Corporate Governance - Performance

7/23/2019 Corporate Governance - Performance

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Corporate governance and firm performance

Sanjai Bhagat

Brian Bolton

AbstractThe primary contribution of this paper is the consistent estimation of the relationship

between corporate governance and performance, by taking into account the inter-relationshipsamong corporate governance, management turnover, corporate performance, corporate capitalstructure, and corporate ownership structure. e make three additional contributions to theliterature!

"irst, we find that better governance as measured by the #ompers, $shii, and %etrick&#$%, '(()* and Bebchuk, +ohen and "errell &B+", '((* indices, stock ownership of board

members, and +-+hair separation is significantly positively correlated with bettercontemporaneous and subse/uent operating performance. f the above four measures, stockownership of board members has the greatest impact on ne0t year1s operating performance.$nterestingly, none of the governance measures are correlated with future stock market performance.

Second, in several instances our inferences regarding the performance-governancerelationship do depend on whether or not one takes into account the endogenous nature of therelationship between governance and performance.

Third, given poor firm performance, the probability of disciplinary management turnoveris positively correlated with stock ownership of board members, and board independence.2owever, given poor firm performance, the probability of disciplinary management turnover is

negatively correlated with better governance measures as proposed by #$% and B+".

JEL +lassification! #)', #)

3eywords! +orporate governance, firm performance, corporate board structure, corporate board ownership

September '((4

5lease address correspondence to Sanjai Bhagat, 6eeds School of Business, 7niversity of+olorado, Boulder, + 8()(9-(:9. [email protected]

1. Introduction

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mailto:[email protected]





$n an important and oft-cited paper, #ompers, $shii, and %etrick &#$%, '(()* study the

impact of corporate governance on firm performance during the :99(s. They find that stock

returns of firms with strong shareholder rights outperform, on a risk-adjusted basis, returns of

firms with weak shareholder rights by 8.4 percent per year during this decade. #iven this result,

serious concerns can be raised about the efficient market hypothesis, since these portfolios could

be constructed with publicly available data. n the policy domain, corporate governance

proponents have prominently cited this result as evidence that good governance &as measured by

#$%* has a positive impact on corporate performance.

There are three alternative ways of interpreting the superior return performance of

companies with strong shareholder rights. "irst, these results could be sample-period specific;

hence companies with strong shareholder rights during the current decade of '(((s may not have

e0hibited superior return performance. $n fact, in a very recent paper, +ore, #uay and <usticus

&'((4* carefully document that in the current decade share returns of companies with strong

shareholder rights do not outperform those with weak shareholder rights. Second, the risk-

adjustment might not have been done properly; in other words, the governance factor might be

correlated with some unobservable risk factor&s*. Third, the relation between corporate

governance and performance might be endogenous raising doubts about the causality

e0planation. There is a significant body of theoretical and empirical literature in corporate

finance that considers the inter-relationships among corporate governance, management

turnover, corporate performance, corporate capital structure, and corporate ownership structure.

2ence, from an econometric viewpoint, to study the relationship between any two of these

variables one would need to formulate a system of simultaneous e/uations that specifies the

relationships among these variables.

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hat if after accounting for sample period specificity, risk-adjustment, and endogeneity,

the data indicates that share returns of companies with strong shareholder rights are similar to

those with weak shareholder rights= hat might we infer about the impact of corporate

governance on performance from this result= $t is still possible that governance might have a

positive impact on performance, but that shareholder rights, as measured by #$%, might not be

the appropriate corporate governance metric.

An impressive set of recent papers have considered alternative measures of corporate

governance, and studied the impact of these governance measures on firm performance. #$%1s

governance measure is an e/ually-weighted inde0 of ' corporate governance provisions

compiled by the $nvestor <esponsibility <esearch +enter &$<<+*, such as, poison pills, golden

parachutes, classified boards, cumulative voting, and supermajority rules to approve mergers.

Bebchuk, +ohen and "errell &B+", '((* recogni>e that some of these ' provisions might

matter more than others and that some of these provisions may be correlated. Accordingly, they

create an ?entrenchment inde0@ comprising of si0 provisions four provisions that limit

shareholder rights and two that make potential hostile takeovers more difficult. They find that

increases in this inde0 &that is, higher entrenchment* are associated with reductions in Tobin1s

and lower abnormal returns during :99(-'((). "urther, they find that the other eighteen $<<+

provisions e0cluded from their inde0 are unrelated to changes in firm value or stock returns.

Thus, they conclude that indices with a small number of the most relevant factors are likely to be

the most appropriate measures of corporate governance.

#illan, 2art>ell and Starks &'(()* also use $<<+ data to create four governance sub-

indices! a board of directors inde0, a corporate charter provisions inde0, a state of incorporation

inde0, and a composite inde0 composed of the other three. They compare governance at the

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industry level relative to governance at the firm level. They find that a firm1s controllable

governance structure pro0ied by the four indices is systematically related to the industry

characteristics such as investment opportunities, product uni/ueness, competitive environment,

and leverage. hile all the previously noted studies use $<<+ data, Brown and +aylor &'((*

use $nstitutional Shareholder Services &$SS* data to create their governance inde0. This inde0

considers 4' corporate governance features such as board structure and processes, corporate

charter issues such as poison pills, management and director compensation and stock ownership.

There is a related strand of the literature that considers corporate board characteristics as

important determinants of corporate governance! board independence &see 2ermalin and

eisbach &:998**, stock ownership of board members &see Bhagat, +arey, and lson &:999**,

and whether the +hairman and + positions are occupied by the same or two different

individuals &see Brickley, +oles, and Carrell &:99D**. +an a single board characteristic be as

effective a measure of corporate governance as indices that consider 4' &as in Brown and

+aylor*, ' &as in #$%* or other multiple measures of corporate charter provisions, and board

characteristics= hile, ultimately, this is an empirical /uestion, on both economic and

econometric grounds it is possible for a single board characteristic to be as effective a measure of

corporate governance. +orporate boards have the power to make, or at least, ratify all important

decisions including decisions about investment policy, management compensation policy, and

board governance itself. $t is plausible that an independent board or board members with

appropriate stock ownership will have the incentives to provide effective monitoring and

oversight of important corporate decisions noted above; hence board independence or ownership

can be a good pro0y for overall good governance. "urthermore, the measurement error in



measuring board independence or board ownership can be less than the total measurement error

in measuring a multitude of board processes, compensation structure, and charter provisions.

ur primary contribution to the literature is a comprehensive and econometrically

defensible analysis of the relation between corporate governance and performance. e take into

account the endogenous nature of the relationship between governance and performance. Also,

with the help of a simultaneous e/uations framework we take into account the inter-relationships

among corporate governance, performance, capital structure, and ownership structure. e make

three additional contributions to the literature!

"irst, instead of considering just a single measure of governance &as prior studies in the

literature have done*, we consider seven different governance measures. e find that better

governance as measured by the #$% and B+" indices, stock ownership of board members, and

+-+hair separation is significantly positively correlated with better contemporaneous and

subse/uent operating performance. f the above four measures, stock ownership of board

members has the greatest impact on ne0t year1s operating performance. Additionally, better

governance as measured by Brown and +aylor, and The +orporate 6ibrary is not significantly

correlated with better contemporaneous or subse/uent operating performance.: Also,

interestingly, board independence is negatively correlated with contemporaneous and subse/uent

operating performance. This is especially relevant in light of the prominence that board

independence has received in the recent EFS and EASGA corporate governance listing

re/uirements.' "inally, none of the governance measures are correlated with future stock market

performance.

: The +orporate 6ibrary &T+6* is a commercial vendor that uses a proprietary weighting scheme to include over ahundred variables concerning board characteristics, management compensation policy, and antitakeover measures inconstructing a corporate governance inde0.' See S+ ruling ?EASG and EFS <ulemaking <elating to +orporate #overnance,@ inhttp://www.sec.gov/rules/sro/3!"#$.htm , and http://www.sec.gov/rules/sro/nyse/3!$%&'$.pdf.

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Second, in several instances our inferences regarding the performance-governance

relationship do depend on whether or not one takes into account the endogenous nature of the

relationship between governance and performance. "or e0ample, the 6S estimate indicates a

significantly negative relation between the #$% inde0 and ne0t year1s Tobin1s , and the #$%

inde0 and ne0t two years1 Tobin1s . 2owever, after taking into account the endogenous nature

of the relationship between governance and performance, we find a negative but statistically

insignificant relation between the #$% inde0 and the one year Tobin1s , and positive and

statistically insignificant for the two years1 Tobin1s .

Third, given poor firm performance, the probability of disciplinary management turnover

is positively correlated with stock ownership of board members, and with board independence.

2owever, given poor firm performance, the probability of disciplinary management turnover is


The above findings have important implications for finance researchers, senior policy

makers, and corporate boards! Efforts to improve corporate governance should focus on stoc(

ownership of board members since it is positively related to both future operating performance,

and to the probability of disciplinary management turnover in poorly performing firms.

5roponents of board independence should note with caution the negative relation between board

independence and future operating performance. 2ence, if the purpose of board independence is

to improve performance, then such efforts might be misguided. 2owever, if the purpose of board

independence is to discipline management of poorly performing firms, then board independence

has merit. "inally, even though the #$% and B+" good governance indices are positively related

to future performance, policy makers and corporate boards should be cautious in their emphasis

on the components of these indices since this might e0acerbate the problem of entrenched

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management, especially in those situations where management should be disciplined, that is, in

poorly performing firms.)

The remainder of the paper is organi>ed as follows. Section two briefly reviews the

literature on the relationship among corporate ownership structure, governance, performance and

capital structure. Section three notes the sample and data, and discusses the estimation procedure.

Section four presents the results on the relation between governance and performance. Section

five focuses on the impact of governance in disciplining management in poorly performing

companies. The final section concludes with a summary.

2. Corporate ownership structure, corporate governance, firm performance, and

capital structure

Some governance features may be motivated by incentive-based economic models of

managerial behavior. Broadly speaking, these models fall into two categories. $n agency models,

a divergence in the interests of managers and shareholders causes managers to take actions that

are costly to shareholders. +ontracts cannot preclude this activity if shareholders are unable to

observe managerial behavior directly, but ownership by the manager may be used to induce

managers to act in a manner that is consistent with the interest of shareholders. 5erformance is

reflected in managerial payoffs, which may be interpreted as including takeovers and managerial

turnover. #rossman and 2art &:98)* describe this problem.

Adverse selection models are motivated by the hypothesis of differential ability that

cannot be observed by shareholders. $n this setting, ownership may be used to induce revelation

of the managerIs private information about cash flow or her ability to generate cash flow, which

) There is considerable interest among senior policy makers and corporate boards in understanding the determinantsof good corporate governance, for e0ample, see )ew *or( +imes, April :(, '((4, page ).H, ?"undamentally;@ ,all-treet Journal , ctober :', '((, page B.8, ?+areer Cournal;@ inancial +imes "T.com, September ':, '((), page :?Jirtue <ewarded.@

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cannot be observed directly by shareholders. 5erformance provides information to the principal

about the ability of the manager, and is therefore reflected in managerial payoffs, which may

include dismissal for poor performance. A general treatment is provided by %yerson &:98D*.

$n the above scenarios, some features of corporate governance may be interpreted as a

characteristic of the contract that governs relations between shareholders and managers.

#overnance is affected by the same unobservable features of managerial behavior or ability that

are linked to ownership and performance.

At least since Berle and %eans &:9)'*, economists have emphasi>ed the costs of diffused

share-ownership; that is, the impact of ownership structure on performance. 2owever, Gemset>

&:98)* argues that since we observe many successful public companies with diffused share-

ownership, clearly there must be offsetting benefits, for e0ample, better risk-bearing. Also, for

reasons related to performance-based compensation and insider information, firm performance

could be a determinant of ownership. "or e0ample, superior firm performance leads to an

increase in the value of stock options owned by management which, if e0ercised, would increase

their share ownership. Also, if there are serious divergences between insider and market

e0pectations of future firm performance then insiders have an incentive to adjust their ownership

in relation to the e0pected future performance. 2immelberg, 2ubbard and 5alia &:999* argue that

the ownership structure of the firm may be endogenously determined by the firm1s contracting

environment which differs across firms in observable and unobservable ways. "or e0ample, if the

scope for per/uisite consumption is low in a firm then a low level of management ownership

may be the optimal incentive contract.4

$nvestors preference for li/uidity would lead to smaller blockholdings given that larger blocks are less li/uid in thesecondary market. Also, as highlighted by Black &:99(* and <oe &:99*, the public policy bias in the 7.S. towards protecting minority shareholder rights increases the costs of holding large blocks.4 The endogeneity of management ownership has also been noted by Censen and arner &:988*! ?A caveat to thealignmentKentrenchment interpretation of the cross-sectional evidence, however, is that it treats ownership ase0ogenous, and does not address the issue of what determines ownership concentration for a given firm or why

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$n a seminal paper, #rossman and 2art &:98)* considered the e0 ante efficiency

perspective to derive predictions about a firm1s financing decisions in an agency setting. An

initial entrepreneur seeks to ma0imi>e firm value with some disciplinary mechanism forcing the

entrepreneur to choose the value-ma0imi>ing level of debt. Eovaes and Lingales &:999* show

that the optimal choice of debt from the viewpoint of shareholders differs from the optimal

choice of debt from the viewpoint of managers. The conflict of interest between managers and

shareholders over financing policy arises because of three reasons. "irst, shareholders are much

better diversified than managers who besides having stock and stock options on the firm have

their human capital tied to the firm &"ama &:98(**. Second, as suggested by Censen &:98H*, a

larger level of debt pre-commits the manager to working harder to generate and pay off the

firm1s cash flows to outside investors. Third, 2arris and <aviv &:988* and Stul> &:988* argue that

managers may increase leverage beyond what might be implied by some ?optimal capital

structure@ in order to increase the voting power of their e/uity stakes, and reduce the likelihood

of a takeover and the resulting possible loss of job-tenure.

hile the above focuses on capital structure and managerial entrenchment, a different

strand of the literature has focused on the relation between capital structure and ownership

structure. #rossman and 2art &:98H* and 2art and %oore &:99(* consider an incomplete

contracting environment where it is difficult to specify all possible future states of nature and

relevant decisions in a contract that can be enforced in a court. $n such an incomplete contracting

environment, e0 ante allocation of control rights under different firm performance outcomes

could be used to provide incentives to managers to make firm-specific human capital

investments.

concentration would not be chosen to ma0imi>e firm value. %anagers and shareholders have incentives to avoidinside ownership stakes in the range where their interests are not aligned, although managerial wealth constraintsand benefits from entrenchment could make such holdings efficient for managers.@

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This brief review of the inter-relationships among corporate governance, management

turnover, corporate performance, corporate capital structure, and corporate ownership structure

suggests that, from an econometric viewpoint, to study the relationship between corporate

governance and performance, one would need to formulate a system of simultaneous e/uations

that specifies the relationships among the abovementioned variables. e specify the following

system of three simultaneous e/uations!

5erformance M f &wnership, #overnance, +apital Structure, L:, ε:*, &:a*

#overnance M f '&5erformance, wnership, +apital Structure, L', ε '*, &:b*

wnership M f 3&#overnance, 5erformance, +apital Structure, L), ε )*, &:c*

where the Li are vectors of control variables and instruments influencing the dependent variables

and the ε i are the error terms associated with e0ogenous noise and the unobservable features of

managerial behavior or ability that e0plain cross-sectional variation in performance, ownership

and governance. The estimation issues for the above e/uations are discussed in the ne0t section.

3. Data, sample, and estimation issues

3.. 0ata and sample

$n this section we discuss the data sources for governance, performance, and other variables,

as well as the sample construction. All variables are described in Table :.

1ompers2 shii and 4etric( 1!nde5: 7sing the $nvestor <esponsibility <esearch +enter &$<<+*

database, #ompers, $shii and %etrick &'(()* construct their #-$nde0. e obtain the #-$nde0

from $<<+. The #-$nde0 scores and provision data are available for appro0imately :,4(( firms

for si0 years! :99(, :99), :994, :998, '((( and '(('; '(( data is available but we do not use it

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since our focus is on the impact of governance on future performance. 6ower #-$nde0 scores are

associated with better governance.

6ebchu(2 7ohen and errell E!nde5: Bebchuk, +ohen and "errell &'((* modify the #-$nde0 to

include only si0 of the ' $<<+ provisions. These si0 provisions are classified board, bylaw

amendments limits, supermajority re/uirements for mergers, supermajority re/uirements for

charter amendments, poison pills and golden parachutes. They call this inde0 the -$nde0 for

?entrenchment inde0.@ The -$nde0 is available for the same period and sample firms as the #-

$nde0. 6ower -$nde0 scores are associated with better governance.

+he 7orporate Library: The +orporate 6ibrary &T+6* is a commercial vendor of corporate

governance indices and risk assessment tools. T+6 utili>es proprietary algorithms to measure

Ngood1 corporate governance. See Table : for details on the construction of the T+6 benchmark

inde0. These scores are available for appro0imately :,4(( firms for '((:, '((', and '(().

2igher T+6 scores are associated with better governance.

6rown and 7aylor 1ov-core! Brown and +aylor &'((* use $SS data to create the governance

&#ovScore* inde0. They use fifty-two categories of firm characteristics and information,

including board si>e, board committee information, stock option e0pensing, and director

ownership. They calculate this measure for appro0imately ',4(( firms for '(('. 2igher

#ovScores are associated with better governance.

6oard 8ariables: e obtain data on board independence, board ownership, and +-+hair

duality from $<<+ and T+6. $<<+ has board data for appro0imately :,4(( firms for :99H to

'((). e have T+6 data for appro0imately :,4(( firms for '((: to '((). Because these two

samples do not completely overlap, we have full information from :99H to '(() for

appro0imately :,'(( firms. e also obtain board si>e, median director ownership, median

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director age and median director tenure from these sources.H The stock ownership variable does

not include options.

9erformance 8ariables! e use +ompustat and +enter for <esearch in Security 5rices &+<S5*

data for our performance variables. e use the annual accounting data from +ompustat for

calculating return-on-assets &?<A@* and Tobin1s . "ollowing Barber and 6yon &:99H*, we

calculate <A as operating income before depreciation divided by total assets. "or robustness,

we also consider operating income after depreciation divided by total assets. Similar to #$%, we

calculate Tobin1s as &total assets O market value of e/uity book value of e/uity deferred

ta0es* divided by total assets. e use the +<S5 monthly stock file to calculate monthly and

annual stock returns. e calculate industry performance measures by taking the four-digit S$+

code average &e0cluding the sample firm* performance for the specific time period.

7E 8ariables: e obtain + ownership, + age, + tenure, and + turnover from

+ompustat1s 0ecucomp database. This database covers appro0imately ',((( firms from :99'

to '((). + ownership is calculated using beneficial stock ownership e0cluding options.

Leverage: +onsistent with Bebchuk, +ohen and "errell &'((*, #raham, 6ang, and Shackleford

&'((*, and 3hanna and Tice &'((4* we compute leverage as &long term debt O current portion of

long term debt* divided by total assets. "or robustness, we also consider alternative definitions

of leverage as suggested by Baker and urgler &'(('*D.

