the determinants of managerial ownership and the ownership
TRANSCRIPT
The determinants of managerial ownership and the ownership-
performance relation
Student name: Huib Raterink Administration number: 664727 Faculty: Economics and Management Department: Finance Supervisor: dr. A. Manconi Date: 18-12-2012
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Acknowledgements
This research could not have been realized without the help of others. I would especially like
to thank my thesis supervisor, dr. A. Manconi, for giving me valuable feedback week after
week and for staying positive at all times. I would also like to thank Marloes Coppoolse for
her never-ending optimism and support, and for putting up with me the past year. Lastly, I
would like to thank my parents and Max for helping me wherever they could.
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Table of contents
1. Introduction ............................................................................................................................ 4
2. Hypothesis development ........................................................................................................ 6
2.1 Determinants of managerial ownership ............................................................................ 6
2.2 The ownership-performance relation ................................................................................ 7
3. Data and methodology ........................................................................................................... 9
3.1 Data ................................................................................................................................... 9
3.2 Econometric problem ..................................................................................................... 12
3.3 Methods .......................................................................................................................... 14
4. Empirical results ................................................................................................................... 18
4.1 Determinants of managerial ownership .......................................................................... 18
4.2.1 Determinants of firm performance: fixed effects estimation ....................................... 19
4.2.2 Determinants of firm performance: IV estimation ...................................................... 20
4.2.3 Determinants of firm performance: sample splits ....................................................... 21
5. Conclusion ............................................................................................................................ 22
6. References ............................................................................................................................ 24
7. Appendix .............................................................................................................................. 26
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1. Introduction
Being the CEO of Apple from 2000 until 2011, the late Steve Jobs earned a yearly salary of
only $1.1 While that may seem strange, other CEOs have had $1 salaries as well. Some
examples include Google’s Eric Schmidt2, Facebook’s Mark Zuckerberg (as of 2013) and Mr.
Jobs’s successor at Apple Tim Cook. Obviously, these men are compensated in other ways.3
In their extensive study on CEO pay, Frydman and Jenter (2010) show that stock-based
compensation like restricted stock grants and option grants have become increasingly
important in the composition of CEO pay. Indeed, Mr. Cook was granted 1,000,000
Restricted Stock Units (RSUs) as a promotion and retention reward when he was appointed.4
A natural question, then, is: why not simply pay these executives with salary? The
answer lies in agency theory. Berle and Means (1932) were the first to note that the separation
of corporate ownership and control results in a conflict of interests between shareholders (the
principal) and managers (the agent). Unlike shareholders, managers do not necessarily want to
maximize firm value. Instead, they may want to engage in empire building, spend money on
pet projects that do not create value or consume corporate assets. Because shareholders cannot
perfectly observe the actions of managers, there is a need for incentive alignment in order to
reduce agency costs. A well known solution is to make managers themselves owners of the
firm by giving them an equity share. This is exactly the reasoning behind Mr. Job’s
compensation package as formulated by Apple:
“While Mr. Jobs served as CEO, the Company believed that
his level of stock ownership significantly aligned his interests
with shareholders’ interests; therefore, his total compensation
consisted of $1 per year.
- Apple’s 2012 Proxy Statement
While the theory described above may sound perfectly reasonable, in the end I am
interested in whether executive ownership really is effective in increasing firm performance.
This is the main topic my thesis. More specifically, I replicate and extend the research of
1 Source: Apple Inc. proxy statements. 2 Source: Google Inc. proxy statements. 3 For instance, Steve Jobs was granted options worth over 500 million USD in 2001 and restricted stock grants worth 75 million USD in 2003 (source: Compustat ExecuComp). 4 Based on the adjusted closing price of Apple stock on the day of his promotion (August 24, 2011), this reward was worth 374,920,000 USD.
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Himmelberg, Hubbard and Palia (1999), hereafter referred to as HHP. My main research
question is as follows:
Is there an empirical link between managerial equity ownership and firm performance?
An impressive body of literature on the relationship between executive ownership and
firm performance already exists. The need for further examination stems from the fact that the
empirical evidence has not led to consensus on the matter. On the contrary, there is a wide
variety in observed ownership-performance relations. I rely on HHP’s methodology because
they point out a serious flaw in many other studies. According to HHP, ownership and
performance are partly determined by common characteristics. Failing to control for these
characteristics would result in spurious correlations. Addressing this endogeneity problem of
(unobserved) omitted variables is at the heart of their study.
Before I focus on the ownership-performance relation, I first examine the determinants
of executive ownership. Whereas Jensen and Meckling (1976) associate low levels of
ownership with suboptimal compensation design, HHP argue that ‘the compensation contracts
observed in the data are endogenously determined by the contracting environment, which
differs across firms in both observable and unobservable ways’. Put differently, the severity of
the moral hazard problem will differ across firms and as a result, some managers may need
less equity for incentive alignment. Apart from gaining insight into how equity compensation
is determined, the variation in ownership explained by unobservable, time-invariant factors
informs us on the possible endogeneity of ownership in regressions on firm performance.
As an extension to the HHP investigation, I try to identify an ownership-performance
relation by separating firms on the basis of share liquidity, agency costs and institutional
ownership. I hypothesize that the relationship is stronger for firms whose shares are relatively
liquid because managers will prefer liquid shares to illiquid ones. Agency costs give an
indication of the scope for moral hazard and could therefore weaken the relationship. Lastly,
institutional investors may want to actively monitor managers because of their large equity
holdings, thereby strengthening the relationship.
My findings are not in favor of using managerial equity as a mechanism for incentive
alignment. Like HHP, I find no convincing evidence for an ownership-performance relation,
after controlling for unobserved heterogeneity. The only evidence I find for a correlation is
tentative at best, and results from splitting the sample on the basis of share liquidity. This
finding suggests that managers may indeed be more motivated to maximize shareholder value
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if they can easily sell their shares. Regarding the determinants of ownership, I reject HHP’s
statement that observable firm characteristics are strong predictors. However, I corroborate
that managerial equity stakes are largely determined by unobserved heterogeneity at the firm
level.
