Mergers and the Market for Busy Directors:

An International Analysis

by

Stephen P. Ferris

Trulaske College of Business

University of Missouri

404F Cornell Hall

Columbia, MO 65211

Tel: (573) 882-9905

E-mail: Ferriss@missouri.edu

Narayanan Jayaraman

Scheller College of Business

Georgia Institute of Technology

800 West Peachtree Street NW

Atlanta, GA 30332

Tel: (404) 894-4389

E-mail: Narayanan.Jayaraman@scheller.gatech.edu

and

Min-Yu (Stella) Liao

Illinois State University

420 State Farm Hall of Business

Normal, IL 61790

Tel: (309) 438-8764

E-mail: mliao@ilstu.edu

12 May 2017

Mergers and the Market for Busy Directors:

An International Analysis

Abstract

Using 57,349 acquisitions from 69 countries, we examine the characteristics and performance of

M&A decisions made by busy boards. We find that busy boards tend to execute mergers in

emerging or foreign markets, favor private targets, finance with both cash and stock, pursue

diversifying mergers, avoid targets with multiple bidders, and long-term underperform relative to

non-busy acquirers. We also discover that the labor market penalizes directors who approve

subsequently bad acquisitions. The market, however, does not reward directors with new

appointments for approving good mergers. Our results are robust to alternative definitions of

directors’ busyness and model specifications.

Keywords: directors; busy boards; governance; mergers and acquisitions

JEL Code: G3; G34

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Mergers and the Market for Busy Directors:

An International Analysis

1. Introduction

The issue of multiple directorships on corporate boards has come under increasing

scrutiny from both academicians and practitioners (Schnake and Williams, 2008; Chasan, 2015),

There is conflicting evidence in the academic literature about the effect of multiple directorships

on firm value and performance. The arguments associated with the effect of these multiple

directorships separates into two distinct channels. The first is a reputation hypothesis and

contends that these individuals gain valuable experience from their multiple board appointments

as well as having the experiences and skills that make them desirable board members in the first

place ( Gilson, 1990; Kaplan and Reishus, 1990; Booth and Deli, 1996; Brickley, Linck and

Coles, 1999; Coles and Hoi, 2003; Harford, 2003; Masulis and Mobbs, 2011)

The competing set of arguments which we refer to as the busyness hypothesis is that

these individuals are over-committed in time and thus are unable to provide the careful

monitoring and diligence that their positions require (Ferris, Jaganathan and Pritchard, 2003).

The literature has not yet established whether the experience or busyness effect is dominant.

More recently, Field, Lowry and Mkrtchyan (2013) suggest that both effects might be present,

with the benefits of reputation accruing to young firms, while the costs due to director busyness

and over-commitment are suffered by large and established firms.

Nor has the literature been able to establish a linkage between firm value, the presence of

busy boards, and major corporate decisions such as mergers and acquisitions (M&A). A notable

exception is the study by Ahn, Jiraporn, and Kim (2010) who show that acquiring firms with

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busy boards experience more negative abnormal returns at the time of deal announcement. Their

analysis, however, is limited to an analysis of solely U.S. firms. In this study, we extend their

analysis by examining the board appointments of a large set of international firms.

The usefulness of such an extension is justified by the evidence presented in Ferris et al

(2017) that busy boards and directors are a global phenomenon. Although busy boards occur

internationally, their impact on corporate M&A decision-making might not be as consistent as

that observed in the U.S. For instance, national cultures help to determine what is allowable as a

punishment and what is desirable as an incentive (Chen, 1995; Williams and Zinkin, 2008).

Therefore, cultural factors are likely to shape perceptions regarding the desirabili ty of individuals

sitting on multiple board seats. Further, the laws and regulations governing business

combinations will vary considerably across countries, effecting the ability of boars to influence

M&A transactions. Finally, national differences in corporate equity ownership structures and

capital market depth will affect the extent to which M&A activity can occur in a country

(LaPorta et al., 1999). For these reasons, the literature requires that the board busyness and

merger activity be further examined using a sample of international firms.

We develop our study around four interrelated issues regarding the nature of international

boards, board busyness, and M&A decisions. First, we explore whether busy boards undertake

more M&A transactions than non-busy boards. Second, we analyze the characteristics and

quality of the M&A decisions made by busy boards. Third, we test whether the global labor

market for corporate directors reacts to the quality of the M&A decisions made by busy

directors. Finally, we compare the long-term operating performance of acquisition made by busy

and non-busy boards following a merger.

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We organize the remainder of our study as follows. Section 2 describes our data

collection and the construction process of our sample. We present our findings regarding the

relation between acquirer returns and busy directors in Section 3. Section 4 contains an analysis

of the labor market effects of M&A decisions on the employment of busy directors. In Section 5

we examine the long-term operating performance of acquirers with busy boards. Section 6

provides a brief summary of our findings and a discussion of their importance to the literature.

2. Sample and Data

2.1 Data Sources and Sample Construction

To begin construction of our sample, we use the Securities Data Corporation’s (SDC)

Mergers and Acquisitions Database to extract acquisitions announced between 1999 and 2012.

Following Ahn, Jiraporn, and Kim (2010), we only include deals that are completed within 1,000

days after the announcement. This results in 57,349 acquisitions from 75 countries. We then

match the SDC acquisition data with BoardEx. BoardEx provides information concerning

demographic, employment, and education data for corporate directors . We require that each firm

in the sample has at least three directors for each year reported in BoardEx. We use Compustat

Global to obtain stock return and other financial data. All financial variables are winsorized at

the 1% and 99% levels. These additional data requirements reduce our sample to 47,360 firm-

deal observations distributed among 69 countries. The sample firms are geographically located

as follows: 24,394 in North America, 166 from South America, 17,950 from Europe, 2732 from

Asia, 267 from Africa, and 1,851 from Oceania.

We undertake several assessments regarding our sample directors and boards. Consistent

with Field, Lowry, and Mkrtchyan (2013), we count directorships held in both public and private

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firms. Each director is classified as an independent director if he/she is a non-executive director.

Consistent with Fitch and Shivdasani (2006) and Field, Lowry, and Mkrtchyan (2013), we

consider independent directors busy if they sit on three or more boards. We then define a board

as busy if 50% or more of its independent directors are busy.

2.2 Sample Summary Statistics

We present a distribution across time and industry for our sample mergers in Table 1.

Panel A provides an annual distribution of the M&As in our sample. Merger activity ranges from

a low of 1,855 in 1999 to a peak of 5,031 in 2007. The years 2006 and 2007 also exhibit high

levels of activity. M&A transactions average 3,382 per year over our sample period.

We present an industry distribution of our sample in Panel B of Table 1. Industries are

classified as per the Fama and French 12 industry classifications. The largest number of M&As

occur in the technology, manufacturing, and health care sectors. The financial and utility

industries report the fewest number of M&A transactions, perhaps due to the extensive

government regulation of these industries (Hale and Hale, 1964; Cox and Portes,1998; Leggio

and Lien, 2000).

Table 2 presents a description of various board and financial/legal characteristics for the

firms in our sample. The first set of variables describes the nature of the board for our sample

firms. We find that the average director holds nearly 4 board seats, with independent directors

holding slightly fewer (i.e., 3.2). Most of the directors on the board are independent (80%), with

58% of these directors being classified as busy. Indeed, the average value of 71% for the busy

board indicator variable implies that a majority of our sample boards can be classified as busy.

The median board size is 11 with the bottom quartile being a board of 8 while the third quartile is

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16 board seats. The average age of our directors is 57 while the mean age for our sample firms is

about 17 years.

There are several important financial/legal characteristics of our sample worth noting.

First, we observe that our sample firms are large, with a median of nearly $ 1.4 billion in sales.

Our firms seem to have strong growth opportunities since their median market-to-book ratio is

1.54. Their use of leverage is modest, with a median debt to total assets ratio of only 0.21.

Nearly three-quarters of our sample firm are located in common law countries, with the

remainder distributed in civil law (24%) and former socialist (2%) countries.

