Endogeneity and the Dynamics of Corporate

Governance∗

M. Babajide Wintokia,†

March 19, 2007

aTerry College of Business, University of Georgia

AbstractThere is growing evidence that corporate governance and firm performance are not strictly exoge-nous; both are jointly determined by (partially) unobservable variables and in addition, corporategovernance adjusts to past firm performance. Ordinary least squares and fixed effects cross-sectionalregression analyses of the relationship between corporate governance and firm performance do notadequately control for these sources of endogeneity and may be biased. I estimate the relationshipbetween governance and performance using a generalized method of moments estimator that con-trols for unobserved heterogeneity, allows governance and performance to be jointly determined andalso allows governance to adjust to changes in past firm performance. In a panel of over 6,000 firmsbetween 1991 and 2003, I find no relationship between any aspect of board structure or inside own-ership, and firm performance. The results stand in contrast with some of those from prior studiesthat suggest an inefficient exogenous relationship between certain aspects of corporate governanceand firm performance. The results support the alternative hypothesis that corporate governancestructures are, in general, efficiently determined by firms in response to their specific contractingand competitive environments.

∗I would like to specially thank Jim Linck, Jeff Netter and Tina Yang for their help in obtaining the boards ofdirectors data and for useful comments. I would also like to thank Audra Boone, Harold Mulherin, Paul Irvine,Chris Stivers, seminar participants at the University of Georgia and the University of Kansas for helpful commentson earlier drafts.

†Email:jwintoki@terry.uga.edu

1 Introduction

Understanding the empirical relationship between corporate governance and firm performance has

been a critical aspect of corporate finance research for several decades, at least ever since Adolf

Berle and Gardiner Means (1932) suggested that dispersed ownership was leading to a divergence

of management and shareholders’ interests. The agency problems of this divergence is magnified,

especially in the U.S., by the fact that ownership is widely dispersed in many of the largest firms.

One way to reduce this agency problem is to concentrate ownership in the hands of management.

Another way for dispersed shareholders to effectively monitor management is by concentrating

the power to monitor managers in the hands of elected directors that represent the dispersed

shareholders.

This reasoning has led a number of researchers to propose that certain governance structures

(e.g. independent and small boards) or concentrated inside ownership are universally good for all

firms and that firms that do not have these particular ownership or board structures have ineffi-

cient governance. Boone, Field, Karpoff, and Raheja (2006) call this (often) implicit assumption

the inefficiency hypothesis. The idea has spawned a large literature that investigates the cross-

sectional relationship between firm performance and governance structure. Finding a significant

relationship between any ownership or any governance structure would provide direct evidence for

the inefficiency hypothesis.

This inefficiency hypothesis viewpoint has been countered by other researchers who, following

arguments presented by Demsetz and Lehn (1985), suggest that while all governance mechanisms

provide benefits, they also impose costs that differ across firms depending on the unique agency

problems the firm faces. Thus firms will endogenously choose value-maximizing governance struc-

tures that trade-off the relative costs and benefits of various governance mechanisms. Proponents

of the endogenous determinants hypothesis argue that if we control for firm specific characteristics,

then in equilibrium, we should expect to find no relationship between any particular aspects of

governance and firm performance.

Nevertheless, understanding the implications of the relationship between firm performance and

corporate governance remains critical to our understanding of corporate governance and may also

1

have important policy or regulatory implications. For example, if we find that board independence

is systematically related to firm performance suggests that there could be economy-wide welfare

benefits to regulation that imposes a uniform level of board independence across all U.S. firms. On

the other hand, a contradictory finding would certainly question the value of any “one size fits all”

regulation. However any inference that can be drawn from empirical analysis will be valid only to the

extent to which we have recognized and controlled for the aspects of the governance/performance

relationship that is endogenous.

In this paper, I argue that there are at least three potential sources of endogeneity when es-

timating the relationship between any aspect of corporate governance and firm performance, all

which any researcher must carefully consider:

(i) Unobserved heterogeneity which would arise if performance and corporate governance are

jointly determined by unobservable firm specific factors

(ii) Simultaneity which would arise if performance and corporate governance is simultaneously

determined in every period in which we are able to observe both performance and governance

(iii) Dynamic endogeneity which would arise from the possibility that contemporaneously observed

governance variables are not strictly exogenous since current corporate governance is likely to

depend on past realizations of performance.

Prior empirical studies have relied on empirical estimation methodologies that ignore one or

more of these sources of endogeneity. Ordinary least squares (OLS) estimation completely ignores

unobserved heterogeneity. Turning to panel data and using fixed effects (FE) estimation eliminates

the unobserved heterogeneity but is valid only if we make the strong assumption that changes in

past performance have no effect on current governance (i.e., if we ignore dynamic endogeneity).

Two-stage least squares estimation techniques using instrumental variable (IV) regression could

potentially eliminate all the sources of endogeneity but it requires us to identify and justify the

use of strictly exogenous instrumental variables. In an environment where all market participants

are even only weakly rational (as we might expect with publicly traded firms), identifying valid

exogenous instruments is very difficult, if not practically impossible (see Keane and Runkle (1992)).

2

The fact that prior studies may have inadvertently relied on inconsistent estimation procedures

may explain why the results from the studies have been mixed. For example, Demsetz and Lehn

(1985), using simple OLS regressions find that management ownership concentration is unrelated

to firm return on equity. In a often cited study, Morck, Shleifer, and Vishny (1988) use piece-

wise OLS regressions to show a hump-shaped relationship between firm performance (measured

by Tobin’s Q) and ownership. However, more recent studies using other econometric techniques

(e.g., two-stage least squares estimation by Loderer and Martin (1997) and Demsetz and Villalonga

(2001)) and fixed effects estimation by Himmelberg, Hubbard, and Palia (1999)) find results that

support those originally reported by Demsetz and Lehn (1985). Research on the relationship be-

tween board structure and firm performance has also produced mixed results. Bhagat and Black

(2002) find no relationship between board independence and firm performance in a two-stage least

squares (IV) regression, while Yermack (1996) actually finds a negative relationship between board

independence and firm performance in an OLS estimation. However, both Yermack (1996) and

Eisenberg, Sundgren, and Wells (1998) find a negative relationship between board size and firm

performance using OLS and fixed effects estimation.

I propose re-examining the relationship between firm performance and board structure using

a general method of moments (GMM) dynamic panel estimator that eliminates the major sources

of endogeneity inherent in the estimation of the relationship between governance and performance.

This method dispenses with the strict exogeneity assumption implicit in OLS and fixed effects es-

timation, and requires only the much more plausible sequential exogeneity. Sequential exogeneity

allows our corporate governance variable to be a function of past and present realizations of per-

formance. This estimator, originally developed in a series of papers by Holtz-Eakin, Newey, and

Rosen (1988) and Arellano and Bond (1991), and further developed by Arellano and Bover (1995)

and Blundell and Bond (1998), provides an excellent econometric specification for dealing with

the endogeneity issues we are likely to encounter in the estimation of the governance/performance

relationship. The basic steps underlying this estimation strategy is as follows. First, the regres-

sion equation of performance on corporate governance is written as a dynamic model that includes

lagged performance as an explanatory variable. Next, we can take first-differences and carry out

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GMM estimation using lagged values of the governance variables, as well as lagged values of per-

formance as GMM instruments. First differencing eliminates unobserved heterogeneity and allows

arbitrary correlation between unobserved firm effects and our governance variables. The choice of

instruments enables us to control for both dynamic endogeneity and simultaneity.

I apply the GMM estimator to estimate the relationship between firm performance (measured by

return on assets) and board structure (board size, independence and leadership) using a panel data

set consisting of over 6,000 firms between 1991 and 2003. In order to compare my results to those

obtained in previous studies, I also estimate this relationship using OLS and fixed effects estimation.

I find that OLS and fixed-effects estimation, which ignore both unobserved heterogeneity and

dynamic endogeneity, suggest that firm performance is significantly negatively related to board size

(a result reported by both Yermack (1996) and Eisenberg, Sundgren, and Wells (1998)). However,

when the estimation is carried out using the GMM dynamic panel estimator, I find no significant

relationship between board size and firm performance. Empirical estimation which controls for the

possibility that firms adjust their board structure in response to past performance, strongly suggests

that there is no relationship between firm performance and board structure. I also estimate the

relationship between insider ownership and firm value (as measured by Tobin’s Q). Fixed-effects

estimates actually suggest a positive relationship between ownership concentration and firm value.

Again, this relationship is not robust to a dynamic estimation strategy that controls for unobserved

heterogeneity, simultaneity and dynamic endogeneity.

These results clearly support the hypothesis that firms endogenously choose efficient board and

ownership structures that suit their unique operating conditions and contracting environments.

Thus, for example, we cannot claim that small boards are universally good for all firms as pre-

vious studies have suggested. Firm performance appears to be driven mostly by variation in the

unobserved individual firm effects where these firm effects might represents such intangibles as

management ability, corporate culture etc., rather than any systematic differences in governance

structures or management ownership.

One of the key contributions of this paper is to show that continuous adjustment of corporate

governance to past performance (dynamic endogeneity) could lead to biased inference when estimat-

4

ing the relationship between governance and performance. The insight that corporate governance is

dynamic is not novel; for example, in their comprehensive survey of the board literature, Hermalin

and Weisbach (2003, p. 8), note that: “...firm performance is both a result of the actions of previous

directors and itself a factor that potentially influences the choice of subsequent directors. Studies

of boards often neglect this issue and thus obtain results that are hard to interpret”. Nevertheless

previous studies have generally ignored this dynamic endogeneity and have not controlled for it in

their empirical estimation. Using a simple model of corporate governance, in which current gover-

nance depends on past performance, I use simulated data to show that in panel data samples with

cross-sectional and time series dimensions that are typically found in corporate governance stud-

ies, these biases can be significant. Even modest amounts of dynamic endogeneity can introduce

substantial bias into our coefficient estimates and produce spurious correlations between dependent

and independent variables if we rely on OLS and fixed effects estimates. I confirm the presence of

dynamic endogeneity in my data set; using tests outlined by Wooldridge (2002) I can clearly reject

the assumption of the strict exogeneity of the board structure and ownership variables.

The ease of implementing the GMM dynamic panel estimator makes it particularly appealing.

By making the assumption that present observable governance are determined by past and present

performance (as well as past and present values of the governance variables), we can use the

past values of performance and corporate governance as instruments in our GMM estimation.

Thus, the researcher has a set of valid instruments (lagged values of performance and corporate

governance) that are already a part of the data set and does not need to search for any external

exogenous instruments. Another appealing aspect of the GMM estimator is that in estimating the

governance/performance relationship, it allows us to treat all the regressors in our model (both the

governance and control variables) as endogenous.