This thesis makes three contributions to the literature. First, it examines the
ownership-performance relation using a very extensive data set (22,463 firm-year
observations) consisting of recent data (2000-2010). Second, it shows that the variables used
by HHP to instrument executive ownership are actually not strong instruments. Third, it
documents how controlling for mediators such as liquidity can help uncover evidence of an
ownership-performance relation.
The remainder of this thesis is organized as follows. In section 2, I discuss the relevant
literature and develop my hypotheses. Section 3 describes our data, discusses the endogeneity
issue in an analytical framework, and describes the tests we run. In section 4, I present my
empirical results and interpret them. Section 5 concludes.
2. Hypothesis Development
2.1 Determinants of managerial ownership
While not directly investigating the determinants of managerial ownership, Demsetz and Lehn
(1985) show that firm risk has a significant and positive effect on the degree of ownership
concentration. According to them, ‘firms that transact in markets characterized by stable
prices, stable technology, stable market shares, and so forth are firms in which managerial
performance can be monitored at relatively low cost. In less predictable environments,
however, managerial behavior simultaneously figures more prominently in a firm’s fortunes
and becomes more difficult to monitor.’ Extending this argument to the determination of
executive ownership, the riskier the firm, the higher the equity stake that should be given to
managers (all else equal). However, there is also a potential offsetting incentive effect since
more equity means more idiosyncratic risk that cannot be diversified away. HHP seek to find
which of these effects prevails by controlling for other variables proxying for the scope for
moral hazard. These include firm size, capital intensity, R&D expenses, advertising expenses,
market power and investment rate. They find that idiosyncratic stock price risk is actually
associated with lower levels of managerial ownership. Moreover, observable firm
characteristics in the contracting environment strongly predict managerial equity levels and in
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predicted ways, i.e. consistent with the principal-agent model. Using the same set of
explanatory variables as HHP, I test if these ‘logical’ relations still hold based on recent data
and therefore formulate the following hypothesis:
H1 Managerial ownership is determined by observable characteristics in the contracting
environment in ways that are consistent with the principal-agent model.
2.2 The ownership-performance relation
As mentioned in the Introduction, there is an impressive body of literature on this relation.
Mørck, Shleifer and Vishny (1988) consider a cross-section of fortune 500 firms and estimate
a piecewise-linear relation between board ownership and Tobin’s Q, the most commonly used
measure of firm valuation/performance. They do this because they suspect that managerial
ownership affects firm valuation differently dependent on the level of equity holdings. More
specifically, two effects are distinguished: the convergence-of-interests and the entrenchment
effect. The latter is explained as follows: ‘A manager who controls a substantial fraction of
the firm’s equity may have enough voting power or influence more generally to guarantee his
employment with the firm. […] With effective power, the manager may indulge his
preference for non-value maximizing behavior’. This theory is more or less supported by their
results, as the relation is increasing for ownership levels between 0% and 5%, decreasing
between 5% en 25% en increasing beyond 25%. The Mørck et al. study was later replicated
by McConnell and Servaes (1990), Cho (1998) and Holderness, Kroszner and Sheehan
(1999), providing mixed results.5 In addition, McConnell and Servaes (1990) use a quadratic
specification of ownership and find an inverted U-shaped relation. Their evidence suggests
that the entrenchment effect dominates the incentive alignment effect when managerial equity
stakes reach 40% to 50%.
An important caveat to the above findings is that ownership is treated as an exogenous
explanatory variable in regressions for firm performance. Jensen and Warner (1988, p. 11)
note that this assumption is likely to result in spurious correlations because executive
ownership and firm performance may be influenced by common characteristics. HHP give
some really intuitive examples. For instance, a firm that has access to a superior monitoring
technology will not need to give its managers high levels of ownership to align incentives. At
the same time, there is less scope for managers to engage in non-value maximizing activities, 5 The relation for the 0-5% range of ownership is corroborated by all, but not for the other ranges.
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resulting in a higher valuation. Not accounting for this characteristic in the firm’s contracting
environment leads to a negative ownership-performance relation, which is obviously
misleading. Hence, many researchers claim that ownership should be treated as endogenous.
This is what Hermalin and Weisbach (1991) do and they also find a significant non-
monotonic relation. Other studies accounting for endogeneity (like Demsetz and Villalonga
(2001) and HHP), however, show that ownership fails to explain variations in firm
performance.
Clearly, the literature does not provide a common view on the ownership-performance
relation. Using Tobin’s Q as measure of firm performance and building on the theory of
Mørck et al., I test the following hypothesis:
H2 Managerial ownership affects firm performance in accordance with the theoretical
notions of incentive alignment and managerial entrenchment.
Because the literature shows that it is generally hard to even identify a relationship between
managerial equity ownership and firm performance, I formulate three hypotheses concerning
specific circumstances that may strengthen or weaken the relation. The first is related to the
liquidity of firms’ shares. Everything else equal, managers will prefer liquid shares to illiquid
shares as they know they can easily convert these into cash at going market prices. I therefore
expect liquid shares to provide managers with stronger incentive effects. Another reason why
liquidity may affect the ownership-performance relation is that firms whose shares are
relatively liquid are probably more transparent than others; traders know their information to
be reliable and trade accordingly. Being more transparent, managers will be more inclined to
pursue value maximization. I thus hypothesize:
H3 The more liquid a firm’s shares are, the more positive is the ownership-performance
relationship.
Besides share liquidity, agency costs may also affect the ownership-performance
relationship. High agency costs indicate a strong need for monitoring because there is a scope
for managers to use corporate assets for their own benefit. The greater this scope, the less
effective an equity stake in the firm is in aligning incentives with shareholders (all else equal).
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My fourth hypothesis is thus:
H4 The lower a firm’s agency costs are, the more positive is the ownership-performance
relationship.
Most of the studies mentioned earlier in this section do not solely focus on managerial
ownership but on ownership structure as a whole. Some, like Demsetz and Villalonga (2001),
include the fraction of shares owned by a firm’s largest shareholders in their regressions for
firm performance because it may influence the relation we try to identify in this thesis. The
reasoning is straightforward: large shareholders have a greater incentive to monitor executives
compared to smaller investors (who typically also lack the required expertise). Since
institutional investors generally hold large amounts of shares, I hypothesize:
H5 The higher the concentration of institutional ownership is, the more positive is the
ownership-performance relationship.