;<0 and =dvertising E5penses: e calculate this variable as advertising e0penses plus research

and development e0penses divided by total assets. Since not all firms report these e0penses for

H e consider the dollar value of stock ownership of the median director as the measure of stock ownership of boardmembers. ur focus on the median director1s ownership, instead of the average ownership, is motivated by the political economy literature on the median voter; see Shleifer and %urphy &'((*, and %ilavonic &'((*. Also,directors, as economic agents, are more likely to focus on the impact on the dollar value of their holdings in thecompany rather than on the percentage ownership.D e consider five alternative measures of leverage, including the two from Baker and urgler &'(('*. ur resultsare /ualitatively similar for the different measures; see Appendi0 .

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all years, we create two dummy variables that are e/ual to one when the relevant measure is

missing. "ollowing 5alia &'((:*, we do this to ma0imi>e sample si>e and to avoid e0cluding

observations and biasing the results related to <PG and advertising intensive firms.

nstrumental 8ariables: The process for empirically determining instrumental variables is not

well specified. The ideal instrument will be correlated with the endogenous regressor, but not

with the error in the structural e/uation. Some econometricians suggest using lagged

endogenous variables as instruments &for e0ample, Cohnston and GiEardo &:99D**. 2owever, if

the endogenous variables are serially correlated, it seems that the lagged values could also be

correlated with the error term, thus rendering them invalid as e0ogenous instruments. Another

suggestion is to use e0ogenous control variables; we do include these other e0ogenous variables

in our first-stage estimation. Additionally, we identify the following four variables as

instruments for performance, governance and ownership.

+reasury -toc(: 5alia &'((:* suggests that a firm is most likely to buy back its stock

when it believes the stock to underpriced relative to where the managers think the price should

be. Thus, the level of treasury stock should be correlated with firm performance and firm value.

e e0pect this measure to be e0ogenous in the governance and ownership e/uations. e use the

ratio of the treasury stock to total assets as the instrument for performance.

6oard ndependence: 2ermalin and eisbach &'(()* review the corporate governance

literature and find that board composition is not correlated with firm performance. 2owever,

recent EFS and EASGA corporate governance guidelines that have been approved by the

S+ give a central role to independent directors. This suggests that board independence might be

correlated with measures of governance which would make it an effective instrument. e use the

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ratio of outsiders on the board to the total number of board members as the measure of board

independence.8

0irector wnership: +ore, 2olthausen, and 6arcker &:999*, and 6inck, Eetter and Fang

&'((4* suggest that percentage stock ownership of a firm1s director may be a substitute form of

governance or monitoring mechanism. Thus, it should be correlated with the governance

measures. Bhagat, +arey and lson &:999* find that there is no consistent relationship between

percentage stock ownership of directors and the performance variables. These findings suggest

that it would make a valid instrument for governance in the performance e/uation.

7E +enure!to!=ge: A + that has had five years of tenure at age H4 is likely to be of

different /uality and have a different e/uity ownership than a + that has had five years of

tenure at age 4(. These +s likely have different incentive, reputation, and career concerns.

#ibbons and %urphy &:99'* provide evidence on this. Therefore, we use the ratio of +

tenure to + age as a measure of + /uality, which will serve as an instrument for +

ownership.

=ll of our analyses involving instrumental variables will include tests for wea(

instruments as suggested by -toc( and *ogo >'%%?2 and the ausman >A#"? test for

endogeneity. This is discussed later in this section, and in Appendi0 A.

-ample: #iven that our primary variables come from several different sources, our analyses

re/uire combining these sources to create our sample. The firms and years covered by these

sources do not completely overlap. e mostly use the largest possible sample in our tests, rather

than using only firms and years for which we have complete data for the entire period. e do

this to ma0imi>e the si>e of the sample and thus the power of the tests. e recogni>e that this

8 $n one specification, we use board independence as the dependent governance variable. $n this case, we useanother instrumental variable for governance in place of board independence! the percent of the board members whoare active +s.

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may induce a sample selection bias; however, e5 ante we have no reason to e0pect this potential

bias to influence our results. "or e0ample, we have no reason to e0pect firms covered by $<<+

to be fundamentally different from firms covered by +ompustat1s 0ecucomp. 2owever, for

robustness, we analy>e the entire sample of firms for the total period &:99(-'((* and a more

consistent sample of firms for a shorter period &'(((-'(('*. Table ' presents the descriptive

statistics and sample si>es for the variables for all available years and for just '(('. Table )

presents the parametric and non-parametric correlation coefficients among the performance and

governance variables.

3.'. Estimation issues

The instruments for performance, governance, and ownership in e/uations &:a*, &:b*, and

&:c* have been discussed above. <egarding the control variables! 5rior literature, for e0ample,

+ore, 2olthausen and 6arcker &:999*, #illan, 2art>ell and Starks &'(()*, and +ore, #uay and

<usticus &'((4*, suggests that industry performance, return volatility, growth opportunities and

firm si>e are important determinants of firm performance. Fermack &:99H* documents a relation

between board si>e and performance. Gemset> &:98)* suggests that small firms are more-likely

to be closely-held suggesting a different governance structure than large firms. "irms with

greater growth opportunities are likely to have different ownership and governance structures

than firms with fewer growth opportunities; see, for e0ample, Smith and atts &:99'*, and

#illan, 2art>ell and Starks &'(()*. Gemset> and 6ehn &:984*, among others, suggest a relation

between information uncertainty about the firm as pro0ed by return volatility and its ownership

and governance structures.

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#iven the abovementioned findings in the literature, in e/uation &:a*, the control

variables include industry performance, log of assets, <PG and advertising e0penses to assets,

board si>e, standard deviation of stock return over the prior five years, and the instrument is

treasury stock to assets. $n e/uation &:b*, the control variables include <PG and advertising

e0penses to assets, board si>e, standard deviation of stock return over the prior five years, and

the instruments are median director percentage ownership and percentage of independent

directors. $n e/uation &:c*, the control variables include log of assets, <PG and advertising

e0penses to assets, board si>e, standard deviation of stock return over the prior five years, and

the instrument is + tenure to + age.

e estimate this system using ordinary least s/uares &6S*, two-stage least s/uares

&'S6S* to allow for potential endogeneity, and three-stage least s/uares &)S6S* to allow for

potential endogeneity and cross-correlation between the e/uations. $f any of the right-hand side

regressors are endogenously determined, 6S estimates of &:* are inconsistent.9 5roperly

specified instrumental variables &$J* estimates such as the two stage least s/uares &'S6S* are

consistent. The problem is which instruments to use, and how many instruments to use.

<egarding the number of instruments, we know we must include at least as many instruments as

we have endogenous variables. The asymptotic efficiency of the estimation improves as the

number of instruments increases, but so does the finite-sample bias &Cohnston and GiEardo

:99D*. +hoosing ?weak instruments@ can lead to problems of inference in the estimation.

9 This point is made in most econometric te0tbook; for e0ample, Cohnston and GiEardo &:99D, page :4)* state,?7nder the classical assumptions 6S estimators are best linear unbiased. ne of the major underpinningassumptions is the independence of regressors from the disturbance term. $f this condition does not hold, 6Sestimators are biased and inconsistent.@ 3ennedy &'((), page :8(* notes, ? $n a system of simultaneous e/uations,all the endogenous variables are random variables a change in any disturbance term changes all the endogenousvariables since they are determined simultaneouslyQAs a conse/uence, the 6S estimator is biased, evenasymptotically.@ %addala &:99', page )8)* observes, ?Qthe simultaneity problem results in inconsistent estimatorsof the parameters, when the structural e/uations are estimated by ordinary least s/uares &6S*.@

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An instrument is ?weak@ if the correlation between the instruments and the endogenous

variable is small. To be valid, the instruments must be e0ogenous and they must be relevant.

2ahn and 2ausman &'(('* define weak instruments by two features. "irst, 'S6S is badly biased

toward the 6S estimate and alternative unbiased estimators such 6imited $nformation

%a0imum 6ikelihood may not solve the problem. Second, the standard &first order* asymptotic

distribution does not give an accurate framework for inference.

Eelson and Start> &:99(* and Bound, Caeger and Baker &:994* were among the first to

discuss how instrumental variables estimation can perform poorly if the instruments are weak.

Eelson and Start> show that the true distribution of the instrumental variables estimator may look

nothing like the asymptotic distribution. They further show that the $J estimator is biased in the

direction of the probability limit of the 6S estimator. Bound, Caeger and Baker generally

define the weak instruments problem as a case where the instrumental variables are only weakly

correlated with the endogenous variable in /uestion. They focus on two related problems. "irst,

if the instruments and the endogenous variables are weakly correlated, then even a weak

correlation between the instruments and the error in the original structural e/uation &which

should be >ero* can lead to large inconsistencies in the $J estimates &this is known as the ?bias@

issue related to weak instruments*. Second, finite sample results can differ substantially from

asymptotic theory. Specifically, $J estimates are generally biased in the same direction as 6S

estimates, with the magnitude of this bias increasing as the ;' of the first-stage regression

between the instruments and the endogenous variable approaches >ero &this is known as the

?si>e@ issue related to weak instruments*.

%ore recently, Stock and Fogo &'((* formali>e the definitions and provide tests to

determine if instruments are weak. They introduce two alternative definitions of weak

:D



instruments. "irst, a set of instruments is weak if the bias of the instrumental variables estimator,

relative to the bias of the 6S estimator, e0ceeds a certain limit b. Second, the set of instruments

is weak if the conventional α -level ald test based on instrumental variables statistics has a

si>e that could e0ceed a certain threshold r . These two definitions correspond to the ?bias@ and

?si>e@ problems mentioned earlier, and yield a set or parameters that define a ?weak instruments

set.@ Appendi0 A discusses the construction of this test in more detail.:(

nce we know that our instruments are valid, we need to compare the 6S estimates with

the $J estimates to determine if $J estimation is truly necessary. To do this, we use the 2ausman

&:9D8* specification test &alternatively known as the u-2ausman or Gurbin-u-2ausman test*.

The test statistic is constructed as follows!

*RR&**Rvar&*R&var&*RR& :

38 :L- 38 :L- 38 :L- h β β β β β β −−′−≡ − .

This statistic has a chi-s/uare distribution with degrees of freedom e/ual to the number of

potentially endogenous regressors &generally two in our analyses*. $f the difference between the

6S and $J estimates is ?large,@ we conclude that 6S is not ade/uate &we use this same test to

compare 6S to 'S6S, 6S to )S6S, and 'S6S to )S6S*. hile this test is sometimes called a

test for endogeneity, it technically evaluates whether or not endogeneity has any effect on the

consistency of the estimates. $f the instruments are valid, we can use this test to suggest which

estimation method should be used.::

:( There are two other weak instrument tests. "irst, 2ahn and 2ausman &'(('* present a test similar in spirit to the

2ausman &:9D8* specification test. Second, the 2ansen-Sargan test compares the second stage residuals with thefirst stage instruments, testing for non-correlation among these variables; see Gavidson and %ac3innon &'((*. e present the Stock and Fogo test results because, in our opinion, its test statistic is easier to interpret; also, the Stockand Fogo test is consistent with the motivation of the prior research on weak instruments; for e0ample, see Bound,Caeger and Baker &:994*. 2owever, we also perform the 2ahn and 2ausman, and the 2ansen-Sargan weakinstrument tests; inferences from these tests are consistent with the reported Stock and Fogo test results. Also, inaddition to the instrument variables discussed above, we consider an alternate set of instrument variables; the resultsnoted below are robust to the consideration of alternate instruments.:: By construction, if the $J variance is larger than the 6S variance, the test statistic will be negative. $n this case,we rely on the 6S estimates because of the smaller variance.

:8



4. Corporate governance and performance

Table summari>es our main results of the relationship between governance and

performance. hile previous studies have used both stock market based and accounting

measures of performance, we primarily rely on accounting performance measures. Stock market

based performance measures are susceptible to investor anticipation. $f investors anticipate the

corporate governance effect on performance, long-term stock returns will not be significantly

correlated with governance even if a significant correlation between performance and

governance indeed e0ists.:'

$n Table , 5anels A through #, we report the results for the relationship between

operating performance &<A* and the following governance measures respectively! #$% inde0,

B+" inde0, T+6 inde0, Brown and +aylor inde0, stock ownership of the board, +-+hair

duality, and board independence. $n each panel we report the 6S, 'S6S, and )S6S estimates of

the e/uations in &:a* and &:b*; we perform 2ausman &:9D8* tests to guide our choice of which

set of estimates to consider for inference purposes. $n each panel, we report three measures of

operating performance! contemporaneous return-on-assets &<A*, ne0t year1s <A, and ne0t

two years1 <A. #iven that information needed to construct the various governance measures

for a particular year are released to market participants some time during the first two /uarters of

the year, the impact of governance on performance will be observed on both the

contemporaneous and subse/uent operating performance. +ore, #uay, and <usticus &'((*

consider just the ne0t year1s operating performance. 2owever, it is possible that to the e0tent

governance impacts performance, operating performance may be impacted for the ne0t several

years. "or this reason, we also consider the ne0t two years1 operating performance.

:' 2owever, to aid the comparison of our results with the e0tant literature, in Appendi0 B we report resultsconsidering stock return and Tobin1s as performance measures.

:9



Table , 5anel A, highlights the relationship between the #$% governance inde0 and

operating performance &<A*. +onsider the results under the ?Ee0t : Fear 5erformance.@ The

2ausman test suggests we consider the 'S6S estimates for inference. The Stock and Fogo &'((*

test indicates that our instruments are appropriate. There is a significant negative correlation

between the #$% inde0 and ne0t year1s <A. #iven that lower #$% inde0 numbers reflect

stronger shareholder rights &better governance*, the above results are consistent with a positive

relation between good governance, as measured by #$%, and operating performance. <esults

using the contemporaneous and ne0t two years1 operating performance are similar. These results

are consistent with #$%1s finding of a positive relation between good governance and

performance for the period :99(-:999, and e0tends their findings to the most recent period,

'(((-'((. 2owever, it is important to note that #$%1s finding of a positive relation between

good governance and performance is based on long-term stock returns as the measure of

performance.:) As noted above, if investors anticipate the effect of corporate governance on

performance, long-term stock returns will not be significantly correlated with governance even if

a significant correlation between performance and governance e0ists. $ndeed, as the results in

Appendi0 B indicate, there is no significant or consistent relation between #$%1s measure of

governance and contemporaneous, ne0t year1s or the ne0t two years1 stock returns, or Tobin1s

.: :4

:) +onsistent with the findings reported here, +ore, #uay and <usticus &'((4* also find a positive relation between

the #$% inde0 and ne0t year1s <A.: These findings are consistent with those of +ore, 2olthausen and 6arcker &:999* who conclude that theirgovernance measures ?more consistently predict future accounting operating performance than future stock market performance.@:4 +onsider the performance-governance relationships estimated in Appendi0 B-$$, 5anel A. The 6S estimateindicates a significantly negative relation between the #$% inde0 and ne0t year1s Tobin1s , and the #$% inde0 andne0t two years1 Tobin1s . 2owever, the 'S6S estimate is negative but statistically insignificant for the one yearTobin1s , and positive and statistically insignificant for the two years1 Tobin1s . The 2ausman &:9D8*specification test suggests that the 'S6S are more appropriate for inferences implying the e0istence of endogeneityin the governance-performance relation.

'(



$n Table , 5anel B, we note the relationship between the B+" governance inde0 and

operating performance. Again, the 2ausman test suggests we consider the 'S6S estimates for

inference, and the Stock and Fogo &'((* test indicates that our instruments are appropriate.

There is a significant negative correlation between the B+" inde0 and ne0t year1s <A. Similar

to the #$% inde0, lower B+" inde0 numbers reflect better governance; hence, these results are

consistent with a positive relation between good governance, as measured by B+", and operating

performance. <esults using the contemporaneous and ne0t two years1 operating performance are

similar. 2owever, similar to #$%, B+"1s finding of a positive relation between good governance

and performance is based on long-term stock returns. The results in Appendi0 B-$$, 5anel B,

indicate there is no significant or consistent relation between B+"1s measure of governance and

contemporaneous, ne0t year1s or the ne0t two years1 stock returns, or Tobin1s .:H :D

The relation between T+61s measure of good governance and operating performance is

detailed in Table , 5anel +. hile this relation is negative and statistically significant for the

contemporaneous year, it is not significant for ne0t year1s and the ne0t two years1 operating

performance.

Table , 5anel G notes a negative but insignificant relation between Brown and +aylor1s

measure of good governance and operating performance. Since this inde0 is available only for

'((', and we have operating data only through '((), we do not report the relation between this

inde0 and ne0t two years1 operating performance.

:H "or robustness, we also estimate the performance-governance relation for each of the seven governance measuresusing the fi0ed effects estimator. The results are consistent with the results reported here. ne positive feature of panel data and the fi0ed effects estimator is that if there are firm-specific time-invariant omitted variables in theestimated e/uation, the coefficients are estimated consistently. 2owever, if the omitted variables are not stationaryover time, the fi0ed effects estimated coefficients are inconsistent; see ooldridge &'(('*. hen the omittedvariables are non-stationary, the instrumental variable techni/ue can yield consistent estimates if the instruments arevalid. As noted above, we use the Stock and Fogo &'((* weak instruments test to ascertain the validity of theinstruments used in Table and Appendi0 B.:D $n Appendi0 + we detail the robustness of the relation between the #$% governance inde0 and abnormal stockreturns, with respect to construction of the abnormal stock return, and sample period.

':



$n Table , 5anel , we note the relation between the dollar value of the median director1s

stock ownership and operating performance. e find a significant and positive relation between

the dollar value of the median director1s stock ownership and contemporaneous and ne0t year1s

operating performance. This relation is positive but insignificant when we consider the operating

performance of the ne0t two years.

The relation between +-+hair separation and operating performance is documented in

Table , 5anel ". +-+hair separation is positively and significantly related to

contemporaneous, ne0t year1s and ne0t two years1 operating performance.:8 This result, along

with the results for #$% and B+", suggests that greater managerial control leads to worse future

operating performance.

The final panel in Table , 5anel #, details the relation between board independence and

performance. Board independence is negatively and significantly related to contemporaneous,

ne0t year1s and ne0t two years1 operating performance. This result is surprising, especially

considering the recent emphasis that has been placed on board independence by the EFS and

EASGA regulations; however, it is consistent with prior literature &for e0ample, 2ermalin and

eisbach &'(()**.

$n summary, these results demonstrate that certain comple0 measures of corporate

governance #$% and B+" and certain simple measures director ownership and +-chair

separation are positively associated with current and future operating performance.:9 ther

:8 Eote that the governance variable +K+hair duality is : if the + is +hair and ( otherwise. 2ence, a negative

relation between +K+hair duality and performance is e/uivalent to a positive relation between +-+hairseparation and performance.:9 The dollar ownership of the median director has the greatest impact on ne0t year1s operating performance. ecompute the elasticities at the sample means from the 'S6S regression coefficients in Table , 5anels A, B, and . A: increase in the dollar ownership of the median director is correlated with an increase in ne0t year1s operating performance by .H:. A : improvement in the #$% inde0 &B+" inde0* is correlated with an increase in ne0tyear1s operating performance by .4: &.'*. The elasticities at the sample medians are as follows! A : increasein the dollar ownership of the median director is correlated with an increase in ne0t year1s operating performance by .H). A : improvement in the #$% inde0 &B+" inde0* is correlated with an increase in ne0t year1s operating performance by .4: &.''*.