The overall contribution of this paper to the corporate governance literature can be summa-

rized as follows. First, I show that in estimating the relationship between firm performance and

corporate governance, most researchers (implicitly or explicitly) ignore the dynamic nature of the

relationship; simulation results strongly suggest that this could introduce substantial biases into

estimation results. I present evidence to suggest that corporate governance (board structure and

5

managerial ownership) is not strictly exogenous; firms adjust their governance and ownership to past

performance. This paper is the first to explore the implications of the dynamic inter-relationship

between performance and any aspect of governance, in the empirical estimation of the perfor-

mance/governance relationship. The empirical framework suggested in this paper is useful not only

in the governance/firm performance context, but is potentially useful in other areas of corporate

finance where unobserved heterogeneity and dynamic endogeneity is prevalent and truly exogenous

instruments are difficult to find. The dynamic estimation methodology complements the ‘struc-

tural model’ approach of Coles, Lemmon, and Meschke (2006) especially in situations where it

is difficult to derive a valid structural model, or in those situation where we are uncertain about

the validity of the model. By allowing our governance variables to be arbitrarily correlated with

historical values of performance and governance variables, we can obtain consistent estimates of the

governance/performance relationship for structural models based on the endogenous relationship

between unobserved firm-specific variables and past values of firm performance.

Another major contribution of this paper is that, using a fairly large panel of firms, over a

thirteen-year period, and employing the dynamic panel data estimator, I find that there is no sig-

nificant relationship between board composition, board size, board leadership, insider ownership

and firm performance. This is the largest panel that has been used to date, to study the perfor-

mance/governance relationship. In particular, the results cast some doubt on the robustness of the

previously documented negative relationship between board size and firm performance. The results

obtained are important not just to academic researchers, but could have implications for regulators,

shareholder activists and groups that promote mandatory, “one size fits all” board structures.

The rest of this paper is structured as follows. In section 2, I present a brief overview of the

sources and implications of endogeneity in empirical corporate governance research and discuss the

advantage of dynamic GMM estimation over other kinds of estimators. In section 3, I present a

Monte Carlo simulation that illustrates the biases of OLS and fixed-effects estimation in the face

of both unobserved heterogeneity and dynamic endogeneity, and also illustrates the power of the

dynamic GMM estimator. In section 4 and 5, I present empirical results from my estimation of

the relationship between firm performance and board structure and between firm value and insider

6

ownership respectively. In section 6, I discuss the implications of my results, and conclude.

2 Review of endogeneity and panel data estimation techniques in

corporate governance

In estimating the empirical relationship between performance and corporate governance, most em-

pirical studies have estimated a variant of the following model:

yit = xitβ + zitγ + ηi + εit, i = 1 . . . N, t = 1 . . . T (2.1)

where yit is some measure of firm performance (e.g. return on assets, stock returns), xit contains

the governance variables of interest, zit contains a set of observable control variables and ηi is a

firm-specific effect (usually assumed to be fixed) that captures unobservable heterogeneity across

firms. Examples of such studies abound in the literature and include Yermack (1996), where the

key governance variable is board size; Agrawal and Knoeber (1996), where the governance variables

include management shareholding and board structure; Himmelberg, Hubbard, and Palia (1999),

where the governance variable of interest is management ownership; Bhagat and Black (2002),

where the governance variables are board size and composition; and Gompers, Ishii, and Metrick

(2003), where the key variable of interest is an index of shareholder protection.

In setting up the empirical specification, most researchers include control variables which are

associated with firm performance that may also be correlated with corporate governance. Thus zit

usually includes observable firm characteristics like firm size, firm risk, industry, growth opportu-

nities etc. In contrast, η (which may or may not explicitly stated by the researcher) is assumed to

represent the unobservable that firm characteristics that may affect firm performance, e.g., man-

agerial productivity, corporate culture, director ability etc.

2.1 OLS and simple instrumental variable (IV) estimation

Both simple OLS (when T = 1) and pooled OLS (when T > 1), ignore unobserved heterogeneity

by assuming that neither the governance variables nor the control variables are correlated with

unobserved firm characteristics, i.e., E(x′itηi) = 0 and E(z′itηi) = 0. It is quite easy to see that

7

this assumption is unlikely to hold when estimating the relationship between performance and

governance. If, for example, η includes such unobservable factors as the ability of the directors,

it not only has a direct impact on performance, but is also likely to be correlated with board

composition. For example, a firm with one very able outside director will not need as many

outsiders as one with only mediocre directors. All these suggest that OLS estimates are likely to

be severely biased.

In corporate governance it is very difficult to use simple instrument variable (2SLS) regressions

to eliminate the bias introduced by unobserved heterogeneity. Valid identification would require us

to find purely exogenous instruments which are not only correlated with the governance variables

(xit) but are independent of the unobserved firm characteristics (ηi) and are not already in the

control variable set (zit). It is easy to see how this could be difficult, in practice, when attempting

to estimate (2.1). The choice of observable instruments (e.g. firm size, growth opportunities,

firm risk etc.) are likely to be correlated with the unobserved firm characteristics. One might

be tempted to use lagged performance (yit−1) as an instrument for xit in (2.1); but this would

be invalid instrument since yit−1 is obviously correlated with the unobserved firm characteristics,

ηi. To further complicate matters, the number of exogenous instruments required for identification

increases with the number of endogenous variables in the empirical specification.

Aside from unobserved heterogeneity, OLS estimation of (2.1) relies on the assumption that

neither the governance variables (xit) nor the control variables (zit) are correlated with the error

term, εit, i.e., E(x′itεit) = 0 and E(z′itεit) = 0. If performance and corporate governance are

simultaneously determined, then this assumption is clearly violated, and OLS will be biased. A

number of researchers (e.g. Bhagat and Black (2002)) have focused on this simultaneity bias and

have suggested the use of simultaneous equations estimation methods as a way to eliminate this

bias. So for example, a typical simultaneous equation system will include the following:

y = f(x, z1, z2, . . . , zk−1, zk) (2.2)

x = g(y, z1, z2, . . . , zk−1) (2.3)

where one of the control variables (zk) has been left out of the governance (x) equation, in order

8

to permit identification of the performance (y) equation. The assumption is that zk is purely

exogenous in the sense that, while it determines performance, it is completely independent of the

governance variable. Again, the practical difficulty of identifying such an instrument is obvious,

and in the absence of strong, underlying structural models, the choice of which element (zk) to

leave of out of (2.3) seems somewhat arbitrary. In addition to all these, simultaneous equations

models do not necessarily address the bias introduced by unobserved heterogeneity (η) from (2.1).

2.2 Fixed-effects (within) estimation and strict exogeneity

In order to solve the serious problems raised by unobserved heterogeneity, researchers (e.g. Yer-

mack (1996) and Himmelberg, Hubbard, and Palia (1999)) have increasinly turned to panel data

estimation methods, especially fixed-effects estimation. Thus a fixed-effects transformation of (2.1),

eliminates the unobserved firm effect (η) and leaves:

∆yit = ∆xitβ + ∆zitγ + ∆εit, i = 1 . . . N, t = 1 . . . T (2.4)

where ∆yit = yit − yi, ∆xit = xit − xi, ∆zit = zit − zi and ∆εit = εit − εi.

However, while the fixed-effects estimation eliminates the unobserved heterogeneity, it could

potentially introduce another source of endogeneity − dynamic endogeneity. Consistent estimation

of (2.4) relies on the assumption that E(∆x′it∆εit) = 0 and E(∆z′it∆εit) = 0. This particular

assumption is much stronger than it at first looks. ∆εit contains information about the error term

across all time periods, since

∆εit = εit − εi = εit −1T

t=T∑t=1

εit (2.5)

Thus, (2.5) implies that for the fixed effects estimation of (4) to be consistent, we have to assume

thatE(x′

isεit) = 0 ∀ t, s (2.6)

This is a strict exogeneity assumption. The implication of this assumption is that the corporate

governance variable (e.g. board structure) that we observe today is completely independent of any

past, present and future shocks or innovations to firm performance. This means that in carrying

out a fixed-effects estimation of (2.1), the researcher is in effect assuming that previous realizations

9

of firm performance have no effect whatsoever on current governance structures. In any estimation

of the relationship between governance and performance, (2.6) is not only a restrictive from an

econometric perspective, but is also likely to be violated from an economic perspective.

There is plenty of empirical to suggest that current governance and ownership structures are

strongly and directly influenced by past performance. Hermalin and Weisbach (1988) finds that,

in U.S. firms, board composition is likely to change following poor performance, a result that is

confirmed in studies by Denis and Sarin (1999), Bhagat and Black (2002) and Easterwood and

Raheja (2007). Kaplan and Minton (1994) also finds that this is also true with Japanese firms.

In a similar vein, Kole (1996), finds that current managerial ownership is strongly influenced by

previous firm performance.

Governance structures can also be indirectly influenced by past performance. At least as far

back as Demsetz and Lehn (1985), researchers have suggested, and empirically demonstrated that

governance structures are determined by the nature and scope of agency costs specific to each firm.

The studies have used observable firm characteristics such as size, growth opportunities, number

of business segments, age and the uncertainty of the firm’s business environment to proxy for

these agency costs. Recent studies including those by Mulherin (2005), Boone, Field, Karpoff, and

Raheja (2006), Coles, Daniel, and Naveen (2006) and Linck, Netter, and Yang (2006a) find that

board structures are closely related to these firm characteristics. Similarly, Himmelberg, Hubbard,

and Palia (1999) and Demsetz and Villalonga (2001) also confirm the original Demsetz and Lehn

(1985) results which suggested that management ownership is related to those firm characteristics

that proxy for agency costs. So if firm characteristics like size and growth opportunities help

determine board structure and these characteristics are in turn determined by prior performance,

then these determinants themselves constitute an additional indirect link between past performance

and current governance.

Thus, fixed-effects estimation of the relationship between firm performance and corporate gov-

ernance as specified in (2.1) is likely to be inconsistent because the strict exogeneity assumption

of (2.6) is likely to be violated if there is a dynamic relationship between firm performance and

10

corporate governance.1

2.3 A consistent estimator for the relationship between corporate governance

and firm performance

Consistent estimation of (2.1) will require the use of an estimation technique which controls for

unobserved heterogeneity and simultaneity while exploiting (or at least controlling for) the dynamic

association between corporate governance and performance.