On a final note, addressing another issue of endogeneity, reverse causality, is not within the
scope of this thesis. Studies like Kole (1996), Cho (1998) and Demsetz et al. (2001) claim that
their evidence suggests that firm performance actually causes managerial ownership, i.e.
executives are rewarded with shares of stock if they do well. While I do not ignore their
findings, there at least seems to be some room for interpretation as HHP point out so I leave
this issue for future research.
3. Data and methodology
3.1 Data
The sample used in this study consists of all Compustat ExeCucomp firms for which
additional information on firm characteristics (accounting information) is available in the
CRSP/Compustat Merged data set over the period 2000-2010. The ExecuComp data set
contains information on various types of executive compensation, including the percentage of
total shares owned by executives.6 It is from this figure that I construct my various ownership
variables. The CRSP/Compustat Merged data set contains information on balance sheet and
6 Excluding still to be exercised options.
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income statement items, which I need for examining the determinants of managerial
ownership and as controls in regressions for firm performance. I retrieved holding period
returns and information on share liquidity from CRSP. Finally, Thomson Reuters provides me
with measures of institutional ownership that I use for a sample split. Table 1 in the Appendix
provides a detailed description of how I constructed each variable and where I retrieved the
(underlying) variables from.
When I merge all the data into one file, I end up with a sample that is, to my
knowledge, much larger than any sample previously used in studies on this particular topic. In
total, I have 22,463 firm-year observations on 2,559 firms over a period of 11 years. To put
this in perspective, HHP, Mørck et al. (1988) and Demsetz et al. (2001) examine 600, 500 and
223 firms, respectively. Moreover, these and several other studies only consider specific
subsets of firms. For example, HHP and Demsetz et al. (2001) require their sample to have no
missing observations on certain variables while Mørck et al. (1988) restrict themselves to
examining Fortune 500 firms. Since I do not impose any such restriction on my observations,
I would argue that besides being more extensive, my sample is also more random because the
sample selection bias is less severe. Lastly, the data I am using has a panel structure, i.e. it
contains repeated observations over the same units (in this case: firms). This allows me to
control for unobserved firm heterogeneity in our regressions with firm-fixed effects. Section
3.2 will elaborate on this.
To get a first impression of the relation between managerial ownership and firm
performance, the scatter plot in figure 1 displays all observed combinations of the fraction of
shares owned by managers and Tobin’s Q, a market-to-book ratio which proxies for firm
performance. Like all of my other variables, fraction of ownership and Tobin’s Q have been
censored at the 1st and 99th percentiles to prevent distortion of our estimates. Note that I
cannot make any inferences on the ownership-performance relationship based solely on figure
1 since both ownership and performance may be influenced by common characteristics (this is
related to the endogeneity problem described previously). Our models described in section 3.3
will try to control for these characteristics.
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Figure 1: Scatter plot of Tobin’s Q on the fraction of shares owned by managers
Figure 1 does not show a clear correlation between managerial ownership and firm
performance. With a bit of imagination, one can discern a somewhat downward sloping
relation in the scatter plot. This observation would not correspond to the theory discussed in
section 2 since I expect managerial ownership to positively affect firm performance, at least at
lower levels of ownership. Then again, when looking at the lowest levels of firm performance
at a given level of managerial ownership, performance seems to be slightly increasing with
ownership. I rely on OLS and instrumental variables models to determine the exact
relationship (if there is one).
Table 2 in the Appendix reports means, medians, standard deviations, minima and
maxima of our dependent and independent variables. A comparison with HHP would be very
informative here but unfortunately, they do not report summary statistics in their study. In any
case, our average fraction of executive ownership of 2.74% is well below the averages found
in other studies. For example, Mørck et al. (1988) and McConnell and Servaes (1990) report
average ownership stakes of 10.6% and 11.84%, 7 respectively. It is unlikely that this
difference is attributable to the nature of our sample since McConnell and Servaes (1990) also
consider a large sample consisting of firms from various industries. A more logical
7 Average executive ownership for their 1986 sample.
05
10
15
Tob
in's
Q
0 .1 .2 .3 .4Fraction of shares owned by managers
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explanation is that these studies are relatively old, dating back to the late 1980s/early 1990s,
and firms’ capital stocks have grown in size since then. In any case, the average fraction of
executive ownership has gone down percentagewise even though absolute values of
managerial ownership have risen over the years (Frydman and Jenter, 2010). Regarding
Tobin’s Q, my observed average of 1.82 is higher than those found in older studies. 8
However, these values are still within one standard deviation of my average � (1.13) so the
difference is not statistically significant.
3.2 Econometric problem
To get a better understanding of why I use the models described later in this section, I first
look at the endogeneity problem in an econometric way by considering the analytical
framework developed by HHP. This framework is based on the assumption that management
compensation contracts, designed by firm owners (shareholders), include shares of equity for
the purpose of aligning managers’ interests with those of the shareholders. I also assume that
other means by which agency costs can be reduced are exhausted so that managerial equity
stakes must address the residual agency costs. Now, let ��� be the fraction of shares owned by
the managers of firm � at time � and let ��� and �� be vectors of observable and unobservable
characteristics (unobserved firm heterogeneity) for firm � at time � . ��� is determined by
observable firm characteristics (���) such as the scope for moral hazard and firm risk but also
by unobservable firm characteristics (�� ) such as a firm’s monitoring technology and
intangible assets. Note that the unobservable firm characteristics are considered to be time-
invariant, i.e. they are relatively constant for each firm. This assumption allows me to use
fixed effects estimation which I will elaborate on later in this section. Putting the above in a
simple model yields:
��� ���� � ��� � ��� (1)
where ��� represents an error term which captures all unobserved factors that are not
correlated over time.
Once managers know what share of equity they are given, they set their effort level,
��� , accordingly. This effort level will also be influenced by the firm’s observable and 8 For example, Mørck et al. (1988) report an average � of 0.85 while Demsetz and Villalonga (2001) find an
average � of 1.13.