In Table 3 we introduce our measurement of busy boards into the analysis of M&A

activity. We observe in Panel A that firms with busy boards are 2.4 times more likely to engage

in M&A transactions than those with non-busy boards. Specifically, we observe that busy boards

account for 33,410 of our sample 47,360 M&A observations. This represents 71% of our sample

compared to only 29% for activity by the non-busy boards.

Panel B presents an analysis of merger deal type made by busy boards. We observe that

few acquirers with busy boards (3.3%) pursue targets that are located in emerging markets. This

might reflect the difficulty of managers of firms located in less developed economies to attract

the interest of large multinationals. Busy boards appear to be less interested in cross-border

mergers (42.6%) compared to domestic acquisitions. Busy boards also have a slight tendency to

favor private targets (51.6%). Busy boards avoid cash only deals (2.0%) and stock only deals

(4.24%). Busy boards rarely pursue a target with multiple bidders (0.48%), and seem to favor a

vertical merger (54.9%). In summary, only a few busy acquirers are from emerging markets, and

firms with busy boards tend to avoid cross-border mergers, favor private targets, finance the

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acquisition with a mix of cash and stock, do not pursue targets with multiple bidders, and often

acquire targets that will not diversify their business.

3. Announcement Period Effects

In this section, we examine how board busyness influences the market’s reaction to a

M&A announcement. The market response should reflect the consensus view of investors

regarding the ability of the merger to create shareholder value. If busy boards are less able to

commit time and attention to an assessment of a target’s value, then that reduced oversight

should be reflected in a negative CAR. If, however, busy boards are better able to identify

valuable targets due to their greater experience and networks, then we should observe positive

CARs at the time of a merger announcement.1

3.1 CARs Across Varying Measures of Board Busyness

We begin our analysis with a comparative examination of the announcement period

CARs calculated for four different measures of board busyness. Board busyness is measured in

four ways: (1) the highest (busy) and lowest decile (not busy) for the total directorships per

director, (2) total directorships per independent director, (3) the percentage of busy independent

directors, and a (4) busy board binary indicator variable where a board is defined as busy if 50%

or more of its independent directors are busy.

We present CARs in Table 4 for various definitions of board busyness for the event

period day -1 to day 0. Longer periods extending to day-2 to day +2 are estimated and provide

equivalent results, but are not reported for brevity. Our first measure of board busyness is the

1 We compute a cumulative abnormal returns (CARs) based on standard market model event

study methodology. We use the MSCI index from Datastream as market index, and the market model parameters are estimated over day -210 to day -11.

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total number of directorships per director. We find that the CARs are significantly negative for

acquirers whose directors hold more directorships and are more negative than those for firms

whose directors are not busy. We obtain comparable results when we calculate board busyness

using the other three measures. We observe consistently more adverse market reaction to merger

announcements by acquirers with busy boards.

In aggregate, these results show that the market reacts more negatively to the

announcement of an acquisition by a firm whose board is busy. This result holds regardless of

how board busyness is measured. Our findings suggest that the market believes that the

acquirer’s busy board has either over-paid for the target or will be unable to provide the

oversight required to generate the anticipated synergies.

3.2 Multivariate Analysis of Board Busyness and Merger Announcements

To examine more comprehensively how the market reacts to merger activity by firms

whose boards are busy, we estimate a series of multivariate regressions in Tables 5 and 6. In

these regressions, we control for three sets of variables: (1) deal characteristics, (2) firm

characteristics and, (3) board characteristics.

Since major corporate decisions such as M&As must be approved by the board, we

control for two aspects of board structure and organization. More specifically, we include board

size and the percentage of independent directors as regressors. Yermack (1996) provides

empirical evidence for a strong inverse relation between firm value and board size. Weisbach

(1988) reports the effect of independent boards on CEO turnover while Brickley et al (1994)

shows that board independence influences the likelihood that a firm adopts a poison pill.

Further, we control for several deal characteristics. We differentiate between related and

diversifying acquisitions since unrelated targets are more challenging to integrate into an existing

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business and actually realize the projected synergies. We control for the presence of multiple

bidders with a binary indicator variable. Edmister and Walkling (1985) find evidence that

acquirers pay a higher bid premium when two or more bidders compete for the same target. We

include a binary indicator variable to identify whether the target firm is private or not. Fuller et.

al. (2002) provide evidence that acquirers experience significantly negative (positive) returns

when they acquire public (private) firms. We control for method of payment since the literature

establishes that bidders experience negative abnormal returns when they use equity to pay for an

acquisition (Amihud et. al., 1990). Finally, consistent with Moeller et al (2004) and Ahn et al

(2010), we control for relative deal size calculated as the target’s market value of equity relative

to the acquirer’s market value of equity.

Finally, we control for several firm characteristics. We control for firm size by using the

log of the firm’s total sales. Moeller et. al. (2004) report that the announcement CARs for

acquirers are two percent higher for smaller size acquirers. We use the firm’s market-to-book

ratio to proxy for its growth opportunities since the potential for future growth will help to

determine how aggressively it pursues a target. We also include controls for firm leverage

which is one measure of firm risk (Hamada, 1972), its age which is related to size and growth

(Evans, 1987), and the legal regime in which it is incorporated (La Porta et al, 2002). In all

regressions, we include industry and year fixed effects to capture systematic shocks to the

merger decision.

We present our initial multivariate analysis of the acquirer’s announcement period CARs

in Table 5. As we do with our univariate examination in Table 4, we use four different measures

of board busyness. In model (1) we use total directorships per director as our measure for board

busyness. We observe that its coefficient is significantly negative, confirming our earlier finding

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of an inverse relation between board busyness and the market’s reaction. The signs for the

control variables are generally as hypothesized. The coefficients for a competed merger (i.e.,

multiple bidders), cash deal, relative deal size, firm size and firms age are consistently significant

and offer explanatory power for the market’s reaction beyond board busyness.

The estimated coefficients for the other three measures of board busyness are also

significantly negative. This confirms the initial results presented in Table 4 which compare the

market’s response to merger announcements made by firms with busy and non-busy boards. The

coefficients in Table 5 are also economically significant. Consider model (2) which uses the total

number of directorships per independent director as its measure of board busyness. The

coefficient is -0.039. This suggests that an additional directorship held by an independent

director decreases the average two-day (i.e., day-1 to day 0) CAR by 0.039%. This represents

40% (-0.039/-0.097) of average acquirer’s announcement period return.2

We conclude from Table 5 that mergers pursued by firms with busy directors are

associated with a significant reduction in shareholder wealth at the time of the announcement.

This result is consistent with the busyness hypothesis described by Ferris et al (2003). It also

implies that the adverse effect of board busyness is not merely a U.S. phenomenon, but exists

internationally.

3.3. A Tipping Point for Board Busyness

These findings suggest that the effect of directors’ busyness on acquirer returns is

negative across the entire range of busyness. Yet the evidence on board size (Yermack, 1996;

Coles et al., 2008) and the conflicting advising and monitoring advantages associated with busy

directors (Field et al. 2013) implies that board busyness might be not uniformly adverse to

2 In un-tabulated results, we estimate the day-1 to day 0 CAR for acquirers to be -0.097 while that for the day -1 to day +1 window to be -0.128.

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shareholder wealth. That is, might there exist an optimal amount of board busyness that balances

the competing demands for monitoring (i.e., less busyness) and advising (i.e., more busyness)? Is

there a point at which the advantages of experience and reputation associated with multiple-

boarded directors tips to the disadvantages of over-commitment and disinterest? To test for this

possibility, we conduct additional tests by estimating a set of piece-wise regressions using the

model developed in Table 5. We present our results in Table 6.

In Panel A of Table 6 we test for a non-linear effect of board busyness by using the

median value to create two segments of our busyness measure. In model (1), we use total

directorships per independent director as our measure for board busyness. Following Ahn,

Jiraporn and Kim (2010), the Busyness < median variable equals the average number of

directorships for the firm if the average directorship of a firm is below the median of the firm’s

country-year group, and zero otherwise. The Busyness > median variable equals the firm’s

average number of directorships if the average directorship of a firm is above the median of the

firm’s country-industry-year group, and zero otherwise. Decomposing the busyness measure into

two sub-measures based on median values allow us to determine whether the high or low levels

of busyness most influences the market’s reaction to a merger announcement.