A promising estimation technique is the GMM dynamic panel data estimation procedure. This

estimator was introduced by Holtz-Eakin, Newey, and Rosen (1988) and Arellano and Bond (1991),

and further developed in a series of papers including Arellano and Bover (1995) and Blundell and

Bond (1998). The dynamic modeling approach has been used in other areas of economics where

the structure of the problem suggests a dynamic relationship between dependent and independent

variables. Examples of these include the empirical measurement of economic growth convergence

(Caselli, Esquivel, and Lefort (1996)), estimation of a labor demand model (Blundell and Bond

(1998)) and the empirical estimation of the relationship between the level of financial intermediary

development and economic growth (Beck, Levine, and Loayza (2000)).

The dynamic GMM estimator dispenses with the strict exogeneity assumption in (2.6), and

instead relies for consistency on a weaker form of exogeneity, sequential exogeneity, which for the

estimation equation in (2.1) can be written as:

E(x′isεit) = 0 ∀ s < t (2.7)

The sequential exogeneity assumption seems to be a more reasonable one to make in the cor-

porate governance and firm performance context. The assumption allows the governance variables

to be determined by past (and present realizations) of performance, but not future values. This

is a fairly reasonable assumption. As Keane and Runkle (1992) point out, if we assume rational

expectations, we expect the governance variables to be affected by past and current innovations

in the performance, but not future innovations in the board structure. This does not mean that1Strict exogeneity as specified in (2.6) also excludes the possibility of simultaneity between performance and

corporate governance. Thus fixed-effects estimation does not control for simultaneity.

11

the economic actors connected with the firm do not adjust their actions in relation to the expected

performance, it simply means that they cannot adjust their actions for future shocks, or unexpected

innovations to performance.

This suggests that the governance/performance relation should be treated as a dynamic unob-

served effects model. Typically, in such models,

xit = f(yit−1, yit−2, . . . , y1,xit−1, zit, ηi) (2.8)

and the model in (2.1) should be estimated as:

yit = ρyit−1 + xitβ + zitγ + ηi + εit, i = 1 . . . N, t = 1 . . . T (2.9)

Arellano and Bond (1991) develop a first difference GMM estimator for the system represented

by (2.8) and (2.9) that exploits the sequential exogeneity assumption of (2.7). Transforming (2.9)

into a system of T − 1 equations in first differences:

∆yi = ∆Xiβ + ∆εi, i = 1 . . . N (2.10)

where Xi includes the governance variables (xit), control variables (zit) and lagged performance,

yit−1. This step eliminates the unobserved heterogeneity and allows us to have a model where our

governance variables can be arbitrarily correlated with any unobserved firm-specific effects.

Next, we can exploit the sequential exogeneity assumption of (2.7) to define a matrix of instru-

ments containing lagged values of performance and the explanatory variables:

Zi =

x0i1 0 0 · · · 0

0 x0i2 0 · · · 0

.... . .

...

0 0 0 · · · x0iT−1

(2.11)

where the rows correspond to the first-differenced equations for periods t = 3, 4, . . . , T and x0i1 ≡

Xi1, x0i2 ≡ Xi1,Xi2, . . ., x0

iT ≡ Xi1,Xi2, . . .XiT−1. We use lagged values of our variables

as GMM instruments for our transformed system which has been first differenced in (2.10). For

example, for the first equation in the system, (y3 − y2) = (X3 −X2)β+ (ε3 − ε2), we can use X1 as

12

an instrument, since the sequential exogeneity assumption means that E(ε3, ε2|X1) = 0. Similarly,

for the second equation in the system, (y4 − y3) = (X4 −X3)β + (ε4 − ε3), we can use X1 and X2

as instruments since E(ε3, ε4|X1,X2) = 0, and we continue in this manner through the system as

defined by (2.10) and (2.11).

We can then carry out GMM estimation based on the moment conditions

E(Z′i∆εi) = 0 (2.12)

In general, the asymptotically efficient GMM estimator based on the moment conditions in (2.12)

minimizes the criterion: [Z′

i(∆yi −∆Xi)]′

W[Z′

i(∆yi −∆Xi)]

(2.13)

The GMM estimator that minimizes this criterion is obtained as:

βGMM =[(∑

i

∆X′iZi

)W

(∑i

∆Z′iXi

)]−1(∑i

∆X′iZi

)W

(∑i

∆Z′iyi

)(2.14)

where the optimal weighting matrix, W = Λ−1, and

Λ = E(Z′i∆εi∆ε

′iZi) (2.15)

The choice of instruments in (2.11) means that we have explicitly modeled the governance

variables to be completely determined by their history, as well as the firm’s past and present

performance. Eliminating the unobserved heterogeneity by first differencing means that we do not

have to worry about the possible correlation between the governance variables and unobserved

firm effects like managerial productivity, director ability etc. We also do not have to impose any

structure on the lag-length of the relationship suggested by (2.9) in order to obtain consistent

estimates of the model in (2.10).

While the particular ‘difference’ GMM estimator obtained by using (2.14) is economically ap-

pealing it does have at least three econometric shortcomings. First, Beck, Levine, and Loayza (2000)

note that if the original model is conceptually one that is in levels, differencing may attenuate the

signal to noise ratio and reduce the power of our tests. Secondly, Arellano and Bover (1995) point

out that variables in levels may be weak instruments for first-differenced equations. Thirdly, first-

13

differencing may exacerbate the impact of measurement errors in the dependent variables (Griliches

and Hausman (1986)).

Arellano and Bover (1995) and Blundell and Bond (1998) suggest that we can mitigate these

shortcomings and improve the GMM estimator by including the equations in levels in the estimation

procedure. We can then use the first-differenced variables as instruments for the equations in levels,

in a ‘stacked’ system of equations that includes the equations in both levels and differences. This

will produce a ‘system’ GMM estimator. The problem that arises here is that the equations in

levels still includes the unobserved heterogeneity. If, however, we are willing to make the additional

assumption that while the governance (and control) variables may be arbitrarily correlated with

the fixed effects, this correlation is constant over time. This is a reasonable assumption, if the

unobserved effects proxies for factors like unobserved director ability, managerial productivity etc.

The assumption leads to an additional orthogonality condition:

E(∆X′itηi) = 0 (2.16)

The ‘system’ GMM estimator enables us to obtain efficient estimates while maintaining all the es-

sential elements of controlling for unobserved heterogeneity, simultaneity and dynamic endogeneity.

2.4 Testing for strict exogeneity

When faced with empirical data, the researcher has to decide whether or not to assume a dynamic

model or to rely on fixed-effects estimation. If the system is not dynamic, GMM estimation might

be less efficient than fixed-effects estimation since dynamic GMM estimation would then involve

using weak instruments for our endogenous variables. So even if we suspect that our system is

dynamically endogenous, an econometric test of endogeneity can certainly better inform our choice

of which panel data estimation technique to employ.

Wooldridge (2002) present a regression-based test for strict exogeneity that is relatively easy

to implement. If Xit contains the explanatory variables, a test of strict exogeneity is obtained by

carrying out fixed-effects estimation on the equation:

yit = Xitβ + Wit+1δ + ηi + εit, t = 1 . . . T − 1 (2.17)

14

where Wit+1 is a subset of Xit+1. Under the null hypothesis of strict exogeneity, δ = 0. Intuitively,

if δ 6= 0, then current governance depends on past performance (or conversely, present performance

affects future corporate governance and ownership).

Thus, if we can reject the hypothesis that δ = 0, fixed-effects estimation is likely to be biased

by the presence of dynamic endogeneity and we are likely to obtain less biased and more consistent

estimates using a dynamic estimation procedure.

3 Estimating the bias from ignoring dynamic endogeneity: Monte

Carlo evidence

Consider the dynamic unobserved effects model:

yit = ρyit−1 + xitβ + ηi + εit, i = 1 . . . N, t = 1 . . . T (3.1)

As discussed in the previous section, OLS estimation will be biased if ρ 6= 0 (since yit−1 is obviously

correlated with ηi) or if any of the variables in xit are correlated with ηi. Similarly, fixed-effects

estimation will be biased if xit = f(yit−1, yit−2, . . . , yit−k), since this would be a violation of the

strict exogeneity assumption required for consistency of fixed effects estimation.

Awareness of the biases introduced from assuming strict exogeneity when explanatory variables

are not strictly exogenous goes at least as far back as Nerlove (1967). Nickel (1981) and Sevestre and

Trognon (1985) show that as N → ∞, the inconsistency of the fixed-effects estimator is O(1/T ),

which means that the inconsistency of fixed effects estimation should decrease as T increases.

However, Judson and Owen (1999) show that even when T is as large as 30, the bias of the fixed

effects estimator can still be quite large and significant. This is of special concern to empirical

researchers in corporate governance because in practice we are often confronted with short panels;

typically, T < 10.2 Thus, it is very unlikely that the typical corporate governance panel will be

long enough (in the time dimension) to eliminate any potential biases of fixed effects estimation.

Since this paper is the first to explore the role of dynamic enogeneity in empirical corporate2For example, any study carried out today that used the the Investor Responsibility Research Center (IRRC) data

that starts from 1996, is limited to a panel of T = 10. If the researcher is concerned about the independence ofyear-to-year data and is forced to sample at large time intervals, the T is even smaller

15

governance research, it is instructive to look at the nature and magnitude of this bias when it is

ignored. Unfortunately, there is no closed form solution for the bias due to dynamic endogeneity if

the dependent variable is endogenous (the best approximation is probably that by Kiviet (1995) but

even this only holds in the case where the dependent variable consists of one autorgressive variable

and one strictly exogenous variable). The exact magnitude (and sign) of the bias will depend on

the exact form of the endogeneity and the relationship between the dependent and independent

variables. Thus, I use Monte Carlo simulations to illustrate the bias from ignoring dynamic endo-

geneity. The simulations also show the ability the dynamic GMM estimator to eliminate the biases

that may arise from dynamic endogeneity. The use of Monte Carlo simulations is very much in the

spirit of other studies in this areas including, for eaxmple, Arellano and Bond (1991), Arellano and

Bover (1995), Kiviet (1995) and Bruno (2005).

Consider the case where the true model (data generating process) for firm performance (yit) is:

yit = βxit + γzit + ηi + εit, εit ∼ N(0, σ2e) (3.2)

In this model performance is determined by an unobservable firm-specific factor, η, an edogenous

governance factor, x, which for example could be board size, and a strictly exogenous factor, z,

which is strictly exogenous in the sense that it neither depends on performance nor does it depend

on the unobservable firm factor.

