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Costs and benefits of "friendly" boards during mergers and acquisitions

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Costs and benefits of "friendly" boards during mergers and acquisitions

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Content COSTS AND BENEFITS OF “FRIENDLY” BOARDS DURING MERGERS AND
ACQUISITIONS
by
Breno Schmidt
A Dissertation Presented to the
FACULTY OF THE GRADUATE SCHOOL
UNIVERSITY OF SOUTHERN CALIFORNIA
In Partial Fulfillment of the
Requirements for the Degree
DOCTOR OF PHILOSOPHY
(BUSINESS ADMINISTRATION)
May 2009
Copyright 2009 Breno Schmidt
Dedication
To Chris Jones, John Matsusaka and Lubos Pastor, for believing that I
could turn it around.
ii
Acknowledgments
I thank Malcolm Baker, Joe Chen, Lauren Cohen, Harry DeAngelo, Luis Diestre, Ran
Duchin, Miguel Ferreira, Huijing Fu, Luis Goncalves-Pinto, Derek Horstmeyer, Ravi
Jagannathan, Shmuel Leshem, Pedro Matos, John Matsusaka, Salvatore Miglietta,
Florian Muenkel, Kevin Murphy, Micah Officer, Oguzhan Ozbas, Josh Shemesh,
Kara Wells, and seminar participants at Boston College, Emory University, Ohio
State University, UC Davis, University of Florida, University of Illinois at Urbana-
Champaign, University of Michigan, University of Notre Dame, USC, Washington
University in St. Louis, and the 2008 FMA PhD Consortium for helpful comments
and suggestions. I am especially grateful to my advisor, John Matsusaka, as well as
to Ran Duchin, Pedro Matos, Salvatore Miglietta, and Oguzhan Ozbas, for long and
fruitful discussions.
iii
Table of Contents
Dedication ii
Acknowledgments iii
List of Tables v
Abstract vi
Chapter 1: Introduction 1
Chapter 2: Data and Empirical Design 11
1 Social Ties 15
2 Proxies for Monitoring and Advisory Needs 22
3 Performance Measures and Other Controls 30
Chapter 3: Empirical Results 36
1 Social Ties and the Dual Role of the Board 36
2 The Effects of Social Ties on Different Samples 40
3 Individual Proxies for Monitoring and Advice 44
4 Social Ties and Extreme Changes in Wealth 48
5 Social Connections to More Board Members 52
6 Endogeneity Concerns 58
Chapter 4: Conclusion 65
Appendix A 71
Appendix B 76
iv
List of Tables
Table 1: Summary Statistics 13
Table 2: CEO-Board Social Ties 18
Table 3: Proxies for Monitoring and Advisory Needs 25
Table 4: Bidder Announcement Returns for Different Samples 31
Table 5: Bidder Announcement Returns and Social Ties 38
Table 6: Social Ties on Different Subsamples 41
Table 7: Individual Proxies for Monitoring/Advisory 44
Table 8: Social Ties and Extreme Changes in Wealth 50
Table 9: Different Measures of Social Ties 55
Table 10:Serial and Non-Serial Acquirers 62
Table 11:Bidder Announcement Returns and Social Ties with Independent Direc-
tors 76
Table 12:Different Measures of Social Ties with Independent Directors 78
Table 13:Social Ties with Independent Directors on Different Subsamples 80
Table 14:Individual Proxies and Ties with Independent Directors 82
Table 15:Serial and Non-Serial Acquirers and Ties with Independent Directors 83
v
Abstract
Although recent regulations call for greater board independence, finance theory pre-
dicts that independence is not always in the shareholders’ interest. In situations where
it is more important for the board to provide advice than to monitor the CEO, more
independent directors can decrease firm value because the CEO is not willing to share
inside information with independent directors. I test this prediction by examining the
connection between takeover returns and board “friendliness” using social ties be-
tween the CEO and board members as a proxy for less independent, more “friendly”
boards. I find that social ties are associated with higher bidder announcement re-
turns when advisory needs are high but with lower returns when monitoring needs
are high. These effects intensify as the proportion of the board socially connected to
the CEO increases and are not driven by correlations between social ties and other
board characteristics. The evidence suggests that friendly boards can have both costs
and benefits depending on the specific needs of the company.
vi
Chapter 1
Introduction
Recent regulations such as the Sarbanes-Oxley Act of 2002 and the NYSE new listing
requirements of 2003 call for greater participation of outside directors in corporate
governance.
1
The new regulations are motivated at least in part by the view that
independent directors are better able to discipline the CEO, an idea with a long pedi-
gree in corporate finance (e.g., Berle & Means (1932), Fama & Jensen (1983), and
Jensen (1993)). Yet, despite the widespread belief among regulators and scholars that
independent directors are good for corporations, there is surprising little evidence to
support the notion that outside board members increase corporate value or efficiency.
2
In addition, theory suggests that in some circumstances less independent boards can
benefit shareholders (e.g., Adams & Ferreira (2007), Harris & Raviv (2008)).
3
This paper tests the largely unexplored hypothesis that less independent, more
“friendly” boards can benefit the shareholders of firms pursuing corporate acquisi-
tions.
4
Theory predicts that less independent directors can be helpful when the im-
portance of board advice surpasses the need to supervise the CEO. This is because the
CEO has no incentive to conceal inside information from friendly directors, which
1
See Duchin et al. (2008) and references therein for more details on such regulatory changes.
2
See e.g., Bhagat & Black (2002) and Hermalin & Weisbach (2003). There is evidence, however,
that outside directors can influence certain corporate tasks, such as CEO turnover (Weisbach (1988)).
3
In a related work, Duchin et al. (2008) find that the effectiveness of outside directors varies
with the costs of acquiring information, which suggests that the impact of board independence on
performance varies across firms.
4
The term “friendly boards,” which I use throughout, is borrowed from Adams & Ferreira (2007).
It is meant to capture the degree to which the board is reluctant to take actions against the CEO.
1
in turn allows for better board counsel (Adams & Ferreira (2007)). To test this pre-
diction, I use observable social connections between the CEO and board members
as a proxy for board friendliness. Following prior literature, I classify firms based
on their specific advisory and monitoring needs. I then examine how the effects of
social ties on the value of the firm vary across these classifications. I find that when
board directors are more likely to possess valuable information about the merger,
higher announcement returns are observed for bidders with more friendly boards.
Conversely, when the need to discipline the manager is a greater concern, social ties
have a negative impact on the acquiring firm’s performance. I also find that these so-
cial connections are prevalent among merger deals that resulted in extreme changes
in shareholders’ wealth, which highlights the potentially large economic impact of
social ties.
Adams & Ferreira (2007) provide a theoretical analysis of the advisory role of
boards. In their model, the board can affect the firm’s value through both disciplining
and advising the CEO. How well it performs each function depends on how much
information executives and board members exchange. When directors are indepen-
dent, the CEO is reluctant to reveal private information. This is because revealing
what really underlies some proposed policy might prompt the board to intervene in
favor of shareholders. The manager thus protects him or herself from monitoring,
though at the cost of not receiving proper advice. Conversely, when board members
do not take actions against the CEO, there is no incentive for the manager to conceal
information from the board. More informed directors in turn improve the overall
quality of board counsel. Friendly boards therefore provide better advice but can-
not supervise managers efficiently. From the shareholders’ perspective, independent
2
boards are desirable when monitoring is more important than advice. When supervis-
ing the CEO is less crucial than the need for feedback from board members, however,
shareholders prefer a less independent, more friendly board.
Harris & Raviv (2008) make a similar argument. In their model, it is costly for
outside board members to obtain inside information about the company. Uninformed
outside directors cannot properly advise the CEO. Consequently, when the value of
inside knowledge is high, shareholders will prefer to delegate control to insiders.
Raheja (2005) also studies how the exchange of information between insiders and
outsiders influences optimal board structure. In her model, outsiders use CEO suc-
cession to motivate insiders to reveal their superior information.
Although the exact channels through which board composition affects firm value
differ in these models, they all share this insight: because independent directors are
less informed, they can impair the board’s ability to serve as valuable advisers to the
CEO. The immediate implication is that less dependent directors can increase firm
value when the advice they provide is sufficiently important for the success of the
merger. Testing this common prediction is the central theme of this paper.
In both Harris & Raviv (2008) and Raheja (2005) independent directors are equiv-
alent to outside board members. In Adams and Ferreira’s model, however, the con-
cept of friendly boards is broader than outside representation. In particular, the model
does not preclude outside board members from being friendly. For instance, friend-
ship ties between CEOs and outside board members can impair board members’
willingness to discipline the CEO, reducing their true independence. This in turn
increases the information flow between the parties and improves the quality of board
advice. This distinction is important because the proportion of outside members on
the board of directors has been traditionally used to measure board independence.
Although I do consider outside representation as a potential measure, my primary
3
gauge for friendly boards relies on social connections between chief executives and
board members. This measure is motivated in part by recent work in management lit-
erature suggesting that social ties foster friendship (e.g., Westphal (1999), Westphal
et al. (2006) and Kroll et al. (2008)) and in part by studies questioning the efficacy
of the regulatory definition of an independent director (e.g., Duchin et al. (2008) and
Cohen et al. (2008a)).
5
If social ties do capture part of the actual level of board independence, Adams
and Ferreira’s hypothesis can be restated in the following terms:
Main hypothesis: Social ties between the CEO and board members affect the value
of the firm as follows:
1. Stronger ties decrease the board’s willingness to discipline the CEO, destroying
the firm’s value when the preferences of the CEO and the shareholders are not
aligned.
2. Social ties facilitate the exchange of information between CEOs and board
members and improve the quality of board advice, increasing the firm’s value
when board advice is important.
The direct implication of this hypothesis is that social ties will benefit sharehold-
ers if, and only if, the gains from improved board advice outweigh the costs from
reduced monitoring. As a prediction, we should observe a negative effect of social
ties on the performance of firms for which board supervision is likely to be very im-
portant. Conversely, when board advice is the main concern, we should observe a
positive effect of social ties.
5
An alternative interpretation is that social ties promote shared values, which enhances corporate
culture. These shared values may improve the alignment of actions (Kreps (1990), Cremer (1993)) of
CEOs and board members. Shared beliefs about the prospects of the acquisition will benefit share-
holders of acquiring companies if, and only if, agency problems is not a main concern.
4
The first step in testing this hypothesis is to identify a corporate decision where
both the monitoring and advisory roles of the board are likely to affect the firm’s
value. One that meets these requirements is the decision to acquire another company.
Mergers are major and complex corporate events that require the board’s approval
and have potentially large effects on shareholders’ wealth. For instance, using an-
nouncement returns as a measure of wealth change, Moeller et al. (2005) report
massive losses in 87 deals between 1998 and 2001: acquiring firm shareholders lost
a total of $397 billion, or an average of $2.31 per dollar spent on the acquisition. Al-
though these are extreme cases, they illustrate well the devastating effects of certain
merger deals.
Focusing on the impact of one corporate event (as opposed to examining the
overall value of the firm) has another advantage. Both Adams & Ferreira (2007) and
Harris & Raviv (2008) are equilibrium models, in which shareholders maximize the
overall value of the firm. In the absence of frictions, these models predict that board
composition will always reflect the optimal choice of shareholders. We therefore
would not be able to observe any effect of board independence on firm value. But
shareholders are likely to take into consideration all corporate policies, not only the
decision to merge, when choosing the board structure. In addition, boards cannot
adjust instantaneously to changes in the economic environment that prompt a merger
opportunity. Therefore, it is possible that we will observe value effects of board
independence following economic shocks that give rise to merger opportunities.
The main hypothesis predicts different effects of board friendliness depending
on specific monitoring and advisory needs. Some classification scheme is therefore
necessary in order to categorize firms across these two dimensions. I rely on prior
literature to construct proxies for the relative importance of board supervision and
counsel.
5
To identify mergers in which board supervision is likely to be more important, I
create an index variable that accounts for many characteristics commonly associated
with agency-driven acquisitions. Existing research suggests that some mergers are re-
lated to agency problems and thus to the board’s supervisory role (e.g., Jensen (1986),
Morck et al. (1990), Harford (1999)). Following this literature, the monitoring index
includes such characteristics as high levels of free cash flows, low CEO equity-based
compensation, few external monitors, and highly entrenched CEOs, among others. I
hypothesize that acquisitions by bidders with higher monitoring needs can be identi-
fied by higher values of this index.
I follow a similar strategy to capture differences in advisory needs across firms.
Specifically, the value created or destroyed by mergers is likely to be affected by the
board’s ability to counsel the CEO. Prior literature associates a number of firms and
business characteristics to the importance of board advice (e.g., Westphal (1999),
Adams (2003), Coles et al. (2008)). Board advice is likely to be more important
during acquisitions made by more complex firms, by inexperienced CEOs, by com-
panies with “expert” board members, and by bidders with informed directors. I use
such characteristics to create the index for the importance of board advice.
The next step is to construct a proxy for friendly boards. I use social ties between
chief executives and board members as a measure of friendly boards. Specifically, I
look for observable social interactions between the CEO and board directors. These
interactions include common membership at the same club, affiliation with the same
charitable foundation, fraternity, art institute, museum, or other nonprofit organiza-
tion, and common seats on university boards of trustees. I also look at cases in which
the CEO and a board member received their MBAs from the same business school
and within one year of each other. Whenever such an interaction is present, I define
the CEO as having a potential social tie to a board member.
6
The findings are consistent with the hypothesis that social ties increase the value
of the bidder when board advice is more important than monitoring the CEO. In a
sample of 7,154 mergers from 1994 to 2006 I find that social ties are associated with
lower mean bidder Cumulative Abnormal Returns (CAR) surrounding the merger
announcement: 0.15% compared to 0.81% for bidders with no social ties. Although
the net effects of social ties are on average small, they vary greatly across different
types of acquisitions. More important, this variation is strongly related to the proxies
for monitoring and advisory needs. In particular, social ties have a strong negative
effect on bidder performance when the monitoring index is above its median level:
even after controlling for many characteristics commonly known to affect CARs,
social ties decrease announcement returns by 1.38 percentage points.
Social ties are not always detrimental to shareholders, however. Perhaps the most
important contribution of this paper is to present evidence that friendly boards can
benefit shareholders. Specifically, I find that social ties are associated with higher
announcement returns when board advice is likely to be important. For the subset
of firms with advice index above the median, social ties increase average CARs by
1.91 percentage points. This difference is more than three times the magnitude of
the unconditional mean announcement return of 0.61%. Moreover, this wedge is
even larger for companies in which the higher advisory needs are accompanied by
lower monitoring concerns: 2.06 or 2.19 percentage points, depending on the spe-
cific measure used. All these results are statistically significant and control for many
characteristics previously found to be associated with bidder announcement returns.
After presenting the results for the typical acquisition, I examine whether social
ties are related to shifts in shareholders’ wealth surrounding some extreme merger
announcements. Moeller et al. (2005) show that in a few mergers between 1998
and 2001 shareholders lost more than $1 billion per acquisition. If a contributing
7
factor to this “value destruction on a massive scale” is that social ties prevent proper
board intervention, we should observe among these deals a disproportional amount of
socially connected firms with high monitoring needs. I find that in 64% of the deals
identified by Moeller et al. (2005), the chief executive of the acquiring firm was
in fact socially connected to the board (compared to an average of 31% for all deals
during the same period). Among these, 83% were deals made by acquiring firms with
above-median values of the monitoring index. I use a Probit analysis to disentangle
the effects of social ties from other companies characteristics. I find that, among
companies with high monitoring needs, social ties increase the probability of extreme
negative announcement returns by more than four times. As before, however, social