Our results show that the effect of outside directorships on acquirer returns is

significantly negative only at higher levels of busyness. At below median levels of busyness the

effect is statistically insignificant. We find comparable results when we use the percentage of

busy independent directors in model (2) to capture board busyness. These results provide further

confirmation of the busyness hypothesis for corporate boards. That is, investors perceive board

busyness as inconsistent with the pursuit of mergers that increase firm value. But these findings

also show that it is not simply busyness that the market finds objectionable. Rather, it is extreme

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busyness which we measure relative to the median that the market dislikes. This result seems to

suggest a tipping point in the number of board appointments that a director holds. That is, the

advantages associated with the networking and advising skills gained from multiple board seats

become eclipsed at some level by the disadvantages due to insufficient time for the directors to

provide adequate monitoring and oversight.

We extend this inquiry regarding the non-linear effect of busyness by now creating three

segments for our busyness variables. In Panel B we use the median and third-quartile value to

create our variables. We can view these three ranges as low, moderate, and high levels of

busyness. This further analysis allows us to gain an even better understanding of what level of

board busyness adversely effects share price. In model (1) we observe that the coefficients are

statistically insignificant for a low level of busyness (below the median) as well as for moderate

busyness (i.e., between the median and third quartile). But for high levels of busyness, the

coefficient is significantly negative. We obtain similar results in model (2) where the percentage

of busy independent directors is our measure of board busyness.

We conclude from these results that it is not busyness per se that the market dislikes, but

rather high levels of busyness. There seems to be a level of busyness where the advantages tip

over and become negative. That is, the reputation, experience and networking advantages that

accompany multi-boarded directors become negative at high levels of external involvement with

other boards as directors have insufficient time to monitor or advise.

4. Merger Quality, Reputation, and the Labor Market for Busy Directors

Fama (1980) and Fama and Jensen (1983) contend that there exists a labor market for

outside directors that functions on the basis of reputation, a position verified by numerous

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empirical studies.3 Since mergers have such an important effect on the profitability and future

growth of a firm, the quality of these M&A decisions should be an important determinant of any

director’s reputation. If reputation is a factor in the hiring and retention of corporate directors,

then the quality of their merger decisions should help explain the number of directorships they

actually hold. In this context, we interpret merger quality as the ability of the merger to generate

value for the shareholders of the acquiring firm.

4.1 Gaining Board Seats

We begin our analysis of the effect of merger quality on subsequent directorship

employment by estimating the likelihood of obtaining additional directorships. We present our

logit regression results in Table 7. The dependent variable Addition assumes a value of 1 if a

director gains an additional directorship. In model 1, the dependent variable is a binary indicator

variable that equals one if a director gains an additional directorship during the first year

following a merger. In model 2, the dependent variable is a binary indicator that equals one if a

director gains an additional directorship during the first two years following a merger. In model

3, the dependent variable is a binary indicator that equals one if a director gains an additional

directorship during the first three years following a merger. In these regressions, we control for

three sets of variables: (1) deal characteristics, (2) firm characteristics, and (3) board

characteristics.

The use of the announcement period CARs to capture merger quality is established in the

corporate finance literature. Lehn and Zhao (2006) argue that the announcement period return is

3 Brickley, Linck, Coles (1999) confirm that CEOs who perform well in the year before retirement receive more

directorships following their retirements. Ferris, Jagannathan, and Pritchard (2003) demonstrate that firm performance positively affects the number of appointments held by a director. Ashraf, Chakrabarti, Fu, and Jayaraman (2010) find that a good merger has a positive effect on nonexecutive directors’ reputations and increases

chances of acquiring new board positions afterward. Alternatively, CEOs of firms who cut dividends (Kaplan and Reishus, 1999), directors who resign following a bankruptcy filing (Gilson, 1990) and directors of firms that restate earnings (Srinivasan, 2005) are likely to receive relatively few directorships.

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an unbiased estimate of whether the merger serves the interest of the acquirer’s shareholders. Liu

and McConnell (2013) interpret the stock price reaction at the time of a merger announcement to

be a measure of the value creation potential of the acquisition attempt for the bidder.

The three models estimated in Table 7 shows that that likelihood of gaining additional

directorships does not depend on the CARs at the time of the announcement. That is, the quality

of the merger deal does not influence the ability of directors to gain more board seats. These

results hold for each of the three years following the merger and offers consistent evidence that

the labor market does not reward director approval of good mergers with more board

appointments.

One might argue that the announcement period CARs are a noisy proxy for the quality of

a M&A decision. Therefore, we redefine a good merger with reference to the top decile of

CARs. Only mergers which generate CARs occurring in the top decile of our sample are

classified as good mergers. We then test to see if mergers generating these top-decile CARs are

associated with additional board appointments for the approving directors.

We present our findings in Table 8. Again, we fail to observe any relation between the

announcement period CARs and additional board appointments. Even when the director has

approved what investors perceive as a good merger, the labor market continues to ignore it when

deciding whom to reward with additional board seats. These findings show that regardless of

how positive the market reacts to a merger, merger quality does not meaningfully affect the

ability of approving directors to gain additional board appointments.

In models (2), (4) and (6), we include Total Directorship as an additional control. This

variable tests whether the labor market perceives a director serving on multiple boards as

knowledgeable and skillful or as over-committed and distracted. We obtain significantly negative

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coefficients for this variable in two of the three models. This suggests that holding multiple

directorships reduces the likelihood of obtaining new board seats even when the mergers are

favorably viewed by the capital market.

The results presented in Table 8 offer two important insights. First, the labor market for

directors does not reward directors for their merger successes. Even those mergers most

favorably viewed by investors appear to be ignored in the processes employed by the labor

market to award new directorships. Further, we find that the number of directorships an

individual holds is inversely related to the likelihood of gaining an additional board seat. This

result holds even after controlling for the perceived quality of the merger.

4.2 Losing Board Seats

Although merger quality does not appear to influence whether approving directors gain

new board seats, it might affect the extent to which they lose a board seat. Loss aversion theory

(Tversky and Kahneman, 1991; Thaler et al; 1997) contends that individuals are more motivated

by the threat of a loss than the possibility of a gain. Hence, the labor market might view the loss

of a board seat as a more effective mechanism to incent directors than the award of a new one.

Further, research in psychology (Taylor, 1991) explains how negative events generate stronger

emotive and social responses than positive events. Thus, a bad merger decision might be more

adverse to a director’s ability to gain new board seats than a good merger is beneficial.

In Table 9 we present our logit analysis of the likelihood of a director losing a board seat

following a merger. In model 1, the dependent variable is a binary indicator variable that equals

one if a director loses a directorship during the first year following a merger. In model 2, the

dependent variable is a binary indicator that equals one if a director loses a directorship during

the first two years following a merger. In model 3, the dependent variable is a binary indicator

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that equals one if a director loses a directorship during the first three years following a merger.

Overall, we observe that merger quality does not appear to affect the likelihood that a director

loses a board seat in the years following a merger.

But similar to our analysis in Table 8, we re-focus our analysis on a subset of extremely

poor mergers. Our findings are presented in Table 10. Specifically, we examine those mergers

whose announcement period CARs are in the bottom decile of our sample. We now define these

as bad mergers. We observe that the coefficient for the CAR variable is positive across all three

sample periods, and statistically significant for two of them. This result is consistent with the

labor market penalizing directors for their approval of bad mergers. We note however, that the

coefficients become significant only in years 2 and 3 post merger, suggesting that it takes about a

year for the market to begin assessing penalties against these directors.

We further find that holding multiple directorships increases the likelihood of losing

board seats. The coefficients of Total Directorship are positive and statistically significant across

all three of our sub-periods. This result is consistent with the negative effect of multiple

directorships on the likelihood of gaining new board seats reported in Table 8.