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unobservable characteristics. For example, managers of firms with a superior monitoring
technology will put in more effort, everything else equal. Hence the following linear relation:
��� ���� � ���� � ��� � ��� (2)
where ��� is an error term. I now have the ingredients to construct a relation for firm
performance, which is captured by Tobin’s Q, a measure of firm value. I assume it depends on
managers’ level of effort and observed and unobserved firm characteristics. Hence:
��� ���� � ���� � ��� � ��� (3)
with ��� being the error term. Inserting equation (2) into (3) allows managerial ownership,
���, to be put back into the equation for firm performance:
��� ����� � ��� � ����� � �� � � ���� � ���� � ��� (4)
Simplifying equation (4) results in the regression specification used by Mørck et al. (1988) to
estimate the effect of ownership on performance with ordinary least squares (OLS):
��� �� � ����� � ����� � ��� (5)
where ��� is the simplified version of �� � � ���� � ���� � ���. The problem with equation
(5) is that the coefficients on managerial ownership and observed firm characteristics can only
be estimated consistently if the error term ��� is uncorrelated with both ��� and ���. Since the
choice of how much shares to give to managers is based partially on unobserved firm
characteristics, cov(���,���) ≠ 0 and thus equation (5) cannot be estimated consistently with
OLS.
In order to remedy the problem formulated above, I control for unobserved firm
heterogeneity by including a firm fixed effect for each firm. This is essentially a firm-specific
dummy that purges the effect of �� from the error term, thereby making OLS a suitable
estimator again.
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3.3 Methods
I estimate equation (6) to test my first hypothesis (‘Managerial ownership is determined by
observable characteristics in the contracting environment in ways that are consistent with the
principal-agent model’):
ln � ���1 ���! " � �#�$��� � �#�$���
� � �#�%���� � &#�%��'������� ()�*+,�-���.�� � /)�*+,�-���.��� � 01�*2��34��*��� 56&'�� � 86&''������ � ��9+��*��:�;%��� ��9+��*��:�;%'������ � ��<;��:���;�6������ ��=��*'����� � �� � ���
(6)
where � and � represent firm and time, � is a firm fixed effect and � is an error term. The
fraction of managerial ownership � is captured in a logarithmic function to be able to express
changes in the explanatory variables into percentage changes in ownership. All of the
explanatory variables, except for #�%��, are proxies for the scope for discretionary spending
or, more generally, for the scope for moral hazard. Important to note here is that I cannot
assume the error term to be independently and identically distributed since a) there is a
potential for serial correlation because of the panel structure of my data (cov(���, ���) ≠ 0) and
b) the variance of the error term is likely to differ across firms (var ��� ≠ σ2, i.e.
heteroskedasticity). To deal with this problem, I use robust standard errors which are clustered
around firms (Peterson, 2009). Like all other regression models discussed in this section, save
the ones on sample splits, I also estimate equation (6) for specifications with pooled data and
industry fixed effects. This will tell us something about the extent to which managerial
ownership is endogenously determined by the contracting environment. I discuss the expected
relations between executive ownership and the explanatory variables, borrowed from HHP,
below.
The effects of #�$� and #�$��, measured as the natural log of sales and the natural log
of sales squared, on managerial ownership are unclear a priori. I include them nonetheless
because Kole (1995) claims that differences in firm size can account for disparities between
studies concerning the ownership-performance relation. HHP offer arguments in favor of a
positive as well as a negative relationship. Firm size could positively affect managerial stakes
because it is harder to monitor managers of large firms and because large firms are likely to
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hire more skilled managers who demand a higher share of ownership. An argument in favor
of a negative relationship is that large firms enjoy economies of scale in monitoring managers.
I include #�$�� to allow for nonlinear relations.
The effect of firm-specific risk, captured by #�%��, on managerial stakes is also not
evident a priori. On the one hand, the riskier a firm, the easier it is for managers to engage in
non-value-maximizing activities while staying unnoticed, as argued by Demsetz and Lehn
(1985). HHP favor a second interpretation. They argue that the added risk to managers’
portfolios from owning volatile stocks is not easily hedged (firm-specific risk cannot even be
hedged at all). Therefore, the risk inherent to firm stock may not give managers the desired
incentive beyond a certain level of ownership. #�%��'���� is simply a dummy variable
that allows me to account for the possibility that firms for which #�%�� can be calculated
(#�%��'���� = 1)9 are inherently different from firms for which it cannot.
)�*+,�-���. and )�*+,�-���.�, the ratios of hard capital to sales and hard capital
to sales squared (to allow for nonlinear relations), represent a firm’s easier to monitor assets.
Hard capital is defined here as a firm’s property, plant and equipment. These are tangible,
long-term assets and thus easily monitored. One would therefore expect )�*+,�-���. to be
negatively related to managerial ownership.
I expect 1�*2��34��*, 6&' , 9+��*��:�;% and <;��:���;�6��� to be positively
related to managerial ownership. I measure a firm’s market power by using the ratio of
operating income to sales, the rationale being that firms with more market power are generally
more profitable. Managers of powerful firms will be less disciplined by market forces,
resulting in a greater need for incentive alignment. This need will also be present for firms
that spend relatively large amounts on research and development (R&D) and advertising since
both types of spending are vulnerable to managerial discretion. The dummy variables
6&''���� and 9+��*��:�;%'���� are included to distinguish between reporting and
non-reporting firms. My last measure for the scope for discretionary spending,
<;��:���;�6���, is given by the ratio of capital expenditures to hard capital. This ratio
proxies for the link between high growth and opportunities for discretionary projects.
9 A minimum of 20 daily returns on a yearly basis is used as a requirement to calculate a meaningful Sigma.
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In addition to the dependent variable based on the fraction of managerial ownership �,
I also use the log of average equity holdings per manager, calculated as the value of common
shares outstanding times the fraction of shares owned by managers divided by the number of
managers, as dependent variable for examining the determinants of executive ownership:
ln����� " � ��� � �� � ��� (7)
where � is the shorthand notation for the observable explanatory variables in equation (6).
Equation (7) serves as an extra test for H1 since I expect to see roughly the same results. The
advantage of using average ownership over the fraction of ownership is that people are
ultimately motivated by money rather than by the fraction of shares they own. Moreover, �
takes into account the number of managers (typically 5 per firm) while � does not.
