43.3097*** 43.0107*** 24.1351 23.3948
(6.8441) (6.8885) (49.0399) (49.0744)
MB x Post Crisis -19.4631 -19.6455 -7.6273 -7.6982
(14.9628) (14.9819) (9.1396) (9.1419)
Firm Size 48.0974*** 47.5478*** 38.1737*** 37.9345***
(10.0901) (10.1480) (5.0271) (5.0581)
Board size 12.2512 13.3395 34.7093** 35.2733**
(26.6843) (26.8336) (17.1215) (17.1729)
Board Ownership -28.5548 -14.3597
(37.6947) (33.2734)
Constant -12.7323 -8.5547 36.4756 38.7834
(96.2806) (96.4015) (69.8019) (70.0129)
Observations 5,515 5,515 5,515 5,515
R-squared 0.0520 0.0522 0.9565 0.9565
Firm Fixed Effect Yes Yes Yes Yes
Industry Year Fixed Effects No No Yes Yes
82
Table 27 Number of Employees Analysis Based on Propensity Score Matched Sample
The sample includes propensity score matched sample of monitoring board firms and non-monitoring board firms of
884 firms between 2004 and 2012. The dependent variable is number of Employees. MB is a dummy variable that
takes the value of one if most of the board’s members are monitoring intensive directors, and zero otherwise. The
MB variable is defined based on a window of 3 years before crisis when the MB status is in the three years. Post
Crisis is a dummy variable that takes the value of 1 if the year is after 2008, and zero if it is before 2007. Firm Size
is the natural log of market value in millions of dollars. Board Size is the natural log of the total directors. Board
Ownership is the percentage of ownership of the board members. Busy Board is a dummy variable that take the
value of one when the majority of independent directors serve in 3 or more boards. Robust standard errors clustered
by firms are in parentheses *** p<0.01, ** p<0.05, * p<0.1.
(1) (2) (3) (4)
VARIABLES
Post Crisis (0/1) 2.288*** 2.263*** 1.852 2.063
(0.567) (0.565) (3.678) (3.678)
MB x Post Crisis -0.988 -0.937 -0.870 -0.822
(1.362) (1.361) (0.686) (0.686)
Firm Size 4.393*** 4.458*** 4.501*** 4.593***
(0.627) (0.648) (0.378) (0.380)
Board Size 3.950* 3.745* 3.441*** 3.203**
(2.033) (1.996) (1.289) (1.293)
Board Ownership 1.683 4.110
(3.740) (2.500)
Busy Board -4.990 -3.489**
(3.272) (1.546)
Constant -19.93*** -20.07*** -18.32*** -18.99***
(7.147) (7.194) (5.242) (5.253)
Observations 5,480 5,480 5,480 5,480
R-squared 0.056 0.058 0.974 0.975
Firm Fixed Effect Yes Yes Yes Yes
Industry Year Fixed Effects No No Yes Yes
83
Table 28 Business Segments Analysis Based on Propensity Score Matched Sample
The sample includes propensity score matched sample of monitoring board firms and non-monitoring board firms of
888 firms between 2004 and 2012. The dependent variable is the number of Business Segments. MB is a dummy
variable that takes the value of one if most of the board’s members are monitoring intensive directors, and zero
otherwise. The MB variable is defined based on a window of 3 years before crisis when the MB status is in the three
years. Post Crisis is a dummy variable that takes the value of 1 if the year is after 2008, and zero if it is before 2007.
Firm Size is the natural log of market value in millions of dollars. Board Size is the natural log of the total directors.
Board Ownership is the percentage of ownership of the board members. Busy Board is a dummy variable that take
the value of one when the majority of independent directors serve in 3 or more boards. Robust standard errors
clustered by firms are in parentheses *** p<0.01, ** p<0.05, * p<0.1.