4.3 The Asymmetric Effect of Bad Mergers

Our findings that directors associated with good mergers go unrewarded while those

approving bad mergers are punished can be understood in the context of several arguments

developed in the behavioral economics, marketing, and psychology literatures. The theory of loss

aversion (Tversky and Kahneman, 1991; Thaler et al., 1997) argues that individuals are more

motivated by the threat of a loss than the possibility of a gain. That is, the threat of a dollar loss

provides more disutility than the corresponding utility of a dollar gain. Thus, loss aversion

implies that an individual’s loss of a board seat is likely to be a more effective motivator than the

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possibility of a future additional appointment, Hence, it is not surprising that the labor market

reacts accordingly to the performance of mergers approved by a director.

Marketing and communication researchers such as Mizerski (1982). Ahluwalia et al

(2000), Dawar and Pillutla (2000), and Henard (2002) describe how a single negative experience

can overwhelm and dominate a set of previous positive outcomes enjoyed by the consumer.

Silver and Wortman (1980), Wortman and Silver (1987), and Tait and Silver (1989) report how

negative life events can persist for years and continue to exert a corrosive influence on

psychological health. Garcia et al (1974) show that it only takes a single trial or experience for

learning to occur with the bad generally dominating the good. Taylor (1991) describes how

negative events evoke stronger cognitive, emotional., and social responses than corresponding

positive ones. Thus, it is very likely that a bad merger decision can be more negative to a

director’s career than a good merger is beneficial.

These results are also consistent with the observation that a bad merger can be more

critical to the viability of the firm than a good merger. A bad merger can result in strategic

misalignment, financial losses, negative cash flow, and prolonged reduced profitability (Duchin

and Schmidt, 2013). A bad merger can bankrupt a firm (Shrieves and Stevens, 1979; Bergstrom

et al., 2005). A good merger increases earnings and market share, but this upside is rarely as

dramatic as the downside of a bad merger. This potential asymmetry in the effects of bad and

good mergers on corporate financial health might also explain the labor market’s differential

response.

Finally, these results are broadly consistent with the literature on the psychology of crime

and punishment. Sigmund et al (2001) examine the role of reputation in fostering cooperative

behavior among selfish agents and conclude it is more effective with punishment than with

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reward. Arvey and Ivancevich (1980) determine that punishment is most effective when the

aversive is at a high level (e.g., the loss of a board seat and its perquisites), is timely (e.g., in the

years immediately following the bad merger) and a rationale is provided (e.g., the subsequently

poor accounting performance).

5. Busy Boards, Acquisitions, and Long-Run Accounting performance

We establish in the preceding analysis that bad mergers as measured by their

announcement period returns affects the likelihood that a director will lose a board seat. In this

section, we examine whether the firm’s subsequent operating performance justifies that initial

market reaction. We then proceed to test whether busy boards tend to make good or bad merger

decisions by examining the long-term operating performance of their post-merger firms.

In Panel A of Table 11 we observe that the correlation between the announcement period

CAR and post-merger ROA is generally positive and statistically significant. The results are even

stronger for those acquirers with busy boards. These results help to justify our use of the

announcement period CARs as a proxy for the quality and subsequent performance of the

merger.

In Panel B, we present our analysis using the un-adjusted or raw ROA. It clearly shows

that acquirers with busy boards significantly underperform relative to acquirers whose boards are

not busy. This suggests that busy boards are less able to acquire targets that are long-term value

increasing for their shareholders.

In Panel C we more rigorously examine this relationship by adjusting for the performance

of industry peers. We present the results using a median industry-adjusted ROA. The results

indicate that the ROA for both sets of mergers is below the industry average. But the

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performance is worse for those undertaken by busy boards. This difference in performance for

mergers between acquirers with busy and non-busy boards is especially pronounced and

statistically significant in years 1 and 2 relative to the merger.

We conclude from this analysis that the market response at the time of merger

announcement is correlated with the acquirer’s subsequent operating performance. This justifies

our use of announcement period CARs as a proxy for the quality of the merger. We further

determine that mergers approved by busy boards underperform relative to those approved by

non-busy boards. This is consistent with arguments that busy boards are too busy to mind their

business (Ferris et al., 2003). This result holds even when we control for peer performance by

estimating industry-adjusted measures of ROA.

6. Summary and Discussion

In spite of increasing scrutiny and restrictions on the practice of multiple boarding by

individual directors and conflicting evidence on its effect on firm value, most of the existing

research on busy boards has been limited to U.S. firms. In this study, however, we explore the

issue of board busyness and its effect on M&A activity with an international sample.

Specifically, we examine over 47,000 acquisitions spanning 69 countries that occur from 1999

through 2012. This analysis allows for us to address the effects of differences in national culture,

regulatory oversight, and equity ownership structures on the ability of busy boards to influence

corporate strategic decision-making.

We initially determine that there are important differences between the M&A activity of

firms with and without busy boards. We discover that busy boards are more frequent purchasers

in the market for corporate control than their non-busy peers. Indeed, firms with busy boards are

19

2.4 times more likely to engage in M&A transactions that those with non-busy boards. Further,

we determine that only a few busy acquirers are from emerging markets and that they tend to

avoid cross-border mergers, favor private targets, finance the acquisition with a mix of cash and

stock, do not pursue targets with multiple bidders and often acquire targets that will not diversify

their business.

We then investigate the characteristics and quality of the M&A decisions made by busy

boards. We first observe that the market reacts negatively to the announcement of an acquisition

by a firm whose board is busy. Our multivariate analysis which controls for various board, deal,

and firm characteristics further confirms that mergers pursued by firms with busy directors are

shareholder-wealth reducing in nature. These results are consistent with the busyness hypothesis

of multiple directorships described by Ferris et al (2003). We further observe that it is not

busyness per se that the market discounts, but rather high levels of busyness. There seems to be a

level of busyness where the advantages due to reputation, experience, and networking tip over

and become negative due to over-commitment.

Our analysis also uncovers important patterns in the labor market for busy directors. We

find that it does not reward directors for merger success with additional board seats. The labor

market, however, does penalize them with seat loss for approving bad mergers. Thus, a bad

merger is more adverse to a director’s ability to gain new board seats than a good merger is

beneficial. These results are consistent with loss aversion and other arguments developed in the

psychology and marketing literatures regarding the asymmetric effects of negative events.

Finally, we also explore the post-merger accounting performance of the acquirers. We

find that the correlation coefficients between an acquirer’s ROA in the three years following the

merger and the announcement period CAR are generally positive and statistically significant.

20

These results help to justify our use of announcement period CARs as a proxy for merger

quality. We also examine raw and industry adjusted ROAs for three years post merger and find

that acquirers with busy boards consistently underperform relative to acquirers whose boards are

not busy.

We conclude from this study that board busyness exerts its own effect on merger activity.

Busy boards are more likely to approve poorly performing mergers. This results holds across

national cultures, legal regimes, and regulatory structures. We find that markets react more

negatively to the announcements of mergers made by busy boards, especially at high levels of

busyness. Further, these same markets punish directors with loss of board seats when the merger

does poorly. Consistent with loss aversion, the market appears to punish directors for bad merger

choices, but does not reward them for good ones.

We believe that the research presented in this study can be meaningfully extended and

generate further insights into the value effects of director busyness. For instance, one could study

the value implications of busy boards as a firm moves through its life cycle or as its equity

ownership changes. Another direction of research can focus on the market, compensation and

demographics of these networked directors who sit on multiple boards of firms from around the

world. Finally, one could examine more closely the cultural interplay between busy directors and

the CEOs as the board evaluates merger targets or other strategic decisions.

21

Appendix: List of Variables and Their Definitions

Variable Definition

Total directorships per

director

The number of total directorships held by each director.

Total directorships per

independent director

The number of total directorships held by each independent director.

Busy director A director who sits on the boards of three or more firms.

Percent of independent

director

The number of independent directors divided by the number of total

directors in each firm

Percent of busy

independent director

The number of busy independent directors divided by the number of total

independent directors.