The endogenous governance factor is determined by the process:

xit = αxit−1 + πzit + λyit−1 + δηi + εit εit ∼ N(0, σ2e) (3.3)

Thus, x is endogenous in two senses. First it exhibits dynamic endogeneity from its relationship to

past performance, y, through the parameter, λ. λ represents the factor by which the governance

variable adjusts to performance and captures the key aspect of dynamic endogeneity. Second, x is

also correlated to the unobserved firm specific factor, η. Any estimation of this system would have

to account for both dynamic endogeneity and unobserved heterogeneity. Of course, the researcher

is unaware of the true model. Most likely the researcher will be estimating (2.1) with the intention

of making some inference from the magnitude and significance level of the estimated coefficient, β.

I simulate the system given by (3.2) and (3.3) and generate panel data sets of time and cross-

16

sectional dimensions that are typical in corporate governance (T = 5, 10) and (N = 500). I then

carry out an estimation (2.1) using OLS, fixed effects and the dynamic GMM estimator of Arellano

and Bond (1991). Details of the data generation procedure are given in an appendix.

Table 1 shows the results of the simulation. Data generation for the reported results was carried

out using the following parameters: γ = 0.6, κ = 0.9, α = 0.7, π = 0.2, δ = 0.53, and a range

of values of the dynamic adjustment factor, λ. The reported coefficients and standard errors are

based on 1,000 replications. I have reported results for different values of λ, since this essentially

captures the dynamic nature of the panel.

The results show that in every case where λ 6= 0, the various estimates of β (except those

obtained by GMM) are biased. OLS estimates are obviously biased largely because of the presence of

unobserved heterogeneity. Fixed-effects estimates are less biased than the OLS estimates since fixed-

effects eliminate the unobserved heterogeneity, but are still biased because of dynamic endogeneity.

Only in the singular case when λ = 0 (in which case there is no dynamic ‘feedback’) are the

fixed-effects estimates unbiased. Of course, as T increases, the bias of the fixed-effects estimates

decreases, but we will need a long time series to eliminate the bias completely. However, as I

have noted earlier, in practice, most corporate governance studies have limited time-series; usually,

T < 10.

Panel A of Table 1 is particularly interesting; it shows the results of the simulation when

the true β = 0. In this case, there is no relationship between performance and the governance

variable. However, OLS and fixed-effects estimate are significantly biased and produce a spurious

correlation between governance and firm performance. For example when the true β is zero and

there is a modest amount of dynamic endogeneity (λ = −0.1), fixed-effects estimation produces

a statistically significant estimate of 0.3787 (standard error of (0.0740) in a sample where T = 5.

Ignoring dynamic endogeneity (and unobserved heterogeneity) would lead us to wrongly infer that

governance was correlated with performance if we carried out estimation using either OLS or fixed

effects.

Table 1 also shows that GMM estimation carried out using the Arellano and Bond (1991) es-3The choice of which set of parameters’ results to report is somewhat arbitrary. The simulation was repeated with

different values of the parameters (ranging from 0 to 1) and the results are similar.

17

timation procedure produces unbiased results regardless of the magnitude of λ. The estimation

procedure uses lagged variables of all the dependent and independent variables as GMM instru-

ments. This procedure not only controls for the dynamic endogeneity, it actually exploits it to

obtain unbiased results. In addition, the GMM procedure does not even require knowledge of the

dynamic process that generates the endogenous variable in order to produce consistent and unbiased

estimation results.

Figure 1 presents a graphical illustration of the bias that results from dynamic endogeneity (and

unobserved heterogeneity). In this particular simulation, the true β is zero. The figure shows the

severity of the bias (β− β) from ignoring either unobserved heterogeneity or dynamic endogeneity.

Fixed effects estimation is negatively biased when the dynamic endogeneity factor (λ) is negative

and positively biased when λ is positive. Even modest of dynamic endogeneity (small values of

|λ|) can lead to significant biases in fixed-effects estimation. Again, we see that only the GMM

estimation produces consistent estimates for β.

Overall, this limited numerical example illustrates the pitfalls inherent in ignoring both dynamic

endogeneity and unobserved heterogeneity in carrying out estimation with panel data samples. The

example also illustrates the relative power of GMM dynamic estimation techniques in cases where

your variable of interest adjusts to past realizations of the dependent variable, which is likely to be

the case with corporate governance variables.

4 Estimating the relationship between board structure and firm

performance

In this section, I estimate the empirical relationship between firm performance, board size, board

composition and board leadership.

4.1 Measuring firm performance

The key measure of firm performance used in this study is return on assets (ROA), where ROA is

defined as operating income before depreciation (COMPUSTAT item #13) divided by fiscal year

18

end total assets (COMPUSTAT item #6). For every firm in the sample, I compute an industry

adjusted ROA which is obtained by subtracting the median industry ROA from the individual

firm’s ROA. Industry is defined by the 2-digit SIC code.

Many studies of the governance/performance relationship have focused on the use of Tobin’s Q

as a measure of firm performance. This can be a problem for a number of reasons. Tobin’s Q (which

is usually defined as some market-to-book ratio) is a strong proxy for growth opportunities and

there is strong theoretical motivation to expect that growth opportunities are a cause, rather than a

consequence, of governance structures. There is also some empirical evidence to support this; both

Boone, Field, Karpoff, and Raheja (2006) and Linck, Netter, and Yang (2006a) find that market-

to-book values determine both board size and board composition. So in my empirical estimation, I

use market-to-book as a control variable, making it inappropriate for use as a performance measure.

One concern that has been raised about using return on assets (ROA) as a measure of per-

formance is that it is sensitive to accounting artifact problems, but as Demsetz and Villalonga

(2001) point out, Tobin’s Q also suffers from accounting artifact problems, and may in fact, be

more severely affected. For one thing, the numerator of both ROA and Tobin’s Q are essentially

the same (book value of assets). In addition, the market-to-book measure assumes that the firm’s

future revenues will be generated purely from tangible assets, which could introduce severe biases

if used to measure the performance of firms whose values lie mostly in intangible assets. These

problems suggest that Tobin’s Q offers few advantages over ROA as a performance measure and it

is probably more appropriate to consider Tobin’s Q as a determinant of governance and used as a

control variable.

4.2 Governance variables

I consider the effect of past performance on three board structure variables - board size, board

composition and board leadership. More precisely, these variables are defined as follows:

• LogBSIZE, the logarithm of the number of directors on the board

• INDEP , the proportion of outside (non-executive) directors on the board

• CEO CHAIR, a dummy variable equal to 1 if the CEO is also the chairman of the board

19

4.3 Control variables

Recent studies (Raheja (2005), Coles, Daniel, and Naveen (2006), Boone, Field, Karpoff, and Raheja

(2006) and Linck, Netter, and Yang (2006a)) suggest that firms will chose their board structures

based on the relative costs and benefits of each governance mechanism. The cost/benefit ratio of any

particular board structure will depend on the unique agency costs that each firm faces and board

structure will reflect the monitoring costs, the private benefits of control and scope and complexity

of each firm’s operations. Thus, in line with prior literature on the determinants of board structure,

I propose using size, age, the number of business segments, growth opportunities, and leverage as

determinants of board structure. These variable might also be related to firm performance itself

and as such they also serve as control variables in our empirical specification.

In summary, my control variables are defined as follows:

• LogMV E, logarithm of the market value of equity.

• MTB, ratio of market-to-book value. This is obtained as market value of equity plus book

value of assets (COMPUSTAT item #6) minus book value of equity (COMPUSTAT item

#60) minus deferred taxes (COMPUSTAT item #74), all divided by book value of assets.

• RETSTD, standard deviation of (the past twelve months) of the firm’s stock returns.

• LogAGE, the logarithm of the firm’s age, where age is computed from the time the firm first

appears on CRSP.

• LogSEGMENTS, the logarithm of the number of business segments.

• DEBT , the ratio of the firm’s long-term debt (COMPUSTAT item #9) to total assets (COM-

PUSTAT item #6).

4.4 Data and Sample Selection

Board structure tends to change very slowly, and is highly persistent. This can lead to attenuation of

the signal-to-noise ratio and reduce the power of empirical tests. Dynamic estimation also requires

us to assume that transient errors are uncorrelated. To overcome these issues, I sample at two-year

20

intervals, and use governance data from 1991, 1993, 1995, 1997, 1999, 2001 and 2003. Longer

sampling intervals might improve the tests, but I am forced to make a trade-off between sampling

interval and the length of the time series (T ), based on data availability.

The board data from this study is taken from the DISCLOSURE database. This is a fairly

large database of governance data for over 7,000 firms starting from 1991. Since my empirical tests

include a number of control variables, I match the data obtained from DISCLOUSRE with data

from CRSP and COMPUSTAT for each of sample years. This leaves me with a sample consisting

of over 6,000 unique firms and over 16,000 firm-years. To the best of my knowledge, this is the

largest panel, to date, that has been used to study the performance/governance relationship.

Table 2 shows the sample characteristics of the data that I use in my estimation. In order to

avoid sample selection issues, I do not require a balanced panel, so that the number of firms differs

from year to year and my estimation strategy uses as many observations as available. The data

also includes a wide selection of both large and small firms, unlike most previous studies that tend

to focus on exclusively either large or small firms.

The distribution of board characteristics appears to be fairly stable over the sample period.

While mean board size declines slightly from 7.71 in 1991 to 7.37 in 2000 (and then increasing to

7.93 in 2003), the median firm has 7 board members throughout most the sample period, with a

small up-tick in 2003. In 1991, in 59% of the firms, the CEO was also the chairman of the board,

this number is 56% in 2003; the median firm has the CEO holding both positions. The average

number of outside directors appears to have increased slightly from two-thirds at the beginning

of the sample period to just over 70% at the end of the sample period. The major variables that

change substantially over the sample period is firm size. Mean and median firm size increased

threefold between 1991 and 2003.

4.5 Econometric tests of strict exogeneity

As I have discussed in previous sections, the strict exogeneity assumption in corporate governance

is likely to be violated and the estimates obtained under this assumption are likely to be biased

and inconsistent. While, there is substantial economic justification to suspect that the governance

21

and control variables are not strictly exogenous, I confirm this with an econometric test of strict

exogeneity.

In the previous section of the paper, I discuss a regression based test of strict exogeneity sug-

gested by Wooldridge (2002). If Xi,t contains the explanatory variables, a test of strict exogeneity

can be obtained by carrying out fixed-effects estimation of the equation:

yi,t = α+ βXi,t + ΩWi,t+2 + ηi + εit, t = 1991, 1993, 1995, . . . , 2001 (4.1)

where Wit+1 is a subset of forward values of the corporate governance and control variables, X. y

is firm performance (ROA). X includes board size (LogBSIZE), board independence (INDEP ),

a dummy variable which is 1 if the CEO is the board chair (CEO CHAIR), firm size (LogMV E),

market-to-book ratio (MTB), standard deviation of stock returns (RETSTD), number of busi-

ness segments (LogSEGMENTS), firm age (LogAGE) and leverage (DEBT ). Under the null

hypothesis of strict exogeneity, Ω = 0. (We lose the last time period (2003) since we use forward

values).