ties are also associated with extreme positive changes in the value of the firm when
advisory needs are high. I identify deals in which the acquiring firm experiences an
increase in value of more than $1 billion, the same threshold used by Moeller et al.
(2005). Close to 58% of these acquisitions were made by a socially connected CEO,
76% of which were by firms with high advisory needs. Using the Probit estimates,
I find that, conditional on high advisory needs, social ties increase the probability of
extreme positive announcement returns by over three times. I interpret these findings
as evidence that social ties can help to explain the occurrence of such extreme deals.
After presenting the main results, I perform two robustness checks. The first uses
the proportion of the board connected to the CEO as the proxy for friendly boards.
Note that the findings discussed above concern the presence of CEO-board social
connections. But if, as I argue, social ties do affect the two main functions of the
board, we should also observe stronger effects as the number of connected board
members increases. To test this implication, I first replicate the experiments above in
a sample consisting only of firms with at least one socially connected board member.
8
I find that in general, social ties with more board members are associated with larger
effects on performance.
It could be that social ties are correlated with other variables that affect perfor-
mance but are not related to board independence. For instance, social ties could
proxy for board expertise or for the quality of directors. If high-quality board mem-
bers are present only in firms with either high advisory or high monitoring needs,
this could potentially induce a spurious result. As an additional robustness test, I
estimate a two-stage selection model that takes into account many of the character-
istics that could affect the existence of social ties. I then use the residuals of this
estimation as a measure of social ties that is orthogonal to such characteristics. As it
turns out, this increases the strength of the results. Across all specifications, social
ties significantly increase performance when advice is important but also decrease
announcement returns for high values of the monitoring index.
Last, it is important to note that these ties are not highly correlated with the com-
monly accepted measure of outside representation. In particular, the proportion of
outside directors does not have a systematic impact on the value of acquiring firms.
This might be an indication that social ties are related to a dimension of (true) board
independence that is not captured by the legal classification of an independent direc-
tor.
6
Therefore, this paper is also related to recent work questioning the efficacy of
the regulatory definition of an independent director (e.g., Duchin et al. (2008) and
Cohen et al. (2008a)).
The approach taken in this paper is driven by what is measurable. On the one
hand, there are surely unobservable factors that could affect the relationship between
CEOs and board members, as well as its impact on the value of the firm (e.g., the
6
Barnea & Guedj (2006) make a similar argument. They study how networks of directors affect
CEO compensation and argue that social networks can be interpreted as a complementary measure of
independence.
9
quality of the match between the firm, the CEO and board members). On the other
hand, it is hard to make general inferences about the quality of the board and the value
of the firm, given the complexity of the issues involved. The goal here is to focus on
how one observable and potentially important factor, CEO-board social connections,
affects the outcome of one particular form of investment, the acquisition of another
company. It is important to keep this caveat in mind when extrapolating the results
documented here to more general statements about governance.
Overall, my results support the view that social ties (and board independence)
affect the quality of corporate decisions. In other words, greater board independence
can have both benefits and costs, which, as theory indicates, depend on the specific
needs of the company. To the extent that social ties can capture one dimension of the
relationship between the CEO and the board, the findings documented here suggest
that complete board independence can sometimes hamper the board’s ability to serve
as valuable advisers to the CEO.
The paper proceeds as follows: Chapter 2 describes the data and the construction
of the social ties measures. Chapter 3 presents the main findings, separating the
effects of social ties when either monitoring or advice is a greater concern. There, I
also study the effects of social ties on extreme changes in the shareholders’ wealth,
and how increases in the proportion of the board connected to the CEO strengthen
the results. Chapter 4 concludes.
10
Chapter 2
Data and Empirical Design
In this section, I describe the sample and the construction of the social ties proxy.
Three data sources are used to obtain information on companies, board members,
and merger activity.
The director profile data was obtained from the BoardEx web site.
1
This database
contains detailed profiles of more than 30,000 executives and board members, cov-
ering virtually all US companies. From these profiles, I collect all information avail-
able for each director. This includes current and previous employment, education,
affiliation to Not-For-Profit (NFP) associations (including religious institutions and
university boards of trustees), club memberships (including fraternities), and other
characteristics such as age and nationality.
I then match the BoardEx data to CRSP and Compustat, from which all financial
and accounting variables are obtained. Because of potential survivorship bias, the
board data is for the period from 1990 to 2007.
2
During this sample period, more
than 80% of all CRSP companies are covered by BoardEx.
1
Cohen et al. (2008b) is one of the first papers to use this database and contains a more detailed
description. See also Ferreira & Matos (2007) and Cohen et al. (2008a).
2
There seems to be strong of survivorship bias in the BoardEx files. Before the 1990s, the propor-
tion of CRSP firms covered drops drastically.
11
The mergers data comes from the Securities Data Company (SDC). After collect-
ing all completed mergers from 1994 to 2007, I impose the following data require-
ments.
3
1. The acquirer is a publicly traded company with daily stock returns available in
CRSP from 230 days before the announcement to the three days that follow it.
The bidder is also required to have Compustat data for the three fiscal years
before the merger announcement.
2. The acquisition was completed, the deal value is more than $1 million and
represents at least 1% of the acquirer’s market value, measured at the fiscal
year end before the announcement.
3. The bidder controls less than 50% of the target before the announcement and
owns 100% of the target’s shares after the transaction.
4. The bidder can be identified in the BoardEx database. In addition, I require
that there is information available on the CEO and at least three board members
(which might include the CEO) starting three years before the announcement
date.
3
These requirements are similar to those in Masulis et al. (2007). The sample begins in 1994
instead of 1990 because I require individuals to have served on the board of directors for at least three
years before the fiscal year preceding the acquisition.
12
Table1: Summary Statistics
The sample consists of 7,154 acquisitions by public bidders from 1994 to 2006. Total As-
sets is in $ Bil. Leverage is long-term debt plus debt in current liabilities over debt plus the
book value of common equity. Tobin’sQ is defined as the market value of assets divided by
the book value of assets. Excess Cash represents excess cash and are computed follow-
ing Dittmar & Mahrt-Smith (2007). N Bus Segments is the number of business segments
in the Compustat Business files. Price Run-up (%) is the bidder’s buy and hold abnormal
return from 230 to 11 days before the announcement. Inst Ownership is the proportion
of shares outstanding in the hands of US independent investors (Chen et al. (2007)).
Income is the three-year income growth used by Morck et al. (1990). E-index is the en-
trenchment index of Bebchuk et al. (2008). Board Size is the number of directors on the
board (from BoardEx). % of Outside Dirs is the proportion of independent directors on the
board (in %). % Equity Based Compensation is the sum of the value of new stock options
granted to the CEO as a percentage of total compensation. (Datta et al. (2001)). CEO
Age represents the age of the CEO (from BoardEx). Board Expertise is the proportion of
board members with financial expertise (i.e., past CEOs/CFOs or executives in companies
with SIC codes 6000-6999). CEO Prior M&A is the number of prior M&As (since 1980) in
which the current bidder CEO was either the CEO or CFO of the acquiring company.CEO
Experience is the CEO’s tenure in the bidder’s industry (in years). Board Experience is
the average tenure of board members in the bidder’s industry (in years). Deal Value is the
value of the deal as reported by SDC (in Millions). RelativeSize is the value of the deal as
reported by SDC over the market value of the acquirer measured at the end of the fiscal
year preceding the announcement (in %). PublicTgt indicates whether the acquisition was
for a publicly traded target. Cash Only represents acquisitions entirely financed by cash.
Diversifying represents mergers in which the target and acquirer are in different four-digit
SIC code industries. The Appendix contains a detailed description of all the variables.
Panel A - Summary Statistics
Mean Std Dev Median 25% 75%
Bidder Characteristics
Total Assets 7:20 39:75 0:78 0:23 2:86
Leverage 0:33 0:94 0:34 0:09 0:54
Tobin’s Q 2:09 2:74 1:45 1:10 2:20
Excess Cash 0:04 0:41 0:14 0:19 0:00
N Bus Segments 1:99 1:64 2:00 1:00 3:00
Price Run-up (%) 19:36 78:31 5:65 16:00 31:82
Inst Ownership 16:21 17:77 8:26 4:17 23:42
Income 0:55 0:79 0:49 0:17 0:90
E-index 2:37 1:34 2:00 1:00 3:00
CEO and Board Characteristics
Board Size 8:96 3:63 8:00 6:00 11:00
% of Outside Dirs 73:23 13:46 75:00 66:67 83:33
% Equity Based Compensation 36:95 29:47 34:49 9:57 60:26
CEO Age 52:97 7:94 53:00 47:00 58:00
Board Expertise 0:11 0:12 0:10 0:00 0:18
CEO Prior M&A 2:71 4:15 1:00 0:00 4:00
CEO Experience 6:55 4:97 5:00 3:00 9:00
Board Experience 6:47 3:36 6:00 3:75 8:60
13
Table 1, Continued
Panel B - Acquisitions by Year
Year N of Deals Deal Value Relative Size Public Tgt Cash Only Diversifying
(Avg -$ Mil) (Avg - in %) (%) (%) (%)
1994 272 244:30 17:12 34:19 27:21 32:72
1995 306 422:86 20:80 37:25 23:86 31:05
1996 429 403:58 19:93 30:77 26:11 38:69
1997 584 422:18 26:45 30:48 25:51 37:67
1998 645 979:91 23:41 33:02 26:20 35:81
1999 612 787:83 23:79 31:21 26:14 37:42
2000 571 1164:84 28:28 32:57 27:32 42:38
2001 521 487:78 28:97 27:83 32:25 36:85
2002 599 281:16 18:84 20:53 42:90 42:57
2003 606 274:63 19:87 22:44 40:76 37:95
2004 688 279:04 18:79 19:48 46:66 35:61
2005 695 592:97 19:17 18:85 44:46 37:55
2006 626 505:47 19:95 18:37 50:32 39:30
Total 7,154 545:76 22:08 26:43 35:09 37:76
A total of 7,154 mergers announced between 1994 and 2006 meet these criteria.
Table 1 summarizes bidder, board, and deal characteristics for the sample. The bidder
characteristics are similar to those in Masulis et al. (2007) and are measured at the
end of the fiscal quarter preceding the announcement. The board composition num-
bers are also similar to recent studies using the more common IRRC database (e.g.,
Duchin et al. (2008)). The average board size is 7.9, with a median of 7.0 directors.
The average proportion of outside directors is about 73%. In terms of deal character-
istics, the average deal value is $418 million. As expected, most acquisitions are for
smaller targets. The average deal value as a percentage of the bidder’s market value
is 21%. Close to 35% of all acquisitions are financed entirely with cash, and more
than 38% of acquisitions are for targets in a different four-digit SIC code industry.
14
1 Social Ties
This section briefly describes the construction of the proxies for social connections
between the CEO and board members. A more detailed description is included in
the next section. The goal is to construct a proxy for “social ties” using observable
membership to institutions outside the working environment, in which social inter-
actions or friendship ties between the CEO and board members could be more easily
developed (e.g., Westphal (1999), Westphal et al. (2006), and Kroll et al. (2008)).
I obtain the affiliation of each director to various “nonbusiness” organizations
from the director profiles in BoardEx. These institutions fall into one of the follow-
ing categories: (i) clubs or fraternities, (ii) Not-For-Profit foundations (NFP), (iii)
university boards of trustees, and (iv) network clubs.
4
When the CEO and a board
member share a common affiliation to one of these institutions, I classify them as
“socially connected.” Following the recent literature on school ties (e.g., Cohen et al.
(2008b) and Nguyen-Dang (2008)), I also classify as socially connected two individ-
uals who earned MBAs from the same school within one year of each other.
To construct the social ties proxy, I first compute the proportion of the board that
is socially connected to the CEO. To correct for the large variation in the number of
affiliations across institutions, I use simulations to subtract from this observed pro-
portion the part that would be expected given the size of the institutions the CEO is
affiliated with. For instance, in the sample, there are more graduates from Harvard
than from Stanford. Thus, we can expect a larger percentage of the board to be con-
nected to the CEO if he or she graduated from Harvard. This would give Harvard
a disproportionate weight in the measure. Subtracting out the expected percentage
4
Section 1 contains a detailed description as well as many examples of each type of institution.
15
of directors connected to a Harvard-educated CEO makes the impact of each institu-
tion on the social tie measure more homogeneous.
5
I refer to this corrected number
throughout as % Friendly Board. It is important to note that none of my conclusions
change if the raw proportion is used instead.
Definition (Social Tie Measures): The following two measures are used as proxies
for social connections:
% Friendly Board: For each acquirer f announcing an acquisition at t,
% Friendly Board is defined as the difference between the actual proportion
of eligible directors connected to the CEO and its expected value, as explained
above. A director is eligible if, at the end of the fiscal yeart 1, he or she has
served as a board member for companyf for at least three years.
Social Ties: For each acquirerf announcing an acquisition att, Social Ties
is a dummy variable that is equal to 1 if % Friendly Board > 0 and is zero
otherwise. It is then an indicator of the presence of social ties with at least one
board member.
The three-year tenure requirement minimizes the possibility that the choice of
a board member is related to the acquisition decision. This is important because
it increases the chances that the composition of the board precedes the decision to
acquire. Although I can not claim causality, it is unlikely that the decision to merge
caused the hiring of a director three years before.
5
Specifically, to estimate the expected proportion of the board connected to CEO i, I simulate
10,000 random boards by sampling from the population of potential directors. For each one of these
simulated boards, I then check the proportion of directors who are socially connected to CEOi. The
average across all simulations is then used to correct the actual proportion of board members with
social ties to CEOi.
16
Throughout, I include all board members in the computation of the proportion
of the board with social ties to the CEO. The results are very similar if only outside
members are included.
6
Table 2 contains the summary statistics for social connections. In Panel A, all
board members are included in the construction of the social ties measure, whereas
in Panel B only outside board members are considered. The first column (% CEOs
Connected) contains the proportion of CEOs for whom % Friendly Board > 0. On
each row, I separate the numbers by the type of organization responsible for the social
tie. For instance, from Panel A, 6.8% of CEOs belong to the same club as at least
one director.
7
The table also presents the mean of both the actual and the simulated
proportions of board members connected to the CEO. The last column (P-Value)
represents the average number of simulations for which the simulated proportion is
larger than the one observed. For example, the first row of Panel A shows that in only
0.62% of all simulations the proportion of directors belonging to the same club as the
CEO was greater than the observed proportion for that particular firm-year.
6
This is because (i) for roughly 75% of the sample, outside directors represent more than 67% of
the board, and (ii) social connections between the CEO and outside directors are much more common
than among insiders (see Table 2). These results are available on request.
7
To be more precise, this number represents the proportion of CEOs for whom the % Friendly
Board measure (i.e., the difference between the actual and simulated proportion) is above zero, where
% Friendly Board is constructed using only ties from Clubs.
17
Table2: CEO-Board Social Ties
This table contains CEO-Board social ties information. Panel A contains information
on CEO social ties with any board members. The Actual column displays the aver-
age proportion of board members socially connected to the CEO (if no tie exists, this
number is set to zero). The Simulated column contains the average proportion con-
nected across 10,000 simulations. P-Value is the proportion of simulations in which
the proportion of the board connected was greater than the one observed. All values
are expressed in percentage points. The first column contains the proportion of CEOs
for whom Actual - Simulated is greater than zero. Panel B contains similar figures but
considers only outside board members. Panel C reports correlations between Social
Ties and other variables. % Friendly Board represents the net proportion of the bid-
der’s board socially connected to the CEO. Board Size is the number of directors on
the board (from BoardEx). % of Outside Dirs is the proportion of independent direc-
tors on the board (in %). Log Total Assets is the logarithm of total assets. Same Club
represents membership to the same club. Same NFP represents membership (either
as a trustee or board member) to the same NFP organization. Same Network Clubs
represents membership (either as a trustee or board member) to the same Network
Club. MBA indicates whether the CEO and a board member earned their MBA degrees
from the same institution and within 2 years of each other.Any Tie correspond to cases