Busy board An indicator variable that equals one if 50% or more of a firm’s

independent directors are busy.

Log of board size Log of total number of directors in each firm.

Competed An indicator variable that equals one if a merger has multiple bidders.

Diversifying M&A An indicator variable that equals one if an acquirer’s industry classification

is different from that of its target. Industry is defined using Fama and

French 49 industry clarification

Private target An indicator variable that equals one if the target of a merger is a private

firm.

Cash deal An indicator variable that equals one if an acquirer pays 100% in cash.

Crosser-border M&A An indicator variable that equals one if an acquirer’s nation is different

from that of its target.

Relative deal size Target market value of equity relative to acquirer’s market value of equity.

Firm size Log of total sales in U.S. dollars of a firm.

Market-to-book ratio The market value of a firm’s equity plus the difference between the book

value of its assets and the book value of its equity at the end of the year,

divided by the book value of the firm’s assets at the end of the year.

Leverage A firm’s total debt divided by its total assets.

Firm age A firm’s age in years since its listing on a public exchange.

Log of CEO directorship Log of the number of directorship held by the CEO of a firm.

CEO tenure CEO’s tenure in years as CEO.

Mean director age The average age of a firm’s directors.

Director age > 61 dummy An indicator variable that equals one if a director is over 61 years old.

22

LAW/MBA/PHD

Indicator variables that equal one if a director holds a LAW/MBA/PHD

degree.

Female An indicator variable that equals one if a director is female.

Emerging

An indicator variable that equals one if an acquirer is from an emerging

market.

Common/Civil/Former

Socialist

Indicator variables that equal one if a firm’s legal origin is based on

English common law, the Napoleonic Code, or is a former socialist,

country, respectively.

ROA A firm’s EBIT divided by its total assets.

Addition (0, 1)/ Addition

(0, 2)/ Addition (0, 3)

Indicator variables that equal one if a director gains additional directorship

during the first year/ first two years/ first three years following a merger.

Reduction (0, 1)/

Reduction (0, 2)/

Reduction (0, 3)

Indicator variables that equal one if a director loses directorship during the

first year/ first two years/ first three years following a merger.

23

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30

Table 1: Sample Distribution of Mergers

This table presents the distribution of the sample mergers. Panel A shows the distribution over the sample

period1999-2012. Panel B presents an industry distribution using the 12 Fama and French industry

clarifications.

Panel A: Sample period distribution

Year Frequency

1999 1,855

2000 3,355

2001 2,609

2002 2,235

2003 2,637

2004 3,224

2005 3,861

2006 4,361

2007 5,031

2008 4,223

2009 2,991

2010 3,757

2011 3,739

2012 3,482

Total 47,360

Panel B: Industry distribution

Industry Classification Frequency

Consumer Non-Durables 3,140

Consumer Durables 1,223

Manufacturing 5,886

Energy 2,609

Chemicals 1,552

Technology 10,592

Communication services 2,095

Utilities 1,601

Basic materials (wholesale and

retail) 3,670

Health care 3,855

Financials 1,197

Other 9,940

Total 47,360

31

Table 2: Board and Financial Characteristics for Acquirers

This table presents summary board and financial statistics for acquirers and their boards. Variable

definitions are contained in the Appendix.

Variable Mean 1st

Quartile

Median 3rd

Quartile

Std. Dev

Total directorships held by each

director

3.83 1.00 3.00 5.00 4.10

Total directorships held by each

independent director

3.20 1.00 2.00 4.00 3.82

Percent of independent directors 0.80 0.70 0.82 0.90 0.18

Percentage of busy independent

directors

0.58 0.43 0.60 0.75 0.24

Busy board dummy 0.71 0.00 1.00 1.00 0.46

Board size 12.39 8.00 11.00 16.00 6.28

Average director age 57.40 54.25 57.75 60.89 5.08

Firm age 17.21 8.00 13.00 20.00 14.13

Sales (in millions) 8737.20 318.30 1378.25 6307.00 22715.5

Market-to-book ratio 2.03 1.20 1.54 2.16 2.53

Leverage 0.23 0.08 0.21 0.34 0.19

Common law 0.74 0.00 1.00 1.00 0.44

Civil law 0.24 0.00 0.00 0.00 0.43

Other (former socialist) 0.02 0.00 0.00 0.00 0.14

32

Table 3: Board Busyness and M&A Activity

Panel A presents the distribution by our sample firms by busyness status. Panel B shows the distribution

of deal type made by firms with busy boards.

Panel A: Merger Activity by Board Busyness

Busy Board Number

Not Busy 13,950

Busy 33,410

Panel B: Merger Deal Type Made by Busy Boards

Acquisition Characteristics Number

Non- Emerging Markets 32,329

Emerging Markets 1,081

Domestic Merger 19,188

International Merger 14,222

Public Target 16,154

Private Target 17,256

Non- Cash Only 26,619

Cash Only 6,791

Non- Stock Only 31,993

Stock Only 1,417

Non-Competed Offer 33,249

Competed Offer 161

Non- Diversified Merger 18,336

Diversified Merger 15,074

33

Table 4: Comparative CARs for Varying Definitions of Board Busyness

This table presents the CARs (-1, 0) of acquirers across busyness status. Board busyness is measured using four different measures: (1) the highest

and lowest deciles of total directorships per director, (2) the highest and lowest deciles of total directorships per independent director, (3) the

highest and lowest deciles of percentage of busy independent directors, and (4) a busy board dummy variable. *, **, *** indicate statistical

significance at the 10%, 5%, and 1% levels, respectively.

Total directorships per director Total directorships per

independent director

Percentage of busy

independent directors Busy Board dummy

Mean Median Mean Median Mean Median Mean Median

Not Busy 0.0597 0.0941 0.0378 0.0055 0.1928 0.0251 -0.0357 -0.0288

Busy -0.3551** -0.0781 -0.3685** -0.1782 -0.3295** -0.1491 -0.1662*** -0.0947

Diff.

(Busy –

Non-busy)

-0.4148* -0.1722** -0.4063* -0.1837** -0.5223** -0.1742*** -0.1304 -0.0659**

p-value 0.0557 0.0223 0.0696 0.0219 0.0276 0.0068 0.1802 0.0246

34

Table 5: The Effect of Board Busyness on Merger Announcement CARs

This table presents the effect of board busyness on acquirers’ cumulative abnormal returns. The variable

definitions are provided in the Appendix. The dependent variable is the CAR for acquirers estimated over

days (-1, 0) relative to the announcement. Board busyness is measured using (1) total directorships per

director, (2) total directorships per independent director, (3) percentage of busy independent directors, and

(4) a busy board binary indicator variable. P-values are provided in parentheses. *, **, *** indicate

statistical significance at the 10%, 5%, and 1% levels, respectively. (1) (2) (3) (4)

Intercept 2.672

2.690 2.641 2.683

Total directorships per director -0.066***

(0.001)

Total directorships per

independent director

-0.039**

(0.033)

Percentage of busy

independent directors

-0.380**

(0.044)

Busy board dummy -0.234**

(0.016)

Percent of independent

directors

0.294

(0.195)

0.177

(0.434)

0.198

(0.385)

0.170

(0.445)

log (Board size) -0.212**

(0.028)

-0.224**

(0.020)

-0.242**

(0.012)

-0.220**

(0.022)

Competed -1.514***

(0.006)

-1.518***

(0.006)

-1.511***

(0.007)

-1.506***

(0.007)

Diversifying M&A -0.065

(0.443)

-0.068

(0.423)

-0.064

(0.453)

-0.065

(0.442)

Private target -0.151*

(0.088)

-0.146*

(0.099)

-0.146

(0.101)

-0.144

(0.104)

Cash deal 0.327***

(0.002)

0.343***

(0.001)

0.341***

(0.001)

0.339***

(0.001)

Relative deal size 0.022***

(<.0001)

0.022***

(<.0001)

0.022***

(<.0001)

0.022***

(<.0001)

Firm size (log of sales) -0.116***

(<.0001)

-0.117***

(<.0001)