Table 3 shows the results of estimating (4.1), with different subsets of the governance and control

variables, Wi,t+2. In every specification in which they are included, the coefficient estimates for

the forward vales of both board size (LogBSIZEt+2) and CEO as board chair (CEO CHAIRt+2)

are significantly different from zero. This suggests that neither of these board variables are strictly

exogenous and both of these variables adjust in response to firm performance. In addition, the

coefficient estimates on the forward values of some control variables (LogMV Et+2, RETSTDt+2

and DEBTt+2) are also significantly different from zero, suggesting that these variables also adjust

to firm performance.

Overall, the results from Table 3 suggest that neither the board structure variables nor the

firm control variables are strictly exogenous. Thus, dynamic endogeneity is likely to be a major

source of bias in estimating the contemporaneous relationship between board structure and firm

performance.

22

4.6 Estimation under the assumption of sequential exogeneity

After having ruled out the possibility that the governance variables are strictly exogenous, I proceed

to investigate the relationship between governance and performance under the sequential exogeneity

assumption. As discussed earlier, sequential exogeneity allows current values of the governance and

control variables to be determined by past values of performance. Thus, I estimate the dynamic

unobserved effects model, specified as:

yit = α+ ρyit−2 + βxit + Γzit + ηi + εit, t = 1991, 1993, 1995, . . . , 2003 (4.2)

under the sequential exogeneity assumption that:

E(εit|yit−1,xit−2,xit−4, . . . ,x1991, zit−2, zit−4, . . . , z1991) = 0 (4.3)

More precisely, if I write my dynamic unobserved model in (4.2) as:

yit = Xit + ηi + εit, t = 1991, 1993, 1995, . . . , 2003 (4.4)

where, Xit now includes the governance variables (xit), the control variables (zit) and lagged per-

formance (yit), GMM estimation, based on Arellano and Bover (1995) can be carried out based on

the following moment conditions:

1. Sequential (or weak) exogeneity is assumed:

E(εit|Xi,t−2,Xi,t−4 . . . ,Xi,1991) = 0 (4.5)

2. Transient errors are uncorrelated:

E(εit|εis) = 0 s 6= t (4.6)

3. Constant correlation between unobserved fixed effect and explanatory variables

E(εit|[Xi,t−2 −Xi,t−4], . . . , [Xi,1993 −Xi,1991]) = 0 (4.7)

Table 4 shows the results of estimating the effect of board structure on performance using

pooled OLS, fixed-effects and the Arellano-Bond-Bover system GMM estimation. Results of the

OLS and fixed-effects estimates are similar to those obtained for other samples by Yermack (1996)

23

and Eisenberg, Sundgren, and Wells (1998). There appears to be a significantly positive relationship

between firm performance and board size; OLS results also suggest a negative relationship between

board composition and firm performance.

However, the picture changes with GMM estimation. Board size is no longer a significant

determinant of firm performance; neither is board composition or board leadership. Size and firm

risk remain significant determinants, although the magnitude of the coefficient on size is much less

than with either OLS or fixed-effects estimates. The coefficient on lagged performance is significant,

further bolstering the argument that the relationship between performance and board structure is

inherently dynamic.

Table 4 also includes the results of a number of specification tests for the validity of the GMM

estimation procedure. If the assumptions of my specification are valid, then by construction, resid-

uals in first differences (m1) should be correlated, but there should be no serial correlation in

second differences (m2).4 Results of these tests confirm that this is indeed the case; m1 = −8.96

(p = 0.00) and m2 = −0.41 (p = 0.68). The specification tests reported also include a Hansen

test for over-identifying restrictions (J-statistic). Under the hypothesis of instrument validity, this

statistic should be distributed χ2, with 162 degrees of freedom. Results reveal a J-statistic of 108.21

(p = 0.24), so we cannot reject the hypothesis that our GMM instruments are valid.

4.7 Robustness tests

4.7.1 Alternative measures of firm performance

I re-estimate the relationship between firm performance and board structure using an alternative

but widely used profitability measure - return on sales (ROS). Table 5 shows the results from the

estimation. The pattern of results is similar to those obtained when return on assets (ROA) is used

the measure of performance. OLS and fixed-effects regressions suggest a negative relation between

board size and firm performance; however, none of the board variables are significant with GMM

estimation under the assumption of sequential exogeneity.4These tests are discussed in Arellano and Bond (1991)

24

4.7.2 Alternative specification: Inclusion of director ownership

I re-estimate the relation between board structure and firm performance with the inclusion of

director ownership, where director ownership is the percentage of the firm’s shares held by the

CEO and other directors. It is possible that increased ownership might by itself reduce some of

the agency costs that may otherwise be reduced by, or be a consequence of other aspects of board

structure.

Table 6 shows the results of the estimation. Again the pattern of results remain largely un-

changed from that obtained if the specification excludes director ownership. OLS and fixed-effects

estimation suggests a negative relationship between board size and firm performance but none

of the board variables are significant with GMM estimation under the assumption of sequential

exogeneity.

5 Estimating the relationship between inside ownership and firm

value

In this section, I estimate the empirical relationship between firm value and inside ownership.

5.1 Measuring firm value

To remain as faithful to prior literature as possible (e.g. Morck, Shleifer, and Vishny (1988),

Himmelberg, Hubbard, and Palia (1999), Demsetz and Villalonga (2001)), I use Tobin’s Q which

is obtained as market value of equity plus book value of assets (COMPUSTAT item #6) minus

book value of equity (COMPUSTAT item #60) minus deferred taxes (COMPUSTAT item #74),

all divided by book value of assets. I compute an industry adjusted Q by subtracting the median

industry Q from the individual firm’s Q. Industry is defined by the 2-digit SIC code.

5.2 Measure of inside ownership

Inside ownership is taken from the DISCLOSURE database. Inside ownership is defined as the

total number of shares owned by the firm’s officers divided by the number of shares outstanding.

25

5.3 Control Variables

Himmelberg, Hubbard, and Palia (1999), building on Demsetz and Lehn (1985) propose a number

of firm characteristics that are determinants of managerial ownership. These variables proxy for the

cross-sectional variation in the scope of agency costs faced, as well as the costs of higher manage-

rial ownership. The variables (which are similar to those used by Demsetz and Villalonga (2001))

proxy for firm size, scope for discretionary managerial spending and managerial risk aversion, all

of which reflect the relative costs and benefits of higher (or lower) inside ownership. However,

these variables may also affect our measure of firm value (Q) and I include them as control vari-

ables in my empirical specification for estimating the effect of inside ownership (IOWN) on firm

value. The proxy variables for firm size is the log of the firm’s sales (LogSALES). The proxy

variables for scope of discretionary managerial spending include: the ratio of tangible assets (prop-

erty, plant and equipment) to sales (ASSETS/SALES), the ratio of capital expenditure to assets

(CAPEX/ASSETS), the ratio of operating income to sales (OP.INCOME/SALES), the ratio of

R&D expenditure to tangible assets (R&D/ASSETS) and the ratio of advertising expenditure to

tangible assets (ADV/ASSETS). I also include dummy variables equal to 1 (and zero otherwise)

in cases where there is neither R&D nor advertising expenditure available for the firm on COMPU-

STAT (RDUM and ADUM). Finally, I sue the standard deviation of stock returns (RETSTD)

to proxy for cost of managerial risk aversion.

5.4 Does ownership adjust to past performance? Tests of strict exogeneity

In order to establish the presence of dynamic endogeneity, I carry out a regression based test of strict

exogeneity of both insider ownership and the control variables. Thus, I carry out a fixed-effects

estimate of the model:

yi,t = α+ βXi,t + ΩWi,t+2 + ηi + εit, t = 1991, 1993, 1995, . . . , 2001 (5.1)

where Wit+1 is a subset of forward values of the insider ownership and control variables, X.

y is industry-adjusted firm value (Q). X includes includes insider ownership (IOWN), log of

sales (LogSALES), tangible assets divided by sales (ASSETS/SALES), capital expenditure di-

26

vided by assets (CAPEX/ASSETS), operating income divided by sales (OP.INCOME/SALES),

R&D expenditure divided by sales (R&D/ASSETS), advertising expenditure divided by sales

(ADV/ASSETS), dummy variables equal to 1 if R&D or advertising expenditure is reported

(RDUM and ADUM). Γ = 0 is the null hypothesis of strict exogeneity and a rejection of this

hypothesis suggests that ownership (or the control variables) adjust to past firm value and that

fixed-effects estimates are likely to be biased.

Table 7 shows the results of estimating (5.1). Overall, the results suggest that neither ownership

nor most of the control variables are strictly exogenous. In a specification which includes forward

values of ownership (IOWNt+2), the coefficient estimate of IOWNt+2 is significantly different from

zero. Similarly, when we include forward values of the control variables, the coefficient estimates of

LogSALESt+2, ASSETS/SALESt+2, CAPEX/ASSETSt+2, OP.INCOME/SALESt+2 are all

significantly different from zero. The inference here is that dynamic endogeneity is likely to be a

source of bias when estimating the relationship between inside ownership and firm value.

5.5 Estimation under the assumption of sequential exogeneity

The evidence for dynamic endogeneity of ownership and the control variables from section 4.5

reinforces the initial premise that ownership and other firm characteristics adjusts to past firm

value. Unobserved heterogeneity would lead to bias OLS estimates while dynamic endogeneity

would lead to bias fixed-effects estimates. System GMM estimation as discussed in section 2.3

and implemented for the firm performance/board structure relationship will provide more reliable

estimates.

Table 8 shows the results of estimating the relationship between firm value and inside ownership.

OLS estimation suggest a negative relationship between inside ownership and firm value; in contrast,

fixed-effects estimates suggests a significantly positive relationship between inside ownership and

firm value. Fixed-effects estimates which control for the unobserved heterogeneity would suggest

that all firm would benefit from high levels of ownership.

In contrast to the OLS and fixed-effects estimates, the system GMM estimates suggest that

there is no systematic relationship between inside ownership and firm value. Estimation that

27

controls for unobserved heterogeneity, simultaneity as well as the fact that ownership and other

firm characteristics adjust to past performance suggests provides support for the hypothesis that

ownership is endogenously determined.