(1) (2) (3) (4)
VARIABLES
Post Crisis (0/1) -0.0389 -1.10e-05 -0.0247 -0.0247
(0.0277) (1.103) (0.376) (0.376)
MB x Post Crisis 0.0783 0.0700 0.0700 0.0700
(0.0685) (0.0701) (0.0701) (0.0701)
Firm Size 0.0458 0.0146 0.0146 0.0146
(0.0353) (0.0388) (0.0388) (0.0388)
Board Size 0.422*** 0.328** 0.328** 0.328**
(0.128) (0.132) (0.132) (0.132)
Board Ownership -0.830*** -0.830*** -0.830***
(0.255) (0.255) (0.255)
Busy Board -0.165 -0.165 -0.165
(0.158) (0.158) (0.158)
Constant 1.327*** 1.811** 1.852*** 1.852***
(0.377) (0.845) (0.537) (0.537)
Observations 5,515 5,515 5,515 5,515
R-squared 0.003 0.073 0.830 0.830
Firm Fixed Effect Yes Yes Yes Yes
Industry Year Fixed Effects No No Yes Yes
84
Table 29 Capital investment Analysis Based on Propensity Score Matched Sample
The sample includes propensity score matched sample of monitoring board firms and non-monitoring board firms of
888 firms between 2004 and 2012. The dependent variable is Capex/Sales. MB is a dummy variable that takes the
value of one if most of the board’s members are monitoring intensive directors, and zero otherwise. The MB
variable is defined based on a window of 3 years before crisis when the MB status is in the three years. Post Crisis is
a dummy variable that takes the value of 1 if the year is after 2008, and zero if it is before 2007. Firm Size is the
natural log of market value in millions of dollars. Board Size is the natural log of the total directors. Board
Ownership is the percentage of ownership of the board members. Busy Board is a dummy variable that take the
value of one when the majority of independent directors serve in 3 or more boards. Robust standard errors clustered
by firms are in parentheses *** p<0.01, ** p<0.05, * p<0.1.
(1) (2) (3) (4)
VARIABLES
Post Crisis (0/1) -0.00314 -0.00320 -0.0182 -0.0186
(0.00195) (0.00196) (0.0166) (0.0167)
MB x Post Crisis -0.00768 -0.00770 -0.0138*** -0.0138***
(0.00723) (0.00723) (0.00310) (0.00310)
Firm Size Sale 0.0126*** 0.0125*** 0.00676*** 0.00665***
(0.00289) (0.00289) (0.00171) (0.00172)
Board Size -0.00697 -0.00679 -0.00277 -0.00254
(0.00811) (0.00809) (0.00581) (0.00583)
Board Ownership -0.00545 -0.00839
(0.0131) (0.0113)
Busy Board -0.00107 -0.00504
(0.00330) (0.00700)
Constant -0.0199 -0.0191 0.0283 0.0297
(0.0298) (0.0300) (0.0237) (0.0238)
Observations 5,515 5,515 5,515 5,515
R-squared 0.016 0.016 0.904 0.904
Firm Fixed Effect Yes Yes Yes Yes
Industry Year Fixed Effects No No Yes Yes
85
Table 30 R&D Analysis Based on Propensity Score Matched Sample
The sample includes propensity score matched sample of monitoring board firms and non-monitoring board firms of
888 firms between 2004 and 2012. The dependent variable is R_D/Sales. MB is a dummy variable that takes the
value of one if most of the board’s members are monitoring intensive directors, and zero otherwise. The MB
variable is defined based on a window of 3 years before crisis when the MB status is in the three years. Post Crisis is
a dummy variable that takes the value of 1 if the year is after 2008, and zero if it is before 2007. Firm Size is the
natural log of market value in millions of dollars. Board Size is the natural log of the total directors. Board
Ownership is the percentage of ownership of the board members. Busy Board is a dummy variable that take the
value of one when the majority of independent directors serve in 3 or more boards. Robust standard errors clustered
by firms are in parentheses *** p<0.01, ** p<0.05, * p<0.1.
(1) (2) (3) (4)
VARIABLES
Post Crisis (0/1) -0.00131 -0.00129 -0.0325*** -0.0324***
(0.00102) (0.00101) (0.0107) (0.0108)
MB x Post Crisis 0.000633 0.000686 8.74e-05 0.000117
(0.00192) (0.00193) (0.00200) (0.00200)
Firm Size Sale -0.00423** -0.00413** -0.00452*** -0.00447***
(0.00176) (0.00181) (0.00110) (0.00111)
Board Size 0.00557 0.00532 0.00572 0.00559
(0.00419) (0.00425) (0.00375) (0.00376)
Board Ownership 0.00442 0.00195
(0.00509) (0.00729)
Busy Board -0.00303 -0.00265
(0.00346) (0.00452)
Constant 0.0678*** 0.0672*** 0.101*** 0.101***
(0.0156) (0.0158) (0.0153) (0.0153)
Observations 5,515 5,515 5,515 5,515
R-squared 0.005 0.005 0.918 0.918
Firm Fixed Effect Yes Yes Yes Yes
Industry Year Fixed Effects No No Yes Yes
86
Table 31 Leverage Analysis Based on Propensity Score Matched Sample
The sample includes propensity score matched sample of monitoring board firms and non-monitoring board firms of
888 firms between 2004 and 2012. The dependent variable is leverage. MB is a dummy variable that takes the value
of one if most of the board’s members are monitoring intensive directors, and zero otherwise. The MB variable is
defined based on a window of 3 years before crisis when the MB status is in the three years. Post Crisis is a dummy
variable that takes the value of 1 if the year is after 2008, and zero if it is before 2007. Firm Size is the natural log of
market value in millions of dollars. Board Size is the natural log of the total directors. Board Ownership is the
percentage of ownership of the board members. Busy Board is a dummy variable that take the value of one when the
majority of independent directors serve in 3 or more boards. Robust standard errors clustered by firms are in
parentheses *** p<0.01, ** p<0.05, * p<0.1.