-0.111***

(<.0001)

-0.114***

(<.0001)

Market-to-book 0.008

(0.728)

0.007

(0.747)

0.007

(0.766)

0.007

(0.769)

Leverage (Debt/Asset) 0.195

(0.442)

0.175

(0.491)

0.178

(0.485)

0.171

(0.500)

Firm age 0.015**

(0.012)

0.015**

(0.014)

0.015**

(0.012)

0.016***

(0.007)

Log (CEO directorship) -0.019

(0.576)

-0.027

(0.444)

-0.028

(0.428)

-0.029

(0.386)

CEO tenure 0.023

(0.349)

0.028

(0.256)

0.030

(0.233)

0.027

(0.273)

Average director age -0.008

(0.469)

-0.007

(0.546)

-0.007

(0.559)

-0.008

(0.461)

Common 0.250

(0.473)

0.202

(0.561)

0.227

(0.515)

0.234

(0.502)

Civil 0.361

(0.294)

0.291

(0.397)

0.296

(0.390)

0.302

(0.378)

35

Table 6: Nonlinear (Piecewise) Regression Analysis of Acquirer Returns and Board Busyness

This table presents the nonlinear effect of board busyness on acquirers’ CARs. The variable definitions

are provided in Appendix 1. The dependent variable is the CAR (-1, 0) of acquirers. Board busyness is

measured using: (1) total directorships per independent director, and (2) percentage of busy independent

directors. In panel A, we create two segments using the median value of busyness. In panel B we create

three segments using the median and third-quartile values. The P-values are provided in parentheses. *,

**, *** indicate statistical significance at the 10%, 5%, and 1% levels, respectively.

Panel A: Two Segment Analysis

Board Busyness Measure

(1) (2) Total directorships per

independent director

Percentage of busy independent

directors

Intercept 2.044

1.934

Busyness < median -0.070

(0.133)

-0.376

(0.236)

Busyness > median -0.043**

(0.031)

-0.314*

(0.086)

Percentage of independent

directors

0.070

(0.747)

0.093

(0.669) log (Board size) -0.238**

(0.010)

-0.250***

(0.007) Competed -1.467***

(0.006)

-1.459***

(0.006) Diversifying M&A -0.074

(0.358)

-0.071

(0.378) Private target -0.124

(0.144)

-0.122

(0.151) Cash deal 0.317***

(0.001)

0.316***

(0.002) Relative deal size 0.019***

(0.001)

0.019***

(0.001) Firm size (log of sales) -0.080***

(0.005)

-0.075***

(0.009) Market-to-book 0.005

(0.824)

0.005

(0.833) Leverage (Debt/Asset) 0.109

(0.652)

0.109

(0.654) Firm age 0.013**

(0.022)

0.013**

(0.020) Log (CEO directorship) -0.026

(0.439)

-0.028

(0.411) CEO tenure 0.028

(0.233)

0.030

(0.207) Average director age -0.003

(0.813)

-0.003

(0.808) Common 0.233

(0.482)

0.249

(0.453) Civil 0.253

(0.441)

0.260

(0.428)

36

Panel B: Three Segment Analysis

Board Busyness Measure

(1) (2) Total directorships per

independent director

Percentage of busy independent

directors

Intercept 2.057

1.967

Busyness < median -0.075

(0.189)

-0.499*

(0.064)

Median < busyness < Q3 -0.048

(0.232)

-0.392*

(0.057)

Busyness > Q3 -0.044**

(0.041)

-0.291*

(0.053)

Percent of independent

directors

0.074

(0.735)

0.097

(0.654) log (Board size) -0.238**

(0.010)

-0.226**

(0.016)

Competed -1.467***

(0.006)

-1.452***

(0.006) Diversifying M&A -0.074

(0.359)

-0.076

(0.345) Private target -0.124

(0.145)

-0.123

(0.148) Cash deal 0.317***

(0.001)

0.314***

(0.002) Relative deal size 0.019***

(0.001)

0.019***

(0.001) Firm size (log of sales) -0.080***

(0.005)

-0.075***

(0.009)

Market-to-book 0.005

(0.826)

0.004

(0.845) Leverage (Debt/Asset) 0.110

(0.650)

0.103

(0.669) Firm age 0.013**

(0.022)

0.014**

(0.015) Log (CEO directorship) -0.026

(0.439)

-0.027

(0.415) CEO tenure 0.028

(0.232)

0.029

(0.219) Average director age -0.003

(0.808)

-0.004

(0.732)

Common 0.233

(0.482)

0.235

(0.479) Civil 0.252

(0.443)

0.251

(0.443)

37

Table 7: Merger Success and the Likelihood of Gaining Additional Directorships

This table tests whether merger success helps directors gain new directorships. The variable definitions

are provided in the Appendix. The dependent variable Addition assumes a value of one if a director gains

an additional directorship on annual basis. In model (1), the dependent variable is a binary indicator that

equals one if a director gains an additional directorship during the first year following a merger. In model

(2), the dependent variable is a binary indicator that equals one if a director gains additional directorship

during the first two years following a merger. In model (3), the dependent variable is a binary indicator

that equals one if a director gains an additional directorship during the first three years following a

merger. P-values are provided in parentheses. *, **, *** indicate statistical significance at the 10%, 5%,

and 1% levels, respectively.

Dependent Variable Addition in (0,1) Addition in (0, 2) Addition in (0, 3)

(1) (2) (3)

Intercept -1.083

0.420

-0.069

CAR (-1, 0) -0.002

(0.476)

-0.003

(0.281)

-0.004

(0.205)

Competed 0.202

(0.179)

-0.102

(0.501)

0.049

(0.736)

Diversifying M&A -0.025

(0.363)

-0.012

(0.632)

-0.041*

(0.090)

Private target 0.002

(0.949)

-0.008

(0.751)

-0.025

(0.336)

Crosser-border M&A 0.066**

(0.023)

0.065**

(0.014)

0.023

(0.374)

Cash deal 0.015

(0.656)

0.011

(0.721)

0.023

(0.441)

Friendly -0.046

(0.238)

-0.075**

(0.036)

-0.027

(0.439)

Relative deal size 0.000

(0.864)

-0.003

(0.329)

-0.003

(0.282)

CEO duality -0.066

(0.618)

-0.059

(0.618)

-0.059

(0.599)

Log (CEO directorship) -0.015

(0.417)

-0.021

(0.230)

0.001

(0.965)

CEO tenure -0.015***

(0.001)

-0.033***

(<.0001)

-0.032***

(<.0001)

% of busy independent

directors

0.630***

(<.0001)

0.414***

(<.0001)

0.221***

(0.003)

Firm size (log of sales) 0.027***

(0.003)

0.028***

(0.001)

0.032***

(<.0001)

Past year stock performance 0.005

(0.217)

0.004

(0.364)

0.009**

(0.017)

Firm age -0.008***

(<.0001)

-0.004**

(0.014)

-0.004**

(0.017)

Leverage (Debt/Asset) 0.203**

(0.023)

0.115

(0.161)

0.200**

(0.011)

Director age > 61 dummy -0.035 0.013 -0.036

38

(0.228) (0.612) (0.167)

LAW -0.121

(0.279)

-0.151

(0.142)

0.000

(0.100)

MBA -0.034

(0.319)

-0.082***

(0.008)

-0.119***

(<.0001)

PHD -0.051

(0.188)

-0.040

(0.263)

-0.082**

(0.018)

Female 0.113**

(0.017)

0.072

(0.102)

0.032

(0.459)

Emerging -0.192**

(0.024)

-0.037

(0.623)

-0.055

(0.461)

Common -0.391

(0.106)

-0.371

(0.100)

0.299

(0.227)

Civil -0.668***

(0.006)

-0.652***

(0.004)

-0.005

(0.985)

39

Table 8: Good Mergers, Board Busyness and the Likelihood of Gaining Additional Directorships

This table tests whether merger success and the number of board seats held by a director help directors

gain new directorship. The variable definitions are provided in the Appendix. The dependent variable

Addition takes the value of one if a director gains an additional directorship on annual basis. The success

of a merger is measured by a binary indicator that equals 1 if an acquirer’s CAR (-1, 0) is among the top

10% of CARs for firms within its country and industry during the year. In models (1) and (2), the

dependent variable is a binary indicator that equals one if a director gains an additional directorship

during the first year following a merger. In model (3) and (4), the dependent variable is a binary indicator

that equals one if a director gains an additional directorship during the first two years following a merger.