where the CEO and board member share at least one of the social ties described above.
Panel A - CEO-Board Social Tie
% CEOs % Board Connected to CEO
Connected Actual Simulated P-Value
Same Club 6:78 0:99 0:14 0:62
Same NFP 27:44 4:52 0:59 2:04
Same Network Clubs 3:30 0:46 0:22 0:93
MBA 1:03 0:11 0:02 0:15
Any Tie 33:15 5:90 1:22 2:90
Panel B - CEO Ties to Independent Directors
% CEOs % Board Connected to CEO
Connected Actual Simulated P-Value
Same Club 5:68 1:07 0:12 0:65
Same NFP 23:99 4:98 0:52 2:45
Same Network Clubs 2:99 0:50 0:20 1:09
MBA 0:90 0:12 0:02 0:15
Any Tie 29:64 6:49 1:09 3:65
Panel C - Correlations
% Friendly Board % Outside Log Total
Board Size Dirs Assets
% Friendly Board 1:000
Board Size 0:196 1:000
% of Outside Dirs 0:182 0:265 1:000
Log Total Assets 0:051 0:592 0:172 1:000
Board Expertise 0:083 0:148 0:155 0:275
18
Across all types of ties, the observed proportion of the board connected is signif-
icantly larger than the one we would expect if directors were chosen at random. This
is consistent with studies finding that the hiring decisions of executives and board
members are influenced by social connections (e.g., Nguyen-Dang (2008)). When
all ties are considered (Any Tie), we see that 33.1% of all CEOs are socially con-
nected. The average proportion of the board connected (including the cases where
no social tie exists) is 5.9%, which makes the average % Friendly Board equal to
4:7% = (5:9% 1:2%). The numbers for the proportion of outside members con-
nected to the CEO are quite similar, which indicates that CEOs’ social connections
are more pervasive among outside board members.
8
Details
This section details the construction of the social ties proxies. To facilitate the exposi-
tion, I begin with a brief description of what is contained in the BoardEx files. Each
director profile is divided into sections containing information on past and current
employment, education and “other activities.” These other activities include current
and past associations to various types of nonprofit organizations, along with the role
played by the director in each of them (e.g., “Trustee” or “Director”). Unfortunately,
BoardEx does not provide a key to uniquely identify each organization. In addition,
there are may cases in which the same organization has different spellings. For in-
stance, The Bryan Rotary Club of Texas is also identified as The Rotary Club of Texas
and The Bryan Rotary Club, Texas USA. To facilitate the matching, a string compar-
ison algorithm was applied to all institutions to identify very similar names. Each
8
The average proportion of outside board members (73%) is not high enough to explain the simi-
larities between the numbers in Panels A and B.
19
resulting tuple was then inspected by hand and a unique key was created to uniquely
identify each institution,
Some of these organizations (such as clubs and fraternities) clearly foster social
interactions. Others, such as membership to professional associations like the Amer-
ican Bar Association, probably do not. To better capture potential social interactions,
I focus on the following types of institutions:
Clubs: These include clubs and fraternities. In most cases, these are eas-
ily defined (e.g., Augusta National Golf Club (118 members), Sigma Xi (88
members)).
Not-For-Profit (NFP): Includes organizations such as the Salvation Army (149
members), the Metropolitan Museum of Art (47 members), the Aspen Institute
(97 members), and the Chicago Symphony Orchestra (58 members). An effort
was made to detect and exclude those NFPs related to businesses or professions
(e.g., Ford Foundation).
Network: Includes network-type organizations such as the World Presidents
Organization (115 members), Young Presidents Organization (219 members),
and the Junior Achievement (270 members).
University Boards: Includes university boards of trustees.
The profiles contain associations to many other organizations that I do not include
in the social ties indexes. For the sake of completeness, these are described below.
It is important to emphasize that these are not included in my social ties measures
because they would probably only introduce noise in the indexes:
Professional: Includes affiliations to professional organizations such as the
American Bar Association (978 members), the American Institute of Certified
Public Accountants (736 members) and the Financial Executives Institute (149
20
members). These professional organizations are not included in the social ties
indexes constructed below, since affiliation is either too common or compul-
sory.
Business: Includes “roundtables” and “councils” such as “council for eco-
nomic development.” As with professional organizations, these are not in-
cluded in the social ties measures.
Other: Includes other organizations that do not fit in the above categories.
Military: Includes affiliation to US Navy (including Marines), Army and Air
Force.
To create a measure of the random ties that are expected to occur given the size
of the organizations the CEO belongs to. The net proportion of the directors tied to
the CEO (actual ties minus expected ties) is then used as a better proxy for social
connections that are not related to the size of the organizations.
Specifically, for each firm-year in the sample, I simulate 10,000 random boards
by sampling from a population of potential directors. To construct this population, I
start with the universe of all directors in the BoardEx database, including directors of
companies that are not in the merger sample. Since membership to a particular orga-
nization is correlated with the state in which the company maintains its headquarters,
I include in the simulations only directors from companies located in the same state
as the bidder.
For each one of these simulated boards, I then check the proportion of directors
that share a common nonprofessional membership with the CEO. This procedure
creates a distribution of the proportion of the board tied to the CEO, conditional
on CEO membership. For each firm-year, the average of this distribution is then
subtracted from the actual proportion of the board connected to the CEO.
21
For example, if companyf announced an acquisition in January 2000, I first look
for all directors who, during the fiscal year ending in 1999, served on the board of
any company whose headquarters is in the same state as that of companyf. If firm
f reported a board size of 10, I draw 10 directors (without replacement) from this
universe. For this simulated board, I then check how many of these directors have
social ties with f’s CEO. This procedure is repeated 10,000 times and the average
proportion of the board socially connected to the CEO is taken to be the “expected”
proportion of social ties, conditional on the memberships of the CEO of companyf.
This “residual” is my measure of the proportion of the board connected to the CEO.
2 Proxies for Monitoring and Advisory Needs
This section describes the construction of the proxies for monitoring and advisory
needs. I start with the proxy based on characteristics commonly associated with a
greater need to supervise the CEO.
Monitoring I rely on previous research to identify situations in which managers
have greater incentives to engage in self-serving acquisitions.
9
Jensen (1986) ar-
gues that managers are more likely to empire build when firms have abundant cash
flows but few profitable investment opportunities. Lang et al. (1991) and Harford
(1999) find supporting evidence in favor of the free cash flow theory. As noted by
Masulis et al. (2007) and others, the commonly used accounting measures of “free
cash flows” do not necessarily correspond to the availability of “excess cash.” Free
cash flows can also be a proxy for better recent firm performance, which could be
9
Other papers have also tried to identify situations in which empire building motives are more
likely to be behind acquisitions, e.g., Morck et al. (1990) and Masulis et al. (2007).
22
correlated with high-quality managers, who in turn tend to make better acquisitions
(Morck et al. (1990)). I follow Dittmar & Mahrt-Smith (2007) to construct a mea-
sure of excess cash that accounts for the many possible reasons firms have to hold
cash. Each year, firms with excess cash above the industry median are defined as
cash-rich firms, which, according to the Free Cash Flow Theory, are more likely to
merge excessively. I use the indicator variable High Excess Cash to identify these
cases.
Managers are also more prone to engage in unnecessary mergers when they own
a smaller share of the company, either directly or through compensation packages
(Jensen & Meckling (1976)). Datta et al. (2001) find a strong positive relation be-
tween the acquiring managers’ equity-based compensation and stock price perfor-
mance around the acquisition announcement. I follow these authors to construct a
measure of Equity-Based Compensation (EBC). EBC is defined as the sum of the
value of new stock options (using the modified Black-Scholes method) granted to the
CEO as a percentage of total compensation. Low EBC is an indicator variable that
equals 1 if the firm’s EBC is lower than the industry median in that given year.
Masulis et al. (2007) find that bidder returns are lower for firms with low gover-
nance indexes and more anti-takeover provisions. One interpretation of their results is
that more entrenched managers are less susceptible to market discipline and therefore
more likely to engage in unnecessary acquisitions. To proxy for entrenched CEOs, I
use the E-index of Bebchuk et al. (2008). As in Masulis et al. (2007), I define High
E-index to represent those firms with an E-index greater than 2.
Chen et al. (2007) show that concentrated holdings by independent long-term
institutions are associated with better postmerger performance, which they attribute
to the active external monitoring role of such institutions. Following Chen et al.
23
(2007), I create a measure of institutional ownership and then define the variable
Low Inst Own to indicate the cases where ownership is below the industry median.
Shleifer & Vishny (1989) suggest that managers have incentives to enter a new
line of business when threatened by poor performance, a view supported by the ev-
idence in Morck et al. (1990). This does not mean, however, that all diversifying
acquisitions are agency-driven. In fact, Matsusaka (1993) finds evidence that ac-
quirer shareholders benefited from diversification acquisitions during the conglom-
erate merger wave of the late 1960s.
10
Therefore, agency-driven diversifying ac-
quisitions are more likely to be the ones that follow bad performance. Like Morck
et al. (1990), I use the change in operating income during the prior three years as
a measure of performance and then create the variable Diversifying Low Inc to
indicate diversifying acquisitions in which the bidder’s past performance falls below
the industry median.
Last, Duchin & Schmidt (2008) argue that the costs of empire building incurred
by the CEO are lower during merger waves and find evidence of both less efficient
mergers and lower poor-performance-driven CEO turnover during periods of high
merger activity. Using the algorithm described in Harford (2005), I create the variable
Wave to indicate periods of intense merger activity.
10
Diversification can also be the result of a value-maximizing strategy, unrelated to agency prob-
lems, as formalized in Matsusaka (2001).
24
Table3: Proxies for Monitoring and Advisory Needs
This table contains information on the distribution of the variables that constitute the monitoring and advisory indexes. Panel A displays
the pairwise (tetrachoric) correlations between these variables. In Panel B, the number of deals is tabulated in a contingency table. Each
cellrc presents the number of deals in which acquiring companies haveMonitor =c andAdvice =r. Panel C reports the frequency of
social ties within each group. Each cellrc reports the proportion of socially connected CEOs for deals in which acquiring companies
haveMonitor =c andAdvice =r. HighExcessCash is a dummy indicating whether the firm’s excess cash is above the industry median
for that given year. Excess cash is computed following Dittmar & Mahrt-Smith (2007). LowEBC represents firms with Equity-Based Com-
pensation lower than the industry median (using all firms in ExecuComp). Wave identifies merger waves using the procedure in Harford
(2005). LowInstOwnership is an indicator variable that identifies cases where institutional ownership, defined as in Chenetal. (2007), is
below the industry median. DiversifyingLow Inc represents diversifying acquisitions following prior three-year below median income
growth. HighE-index represents high entrenchment levels as measured by the E-index of Bebchuketal. (2008). It is equal to 1 when the
E-index is greater than 2. YoungCEO is a dummy variable equal to 1 if the CEO is younger than the median CEO age of 52. InexpCEO
& Exp Board represents firm-years where both the experience of the CEO in the bidder’s industry is below the median and the average
experience board members is above the median. Multi Seg is a binary variable indicating whether the company reports more than one
segment in the Compustat Business files. Diverse High Inc represents diversifying acquisitions following prior three-year above
median income growth. ExpertBoard indicates the existence of at least one board member with financial expertise. InformedDirector is
a binary variable that equals 1 when at least one of the bidder’s independent board members also serves as a board member for another
company in the same four-digit SIC industry code as the target. It is set to zero if the target and the bidder are in the same industry.
Panel A - Correlations
High Low Merger Low Inst Diverse High Young Inexp Multi Diverse Expert Info
Cash EBC Wave Owner Low Inc E-index CEO CEO Seg High Inc Board Dirs
High Excess Cash 1:00
Low EBC 0:01 1:00
Wave 0:01 0:00 1:00
Low Inst Owner 0:02 0:17 0:02 1:00
Diverse Low Inc 0:05 0:02 0:04 0:05 1:00
High E-index 0:07 0:00 0:11 0:09 0:04 1:00
Young CEO 0:07 0:04 0:02 0:02 0:13 0:02 1:00
Inexp CEO/Exp Board 0:01 0:00 0:02 0:02 0:06 0:02 0:04 1:00
Multi Seg 0:00 0:09 0:20 0:04 0:14 0:16 0:07 0:07 1:00
Divere High Inc 0:07 0:01 0:05 0:01 0:23 0:06 0:12 0:08 0:04 1:00
Expert Board 0:02 0:02 0:05 0:06 0:05 0:09 0:08 0:02 0:01 0:09 1:00
Continued on next page
25
Table 3, Continued
Panel A - Correlations
High Low Merger Low Inst Diverse High Young Inexp Multi Diverse Expert Informed
Cash EBC Wave Owner Low Inc E-index CEO CEO Seg High Inc Board Dirs
Info Dir 0:01 0:02 0:01 0:01 0:14 0:01 0:01 0:04 0:01 0:22 0:00 1:00
Social Tie 0:02 0:08 0:02 0:07 0:04 0:07 0:16 0:01 0:07 0:07 0:08 0:03
Panel B - Number of Deals Panel C - Proportion of Social Ties
Advice Advice
Monitor 0 1 2 3 4 Total Monitor 0 1 2 3 4 Total
0 164 457 493 186 37 1,337 0 0.17 0.24 0.26 0.31 0.29 0.25
1 310 955 1,085 385 69 2,804 1 0.27 0.27 0.27 0.23 0.27 0.26
2 228 680 779 304 65 2,056 2 0.36 0.39 0.31 0.18 0.16 0.32
3 77 277 286 95 21 756 3 0.45 0.48 0.33 0.31 0.19 0.39
4 16 100 70 13 2 201 4 0.56 0.75 0.45 0.15 1.00 0.59
Total 795 2,469 2,713 983 194 7,154 Total 0.30 0.34 0.29 0.24 0.24 0.30
26
Using the six indicator variables defined above, I create the index Monitor as
Monitor = Wave + High Excess Cash + High E-index + Low Inst Own + Low EBC
+ Diversifying Low Inc (2.1)
Companies with higher values of Monitor are assumed to have a greater need for
more board supervision.
It is possible that the individual indicator variables that constitute Monitor are
correlated. Instead, Panel A of Table 3 shows that the correlations between these
variables are generally low.
11
The largest correlations are between Low EBC and Low
Inst Own (0.17) and between Wave and High E-index (0.11). All other coefficients
are below 10% in magnitude.
Advice Board advice is likely to be valuable when directors possess pertinent in-
formation that the CEO does not have. I use several measures to identify acquisitions
in which we can expect a higher degree of complementarity between the knowledge
possessed by the CEO and that possessed by board members.
In general, it should be more costly for insiders to acquire information about a tar-
get in a different industry, ceteris paribus. Therefore, bidders in diversifying mergers
are potential candidates to enjoy greater benefits from board advice. To differentiate
between diversifying acquisitions that could be driven by agency, I consider as one
of the proxies for advisory needs only diversifying acquisitions that follow good per-
formance. Specifically, I define Diversifying High Inc to indicate diversifying
acquisitions that follow above industry median increases in operating performance.
11
Since each variable in the Monitor index is binary, tetrachoric correlations rather than the more
common Pearson correlation coefficients are estimated.
27
I then focus on a subset of diversifying acquisitions where the cost of becoming
informed about the target is expected to be especially low for some board members.
One such situation is when some of the bidder’s outside directors also serve on the
board of another company in the target’s industry. These “informed” directors are
likely to be intimately related to both businesses and thus better prepared to assess
the future prospects of the merger. The variable Informed Director identifies these
situations.
Chief executives are also likely to benefit from board advice when the bidder is
a more complex firm. Following prior literature, I look at the number of business
segments as a measure of firm complexity (e.g., Coles et al. (2008)). The variable
Multi Segments identify bidders with more than one business segment.
I also look at boards in which there are directors with financial expertise (Guner.
et al. (2008)). The variable Expert Board identifies those boards in which at least
one director is also either a CFO or a top executive in a bank (CEO, CFO, COO, or
Vice President).
Last, I use two variables to proxy for the CEO experience. The variable Inexp
CEO & Exp Board indicates cases in which (i) the number of years the CEO has
worked in the industry is below the median and (ii) the average experience of board
members is above the industry median. Young CEO indicates cases in which the
CEO’s age is below the industry median.
These indicator variables are then used to create an index of advisory needs.
Advice = Multi Segments + Expert Board + Inexp CEO & Exp Board + Young CEO
+ Informed Director + Diversifying High Inc (2.2)
28
High values of Advice should then proxy for acquisitions in which the CEO is more
likely to benefit from the board’s advice.
Similar to the variables that constitute the monitoring index, these advisory vari-
ables are not highly correlated to each other. From Panel A of Table 3, the largest co-
efficients are from the correlation between Diversifying High Inc and Informed
Director (0.22) and between Diversifying High Inc and Young CEO (0.12). All
other coefficients are below 10% in magnitude.
Again from Panel A, we can see that the advisory variables are not strongly cor-