(1) (2) (3) (4)
VARIABLES
Post Crisis (0/1) 0.0176*** 0.0174*** 0.00180 0.00230
(0.00384) (0.00385) (0.0308) (0.0308)
MB x Post Crisis 0.00156 0.00179 0.00240 0.00266
(0.00945) (0.00945) (0.00574) (0.00574)
Firm Size Sale -0.0323*** -0.0321*** -0.0356*** -0.0353***
(0.00490) (0.00494) (0.00316) (0.00318)
Board Size 0.0377** 0.0369** 0.0442*** 0.0432***
(0.0148) (0.0149) (0.0108) (0.0108)
Board Ownership 0.000583 0.00973
(0.0259) (0.0209)
Busy Board -0.0279*** -0.0267**
(0.0103) (0.0130)
Constant 0.327*** 0.328*** 0.372*** 0.370***
(0.0457) (0.0457) (0.0439) (0.0440)
Observations 5,515 5,515 5,515 5,515
R-squared 0.042 0.043 0.789 0.789
Firm Fixed Effect Yes Yes Yes Yes
Industry Year Fixed Effects No No Yes Yes
87
Appendix
1. Variables Definitions
This appendix defines the variables used in the study. Board data is taken from the Institutional
Shareholder Services database (formerly Risk Metrics). Accounting and operating segment data
from Compustat and Stock Return data are taken from the Center for Research on Security Prices
(CRSP).
Variable Description
# of Dependent Directors (DD) Inside director
# of Linked Directors (LD) Affiliate outsider director
# of Independent Directors (ID) Directors with no material connection to the company
Total Directors (TD) Number of total members of the board
Board Size The natural log of the total directors
Board Composition Number of independent directors divided by the
number of the total directors.
Busy Board A dummy variable that takes the value of one when the
majority of independent directors serve in 3 or more
boards
Board Ownership The percentage of ownership of the board members
Audit Committee Member Number of members of the audit committee
Compensation Committee Member Number of members of the compensation committee
88
Appendix, continued
Variable Description
Nom./Corp.Gov Committee Member Number of members of the nominating or the
corporate governance committees. If a member
participates in both committees, it only considers one
of those.
# Independent Director M. Committees Sum of the number of members of the audit committee
plus the number of members of the compensation
committee, plus the number of members of the
nominating or the corporate governance committees.
Monitoring Intensive Directors (MID) Dummy variable that takes the value of one if a
director serves in at least two of the three monitoring
committees (audit, compensation and nominating or
corporate governance), and 0 otherwise.
Monitoring Board (MB) Dummy variable that takes the value of one if most of
the board’s members are monitoring intensive
directors, and zero otherwise (Faleye et al., 2011).
89
Appendix, continued
Variable Description
Q Tobin’s Q estimated as the book assets minus book equity
plus market value of equity minus deferred taxes all
divided by book assets.
ROA Operational income before depreciation divided by total
assets
Leverage Long-term debt divided by total assets
Rev/Employ Total revenue divided by number of employees
Capex/Sales Capital expenditure divided by sales
Firm Size (ln Market Value) The natural log of market value
Capex Capital expenditure
R&D/Assets Research and development divided by total assets
Cash holding The cash and short-term investments divided by total
assets
# Business Segment Number of segments
Stock Return Annual stock price return
90
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Biography
Sandra Constanza Gaitan Riaño is a Professor at Universidad EAFIT in the area of
Corporate Finance. She is the Coordinator of Research Projects of the Master in
Financial Administration (MAF) at Universidad EAFIT in Medellin. She received
her Master in Financial Administration (MAF), Specialist in Finance and Master in
Business Administration (MBA) at Universidad EAFIT. She graduated in Civil
Engineering from the Universidad Nacional de Colombia, Facultad de Minas. Her
research interests are related to topics in corporate governance, merger and
acquisition, international finance.