6 Conclusion

In this paper, I have argued that past studies that have estimated the relationship between cor-

porate governance and performance may be biased either because of unobserved heterogeneity, or

because of the presence of dynamic endogeneity. Dynamic endogeneity arises because observable

governance structures are likely to be determined by past realizations of performance, or past shocks

to performance. A simple numerical example and simulation, illustrates the fact that in panel data

samples with cross-sectional and time dimensions that are fairly typical in corporate governance

studies, these biases can be significant, and lead to the wrong inference. This suggests that it is

definitely worth re-examining previous studies that have investigated the relationship between some

aspect of corporate governance and firm performance.

To overcome the biases introduced by unobserved heterogeneity and dynamic endogogeneity, I

have suggested and discussed the GMM dynamic panel data estimator. This estimation procedure

eliminates the unobserved heterogeneity while controlling for dynamic endogeneity; in fact the dy-

namic GMM estimator exploits the relationship between current governance and past performance

to identify GMM instruments that enable unbiased and consistent estimation. Previous research,

using OLS and fixed-effects estimation, both of which assume strict exogeneity (hence ignoring

dynamic endogeneity) suggests a negative relationship between some aspects of board structure

(notably board size) and firm performance. However, dynamic GMM estimation suggests that no

particular aspect of board structure (board size, composition, or leadership) is related firm per-

formance; a result that certainly calls into question those from previous studies. The results also

strongly indicate that while our understanding of the complex interplay between corporate gov-

ernance and firm performance continues to develop, great care must be taken in specifying and

drawing inference from empirical investigation of the relationship between firm performance and

corporate governance.

28

Appendix: Numerical procedure for simulation

The numerical procedure for generating the data is an extension of that originally used by Kiviet

(1995).5 The outline is follows.

The exogenous regressor is defined as follows:

zt = κzt−1 + ξt ξt ∼ N(0, σ2ξ ) (6.1)

Define a latent variable, νt, where

νt = ρνt−1 + γzt + εt εt ∼ N(0, σ2ε ) (6.2)

Let L denote the lag operator. Substituting (6.1) into (6.2) gives:

νt =γξt

(1− ρL)(1− κL)+

εt(1− ρL)

= γϕt + ψt (6.3)

νt is the weighted sum of ϕt and ψt, which are AR(2) and AR(1) respectively. Data for zt and

ψt can be obtained from the distribution of (ξ0, . . . , ξT ) and (ε0, . . . , εT ) as follows:

z0 = ξ0(1− κ2)1/2, zt = κzt−1 + ξt, t = 1, . . . , T (6.4)

ψ0 = ε0(1− ρ2)1/2, ψt = ρψt−1 + εt, t = 1, . . . , T (6.5)

Similarly, ϕ is obtained as follows:

ϕ0 = ξ0[var(ϕt)]1/2

ϕ1 = ϕ0cor(ϕt, ϕt−1) + ξ1[var(ϕt)]1/21− [cor(ϕt, ϕt−1)]21/2

ϕt = (ρ+ κ)ϕt−1 − ρκϕt−2, t = 2, . . . , T (6.6)

where

cor(ϕt, ϕt−1) = cov(ϕt, ϕt−1)/var(ϕt) = (ρ+ κ)/(1 + ρκ)

cor(ϕt, ϕt−2) = cov(ϕt, ϕt−1)/var(ϕt) = (ρ+ κ)2/(1 + ρκ)− ρκ

var(ϕt) = σ2ξ [1− (ρ+ κ)cor(ϕt, ϕt−1) + ρκcor(ϕt, ϕt−2)]−1 (6.7)

5This procedure has been turned into a STATA module, xtarsim, by Giovanni Bruno. See Bruno (2005) for details.

29

The dependent variable yt is then obtained as

yt = ρνt−1 + γzt + εt + xt + η, η ∼ N(0, σ2η) (6.8)

The endogenous variable, xt is given by the scheme:

x0 = πzt + δη + εt, xt = αxt−1 + λyt−1 + πzt + δη + εt, t = 1, . . . , T, ε ∼ N(0, σ2ε) (6.9)

Since I do not generate the lagged dependent variable, I set ρ = 0 and generate the panel data

variables, y, x and z using (6.4), (6.8) and (6.9) respectively.

30

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33

Table 1Measuring the bias when explanatory variables is endogenous: Simulation results

In this table, I report the simulation results from estimating a model with OLS, fixed effects (FE) and GMM when theexplanatory variable is dynamically endogenous and correlated with unobservable effects. The model estimated is: yit =xitβ + zitγ + ηi + νit. The true data generating process (DGP) for zit is zit = κzit−1 + ξit; and that for xit is xit =αxit−1 + πzit + λyit−1 + δηi + εit. Thus, xit is endogenously related to yit−1 through the parameter λ and is endogenouslyrelated to the unobserved heterogeneity, η. The parameters used for generating the data are : β = 0; γ = 0.6, κ = 0.9, α = 0.7,π = 0.2, δ = 0.5.

Panel A: β = 0, N = 500, T = 5

OLS FE GMM OLS FE GMM OLS FE GMM

λ = −0.8 λ = 0.8 λ = 0.5

β − β -0.0993 0.1467 0.0054 0.1018 −0.0559 −0.0001 0.1461 −0.0706 0.0001

(0.0132) (0.0160) (0.0395) (0.0052) (0.0102) (0.0220) (0.0077) (0.0139) (0.0305)

γ − γ −0.0581 −0.0128 −0.0045 −0.1670 0.0230 −0.0010 −0.1776 0.0243 0.0008

(0.0108) (0.0108) (0.0525) (0.0110) (0.0115) (0.0484) (0.0114) (0.0111) (0.0457)

λ = −0.1 λ = 0.1 λ = 0.0

β − β 0.6335 0.3787 0.0228 0.3222 −0.0739 0.0007 0.4370 −0.0008 0.0066

(0.0189) (0.0740) (0.1433) (0.0125) (0.0319) (0.0691) (0.0153) (0.0464) (0.0990)

γ − γ −0.2440 −0.0857 −0.0010 −0.2134 0.0197 −0.0010 −0.2287 0.0003 0.0020

(0.0089) (0.0194) (0.0419) (0.0100) (0.0133) (0.0446) (0.0096) (0.0153) (0.0431)

Panel B: β = 0, N = 500, T = 10

OLS FE GMM OLS FE GMM OLS FE GMM

λ = −0.8 λ = 0.8 λ = 0.5

β − β −0.0989 0.0525 0.0034 0.0977 −0.0185 −0.0007 0.1405 −0.0234 0.0000

(0.0105) (0.0079) (0.0158) (0.0045) (0.0046) (0.0087) (0.0061) (0.0063) (0.0121)

γ − γ −0.0590 −0.0060 −0.0014 −0.1670 0.0230 0.0010 −0.1743 0.014 0.0009

(0.0085) (0.0055) (0.0174) (0.0110) (0.0115) (0.0484) (0.0091) (0.0067) (0.0150)

λ = −0.1 λ = 0.1 λ = 0.0

β − β 0.6139 0.1312 0.0153 0.3093 −0.0226 0.0020 0.4205 −0.0005 0.0057

(0.0156) (0.0348) (0.0552) (0.0101) (0.0143) (0.0257) (0.0126) (0.0209) (0.0377)

γ − γ −0.2389 −0.0357 −0.0016 −0.2080 0.0086 0.0004 −0.2231 0.0002 0.0006

(0.0072) (0.0108) (0.0161) (0.0083) (0.0076) (0.0145) (0.0080) (0.0086) (0.0144)

Panel C: β = 0.3, N = 500, T = 5

OLS FE GMM OLS FE GMM OLS FE GMM

λ = −0.8 λ = 0.8 λ = 0.5

β − β -0.1001 0.1483 0.0066 0.1018 −0.0563 0.0009 0.1464 −0.0714 0.0004

(0.0130) (0.0159) (0.0330) (0.0053) (0.0092) (0.0361) (0.0073) (0.0135) (0.0427)

γ − γ −0.0589 −0.0127 −0.0050 −0.1662 0.0227 0.0006 −0.1786 0.0243 0.0028

(0.0105) (0.0106) (0.0530) (0.0109) (0.0109) (0.0459) (0.0109) (0.0116) (0.0462)

λ = −0.1 λ = 0.1 λ = 0.0

β − β 0.6331 0.3781 0.0267 0.3222 −0.0735 0.0028 0.4369 0.0008 0.0128

(0.0189) (0.0716) (0.1560) (0.0126) (0.0322) (0.0776) (0.0153) (0.0463) (0.0955)

γ − γ −0.2439 −0.0859 −0.0024 −0.2134 0.0197 −0.0009 −0.2287 0.0000 0.0008

(0.0088) (0.0190) (0.0422) (0.0100) (0.0132) (0.0439) (0.0153) (0.0463) (0.0955)Notes

1. All estimated biases and standard errors are based on 1,000 replications. Standard errors of biases are in parentheses.

2. GMM estimation is carried out using the Arellano and Bond (1991) estimation procedure. Instruments used are:

yt−2, yt−3, . . . , y1, xt−1, xt−2, . . . , x1, zt−1, zt−2, . . . , z1

34

Table 2Summary statistics of board and control variables

The table contains the sample characteristics of the board and control variables of the firms used in the study. The

board variables data comes from the DISCLOSURE database. The control variables data comes from CRSP and

COMPUSTAT. Board size is the total number of directors on the board. CEO Chair is 1 if the CEO is also the

chairman of the board, 0 otherwise. Board Independence is the percentage of directors who are not employees of

the firm. Firm Size is the market value of equity. Segments is the number of business segments the firm operates

in, as reported by COMPUSTAT. Firm Age is computed based on the year the firm first appears on CRSP. Debt

is the ratio of long-term debt to total assets. RETSTD is the standard deviation of the firm’s stock returns in the

previous twelve months. Market-to-book is obtained as the value of equity plus book value of assets minus book

value of equity minus deferred taxes, all divided by book value of assets

Panel A: Mean (Median) Values of Board Variables

1991 1993 1995 1997 1999 2001 2003

Board Size 7.79 7.59 7.39 7.49 7.37 7.59 7.93

(7.00) (7.00) (7.00) (7.00) (7.00) (7.00) (8.00)

CEO Chair 0.59 0.60 0.59 0.59 0.59 0.59 0.56

(1.00) (1.00) (1.00) (1.00) (1.00) (1.00) (1.00)

Board Independence 0.63 0.64 0.64 0.67 0.67 0.67 0.71

(0.66) (0.67) (0.66) (0.70) (0.69) (0.70) (0.71)

Panel B: Mean (Median) Values of Control Variables

1991 1993 1995 1997 1999 2001 2003

Firm Size (millions) $1,250 $1,240 $1,430 $2,060 $3,130 $2,610 $3,000

(100) (114) (131) (186) (186) (264) (358)

Segments 1.60 1.53 1.46 1.49 2.36 2.50 2.35

(1.00) (1.00) (1.00) (1.00) (1.00) (1.00) (1.00)