related with the monitoring variables either. Apart from Diversifying High Inc
and Diversifying Low Inc, which are naturally negatively correlated, all other
variables are not strongly correlated. Multi Segments is correlated with Wave (-0.20)
and High E-index (-0.16). Because of the way Informed Director is defined, its cor-
relation with Diversifying Low Inc is also among the highest we observe (0.14).
Finally, the correlation coefficient between Diversifying Low Inc and Young
CEO is -0.13.
Panel B of Table 3 shows the distribution of the merger deals across the monitor-
ing and advisory indexes. There are 5,817 deals in which the Monitor is greater than
zero (81% of the sample) and 6,359 in which the Advice is 1 or more (89% of the
sample). Across each dimension, an index of 2 or more roughly divides the sample
in half. For the advisory index this corresponds to 3,890 deals or 54% of the sample,
while for the monitoring index, these numbers are 3,013 and 42%, respectively. As
the value of the indexes increases, the number of firms drops drastically. Only 16%
of the deals have Advice 3 and an even lower proportion, 14%, have Monitor 3.
29
3 Performance Measures and Other Controls
This section describes the construction of the performance measures used in the tests
that follow, as well as the controls for many firm and deal characteristics that have
been shown to affect bidder performance.
Announcement returns As in Moeller et al. (2004), Masulis et al. (2007), and
many others, I measure bidder announcement effects by market model adjusted stock
returns around merger announcements. Market model estimates are obtained using
the daily CRSP value-weighted index as a proxy for returns on the market portfo-
lio. The estimation period is from 230 days to 11 days before the announcement.
Announcement dates are obtained from SDC, and three-day cumulative abnormal
returns (CAR) are computed around these dates.
Table 4 contains the average bidder announcement returns for different samples.
The first column (All) includes all the deals that fall into the categories described by
each row. For instance, the table reports that the average CAR for the entire sample
is 0.61%, whereas for acquisitions that took place during merger waves it is 0.37%.
In the second and third columns, I separate the deals in which the bidder’s CEO is
socially connected to at least one director in that same company’s board (Social Ties)
from those in which no such ties are present (No Ties). The last column displays the
difference between the former and the latter. A negative number in this last column
thus indicates that the average announcement return is lower when social ties are
present.
30
Table4: Bidder Announcement Returns for Different Samples
This table contains average Cumulative Abnormal Returns (CAR) for different samples.
The first column displays average CARs across all the deals that fall into each of the cat-
egories described by each row. In the second and third columns, I separate the deals
in which the bidder’s CEO is socially connected to at least one of the outside direc-
tors in that same company’s board (Social Ties) from those in which no such ties are
present (No Ties). The last column contains the difference between the former and the
latter. A negative number thus indicates that the average announcement return is lower
when social ties are present. Wave identifies merger waves using the procedure in Har-
ford (2005). High Excess Cash is a dummy indicating whether the firm’s excess cash
is above the industry median for that given year. Excess cash is computed following
Dittmar & Mahrt-Smith (2007). Low EBC represents firms with Equity-Based Compen-
sation lower than the industry median (using all firms in ExecuComp). High E-index rep-
resents high entrenchment levels as measured by the E-index of Bebchuk et al. (2008).
It is equal to 1 when the E-index is greater than 2. Young CEO is a dummy variable
equal to 1 if the CEO is younger than the median CEO age of 52. Multi Segments is
a binary variable indicating whether the company reports more than one segment in the
Compustat Business files. Diversifying represents mergers in which the target and ac-
quirer are in different four-digit SIC code industries.Informed Director is a binary vari-
able that equals 1 when at least one of the bidder’s independent board members also
serves as a board member for another company in the same four-digit SIC industry
code as the target. It is set to zero if the target and the bidder are in the same indus-
try. Expert Board indicates the existence of at least one board member with financial
expertise. Inexp CEO & Exp Board represents firm-years where both the experience
of the CEO in the bidder’s industry is below the median and the average experience
board members is above the median. Diversifying High Inc represents diversify-
ing acquisitions following prior three-year above median income growth. Diversifying
Low Inc represents diversifying acquisitions following prior three-year below median
income growth. Low Inst Ownership is an indicator variable that identifies cases where
institutional ownership, defined as in Chen et al. (2007), is below the industry median.
All Social Ties No Social Ties (1) - (2)
(1) (2)
Full Sample
0:612*** 0:158 0:812*** 0:654***
(0:074) (0:129) (0:089) (0:160)
More Monitoring
Wave 0:370* 0:475 0:776*** 1:252***
(0:176) (0:286) (0:221) (0:375)
High Excess Cash 0:808*** 0:100 1:227*** 1:327***
(0:122) (0:206) (0:151) (0:262)
High E-index 1:232*** 1:711*** 0:820** 0:891*
(0:217) (0:296) (0:311) (0:433)
Low EBC 0:113 0:878*** 0:533* 1:411***
(0:174) (0:259) (0:231) (0:346)
Low Inst Ownership 0:263* 0:448* 0:649*** 1:097***
(0:123) (0:183) (0:160) (0:256)
Diversifying Low Inc 0:514*** 0:305 0:937*** 1:243***
(0:137) (0:226) (0:170) (0:287)
More Advice
Continued on next page
31
Table 4, Continued
All Social Ties No Social Ties (1) - (2)
(1) (2)
Young CEO 0:617*** 1:069** 0:473** 0:596
(0:160) (0:341) (0:180) (0:373)
Multi Segments 0:774*** 0:730*** 0:791*** 0:061
(0:105) (0:202) (0:123) (0:235)
Inexp CEO & Exp Board 0:528** 0:111 0:797*** 0:908*
(0:192) (0:328) (0:235) (0:420)
Expert Board 0:479*** 0:314* 0:564*** 0:250
(0:092) (0:148) (0:117) (0:195)
Diversifying High Inc 1:014*** 1:943*** 0:736** 1:208*
(0:227) (0:437) (0:264) (0:538)
Informed Director 0:187 0:531 0:006 0:525
(0:227) (0:371) (0:286) (0:477)
For the entire sample, mean announcement returns when social ties are present
are smaller than when no such ties exist. This difference of -0.65% is also significant
at 1% level. This indicates that, unconditionally, social ties tend to have a negative
effect on overall bidder returns. To the extent that social ties proxy for some sort
of board dependence, this result is consistent with less independent boards making
worse acquisitions. But the average effect of social ties on performance varies greatly
across different types of firms and deals. I start by showing how monitoring needs
exacerbate the negative effects of these ties.
Across all variables that constitute the monitoring index, average announcement
returns are lower when social ties are present, and the differences are generally
significant. For example, on average, wave acquisitions with socially connected
CEOs yield -0.47% CARs. When no such connections exist, returns are much larger
(0.78%). The difference of -1.20%, roughly twice the magnitude of the overall aver-
age CAR, is also significant at 1% level. Social connections have the largest effect
when we concentrate on firms with high levels of excessive cash (-1.33%). While the
32
average bidder with no social ties earns a positive and significant return of 1.23%,
acquiring firms in which the CEO is connected to the board experience a slightly
negative CAR of -0.10%. The smallest negative effect across the monitoring vari-
ables is for firms with an E-index above the median (-0.89%). For these firms, the
effects of social ties are only marginally significant.
When board advice is likely to be important, the negative effects of social ties
disappear. With the exception of the sample of inexperienced CEO and experienced
board members (Inexp CEO & Exp Board), where this difference is negative and
slightly significant, social ties are not associated with lower performance when advi-
sory needs are high. But, at least unconditionally, they are not associated with signifi-
cantly higher returns either. The largest difference across the advisory variables is for
diversifying acquisitions. In this case, social ties are associated with 1.21 percentage
points higher abnormal returns, a difference that is significant at the 10% level. As
I show in the next section, when we control for firm and deal characteristics, social
ties do have a positive and significant effect on performance when advisory needs are
high. But before we go into the main regression specification, it is helpful to describe
the controls included.
Bidder characteristics Firm size has been shown to affect bidder performance.
For instance, Moeller et al. (2004) find that firm size is negatively correlated with
the bidder’s CAR, which they attribute to managerial hubris (Roll (1986)). In the
first column of Table 5, bidder announcement returns are regressed on many firm and
deal characteristics. I use total assets as a proxy for firm size and find, like others,
that size is inversely related to CARs. The coefficient of -0.39 is significant at the 1%
level (T-stat of -3.74).
33
Tobin’s Q is also found to affect announcement returns (e.g., Lang et al. (1991)
and Moeller et al. (2004)). Including Tobin’s Q as a control variable is problematic
because Q might be determined endogenously. I follow Gillan et al. (2006) and
Masulis et al. (2007) and substitute individual market to book ratios by the industry
median (using all companies in Compustat). Tobin’s Q is defined as the market value
of assets divided by the book value of assets, where the market value of assets equals
the book value of assets plus the market value of common equity less the sum of the
book value of common equity and balance sheet deferred taxes. Similar to Masulis
et al. (2007) I find a small negative effect of Tobin’s Q on CARs (-0.34). But whereas
they find a slightly significant effect, my estimates are not significantly different from
zero (Column (1), Table 5).
I also include the acquirer’s leverage as another control. Leverage is long-term
debt plus debt in current liabilities over long-term debt plus debt in current liabil-
ities plus the book value of common equity. Like Tobin’s Q, leverage is likely to
be endogenous, so I again substitute individual leverage measures by their industry
counterparts. Again similar to Tobin’s Q, industry leverage does not seem to have a
significant effect on CARs. The coefficient on this variable is 0.04 with a T-stat of
0.47.
To account for past performance of the bidder, I include the Price Run-up, as
defined in Masulis et al. (2007): Price Run-up is the bidder’s buy and hold abnormal
return from 230 to 11 days before the announcement. The CRSP value-weighted
index is used as the benchmark. Like those authors, I also find a significant negative
effect of Price Run-up on CARs. The coefficient of -0.36 (T-stat of -3.42) suggests
that investors discount the price of firms announcing acquisitions more when these
34
experienced larger prior performance, consistent with the asymmetry of information
theory of Myers & Majluf (1984).
Last, the number of board members (Board Size) and the proportion of outside
directors (% Outside Dirs) are also included. Yermack (1996) documents that larger
boards are associated with lower firm value. I find no effect of Board Size on CARs
in the main regression specification (Column (1) of Table 5). Interestingly, % Out-
side Dirs has a negative effect on announcement returns. I discuss this finding in
Section 3.
Deal characteristics Acquirer announcement returns seem to be related to the
method of payment and the type of target (e.g., Chang (1998), Moeller et al. (2004),
and Officer et al. (2008)). To account for this variation, I include controls for the
type of target (Public, Private, and Subsidiary) and medium of payment (Cash Only
and Stock Deal). Because the choice of medium of exchange is often related to the
target characteristics (Officer et al. (2008)), I include interactions between the target
type and the type of payment. In the first column of Table 5 I find that the strongest
effect is for acquisitions of public targets that are financed with cash. On average,
announcement returns of bidders in these deals are -3.24 percentage points lower (T-
stat of -3.24). Conversely, deals in which subsidiaries were acquired with cash only
earn higher returns (0.99 percentage points difference with a T-stat of 3.98).
I include the Relative Deal Size to control for the size of the deal. Relative Deal
Size is the value of the deal as reported by SDC over the market value of the acquirer
measured at the end of the fiscal year preceding the announcement (in %). Acqui-
sitions in which Relative Deal Size is higher are also associated with higher CARs.
The coefficient of 0.99 is significant at the 1% level.
35
Chapter 3
Empirical Results
Having described the sample and the construction of the main variables, I turn to em-
pirical analysis of how friendly boards affect bidder announcement returns. Perhaps
the most important goal of this study is to test the prediction of Adams & Ferreira
(2007) and Harris & Raviv (2008) that less independence is advantageous when advi-
sory needs are high. I first examine whether part of the variation in the announcement
returns of acquiring firms can be explained by the presence of social ties. I then look
at how these effects vary across firms with different advisory or monitoring needs.
1 Social Ties and the Dual Role of the Board
In Table 5, bidder announcement returns are regressed on the controls discussed
above plus two different proxies for board independence: % Outside Dirs, repre-
senting the proportion of outside directors on the board, and Social Ties. All regres-
sions include year dummies (not reported) and robust standard errors clustered at the
industry level (four-digit SIC codes).
In the first column of Table 5, the main variable of interest is the social ties indica-
tor (Social Ties). On average, social ties have only a small and marginally significant
effect on CARs. The coefficient of -0.26 (T-stat of -1.83) indicates that the social
ties decrease bidder performance by 26 basis points on average. This is smaller than
the unconditional effect of social ties on average CARs (-0.65% from Table 4). Per-
haps surprisingly, the effect of outside representation is also negative, although not
36
significant at 5% level. This is consistent with other studies that find only a small,
insignificant effect of outside representation on merger announcement returns (e.g.,
Byrd & Hickman (1992), Matsusaka (1993), and Masulis et al. (2007)). In fact, Byrd
& Hickman (1992) find a strong negative effect of outside representation on CARs
when independent directors exceed 60% of the board, a condition that is met by more
than 75% of my sample (Table 1).
As advisory needs increase, the effect of social ties on bidder performance shifts
from slightly negative to significantly positive. In Column (2) of Table 5, the social
ties indicator is multiplied by the proxy for advisory needs. The coefficient on the
interaction is negative and highly significant (1.43 with a T-stat of 4.12). The opposite
result is found when Social Ties is interacted with Monitor; the coefficient on the
interaction is negative and also significant (-0.75 with a T-stat of -2.78). Similar
effects are found when both indexes for monitoring and advisory are included (the
correlation between the two indexes is less than 1%). This result indicates that social
ties can be beneficial to acquiring firms in which board advice is important. On
the other hand, as predicted by theory, social ties have a negative effect on CARs
when CEO supervision is likely to be a more important concern, as can be seen from
Columns (3) and (4).
37
Table5: Bidder Announcement Returns and Social Ties
This table contains the estimates of regressions of bidder announcement returns on
many controls and the proxies for social ties, monitoring needs, and advisory needs.
Social Tie is a dummy variable equal to 1 if the CEO is socially connected to at least
one independent board member, and 0 otherwise. Monitor is the summation of indica-
tor variables for High Excess Cash, Diversifying acquisitions following bad performance,
Low EBC, Low Institutional Ownership, Merger Waves, and High E-index. Advice is the
summation of indicator variables for Multi-segment Firms, Y oung CEO, Inexperienced
CEO/Experienced Board, Expert Board, Diversifying acquisitions following good perfor-
mance, and Informed Directors. These individual components are described in Table 4.
Social Tie Advice , % of Outside Dirs Advice , represents interactions between
the Social Tie and the advisory needs index Advice . The interactions with the moni-
toring index are defined analogously. Industry Leverage is the median leverage in the
acquirer’s industry. Industry Tobin’s Q is the median Tobin’s Q in the acquirer’s indus-
try. Subsidiary Cash Only , Private Tgt Stock Deal, Public Tgt Cash Only,Private
Tgt Cash Only and Public Tgt Stock Deal are the interactions between the dum-
mies representing the target and deal types (the ommitted group is SubsidiaryStock
Deal), where Cash Only represents acquisitions entirely financed by cash and Stock Deal
represents acquisitions paid at least partially with stocks. The construction of each vari-
able is described in detail in the Appendix. All variables are measured at the end of
the fiscal year preceding the announcement date. All regressions include year dum-
mies (not reported). Robust standard errors clustered at industry level are in parenthe-
ses. , , represents significance at the 10%, 5% and 1% level, respectively.
(1) (2) (3) (4) (5)
Social Tie Advice 1:429*** 1:377***
(0:347) (0:338)
Social Tie Monitor 0:748*** 0:652**
(0:269) (0:257)
% of Outside Dirs Advice 0:647
(0:596)
% of Outside Dirs Monitor 0:633
(0:758)
Social Tie 0:260* 2:505*** 0:821* 1:476***
(0:142) (0:562) (0:416) (0:425)
Monitor 0:060 0:203* 0:231** 0:531
(0:067) (0:106) (0:113) (0:563)
Advice 0:066 0:330** 0:344** 0:548
(0:121) (0:158) (0:162) (0:415)
% of Outside Dirs 1:293* 1:259* 1:321* 1:274* 1:099
(0:659) (0:667) (0:675) (0:661) (1:651)
Board Size 0:049 0:050 0:051 0:051 0:040
(0:030) (0:031) (0:032) (0:032) (0:031)
Log Total Assets 0:393*** 0:417*** 0:370*** 0:398*** 0:404***
(0:105) (0:106) (0:104) (0:106) (0:104)
Industry Leverage 0:043 0:059 0:033 0:054 0:036
(0:091) (0:092) (0:099) (0:094) (0:091)
Industry Tobin’s Q 0:341 0:335 0:335 0:334 0:344
(0:270) (0:281) (0:264) (0:279) (0:269)
Price Run-up 0:362*** 0:392*** 0:357*** 0:387*** 0:364***
Continued on next page
38
Table 5, Continued
(1) (2) (3) (4) (5)
(0:106) (0:102) (0:104) (0:102) (0:105)
Ceo Ownership 0:008 0:011 0:008 0:012 0:007