''



measures seem to be less reliable indicators of performance. $t is also important to note that the

estimation method used does matter in certain cases. "or e0ample, in 5anel " for ne0t year1s

performance, we see that using 6S, +-+hair duality is significantly positively related with

operating performance, while it is significantly negatively related with operating performance

under both instrumental variables approaches. "or this reason, we believe it is important to rely

on inferences after controlling for the endogeneity between governance and performance.'(

5. Corporate governance and management turnover

The preceding analysis focused on the relation between governance and performance

generally. 2owever, governance scholars and commentators suggest that governance is

especially critical in imposing discipline and providing fresh leadership when the corporation is

performing particularly poorly. $t is possible that governance matters most in only certain firm

events, such as the decision to change senior management. "or this reason, we study the

relationship between governance, performance, and + turnover.

7sing +ompustat1s 0ecucomp database, we identify :,9') + changes from :99) to

'((). Table 4 documents the number of disciplinary and non-disciplinary + turnovers during

this period. ur criteria for classifying a + turnover as disciplinary or non-disciplinary is

similar to that of eisbach &:988*, #ilson &:989*, 2uson, 5arrino, and Starks &'((:*, and "arrell

and hidbee &'(()*. + turnover is classified as ?non-disciplinary@ if the + died, if the

+ was older than H), if the change was the result of an announced transition plan, or if the

'( Also, consider the performance-governance relationships estimated in Appendi0 B-$$, 5anel A. The 6S estimateindicates a significantly negative relation between the #$% inde0 and ne0t year1s Tobin1s , and the #$% inde0 andne0t two years1 Tobin1s . 2owever, the 'S6S estimate is negative but statistically insignificant for the one yearTobin1s , and positive and statistically insignificant for the two years1 Tobin1s . The 2ausman &:9D8*specification test suggests that the 'S6S are more appropriate for inferences. Similarly, as detailed in Appendi0 B-$$,5anel B, the 6S and 'S6S estimates for the relation between the B+" inde0 and future Tobin1s are statisticallyand economically different. Again, the 2ausman &:9D8* specification test suggests that the 'S6S are moreappropriate for inferences.

')



+ stayed on as chairman of the board for more than a year. + turnover is classified as

?disciplinary@ if the + resigned to pursue other interests, if the + was terminated, or if no

specific reason is given.

e consider a multinomial logit regression.': The dependent variable is e/ual to ( if no

turnover occurred in a firm-year, : if the turnover was disciplinary, and ' if the turnover was

non-disciplinary. e consider the past two years1 stock return as the performance measure. e

estimate the following baseline e/uation!

Type of + Turnover M g 1&5ast ' years1 stock return, L1, 1* &'a*

The L1 vector of controls includes + ownership, + age, + tenure, firm si>e, industry

return and year dummy variables. These control variables are motivated by a substantial e0tant

literature on performance and + turnover; for e0ample, see 2uson, 5arrino, and Starks

&'((:*, "arrell and hidbee &'(()*, and ngel, 2ayes and ang &'(()*. To determine the role

that governance plays in + turnover, we create an interactive variable that is e/ual to &5ast '

years1 stock return 5 #overnance*. The reason behind this is that if the firm is performing

ade/uately, good governance should not lead to + turnover; only when performance is poor

do we e0pect better governed firms to be more likely to replace the +. To measure this effect,

we estimate the following modified version of e/uation &'a*!

Type of + Turnover M g 2&5ast ' years1 stock return, , #overnance,&5ast ' years1 stock return 5 #overnance*, L1, 2* &'b*

Table H highlights the relation between different measures of governance and disciplinary +

turnover. Table H, 5anel A, details the multinomial logit regression results for the determinants of

disciplinary + turnover. +onsider first the baseline results without governance variables in the

': e also considered a fi0ed effects logit estimator model. 2owever, there are concerns regarding the bias of suchan estimator. #reene &'((* documents that when the time periods in panel data are five or less &as is the case inthis study*, nonlinear estimation may produce coefficients that can be biased in the range of )' to H8.

'



regression. The baseline results indicate that a firm1s stock market returns during the previous

two years, + stock ownership, and + tenure are significantly negatively related to

disciplinary + turnover; these findings are consistent with the prior literature noted above.

$nterestingly, we find that the prior two years1 returns of similar firms in the industry is

significantly positively related to disciplinary + turnover.

Goes good governance have an impact on disciplinary + turnover directly, or is

governance related to disciplinary turnover only in poorly performing companies= The results in

Table H, 5anel A, shed light on this /uestion. Eote that when the governance variables are

included, the prior return variable is not significant in five of the seven cases, suggesting that bad

performance alone is not enough to lead to a change in senior management. Also note that the

governance variable by itself is statistically not significant in most cases.'' This suggests that

good governance per se is not related to disciplinary turnover. The coefficient of the interactive

term &5ast ' years1 stock return 5 #overnance* sheds light on the /uestion whether governance is

related to disciplinary turnover only for poorly performing firms. The interactive term suggests

that good governance as measured by the dollar value of the median director1s stock ownership

and the percentage of directors who are independent, increases the probability of disciplinary

turnover for poorly performing firms.') ' Both the #$% and B+" measures of good governance

are negatively related to the probability of disciplinary turnover for poorly performing firms.

"inally, when the + is also the +hairman, he is more likely to e0perience disciplinary

turnover given poor firm performance.

'' The e0ceptions are! the T+6 governance inde0 which is positively related to disciplinary + turnover. Also,when the + is also the +hairman, he is less likely to e0perience disciplinary turnover.') The finding of the probability of disciplinary + turnover &given poor prior firm performance* increasing withgreater board independence is consistent with the e0tant literature, for e0ample, see "ich and Shivdasani &'((4*, andeisbach &:988*.' Appendi0 " highlights the economic importance of the dollar ownership of the median director and boardindependence as governance variables.

'4



Table H, 5anel B, details the multinomial logit regression results for the determinants of

non-disciplinary + turnover. e do not e0pect any relation between good governance and

non-disciplinary + turnover both unconditionally, and conditional on poor prior performance;

the results in 5anel B are consistent with this.

Gue to data limitations the sample periods and sample si>es for the various governance

measures are different in Table H, 5anels A and B. $t is possible that the significant relationship

between a governance measure and disciplinary turnover in a poorly performing firm may be

sample-period specific, or is being influenced by the different sample si>es. To address this

concern, we consider disciplinary turnovers only for the period '((( through '((' for all

governance measures in 5anel +. The results in 5anel + are consistent and stronger in some

cases with the results in 5anel A.

6. ummar! and conclusions

ur primary contribution to the literature is the consistent estimation of the relationship

between corporate governance and performance2 by ta(ing into account the inter!relationships

among corporate governance2 management turnover2 corporate performance2 corporate capital

structure2 and corporate ownership structure. e make three additional contributions to the

literature!

"irst, instead of considering just a single measure of governance &as prior studies in the

literature have done*, we consider seven different governance measures. e find that better

governance as measured by the #$% and B+" indices, stock ownership of board members, and

+-+hair separation is significantly positively correlated with better contemporaneous and

subse/uent operating performance. f the above four measures, stock ownership of board

'H



members has the greatest impact on ne0t year1s operating performance. Additionally, better

governance as measured by Brown and +aylor & that considers 4' separate charter provisions and

board characteristics*, and The +orporate 6ibrary &that considers over a hundred variables

concerning board characteristics, management compensation policy, and antitakeover measures*

is not significantly correlated with better contemporaneous or subse/uent operating performance.

Also, interestingly, board independence is negatively correlated with contemporaneous and

subse/uent operating performance. This is especially relevant in light of the prominence that

board independence has received in the recent EFS and EASGA corporate governance

listing re/uirements. "inally, none of the governance measures are correlated with future stock

market performance.

Second, in several instances our inferences regarding the performance-governance

relationship do depend on whether or not one takes into account the endogenous nature of the

relationship between governance and performance. "or e0ample, the 6S estimate indicates a

significantly negative relation between the #$% inde0 and ne0t year1s Tobin1s , and the #$%

inde0 and ne0t two years1 Tobin1s . 2owever, the 'S6S estimate is negative but statistically

insignificant for the one year Tobin1s , and positive and statistically insignificant for the two

years1 Tobin1s . The 2ausman &:9D8* specification test suggests that the 'S6S estimates are

more appropriate for inferences. Similarly, the 6S and 'S6S estimates for the relation between

the B+" inde0 and future Tobin1s are statistically and economically different. Again, the

2ausman &:9D8* specification test suggests that the 'S6S estimates are more appropriate for

inferences. $n both cases the 'S6S results suggest no relationship between the #$% inde0 and

future Tobin1s , and the B+" inde0 and future Tobin1s . "or this reason, we believe it is

'D



important to rely on inferences after controlling for the endogeneity between governance and

performance.

Third, given poor firm performance, the probability of disciplinary management turnover

is positively correlated with stock ownership of board members, and with board independence.

2owever, given poor firm performance, the probability of disciplinary management turnover is


The above findings have important implications for finance researchers, senior policy

makers, and corporate boards! Efforts to improve corporate governance should focus on stoc(

ownership of board members since it is positively related to both future operating performance,

and to the probability of disciplinary management turnover in poorly performing firms.

5roponents of board independence should note with caution the negative relation between board

independence and future operating performance. 2ence, if the purpose of board independence is

to improve performance, then such efforts might be misguided. 2owever, if the purpose of board

independence is to discipline management of poorly performing firms, then board independence

has merit. "inally, even though the #$% and B+" good governance indices are positively related

to future performance, policy makers and corporate boards should be cautious in their emphasis

on the components of these indices since this might e0acerbate the problem of entrenched

management, especially in those situations where management should be disciplined, that is, in

poorly performing firms.

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'8



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Censen, %ichael, :98H, Agency costs of free cash flow, corporate finance, and takeovers, =merican Economic ;eview DH, )')-)'9.

Censen, %ichael, and illiam %eckling, :9DH, Theory of the firm! %anagerial behavior, agencycosts, and ownership structure, Journal of inancial Economics ), )(4-)H(.

Censen, %ichael, and Cerold B. arner, :988, The distribution of power among corporatemanagers, shareholders and directors, Journal of inancial Economics '(, )-'.

Cohnston, Cack and Cohn GiEardo, :99D, Econometric 4ethods, "ourth edition, The %c#raw-2ill +ompanies.

3ennedy, 5eter, '((), = 1uide to Econometrics, "ifth dition, %$T 5ress.

3hanna, E. and S. Tice, '((4, 5ricing, e0it, and location decisions of firms! vidence on the roleof debt and operating efficiency, Journal of inancial Economics D4, )9D-'8.

6inck, Cames S., Ceffry %. Eetter and Tina Fang, The determinants of board structure, 7niversityof #eorgia working paper, '((4.

%addala, #.S., :99', ntroduction to Econometrics, Second dition, %ac%illan.

):



%ilanovic, Branko, Go more une/ual countries redistribute more= Goes the median Joterhypothesis hold=, orld Bank policy research working paper series, +arnegiendowment for $nternational 5eace, '((

%orck, <andall, Andrei Shleifer, and <obert . Jishny, :988, %anagement ownership and

market valuation, Journal of inancial Economics '(, '9)-):4.

%yerson, <oger, :98D, $ncentive compatibility and the bargaining problem, Econometrica D,H:-D).

Eelson, +harles <. and <ichard Start>, :99(, Some further results on the e0act small sample properties of the instrumental variables estimator, Econometrica 48, 9HD-9DH.

Eovaes, alter, and 6uigi Lingales, :999, +apital structure choice under a takeover threat,7niversity of +hicago working paper.

5alia, Garius, '((:, The endogeneity of managerial compensation in firm valuation! A solution, ;eview of inancial -tudies :, D)4-DH.

<oe, %ark C., :99, -trong managers2 wea( owners: +he political roots of =merican corporate

finance, 5rinceton 7niversity 5ress, 5rinceton, EC.

<othenberg, Thomas C., :98, UAppro0imating the Gistributions of conometric stimators andTest Statistics,U +hapter :4 in andboo( of Econometrics, Jolume $$, edited by Lvi#riliches and %ichael G. $ntriligator, Amsterdam! Eorth 2olland, 88:-9)4.

Shleifer, Andrei and 3evin %. %urphy, 5ersuasion in politics, =merican economic association

papers and proceedings, Jol. 9, Eo. ', %ay '((

Smith, +lifford . and <oss 6. atts, :99', The investment opportunity set and corporatefinancing, dividend and compensation policies, Journal of inancial Economics )', 'H)-'9'.

Staiger, Gouglas and Cames 2. Stock, :99D, ?$nstrumental Jariables <egression with eak$nstruments,@ Econometrica H4&)*, 44D-48H.

Stock, Cames 2., and %otohiro Fogo, 2**+, -esting %or eak instrments in linear/0 regression, in ..3. Andres and J.4. "tock, eds., Identifcation andInerence or Econometric Models: Essays in Honor o Thomas J.Rothenberg. Cam&ridge! Cam&ridge 5ni6ersity ress.

"tl8, ene :, 19((, :anagerial control o% 6oting rights! ;inancing policiesand the market %or corporate control, Journal o inancial Economics 2*, 2<)<+.

)'



eis&ach, :ichael "., 19((, $tside directors and C#$ trno6er, Journal oinancial Economics 2*, +'2)+=*.

ooldridge, J.:., 2**2, #conometric Analysis o% Cross "ection and anelata, :/- ress, Cam&ridge, :assachsetts.

>ermack, a6id, 199=, 4igher market 6alation %or ?rms ith a small &oardo% direc@tors, Journal o inancial Economics +*, 1(<)211.

))



Table :Gescription of variablesThis table presents descriptions of variables used in this study. $t also shows the years for which we have data available and the total number of observations wehave of each variable. The full sample period is from :99( to '((.

9anel =: 1overnance 8ariables*ears

=vailable -ample -iCe

&A* #$% #-$nde0The #-$nde0 is constructed from data compiled by the $nvestor <esponsibility <esearch +enter &U$<<+U*, as described in #ompers,

$shii, %etrick &'(()*. A firmIs score is based on the number of shareholder rights-decreasing provisions a firm has. The inde0 ranges

from a feasible low of ( to a high of '. A high #-Score is associated with weak shareholder rights, and a low #-Score is associated

with high shareholder rights.

AA%2 AA32

AA$2 AA"2

'%%%2 '%%'

%2'

&B* B+" -$nde0The -$nde0 is constructed from $<<+ data as described in Bebchuk, +ohen, "errell &'((*. $t uses a H-provision subset of the #-

$nde0. The inde0 ranges from a feasible low of ( to a high of H; a high score is associated with weak shareholder rights and a low

score is associated with high shareholder rights.

AA%2 AA32

AA$2 AA"2

'%%%2 '%%'

%2'

&+* T+6 Benchmark ScoreThe +orporate 6ibrary is a commercial vendor of corporate governance data, analysis and risk assessment

tools. The benchmark score is based on the following criteria! whether the board is classified, whether the outside directors constitute

a majority on the board, whether the board has an independent chairman or lead director, whether the audit committee consists of only

independent directors, whether the board has adopted a formal governance policy, number of directors with more than fifteen years

tenure, number of directors who serve on more than four boards, number of directors older than seventy years old, and +

compensation structure. The inde0 ranges from a feasible low of ( to a high of :((. "or more

information, see www.thecorporatelibrary.com.

'%%!'%%3 2#%

&G* B+ #ovScoreThe #ovScore is constructed from data compiled by $nstitutional Shareholder Services &U$SSU*, as described in Brown, +aylor &'((*.

"ifty-two firm characteristics and provisions are used to assign a score to each firm. The feasible range of scores is from ( to 4'. A

high score is associated with better corporate governance.

'%%' '2$3"

&* Board $ndependenceThe number of unaffiliated independent directors divided by the total number of board members.

This measure is constructed from data provided by $<<+ and T+6.

AA&!'%%3 #2A"%

&"* %edian Girector Gollar Jalue wnershipThe dollar value of the stock ownership K voting power is calculated for all directors. e take the median directorIs holdings as the

governance measure as this individual can be viewed as having the IswingI vote in governance related matters. This variable is

calculated from data provided by $<<+ and T+6.

AA"!'%%' &2'&

&#* %edian Girector 5ercent Jalue wnershipThe percentage ownership of the firmIs total voting power is calculated for all directors. e take the median directorIs ownership as

the governance measure as this individual can be viewed as having the IswingI vote in governance related matters. This variable is

calculated from data provided by $<<+ and T+6.

AA"!'%%' &2'&

)



Table : &continued*

9anel =: 1overnance 8ariables >continued?

*ears


&2* + +hair K GualityA dummy variable e/ual to : if the + is also the chairman of the board. This measure is constructed from data provided by $<<+

and T+6.

/AA"!'%%' /'2$'/

&$* Alternative #overnance %easures$n some anslyses, we consider si0 alternative measures of corporate governance! &:* the percentage of directors who are currently

active +s, &'* the percentage of directors currently serving on more than four boards, &)* the percentage of directors who have

served on the sample firmIs board for more than fifteen years, &* the percentage of directors who are older than seventy years old, &4*the percentage of directors who are women, and &H* the percentage of directors who do not own any stock in the sample firm.

/AA"!'%%' /$2A&

to

/#2AA3

9anel 6: 9erformance 8ariables

*ears


&A* <eturn on Assetse measure <A as operating income divided by end of year total assets &+ompustat data item H*. $n general, following Barber and

6yon &:99H*, we use operating income before depreciation &+ompustat data item :)*. 7nless otherwise noted, this is our measure for

<A. $n some cases, we use operating income after depreciation &+ompustat data item :D8*. These cases are pointed out e0plicitly.

/AA%!'%% '/2&"/

&B* Stock <eturne use the +<S5 monthly stock file to calculate one-year compound returns, including dividends. /AA%!'%% /&2A3&

&+* TobinIs e use the TobinIs measure as in #ompers, $shii and %etrick&'(()*! &Book Jalue of Assets O %arket Jalue of +ommon Stock -

Book Jalue of +ommon Stock - Geferred Ta0es* K Book Jalue of Assets.

/AA%!'%% /#2$"#

&G* 6ast ' Fears 5erformance"or <A and TobinIs , we use the average measure for years t-' and t-:. "or Stock <eturn, we use the one-year compound return

for years t-' and t-:.

/AA%!'%% /&2''" !

/A2A''

&* $ndustry 5erformance"or all industry performance measures, we calculate the mean performance for each S$+ four-digit classification. e do this for

<A, return, and TobinIs as discussed above. ne-year and two-year performance is calculated as above.

/AA%!'%% /"2$%3 !