In model (5) and (6), the dependent variable is a binary indicator that equals one if a director gains an

additional directorship during the first three years following a merger. P-values are provided in

parentheses. *, **, *** indicate statistical significance at the 10%, 5%, and 1% levels, respectively.

Dependent variable Addition in (0,1) Addition in (0,2) Addition in (0,3)

(1) (2) (3) (4) (5) (6)

Intercept -1.154 -1.120

0.137

0.148

-0.156 -0.122

Top 10% CARs

dummy

0.075

(0.388)

0.070

(0.423)

0.071

(0.374)

0.069

(0.385)

0.157

(0.040)

0.151

(0.047)

Total directorship

-0.006***

(0.006)

-0.002

(0.342)

-0.006***

(0.002)

Competed 0.112

(0.338)

0.107

(0.360)

-0.038

(0.734)

-0.040

(0.723)

0.042

(0.696)

0.037

(0.729)

Diversifying M&A -0.023

(0.309)

-0.023

(0.315)

-0.018

(0.399)

-0.018

(0.401)

-0.036

(0.076)

-0.036

(0.078)

Private target -0.013

(0.597)

-0.012

(0.613)

-0.024

(0.288)

-0.024

(0.292)

-0.023

(0.286)

-0.023

(0.297)

Crosser-border M&A 0.018

(0.465)

0.018

(0.472)

0.044**

(0.046)

0.044**

(0.046)

0.011

(0.601)

0.011

(0.609)

Cash deal 0.004

(0.900)

0.003

(0.909)

0.005

(0.843)

0.005

(0.846)

0.000

(0.991)

0.000

(0.996)

Friendly -0.030

(0.381)

-0.034

(0.322)

-0.053*

(0.089)

-0.054*

(0.082)

-0.016

(0.604)

-0.020

(0.520)

Relative deal size 0.000

(0.848)

0.001

(0.831)

-0.001

(0.782)

-0.001

(0.789)

-0.001

(0.687)

-0.001

(0.702)

CEO duality -0.057

(0.592)

-0.064

(0.550)

-0.067

(0.477)

-0.070

(0.463)

-0.040

(0.659)

-0.046

(0.612)

Log (CEO

directorship)

-0.013

(0.361)

-0.011

(0.452)

-0.002

(0.876)

-0.001

(0.922)

0.001

(0.928)

0.003

(0.785)

CEO tenure -0.019***

(<.0001)

-0.019***

(<.0001)

-0.032***

(<.0001)

-0.032***

(<.0001)

-0.031***

(<.0001)

-0.031***

(<.0001)

Percentage of busy

independent directors

0.605***

(<.0001)

0.611***

(<.0001)

0.430***

(<.0001)

0.432***

(<.0001)

0.237***

(<.0001)

0.243***

(<.0001)

Firm size (log of

sales)

0.029***

(<.0001)

0.028***

(<.0001)

0.029***

(<.0001)

0.029***

(<.0001)

0.034***

(<.0001)

0.034***

(<.0001)

Past year stock

performance

0.004

(0.332)

0.004

(0.341)

0.003

(0.525)

0.003

(0.529)

0.008**

(0.034)

0.008**

(0.035)

Firm age -0.006***

(<.0001)

-0.006***

(<.0001)

-0.004***

(0.005)

-0.004***

(0.005)

-0.004***

(0.002)

-0.004***

(0.002)

Leverage

(Debt/Asset)

0.263

(0.001)

0.264

(0.001)

0.194

(0.005)

0.195

(0.005)

0.265

(<.0001)

0.266

(<.0001)

Director age > 61

dummy

-0.033

(0.177)

-0.038

(0.128)

0.002

(0.930)

0.001

(0.976)

-0.033

(0.136)

-0.037*

(0.094)

40

LAW -0.139

(0.130)

-0.135

(0.142)

-0.151*

(0.072)

-0.150*

(0.074)

-0.006

(0.940)

-0.002

(0.976)

MBA -0.036

(0.214)

-0.039

(0.181)

-0.053**

(0.042)

-0.054**

(0.039)

-0.087***

(0.001)

-0.090***

(0.001)

PHD -0.061*

(0.064)

-0.0610*

(0.062)

-0.038

(0.195)

-0.039

(0.193)

-0.077***

(0.009)

-0.077***

(0.008)

Female 0.078*

(0.051)

0.070*

(0.079)

0.058

(0.114)

0.056

(0.130)

0.037

(0.297)

0.030

(0.401)

Emerging -0.158**

(0.028)

-0.136*

(0.060)

-0.014

(0.827)

-0.007

(0.910)

0.002

(0.969)

0.023

(0.711)

Common -0.173

(0.433)

-0.169

(0.444)

-0.226

(0.256)

-0.225

(0.259)

0.222

(0.291)

0.226

(0.282)

Civil -0.425*

(0.054)

-0.407*

(0.065)

-0.498**

(0.012)

-0.493**

(0.013)

-0.080

(0.705)

-0.062

(0.767)

41

Table 9: Merger Success and the Likelihood of Losing a Directorship

This table tests whether merger success prevents directors from losing a directorship. The variable

definitions are provided in the Appendix. The dependent variable Reduction takes the value of one if a

director loses a directorship on annual basis. In model (1), the dependent variable is a binary indicator that

equals one if a director loses a directorship during the first year following a merger. In model (2), the

dependent variable is a binary indicator that equals one if a director loses a directorship during the first

two years following a merger. In model 3, the dependent variable is a binary indicator that equals one if a

director loses a directorship during the first three years following a merger. P-values are provided in

parentheses. *, **, *** indicate statistical significance at the 10%, 5%, and 1% levels, respectively.

Dependent Variable Reduction in (0,1) Reduction in (0, 2) Reduction in (0, 3)

(1) (2) (3)

Intercept -5.054

-4.968

-4.779

CAR(-1, 0) -0.009

(0.200)

-0.003

(0.225)

-0.001

(0.581)

Competed -0.051

(0.730)

0.021

(0.871)

0.107

(0.388)

Diversifying M&A 0.041

(0.107)

0.017

(0.435)

0.051**

(0.015)

Private target 0.015

(0.584)

-0.002

(0.948)

-0.012

(0.596)

Crosser-border M&A

0.029

(0.283)

0.047**

(0.049)

0.054**

(0.016)

Cash deal

0.016

(0.621)

-0.019

(0.495)

-0.019

(0.470)

Friendly

-0.019

(0.610)

0.038

(0.236)

0.030

(0.332)

Relative deal size

0.002

(0.404)

0.003

(0.184)

0.002

(0.424)

CEO duality

0.014

(0.909)

0.281***

(0.005)

0.184*

(0.059)

Log (CEO directorship) -0.004

(0.813)

-0.018

(0.241)

-0.011

(0.463)

CEO tenure 0.032***

(<.0001)

0.035***

(<.0001)

0.039***

(<.0001)

Percentage of busy

independent directors

1.371***

(<.0001)

1.323***

(<.0001)

1.462***

(<.0001)

Firm size (log of sales)

0.042***

(<.0001)

0.044***

(<.0001)

0.047***

(<.0001)

Past year stock performance

0.002

(0.630)

-0.001

(0.728)

0.000

(0.943)

Firm age -0.006***

(<.0001)

-0.004***

(0.003)

-0.005***

(<.0001)

Leverage (Debt/Asset)

-0.360***

(<.0001)

-0.151**

(0.046)

-0.124*

(0.082)

Director age > 61 dummy

0.021

(0.431)

0.019

(0.411)

0.012

(0.593)

LAW -0.140

(0.184)

-0.232**

(0.013)