(0:035) (0:035) (0:037) (0:037) (0:035)
Relative Deal Size 0:870** 0:858** 0:887** 0:873** 0:863**
(0:378) (0:372) (0:380) (0:374) (0:376)
CEO Prior M&A 0:210 0:194 0:210 0:192 0:209
(0:144) (0:154) (0:144) (0:155) (0:144)
Public Tgt Stock Deal 3:240*** 3:243*** 3:242*** 3:241*** 3:243***
(0:682) (0:674) (0:661) (0:663) (0:688)
Public Tgt Cash Only 0:363 0:347 0:323 0:309 0:372
(0:262) (0:287) (0:271) (0:285) (0:262)
Private Tgt Stock Deal 0:717 0:691 0:731 0:708 0:706
(0:708) (0:688) (0:716) (0:685) (0:705)
Private Tgt Cash Only 0:012 0:037 0:025 0:044 0:019
(0:276) (0:278) (0:275) (0:284) (0:274)
Subsidiary Cash Only 0:986*** 0:949*** 0:970*** 0:934*** 0:988***
(0:248) (0:239) (0:247) (0:240) (0:248)
R-squared 0:041 0:046 0:042 0:047 0:041
Observations 7,154 7,154 7,154 7,154 7,154
Similar patterns are not observed when friendly boards are proxied by outside
representation. The last column of Table 5 shows no significant effects of % Out-
side Dirs on bidder announcement returns, even after conditioning on advisory or
monitoring needs. This might indicate that actual board independence is not entirely
captured by the regulatory definition of an outside director.
Overall, the results in Table 5 are consistent with the assertion that board inde-
pendence (as measured by social ties) affects the board’s two main functions. In
particular, I find evidence that less independence can increase the wealth of bidder
shareholders in situations where the board’s advice is crucial. In the next section, I
take a closer look at different sets of firms that tend either to benefit more from board
feedback or to have lower concerns about CEO supervision or both.
39
2 The Effects of Social Ties on Different Samples
In this section, I look at how the effects of social ties vary across different samples.
An advantage of this approach is that it allows an easy interpretation of the mag-
nitudes of the effects of social ties on each group of firms. If friendly boards are
beneficial at times when feedback from the board is important, then in a subsample
of firms in which Advice takes higher levels, we should observe higher announcement
returns for firms with socially connected CEOs. Analogously, for those firms with
high values of Monitor we should observe a negative effect of social ties on bidder
performance.
In Table 6, I estimate the main regression specification for different subsamples.
The first two columns correspond to those deals in which the value of Advice is equal
or greater than its median value of 2. For this subset of firms, corresponding to
roughly half of the sample, social ties significantly increase bidder abnormal returns.
The coefficient of 1.17 implies that, for those deals, social ties are associated with
1.17 percentage points higher CARs. To put this into perspective, note that this is a
stronger average effect than that of a one standard deviation increase in either firm
size or relative deal size.
1
1
A one standard deviation increase in the log of firm size produces a decrease in CARs of 1:92
0:51 =0:97 percentage points, whereas a one standard deviation increase in relative deal size has
an impact of 0:57 0:48 = 0:27.
40
Table6: Social Ties on Different Subsamples
This table contains the estimates of regressions of bidder announcement returns on all
control variables described in Table 5. Each regression is run on a different subsample,
depending on the value of the monitor/advisor proxies. For brevity, this table does not re-
port the interactions between the type of target and method of payment (see description
in Table 5), even though they are included. SocialTie is a dummy variable equal to 1 if the
CEO is socially connected to at least one independent board member, and 0 otherwise.%
of Outside Dirs is the proportion of independent directors on the board (in %). Board Size
is the number of directors on the board (from BoardEx). Log Total Assets is the logarithm
of total assets. Industry Leverage is the median leverage in the acquirer’s industry. In-
dustry Tobin’s Q is the median Tobin’s Q in the acquirer’s industry. Price Run-up is the
bidder’s buy and hold abnormal return from 230 to 11 days before the announcement.
The CRSP value-weighted index is used the benchmark. Ceo Ownership is the propor-
tion of the firm owned by the CEO at the end of the fiscal year preceding the acquisition
announcement (from ExecuComp and excluding options). Missing values are set to zero,
and a dummy indicating missing values is included. Relative Deal Size is the value of the
deal as reported by SDC over the market value of the acquirer measured at the end of the
fiscal year preceding the announcement. CEO Prior M&A is the (logarithm of the) number
of prior M&As (since 1980) in which the current bidder CEO was either the CEO or CFO
of the acquiring company. Subsidiary Cash Only , Priv Tgt Stock Deal, Pub Tgt
Cash Only, Priv Tgt Cash Only and Pub Tgt Stock Deal are the interactions between
the dummies representing the target and deal types (the ommitted group isSubsidiary
Stock Deal), where Cash Only represents acquisitions entirely financed by cash and
Stock Deal represents acquisitions paid at least partially with stocks. The construction of
each variable is described in detail in the Appendix. All variables are measured at the
end of the fiscal year preceding the announcement date. All regressions include year
dummies (not reported). Robust standard errors clustered at industry level are in paren-
theses. ,, represents significance at the 10%, 5% and 1% level, respectively.
High Advice High Monitor
High Advice
Low Monitor
Advice Index (> 1) (> 2) (> 1) (> 2)
Monitor Index (> 1) (> 2) ( 1) ( 2)
Social Tie 1:170*** 1:905** 1:385***1:243** 2:063*** 2:188**
(0:362) (0:820) (0:304) (0:495) (0:511) (0:851)
% of Outside Dirs 1:114 2:694* 0:107 0:503 2:245 1:630
(0:894) (1:552) (1:360) (1:876) (1:364) (1:923)
Board Size 0:015 0:002 0:077** 0:042 0:001 0:005
(0:047) (0:096) (0:036) (0:051) (0:058) (0:104)
Log Total Assets 0:508***0:444** 0:379***0:282** 0:443***0:391*
(0:126) (0:187) (0:109) (0:113) (0:136) (0:201)
Industry Leverage 0:217 0:856* 0:046 0:881 0:314* 0:849
(0:217) (0:470) (0:104) (0:542) (0:185) (0:555)
Industry Tobin’s Q 0:289 0:151 0:303 1:302** 0:334 0:066
(0:305) (0:397) (0:427) (0:517) (0:229) (0:532)
Price Run-up 0:262 0:342 0:416** 0:523 0:229 0:302
(0:177) (0:280) (0:177) (0:482) (0:328) (0:303)
Ceo Ownership 0:061* 0:071 0:067** 0:143*** 0:066 0:118
(0:035) (0:103) (0:032) (0:035) (0:066) (0:092)
Continued on next page
41
Table 6, Continued
High Advice High Monitor
High Advice
Low Monitor
Advice Index (> 1) (> 2) (> 1) (> 2)
Monitor Index (> 1) (> 2) ( 1) ( 2)
Relative Deal Size 0:483 0:213 1:007** 0:684 0:260 0:633
(0:293) (1:230) (0:456) (0:979) (0:238) (1:294)
CEO Prior M&A 0:393** 1:057*** 0:160 0:104 0:472** 1:023***
(0:155) (0:309) (0:178) (0:235) (0:195) (0:294)
Pub Tgt Stock Deal 3:794***3:097*** 2:442***1:375** 4:597***4:512***
(0:801) (0:826) (0:669) (0:535) (1:160) (0:842)
Pub Tgt Cash Only 0:190 1:717*** 0:657* 0:766 0:312 1:578**
(0:352) (0:544) (0:359) (0:642) (0:542) (0:646)
Priv Tgt Stock Deal 0:803 1:582 1:515* 0:101 0:217 1:313
(1:069) (1:515) (0:894) (1:522) (1:383) (1:358)
Priv Tgt Cash Only 0:199 0:836 0:411 0:269 0:324 0:634
(0:378) (0:693) (0:295) (0:598) (0:507) (0:738)
Sub Cash Only 0:579* 2:313*** 1:309*** 1:385* 0:547 2:204***
(0:340) (0:575) (0:336) (0:755) (0:466) (0:626)
R-squared 0:040 0:053 0:053 0:056 0:040 0:058
Observations 3,890 1,177 3,013 957 2,255 1,046
According to the main hypothesis, firms with higher advisory needs would benefit
more from social ties. In fact, if we restrict the sample further to those deals in which
Advice is greater than 2 (its median value), the estimated positive effects of social ties
also increase. From the second column of Table 6 we can see that acquiring firms
with socially connected CEOs earn 1.91 percentage points higher returns on average
(T-stat of 2.32).
The opposite occurs for deals in which high monitoring needs are expected.
When the monitor index is 2 (its median value) or more, social ties reduce the bid-
der performance by -1.38 percentage points (T-stat of -4.56). This is in line with the
argument that social ties reduce the board’s ability to discipline the CEO, which is
especially valuable at times of greater monitoring needs. When we look at the sample
42
of firms with higher values of monitoring (Monitor> 2) we still find that social ties
reduce the CARs of those firms.
According to Adams and Ferreira’s (2007) hypothesis, the positive effects of
friendly boards should be stronger for firms in which the importance of the board’s
advice surpasses the need to supervise the CEO. In the last two columns of Ta-
ble 6, I look at firms for which this situation is more likely to occur. When
Monitor 1 < Advice, social ties are associated with an increase in announce-
ment returns of 2.06 percentage points. This larger effect is perhaps not surprising,
given the signs and significant of the coefficients in the first four columns. For the
stricter requirement that Monitor 2 < Advice, the effect is even stronger. In this
case, the difference between the returns of firms with socially connected CEOs and
firms where no such connection exists is about 2.19 percentage points (T-stat of 2.57).
Last, the effects of outside representation on CARs are again generally insignif-
icant. With the exception of the sample with Advice > 2, where the coefficient on
% Outside Dirs is negative and marginally significant (-2.69 with a T-stat of1:74),
outside representation has no significant effect on announcement returns. This mir-
rors the results found in Table 5 and strengthens the assumption that social ties are
capturing something different than the usual measure of board independence.
In all results above, the indexes of Advice and Monitor were used as a gauge
for monitoring and advisory needs. Because each component that constitutes these
variables can also be a proxy, it is interesting to see whether the patterns documented
above can be found when these individuals components are used instead.
43
3 Individual Proxies for Monitoring and Advice
In the previous section, I showed that social ties increase bidder announcement re-
turns for high values of Advice and decrease CARs for high values of Monitor. In this
section, I examine the marginal contribution of each element included in the advice
and monitoring indexes.
Table 7 presents the effects of social ties on bidder performance controlling of
the need of board advice and CEO supervision. The individual characteristics com-
monly associated with monitoring and advisory needs (described in Section 2) are
interacted with the dummy variable that represents socially connected CEOs (So-
cial Ties). These regressions include all the controls displayed in the first column of
Table 5, but for ease of exposition I include only the coefficients of interest.
Table7: Individual Proxies for Monitoring/Advisory
This table contains the estimates of regressions of bidder announcement returns on all
variables described in Table 5 along with the proxies for monitoring needs, and advisory
needs. The main coefficients of interest are the interactions between the social ties vari-
able and these proxies. For brevity, the table reports only these coefficients, although all
controls present in Table 5 are included in the regressions. Tie Proxy is the interac-
tion between the Social Tie dummy and the proxy. Tie is a dummy variable equal to 1 if
the CEO is socially connected to at least one independent board member, and 0 other-
wise. Proxy represents the monitoring and advisory needs proxies. Tie Proxy is the
interaction between the Social Tie dummy and the proxy. Tie is a dummy variable equal
to 1 if the CEO is socially connected to at least one independent board member, and 0
otherwise. Proxy represents the monitoring and advisory needs proxies. The construc-
tion of each variable is described in detail in the Appendix. All variables are measured at
the end of the fiscal year preceding the announcement date. All regressions include year
dummies (not reported). Robust standard errors clustered at industry level are in paren-
theses. ,, represents significance at the 10%, 5% and 1% level, respectively.
Panel A - Social Ties and Advice
Multi Less Exp Diversifying Inexp CEO Expert Informed
Segments CEO High Inc Exp Board Board Director
Tie Proxy 1:141** 1:654*** 2:320*** 0:183 1:718*** 3:149**
(0:539) (0:522) (0:638) (0:710) (0:515) (1:176)
Tie 0:851*** 0:645*** 0:567*** 0:259 1:287*** 0:354**
Continued on next page
44
Table 7, Continued
(0:245) (0:220) (0:151) (0:159) (0:338) (0:136)
Proxy 0:345 0:747** 0:157 0:171 0:235 0:285
(0:343) (0:354) (0:334) (0:387) (0:263) (0:281)
R-squared 0:041 0:044 0:043 0:040 0:043 0:041
Observations 7,154 5,630 7,154 7,154 7,154 7,154
Panel B - Social Ties and Monitoring
High Excess High Low Low Inst Merger Diversifying
Cash E-index EBC Ownership Wave Low Inc
Tie Proxy 1:523*** 0:461 0:863** 0:478 0:290 0:833**
(0:509) (0:587) (0:424) (0:350) (0:522) (0:335)
Tie 0:373 0:079 0:091 0:109 0:217 0:074
(0:272) (0:291) (0:231) (0:204) (0:196) (0:171)
Proxy 0:492*** 0:754 0:209 0:480** 0:115 0:028
(0:175) (0:520) (0:310) (0:229) (0:335) (0:209)
R-squared 0:042 0:046 0:052 0:041 0:041 0:041
Observations 7,154 3,372 3,174 7,154 7,154 7,154
Note: All regressions include the same controls as in Table 5.
Panel A contains the results for the different components of Advice. In general,
the interaction between social ties and the proxies for board advice imply positive
effects on performance. In all but one instance (Inexp CEO & Exp Board), these in-
teractions are significant at 5% level.
2
In many cases, the effects are quite strong. In
terms of magnitude, the largest effect is found when the social tie is with an informed
director, that is, a board member who (during a diversifying acquisition) sits on the
board of companies in both the target and the acquiring firm’s industries. When such
director exists, social ties increase CARs by 3:15 0:35 = 2:8 percentage points.
3
A
2
The reason for the different sample sizes across specifications is data availability.
3
To be precise, the “interaction” between social ties and informed directors is computed as follows.
This variable is equal to 1 if (i) the acquisition is diversifying, (ii) there is an informed director in the
acquiring company’s board, and (iii) the CEO has a social connection with that informed director.
45
large effect is also found for diversifying acquisitions following good performance.
The marginal effect of social ties on CARs is around 2.32 percentage points (T-stat of
3:63). This corresponds to a total difference of 2:32 0:57 = 1:75 percentage points
when compared to diversifying acquisitions without social ties. We also observe a
positive and significant effect of social ties on acquisitions made by multi-segment
companies, by young CEOs, and by companies with expert boards. That is, for all but
one of the measures of advisory needs included in Advice, changes in bidder share-
holders’ wealth surrounding the announcement of the merger are positively related
to socially connected CEOs.
I turn now to the effects of social ties when monitoring needs are likely to be
high. In Panel B of Table 7, the individual characteristics commonly associated with
monitoring needs described in Section 2 are interacted with the dummy variable
that represents socially connected CEOs (Social Ties). Similar to the regressions for
advisory measures, I include (but not report) all the controls displayed in the first
column of Table 5.
Firms with high excess cash experience a substantial drop in announcement re-
turns when social ties are present (Column (1)). The negative and significant co-
efficient of -1.52 can be interpreted as the additional (negative) effect in CAR (in
percentage points) that firms with a socially connected CEO experience when they
have high levels of excess cash. The corresponding level coefficients, 0.37 for Social
Ties and 0.49 for High Excess Cash, are much smaller in magnitude and significance.
Because these are all indicator variables, the net effect of social ties for firms with
high excess cash is also negative (1:52 + 0:37 + 0:49 =0:66). In other words,
The coefficient thus measure the impact of social ties with informed directors during diversifying
acquisitions on CARs.
46
during the three days surrounding the announcement, shareholders of firms that (ac-
cording to the Free Cash Flow Theory) are more likely to empire build lost money in
acquisitions where the bidder’s CEO was socially connected to the board.
Negative effects of social ties on performance are also felt more heavily in com-
panies with low equity-based compensation contracts: the interaction between Social
Ties and Low EBC is -0.09 and significant at 5% level. Similar magnitudes are found
when monitoring is proxied by Diversifying Low Inc. For all the variables, High
E-index, Low Inst Own, and Wave, the interaction coefficients are also negative but
not significant.
Bidder returns for acquisitions that are more likely to be driven by managers di-
versifying personal risk (Morck et al. (1990)) are also penalized by social ties. The
interaction between Diversifying Low Inc and Social Ties is negative and signif-
icant at 5% level. The magnitude of the marginal effect is similar to that found for
low equity-based compensation, -0.83 percentage points. For the other components
of the monitoring index, I also find negative, although not statistically significant,
effects. The pattern that emerges when we consider the group, however, is that the
market penalizes more heavily bidders with socially connected CEOs with higher
supervisory needs.
Even when we look at the individual components of the advisory and monitoring
indexes as proxies for the specific needs of the acquiring firm, social ties seem to
have an significant effect on performance. In the next section, I show how social
ties also seem to play a role in the extreme changes in the shareholders’ wealth that
surround some merger announcement.
47
4 Social Ties and Extreme Changes in Wealth
Moeller et al. (2005) report massive losses in 87 deals between 1998 and 2001 in
which shareholders lost at least $1 billion per deal. If a contributing factor to this
“value destruction on a massive scale” is that social ties prevent proper board inter-
vention, we should observe among these deals a disproportional amount of socially
connected firms with high monitoring needs. In this section, I show that this is in-
deed the case. In addition, social ties significantly increase the probability of extreme
announcement returns, even after controlling for the specific characteristics of the
bidder. This is true for both value destruction (conditional on high monitoring needs)
as well as value creation (conditional on high advisory needs).
I use the same sample period and threshold as Moeller et al. (2005). Specifi-
cally, for all mergers taking place between 1998 and 2001 and satisfying my sample
requirements, extreme losses are defined as deals in which shareholders lost more