'/2A%'

)4



Table : &continued*

9anel 0: ther 8ariables

*ears


&A* Assets+ompustat data item H AA%!'%% '2'$$

&B* GebtTotal debt K Total Assets. Gebt is total long-term debt, +ompustat data item 9 O +ompustat data item ). AA%!'%% #23"

&+* 0penses<PG and Advertising 0penses K Total Assets. <PG is +ompustat data item H and advertising is data item 4. Similar to 5alia

&'((:*, we use a dummy variable to identify firms for which this variable is not missing.

AA%!'%% '2'3%

&G* Board Si>e

The total number of directors, obtained from $<<+ and T+6. AA&!'%%3 #2AA3&* + Age

The age of the +, obtained from 0ecucomp and T+6. AA'!'%%3 %2AA%

&"* + TenureThe number of years the + has been +, obtained from 0ecucomp and T+6. AA'!'%%3 %2&$

&#* Girector AgeThe median directorIs age, obtained from $<<+ and T+6. AA"!'%%3 $23&%

&2* Girector TenureThe number of years the median director has been on the board, obtained from $<<+ and T+6. AA"!'%%3 $23&%

&$* <isk The standard deviation of the monthly stock return for the five preceding years. AA%!'%% $2'#'

9anel 7: wnership8ariables*ears


&A* + wnershipThe percent of the firmIs stock owned by the +. This variable is constructed from the 0ecucomp database. AA'!'%%3 32%

&B* $nstitutional wnershipThe percent of stock owned by all institutions. This variable is constructed from the $<<+ database. AA&!'%%3 #2$#&

&+* fficer P Girector wnershipThe percent of the firmIs stock owned by all officers and directors. This variable is provided by T+6. AA&!'%%3 2A

)H



Table 'Gescriptive statisticsThis table presents the mean, median and number of observations for the primary performance, governance and control variables used in this study. Statistics forall available years and for '((' only are presented.

#ean #edian $ of %&s. #ean #edian $ of %&s.

=. 1overnance 8ariables

6og W Jalue, %edian Girector :4.'H :).'89 H,:'H :.(9( :'.4H :,8'Gollar value, median director ,'4D,D)8 49(,48' H,:'H :,):4,4:D '8H,:(9 :,8'

holdings, median director (.:9 (.( H,:): (.:( (.(' :,8:

#$% #-$nde0 9.(:4 9.((( :(,:': 9.()( 9.((( :,89

B+" -$nde0 '.(D( '.((( :(,:': '.'' '.((( :,89

B+ #ovScore ''.H9 ''.((( ',4)8 ''.H9 ''.((( ',4)8

T+6 benchmark score H(.:D H:.((( ,D(: 4H.D4( 44.((( :,4)

independent directors H(.'8 H.D: :D,98( H).8 HH.HD :,99D

+K+hair duality H.8' :((.(( :',4': HH.9( :((.(( :,99

directors, +s '4.9) '(.(( :D,99) '4. ').(8 :,99D

directors, on O boards .8' (.(( :H,(4' H.) (.(( :,99D

directors, :4O years tenure :H.: (.(( :H,'98 :.H: 9.(9 :,99D

directors, over D( 9.)8 (.(( :4,9H 8.(' (.(( :,99D

directors, women D.H4 D.: :H,H(4 8.94 9.(9 :,99D

directors, ( shares ::.'9 (.(( :H,4'9 ').8) ::.:: :,99D

6. 9erformance 8ariables

<eturn, annual :D.:) :'.DH :H,9)H -:'.99 -:(.D4 :,84

<A, annual :).8( :).4 ':,H8: ::.(( :(.88 :,H8(

TobinIs , annual '.(D' :.4(8 :D,48D :.H): :.'98 :,4H

7. ther 8ariables

Assets &0 W:,(((,(((* :,): :,''H ','44 ',D( ','9) :,D'D

+ Age 4.H'8 44.((( :(,99( 4.9' 44.((( :,D

+ Tenure 8.849 D.9(9 :(,H4: H.9: .((( ',:)

Girector tenure, average D.4) 4.(H( :9,D:8 8.DH: 8.)(( :,9'(

+ holdings, '.9' (.) :),( '.H (.): :,498

'll 'vaila&le (irm )ears 2**2 %nl!

)D



Table )+orrelation coefficientsThis table presents the correlation coefficients for the performance and governance variables. The performancevariables are in 5anel A and the governance variables are in 5anel B. The 5earson correlation coefficients are abovethe diagonal and the Spearman rank correlation coefficients are below the diagonal. Significant coefficients at the:, 4, and :( levels are noted by XXX, XX and X, respectively.

9anel =:

"eturn "%' +o&ins -

<eturn (.)4XXX (.D4XXX

<A (.)':XXX (.:9HXXX

TobinIs (.48XXX (.'4:XXX

9anel 6:

#$% #-

$nde0

B+" -

$nde0

T+6

Benchmark

Score

B+

#ovScore

$nde-

pendent

Girector

2oldings

+ K +hair

Guality

(.D:9XXX -(.)'DXXX -(.:(4XXX (.'D4XXX (.((4 (.(88XXX

(.D'HXXX -(.)48XXX -(.:H:XXX (.'H)XXX -(.(8)XXX (.(H'XX

-(.))XXX -(.)DDXXX (.):XXX (.(88XXX -(.::HXXX -(.'(:XXX

-(.::XXX -(.:H9XXX (.)::XXX (.)4XXX -(.(:) (.(89XXX

(.'8HXXX (.'H)XXX (.(H9XX (.)4XXX -(.:DXXX (.:8)XXX

(.(:) -(.(D)XXX -(.:'4XXX -(.()' -(.::XXX (.()X

(.(9XXX (.(H8XX -(.:D9XXX (.(D8XX (.:9XXX (.(8X

#$% #-$nde0

B+" -$nde0

T+6 Benchmark

Score

B+ #ovScore

$ndependent

Girector 2oldings

+ K +hair Guality

)8



Table Simultaneous /uations System stimation, 5erformance %easured by <eturn on AssetsThis table presents the coefficient estimates for performance, governance and + ownership as estimated in the following system!

&:a* 5erformance M f &wnership, #overnance, 6og&Assets*, $ndustry 5erformance, Gebt K Assets, &<PG andAdvertising 0penses* K Assets, Board Si>e, Stock Jolatility, Treasury Stock K Assets, 1*,

&:b* #overnance M f ' &5erformance, wnership, +apital Structure, &<PG and Advertising 0penses* K Assets, BoardSi>e, Stock Jolatility, %edian Girector wnership 5ercentage, 5ercentage $ndependent Girectors, 2*

&:c* wnership M f 3 &5erformance, #overnance, 6og&Assets*, +apital Structure, &<PG and Advertising 0penses* KAssets, Board Si>e, Stock Jolatility, + Tenure K + Age, '*

nly the coefficients for governance and + ownership from the first e/uation &:a* are presented in the table. 5erformance ismeasured by <eturn on Assets &?<A@*. wnership is measured by the percent of stock owned by the + at time t in all panels&?+ wn@*. #overnance is measured by a different variable in each panel. All governance variables are as of time t . $n 5anel A,the #ompers, $shii and %etrick &'(()* 1-$nde0 is used as the governance variable. $n 5anel B, the Bebchuk, +ohen and "errell&'((* E!$nde0 is used as the governance variable. $n 5anel +, T+6 Benchmark score is used as the governance variable. $n 5anel G,the Brown and +aylor &'((* #ovScore is used as the governance variable &data is available only for '(('*. $n 5anel , the dollarvalue of the median director1s stock holdings is used as the governance variable. $n 5anel ", a dummy variable e/ual to : if the +is also the +hair of the board, ( otherwise, is used as the governance variable. $n 5anel #, the percent of directors who areindependent is used as the governance variable. &$n 5anel # e/uation &:b*, the right-hand side variable ?5ercentage $ndependentGirectors@ is replaced with 5ercentage of Girectors ho Are Active +s.* <esults are presented using performance in time t , tD2 and tD to tD'. ach system is estimated using 6S, 'S6S, and )S6S. The 2ausman &:9D8* specification test is performed on eachsystem to determine which estimation method is most appropriate. The null hypothesis is that the methods are e/uivalent, so wereject the null for high h-statistics. The Stock and Fogo &'((* test for weak instruments is also performed. The -Jalue from thefirst-stage regression for each of the three potentially endogenous regressors is presented. $f the -Jalue e0ceeds the critical value

&using 4 bias* from Stock and Fogo &'((*, the instruments are deemed to be valid. The number of observations used in each panel-performance period varies so to ma0imi>e the sample si>e for the panel-performance period. +oefficient estimates are presented, with p-values in parentheses.

)9



Table 5anel A! #ompers, $shii and %etrick &'(()* #-$nde0 is the governance measure &U#ovU*<eturn on Assets is the performance measure &U<AU*

% Estimate p!value % Estimate p!value % Estimate p!value<A M #ov -(.((: >%.? <A M #ov -(.((: >%.%3? <A M #ov -(.((: >%.%'?

+ wn (.(4) >%.%? + wn (.(D >%.%%? + wn (.('( >%.%?

2 2 2

<A M #ov -(.((8 >%.%%? <A M #ov -(.((D >%.%? <A M #ov -(.((4 >%.%'?+ wn (.'': >%.%%? + wn (.)4) >%.%%? + wn (.(84 >%.%?

3 3 3<A M #ov -(.((9 >%.%%? <A M #ov -(.((D >%.%'? <A M #ov -(.((4 >%.%'?

+ wn (.''4 >%.%%? + wn (.)49 >%.%%? + wn (.(9 >%.%#?

Sample Si>e ,48D Sample Si>e ,44( Sample Si>e ),(9

/ausman 01 pecification +est /ausman 01 pecification +est /ausman 01 pecification +esth-statistic p-value h-statistic p-value h-statistic p-value

6S v. 'S6S :)).H( &(.((* 6S v. 'S6S :4D.D( &(.((* 6S v. 'S6S 99.:H &(.((*6S v. )S6S -D4.'( - 6S v. )S6S -H'.:( - 6S v. )S6S -:)9.(( -'S6S v. )S6S -:H.'( - 'S6S v. )S6S -:'.9( - 'S6S v. )S6S -)(.( -

toc and )ogo 02**4 7ea Instruments +est toc and )ogo 02**4 7ea Instruments +est toc and )ogo 02**4 7ea Instruments +est

"irst-Stage -Jalue +riticalJalue "irst-Stage -Jalue +riticalJalue "irst-Stage -Jalue +riticalJalue <A :D.'4 :8.DH <A ::8.H :8.DH <A :()H.'8 :8.DH #ov H4.'9 :8.DH #ov H.9' :8.DH #ov H.( :8.DH + wn :(9.'( :8.DH + wn :(D.(4 :8.DH + wn 8).99 :8.DH

Contemporaneous 8erformance 9e:t 1 )ear 8erformance 9e:t 2 )ears 8erformance

(



Table 5anel B! Bebchuk, +ohen and "errel &'((* -$nde0 is is the governance measure &U#ovU*<eturn on Assets is the performance measure &U<AU*

% Estimate p!value % Estimate p!value % Estimate p!value<A M #ov -(.(( >%.%%? <A M #ov -(.((4 >%.%%? <A M #ov -(.((' >%.%%?

+ wn (.(' >%.%? + wn (.(H' >%.%%? + wn (.(:4 >%.'3?

2 2 2

<A M #ov -(.(:H >%.%%? <A M #ov -(.(:4 >%.%? <A M #ov -(.(:( >%.%?+ wn (.:8( >%.%'? + wn (.): >%.%%? + wn (.(49 >%.3?

3 3 3<A M #ov -(.(:H >%.%%? <A M #ov -(.(:4 >%.%? <A M #ov -(.((9 >%.%'?

+ wn (.:8H >%.%'? + wn (.)(8 >%.%%? + wn (.(D >%.'%?

Sample Si>e ,48D Sample Si>e ,44( Sample Si>e ),(9

/ausman 01 pecification +est /ausman 01 pecification +est /ausman 01 pecification +est

h-statistic p-value h-statistic p-value h-statistic p-value6S v. 'S6S :)H.)( &(.((* 6S v. 'S6S :D.'( &(.((* 6S v. 'S6S :(D.(( &(.((*6S v. )S6S -'4H.(( - 6S v. )S6S -:89.(( - 6S v. )S6S 94.) &(.((*'S6S v. )S6S -D.4( - 'S6S v. )S6S :D'.:( &(.((* 'S6S v. )S6S -:'H.(( -


"irst-Stage -Jalue +riticalJalue "irst-Stage -Jalue +riticalJalue "irst-Stage -Jalue +riticalJalue <A :D.'4 :8.DH <A ::8.H :8.DH <A :()H.'8 :8.DH #ov 4H.D8 :8.DH #ov 44.8H :8.DH #ov :.H9 :8.DH + wn :(9.'( :8.DH + wn :(D.(4 :8.DH + wn 8).99 :8.DH


:



Table 5anel +! T+6 Benchmark Score is the governance measure &U#ovU*<eturn on Assets is the performance measure &U<AU*

% Estimate p!value % Estimate p!value % Estimate p!value<A M #ov -(.((: >%.%$? <A M #ov -(.((: >%.'$? <A M #ov (.((( >%.$#?

+ wn (.(HD >%.%3? + wn (.(D) >%.%'? + wn (.(:) >%.&%?

2 2 2

<A M #ov -(.(( >%.%'? <A M #ov -(.((' >%.$? <A M #ov -(.((: >%.'?+ wn -(.(: >%.A3? + wn (.:H: >%.3%? + wn (.('' >%."?

3 3 3<A M #ov -(.(( >%.%'? <A M #ov -(.((' >%.$? <A M #ov -(.((: >%.'%?

+ wn -(.(:: >%.A? + wn (.:H >%.'A? + wn (.(:) >%.A%?

Sample Si>e ',:9H Sample Si>e ',:)4 Sample Si>e 9D'

/ausman 01 pecification +est /ausman 01 pecification +est /ausman 01 pecification +est

h-statistic p-value h-statistic p-value h-statistic p-value6S v. 'S6S HH.H' &(.((* 6S v. 'S6S H4.:4 &(.((* 6S v. 'S6S )9.H' &(.(H*6S v. )S6S :'.H &(.99* 6S v. )S6S -:.): - 6S v. )S6S :H.)( -'S6S v. )S6S -:.9 - 'S6S v. )S6S -:.)H - 'S6S v. )S6S -D.48 -


"irst-Stage -Jalue +riticalJalue "irst-Stage -Jalue +riticalJalue "irst-Stage -Jalue +riticalJalue <A 8).HD :8.DH <A 9.)9 :8.DH <A )H.4H :8.DH #ov '.' :8.DH #ov ''.) :8.DH #ov '(.:( :8.DH + wn 4(.(( :8.DH + wn H.H8 :8.DH + wn ').88 :8.DH


'



Table 5anel G! Brown and +aylor &'((* #ovScore is the governance measure &U#ovU*<eturn on Assets is the performance measure &U<AU*

% Estimate p!value % Estimate p!value<A M #ov -(.((: >%.$'? <A M #ov (.((( >%."? 9'

+ wn (.:: >%.%%? + wn (.(88 >%.%$?

2 2

<A M #ov -(.((H >%.%3? <A M #ov -(.(() >%.3&?+ wn (.:8H >%.''? + wn (.:(9 >%.$$?

3 3<A M #ov -(.((H >%.%3? <A M #ov -(.(() >%.3#?

+ wn (.:)H >%.3#? + wn -(.(88 >%.&3?

Sample Si>e 8:( Sample Si>e DD:

/ausman 01 pecification +est /ausman 01 pecification +est

h-statistic p-value h-statistic p-value6S v. 'S6S ''.'8 &(.DD* 6S v. 'S6S :.:) &(.99*6S v. )S6S -4:.(( - 6S v. )S6S -').D( -'S6S v. )S6S -'.4( - 'S6S v. )S6S -:D.H( -

toc and )ogo 02**4 7ea Instruments +est toc and )ogo 02**4 7ea Instruments +est

"irst-Stage -Jalue +riticalJalue "irst-Stage -Jalue +riticalJalue <A 4(.9( :8.DH <A ':.': :8.DH #ov :9.'9 :8.DH #ov :D.'8 :8.DH + wn :).48 :8.DH + wn ::.)4 :8.DH


)







Table 4<easons for + turnover This table presents the classifications for reasons why + turnover occurred in a specific year. 6e0is-Ee0is archives were reviewedto determine the stated reason for why a + left the firm. + turnover data was obtained from +ompustat1s 0ecucomp database.+ Turnover is classified as ?Eon-disciplinary@ if the + died, if the + was older than H), if the change was the result of anannounced transition plan, or if the + stayed on as chairman of the board for a nontrivial length of time. + Turnover isclassified as ?Gisciplinary@ if the + resigned to pursue other interests, if the + was fired, or if no specific reason is given.

&:* &'* &)* &* &4* &H* &D*

Geceased lder Than H)

<etired K

Succession

5lan

+ Stayed

as +hair

+orporate

+ontrol <esigned Terminated

Eo <eason

#iven

Eo

$nformation Total

:99) : ' :) ( :' ) ( ( )4

:99 : :) 4 '8 ' ') ' : ( ::4

:994 4 :4 4' 4: : ( :DH

:99H ) :' 4 )8 4 : :H4

:99D : :) H: )8 H D 4 ' ( :D)

:998 :D 4D ( :D 4D 4 ) : '(:

:999 : :9 HH : H) : ' : :98

'((( ) : 8: 4 8 8 4 ) : '

'((: H ') D9 4 D DH H ( '44

'((' ) :D )H : D' 9 ( ( :8'

'(() ' '' ) )H : H9 :( ) ' :D9

+otal 3* 16 5 41 54 52 55 2* 1,23

of Total :.H 8.D )(.: ':.D '.8 )(.8 '.9 :.( (.4

9on;Disciplinar! +urnover Disciplinar! +urnover

H



Table H%ultinomial logit models for + turnover This table presents the results from multinomial logistic regressions estimating the probability of + Turnover. The dependentvariables are type of + turnover! : M Gisciplinary turnover, ' M Eon-disciplinary turnover, ( M no turnover. Eo turnover is the baseline category. Baseline results are presented in the first column; all other columns present results including #overnance and&5erformance 5 #overnance* variables. 5erformance is measured as the compound stock return for the two years prior to the year ofturnover. The governance variables are described in Table $. The other control variables are also described in Table $. Fear dummyvariables are included but are not shown. 5anel A presents the results for disciplinary turnover for all available years; 5anel B presents the results for non-disciplinary turnover for all available years. 5anel + presents the results for disciplinary turnover for'((( to '((' only; 5anel G presents the results for non-disciplinary turnover for '((( to '((' only. +oefficients are presented and p-values are in parentheses.

D



9anel =: 0isciplinary +urnover2 all available years

Baseline

5erformance #$% #-$nde0 B+" -$nde0

T+6

Benchmark

Score B+ #ovScore

W Jalue of

%edian

GirectorIs

2oldings

+ K +hair

Guality

&M: if Gual*

of Girectors

$ndependent

-::.'(( -9.' -9.HH -.9:D -'.')' -'.D4) -.:' -).HD)>%.%%? >%.%%? >%.%%? >%.%%? >%.'$? >%.%%? >%.%%? >%.%%?