Firm Age 15.04 14.42 13.53 13.42 13.38 14.18 15.95

(10.00) (10.00) (9.00) (8.00) (8.00) (9.00) (10.00)

Debt 0.17 0.15 0.16 0.16 0.17 0.16 0.15

(0.12) (0.10) (0.11) (0.11) (0.11) (0.09) (0.09)

RETSTD 14.29% 14.62% 12.13% 14.30% 17.87% 21.62% 17.64%

(12.68) (12.36) (10.77) (12.48) (15.48) (18.41) (15.13)

Market-to-Book 1.92 2.07 1.93 2.11 2.49 1.94 2.15

(1.24) (1.49) (1.50) (1.58) (1.32) (1.38) (1.63)

Number of observations 2,492 2,913 3,025 3,261 3,160 2,754 2,398

35

Table 3Does board structure adjust to past performance? Tests of strict exogeneity

In this table, I report results from the fixed-effects estimation of the model:

yi,t = α + βXi,t + ΩWi,t+2 + ηi + εit, t = 1991, 1993, 1995 . . . 2001

where Wit+1 is a subset of forward values of the corporate governance and control variables, X. y is firm performance

(ROA). X includes board size (LogBSIZE), board independence (INDEP ), a dummy variable which is 1 if the

CEO is the board chair (CEO CHAIR), firm size (LogMV E), market-to-book ratio (MTB), standard deviation

of stock returns (RETSTD), number of business segments (LogSEGMENTS), firm age (LogAGE) and leverage

(DEBT ). Ω = 0 is the null hypothesis of strict exogeneity. All t-statistics (in parentheses) are based on robust

standard errors. Year dummies are included in all specifications.

Dependent Variable:ROA(t) 1 2 3 4 5

LogBSIZE(t) −0.0267∗ −0.0282∗ −0.0247∗ −0.0234∗ −0.0251∗

(−3.59) (−3.80) (−3.29) (−3.13) (−3.32)

INDEP (t) 0.0082 0.0088 0.0059 0.0055 0.0050(0.81) (0.86) (0.57) (0.54) (0.49)

CEO CHAIR(t) 0.0005 0.0005 −0.0005 −0.0005 −0.0009(0.21) (0.20) (−0.17) (−0.16) (−0.33)

LogMV E(t) 0.0442∗ 0.0438∗ 0.0441∗ 0.0443∗ 0.0416∗

(18.13) (17.98) (17.87) (17.99) (14.74)

MTB(t) 0.0008 0.0008 0.0008 0.0009 0.0011(0.75) (0.79) (0.80) (0.76) (0.97)

RETSTD(t) −0.0085 −0.0082 −0.0091 −0.0091 −0.0371∗

(−0.56) (−0.55) (−0.59) (−0.59) (−2.24)

LogSEGMENTS(t) −0.0070∗ −0.0068∗ −0.0066∗ −0.0067∗ −0.0042(−2.39) (−2.34) (−2.27) (−2.29) (−1.34)

LogAGE(t) −0.0083∗ −0.0087∗ −0.0093∗ −0.0089∗ −0.0059(−2.20) (−2.21) (−2.49) (−2.35) (−0.33)

DEBT (t) −0.0869∗ −0.0864∗ −0.0866∗ −0.0869∗ −0.0758∗

(−5.51) (−5.46) (−5.44) (−5.43) (−4.51)

LogBSIZE(t + 2) −0.0136∗ −0.0116∗ −0.0150∗

(−2.08) (−1.72) (−2.18)

INDEP (t + 2) −0.0030 −0.0023 −0.0027(−0.31) (−0.23) (−0.26)

CEO CHAIR(t + 2) 0.0064∗ 0.0062∗ 0.0055∗

(2.26) (2.20) (1.95)

LogMV E(t + 2) 0.0076∗

(2.64)

MTB(t + 2) 0.0003(0.17)

RETSTD(t + 2) 0.0997∗

(−5.05)

LogSEGMENTS(t + 2) −0.0041(−1.43)

LogAGE(t + 2) −0.0060(−0.19)

DEBT (t + 2) −0.0257∗

(−2.19)∗ significant at the ten percent level or smaller

36

Table 4Does board structure affect current firm performance?

In this table, I report results from the estimation of the model:

yit = α + βxit + Γzit + ηi + εit, t = 1991, 1993, 1995 . . . 2003

yit is return on assets (ROA); xit includes board size (LogBSIZE), board independence (INDEP ) and a dummyvariable which is 1 if the CEO is the board chair (CEO CHAIR); zit includes firm size (LogMV E), market-to-bookratio (MTB), standard deviation of stock returns (RETSTD), number of business segments (LogSEGMENTS),firm age (LogAGE) and leverage (DEBT ). t-statistics are reported in parentheses. All t-statistics are based onrobust standard errors. a, b, c represent significance at the one percent, five percent and ten percent level respectively.Year dummies are included in all specifications.

Dependent Variable (ROA) Pooled Fixed SystemOLS Effects GMM

LogBSIZE −0.0262a −0.0261a −0.0142(−7.25) (−4.52) (−0.53)

INDEP −0.0266a 0.0202b −0.0248(−4.37) (2.52) (−0.53)

CEO CHAIR 0.0025 0.0003 −0.0097(1.33) (0.14) (−0.68)

LogMV E 0.0234a 0.0429a 0.0106b

(36.11) (23.89) (2.51)

MTB −0.0025a −0.0014 −0.0011(−3.63) (−1.48) (−0.68)

RETSTD −0.2047a −0.0117 −0.2164a

(−12.13) (−0.95) (−2.19)

LogSEGMENTS −0.0087a −0.0074a 0.0054(−5.81) (−3.26) (0.98)

LogAGE 0.0056a 0.0008 −0.0045(5.42) (0.29) (−1.27)

DEBT −0.0307a −0.0625a −0.0103(−4.00) (−4.62) (−0.26)

ROA(t− 2) 0.3864a

(6.36)

R2 0.15 0.12

m1 −8.96(0.00)

m2 0.41(0.68)

J-statistic 174.04(0.24)

Notes

1. m1 and m2 are tests for first-order and second-order serial correlation in the first-differenced residuals asymp-totically distributed as N(0, 1) under the null of no serial correlation. p-values are in parentheses

2. J-statistic is from the Hansen test of over-identifying restrictions,asymptotically distributed χ2, under the nullof instrument validity p-values are in parentheses.

3. The instruments used in the GMM estimation are:differenced equations: yit−6,yit−8,. . . ,yi,1991, xit−6,xit−8,. . . ,xi,1991,zit−6,zit−8,. . . ,zi,1991; level equations: ∆yit−6,∆xit−6, ∆zit−6

37

Table 5Does board structure affect current firm performance? Alternative performance measure

In this table, I report results from the estimation of the model:

yit = α + βxit + Γzit + ηi + εit, t = 1991, 1993, 1995 . . . 2003

yit is return on sales (ROS); xit includes board size (LogBSIZE), board independence (INDEP ) and a dummyvariable which is 1 if the CEO is the board chair (CEO CHAIR); zit includes firm size (LogMV E), market-to-bookratio (MTB), standard deviation of stock returns (RETSTD), number of business segments (LogSEGMENTS),firm age (LogAGE) and leverage (DEBT ). t-statistics are reported in parentheses. All t-statistics are based onrobust standard errors. a, b, c represent significance at the one percent, five percent and ten percent level respectively.Year dummies are included in all specifications.

Dependent Variable (ROS) Pooled Fixed SystemOLS Effects GMM

LogBSIZE −0.0423a −0.0320a −0.0013(−8.71) (−4.42) (−0.04)

INDEP −0.0312a 0.0189c −0.0294(−3.95) (1.84) (−0.65)

CEO CHAIR 0.0096a 0.0044 −0.0090(3.75) (1.48) (−0.65)

LogMV E 0.0283a 0.0459a 0.0134a

(34.64) (19.61) (2.80)

MTB −0.0041a 0.0023b −0.0001(−4.77) (2.08) (−0.02)

RETSTD −0.2846a −0.0520a −0.1238c

(−12.34) (−3.27) (−1.64)

LogSEGMENTS −0.0130a −0.0104a 0.0054(−6.22) (−3.47) (0.98)

LogAGE 0.0057a 0.0074b −0.0084b

(3.97) (2.06) (−2.03)

DEBT 0.0088 0.0118 −0.0253(1.06) (−0.95) (−0.57)

ROA(t− 2) 0.4174a

(6.40)

R2 0.14 0.10

m1 −6.89(0.00)

m2 −0.61(0.54)

J-statistic 178.28(0.18)

Notes

1. m1 and m2 are tests for first-order and second-order serial correlation in the first-differenced residuals asymp-totically distributed as N(0, 1) under the null of no serial correlation. p-values are in parentheses

2. J-statistic is from the Hansen test of over-identifying restrictions,asymptotically distributed χ2, under the nullof instrument validity p-values are in parentheses.

3. The instruments used in the GMM estimation are:differenced equations: yit−6,yit−8,. . . ,yi,1991, xit−6,xit−8,. . . ,xi,1991,zit−6,zit−8,. . . ,zi,1991;level equations: ∆yit−6,∆xit−6, ∆zit−6

38

Table 6Does board structure affect current firm performance? Inclusion of director ownership

In this table, I report results from the estimation of the model:

yit = α + βxit + Γzit + ηi + εit, t = 1991, 1993, 1995 . . . 2003

yit is return on assets (ROA); xit includes board size (LogBSIZE), board independence (INDEP ), a dummy variablewhich is 1 if the CEO is the board chair (CEO CHAIR) and director ownership (DIR OWN); zit includes firmsize (LogMV E), market-to-book ratio (MTB), standard deviation of stock returns (RETSTD), number of businesssegments (LogSEGMENTS), firm age (LogAGE) and leverage (DEBT ). t-statistics are reported in parentheses.All t-statistics are based on robust standard errors. a, b, c represent significance at the one percent, five percent andten percent level respectively. Year dummies are included in all specifications.