than $1 billion dollars in the three-day window surrounding the announcement. Ex-
treme gains are defined analogously. Both gains and losses are measured using 2001
dollars.
The first finding is that social ties are much more common among acquirers ex-
periencing extreme deals than among other bidders. Eighty-three deals are classified
as extreme losses in my sample.
4
In 53 (64%) of these, the CEO of the acquirer
is socially connected to the board. This is in sharp contrast with the sample aver-
age of 31% during the same period. Of these 53 deals, 44 are for companies with
Monitor 2. Social ties are also prevalent among acquiring firms that experience
extreme positive announcement returns. I find that in 50 acquisitions between 1998
4
Four deals classified as extreme losses in Moeller et al. (2005) are not included in my sample.
This is due to the extra requirement that acquirers be identified in BoardEx with at least three directors.
48
and 2001 shareholders gained $1 billion or more. Of these, 29 (or 58%) were made
by socially connected CEOs, 22 of them by bidders with Advice 2.
These numbers, though suggestive, do not take into consideration potentially im-
portant firm/deal characteristics. A more informative approach may be to study how
the probability of extreme gains or losses is affected by the presence of social ties. In
Table 8, I use a Probit analysis to examine these effects. The specification is
Prob(Extreme Event) = ( +
Ties
Social Ties +
1
Controls)
where Extreme Event is either Loss > $1bi (first two columns) or Gain > $1bi
(last two columns) and () represents the cumulative distribution function of the
standard normal distribution. For each type of extreme event, I split the sample based
on what the main hypothesis predicts will be the strongest effects of social ties.
49
Table8: Social Ties and Extreme Changes in Wealth
This table contains the estimates of probit regressions. The dependent variable is 1 if an
extreme event occurred and zero otherwise. Extreme events are merger deals in which
shareholders lose or gain more than $1 billion during the announcement period. The col-
umn Monitor 2 contains only deals in which the monitoring index is at least 2. The
other columns are defined analogously. % of Outside Dirs is the proportion of indepen-
dent directors on the board (in %). Board Size is the number of directors on the board
(from BoardEx). Log Total Assets is the logarithm of total assets. Industry Leverage
is the median leverage in the acquirer’s industry. Industry Tobin’s Q is the median To-
bin’s Q in the acquirer’s industry. Price Run-up is the bidder’s buy and hold abnormal
return from 230 to 11 days before the announcement. The CRSP value-weighted index
is used the benchmark. Ceo Ownership is the proportion of the firm owned by the CEO
at the end of the fiscal year preceding the acquisition announcement (from ExecuComp
and excluding options). Missing values are set to zero, and a dummy indicating miss-
ing values is included. Relative Deal Size is the value of the deal as reported by SDC
over the market value of the acquirer measured at the end of the fiscal year preced-
ing the announcement. CEO Prior M&A is the (logarithm of the) number of prior M&As
(since 1980) in which the current bidder CEO was either the CEO or CFO of the acquir-
ing company. Subsidiary Cash Only , Private Tgt Stock Deal, Public Tgt Cash
Only, Private Tgt Cash Only and Public Tgt Stock Deal are the interactions between
the dummies representing the target and deal types (the ommitted group isSubsidiary
Stock Deal), where Cash Only represents acquisitions entirely financed by cash and
Stock Deal represents acquisitions paid at least partially with stocks. The construction of
each variable is described in detail in the Appendix. All variables are measured at the
end of the fiscal year preceding the announcement date. All regressions include year
dummies (not reported). Robust standard errors clustered at industry level are in paren-
theses. ,, represents significance at the 10%, 5% and 1% level, respectively.
Loss> $1 bi Gain> $1 bi
Monitor 2 Monitor< 2 Advice 2 Advice< 2
Social Tie 0:409*** 0:300* 0:318** 0:571
(0:154) (0:168) (0:160) (0:536)
% of Outside Dirs 1:261*** 1:719 1:179* 0:168
(0:423) (1:444) (0:617) (0:731)
Board Size 0:047*** 0:093** 0:001 0:128***
(0:016) (0:044) (0:027) (0:040)
Log Total Assets 0:615*** 0:833*** 0:423*** 1:195***
(0:074) (0:093) (0:057) (0:192)
Industry Leverage 0:208 1:061** 0:595*** 1:555***
(0:154) (0:432) (0:219) (0:542)
Industry Tobin’s Q 0:533*** 0:461*** 0:322*** 0:719***
(0:078) (0:130) (0:104) (0:225)
Price Run-up 0:169*** 0:198*** 0:122* 0:509***
(0:062) (0:075) (0:070) (0:126)
Ceo Ownership 0:059 0:050 0:118** 0:185***
(0:118) (0:102) (0:054) (0:047)
Relative Deal Size 0:583*** 0:098*** 0:048 0:870*
(0:187) (0:032) (0:099) (0:462)
CEO Prior M&A 0:027 0:069 0:018 0:486***
Continued on next page
50
Table 8, Continued
Loss> $1 bi Gain> $1 bi
Monitor 2 Monitor< 2 Advice 2 Advice< 2
(0:126) (0:168) (0:089) (0:160)
Public Tgt Stock Deal 0:104 1:073** 0:389** 0:551
(0:181) (0:424) (0:153) (0:521)
Public Tgt Cash Only 0:068 0:788* 0:070 0:840
(0:228) (0:403) (0:387) (0:764)
Private Tgt Stock Deal 0:650*** 0:538 0:603*** 1:037*
(0:191) (0:640) (0:231) (0:617)
Private Tgt Cash Only 0:836**
(0:350)
Subsidiary Cash Only 0:617 0:403 0:388 0:188
(0:391) (0:431) (0:283) (0:696)
R-squared 0:388 0:486 0:329 0:530
Observations 1,085 1,156 1,159 959
In the first column of the Table 8, I estimate the Probit coefficients in a sample
that includes only bidders with Monitor 2. As predicted by the main hypothesis,
social ties significantly affect the probability of extreme losses. The coefficient of
0.41 is significant at the 1% level and indicates a substantial impact of the presence
of CEO-board social connections on the probability of extreme losses. Interestingly,
the proportion of outside directors also has a significant effect. For bidders with
high monitoring needs, more outside directors significantly decrease the probability
of mass value destruction (coefficient of -1.26). When we look at the complement
set of companies (Monitor < 2), the effects of social ties and outside representation
disappear. This suggests that social ties increase the likelihood of large value destruc-
tion only for companies with high monitoring needs. In particular, extreme losses by
bidders with low monitoring needs do not seem to be affected by either social ties or
outside representation. These results are again consistent with the main hypothesis.
51
Moreover, they highlight a potentially large impact of social ties on the shareholders’
wealth.
The next experiment looks for the cases in which shareholders gained more than
$1 billion. Similarly, social ties seem to affect the probability of extreme gains only
when advisory needs are high. From Column Advice 2 in Table 8, the coefficient
on the social tie dummy is 0.32 and is significant at the 5% level. As can be seen
from the results in the last column of this table, social ties do not have a significant
impact when companies do not have high advisory needs.
To get a better sense of the impact of social ties on the probability of extreme
events, I estimate conditional probabilities using the values from Table 8. Focusing
first on firms with high monitoring needs, I find that while the probability of a loss
of at least $1 billion is only 2.62% when no social ties exist, this number increases
over four times to 11.67% in the presence of social ties. The impact of social ties on
the probability of extreme gains is also economically significant. Although there is
only a 1.83% chance that shareholders of firms with no social connections but high
advisory needs will enjoy a gain of over $1 billion, this number increases over three
times to 6.12% when social ties are present.
The previous tests look at how the presence of social ties affect the value of the
bidder during mergers and acquisitions. In the next section, I show that these effects
tend to be stronger if a larger proportion of the board is connected to the CEO.
5 Social Connections to More Board Members
So far I have classified the acquiring firm’s CEO as socially connected if the (net)
proportion of the board tied to the CEO is positive. But the main hypothesis is also
consistent with the effects of social ties increasing with the portion of the board that
52
is “friendly” to the CEO. In this section, I test this implication. The main variable
of interest is % Friendly Board, which increases with the number of board members
connected to the CEO.
In Table 2, I showed that about 33.1% of the CEOs in my sample have some
social connection with at least one board member. In the absence of data limitations,
% Friendly Board = 0 indicates that there are no social ties between the CEO and
any board member.
5
But even in this case, inferences based on % Friendly Board
(which is observed for only a subset of companies) do not necessarily apply to the
entire population. This is because the subset of companies in which the CEO is
socially connected may not be a random sample. This situation is similar to the
classic selectivity problem found in labor economics literature (Heckman (1979)). In
fact, if the estimator for the expected proportion of connections used to compute the
% Friendly Board is unbiased, then the values in Table 2 indicate that social ties are
not random.
I use two approaches to deal with this problem. The first is to account for the
selectivity problem directly. I discuss this method in the next section. Alternatively,
we can concentrate on firms for which social ties are observable.
Recall that the goal is to examine how more “friendly” boards affect the impact
of social ties on bidder announcement returns. If we concentrate on firms for which
at least some social tie is present (i.e., Social Ties = 1), then we can use % Friendly
Board to say something about how additional ties affect the value of the bidder, con-
ditional on % Friendly Board> 0. The idea is then to estimate the following model:
5
% Friendly Board = 0 actually means that there is no social connection in excess of what we
expect given the CEO’s affiliations (as discussed in Section 2).
53
E[CARjSocial Ties = 1] =
0
% Friendly Board Proxy +
1
% Friendly Board
+
2
Proxy +
Controls (3.1)
In this case, testingH
0
:
0
> 0 is equivalent to testing whether–for firms with
observable social ties–more friendly boards increase announcement returns.
The results are shown in Table 9. In the first three columns of Panel A, I esti-
mate ( 3.1) using the indicator variables Advice > 1 and Monitor > 1 as proxies
for more advice and monitoring needs, respectively. Because of the low correlation
between the two indexes, the coefficients on Columns (1) and (2) are similar to when
both measures are included in Column (3). Column (1) indicates that, when advi-
sory needs are high, companies with CEOs connected to more directors earn higher
announcement returns. The coefficient for the interaction between Advice > 1 and
Social Ties is 6.56, with a T-stat of 2.17. The opposite occurs when we look at firms
with more monitoring needs: from Column (2) the coefficient on the interaction of
Monitor > 1 and Social Ties is negative (-7.70) and significant (T-stat of -2.29).
The results suggest that, as the proportion of directors with social ties to the CEO
increases, the patterns I document in the previous section get stronger. In particu-
lar, stronger ties are associated with higher announcement returns when the advisory
index is at or above its median level. Conversely, social ties are detrimental when
monitoring needs increase.
54
Table9: Different Measures of Social Ties
This table contains the estimates of regressions of bidder announcement returns on many
controls and the proxies for social ties, monitoring and advisory needs. The main co-
efficients of interest are the interactions between the social ties variable and the mon-
itoring/advisory proxies. % Frd Brd Mon > 1 is the interaction between the Social
Tie dummy and the Monitor > 1 indicator. % Friend Board Adv > 1 is defined
analogously. Monitor > 1 indicates deals in which the Monitor index is above 1. Ad-
vice > 1 is defined analogously. % Frd Brd represents the net proportion of the bid-
der’s board socially connected to the CEO. % Frd Brd represents the net proportion
of the bidder’s board socially connected to the CEO. % of Outside Dirs is the pro-
portion of independent directors on the board (in %). The construction of each vari-
able is described in detail in the Appendix. All variables are measured at the end of
the fiscal year preceding the announcement date. All regressions include year dum-
mies (not reported). Robust standard errors clustered at industry level are in parenthe-
ses. , , represents significance at the 10%, 5% and 1% level, respectively.
Panel A - Interactions
Social Tie Measure is Social Tie Measure is
% Frd Brds Residual % Frd Brd
(Only Firms with Social Ties) (Two-Step Heckman)
(1) (2) (3) (4) (5) (6)
% Frd BrdAdv> 1 6:564** 5:382* 1:202*** 1:100***
(3:029) (2:747) (0:268) (0:250)
% Frd BrdMon> 1 7:705** 6:254** 0:932*** 0:794***
(3:359) (3:100) (0:258) (0:248)
Monitor> 1 0:419 0:413 0:171 0:079
(0:524) (0:495) (0:141) (0:150)
Advice> 1 0:752 0:719 0:127 0:068
(0:506) (0:498) (0:184) (0:188)
% Frd Brd 3:194 3:539 0:712 0:755*** 0:271* 0:336**
(2:187) (2:152) (2:420) (0:151) (0:138) (0:145)
R-squared 0:057 0:055 0:066 0:046 0:044 0:049
Observations 2,186 2,186 2,186 7,154 7,154 7,154
Panel B - Subsamples
Social Tie Measure is Social Tie Measure is
% Frd Brds Residual % Frd Brd
(Only Firms with Social Ties) (Two-Step Heckman)
Adv> 1 Mon> 1 Adv> 1 Adv> 1 Mon> 1 Adv> 1
Mon 1 Mon 1
% Frd Brd 2:091 3:762** 4:112 0:454*** 0:670*** 0:822***
(1:603) (1:658) (2:512) (0:142) (0:143) (0:209)
% of Outside Dirs 1:587 1:655 2:260 1:070 0:128 1:993
(2:089) (1:637) (2:799) (0:878) (1:374) (1:367)
Continued on next page
55
Table 9, Continued
R-squared 0:099 0:046 0:119 0:039 0:055 0:038
Observations 1,086 1,085 609 3,890 3,013 2,255
Adv> 2 Mon> 2 Adv> 2 Adv> 2 Mon> 2 Adv> 2
Mon 2 Mon 2
% Frd Brd 6:307** 2:785 8:633** 0:913** 0:578*** 1:028***
(2:979) (2:007) (3:820) (0:349) (0:122) (0:369)
% of Outside Dirs 2:974 0:264 5:889 2:446 0:435 1:102
(4:602) (2:075) (5:775) (1:593) (1:880) (1:996)
R-squared 0:183 0:100 0:199 0:054 0:058 0:059
Observations 284 420 246 1,177 957 1,046
In the first three columns of Panel B, I focus on the effects of % Friendly Board
on different subsamples, similar to the experiments in Table 6. All controls discussed
in that table are also included here, but only the coefficients on % Friendly Board are
reported. Across all subsamples, the coefficients go in the direction predicted by the
main hypothesis. Possibly because of a much decreased sample size, only half of the
coefficients are significant. Taken as a group, however, these results still support the
prediction that stronger ties increase the effects of friendly boards.
Controlling for Correlations and Selectivity
It could be that social ties are correlated with other firm characteristics that affect
the bidder’s performance but are not related to board independence. If this were
the case, the results presented above could be spurious. In this section, I show that
even after controlling for many characteristics that could affect the proportion of the
board socially connected to the CEO, I find that friendly boards are beneficial when
advisory needs are high.
56
One possibility to deal with correlations across variables is to “orthogonalize” the
% Friendly Board measure. As discussed above, % Friendly Board is observed only
in firms with at least one CEO-board member social connection. Because the sample
of firms for which social ties are observed can be nonrandom, inferences based on
this subset of companies do not apply to the entire population.
6
To deal with this selectivity problem, I use a two-stage correction based on Heck-
man (1979). To simplify the model, I assume that the same characteristics that de-
termine the probability of observing a social tie also determine the proportion of the
board socially connected, conditional on social ties being observed. The canonical
specification for this relationship has the form:
% Friendly Board = X
0
+"
Pr(Social Ties = 1) = (
0
X)
where () represents the cumulative distribution function of the standard nor-
mal distribution and X is a vector of controls. In my specification, X consists of
the same firm/deal controls reported in Column (1) of Table 5, along with the indi-
vidual components of Advice and Monitor (from Equations ( 2.1) and ( 2.2)).
7
The
6
Not only social ties per se but the availability of information about club memberships and NFP
affiliations may not be random.
7
Specifically,X includes the following firm/deal controls: % Outside Dirs, Board Size, Log To-
tal Assets, Industry Leverage, Industry Tobin’s Q, Price Run-up, CEO Ownership, Relative Deal
Size, CEO Prior M&A, Public Tgt Stock Deal, Public Tgt Cash Only, Private Tgt Stock
Deal, Private Tgt Cash Only and Subsidiary Cash Only. The individual proxies for monitor-
ing/advisory included are Wave, High Excess Cash, High E-index, Low EBC, Low Inst Own, Diversify-
ing Low Inc, Young CEO, Multi Segments, Inexp CEO & Exp Board, Diversifying High Inc,
Expert Board, and Informed Director.
57
(standardized) residuals of the two-step Heckman estimation are then used as the or-
thogonalized social ties measure. By construction, these measures are not linearly
related to any of the controls inX. In particular, they are uncorrelated with all the
proxies for monitoring and advisory needs.
The results are shown in Table 9.
8
In the last three columns of Panel A, I show
that the effect of friendly boards on CARs is strongly related to the needs of moni-
toring and advise. From Column (6), the marginal effect of a one standard deviation
increase in the (standardized) measure of friendly boards when advisory needs are
high corresponds to an increase in the average CAR of 1.10 percentage points. The
marginal effect of a similar shock decreases CARs by -0.79 percentage points when
Monitor> 1.
From Panel B, the effects of a one standard deviation increase in friendly boards
increases bidder announcement returns by 0.82 (T-stat of 3:93) when Monitor 1<
Advice and 1.03 (T-stat of 2:78) when Monitor 2 < Advice. Both results are
consistent with the notion that more friendly boards are beneficial to the bidder when
advisory needs are high.
6 Endogeneity Concerns
Endogeneity is a common concern in empirical studies in Corporate Finance. The
main goal of this paper is to test a class of equilibrium models of board composition
that specify the circumstances under which board independence increases the value
of the firm. Since board composition and firm value are both endogenous variables,
8
To facilitate the interpretation, the coefficients are expressed in terms of the impact on CARs (in
%) of a one standard deviation increase in of the independent variable. That is, the reported coefficients
are transformations of the type