-'.('9 -(.( -(.8H( -.)9( -'.D (.4'9 -:.4'H (.')>%.%%? >%.#? >%."? >%.%'? >%.$#? >%.&&? >%.%%? >%.#'?

:.(D9 :.4(H :.4: (.9H: :.)4) :.(4: :.(48 :.:(:>%.%%? >%.%%? >%.%%? >%.%3? >%.'? >%.%%? >%.%%? >%.%%?

- -(.((9 (.(') (.(:9 -(.(H -(.(): -(.DH( -(.:! >%."? >%.##? >%.%? >%.'? >%.$%? >%.%%? >%.'&?

- -(.''( -(.D(( (.(: (.()8 -(.'(8 -(.88D -).449! >%.? >%.%? >%.&? >%."? >%.%3? >%.%#? >%.%%?

-:(.') -H.:)4 -H.(H -D.H)H -:H.) -9.):H -8.D:4 -:(.9'>%.%%? >%.%&? >%.%#? >%.%? >%.'%? >%.%%? >%.%%? >%.%%?

-(.(D9 -(.(H9 -(.(H9 -(.(8H -(.''H -(.(8 -(.()D -(.(88>%.%? >%.'$? >%.'$? >%.%? >%.%&? >%.%A? >%.? >%.%3?

(.(:: (.(:8 (.(:9 (.()' (.(4: (.(:4 (.(:' (.(::>%.'"? >%.'$? >%.'3? >%.%'? >%.%"? >%.'? >%.'#? >%.'#?

-(.('9 -(.(9 -(.(8 -(.(H -(.(' -(.('D -(.(): -(.()(>%.%'? >%.%? >%.%? >%.%? >%.'#? >%.%#? >%.%'? >%.%'?

Fears $ncluded :99)-'(() :99) , I94, I98,

I((, I('

:99) , I94, I98,

I((, I('

'((:-'(() '((' :998-'((' :99H-'(() :99H-'(()

Sample Si>e 8,9H4 ),)'9 ),)'9 ),88 D88 ,DHH H,8D: D,'D8

Governance Variable

+ Tenure

+ Age

&<eturn, 6ast ' years 0

#overnance

+ wn

$ntercept

Si>e &Assets*

<eturn, 6ast ' years

$ndustry <eturn, 6ast '

years

#overnance

8



9anel 6: )on!disciplinary +urnover2 all available years

Baseline


T+6

Benchmark

Score B+ #ovScore

W Jalue of

%edian

GirectorIs

2oldings

+ K +hair

Guality

&M: if Gual*

of Girectors

$ndependent

-:).H9H -::.4(H -::.489 -:(.(:: -D.4DD -9.8(9 -:'.(4) -::.HH4>%.%%? >%.%%? >%.%%? >%.%%? >%.%%? >%.%%? >%.%%? >%.%%?

-(.))) (.)'D (.::) -(.(8 -:.D -:.4(D -(.'H8 (.''9>%.%$? >%.#%? >%."%? >%.A#? >%.&&? >%.'? >%.33? >%.&3?

(.:8D (.4H' (.4H -(.:) (.)4) (.)D4 (.:4( (.'4>%.3? >%.'? >%.'? >%.#? >%.#%? >%."? >%.$#? >%.3'?

- (.(: (.(D( (.((4 -(.(HD -(.(:H -:.(D: -(.(D:! >%.&$? >%.'$? >%.&%? >%.3? >%.&#? >%.%%? >%."?

- -(.(H -(.:H -(.(( (.(4 (.(8: (.(( -(.8'! >%.$%? >%.3"? >%."'? >%.#A? >%.''? >%.A%? >%.'#?

-:9.'D: -:D.'9H -:D.(9( -:4.'( -8.)8H -:4.)4( -:8.'8' -:9.H>%.%%? >%.%%? >%.%%? >%.%%? >%.%#? >%.%%? >%.%%? >%.%%?

-(.(:4 -(.(H4 -(.(H' -(.(:' -(.(D) (.((: (.(49 -(.('(>%.&%? >%.$? >%.#? >%.##? >%.3? >%.A#? >%.%&? >%.$?

(.:)) (.:)) (.:)) (.:)( (.:') (.:'9 (.:)H (.:)H>%.%%? >%.%%? >%.%%? >%.%%? >%.%%? >%.%%? >%.%%? >%.%%?

(.(:8 (.(:H (.(:D (.('8 (.('' (.(:( (.(:: (.(:)>%.%%? >%.%? >%.%A? >%.%%? >%.'&? >%.A? >%.? >%.%&?

Fears $ncluded :99)-'(() :99) , I94, I98,

I((, I('

:99) , I94, I98,

I((, I('

'((:-'(() '((' :998-'((' :99H-'(() :99H-'(()

Sample Si>e 8,9H4 ),)'9 ),)'9 ),88 D88 ,DHH H,8D: D,'D8

+ Tenure

+ Age

#overnance


#overnance

+ wn

$ntercept

Governance Variable

Si>e &Assets*



years

9



9anel 7: 0isciplinary +urnover2 '%%% to '%%' only

Baseline


T+6

Benchmark

Score B+ #ovScore

W Jalue of

%edian

GirectorIs

2oldings

+ K +hair

Guality

&M: if Gual*

of Girectors

$ndependent

-).:9D -'.() -'.) -4.))( -'.')' -'.':8 -).:8( -'.D9:>%.%%? >%.%$? >%.%'? >%.%%? >%.'$? >%.%? >%.%%? >%.%%?

-'.)) :.8' -(.D': -).DD9 -'.D :.(( -:.H:' (.)'H>%.%%? >%.'? >%.3'? >%.#? >%.$#? >%.3&? >%.%%? >%.#?

:.(:: :.( :.)99 (.HH4 :.)4) :.(H: :.(H :.(D4>%.%%? >%.%%? >%.%%? >%.''? >%.'? >%.%%? >%.%%? >%.%%?

- -(.(( -(.((9 (.(:8 -(.(H -(.(H' -(.D9( -(.9::! >%.3"? >%.A'? >%.'? >%.'? >%.'&? >%.%%? >%.%A?

- -(.8( -(.8DD (.()) (.()8 -(.'8 -:.)8: -.:H! >%.%%? >%.%%? >%.A? >%."? >%.%%? >%.%? >%.%%?

-4.9:) -4.'9) -.99D -D.)8 -:H.) -4.D' -4.4HD -H.H94>%.%&? >%.? >%.? >%.%A? >%.'%? >%.%#? >%.%#? >%.%?

-(.(): -(.:(' -(.:(: -(.(9D -(.''H -(.(: (.((: -(.():>%.$"? >%.? >%.$? >%.&? >%.%&? >%."? >%.AA? >%.$"?

(.(:' (.((9 (.((9 (.() (.(4: (.(:( (.(:H (.(:H>%.%? >%.&'? >%.&%? >%.%? >%.%"? >%.A? >%.'&? >%.'#?

-(.(48 -(.(4 -(.(4 -(.(48 -(.(' -(.(H' -(.(4 -(.(H:>%.%%? >%.%'? >%.%'? >%.%? >%.'#? >%.%%? >%.%? >%.%%?

Fears $ncluded '(((-(' '(((, '((' '(((, '((' '((:-(' '((' '(((-(' '(((-(' '(((-('

Sample Si>e ),)H ',()H ',()H ',:94 D88 ),:HH ),''8 ),''8

+ Tenure

+ Age


#overnance

+ wn

Si>e &Assets*


years

#overnance

$ntercept

<eturn, 6ast ' year

<overnance =aria&le

4(



9anel 0: )on!disciplinary +urnover2 '%%% to '%%' only

Baseline


T+6

Benchmark

Score B+ #ovScore

W Jalue of

%edian

GirectorIs

2oldings

+ K +hair

Guality

&M: if Gual*

of Girectors

$ndependent

-9.:8H -8.99 -8.HH -9.HDD -D.4DD -8.D' -8.8)4 -9.'4(>%.%%? >%.%%? >%.%%? >%.%%? >%.%%? >%.%%? >%.%%? >%.%%?

-(.))) -(.DH -(.(( -(.4:: -:.D -:.9 -(.')9 (.'49>%.? >%.&3? >%.&? >%.#'? >%.&&? >%.A? >%.3? >%.&A?

(.:(8 (.H8: (.D(9 -(.):( (.)4) (.') (.:8 (.:88>%.#'? >%.%"? >%.%#? >%.$? >%.#%? >%.? >%.$? >%.$3?

- (.(: (.(D8 (.((' -(.(HD -(.('8 -:.:)) (.')H! >%.#%? >%.'&? >%."#? >%.3? >%.$$? >%.%%? >%.$#?

- (.(:D (.() (.((H (.(4 (.(8 -(.:4' -(.8D4! >%.""? >%.""? >%."'? >%.#A? >%.'#? >%.&"? >%.3#?

-:.(D) -:).8H -:).'() -:(.:(( -8.)8H -:).:H8 -:).'4 -:).49)>%.%%? >%.%%? >%.%%? >%.%%? >%.%#? >%.%%? >%.%%? >%.%%?

(.(') -(.(4 -(.(( (.(( -(.(D) (.(: (.(8) (.((4>%.$&? >%.3"? >%.? >%.? >%.3? >%.#'? >%.%? >%."A?

(.::H (.::: (.::: (.:'( (.:') (.::H (.::4 (.::D>%.%%? >%.%%? >%.%%? >%.%%? >%.%%? >%.%%? >%.%%? >%.%%?

(.(:9 (.('' (.(') (.(:D (.('' (.(:D (.(': (.(:9>%.%? >%.%#? >%.%&? >%.? >%.'&? >%.%#? >%.%'? >%.%?

Fears $ncluded '(((-(' '(((, '((' '(((, '((' '((:-(' '((' '(((-(' '(((-(' '(((-('

Sample Si>e ),)H ',()H ',()H ',:94 D88 ),:HH ),''8 ),''8

+ Tenure

+ Age


#overnance

+ wn

Si>e &Assets*


years

#overnance

$ntercept


<overnance =aria&le

4:



'ppendi: '. toc and )ogo 02**4 wea instrument test

+onsider the single e/uation instrumental variables model,

u* y += β ,

where the reduced form e/uation for * can be e0pressed as,

.8 * +Π= '4

This is the standard instrumental variables, or two-stage least s/uares set up. The 'S6S

estimator of β is!

( )

( )* 9 *

y 9 *

E

E -L-

′

′='

Rβ where, 9 ′′= −:*& .

<othenberg &:98* e0presses -L- 'Rβ e/uivalently as!

,*K&*K'&:

*K&*R&

'

':

' µ µ ζ

µ ζ

σ

σ β β µ

88 8

8uu

88

uu-L-

-

-

+++

=−

where,'

:

*& Π′Π′

′Π′=

E E

u E

uu

uσ

ζ ,'

:

*& Π′Π′

′Π′=

E E

8 E

88

8 σ

ζ

':

*&88 uu

E

8u

u 9 8 -

σ σ

′= ,

88

88

8 9 8 -

σ

′=

88

σ µ

Π′Π′='

.

Stock and Fogo argue that for 'S6S, the sets of weak instruments can be characteri>ed in

terms of the minimum eigenvalue of the matri0 version of'

'

F

µ , where ' F is the dimension of

the Π coefficient vector. The Stock and Fogo test-statistic is based on the statistic derived in

+ragg and Gonald &:99)*. The approach is based on the eigenvalue of the matri0 analog of the

'4 This development follows from <othenberg &:98*.

4'



-statistic from the first-stage, reduced form regression. 7sing the same notation as above, this

value is!

'

':

': RR

F

* 9 * 1 vv Cvv

+

−⊥⊥−

∑

′′

∑= ⊥

where,

the superscript ?⊥ @ denotes the residuals from the projection of a variable on X and

*&

*&R

': F F +

* 4 *

88 −−

′=Σ . The test statistic is the minimum eigenvalue of 1+ ! *.&minmin + 1eval g =

This definition of 1+ puts it in -statistic form. "rom this, they obtain the limiting null

distribution of this test statistic using weak instrument asymptotics.

The underlying premise of weak instruments is that the instrument vector L is only

weakly related to the endogenous regressor F, given V. Stock and Fogo show that, under certain

assumptions, the probability limit of the 6S estimator is θ β β +→

d R

, where 8u88 ∑∑=

−:

θ .

"or 'S6S, they show that the distributions of the bias and ald statistic can be e0pressed as

follows!

β β −-L- 'R d

→ '

:

:'

:'

:

vv88 uu

−−

∑σ

-L- , '

d

→*':&

'

'

:''

:

:

'

:

:'

vvvvvn

vvv−−

−

′+

′−

′ ρ

where ':

': −−

∑∑= uu8u88 σ ρ .

Gefining,

4)



' F

λ λ ′=Λ , '

:'

: −

∑Ω=88

7 λ , G GG G BBBB :−−=Ω ,

+

7 + =Π=Π , where + is a fi0ed '

F 0 n matri0 with bounded elements c7 if ≤ , and,

B + p

→′−: .

Stock and Fogo further show that :v has a non-central ishart distribution with non-centrality

matri0 Λ=′' F λ λ . This non-centrality matri0 is the weak instrument limit of the concentration

matri0!

Λ→∑Π′Π′∑

−−

''

:'

:

F

p

88 88 .

Thus, the test statistic g min has the weak instrument asymptotic distribution of the minimum

eigenvalue of a non-central ishart distribution, divided by F ' where the non-centrality

parameter is Λ' F .

#iven this, Stock and Fogo characteri>e the weak instrument set in terms of the

eigenvalues of Λ in two ways! one based on the bias of the estimator and one based on the si>e

distortions of the associated ald statistic. They consider the relative'H s/uared bias of the

instrumental variables estimator relative to the 6S estimator!

*R&*R&

*R&*R&'

β β β β

β β β β

γ γ

γ γ

−∑′−

−∑′−=

⊥⊥

⊥⊥

E E

E E 6

8 8

+ .

7nder weak instrument asymptotics, the denominator has the limit,

ρ ρ σ β β β β γ γ ′→−∑′− ⊥⊥ uu E E *R&*R& .

"rom this, the s/uare of the asymptotic relative bias is

'H They use relative bias for cases when n Y : so that the bias is comparable across estimates of β . They do this

by standardi>ing the regressors ⊥* so that they have unit standard deviation and are orthogonal.

4



ρ ρ

β β β β σ

γ γ

′

−∑′−=

⊥⊥

∞→−

*R&*R&lim:' 8 8

+ uu

E E 6 .

Assuming (>′ ρ ρ , this leads to the worst-case scenario of asymptotic bias!

6 6 4a5

:(!ma0 ≤′<= ρ ρ ρ .

They consider the worst-case scenario because they deem instruments to be valid if they lead to

reliable inferences for all possible degrees of simultaneity ρ , and they consider all other

instruments weak. They define the weak instrument set to be those instruments that have the

potential of leading to bias greater than some value. This weak instrument set can be stated as

Z![ b 6 , 4a5 6ias ≥= Thus, the strength of an instrument is determined by the parameters of the

reduced form e/uation &the first-stage regression*.

$n terms of si>e, they consider instruments strong from the perspective of the ald test if

the si>e of the test is close to its level for all possible manipulations of the instrumental variables

regression model. They denote the ald test statistic based on the instrumental estimator as

38 , . 7sing first-order asymptotics, this statistic has a chi-s/uared null distribution with n

degrees of freedom, divided by n. Thus, the actual rejection rate under the null hypothesis can

be stated as

n, ; n

8 +

';

\5r (

α

β

χ >= ,

where α is the nominal level of the test and'

;α χ n is the α -level critical value of the chi-

s/uared distribution with n degrees of freedom. $n similar notation, the si>e-based weak

instrument set consists of instruments that can lead to a si>e of at least α >r ,

Z![ r ; , 4a5

-iCe ≥= .

44



7sing these two definitions of weak instruments sets based on bias and si>e, Stock and

Fogo calculate critical values for the ma0imum bias and si>e. The critical values essentially

compare the ma0imum bias and si>e with the minimum eigenvalue of Λ , the first-stage !

statistic. Their framework produces the following weak instrument testing process. The null

hypothesis is that the instruments are weak, while the alternative is that they are not. The

following is stated for the test based on 'S6S bias with a significance level of S:((δ !

--L- bias, @ ',( ! ∈Ζ vs. --L- bias = , @ ',! ∉Ζ .

The test procedure is!

<eject ( @ if *,,;& '',min δ n F bd g -L- bias≥ ,

where,

**,;&&*,,;& '','':

''', :,'n F b F F n F bd -L- bias F -L- bias

δ χ δ

−

−= ,

and,

*&'

:,'m F δ

χ −

is the percentile of the non-central chi-s/uared distribution with ' F

degrees of freedom, non-centrality parameter m , and the function -L- bias ', is the weak

instrument minimum eigenvalue of Λ from above. %ore simply put, the researcher can

compare the minimum eigenvalue of + 1 the -statistic from the first-stage regression to a

critical value. $f the !statistic e0ceeds the critical value &based on either bias or si>e, number of

instruments, and number of endogenous regressors*, then the researcher can reject the null

hypothesis that the instruments are weak and conclude that they are valid. $f the first-stage -

statistic is less than the critical value, the instruments are weak and the 'S6S estimates are

potentially biased and inconsistent. +ritical values are provided for si>e and for bias, for

different levels of significance, for different numbers of endogenous regressors and for different

number of instruments.

4H



'ppendi: >. <overnance, stoc returns and +o&in?s -

Appendi0 Table B-$ is similar to Table , e0cept that the appendi0 table considers stock

returns as the performance measure. As noted earlier, if investors anticipate the corporate

governance effect on performance, long-term stock returns will not be significantly correlated

with governance even if a significant correlation between performance and governance indeed

e0ists. &$n Table , the performance measure was based on accounting data! return on assets.*

Appendi0 Table B-$, 5anel A indicates there is no consistent or significant relation

between #$%1s measure of governance and contemporaneous, ne0t year1s or the ne0t two years1

stock returns. Appendi0 Table B-$, 5anels B through # indicate there is no consistent or

significant relation between the other measures of governance considered in this study &B+"

inde0, T+6 inde0, Brown and +aylor inde0, director stock ownership, +K+hair duality, and

board independence* and contemporaneous, ne0t year1s or the ne0t two years1 stock returns.

Appendi0 Table B-$$ is similar to Table , e0cept that this appendi0 table considers

Tobin1s as the performance measure. The results in Appendi0 Table B-$$, 5anels A through #

indicate there is no consistent or significant relation between the measures of governance

considered in this study &#$% inde0, B+" inde0, T+6 inde0, Brown and +aylor inde0, director

stock ownership, +K+hair duality, and board independence* and contemporaneous, ne0t year1s

or the ne0t two years1 Tobin1s .

e note that the method for estimating the system of simultaneous e/uations does matter.

"or e0ample, in Appendi0 Table B-$$, 5anel A, the 6S estimates suggest a significant

relationship between the #$% inde0 and Tobin1s , whereas the 'S6S estimates indicate no

significant relationship between the #$% inde0 and Tobin1s . The 2ausman test indicates that

the 'S6S estimates are better specified. Again, in Appendi0 Table B-$$, 5anel B, the 6S

4D



estimates suggest a significant relationship between the B+" inde0 and Tobin1s , whereas the

'S6S estimates indicate no significant relationship between the B+" inde0 and Tobin1s . nce

again, the 2ausman test indicates that the 'S6S estimates are better specified.