To test my second hypothesis (‘Managerial ownership affects firm performance in
accordance with the theoretical notions of incentive alignment and managerial
entrenchment’) I estimate the impact of managerial ownership on firm value, represented by
Tobin’s Q, in several ways. First, I use two specifications of managerial ownership in a
regression for Q while controlling for unobserved firm characteristics:
��� ���� � ����� � ���� � �� � ��� (8)
��� ��1�� � ��2�� � ��3�� � &��� � �� � ��� (9)
McConnel and Servaes (1990) use the quadratic specification in their study as in (8)
while Mørck et al. (1988) use the spline specification as in (9) to allow for different slope
coefficients for different regions of ownership levels (see table 1 in the Appendix for the
interpretation of �1, �2 and �3). These specifications may allow me to identify when, if at
all, the entrenchment effect dominates the incentive effect. I also estimate equations (8) and
(9) using only the managerial ownership variables and the non-investment set of observable
variables. Once again, I estimate all specifications for pooled data, industry effects and fixed
effects, resulting in a total of eighteen regressions.
An alternative approach to solving the endogeneity problem is instrumental variables
estimation. A good instrument of � and �� is correlated with these variables (the instrument
should be relevant) but not with the error term in the � regression and not with � itself (the
instrument should be exogenous). If the instrument does affect performance by itself, then it
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should be included in the Q regression to prevent biasedness of the estimators. The idea here
is that the instrumental variable affects firm performance only through its effect on
managerial ownership, resulting in better estimates for � and ��. I use the same variables as
HHP to instrument the ownership variables, namely #�$� , #�$�� , #�%�� and
#�%��'���� 10 . Applying the two stage least squares (2SLS) method results in the
following model:
��� "� ��� � �� � ��� (10)
���� � ���� � �� � ��� (11)
��� A ��B �� ��B ��� � ��� � �� � $�� (12)
where ", and A are constants, � is the full set of control variables, � is the set of
control variables without #�$�, #�$��, #�%�� and #�%��'����, � is the firm fixed effect
and �, � and $ are error terms. Equation (10) and (11) make up the first stage of the 2SLS
method that provides me with the fitted values of the endogenous ownership variables needed
for the second stage, denoted by equation (12). A first-stage F-test will make clear whether
my instruments are weak or strong based on the variation they explain in � and �� while a
Hansen’s J-test informs me about the exogeneity of the instruments. Again, I also estimate the
IV model using pooled data and industry fixed effects.
Lastly, I try to identify an ownership-performance relation by splitting the sample
based on firm liquidity, agency costs and institutional ownership. Firm liquidity is proxied by
share turnover and two differently computed bid-ask spreads, agency costs are proxied by the
ratio of cash to assets and by the ratio of SG&A expenses to sales, and institutional ownership
is proxied by the fraction of shares held by institutional investors and by institutional
blockholders (see the table 1 in the Appendix for more details on these variables). To test H3,
H4 and H5, I run the same regression as in equation (8) on samples split at the median of the
variable of interest for each year and for the entire sample period. In addition, I also split the
samples into quantiles for each year and for the entire sample period. This makes for a total of
56 regressions (considering I only use firm fixed effects specifications here). Then I compare
the coefficients on ownership for the top and bottom 20% of the sample, expecting a greater
difference when comparing the top 50% against the bottom 50%. I formally test for the
difference between coefficients with a Chow test.
10 There are reasons to believe that these are correlated with ownership but not with performance nor with omitted variables (HHP, p. 379).
18
4. Empirical results
4.1 Determinants of ownership
Table 3A in the Appendix reports the estimated coefficients on the determinants of the
fraction of executive equity ownership.11 Turning first to the estimates of the pooled model in
column (1), I observe that only R&D expenditures have a significant (and unexpected) impact
on managerial stakes, apart from the coefficient on #�$�� which is only marginally
significant. This is quite a surprising result, considering that HHP mention that the fraction of
executive ownership is affected by nearly all regressors for pooled data. Moving to column
(2) where I control for unobserved industry heterogeneity by including industry fixed effects,
I observe that the coefficient on Size2 is no longer significant while the effect of R&D
expenses persists, although it is smaller. In terms of economic significance, a one-standard-
deviation increase in R&D expenses leads to an average decrease in ownership stakes of
2.81%,12 which is a modest effect. Moreover, the coefficient on <;��:���;�6��� has now
become marginally significant and, like the one on 6&', with a sign that runs counter to my
theoretical predictions. Finally, column (3) reports estimates for the specification that includes
firm fixed effects. Now, increases in R&D expenses no longer negatively affect ownership
stakes (suggesting a correlation with unobserved firm characteristics) while the effect of a
firm’s investment rate has become slightly more pronounced in terms of statistical
significance. The economic impact of a one-standard-deviation increase in <;��:���;�6���
is a decrease of 1.88% 13 in managerial stock holdings, a negligible effect. A possible
explanation for this negative relation is that capital expenditures are investments in fixed
assets and, as I have argued before, these are easily monitored. The estimates in Table 3A
provide little support for my first hypothesis.
Table 3B reports estimates for the determinants of managerial ownership when
ownership is measured as the log of average equity holdings per manager. Again, column (1)
reports the estimates of the pooled model while columns (2) and (3) control for industry and
firm fixed effects, respectively. Although columns (1) and (2) suggest that there is a
significant relationship between some observable characteristics and ownership, only the
effect of idiosyncratic firm risk represented by #�%�a survives the inclusion of firm fixed
11 Note that our dummy variables SigmaDummy, R&DDummy and AdvertisingDummy are omitted from all regressions because of collinearity. 12 1.2714 F 0.0945 J 4.2789 13 0.4044 F 0.1990 J 4.2789
19
effects. This again provides evidence for a correlation between the observed and unobserved
characteristics. Consequently, estimates of the model with firm dummies will be more reliable
than those of the other models. In terms of economic significance, a one-standard-deviation
increase in #�%�� will result in a decrease average managerial stock holdings of 26.07%14,
which is considerable but not implausibly high. This finding is in line with the interpretation
that rewarding managers with risky firm stock comes at the cost of less portfolio
diversification and therefore may not lead to the desired incentive effects.