x
=
x

x
, where
x
is the standard deviation of the regressor.
58
one should exercise care in interpreting contemporaneous relations between these
two quantities. This point is well emphasized by Hermalin & Weisbach (2003). In
this section, I describe and test the assumptions necessary for a correct interpretation
of the results found above.
Consider once more the main predictions of Adams & Ferreira’s model in the
context of M&A’s: friendly boards would increase the value of the acquirer in situa-
tions where board advice is really important for the success of the merger, but would
be detrimental to the acquiring firm when monitoring is a more important concern.
To test this prediction, I constructed proxies for monitoring and advisory needs and
examined the effects of friendly boards on announcement returns, conditional on the
importance of each role of the board to the success of that particular merger. The
main question is: is this a valid test? Or more precisely, under which assumptions
would this be a well specified test of Adams & Ferreira (2007)?
Consider first the situations in which it would not. If merger opportunities are
predictable and boards adjust instantaneously, optimally constituted boards will in-
corporate the decision to merge. In this case, observable cross-sectional relations
between board composition and announcement returns would be spurious. These
assumptions, however, are unlikely to hold in reality.
If, in contrast, mergers are not predictable in advance and if boards are sticky,
then, at the merger announcement, board composition will not necessarily be optimal.
More specifically, I assume the following:
Assumptions: Suppose that the following conditions hold:
1. The CEO only reveals private information to friendly board members.
2. Boards cannot adjust instantaneously.
59
3. Mergers are unpredictable three years before the announcement date.
For now, suppose that for a given set of firms all assumptions hold and consider
the following timeline. At time t = 0, shareholders create the board to maximize
the overall value of the firm. The level of board friendliness is determined by the
importance of monitoring and advice. Because of Assumption 3, shareholders do not
take in account the possibility of future mergers opportunities three or more years
in the future. At time t = 1, a merger opportunity arises. Shareholders cannot
restructure the board to reflect the importance of board monitoring and advice on
the success of the merger and its impact on the value of the firm. Because firms are
in disequilibrium, those with more friendly boards than optimal are more likely to
engage in empire building acquisitions. Conversely, firms with board that are more
independent than optimal will lack proper advice. The results found in this paper
would then be consistent with Adams & Ferreira (2007). Because these results rely
on these assumptions, it is important to check their plausibility.
The first assumption comes from Adams & Ferreira (2007). The second assump-
tion, that board composition is sticky, is supported by the data: from Table 1, the
median tenure of a director in the company during the 13 years of my sample is 6
years. The final assumption about the predictability of mergers is crucial and de-
serves a more detailed analysis.
Assumption 3 is likely to hold for some firms but not all. If one could identify
the set of firms for which this assumption holds, it would then be possible to test
if the results found above are likely to be spurious. Consider the extreme opposite
scenario in which at timet = 0 shareholders choose the board taking into account
all future merger opportunities. Because merger opportunities do not change board
60
composition, these firms will be at optimum and there should be no relation between
board composition and announcement returns.
This argument implies that if we split the sample between deals in which the
merger opportunity was not reflected in the composition of the board and deals in
which it was, we should ideally find that the results only hold in the former case.
Future mergers opportunities are likely to be reflected in the board composition
of serial acquirers. These are companies which actively pursue a strategy of growth
through acquisitions.
9
I use serial acquirers to proxy for companies for which As-
sumption 3 is unlikely to hold.
In Table 10, companies are categorized by the number of acquisitions made dur-
ing the sample period. I then repeat the experiement in Table 6. If the board of
directors of serial acquirers optimally incorporate future merger plans, then board
composition should not affect announcement returns. The results described in Ta-
ble 6 should hold only for companies that are not serial acquirers.
9
For instance, Cisco is typically taken as the prototype serial acquirer (e.g., Ahern (2008)).
61
Table10: Serial and Non-Serial Acquirers
This table contains the estimates of regressions of bidder announcement returns on all
control variables described in Table 5. Each regression is run on a different subsam-
ple, depending on the value of the monitor/advisor proxies and on the total number of
acquisitions by the acquirer. In all regressions, only the largest acquisition made by
the firm (in terms of relative deal value) is included. Column All contains the results
for this restricted sample. Columns 1 Deal, 2 Deals, 3 Deals include only firms that
acquired either once, twice or three times or more, respectively. In Panel A, all firms
are included. In Panels B and C, only deals with either high monitoring needs or high
advisory needs, respectively, are included. For brevity, this table only reports the co-
efficients of interest, but all variables in the first column of Table 5 are included in the
regressions.Social Tie is a dummy variable equal to 1 if the CEO is socially connected
to at least one independent board member, and 0 otherwise.% of Outside Dirs is the
proportion of independent directors on the board (in %). The construction of each vari-
able is described in detail in the Appendix. All variables are measured at the end of
the fiscal year preceding the announcement date. All regressions include year dum-
mies (not reported). Robust standard errors clustered at industry level are in parenthe-
ses. , , represents significance at the 10%, 5% and 1% level, respectively.
Panel A - All Deals
All 1 Deal 2 Deals 3 Deals
Social Tie 0:299 0:561 0:594 0:721
(0:338) (0:557) (1:088) (0:547)
% of Outside Dirs 2:756* 3:715 0:136 3:385
(1:457) (2:916) (2:256) (2:086)
R-squared 0:073 0:051 0:097 0:130
Observations 2,518 985 567 966
Panel B - High Monitoring Needs (Monitor 2)
All 1 Deal 2 Deals 3 Deals
Social Tie 2:979***5:046*** 0:129 2:429**
(0:568) (0:755) (1:164) (1:055)
% of Outside Dirs 0:348 0:853 0:947 3:079
(2:240) (3:449) (4:396) (3:374)
R-squared 0:107 0:172 0:128 0:158
Observations 1,072 410 228 434
Panel C - High Advisory Needs (Advice 2)
All 1 Deal 2 Deals 3 Deals
Social Tie 2:660*** 3:978*** 3:897** 0:041
(0:662) (0:951) (1:571) (0:800)
% of Outside Dirs 2:404 1:449 3:757 2:604
Continued on next page
62
Table 10, Continued
All 1 Deal 2 Deals 3 Deals
(1:483) (3:140) (2:326) (1:835)
R-squared 0:077 0:107 0:156 0:127
Observations 1,384 533 323 528
Once companies are categorized by the number of acquisitions, I chose the most
important acquisition for each company (as a proportion of the market value of the
acquirer). The first column of Table 10 presents the results for this restricted sample.
In Panel A, all deals are included. The general picture that emerges is that so-
cial ties do not have a signficant effect on announcement returns, regardless of the
number of acquisitions made by the company. However, as before, conditional on
high monitoring needs, social ties have a strong and negative effect. Column All,
announcement returns are -2.979 percentage points lower in the presence of social
ties (significant at the 1% level). This is stronger than the effect of social ties on
high monitoring needs companies in the unrestricted sample (-1.24 from Table 6),
which suggests a weaker effect for serial acquirers. This is confirmed in the subse-
quente columns in Panel B: the effect of social ties on high monitoring needs firms
is stronger for non-serial acquirers. Panel C looks at companies with high advisory
needs. Again, for this set of companies, the effect of social ties on announcement
returns is stronger for non-serial acquirers.
In general, the pattern documented in Table 10 is consistent with the Theory of
Friendly Boards: for companies whose board compositions are more likely to take
into consideration future merger deals, announcement returns should not be system-
atically related to any board characterisitic.
63
However, if mergers are unpredictable when boards are formed and board compo-
sition is sticky, then we should expect a relationship between announcement returns
and friendly board consistent with the results in Table 10.
64
Chapter 4
Conclusion
This paper tests the hypothesis that less independent, more “friendly” boards can
sometimes benefit the shareholders of firms pursuing corporate acquisitions. Theory
predicts that director independence can be harmful when the importance of board
advice surpasses the need to supervise the CEO. To test this prediction, I use observ-
able social connections between the CEO and board members as a proxy for friendly
boards.
I find that when board directors tend to possess valuable information about the
merger, higher announcement returns are observed for bidders with more friendly
boards. The magnitudes of the effects can be large, about two to three times the
average bidder announcement return of 0.61%. Also as predicted by theory, when
the need to discipline the manager is a greater concern, social ties seem to have a
negative impact on the acquiring firm’s performance.
The same patterns are not observed when the regulatory definition of an inde-
pendent director is used instead. This may indicate that social ties are related to a
different dimension of true board independence.
In addition to the effects on the average bidder, I look at how social ties affect
the probability of extreme changes in the shareholders’ wealth around the time of the
announcement. For companies with high monitoring needs, social ties increase the
probability of losses in excess of $1 billion by over four times. In contrast, when
65
advisory needs are high, social ties increase the chances of gains of $1 billion or
more by over three times.
The effect of social ties on the dual role of the board (and thus on firm value)
generally increase in the proportion of board members connected to the CEO. More
friendly boards correspond to larger effects. Also, the results documented here cannot
be explained by correlations between the social tie measure and other firm or board
characteristics.
My findings are not dependent on the inclusion of any of the individual elements
used to classify firms into advisory and monitoring needs. In particular, when used
in isolation, most of these proxies seem powerful enough to deliver the results.
Perhaps the most important contribution of this research is to identify situations
in which friendly boards have a systematic positive effect on the value of the firm,
seemingly through its influence on the dual role of the board. If social ties do capture
part of the actual level of interdependence between the CEO and board members,
then the results described here support the view that greater board independence is
not always efficient. Rather, board composition should take into account the trade-off
between the need to discipline the CEO and the importance of board advice.
66
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Appendix A: Variable Definitions
Below is a description of all variables used in this paper.
Income is the three-year income growth used by Morck et al. (1990). It is
defined as log(I(t1))log(I(t4)), whereI(t1) is the sum of net income,
interest, and deferred taxes for the fiscal year preceding the announcement.
Advice is the summation of indicator variables for Multi-segment Firms, Young
CEO, Inexperienced CEO/Experienced Board, Expert Board, Diversifying ac-
quisitions following good performance, and Informed Directors.
Board Experience is the average tenure of board members in the bidder’s four-
digit SIC code industry (in years).
Board Expertise is the proportion of board members who have either held a
CEO or CFO position in another company or worked as an executive in a fi-
nancial company (SIC codes 6000-6999).
Board Size is the number of directors on the board (from BoardEx).
CEO Age represents the age of the CEO (from BoardEx).
71
CEO Experience is the CEO’s tenure in the bidder’s industry (in years). Specif-
ically, it is the number of years the manager has been the CEO of the bidder or
other companies in the same four-digit SIC code.
CEO Prior M&A is the number of prior M&As (since 1980) in which the cur-
rent bidder CEO was either the CEO or CFO of the acquiring company.
Cash Only represents acquisitions entirely financed by cash.
Ceo Ownership is the proportion of the firm owned by the CEO at the end of
the fiscal year preceding the acquisition announcement (from ExecuComp and
excluding options). Missing values are set to zero, and a dummy indicating
missing values is included.
Deal Value is the value of the deal as reported by SDC (in Millions).
Diversifying High Inc represents diversifying acquisitions following prior
three-year above median income growth.
Diversifying Low Inc represents diversifying acquisitions following prior
three-year below median income growth.
Diversifying represents mergers in which the target and acquirer are in different
four-digit SIC code industries.
E-index is the entrenchment index of Bebchuk et al. (2008).
Excess Cash is defined as in Dittmar & Mahrt-Smith (2007). Each year,
the following regression is estimated (using all companies in Compustat):
ln