Dependent Variable (ROA) Pooled Fixed SystemOLS Effects GMM

LogBSIZE −0.0283a −0.0262a −0.0073(−7.74) (−4.47) (−0.29)

INDEP −0.0291a 0.0163b −0.0258(−4.74) (1.99) (−0.60)

CEO CHAIR 0.0028 0.0008 −0.0188(1.43) (0.36) (−1.37)

DIR OWN 0.1419a 0.0332c 0.0171(8.27) (1.83) (0.70)

LogMV E 0.0241a 0.0431a 0.0115a

(36.62) (23.66) (2.79)

MTB −0.0028a −0.0013 −0.0018(−3.87) (−1.41) (−0.35)

RETSTD −0.2044a −0.0094 −0.2195a

(−11.80) (−0.75) (−2.74)

LogSEGMENTS −0.0086a −0.0071a 0.0040(−5.69) (−3.11) (0.73)

LogAGE 0.0057a 0.0006 −0.0053(5.53) (0.20) (−1.51)

DEBT −0.0338a −0.0653a −0.0215(−4.36) (−4.62) (−0.53)

ROA(t− 2) 0.3908a

(6.45)

R2 0.15 0.12

m1 −8.93(0.00)

m2 0.28(0.78)

J-statistic 186.72(0.31)

Notes

1. m1 and m2 are tests for first-order and second-order serial correlation in the first-differenced residuals asymp-totically distributed as N(0, 1) under the null of no serial correlation. p-values are in parentheses

2. J-statistic is from the Hansen test of over-identifying restrictions,asymptotically distributed χ2, under the nullof instrument validity p-values are in parentheses.

3. The instruments used in the GMM estimation are:differenced equations: yit−6,yit−8,. . . ,yi,1991, xit−6,xit−8,. . . ,xi,1991,zit−6,zit−8,. . . ,zi,1991;level equations: ∆yit−6,∆xit−6, ∆zit−6

39

Table 7Summary statistics of ownership and control variables

The table contains the summary statistics of ownership and control variables of the firms used in the study. Theownership variables data comes from the DISCLOSURE database. The control variables data comes from CRSPand COMPUSTAT. IOWN is the percentage of the firm’s shares owned by the firm’s chief officers, LogSALESis the logarithm of the firm’s total revenue, ASSETS/SALES is tangible assets (property, plant and equipment)divided by sales, CAPEX/ASSETS is capital expenditure divided by tangible assets, OP.INCOME/SALES isoperating income divided by sales, R&D/ASSETS is R&D expenditure divided by tangible assets, ADV/ASSETSis advertising expenditure divided by tangible assets.

Panel A: Mean (Median) Values of Ownership Variable

1991 1993 1995 1997 1999 2001 2003

IOWN 0.213 0.216 0.236 0.180 0.181 0.161 7.93

(0.135) (0.132) (0.165) (0.090) (0.085) (0.073) (8.00)

Panel B: Mean (Median) Values of Control Variables

1991 1993 1995 1997 1999 2001 2003

LogSALES 18.62 18.62 18.70 18.90 18.98 19.16 $3,000

(18.54) (18.52) (18.61) (18.77) (18.87) (19.09) (358)

ASSETS/SALES 0.737 0.744 0.704 0.663 0.787 0.680 0.702

(0.400) (0.380) (0.351) (0.350) (0.350) (0.378) (0.410)

CAPEX/ASSETS 0.124 0.146 0.166 0.178 0.158 0.143 0.100

(0.095) (0.104) (0.123) (0.131) (0.124) (0.104) (0.076)

OP.INCOME/SALES 0.096 0.095 0.098 0.097 0.090 0.16 0.15

(0.094) (0.098) (0.103) (0.109) (0.098) (0.09) (0.09)

R&D/ASSETS 0.119 0.172 0.172 0.256 0.215 0.288 0.284

(0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.008)

ADV/ASSETS 0.063 0.071 0.050 0.060 0.049 0.094 0.056

(0.000) (0.000) (0.000) (0.000) (0.000) (0.00) (0.00)

Number of observations 2,408 2,913 3,025 3,261 3,160 2,754 2,398

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Table 8Does insider ownership adjust to past firm value? Tests of strict exogeneity

In this table, I report results from the fixed-effects estimation of the model:

yi,t = α + βXi,t + ΩWi,t+2 + ηi + εit, t = 1991, 1993, 1995 . . . 2001

where Wit+1 is a subset of forward values of the insider ownership and control variables, X. y is industry-adjusted

firm value (Q). X includes insider ownership(IOWN), log of sales (LogSALES), tangible assets divided by sales

(ASSETS/SALES), capital expenditure divided by assets (CAPEX/ASSETS), operating income divided by sales

(OP.INCOME/SALES), R&D expenditure divided by assets (R&D/ASSETS), advertising expenditure divided

by assets (ADV/ASSETS), dummy variables equal to 1 if R&D or advertising expenditure is reported (RDUM

and ADUM). Ω = 0 is the null hypothesis of strict exogeneity. All t-statistics (in parentheses) are based on robust

standard errors. Year dummies are included in all specifications.Dependent Variable:Q(t) 1 2 3

IOWN(t) 0.2527∗ 0.2191∗ 0.2196∗

(1.92) (1.66) (1.66)

LogSALES(t) −0.4051∗ −0.6832∗ −0.6801∗

(−6.09) (−7.02) (−7.11)

ASSETS/SALES(t) 0.0014 −0.0235 −0.0220(0.04) (−0.47) (−0.64)

CAPEX/ASSETS(t) 1.1093∗ 1.4311∗ 1.5070∗

(3.81) (4.50) (4.42)

OP.INCOME/SALES(t) 2.8157∗ 2.6408∗ 2.6305∗

(6.86) (6.36) (6.37)

R&D/ASSETS(t) 0.0701 0.0096 0.0034(0.28) (0.15) (0.01)

ADV/ASSETS(t) −0.1663∗ 0.1149 0.1828∗

(−3.33) (1.53) (2.54)

RDUM(t) 0.0536 0.0833 0.0833(0.54) (0.77) (0.75)

ADUM(t) −0.0254 −0.0614 −0.0512(−0.30) (−0.91) (−0.76)

IOWN(t + 2) 0.2037∗ 0.1791 0.1779(1.69) (1.36) (1.34)

LogSALES(t + 2) 0.6123∗ 0.6136∗

(5.60) (5.66)

ASSETS/SALES(t + 2) 0.0544∗ 0.0563∗

(1.84) (1.91)

CAPEX/ASSETS(t + 2) 1.8247∗ 1.6300∗

(4.08) (3.75)

OP.INCOME/SALES(t + 2) −0.9730∗ −0.9591∗

(−2.04) (−2.01)

R&D/ASSETS(t + 2) 0.2721(0.99)

ADV/ASSETS(t + 2) 0.2466∗

(2.39)

RDUM(t + 2) −0.0543(0.12)

ADUM(t + 2) −0.0028(−0.52)

∗ significant at the ten percent level or smaller

41

Table 9Does insider ownership affect firm value?

In this table, I report results from the estimation of the model:

yit = α + βxit + Γzit + ηi + εit, t = 1991, 1993, 1995 . . . 2003

y is industry-adjusted firm value (Q), x is insider ownership (IOWN), log of sales (LogSALES), tangible assetsdivided by sales (ASSETS/SALES), capital expenditure divided by assets (CAPEX/ASSETS), operating incomedivided by sales (OP.INCOME/SALES), R&D expenditure divided by assets (R&D/ASSETS), advertising ex-penditure divided by assets (ADV/ASSETS), dummy variables equal to 1 if R&D or advertising expenditure isreported (RDUM and ADUM). All t-statistics (in parentheses) are based on robust standard errors. a, b, c representsignificance at the one percent, five percent and ten percent level respectively. Year dummies are included in allspecifications.

Dependent Variable:Q Pooled Fixed SystemOLS Effects GMM

IOWN −0.1354c 0.2710a −0.0205(−1.88) (2.69) (−0.04)

LogSALES −0.0708a −0.5034a 0.0257(−5.69) (−9.19) (0.37)

ASSETS/SALES −0.0138 −0.0384 −0.0582(−0.89) (−1.23) (−0.64)

CAPEX/ASSETS 2.4260a 1.4764a −1.9554(8.33) (5.36) (−1.29)

OP.INCOME/SALES 0.5134a 2.6677a 1.3881c

(3.12) (9.26) (1.76)

R&D/ASSETS 0.5023a −0.2849 0.3561c

(3.08) (−0.69) (1.68)

ADV/ASSETS −0.2597a −0.1784a −0.0381(−3.34) (−3.74) (−0.41)

RDUM 0.3974a 0.1514c 0.3025(8.48) (1.81) (1.20)

ADUM 0.1205a −0.0492 −0.1120(2.61) (−0.91) (−0.75)

Q(t− 1) 0.3139a

(4.45)

R2 0.06 0.05

m1 −3.98(0.00)

m2 1.44(0.15)

J-statistic 126.07(0.25)

Notes

1. m1 and m2 are tests for first-order and second-order serial correlation in the first-differenced residuals asymp-totically distributed as N(0, 1) under the null of no serial correlation. p-values are in parentheses

2. J-statistic is from the Hansen test of over-identifying restrictions,asymptotically distributed χ2, under the nullof instrument validity p-values are in parentheses.

3. The instruments used in the GMM estimation are:differenced equations: yit−6,yit−8,. . . ,yi,1991, xit−6,xit−8,. . . ,xi,1991,zit−6,zit−8,. . . ,zi,1991;level equations: ∆yit−6,∆yit−8,. . . ,∆yi,1991,∆xit−6,∆xit−8,. . . ,∆xi,1991, ∆zit−6,∆zit−8,. . . ,∆zi,1991

42

Figure 1The relationship between estimation bias and dynamic endogeneity

This figure graphically illustrates the bias introduced by dynamic endogeneity when carrying out OLS andfixed-effect estimation. The model estimated is: yit = xitβ + zitγ + ηi + νit. The true data generatingprocess (DGP) for zit is zit = κzit−1 + ξit; and that for xit is xit = αxit−1 + πzit + λyit−1 + δηi + εit. Thus,xit is endogenously related to yit−1 through the parameter λ and is endogenously related to the unobservedheterogeneity, η. The parameters used for generating the data are : β = 0; γ = 0.6, κ = 0.9, α = 0.7, π = 0.2,δ = 0.5. I use a range of λ from −0.9 to 0.9. Details of the simulation are included in an appendix. Thevertical axis gives the bias in the estimate of β. The horizontal axis represents the magnitude of dynamicendogeneity, λ. The dimensions of the panel data set are T = 5 and N = 500.

-0.2

0

0.2

0.4

0.6

0.8

1

-0.9 -0.7 -0.5 -0.3 -0.1 0.1 0.3 0.5 0.7 0.9

λ (Dynamic endogeneity)

Bia

s

OLS

FE

GMM

Notes1. All estimated biases and standard errors are based on 1,000 replications. Standard errors of biases are in

parentheses.

2. GMM estimation is carried out using the Arellano and Bond (1991) estimation procedure. Instruments usedare: yt−2, yt−3, . . . , y1, xt−1, xt−2, . . . , x1, zt−1, zt−2, . . . , z1

43