-0.195**

(0.027)

MBA -0.056*

(0.082)

-0.080***

(0.004)

-0.057**

(0.033)

PHD -0.079** -0.023 0.034

42

(0.030) (0.464) (0.247)

Female 0.003

(0.951)

0.056

(0.160)

-0.005

(0.892)

Emerging -0.224***

(0.005)

-0.200***

(0.004)

-0.206***

(0.002)

Common 0.079

(0.802)

0.330

(0.253)

0.083

(0.738)

Civil -0.094

(0.767)

0.178

(0.536)

-0.088

(0.724)

43

Table 10: Bad Mergers, Board Busyness and Directorship Loss

This table examines tests whether the poor performance of a merger and the number of board seats held

by a director lead to the loss of a directorship held by independent busy directors. The variable definitions

are provided in the Appendix. The dependent variable Reduction takes the value of one if a director loses

directorship on annual basis. The poor performance of a merger is measured as a binary indicator that

equals 1 if an acquirer’s CAR (-1, 0) is among the bottom 10% of CARs for all firms within its country

and industry for the year. In models (1) and (2), the dependent variable is a binary indicator that equals

one if a director loses a directorship during the first year following a merger. In models (3) and (4), the

dependent variable is a binary indicator that equals one if a director loses a directorship during the first

two years following a merger. In models (5) and (6), the dependent variable is a binary indicator that

equals one if a director loses a directorship during the first three years following a merger. P-values are

provided in parentheses. *, **, *** indicate statistical significance at the 10%, 5%, and 1% levels,

respectively.

Dependent variable Reduction in (0,1) Reduction in (0,2) Reduction in (0,3)

(1) (2) (3) (4) (5) (6)

Intercept -4.579

-4.625

-4.606 -4.628

-4.553 -4.589

Bottom 10% CARs

dummy

0.037

(0.639)

0.036

(0.652)

0.164**

(0.017)

0.163**

(0.017)

0.183***

(0.005)

0.183***

(0.005)

Total directorship

0.012***

(<.0001)

0.006***

(0.001)

0.009***

(<.0001)

Competed 0.037

(0.737)

0.046

(0.672)

0.08

(0.386)

0.088

(0.360)

0.190**

(0.038)

0.198**

(0.031)

Diversifying M&A 0.024

(0.259)

0.024

(0.261)

0.012

(0.523)

0.012

(0.525)

0.033*

(0.066)

0.033*

(0.067)

Private target 0.009

(0.683)

0.008

(0.724)

0.007

(0.746)

0.006

(0.733)

-0.005

(0.784)

-0.006

(0.739)

Crosser-border M&A 0.022

(0.331)

0.023

(0.321)

0.043**

(0.031)

0.043**

(0.031)

0.051***

(0.008)

0.051***

(0.008)

Cash deal -0.001

(0.959)

-0.001

(0.958)

-0.021

(0.377)

-0.021

(0.379)

-0.013

(0.562)

-0.013

(0.568)

Friendly -0.016

(0.617)

-0.008

(0.805)

0.034

(0.228)

0.038

(0.179)

0.038

(0.157)

0.044

(0.100)

Relative deal size 0.001

(0.721)

0.001

(0.752)

0.001

(0.469)

0.001

(0.483)

0.000

(0.938)

0.000

(0.901)

CEO duality -0.032

(0.748)

-0.021

(0.834)

0.208**

(0.011)

0.213**

(0.010)

0.175**

(0.027)

0.182**

(0.022)

Log (CEO

directorship)

-0.013

(0.327)

-0.017

(0.203)

-0.019

(0.102)

-0.021*

(0.074)

-0.022**

(0.046)

-0.025**

(0.024)

CEO tenure 0.025***

(<.0001)

0.024***

(<.0001)

0.031***

(<.0001)

0.030***

(<.0001)

0.034***

(<.0001)

0.033***

(<.0001)

Percentage of busy

independent directors

1.353***

(<.0001)

1.344***

(<.0001)

1.340***

(<.0001)

1.335***

(<.0001)

1.449***

(<.0001)

1.442***

(<.0001)

Firm size (log of

sales)

0.049***

(<.0001)

0.050***

(<.0001)

0.047***

(<.0001)

0.048***

(<.0001)

0.049***

(<.0001)

0.050***

(<.0001)

Past year stock

performance

0.000

(0.905)

0.001

(0.867)

-0.003

(0.434)

-0.003

(0.450)

-0.002

(0.597)

-0.002

(0.630)

Firm age -0.005***

(<.0001)

-0.005***

(<.0001)

-0.004***

(0.001)

-0.004***

(0.001)

-0.004***

(0.001)

-0.004***

(0.001)

44

Leverage

(Debt/Asset)

-0.328***

(<.0001)

-0.331***

(<.0001)

-0.144**

(0.025)

-0.145**

(0.024)

-0.118*

(0.054)

-0.119*

(0.051)

Director age > 61

dummy

0.026

(0.252)

0.037

(0.105)

0.017

(0.394)

0.022

(0.266)

0.007

(0.698)

0.015

(0.425)

LAW -0.096

(0.262)

-0.105

(0.217)

-0.128*

(0.088)

-0.132*

(0.078)

-0.094

(0.183)

-0.100

(0.155)

MBA -0.043

(0.118)

-0.036

(0.185)

-0.066***

(0.006)

-0.063***

(0.009)

-0.043*

(0.055)

-0.039*

(0.087)

PHD -0.079**

(0.010)

-0.078**

(0.010)

-0.039

(0.141)

-0.039

(0.141)

0.014

(0.577)

0.014

(0.575)

Female 0.008

(0.832)

0.024

(0.540)

0.036

(0.281)

0.043

(0.194)

-0.033

(0.302)

-0.021

(0.502)

Emerging -0.176***

(0.009)

-0.222***

(0.001)

-0.184***

(0.002)

-0.206***

(0.001)

-0.198***

(<.0001)

-0.232***

(<.0001)

Common -0.108

(0.673)

-0.116

(0.649)

0.177

(0.453)

0.174

(0.461)

0.122

(0.572)

0.117

(0.587)

Civil -0.240

(0.347)

-0.277

(0.277)

0.044

(0.854)

0.026

(0.911)

-0.012

(0.956)

-0.038

(0.860)

45

Table 11: Post Merger Operating performance and Board Busyness

This table tests post-merger operating performance as measured by ROA up to 3 years following an

acquisition. Panel A presents the correlations between the CARs (-1, 0) and post-merger ROA. Panel B

compares post-merger raw ROA between acquirers with busy and non-busy boards. Panel C presents a

comparable analysis, but uses an industry-adjusted ROA. P-values are provided in the parentheses. *, **,

*** indicate statistical significance at the 10%, 5%, and 1% levels, respectively.

Panel A: Correlations Between Operating Performance and Market Reaction

Acquirers with Non-Busy boards

ROA in year 0 ROA in year 1 ROA in year 2 ROA in year 3

CAR (-1, 0) 0.057*** 0.047** 0.002 -0.026

p-value 0.0025 0.0128 0.9064 0.17

Acquirers with Busy boards

ROA in year 0 ROA in year 1 ROA in year 2 ROA in year 3

CAR (-1, 0) 0.025** 0.031** 0.024* 0.041***

p-value 0.046 0.0141 0.0575 0.0012

Panel B: Comparative Raw ROA Non-Busy board Busy Board Difference in ROA (Busy – Non-busy)

Year 0 0.073 0.067 -0.006**

(0.036)

Year 1 0.068 0.061 -0.007***

(0.008)

Year 2 0.068 0.059 -0.009***

(0.004)

Year 3 0.066 0.062 -0.004

(0.255)

Panel C: Comparative Industry-adjusted ROA Non-Busy board Busy Board Difference in ROA (Busy – Non-busy)

Year 0 -0.009 -0.013 -0.004

(0.138)

Year 1 -0.009 -0.015 -0.007***

(0.009)

Year 2 -0.009 -0.017 -0.008***

(0.008)

Year 3 -0.010 -0.013 -0.003

(0.295)