48



Appendi0 B-$Simultaneous /uations System stimation, 5erformance %easured by Stock <eturnThis table presents the coefficient estimates for performance, governance and + ownership as estimated in the following system!

&:a* 5erformance M f &#overnance, wnership, 6og&Assets*, $ndustry 5erformance, Gebt K Assets, &<PG and Advertising0penses* K Assets, Board Si>e, Stock Jolatility, Treasury Stock K Assets, 1*



nly the coefficients for governance and + ownership from the first e/uation &:a* are presented in the table. 5erformance ismeasured by Stock <eturn &?<eturn@*. wnership is measured by the percent of stock owned by the + at time t in all panels&?+ wn@*. #overnance is measured by a different variable in each panel. All governance variables are as of time t . $n 5anel A,the #ompers, $shii and %etrick &'(()* 1-$nde0 is used as the governance variable. $n 5anel B, the Bebchuk, +ohen and "errell&'((* E!$nde0 is used as the governance variable. $n 5anel +, T+6 Benchmark score is used as the governance variable. $n 5anel G,the Brown and +aylor &'((* #ovScore is used as the governance variable &data is available only for '(('*. $n 5anel , the dollarvalue of the median director1s stock holdings is used as the governance variable. $n 5anel ", a dummy variable e/ual to : if the +is also the +hair of the board, ( otherwise, is used as the governance variable. $n 5anel #, the percent of directors who areindependent is used as the governance variable. &$n 5anel # e/uation &:b*, the right-hand side variable ?5ercentage $ndependentGirectors@ is replaced with 5ercentage of Girectors ho Are Active +s.* <esults are presented using performance in time t , tD2 and tD to tD'. ach system is estimated using 6S, 'S6S, and )S6S. The 2ausman &:9D8* specification test is performed on eachsystem to determine which estimation method is most appropriate. The null hypothesis is that the methods are e/uivalent, so wereject the null for high h-statistics. The Stock and Fogo &'((* test for weak instruments is also performed. The -Jalue from the

first-stage regression for each of the three potentially endogenous regressors is presented. $f the -Jalue e0ceeds the critical value&using 4 bias* from Stock and Fogo &'((*, the instruments are deemed to be valid. The number of observations used in each panel-performance period varies so to ma0imi>e the sample si>e for the panel-performance period. +oefficient estimates are presented, with p-values in parentheses.

49



'ppendi: >;I 5anel A! #ompers, $shii and %etrick &'(()* #-$nde0 is the governance measure &U#ovU*

Stock return is the performance measure &U<eturnU*

% Estimate p!value % Estimate p!value % Estimate p!value

<eturn M #ov -(.(() >%.3#? <eturn M #ov -(.(() >%.? <eturn M #ov -(.((' >%.'$?

+ wn (.': >%./3? + wn -(.(4 >%.#/? + wn -(.(D) >%.''?

2 2 2

<eturn M #ov -(.((' >%.A/?

<eturn M #ov -(.(:' >%.$#?

<eturn M #ov (.((H >%.$"?

+ wn (.94 >%.33? + wn (.:99 >%.#/? + wn (.D:4 >%.%%?

3 3 3

<eturn M #ov -(.((' >%.A/? <eturn M #ov -(.(:' >%.$"? <eturn M #ov (.((H >%.$A?

+ wn (.4: >%.3/? + wn (.''4 >%.&"? + wn (.D(: >%.%/?

Sample Si>e ,H:9 Sample Si>e ,484 Sample Si>e ),)'

/ausman 01 pecification +est

h -statistic p -value h -statistic p -value h -statistic p -value

6S v. 'S6S 8:.H9 &(.((* 6S v. 'S6S 88.) &(.((* 6S v. 'S6S 9).H: &(.((*

6S v. )S6S -:H'8.(( - 6S v. )S6S -'H).(( - 6S v. )S6S :.)H &(.98*

'S6S v. )S6S -.D( - 'S6S v. )S6S -4(.D( - 'S6S v. )S6S )(.8) &(.)'*

toc and )ogo 02**4 7ea Instruments +est

"irst-Stage

-Jalue

+ritical

Jalue

"irst-Stage

-Jalue

+ritical

Jalue

"irst-Stage

-Jalue

+ritical

Jalue

<eturn :'D.H8 :8.DH <eturn :.:' :8.DH <eturn ):).D4 :8.DH

#ov HH.:: :8.DH #ov H4.8H :8.DH #ov D.9 :8.DH

+ wn ::(.(' :8.DH + wn :(D.D: :8.DH + wn 8.:8 :8.DH


H(



'ppendi: >;I 5anel B! Bebchuk, +ohen and "errel &'((* -$nde0 is is the governance measure &U#ovU*



<eturn M #ov -(.(() >%.&$? <eturn M #ov (.((: >%.A%? <eturn M #ov -(.((: >%.&&?

+ wn (.':D >%./3? + wn -(.()8 >%."%? + wn -(.(H9 >%.'$?

2 2 2

<eturn M #ov -(.((D >%."$?

<eturn M #ov -(.('' >%.$A?

<eturn M #ov (.(:' >%.$$?

+ wn (.44 >%.'? + wn (.:49 >%.#A? + wn (.D4' >%.%/?

3 3 3

<eturn M #ov -(.((D >%."$? <eturn M #ov -(.('' >%.$A? <eturn M #ov (.(:: >%.$A?

+ wn (.4(' >%.3#? + wn (.:8( >%.##? + wn (.D(8 >%.%/?

Sample Si>e ,H:9 Sample Si>e ,484 Sample Si>e ),)'



6S v. 'S6S 8:.D: &(.((* 6S v. 'S6S 9(.D4 &(.((* 6S v. 'S6S :().D( &(.((*

6S v. )S6S -4'H.(( - 6S v. )S6S -H4D.(( - 6S v. )S6S )).44 &(.''*

'S6S v. )S6S -:DH.(( - 'S6S v. )S6S -:DH.(( - 'S6S v. )S6S :)8.9( &(.((*


"irst-Stage

-Jalue

+ritical

Jalue

"irst-Stage

-Jalue

+ritical

Jalue

"irst-Stage

-Jalue

+ritical

Jalue

<eturn :'D.H8 :8.DH <eturn :.:' :8.DH <eturn ):).D4 :8.DH

#ov 4D.H :8.DH #ov 4H.8 :8.DH #ov '.9 :8.DH

+ wn ::(.(' :8.DH + wn :(D.D: :8.DH + wn 8.:8 :8.DH


H:



'ppendi: >;I 5anel +! T+6 Benchmark Score is the governance measure &U#ovU*



<eturn M #ov (.((( >%.&#? <eturn M #ov (.((' >%./$? <eturn M #ov (.((( >%.A?

+ wn -(.(H: >%.&#? + wn (.()8 >%."/? + wn -(.(' >%.&#?

2 2 2

<eturn M #ov -(.((: >%.""?

<eturn M #ov (.((( >%.A"?

<eturn M #ov -(.((: >%.A%?

+ wn -(.):H >%.&$? + wn -(.9'4 >%.'? + wn (.)8( >%.3"?

3 3 3

<eturn M #ov -(.((: >%."&? <eturn M #ov -(.((: >%.A#? <eturn M #ov -(.((: >%.A%?

+ wn -(.)(( >%.&#? + wn -(.9): >%.'? + wn (.)D8 >%.3"?

Sample Si>e ',':4 Sample Si>e ',:44 Sample Si>e 98:



6S v. 'S6S DH.H( &(.((* 6S v. 'S6S 8(.'D &(.((* 6S v. 'S6S .D9 &(.('*

6S v. )S6S 4'.:8 &(.((* 6S v. )S6S -:).4( - 6S v. )S6S -(.8 -

'S6S v. )S6S -4.D8 - 'S6S v. )S6S -(.) - 'S6S v. )S6S -:.:) -


"irst-Stage

-Jalue

+ritical

Jalue

"irst-Stage

-Jalue

+ritical

Jalue

"irst-Stage

-Jalue

+ritical

Jalue

<eturn 8.49 :8.DH <eturn :)9.'4 :8.DH <eturn H).)( :8.DH

#ov 'D.H9 :8.DH #ov '8.8H :8.DH #ov '(.:D :8.DH

+ wn 4(.)) :8.DH + wn H.D :8.DH + wn ').84 :8.DH


H'



'ppendi: >;I 5anel G! Brown and +aylor &'((* #ovScore is the governance measure &U#ovU*


% Estimate p!value % Estimate p!value

<eturn M #ov (.((: >%.#$? <eturn M #ov (.((D >%./%? 9'

+ wn (.()D >%.#A? + wn -(.'94 >%.'3?

2 2

<eturn M #ov (.(( >%.&&?

<eturn M #ov -(.((: >%.A$?

+ wn :.(H >%.%&? + wn -:.DH( >%.%A?

3 3

<eturn M #ov (.((H >%.$/? <eturn M #ov -(.((9 >%.$"?

+ wn :.:D( >%.%3? + wn -'.': >%.%'?

Sample Si>e 8: Sample Si>e 8()


h -statistic p -value h -statistic p -value

6S v. 'S6S '8.D: &(.)* 6S v. 'S6S :H.9H &(.94*

6S v. )S6S -H.DD - 6S v. )S6S '.9D &:.((*

'S6S v. )S6S -::.( - 'S6S v. )S6S .) &:.((*


"irst-Stage

-Jalue

+ritical

Jalue

"irst-Stage

-Jalue

+ritical

Jalue

<eturn 4(.9: :8.DH <eturn '8.'9 :8.DH

#ov :9.: :8.DH #ov :H.8H :8.DH

+ wn :.' :8.DH + wn :'.(' :8.DH


H)



'ppendi: >;I 5anel ! 6og of Gollar Jalue of the median directorIs stock ownership is the governance measure &U#ovU*



<eturn M #ov (.(48 >%.%%? <eturn M #ov -(.('( >%.%%? <eturn M #ov -(.((8 >%.%%?

+ wn (.(') >%."&? + wn (.((9 >%.A? + wn -(.((H >%.A'?

2 2 2

<eturn M #ov -(.(:: >%.?

<eturn M #ov (.((H >%.&A?

<eturn M #ov -(.((: >%.""?

+ wn -(.'48 >%.$&? + wn (.::9 >%."%? + wn (.448 >%.%/?

3 3 3

<eturn M #ov (.(:) >%.3#? <eturn M #ov (.(() >%."'? <eturn M #ov -(.(() >%.&#?

+ wn (.) >%.3/? + wn -(.'(: >%.&#? + wn (.H:' >%.%%?

Sample Si>e 4,:4: Sample Si>e 4,:(H Sample Si>e ),8)'



6S v. 'S6S :.9( &(.((* 6S v. 'S6S ':8.8( &(.((* 6S v. 'S6S :)9.( &(.((*

6S v. )S6S -:844.(( - 6S v. )S6S -:489.(( - 6S v. )S6S -''.'( -

'S6S v. )S6S -(.H: - 'S6S v. )S6S -D4H.(( - 'S6S v. )S6S D.: &(.(:*


"irst-Stage

-Jalue

+ritical

Jalue

"irst-Stage

-Jalue

+ritical

Jalue

"irst-Stage

-Jalue

+ritical

Jalue

<eturn :D(.'( :8.DH <eturn :H'.)H :8.DH <eturn )49.)H :8.DH

#ov :H9.(H :8.DH #ov :9(.: :8.DH #ov :4:.(: :8.DH

+ wn :'H.H' :8.DH + wn :'.H :8.DH + wn 98.'9 :8.DH

9e:t 1 )ear 8erformance 9e:t 2 )ears 8erformanceContemporaneous 8erformance

H



'ppendi: >;I 5anel "! + K +hair Guality &: if + is +hair, ( otherwise* is the governance measure &U#ovU*



<eturn M #ov (.(' >%./'? <eturn M #ov (.(:D >%.3&? <eturn M #ov (.((9 >%.'?

+ wn (.:H9 >%.'%? + wn -(.(49 >%.&$? + wn -(.()H >%.$'?

2 2 2

<eturn M #ov -(.(H >%.#%?

<eturn M #ov -(.:8 >%.'A?

<eturn M #ov -(.(4( >%.3'?

+ wn (.44D >%.'%? + wn (.HH' >%.'/? + wn (.8() >%.%%?

3 3 3

<eturn M #ov -(.(4: >%.#&? <eturn M #ov -(.:)' >%.3? <eturn M #ov -(.(4' >%.3%?

+ wn (.)9 >%.3/? + wn (.9 >%.%? + wn (.D >%.%%?




6S v. 'S6S '9.) &(.)9* 6S v. 'S6S 8:.'8 &(.((* 6S v. 'S6S ::'.)( &(.((*

6S v. )S6S -)4D.(( - 6S v. )S6S -:)'.(( - 6S v. )S6S HD.D( &(.((*

'S6S v. )S6S -::(.(( - 'S6S v. )S6S -:'.(( - 'S6S v. )S6S -):.)( -


"irst-Stage

-Jalue

+ritical

Jalue

"irst-Stage

-Jalue

+ritical

Jalue

"irst-Stage

-Jalue

+ritical

Jalue

<eturn :D(.'( :8.DH <eturn :H'.)H :8.DH <eturn )49.)H :8.DH

#ov '8.( :8.DH #ov )9.) :8.DH #ov )H.H4 :8.DH

+ wn :'H.H' :8.DH + wn :'.H :8.DH + wn 98.'9 :8.DH


H4



'ppendi: >;I 5anel #! 5ercentage of directors who are independent is the governance measure &U#ovU*a



<eturn M #ov -(.()H >%.$? <eturn M #ov -(.()8 >%./? <eturn M #ov -(.()8 >%.%?

+ wn (.:H' >%.'? + wn -(.(DD >%.$#? + wn -(.(H) >%.'"?

2 2 2

<eturn M #ov -(.:HD >%.$'?

<eturn M #ov -(.'4D >%.3'?

<eturn M #ov -(.(94 >%.3A?

+ wn (.:DD >%.#&? + wn -(.:8D >%.#$? + wn (.H9 >%.%#?

3 3 3

<eturn M #ov -(.:H) >%.$3? <eturn M #ov -(.'4H >%.3'? <eturn M #ov -(.(94 >%.3A?

+ wn (.:48 >%.#"? + wn -(.:94 >%.#? + wn (.H9 >%.%#?




6S v. 'S6S H.8D &(.((* 6S v. 'S6S 4(.D9 &(.(:* 6S v. 'S6S .9 &(.('*

6S v. )S6S -:4.(( - 6S v. )S6S H.H &:.((* 6S v. )S6S :H.88 &(.94*

'S6S v. )S6S -:H.'( - 'S6S v. )S6S -:(.:( - 'S6S v. )S6S -(.: -


"irst-Stage

-Jalue

+ritical

Jalue

"irst-Stage

-Jalue

+ritical

Jalue

"irst-Stage

-Jalue

+ritical

Jalue

<eturn :D(.:4 :8.DH <eturn :H'.8 :8.DH <eturn )49.D4 :8.DH

#ov H.:H :8.DH #ov 48.:4 :8.DH #ov )9.4) :8.DH

+ wn :::.:D :8.DH + wn :(H.H( :8.DH + wn D9.:' :8.DH


a $n e/uation &:b* with U#ovU as the dependent variable, we replace the e0planatory variable Upercentage of directors who are independentU with Upercentage of directors who are +sU.

HH



Appendi0 B-$$Simultaneous /uations System stimation, 5erformance %easured by Tobin1s This table presents the coefficient estimates for performance, governance and + ownership as estimated in the following system!

&:a* 5erformance M f &#overnance, wnership, 6og&Assets*, $ndustry 5erformance, Gebt K Assets, &<PG and Advertising0penses* K Assets, Board Si>e, Stock Jolatility, Treasury Stock K Assets, 1*



nly the coefficients for governance and + ownership from the first e/uation &:a* are presented in the table. 5erformance ismeasured by Tobin1s &?@*. wnership is measured by the percent of stock owned by the + at time t in all panels &?+wn@*. #overnance is measured by a different variable in each panel. All governance variables are as of time t . $n 5anel A, the#ompers, $shii and %etrick &'(()* 1-$nde0 is used as the governance variable. $n 5anel B, the Bebchuk, +ohen and "errell &'((*

E!$nde0 is used as the governance variable. $n 5anel +, T+6 Benchmark score is used as the governance variable. $n 5anel G, theBrown and +aylor &'((* #ovScore is used as the governance variable &data is available only for '(('*. $n 5anel , the dollar valueof the median director1s stock holdings is used as the governance variable. $n 5anel ", a dummy variable e/ual to : if the + is alsothe +hair of the board, ( otherwise, is used as the governance variable. $n 5anel #, the percent of directors who are independent isused as the governance variable. &$n 5anel # e/uation &:b*, the right-hand side variable ?5ercentage $ndependent Girectors@ isreplaced with 5ercentage of Girectors ho Are Active +s.* <esults are presented using performance in time t , tD2 and tD to

tD'. ach system is estimated using 6S, 'S6S, and )S6S. The 2ausman &:9D8* specification test is performed on each system todetermine which estimation method is most appropriate. The null hypothesis is that the methods are e/uivalent, so we reject the nullfor high h-statistics. The Stock and Fogo &'((* test for weak instruments is also performed. The -Jalue from the first-stage

regression for each of the three potentially endogenous regressors is presented. $f the -Jalue e0ceeds the critical value &using 4 bias* from Stock and Fogo &'((*, the instruments are deemed to be valid. The number of observations used in each panel- performance period varies so to ma0imi>e the sample si>e for the panel-performance period. +oefficient estimates are presented, with

p-values in parentheses.

HD



'ppendi: >;II 5anel A! #ompers, $shii and %etrick &'(()* #-$nde0 is the governance measure &U#ovU*

TobinIs is the performance measure &UU*


M #ov -(.(H) >%.%%? M #ov -(.(H >%.%%? M #ov -(.()4 >%.%%?

+ wn (.8D( >%.%#? + wn (.4(D >%.''? + wn (.H9H >%./3?

2 2 2

M #ov -(.:'( >%.%#?

M #ov -(.(( >%.$%?

M #ov (.('H >%.#3?

+ wn H.H8 >%.%%? + wn 8.)HD >%.%%? + wn :(.9)( >%.%%?

3 3 3

M #ov -(.::4 >%.%A? M #ov -(.()D >%.$3? M #ov (.('9 >%.#/?

+ wn D.((( >%.%%? + wn 8.HHD >%.%%? + wn ::.(9: >%.%%?