Everything considered, the results presented in Table 3A and 3B do not support the
hypothesis that observable characteristics in firms’ contracting environments determine
executive ownership in ways that are consistent with the principal-agent model. In fact,
ownership is not even affected at all by nearly all variables. This contradicts the findings of
HHP. Because ownership is largely explained by unobserved factors, it is essential to control
for unobserved firm heterogeneity when regressing firm performance (�) on ownership (�).
4.2.1 Determinants of firm performance: fixed effects estimation
Moving on to the determinants of �, table 4A and 4B report, respectively, the estimated
coefficients for the quadratic and spline specifications of ownership. Turning first to column
(1) of table 4A, both � and �� have a statistically significant effect on � when neither
observable firm characteristics nor any fixed effects (except for year effects) are controlled
for. This finding corresponds to those of studies that fail to account for the endogeneity
problem. If the observed estimates for � and �� in column (1) were trustworthy, it would
imply an inverted U-shape relation between � and � with an inflection point at an ownership
level of 21.59%, 15 a finding that is perfectly in line with my second hypothesis (the
entrenchment effect supposedly dominates the incentive effect when the fraction of ownership
exceeds 21.59%). In terms of economic significance, a one-standard-deviation increase in �
would result in an average increase of 6.98%16 in �. However, introducing industry effects in
column (2) results in an � that is only marginally significant and its significance is lost
altogether when I control for unobserved firm heterogeneity in column (3). Controlling for the
full set of control variables (columns (4) to (6)) and for the non-investment set of control
variables (columns (7) to (9)) also yields ownership coefficients that are practically never
statistically different from zero. I conclude from Table 4A that ownership is indeed an
14 25.3101 F 100 F 0.0103 15 This is calculated by taking the first order derivative of � with respect to ��� and solving for ���. 16 �2.4347 F 0.0601 5.6385 F 0.0601�� J 1.8187
20
endogenous variable in regressions for firm performance and that � and � are actually not
related, even though studies based on cross-sectional data claim the opposite. To my support,
HHP find roughly the same significance pattern for the quadratic specification of �.
The reported estimates from the spline specification of ownership in Table 4B provide
even less evidence for an ownership-performance relation. Only �1 is statistically significant
in the pooled model without any other controls (column (1)). Its coefficient of 2.7106
indicates an increase of 2.73%17 in firm performance when ownership below a level of 5% is
raised by one standard deviation, an economic effect that can hardly be called significant. The
inclusion of industry effects, firm effects and observable contracting determinants in columns
(2) through (8) results in insignificant ownership coefficients, apart from the one on �2 in
column (7). Although it is not obvious why there is some significance here, I know this
estimate to be unreliable because it is not robust to the inclusion of industry and firm effects.
In conclusion, Table 4A and 4B cast serious doubt on empirical findings from studies
that fail to control for unobserved heterogeneity, be it at the industry level or at the firm level.
Both specifications of ownership are not even robust to the inclusion of only observable firm
characteristics. Something else worth mentioning is that practically all ownership variables
have the predicted signs, even though they are not statistically significant. For example,
nearly all estimations in Table 4A signal an inverted U-shaped pay-performance relation
(corroborating McConnell and Servaes (1990)) while the signs on �1, �2 and �3 in Table
4B suggest that increasing ownership up to a level of 25% increases performance and
decreases it thereafter.
4.2.2 Determinants of firm performance: IV estimation
Can I now safely conclude that managerial stakes do not influence performance? No, because
the panel data method used previously might not be the best method to estimate the real
population parameter on ownership. For instance, fixed effects estimation does not solve the
problem of time-varying omitted variables (Wooldridge, 2009). Table 5 reports instrumental
variable estimates for � and �� with #�$� , #�$�� , #�%�� and #�%��'���� 18 as
instruments. As usual, column (1) includes no fixed effects apart from year effects, column
(2) includes industry effects and column (3) includes firm effects. One immediately notices
the extremely high (and insignificant) estimates and corresponding standard errors on � and
17 2.7106 F 0.0183 J 1.8187 18 Which is again dropped in both stages of 2SLS because of being collinear to one of the other explanatory variables.
21
��. This is what happens when the instrumental variables explain only a small fraction of the
variation in the instrumented variables (Wooldridge, 2009, p. 511 and p. 515). Our first-stage
F-test statistics confirms the weak correlation between the instruments and the ownership
variables. According to Stock and Watson’s rule of thumb (Stock and Watson, 2011), this
statistic should be at least 10 in order to reject the hypothesis that the instruments are jointly
zero. Clearly, I cannot reject this hypothesis for any of the estimations reported in Table 5.
Moreover, one could have already predicted the above findings simply by looking at Table
3A: none of the instruments are significantly related to the fraction of executive equity
holdings. Even though I cannot reject the exogeneity of my instruments (the p-value of a
Hansen’s J-test is above 0.10 in each column), the ones used by HHP are certainly not
suitable for this particular sample. Because the other explanatory variables do not satisfy the
requirements for an instrumental variable either, IV estimation does not help identify the
effect of ownership on performance.
4.2.3 Determinants of firm performance: sample splits
Turning to the last possibility of detecting an ownership effect, Table 6 reports results from
splitting the sample on the basis of the liquidity of a firm’s shares. Three proxies for liquidity
are used: share turnover, a bid-ask spread based on end-of-month closing prices and a bid-ask
spread based on daily closing prices. Column (1) reports estimates for firms which have a
share turnover above the median share turnover, which is calculated for each sample year.
Column (2) reports estimates for firms which have a share turnover below the median share
turnover. Unobserved firm heterogeneity is controlled for in both columns, which is also true
for columns (3)-(6). The coefficient on � is marginally significant in both columns, with a
positive sign for firms that have a share turnover above the median and with a negative sign
for firms that belong to the below-median group. In line with our predictions, the observed
signs suggest that managers who are actually able to sell their shares at market prices are
more motivated to maximize shareholder value than managers who are rewarded with less
liquid stock. The negative coefficient in column (2) even indicates that compensation in the
form of illiquid firm stock has an adverse effect on firm performance. Columns (3) and (4),
which report estimates for firms that face below and above median end-of-month bid-ask
spreads, 19 respectively, support the previously mentioned findings. The coefficients on � and
�� imply an inverted U-shaped relation between managerial stock holdings and performance
19 Recall that low bid-ask spreads are associated with liquid shares.
22
with an inflection point at an ownership level of 17.53% for firms in the above-median group.