Cash
i
NA
i
+ 1

=
0
+
1
ln (NA
i
))+
2
FCF
i
NA
i
+
3
NWC
i
NA
i
+
4
(IndSigma
i
)+

5
d
MV
i
NA
i
+"
i
, where Cash is cash and equivalents (item 1),NA represents net
72
assets (item 3 - item 1), FCF is operating income (item 13) minus current lia-
bilities (item 5) minus cash (item 1), IndSigma is the industry average of prior
10 year standard deviation of FCF/NA,
d
MV represents past three-year sales
growth and is used as an instrument for market to book, RD stands for R&D
expenditures (item 46) and is set to zero when missing. The residuals are used
to compute excess cash at timet + 1.
High E-index represents high entrenchment levels as measured by the E-index
of Bebchuk et al. (2008). It is equal to 1 when the E-index is greater than 2.
High Excess Cash is a dummy indicating whether the firm’s excess cash is
above the industry median for that given year (using all companies in Compu-
stat).
Industry Leverage represents the acquirer’s industry median leverage across all
Compustat firms (classified using four-digit SIC codes). See Leverage.
Industry Tobin’s Q is the acquirer’s industry median Tobin’s Q across all Com-
pustat firms (using four-digit SIC codes). See Tobin’s Q.
Informed Director is a binary variable that equals 1 when at least one of the
bidder’s independent board members also serves as a board member for another
company in the same four-digit SIC industry code as the target. It is set to zero
if the target and the bidder are in the same industry.
Inst Ownership is the proportion of shares outstanding in the hands of US in-
dependent investors, corresponding to the CDA/Spectrum institutional classi-
fication types 3 and 4 (Chen et al. (2007)).
73
Less Experienced CEO is a dummy variable equal to 1 if the CEO is younger
than the median CEO age of 52.
Leverage is long-term debt (Compustat item 9) plus debt in current liabilities
(Compustat item 34) over long-term debt plus debt in current liabilities plus
the book value of common equity (Compustat item 60).
Log Total Assets is the logarithm of total assets.
Low EBC represents firms with Equity-Based Compensation lower than the
industry median (using all firms in ExecuComp).
Monitor is the summation of indicator variables for High Excess Cash, Diver-
sifying acquisitions following bad performance, Low EBC, Low Institutional
Ownership, Merger Waves, and High E-index.
Multi Segments is a binary variable indicating whether the company reports
more than one segment in the Compustat Business files.
N Bus Segments is the number of business segments in the Compustat Business
files.
Price Run-up is the bidder’s buy and hold abnormal return from 230 to 11
days before the announcement. The CRSP value-weighted index is used the
benchmark.
Public Tgt indicates whether the acquisition was for a publicly traded target.
Relative Deal Size is the value of the deal as reported by SDC over the mar-
ket value of the acquirer measured at the end of the fiscal year preceding the
announcement.
74
Social Tie is a dummy variable equal to 1 if the CEO is socially connected to
at least one independent board member, and 0 otherwise.
Tobin’s Q is defined as the market value of assets divided by the book value of
assets (Compustat item 6), where the market value of assets equals the book
value of assets plus the market value of common equity less the sum of the
book value of common equity (Compustat item 60) and balance sheet deferred
taxes (Compustat item 74).
Total Assets is Compustat item 6 (in $ Bil).
Wave identifies merger waves using the procedure in Harford (2005).
% Equity Based Compensation is the Equity Based Compensation measure
used by Datta et al. (2001). It is defined as the sum of the value of new stock
options (using the modified Black-Scholes method) granted to the CEO as a
percentage of total compensation.
% Friendly Board represents the net proportion of the bidder’s board socially
connected to the CEO. It is the difference between the actual proportion of
the directors with ties to the CEO and the average proportion from 10,000
simulated boards.
% of Outside Dirs is the proportion of independent directors in the board (in
%).
75
Appendix B: Social Ties with
Independent Directors
Throughout the paper, the social ties measure include connections between the CEO
and any board member, independent or not. In this section, I present results in which
CEO social ties are measured exclusively with independent board members.
Table11: Bidder Announcement Returns and Social Ties with Independent Directors
This table contains the estimates of regressions of bidder announcement returns on many
controls and the proxies for social ties, monitoring needs, and advisory needs. Social Tie
is a dummy variable equal to 1 if the CEO is socially connected to at least one indepen-
dent board member, and 0 otherwise. Monitor is the summation of indicator variables for
High Excess Cash, Diversifying acquisitions following bad performance, Low EBC, Low
Institutional Ownership, Merger Waves, and High E-index. Advice is the summation of
indicator variables for Multi-segment Firms, Y oung CEO, Inexperienced CEO/Experienced
Board, Expert Board, Diversifying acquisitions following good performance, and Informed
Directors. These individual components are described in Table 4. Social Tie Advice
, % of Outside Advice , represents interactions between the Social Tie and the advi-
sory needs index Advice . The interactions with the monitoring index are defined anal-
ogously. Industry Leverage is the median leverage in the acquirer’s industry. Industry
Tobin’s Q is the median Tobin’s Q in the acquirer’s industry. Subsidiary Cash , Pri-
vate Stock, Public Cash,Private Cash and Public Stock are the interactions
between the dummies representing the target and deal types (the ommitted group is
SubsidiaryStock), where Cash represents acquisitions entirely financed by cash and
Stock represents acquisitions paid at least partially with stocks. The construction of each
variable is described in detail in the Appendix. All variables are measured at the end
of the fiscal year preceding the announcement date. All regressions include year dum-
mies (not reported). Robust standard errors clustered at industry level are in parenthe-
ses. , , represents significance at the 10%, 5% and 1% level, respectively.
(1) (2) (3) (4) (5)
Continued on next page
76
Table 11, Continued
(1) (2) (3) (4) (5)
Social Tie Advice 1:417*** 1:367***
(0:316) (0:306)
Social Tie Monitor 0:596** 0:472**
(0:249) (0:233)
% of Outside Advice 0:647
(0:596)
% of Outside Monitor 0:633
(0:758)
Social Tie 0:234* 2:439*** 0:633 1:671***
(0:136) (0:469) (0:432) (0:373)
Monitor 0:062 0:132 0:158 0:531
(0:067) (0:098) (0:103) (0:563)
Advice 0:067 0:287** 0:298** 0:548
(0:120) (0:142) (0:146) (0:415)
% of Outside 1:251* 1:222* 1:284* 1:236* 1:099
(0:660) (0:671) (0:682) (0:669) (1:651)
Board Size 0:048 0:048 0:050 0:049 0:040
(0:030) (0:030) (0:031) (0:031) (0:031)
Log Total Assets 0:395*** 0:420*** 0:376*** 0:406*** 0:404***
(0:105) (0:106) (0:104) (0:107) (0:104)
Industry Leverage 0:043 0:062 0:033 0:057 0:036
(0:091) (0:094) (0:098) (0:095) (0:091)
Industry Tobin’s Q 0:340 0:345 0:333 0:343 0:344
(0:271) (0:282) (0:265) (0:281) (0:269)
Price Run-up 0:362*** 0:390*** 0:359*** 0:388*** 0:364***
(0:106) (0:102) (0:104) (0:102) (0:105)
Ceo Ownership 0:008 0:011 0:009 0:012 0:007
(0:035) (0:036) (0:036) (0:037) (0:035)
Relative Deal Size 0:869** 0:862** 0:882** 0:873** 0:863**
(0:378) (0:374) (0:379) (0:375) (0:376)
CEO Prior M&A 0:207 0:191 0:207 0:189 0:209
(0:144) (0:153) (0:144) (0:154) (0:144)
Public Stock 3:237*** 3:232*** 3:241*** 3:233*** 3:243***
(0:685) (0:675) (0:666) (0:668) (0:688)
Public Cash 0:363 0:370 0:323 0:334 0:372
(0:262) (0:284) (0:272) (0:285) (0:262)
Private Stock 0:718 0:679 0:717 0:684 0:706
(0:708) (0:686) (0:714) (0:683) (0:705)
Private Cash 0:011 0:032 0:025 0:038 0:019
(0:276) (0:277) (0:274) (0:281) (0:274)
Subsidiary Cash 0:986*** 0:947*** 0:973*** 0:936*** 0:988***
(0:248) (0:240) (0:247) (0:241) (0:248)
R-squared 0:040 0:046 0:042 0:046 0:041
Observations 7,154 7,154 7,154 7,154 7,154
77
Table12: Different Measures of Social Ties with Independent Directors
This table contains the estimates of regressions of bidder announcement returns on many
controls and the proxies for social ties, monitoring and advisory needs. The main co-
efficients of interest are the interactions between the social ties variable and the mon-
itoring/advisory proxies. % Frd Brd Mon > 1 is the interaction between the So-
cial Tie dummy and the Monitor > 1 indicator. % Friend Board Adv > 1 is de-
fined analogously. Monitor > 1 indicates deals in which the Monitor index is above
1. Advice > 1 is defined analogously. % Frd Brd represents the net proportion of
the bidder’s board socially connected to the CEO. % Frd Brd represents the net pro-
portion of the bidder’s board socially connected to the CEO. % of Outside is the pro-
portion of independent directors on the board (in %). The construction of each vari-
able is described in detail in the Appendix. All variables are measured at the end of
the fiscal year preceding the announcement date. All regressions include year dum-
mies (not reported). Robust standard errors clustered at industry level are in parenthe-
ses. , , represents significance at the 10%, 5% and 1% level, respectively.
Panel A - Interactions
Social Tie Measure is Social Tie Measure is
% Frd Brds Residual % Frd Brd
(Only Firms with Social Ties) (Two-Step Heckman)
(1) (2) (3) (4) (5) (6)
% FB Adv> 1 4:564 4:036 1:091*** 1:019***
(3:189) (2:966) (0:245) (0:226)
% FB Mon> 1 5:588 4:879 0:823*** 0:724***
(3:414) (3:246) (0:260) (0:253)
Monitor> 1 0:398 0:282 0:169 0:083
(0:630) (0:619) (0:141) (0:150)
Advice> 1 0:844 0:731 0:130 0:075
(0:640) (0:642) (0:185) (0:190)
% FB 1:091 3:503 1:800 0:663*** 0:247* 0:292**
(2:271) (2:271) (2:479) (0:135) (0:144) (0:132)
R-squared 0:061 0:058 0:069 0:045 0:043 0:047
Observations 1,990 1,990 1,990 7,154 7,154 7,154
Panel B - Subsamples
Social Tie Measure is Social Tie Measure is
% FBs Residual % FB
(Only Firms with Social Ties) (Two-Step Heckman)
Adv> 1 Mon> 1 Adv> 1 Adv> 1 Mon> 1 Adv> 1
Mon 1 Mon 1
% FB 1:905 2:343 3:082 0:433*** 0:581*** 0:759***
(1:866) (1:630) (3:186) (0:144) (0:142) (0:218)
% of Outside 1:831 3:694** 3:622 1:066 0:037 2:093
(2:161) (1:478) (2:992) (0:892) (1:354) (1:374)
Continued on next page
78
Table 12, Continued
R-squared 0:103 0:048 0:122 0:039 0:053 0:036
Observations 974 989 559 3,890 3,013 2,255
Adv> 2 Mon> 2 Adv> 2 Adv> 2 Mon> 2 Adv> 2
Mon 2 Mon 2
% FB 5:073 1:215 4:856 1:037** 0:428*** 1:173***
(3:683) (1:812) (4:338) (0:396) (0:104) (0:416)
% of Outside 1:448 1:110 2:997 2:522 0:586 1:206
(5:324) (2:300) (6:237) (1:505) (1:935) (1:909)
R-squared 0:190 0:102 0:203 0:056 0:054 0:061
Observations 254 387 225 1,177 957 1,046
79
Table13: Social Ties with Independent Directors on Different Subsamples
This table contains the estimates of regressions of bidder announcement returns on all
control variables described in Table 5. Each regression is run on a different subsample,
depending on the value of the monitor/advisor proxies. For brevity, this table does not re-
port the interactions between the type of target and method of payment (see description
in Table 5), even though they are included. SocialTie is a dummy variable equal to 1 if the
CEO is socially connected to at least one independent board member, and 0 otherwise.%
ofOutside is the proportion of independent directors on the board (in %). BoardSize is the
number of directors on the board (from BoardEx). LogTotalAssets is the logarithm of total
assets. Industry Leverage is the median leverage in the acquirer’s industry. Industry To-
bin’s Q is the median Tobin’s Q in the acquirer’s industry. Price Run-up is the bidder’s buy
and hold abnormal return from 230 to 11 days before the announcement. The CRSP value-
weighted index is used the benchmark. CeoOwnership is the proportion of the firm owned
by the CEO at the end of the fiscal year preceding the acquisition announcement (from Ex-
ecuComp and excluding options). Missing values are set to zero, and a dummy indicating
missing values is included. RelativeDealSize is the value of the deal as reported by SDC
over the market value of the acquirer measured at the end of the fiscal year preceding
the announcement. CEO Prior M&A is the (logarithm of the) number of prior M&As (since
1980) in which the current bidder CEO was either the CEO or CFO of the acquiring com-
pany. Subsidiary Cash Only , Priv Stock, Pub Cash, Priv Cash and Pub Stock
are the interactions between the dummies representing the target and deal types (the om-
mitted group isSubsidiary Stock), where Cash represents acquisitions entirely financed
by cash andStock represents acquisitions paid at least partially with stocks. The construc-
tion of each variable is described in detail in the Appendix. All variables are measured at
the end of the fiscal year preceding the announcement date. All regressions include year
dummies (not reported). Robust standard errors clustered at industry level are in paren-
theses. ,, represents significance at the 10%, 5% and 1% level, respectively.
High Advice High Monitor
High Advice
Low Monitor
Advice Index (> 1) (> 2) (> 1) (> 2)
Monitor Index (> 1) (> 2) ( 1) ( 2)
Social Tie 1:136*** 2:140** 1:258***0:922*** 1:882*** 2:435**
(0:321) (0:889) (0:277) (0:309) (0:496) (0:926)
% of Outside 1:287 2:987* 0:343 0:340 2:490* 1:866
(0:913) (1:531) (1:362) (1:885) (1:413) (1:912)
Board Size 0:014 0:005 0:074** 0:036 0:004 0:011
(0:045) (0:099) (0:036) (0:050) (0:056) (0:106)
Log Total Assets 0:500***0:453** 0:393***0:317*** 0:436***0:400**
(0:124) (0:184) (0:110) (0:111) (0:136) (0:198)
Industry Leverage 0:217 0:872* 0:046 0:856 0:303 0:847
(0:217) (0:477) (0:105) (0:547) (0:184) (0:553)
Industry Tobin’s Q 0:298 0:179 0:292 1:294** 0:355 0:043
(0:307) (0:399) (0:426) (0:521) (0:227) (0:534)
Price Run-up 0:260 0:343 0:420** 0:501 0:221 0:296
(0:177) (0:281) (0:177) (0:481) (0:327) (0:303)
Ceo Ownership 0:059* 0:073 0:070** 0:141*** 0:062 0:116
(0:035) (0:103) (0:031) (0:036) (0:067) (0:091)
Relative Deal Size 0:485 0:252 1:000** 0:748 0:263 0:695
Continued on next page
80
Table 13, Continued
High Advice High Monitor
High Advice
Low Monitor
Advice Index (> 1) (> 2) (> 1) (> 2)
Monitor Index (> 1) (> 2) ( 1) ( 2)
(0:295) (1:230) (0:455) (1:004) (0:241) (1:293)
CEO Prior M&A 0:407** 1:062*** 0:142 0:080 0:493** 1:034***
(0:156) (0:306) (0:174) (0:231) (0:197) (0:288)
Public Stock 3:792***3:127*** 2:427***1:336** 4:603***4:533***
(0:797) (0:824) (0:678) (0:539) (1:163) (0:843)
Public Cash 0:193 1:823*** 0:645* 0:776 0:328 1:708**
(0:352) (0:546) (0:356) (0:639) (0:544) (0:655)
Private Stock 0:786 1:516 1:485 0:164 0:176 1:259
(1:067) (1:487) (0:895) (1:514) (1:380) (1:329)
Private Cash 0:201 0:834 0:412 0:250 0:330 0:650
(0:377) (0:689) (0:296) (0:594) (0:503) (0:733)
Subsidiary Cash 0:577* 2:324*** 1:304*** 1:409* 0:550 2:229***
(0:340) (0:569) (0:333) (0:759) (0:464) (0:617)
R-squared 0:040 0:055 0:052 0:053 0:038 0:060
Observations 3,890 1,177 3,013 957 2,255 1,046
81
Table14: Individual Proxies and Ties with Independent Directors
This table contains the estimates of regressions of bidder announcement returns on all
variables described in Table 5 along with the proxies for monitoring needs, and advisory
needs. The main coefficients of interest are the interactions between the social ties vari-
able and these proxies. For brevity, the table reports only these coefficients, although all
controls present in Table 5 are included in the regressions. Tie Proxy is the interac-
tion between the Social Tie dummy and the proxy. Tie is a dummy variable equal to 1 if
the CEO is socially connected to at least one independent board member, and 0 other-
wise. Proxy represents the monitoring and advisory needs proxies. Tie Proxy is the
interaction between the Social Tie dummy and the proxy. Tie is a dummy variable equal
to 1 if the CEO is socially connected to at least one independent board member, and 0
otherwise. Proxy represents the monitoring and advisory needs proxies. The construc-
tion of each variable is described in detail in the Appendix. All variables are measured at
the end of the fiscal year preceding the announcement date. All regressions include year
dummies (not reported). Robust standard errors clustered at industry level are in paren-
theses. ,, represents significance at the 10%, 5% and 1% level, respectively.
Panel A - Social Ties and Advice
Multi Less Exp Diversif Inexp CEO Expert Informed
Segments CEO High Inc Exp Board Board Director
Tie Proxy 1:135** 1:506*** 1:953*** 0:079 1:768*** 3:270**
(0:437) (0:518) (0:671) (0:610) (0:416) (1:345)
Social Tie 0:818*** 0:546*** 0:493*** 0:267** 1:290*** 0:342**
(0:236) (0:187) (0:151) (0:129) (0:197) (0:141)
Proxy 0:309 0:661* 0:291 0:250 0:201 0:293
(0:309) (0:363) (0:348) (0:334) (0:239) (0:268)
R-squared 0:041 0:043 0:043 0:040 0:043 0:041
Observations 7,154 5,630 7,154 7,154 7,154 7,154
Panel B - Social Ties and Monitoring
High Excess High Low Low Inst Merger Diversif
Cash E-index EBC Ownership Wave Low Inc
Tie Proxy 1:458*** 0:439 0:783* 0:361 0:085 0:579
(0:489) (0:519) (0:396) (0:356) (0:437) (0:385)
Social Tie 0:375 0:009 0:072 0:126 0:233 0:112
(0:287) (0:286) (0:219) (0:227) (0:195) (0:186)
Proxy 0:431** 0:779 0:133 0:435* 0:182 0:134
(0:197) (0:495) (0:277) (0:217) (0:310) (0:219)
R-squared 0:042 0:046 0:051 0:041 0:040 0:041
Observations 7,154 3,372 3,174 7,154 7,154 7,154
Note: All regressions include the same controls as in Table 5.
82
Table15: Serial and Non-Serial Acquirers and Ties with Independent Directors
This table contains the estimates of regressions of bidder announcement returns on all
control variables described in Table 5. Each regression is run on a different subsample, de-
pending on the value of the monitor/advisor proxies and on the total number of acquisitions
by the acquirer. In all regressions, only the largest acquisition made by the firm (in terms of
relative deal value) is included. Column All contains the results for this restricted sample.
Columns 1 Deal, 2 Deals, 3 Deals include only firms that acquired either once, twice or
three times or more, respectively. In Panel A, all firms are included. In Panels B and C, only
deals with either high monitoring needs or high advisory needs, respectively, are included.
For brevity, this table only reports the coefficients of interest, but all variables in the first
column of Table 5 are included in the regressions.Social Tie is a dummy variable equal to
1 if the CEO is socially connected to at least one independent board member, and 0 other-
wise.%ofOutside is the proportion of independent directors on the board (in %). The con-
struction of each variable is described in detail in the Appendix. All variables are measured
at the end of the fiscal year preceding the announcement date. All regressions include year
dummies (not reported). Robust standard errors clustered at industry level are in paren-
theses. ,, represents significance at the 10%, 5% and 1% level, respectively.
Panel A - All Deals
All 1 Deal 2 Deals 3 Deals
Social Tie 0:169 0:565 0:323 0:046
(0:344) (0:612) (0:866) (0:605)
% of Outside 2:744* 3:652 0:172 3:432
(1:468) (2:894) (2:261) (2:105)
R-squared 0:073 0:051 0:097 0:129
Observations 2,518 985 567 966
Panel B - High Monitoring Needs (Monitor 2)
All 1 Deal 2 Deals 3 Deals
Social Tie 2:492***5:070*** 0:451 0:960
(0:448) (0:825) (1:341) (0:776)
% of Outside 0:001 1:738 0:820 3:237
(2:298) (3:646) (4:409) (3:297)
R-squared 0:100 0:170 0:128 0:148
Observations 1,072 410 228 434
Panel C - High Advisory Needs (Advice 2)
All 1 Deal 2 Deals 3 Deals
Social Tie 2:942*** 3:870*** 4:109*** 1:018
(0:619) (0:876) (1:294) (0:951)
% of Outside 2:871* 1:720 4:460** 2:954
(1:493) (3:100) (2:206) (1:905)
Continued on next page
83
Table 15, Continued
All 1 Deal 2 Deals 3 Deals
R-squared 0:079 0:105 0:156 0:128
Observations 1,384 533 323 528
84

Abstract (if available)

Abstract Although recent regulations call for greater board independence, finance theory predicts that independence is not always in the shareholders' interest. In situations where it is more important for the board to provide advice than to monitor the CEO, more independent directors can decrease firm value because the CEO is not willing to share inside information with independent directors. I test this prediction by examining the connection between takeover returns and board friendliness using social ties between the CEO and board members as a proxy for less independent, more friendly boards. I find that social ties are associated with higher bidder announcement returns when advisory needs are high but with lower returns when monitoring needs are high. These effects intensify as the proportion of the board socially connected to the CEO increases and are not driven by correlations between social ties and other board characteristics. The evidence suggests that friendly boards can have both costs and benefits depending on the specific needs of the company.

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Asset Metadata

Creator Schmidt, Breno (author)

Core Title Costs and benefits of "friendly" boards during mergers and acquisitions

School Marshall School of Business

Degree Doctor of Philosophy

Degree Program Business Administration

Publication Date 04/29/2009

Defense Date 03/31/2009

Publisher University of Southern California (original), University of Southern California. Libraries (digital)

Tag governance,mergers and acquisitions,OAI-PMH Harvest,social networks

Language English

Contributor Electronically uploaded by the author (provenance)

Advisor Matsusaka, John (committee chair), DeAngelo, Harry (committee member), Leshem, Shmuel (committee member), Matos, Pedro (committee member), Ozbas, Oguzhan (committee member)

Creator Email breno.schmidt@gmail.com,brenosch@usc.edu

Permanent Link (DOI) https://doi.org/10.25549/usctheses-m2125

Unique identifier UC1272989

Identifier etd-Schmidt-2873 (filename),usctheses-m40 (legacy collection record id),usctheses-c127-224147 (legacy record id),usctheses-m2125 (legacy record id)

Legacy Identifier etd-Schmidt-2873.pdf

Dmrecord 224147

Document Type Dissertation

Rights Schmidt, Breno

Type texts

Source University of Southern California (contributing entity), University of Southern California Dissertations and Theses (collection)

Repository Name Libraries, University of Southern California

Repository Location Los Angeles, California

Repository Email cisadmin@lib.usc.edu