Sample Si>e ),9H: Sample Si>e ),9)( Sample Si>e ',9(H



6S v. 'S6S :().8( &(.((* 6S v. 'S6S :'8.9( &(.((* 6S v. 'S6S :').H( &(.((*

6S v. )S6S -::.9( - 6S v. )S6S (.9' &:.((* 6S v. )S6S :8.49 &(.9:*

'S6S v. )S6S -:D.( - 'S6S v. )S6S -:).( - 'S6S v. )S6S -D.:' -


"irst-Stage

-Jalue

+ritical

Jalue

"irst-Stage

-Jalue

+ritical

Jalue

"irst-Stage

-Jalue

+ritical

Jalue

4D.8' :8.DH H9.) :8.DH HD.)H :8.DH

#ov 49.99 :8.DH #ov 49.) :8.DH #ov ).() :8.DH

+ wn 9H.4 :8.DH + wn 9).4' :8.DH + wn D.(' :8.DH


H8



'ppendi: >;II 5anel B! Bebchuk, +ohen and "errel &'((* -$nde0 is is the governance measure &U#ovU*



M #ov -(.:8H >%.%%? M #ov -(.:) >%.%%? M #ov -(.:'4 >%.%%?

+ wn (.94 >%.3%? + wn (.:99 >%.&3? + wn (.)D8 >%.'?

2 2 2

M #ov -(.'4: >%.%$?

M #ov -(.(D9 >%.$%?

M #ov (.(4) >%.#3?

+ wn 4.9(4 >%.%%? + wn 8.:H' >%.%%? + wn ::.(9: >%.%%?

3 3 3

M #ov -(.')9 >%.%&? M #ov -(.(H4 >%.$"? M #ov (.(D8 >%.&/?

+ wn H.8(8 >%.%%? + wn 8.9: >%.%%? + wn ::.DD4 >%.%%?

Sample Si>e ),9H: Sample Si>e ),9)( Sample Si>e ',9(H



6S v. 'S6S 94.9' &(.((* 6S v. 'S6S :').(( &(.((* 6S v. 'S6S :'H.'( &(.((*

6S v. )S6S -8(.)9 - 6S v. )S6S -H.4( - 6S v. )S6S -4:.:( -

'S6S v. )S6S -4:.9( - 'S6S v. )S6S -)D.9( - 'S6S v. )S6S -''.'( -


"irst-Stage

-Jalue

+ritical

Jalue

"irst-Stage

-Jalue

+ritical

Jalue

"irst-Stage

-Jalue

+ritical

Jalue

4D.8' :8.DH H9.) :8.DH HD.)H :8.DH

#ov 9.9( :8.DH #ov 9.(9 :8.DH #ov )H.4: :8.DH

+ wn 9H.4 :8.DH + wn 9).4' :8.DH + wn D.(' :8.DH


H9



'ppendi: >;II 5anel +! T+6 Benchmark Score is the governance measure &U#ovU*



M #ov -(.(:' >%.%%? M #ov (.((' >%.3A? M #ov -(.(( >%.3'?

+ wn (.D:9 >%./%? + wn (.H)H >%.%A? + wn (.'99 >%.$"?

2 2 2

M #ov -(.((D >%.#3?

M #ov (.((( >%.AA?

M #ov -(.((4 >%.#3?

+ wn H.('( >%.%/? + wn .DD9 >%.%/? + wn .:: >%.%#?

3 3 3

M #ov -(.(() >%."A? M #ov (.((' >%.A'? M #ov -(.(() >%."&?

+ wn H.)'D >%.%%? + wn .88D >%.%/? + wn .)' >%.%$?

Sample Si>e :,88 Sample Si>e :,8)4 Sample Si>e 8)



6S v. 'S6S DD.' &(.((* 6S v. 'S6S D.4 &(.((* 6S v. 'S6S D.)( &(.(:*

6S v. )S6S -:9.9( - 6S v. )S6S -:8.'( - 6S v. )S6S :4.9 &(.9D*

'S6S v. )S6S ).): &:.((* 'S6S v. )S6S -:.9 - 'S6S v. )S6S 4.44 &:.((*


"irst-Stage

-Jalue

+ritical

Jalue

"irst-Stage

-Jalue

+ritical

Jalue

"irst-Stage

-Jalue

+ritical

Jalue

)D.4 :8.DH '.'( :8.DH 'D.: :8.DH

#ov ').H) :8.DH #ov ':.D( :8.DH #ov '(.:8 :8.DH

+ wn '.D4 :8.DH + wn (.D' :8.DH + wn ').)' :8.DH


D(



'ppendi: >;II 5anel G! Brown and +aylor &'((* #ovScore is the governance measure &U#ovU*


% Estimate p!value % Estimate p!value

M #ov -(.((8 >%.3#? M #ov -(.(() >%.##? 9'

+ wn :.(: >%.%'? + wn (.44: >%.3'?

2 2

M #ov -(.(:8 >%.&'?

M #ov -(.(D) >%.%"?

+ wn 4.(H >%.%/? + wn :.D:) >%.A?

3 3

M #ov -(.(:( >%.##? M #ov -(.(D' >%.%"?

+ wn H.'9: >%.%%? + wn :.8: >%.$$?

Sample Si>e D:H Sample Si>e H89


h -statistic p -value h -statistic p -value

6S v. 'S6S '4.HD &(.49* 6S v. 'S6S '(.D &(.84*

6S v. )S6S D.8 &:.((* 6S v. )S6S D.)' &:.((*

'S6S v. )S6S '.D' &(.H* 'S6S v. )S6S -:4.4( -


"irst-Stage

-Jalue

+ritical

Jalue

"irst-Stage

-Jalue

+ritical

Jalue

:D.D :8.DH ').) :8.DH

#ov :H.) :8.DH #ov :.HD :8.DH

+ wn ::.'' :8.DH + wn :(.(8 :8.DH


D:



'ppendi: >;II 5anel ! 6og of Gollar Jalue of the median directorIs stock ownership is the governance measure &U#ovU*



M #ov (.)9( >%.%%? M #ov (.')D >%.%%? M #ov (.')) >%.%%?

+ wn -(.(4 >%.A%? + wn (.()( >%.A? + wn -(.(8 >%.A/?

2 2 2

M #ov (.(H: >%.'3?

M #ov (.('' >%.&/?

M #ov (.((( >%.AA?

+ wn H.H() >%.%%? + wn D.H' >%.%%? + wn 8.4(9 >%.%%?

3 3 3

M #ov (.('' >%.&#? M #ov -(.(:9 >%.&$? M #ov -(.(H) >%./A?

+ wn 9.9(: >%.%%? + wn :(.98( >%.%%? + wn :'.)) >%.%%?

Sample Si>e ,:: Sample Si>e ,)D9 Sample Si>e ),'49



6S v. 'S6S :):.'( &(.((* 6S v. 'S6S :)4.)( &(.((* 6S v. 'S6S ::.4( &(.((*

6S v. )S6S -D(.4( - 6S v. )S6S -:(8.(( - 6S v. )S6S -HH.D( -

'S6S v. )S6S -8:.'( - 'S6S v. )S6S -D).D( - 'S6S v. )S6S -(.9( -


"irst-Stage

-Jalue

+ritical

Jalue

"irst-Stage

-Jalue

+ritical

Jalue

"irst-Stage

-Jalue

+ritical

Jalue

H(.:: :8.DH D:.': :8.DH D:.4 :8.DH

#ov :'D.) :8.DH #ov ::.)H :8.DH #ov :)(.9 :8.DH

+ wn ::(.(4 :8.DH + wn :(D.49 :8.DH + wn 8H.84 :8.DH


D'



'ppendi: >;II 5anel "! + K +hair Guality &: if + is +hair, ( otherwise* is the governance measure &U#ovU*



M #ov (.(49 >%.3&? M #ov (.(9 >%.3&? M #ov (.(D' >%./A?

+ wn :.':4 >%.%/? + wn (.8) >%.%3? + wn (.9)) >%.%3?

2 2 2

M #ov (.9(' >%./?

M #ov (.9) >%.'?

M #ov (.:(: >%."/?

+ wn 4.)4 >%.%%? + wn 4.9D >%.%%? + wn 8.': >%.%%?

3 3 3

M #ov (.8(4 >%./&? M #ov (.') >%.3/? M #ov -(.(8) >%."?

+ wn D.D4 >%.%%? + wn 8.(DH >%.%%? + wn 9.8) >%.%%?

Sample Si>e ,:: Sample Si>e ,)D9 Sample Si>e ),'49



6S v. 'S6S 4D.'' &(.((* 6S v. 'S6S :::.'( &(.((* 6S v. 'S6S ::.8( &(.((*

6S v. )S6S D'8.:( &(.((* 6S v. )S6S 4D.8( &(.((* 6S v. )S6S :99.:( &(.((*

'S6S v. )S6S -:'(.(( - 'S6S v. )S6S -'H:.(( - 'S6S v. )S6S -H.(: -


"irst-Stage

-Jalue

+ritical

Jalue

"irst-Stage

-Jalue

+ritical

Jalue

"irst-Stage

-Jalue

+ritical

Jalue

H(.:: :8.DH D:.': :8.DH D:.4 :8.DH

#ov '4.'( :8.DH #ov )'.)9 :8.DH #ov 49.HD :8.DH

+ wn ::(.(4 :8.DH + wn :(D.49 :8.DH + wn 8H.84 :8.DH

9e:t 2 )ears 8erformanceContemporaneous 8erformance 9e:t 1 )ear 8erformance

D)



'ppendi: C. "o&ustness of <I# <;inde: relation to a&normal returns

#ompers, $shii and %etrick &'(()* show that a trading strategy long firms with high

shareholders rights &?Gemocracy@* and short firms with low shareholder rights &?Gictatorship@*

generated an abnormal return of 8. per year during their sample period of September :99( to

Gecember :999. They estimate a four-factor model as in +arhart &:99D*. The four factors

include a market factor &?<%<"@*, a si>e factor &?S%B@*, a book-to-market factor &?2%6@*,

and a momentum factor &?%omentum@*. They obtain the first three factors from 5rofessor 3en

"rench1s website and they replicate +arhart1s methodology to obtain the momentum factor.

'D

$n

this model, the intercept represents the abnormal monthly return. Their main results from their

Table J$ are as follows &standard errors in parentheses, significance at the 4 and : levels is

indicated by X and XX, respectively*!

α RMRF SMB HML Momentum

Democrac! ; Dictatorship *.1@@ ;*.*4 ;*.22@ ;*.55@@ ;*.*1

&(.'H* &(.(D* &(.(9* &(.:(* &(.(D*

Original GIM Results: 9!99" # !$!999

The α of (.D: represents the monthly abnormal return, e/uivalent to an annual abnormal

return of 8.. $n their Table J$$, they show that this result is robust using e/ual-weighted

portfolios rather than value-weighted, to industry adjustments, to alternate definitions of

democracy and dictatorship portfolios and other tests.

e reproduce this analysis during the #$% sample period and find similar results. e

then replicate the above analysis for the five years following the initial #$% period Canuary

'((( to Gecember '((. e find that the #$% results do not hold during this time period, nor

'D http!KKmba.tuck.dartmouth.eduKpagesKfacultyKken.frenchK

D



do they hold for the full period of available data September :99( to Gecember '((.'8 $n fact,

the abnormal return becomes negative &though insignificant* for the five years immediately

following the #$% period as noted below &standard errors in parentheses, significance at the 4

and : levels is indicated by X and XX, respectively*!


Democrac! ; Dictatorship ;*.35 *.12 ;*.*1 ;*.54@@ *.*

&(.H* &(.::* &(.:(* &(.:)* &(.(4*

Out#o%#Sam&le Results: !$""" # !$$""'


Democrac! ; Dictatorship *.31 *.*3 ;*.12 ;*.6*@@ *.*

&(.')* &(.(H* &(.(H* &(.(8* &(.(*

Full Sam&le Results: 9!99" # !$$""'

Also, we find that the estimation of this model is sensitive to the construction of the

momentum factor. +<S5 publishes a momentum factor that is similar to the +arhart factor, but it

allows for small firms and large firms having different momentum characteristics.'9 All firms are

sorted based on si>e. The momentum factor, H402 is the average return on the two top

portfolios minus the average return on the two bottom portfolios!

H40 I >-mall ,inners D 6ig ,inners? K >-mall Losers D 6ig Losers?.

hen we use the H40 momentum factor in the #$% analysis instead of the +arhart-based

momentum factor, the (.D: monthly abnormal return declines to an insignificant (.8 &t-

statistic of :.89*. "or the full sample period of more than : years, the abnormal return falls

even further to an insignificant (.:9 per month as shown below &standard errors in parentheses,

significance at the 4 and : levels is indicated by X and XX, respectively*!

'8 +ore, #uay and <usticus &'((4* find similar results through Gecember '(().'9 This factor is also available on 5rofessor 3en "rench1s website.

D4



α RMRF SMB HML (M)

Democrac! ; Dictatorship *.4 ;*.*2 ;*.21@@ ;*.4@@ *.1

&(.'H* &(.(D* &(.(8* &(.:(* &(.(D*

Results *it+ ,RS- Momentum Factor: 9!99" # !$!999

α RMRF SMB HML (M)

Democrac! ; Dictatorship *.1 *.*5 ;*.15 ;*.5@@ *.15@@

&(.''* &(.(H* &(.(H* &(.(8* &(.(*

Full Sam&le Results. ,RS- Momentum Factor: 9!99" # !$$""'

These robustness tests demonstrate the sensitivity of the #$% results to the sample

period, and the momentum factor used in the construction of abnormal stock returns.

DH



'ppendi: D. >oard structure and performance

$n the main part of the paper we focus on board characteristics such as board ownership,

board independence, and +-+hair duality. 2ere, we provide evidence on the relation between

five additional board characteristics and performance. Appendi0 Table G is similar to Table ,

e0cept that the appendi0 table considers the relation of the following si0 board characteristics in

5anels A through ", respectively! &5anel A* percent of directors who are currently active +s,

&5anel B* percent of directors who are currently on more than four boards, &5anel +* percent of

directors who have at least fifteen years tenure on the sample firm1s board, &5anel G* percent of

directors who are older than D( years, &5anel * percent of directors who are women, and &5anel

"* percent of directors who own >ero shares of stock.

The results show that the percent of directors who are +s, the percent of directors on

more than four boards, and the percent of directors who do not own any firm stock are each

negatively associated with future operating performance.)( The percent of directors with more

than fifteen years tenure, the percent of directors older than D(, and the percent of directors who

are women are each positively associated with future operating performance.

)( $n a recent paper, "ich and Shivdasani &'((4* find that firm performance suffers when a majority of outsidedirectors are busy; they define a busy director as one who sits on three or more boards. This is consistent with theabove finding that the percent of directors on more than four boards is negatively correlated with future operating performance.

DD



Appendi0 GSimultaneous /uations System stimation, 5erformance %easured by <eturn on Assets Alternative #overnance %echanisms

This table presents the coefficient estimates for performance, governance and + ownership as estimated in the following system!&:a* 5erformance M f &wnership, #overnance, 6og&Assets*, $ndustry 5erformance, Gebt K Assets, &<PG and

Advertising 0penses* K Assets, Board Si>e, Stock Jolatility, Treasury Stock K Assets, 1*,



nly the coefficients for governance and + ownership from the first e/uation &:a* are presented in the table. 5erformance ismeasured by <eturn on Assets &?<A@*. wnership is measured by the percent of stock owned by the + at time t in all panels&?+ wn@*. #overnance is measured by a different variable in each panel. All governance variables are as of time t . $n 5anel A,the percentage of directors who are active +s is used as the governance variable. $n 5anel B, the percentage of directors who areone more than four boards is used as the governance variable. $n 5anel +, the percentage of directors with more than fifteen yearstenure on the sample firm1s board is used as the governance variable. $n 5anel G, the percentage of directors who are older thanseventy years old is used as the governance variable. $n 5anel , the percentage of directors who are women is used as thegovernance variable. $n 5anel ", the percentage of directors who do not own any stock in the sample firm is used as the governancevariable. <esults are presented using performance in time t , tD2 and tD to tD'. ach system is estimated using 6S, 'S6S, and)S6S. The 2ausman &:9D8* specification test is performed on each system to determine which estimation method is mostappropriate. The null hypothesis is that the methods are e/uivalent, so we reject the null for high h-statistics. The Stock and Fogo&'((* test for weak instruments is also performed. The -Jalue from the first-stage regression for each of the three potentiallyendogenous regressors is presented. $f the -Jalue e0ceeds the critical value &using 4 bias* from Stock and Fogo &'((*, the

instruments are deemed to be valid. The number of observations used in each panel-performance varies so to ma0imi>e the samplesi>e for the panel-performance period. +oefficient estimates are presented, with p-values in parentheses.

D8



'ppendi: D 5anel A! The percent of directors who are currently active +s is the governance measure &U#ovU*

<eturn on Assets is the performance measure &U<AU*


<A M #ov -(.()H >%.%%? <A M #ov -(.(: >%.%%? <A M #ov -(.(' >%.%%?

+ wn (.(HD >%.%%? + wn (.(84 >%.%%? + wn (.((D >%.#'?

2 2 2

<A M #ov -(.'D) >%.%%?

<A M #ov -(.):H >%.%%?

<A M #ov -(.''H >%.%'?

+ wn (.(4' >%.&'? + wn (.':' >%.%&? + wn -(.:(H >%.3$?

3 3 3

<A M #ov -(.'94 >%.%%? <A M #ov -(.)D >%.%%? <A M #ov -(.)(4 >%.%%?

+ wn -(.('( >%."? + wn (.:': >%.'"? + wn -(.')4 >%.%?

Sample Si>e 4,(84 Sample Si>e 4,(H Sample Si>e ),8:'



6S v. 'S6S :):.)( (.(( 6S v. 'S6S :H:.9( (.(( 6S v. 'S6S :().( (.((

6S v. )S6S -9H.'( - 6S v. )S6S -:94.(( - 6S v. )S6S :H9.(( (.((

'S6S v. )S6S 99.: (.(( 'S6S v. )S6S ).88 (.(' 'S6S v. )S6S 49.D( (.((


"irst-Stage

-Jalue

+ritical

Jalue

"irst-Stage

-Jalue

+ritical

Jalue

"irst-Stage

-Jalue

+ritical

Jalue

<A :H9.() :8.DH <A :)'. :8.DH <A :((4.8H :8.DH

#ov )D.(: :8.DH #ov )).9D :8.DH #ov '.D9 :8.DH

+ wn :').: :8.DH + wn :'(.D8 :8.DH + wn 9H.:4 :8.DH


D9



'ppendi: D 5anel B! The percent of directors who are on more than four boards is the governance measure &U#ovU*



<A M #ov -(.(4' >%.%'? <A M #ov -(.()D >%./'? <A M #ov -(.(:9 >%.3A?

+ wn (.(D >%.%%? + wn (.(9) >%.%%? + wn (.(:' >%.$3?

2 2 2

<A M #ov -9.::: >%.%%?

<A M #ov -:'.DD8 >%.%%?

<A M #ov -)(.(9 >%.%A?

+ wn -:.D84 >%.%%? + wn -'.DHD >%.%%? + wn -4.) >%.//?

3 3 3

<A M #ov -::.H4 >%.%%? <A M #ov -:9.H': >%.%%? <A M #ov -D.('H >%.%%?

+ wn -).DD' >%.%%? + wn -H.'9 >%.%%? + wn -:'.()( >%.%%?