In addition, the impact of a one-standard-deviation increase in � results in a 7.70%20 change
in �, which is large enough to be economically significant. In columns (5) and (6), however,
the ownership coefficients are no longer significant and this is reflected by the p-value of the
Chow test. Whereas the ownership variables were statistically significantly different before
(p-values below or practically below 0.10), I cannot reject the hypothesis that the ownership
coefficients are different for columns (5) and (6). This also holds for all estimated coefficients
from regressions on samples split at the median of each liquidity variable calculated for the
entire sample period. The same is true when all previously described regressions are done for
the top and bottom 20% firms in terms of share liquidity. Although this casts some doubt on
the results reported in Table 6, 21 separating firms by share liquidity does seem to be a
potential starting point on the basis of which an ownership-performance relation can be
identified. I therefore do not accept, nor reject H3.
Regarding all regressions for samples split on the basis of variables proxying agency
costs and institutional ownership, I did not include any tables because I nowhere find
significant ownership coefficients. Therefore, there is no evidence that supports H4 and H5.
5. Conclusion
This thesis replicates and extends the study by Himmelberg, Hubbard and Palia
(1999), who found little evidence that executive equity holdings and firm performance are
related. Given that my research is based on recent data (2000-2010) and a very extensive data
set compared to other research in the field, I first show that managerial stakes are hardly
determined by observable factors in the contracting environment. As a result, I reject the
hypothesis that managerial ownership is determined by observable characteristics in ways
consistent with the principal-agent model. Instead, I find that the variation in ownership is
largely determined by unobserved factors at the firm level. This signals the need for
controlling for unobserved firm heterogeneity in the regression for firm performance
(represented by Tobin’s Q). Failing to do so would result in inconsistent estimates for the
explanatory variables. I also find that the effect of executive ownership on performance is not
20 �2.8138 F 0.0601 8.0263 F 0.0601��/1.8187 21 Since we expected that the difference between ownership coefficients would be more pronounced when comparing the top and bottom 20% firms.
23
robust to the inclusion of industry and firm dummies, thereby casting serious doubt on the
findings of studies that fail to account for unobserved heterogeneity.
As an alternative to fixed effects, I use instrumental variables to control for the
endogeneity of ownership in the � regression. My results show that the instruments used by
HHP are certainly not suitable for my sample. Again, I do not find evidence for a correlation
between the two variables of interest.
Extending the HHP study, I try to identify an ownership-performance relation by
separating firms on the basis of share liquidity, agency costs and institutional ownership.
Hypothesizing that the relation is stronger for firms with liquid shares, low agency costs and
high institutional ownership, I only find some tentative evidence for a correlation when our
sample is split on the basis of liquidity. This suggests that managers are more motivated and
thus perform better when they are rewarded with liquid stocks instead of illiquid stocks.
All in all, my results provide little evidence by which to reject the hypothesis that
managerial equity ownership and firm performance are unrelated. However, I do not ignore
the fact that certain aspects of my thesis are being questioned by other researchers (and maybe
rightly so). For example, Demsetz and Villalonga (2001) provide arguments against the use of
Tobin’s Q as a proxy for firm performance22 and point out that managers do not necessarily
share common interests. Zhou (2001) even devotes an entire article to the HHP study,
criticizing the use of within variation for resolving the issue at hand. Additionally, I noted
earlier that fixed effects estimation does not solve the problem of time-varying omitted
variables (Wooldridge, 2009). Lastly, my thesis does not address the possibility that
managerial ownership is actually determined by firm performance as argued by Kole (1996),
though HHP claim that their results allow for a different interpretation.
Future research could focus on a variety of issues pertaining to the ownership-
performance relation. Instrumental variable estimation still seems like a viable method for
identifying a relationship, the difficulty being finding proper instruments. One should look for
policy or regulatory changes that do not affect firm value but do affect ownership on a cross-
sectional level. Another avenue of inquiry is exploring other factors that strengthen or weaken
the relationship. For example, CEOs are more likely to influence firm value than COOs
(Chief Operations Officers) which may be reflected by different ownership coefficients.
Finally, stock options may be used as alternative mechanism for incentive alignment.
22 As well as against its most logical replacement, accounting profit.
24
6. References
Berle, A., Means, G., 1932. The Modern Corporation and Private Property. New York:
Harcourt, Brace and World.
Cho, M. 1998. Ownership structure, investment, and the corporate value: an empirical
analysis. Journal of Financial Economics, vol. 47, pp. 103-121.
Demsetz, H., Lehn, K., 1985. The structure of corporate ownership: causes and consequences.
Journal of Political Economy, vol. 93, pp. 1155-1177.
Demsetz, H., Villalonga, B., 2001. Ownership structure and corporate performance. Journal
of Corporate Finance, vol. 7, pp. 209-233.
Frydman, C., Jenter, D., 2010. CEO Compensation. Annual Review of Financial Economics,
vol. 2, pp. 75-102.
Hermalin, B., Weisbach, M., 1991. The effects of board compensation and direct incentives
on firm performance. Financial Management, vol. 20, pp. 101-112.
Himmelberg, C.P., Hubbard, R.G., Palia, D., 1999. Understanding the determinants of
managerial ownership and the link between ownership and performance. Journal of
Financial Economics, vol. 53, pp. 353-384.
Holderness, C., Kroszner, R., Sheehan, D., 1999. Were the good old days that good?
Evolution of managerial stock ownership and corporate governance since the great
depression. Journal of Finance, vol. 54, pp. 435-469.
Jensen, M., Meckling, W., 1976. Theory of the firm: managerial behavior, agency costs and
ownership structure. Journal of Financial Economics, vol. 3, pp. 305-360.
Jensen, M., Warner, J., 1988. The distribution of power among corporate managers,
shareholders, and directors. Journal of Financial Economics, vol. 20, pp. 3-24.
Kole, S., 1996. Managerial ownership and firm performance: incentives or rewards? Advances
in Financial Economics, vol. 2, pp. 119-149.
McConnell, J., Servaes, H., 1990. Additional evidence on equity ownership and corporate
value. Journal of Financial Economics, vol. 27, pp. 595-612.