6S v. 'S6S :'9.(( (.(( 6S v. 'S6S ::D.8( (.(( 6S v. 'S6S 4D.)8 (.((

6S v. )S6S -(.() - 6S v. )S6S -4D:).(( - 6S v. )S6S :H'.'( (.((

'S6S v. )S6S -)8(.(( - 'S6S v. )S6S -)).(( - 'S6S v. )S6S -:D.H( -


"irst-Stage

-Jalue

+ritical

Jalue

"irst-Stage

-Jalue

+ritical

Jalue

"irst-Stage

-Jalue

+ritical

Jalue

<A :H9.() :8.DH <A :)'. :8.DH <A :((4.8H :8.DH

#ov D.)H :8.DH #ov .D: :8.DH #ov ):.( :8.DH

+ wn :').: :8.DH + wn :'(.D8 :8.DH + wn 9H.:4 :8.DH


8(



'ppendi: D 5anel +! The percent of directors with more than :4 years tenure is the governance measure &U#ovU*



<A M #ov (.(' >%.%%? <A M #ov (.(49 >%.%%? <A M #ov (.('( >%.%%?

+ wn (.(4H >%.%'? + wn (.(HH >%.%/? + wn (.(() >%."&?

2 2 2

<A M #ov :.84' >%.%%?

<A M #ov '.(H4 >%.%%?

<A M #ov :.'(: >%.%%?

+ wn -).4H >%.%%? + wn -).D4D >%.%%? + wn -'.:H8 >%.%%?

3 3 3

<A M #ov :.98( >%.%%? <A M #ov '.'(4 >%.%%? <A M #ov :.'9 >%.%%?

+ wn -.('' >%.%%? + wn -.)4 >%.%%? + wn -'.48( >%.%%?




6S v. 'S6S )4.:( (.(( 6S v. 'S6S )'9.( (.(( 6S v. 'S6S ')H.:( (.((

6S v. )S6S -'H(.(( - 6S v. )S6S -''D.(( - 6S v. )S6S -'8:.9( -

'S6S v. )S6S -84.9( - 'S6S v. )S6S -:('.(( - 'S6S v. )S6S -:D'.(( -


"irst-Stage

-Jalue

+ritical

Jalue

"irst-Stage

-Jalue

+ritical

Jalue

"irst-Stage

-Jalue

+ritical

Jalue

<A :H9.() :8.DH <A :)'. :8.DH <A :((4.8H :8.DH

#ov 84.:' :8.DH #ov 8(.D8 :8.DH #ov 49.H) :8.DH

+ wn :').: :8.DH + wn :'(.D8 :8.DH + wn 9H.:4 :8.DH

9e:t 2 )ears 8erformanceContemporaneous 8erformance 9e:t 1 )ear 8erformance

8:



'ppendi: D 5anel G! The percent of directors who are older than D( years is the governance measure &U#ovU*



<A M #ov (.((8 >%.$? <A M #ov (.(:' >%.3#? <A M #ov (.('8 >%.%/?

+ wn (.(D) >%.%%? + wn (.(9: >%.%%? + wn (.((8 >%.&#?

2 2 2

<A M #ov ).D)8 >%.%%?

<A M #ov ).9H) >%.%%?

<A M #ov ).(H4 >%.%%?

+ wn -'.)' >%.%%? + wn -'.)'8 >%.%%? + wn -:.9DD >%.%%?

3 3 3

<A M #ov ).D): >%.%%? <A M #ov .))9 >%.%%? <A M #ov ).)49 >%.%%?

+ wn -'.D' >%.%%? + wn -).()9 >%.%%? + wn -'.98 >%.%%?




6S v. 'S6S :).(( (.(( 6S v. 'S6S :.H( (.(( 6S v. 'S6S :().4( (.((

6S v. )S6S 4H8.(( (.(( 6S v. )S6S -)H:.(( - 6S v. )S6S ':).( (.((

'S6S v. )S6S -:D'.(( - 'S6S v. )S6S -:D.(( - 'S6S v. )S6S -:::.(( (.((


"irst-Stage

-Jalue

+ritical

Jalue

"irst-Stage

-Jalue

+ritical

Jalue

"irst-Stage

-Jalue

+ritical

Jalue

<A :H9.() :8.DH <A :)'. :8.DH <A :((4.8H :8.DH

#ov '8.'4 :8.DH #ov 'D.(: :8.DH #ov :9.:9 :8.DH

+ wn :').: :8.DH + wn :'(.D8 :8.DH + wn 9H.:4 :8.DH


8'



'ppendi: D 5anel ! The percent of directors who are women is the governance measure &U#ovU*



<A M #ov (.:(: >%.%%? <A M #ov (.:'' >%.%%? <A M #ov (.(H >%.%%?

+ wn (.(DD >%.%%? + wn (.:(( >%.%%? + wn (.(:H >%.%?

2 2 2

<A M #ov :.D9D >%.%%?

<A M #ov '.(8( >%.%%?

<A M #ov :.44 >%.%%?

+ wn :.(4( >%.%%? + wn :.))' >%.%%? + wn (.8'9 >%.%%?

3 3 3

<A M #ov :.H)H >%.%%? <A M #ov :.999 >%.%%? <A M #ov :.):( >%.%%?

+ wn :.''H >%.%%? + wn :.48 >%.%%? + wn (.9DD >%.%%?




6S v. 'S6S :98.)( (.(( 6S v. 'S6S ')'.D( (.(( 6S v. 'S6S :H).(( (.((

6S v. )S6S -4)8H.(( - 6S v. )S6S -:89.)( - 6S v. )S6S -84.8( -

'S6S v. )S6S -9H).(( - 'S6S v. )S6S -DD.'( - 'S6S v. )S6S -H4.(( -


"irst-Stage

-Jalue

+ritical

Jalue

"irst-Stage

-Jalue

+ritical

Jalue

"irst-Stage

-Jalue

+ritical

Jalue

<A :H9.() :8.DH <A :)'. :8.DH <A :((4.8H :8.DH

#ov 9'.H :8.DH #ov 9H.D :8.DH #ov DD.D :8.DH

+ wn :').: :8.DH + wn :'(.D8 :8.DH + wn 9H.:4 :8.DH


8)



'ppendi: D 5anel "! The percent of directors who own >ero shares of stock is the governance measure &U#ovU*



<A M #ov -(.(9H >%.%%? <A M #ov -(.(DD >%.%%? <A M #ov -(.((9 >%.$%?

+ wn (.(D9 >%.%%? + wn (.(9D >%.%%? + wn (.(:' >%.$/?

2 2 2

<A M #ov -'.)'' >%.%%?

<A M #ov -'.8'4 >%.%%?

<A M #ov -).('4 >%.%%?

+ wn (.): >%.%'? + wn (.)8' >%.%3? + wn (.'' >%.''?

3 3 3

<A M #ov -'.'D) >%.%%? <A M #ov -'.D'' >%.%%? <A M #ov -).'9' >%.%%?

+ wn (.H49 >%.%%? + wn :.(( >%.%%? + wn :.:(9 >%.%%?




6S v. 'S6S :9'.H( (.(( 6S v. 'S6S :4H.D( (.(( 6S v. 'S6S 4'.(9 (.((

6S v. )S6S :H'8.(( (.(( 6S v. )S6S -::.'( - 6S v. )S6S -'9:.'( -

'S6S v. )S6S -)'4.(( - 'S6S v. )S6S -').:( - 'S6S v. )S6S -8H.(( -


"irst-Stage

-Jalue

+ritical

Jalue

"irst-Stage

-Jalue

+ritical

Jalue

"irst-Stage

-Jalue

+ritical

Jalue

<A :H9.() :8.DH <A :)'. :8.DH <A :((4.8H :8.DH

#ov )H.(H :8.DH #ov 'D.() :8.DH #ov D.:8 :8.DH

+ wn :').: :8.DH + wn :'(.D8 :8.DH + wn 9H.:4 :8.DH


8



'ppendi: A. ensitivit! of results to alternative measures of leverage

$t is possible that the results reported in section regarding the performance-governance relation

are sensitive to the construction of the leverage variable. $n the capital structure literature, there does no

appear to be any agreed upon Nbest1 measure of leverage. "or our primary analyses, we use the measure

that appears fre/uently in corporate finance studies! All long term debt divided by assets.

To test the sensitivity of our results to this definition of leverage, we run the analyses in Table

using the following si0 definitions of leverage!

&:*

s+otal=sset

bt Long+erm0e&This is used in Table includes current portion of long term debt.*

&'* s+otal=sset

bt Long+erm0e&0cluding current portion of long term debt.*

&)* s+otal=sset

6oo(Euity s+otal=sset −

&* s+otal=sset

iabilities+otal6oo(L

&4* s+otal=sset

6oo(Euity s+otal=sset − &5er, Baker P urgler &'(('*.):*

&H*ty 4ar(etEui 6oo(Euity s+otal=sset

6oo(0ebt

+−&5er, Baker P urgler &'(('*.*

Again, we run the three-e/uation system allowing for potential endogeneity between

performance, governance and ownership. e estimate each system using 6S, 'S6S, and )S6S. e

use the Stock and Fogo &'((* weak instrument test and the 2ausman &:9D8* specification test to

determine which estimation method is most appropriate.

): Gefinitions &)* and &4* differ in the +ompustat variables used, specifically for Book /uity. Gefinition &)* uses+ompustat data item ]':H, ?Stockholders1 /uity.@ Gefinition &4* defines Book /uity as total assets less totalliabilities &item :8:* and preferred stock &item :(* plus deferred ta0es &item )4* and convertible debt &item D9*. Thecorrelation between the leverage variables based on the two definitions is (.9(.



$n the following table, we only present the coefficients and p-values &in parentheses* for the

governance variable in the performance e/uation &e/uation :A*, with return on assets as the performance

variable. nly the results from the estimation method deemed most appropriate by the specification

tests are presented. e present the results for all three different time periods &contemporaneous, ne0t

year1s <A, and ne0t two years1 <A* and for all seven different governance variables. The results are

/ualitatively very similar across the different definitions of leverage. Both the coefficients and p-values

vary little with the first five definitions of leverage; in a few cases, using the Baker and urgler &'(('*

market leverage variable does impact the statistical significance levels. verall, this evidence suggests

that our results regarding the relation between performance and governance are robust to alternative

definitions of leverage.



Appendi0 TableSensitivity of results to alternative measures of leverage<esults from estimating the performance governance model similar to Table using si0 different measures of leverage! &:* 6ong termdebt K assets &same as in Table *; &'* 6ong term debt, including current portion K assets; &)* &Assets book e/uity* K assets; &* Bookliabilities K assets; &4* &Assets book e/uity* K assets, as in Baker and urgler &'(('*; and, &H* Book debt K &Assets book e/uity Omarket e/uity*, as in Baker and urgler. e estimate the complete system of e/uations :a, :b, and :c using each of these si0definitions of leverage. $n this table we focus on e/uation :a, and on the coefficient on the governance parameter. e estimate eachsystem with each leverage variable for each of the seven measures of governance. "inally, we estimate each version for return onassets &?<A@* in three time periods! 5anel A uses contemporaneous <A, 5anel B uses ne0t year1s <A, and 5anel + uses ne0t twoyears1 <A. e present only the coefficient on the governance parameter; p-values are in parentheses. The sample si>e for each iscomparable, though not e0actly the same as, to the sample si>es in Table . All systems are estimated using 6S, 'S6S and )S6S.e perform the 2ausman &:9D8* specification test and the Stock and Fogo &'(()* weak instrument tests. e only present the resultfrom the estimation method &6S, 'S6S or )S6S* that is determined to be most appropriate. Eoted below are the estimatedcoefficients and significance levels for the governance variable in e/uation &:a*.

De&t =aria&le De&t =aria&le De&t =aria&le De&t =aria&le De&t =aria&le De&t =aria&le

&6T Gebt O ST 6T Gebt* K

Assets

6ong Term Gebt K

Assets

&Assets - /uity* K

Assets

6iabilities K

Assets

Baker-urgler -

Book 6everage

Baker-urgler -

%arket 6everage

9anel =:

<At M f &<overnance, 5erformance, 6everage, +ontrols*

#$% #-$nde0 -(.((8 -(.((D -(.((D -(.((D -(.((D -(.(()

>%.%%? >%.%3? >%.%? >%.%'? >%.%3? >%.'?

B+" -$nde0 -(.(:H -(.(:4 -(.(:4 -(.(: -(.(: -(.((9

>%.%%? >%.%'? >%.%? >%.%? >%.%? >%.%?

T+6 Benchmark Score -(.(( -(.(( -(.(() -(.(() -(.(() -(.(('

>%.%'? >%.%'? >%.%$? >%.%&? >%.%$? >%.'?

B+ #ovScore (.((( -(.((: (.((( (.((( -(.((: (.(((>%.$'? >%.'#? >%.#&? >%.#&? >%.3#? >%.&3?

Girector W wnership (.((D (.((8 (.((D (.((D (.((D (.((D

>%.%%? >%.%%? >%.%%? >%.%%? >%.%%? >%.%%?

+ - +hair duality -(.(D -(.(D( -(.(H8 -(.(H9 -(.(HH -(.(H4

>%.%? >%.%? >%.%? >%.%? >%.%'? >%.%?

Board $ndependence -(.:)' -(.:)' -(.:'9 -(.:'9 -(.:)) -(.:(

>%.%%? >%.%%? >%.%%? >%.%%? >%.%%? >%.%%?



De&t =aria&le De&t =aria&le De&t =aria&le De&t =aria&le De&t =aria&le De&t =aria&le

&6T Gebt O ST 6T Gebt* K

Assets

6ong Term Gebt K

Assets

&Assets - /uity* K

Assets

6iabilities K

Assets

Baker-urgler -

Book 6everage

Baker-urgler -

%arket 6everage

9anel 6:

<AtO: M f &<overnance, 5erformance, 6everage, +ontrols*

#$% #-$nde0 -(.((D -(.((D -(.((H -(.((H -(.((4 -(.(()

>%.%? >%.%3? >%.%? >%.%&? >%.%? >%.3&?

B+" -$nde0 -(.(:4 -(.(:H -(.(:) -(.(:' -(.(:: -(.((8

>%.%? >%.%? >%.%'? >%.%? >%.%$? >%.$?

T+6 Benchmark Score -(.((' -(.(() -(.((: -(.((: -(.((: -(.((:

>%.$? >%.%#? >%.%? >%.'? >%.$? >%.&3?

B+ #ovScore (.((( (.((( (.((: (.((: (.((( (.(((

>%."? >%.#? >%.'? >%.3"? >%.A? >%.&?Girector W wnership (.((H (.((8 (.((H (.((H (.((D (.((D

>%.%? >%.%%? >%.%%? >%.%%? >%.%%? >%.%%?

+ - +hair duality -(.(HH -(.(HH -(.(H' -(.(H' -(.(44 -(.(4D

>%.%%? >%.%'? >%.%? >%.%? >%.%'? >%.%?

Board $ndependence -(.:'( -(.:): -(.::D -(.::4 -(.:: -(.:'H

>%.%%? >%.%%? >%.%%? >%.%%? >%.%%? >%.%%?

9anel 7:

<AtO' to tO: M f &<overnance, 5erformance, 6everage, +ontrols*

#$% #-$nde0 -(.((4 -(.(( -(.(( -(.(( -(.((4 -(.((

>%.%'? >%.%#? >%.%$? >%.%? >%.%3? >%.%?

B+" -$nde0 -(.(:( -(.((9 -(.((9 -(.((9 -(.(:( -(.((9

>%.%? >%.%$? >%.%3? >%.%3? >%.%'? >%.%'?

T+6 Benchmark Score -(.((: -(.((: -(.((: -(.((: -(.((: -(.((:

>%.'? >%.A? >%.'$? >%.'? >%.'? >%.'$?

Girector W wnership (.((' (.((' (.((' (.((' (.((' (.(()

>%.&? >%.'#? >%.#? >%."? >%.'? >%.%#?

+ - +hair duality -(.()H -(.()' -(.()' -(.()' -(.()4 -(.()D

>%.%%? >%.%3? >%.%? >%.%? >%.%? >%.%?

Board $ndependence -(.(H9 -(.(4D -(.(H' -(.(H: -(.(H4 -(.(D:

>%.%? >%.%&? >%.%'? >%.%'? >%.%'? >%.%?



'ppendi: (. 8redicted pro&a&ilit! of disciplinar! turnover

$n the multinomial logit model, the probability of turnover can be estimated using the

following e/uations!

9rob&Gisciplinary Turnover B M 5* M*_e0p&*e0p&:\

*e0p&

5 5

5

n )on0isc+ur 0isc+urn

0isc+urn

∗+∗+∗β β

β , and

9rob&Eon-Gisciplinary Turnover B M 5* M*_e0p&*e0p&:\

*e0p&

5 5

5

n )on0isc+ur 0isc+urn

n )on0isc+ur

∗+∗+∗β β

β .

The probability of no turnover would e/ual one minus the two above probabilities. The B vector

can be any values of the parameters; the mean or median values are reasonable values to start

with.

e calculate the predicted probability of disciplinary and non-disciplinary turnover,

using the coefficient estimates from Table H and both the mean and median values of the B

vector parameters. To test the sensitivity of these predicted values to the interactive terms &5ast

<eturn 5 #overnance*, we make each interactive term one standard deviation Nworse1 and use

this as the B value in the above calculation. "or all of the other parameters in the B vector, we

leave them unchanged at their mean or median values. This allows us to focus on the sensitivity

of the probability of disciplinary turnover with respect to the interactive terms only.

The following table shows the probability of disciplinary turnover with the dollar

ownership of the median director and board independence as governance variables. $n the first

and third columns, we present the predicted values with all parameters at their mean and median

values, respectively. $n the second and fourth columns, we perturb the value of the interaction

term parameter, making it one standard deviation Nworse.1 nly the interaction term is changed;

all other parameters remain at their mean or median value. The coefficient estimates are taken

from Table H; the parameters that are not changed include the intercept, past two years1 stock



return, the governance variable, + ownership, firm si>e, + age, + tenure, and year

dummy variables.

The predicted probabilities in this table put the coefficient estimates from Table H into

perspective. hen all parameters are measured at their mean values, the probability of

disciplinary turnover is '.'8 with the dollar ownership of the median director as the

governance variable; this increases to :'.44 when the &5ast <eturn 5 Girector W wnership*

interaction term decreases by one standard deviation. The results are /ualitatively similar when

the median values of the parameters are used. These results highlight the economic importance of

the dollar ownership of the median director and board independence as governance variables.

Also, these results suggest that the dollar ownership of the median director might be more

important than board independence as a governance variable.

8redicted 8ro&a&ilit!

of Disciplinar!

+urnover

8rediction, Interaction

+erm one tandard

Deviation 7orse

8redicted 8ro&a&ilit!

of Disciplinar!

+urnover

8rediction, Interaction

+erm one tandard

Deviation 7orse

Girector W wnership '.'8 :'.44 ).48 :8.H4

Board $ndependence '.9( D.9H .:: :(.D'

8ro&a&ilit! of Disciplinar! +urnover

Mean Values o% -arameters Me/ian Values o% -arameters



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Corporate Governance - Performance