Morck, R., Schleifer, A., Vishny, R., 1988. Management ownership and market valuation.
Journal of Financial Economics, vol. 20, pp. 293-315.
Petersen, M.A., 2009. Estimating standard errors in finance panel data sets: comparing
approaches. Review of Financial Studies, vol. 22, pp. 435-480.
Wooldridge, J.M., 2009. Introductory Economics. Fourth edition. South-Western.
Zhou, X., 2001. Understanding the determinants of managerial ownership and the link
25
between ownership and performance: comment. Journal of Financial Economics,
Vol. 62, No. 3, pp. 559-571.
26
7. Appendix
Table 1
Variable Descriptions
� Tobin’s Q, a measure of firm performance. It is a market-to-book ratio
defined as firm value divided by the replacement value of the firm’s assets. I
calculate firm value by multiplying share price at fiscal year-end with the
number of common shares outstanding and adding the book value of total
liabilities. The replacement value of a firm’s assets is equal to the book
value of total assets. I retrieved these variables from Compustat and their
variables names are PRCC_F, CSHO, LT and AT, respectively.
� The total fraction of a firm’s shares owned by its managers. It is constructed
by dividing the Compustat ExecuComp variable
SHROW_EXCL_OPTS_PCT by 100. Other variables that represent
managerial ownership are adaptations of �.
�� The total fraction of a firm’s shares owned by its managers squared.
�1 The fraction of a firm’s shares owned by its managers up to and including
0.05.
�2 The fraction of a firm’s shares owned by its managers beyond 0.05 up to and
including 0.25. For example, if �=0.25 for company X, then �1=0.05 and
�2=0.20.
�3 The fraction of a firm’s shares owned by its managers beyond 0.25.
� Average equity holdings per manager. It is calculated by multiplying share
price at fiscal year-end with the number of common shares outstanding
times the fraction of shares held by top managers (�) divided by the number
of top managers. � is expressed in millions of dollars.
#�$� The natural logarithm of a firm’s sales. Firms’ sales are retrieved from
Compustat, where the variable is named SALE. #�$� is expressed in
millions of dollars.
#�$�� The natural logarithm of a firm’s sales squared. #�$�� is expressed in
millions of dollars squared.
#�%�� Share price volatility resulting from firm-specific risk. It is constructed by
first estimating ‘normal’ daily returns for each firm-year combination using
the CAPM model and then taking the standard deviation of the residuals.
Daily returns and market returns used for constructing #�%�� are retrieved
from CRSP and the Fama-French website, respectively.
#�%��'���� A dummy variable that takes the value 1 if a firm reports the required
information to construct #�%�� and 0 otherwise (at least 20 daily returns on
27
a yearly basis are required to construct a meaningful #�%��).
)�*+,�-���. A capital-to-sales ratio calculated as a firm’s power, plant and equipment
(representing ‘hard’ capital) divided by its sales. Power, plant and
equipment and sales are retrieved from Compustat. Their variable names are
PPENT and SALE, respectively.
)�*+,�-���.� A firm’s hard capital-to-sales ratio squared.
1�*2��34��* Operating income-to-sales, a ratio that proxies for a firm’s market power. It
is a measure for the scope for discretionary spending. Operating income is
retrieved from Compustat, where it is named OIBDP.
6&' The ratio of R&D expenditures to hard capital, a proxy for the scope for
discretionary spending. R&D expenditures are named RDIP in Compustat.
6&''���� A dummy variable that takes the value 1 if a firm reports R&D expenses and
0 if it does not.
9+��*��:�;% The ratio of a firm’s advertising spending to hard capital. Like MarketPower
and R&D, it proxies for the scope for discretionary spending. Advertising
expenses are retrieved from Compustat. Its variable name is XAD.
9+��*��:�;%'���� A dummy variable that takes the value 1 if a firm reports any advertising
expenses and 0 if it does not.
<;��:���;�6��� The ratio of capital expenditures to hard capital, another measure for the
scope for discretionary spending. Capital expenditures are retrieved from
Compustat. Its variable name is CAPX.
Share turnover Share turnover is calculated as the number of shares traded during a firm’s
fiscal year divided by the number of shares outstanding in that year. Their
Compustat variable names are CSHTR_F and CSHO, respectively. Share
turnover is not included in any regression but it is used to split the sample on
the basis of a firm’s share liquidity.
Bid-ask spread A second measure for a firm’s share liquidity. Two differently calculated
bid-ask spreads are used throughout this paper. One is a bid-ask spread
based on end of month bid-ask spreads which are readily available from
CRSP. This monthly bid-ask spread variable is named Spread Between Bid
and Ask. The other is a bid-ask spread based on daily differences between
bid and ask prices. Both are expressed in dollars and are named Closing Bid
and Closing Ask in CRSP.
Cash/assets A ratio that proxies for agency costs. Like our proxies for a firm’s share
liquidity, this variable is used for splitting our sample into ‘high’ and ‘low’
groups. Cash and assets are retrieved from Compustat. Their variable names
are CH and AT.
SG&A/sales The ratio of selling, general and administrative expenses to sales, a second
proxy for agency costs. SG&A expenses are retrieved from Compustat,
where it is named XSGA.
28
Institutional ownership Institutional ownership represents the fraction of shares that is held by
institutional investors. Like the proxies for share liquidity and agency costs,
it is used for splitting the sample. Institutional ownership is retrieved from
the Thomson Reuters database, where it is named Total Institutional
Ownership.
Instituional block ownership A second measure of institutional ownership. This variable is similar to the
previous one, except for the fact that it only includes institutional ownership
beyond a considerable level. This level is not explicitly mentioned by
Thomson Reuters but if one relies on the description of a variable called
‘Number of >5% Institutional Block Ownerships’, it is 5% of a firm’s
shares. The Thomson Reuters variable name for institutional block
ownership is Total Ownership by Institutional BlockHolders.
29
Table 2
Summary statistics
The table below reports summary statistics on all dependent and independent variables. These are all described
in detail in table 1 of the Appendix. The sample consists of all firms from Compustat’s ExecuComp data set of
which additional information on firm characteristics is available in the CRSP/Compustat Merged data set over
the period 2000—2010. The top and bottom 1% of observations on �, � and variables underlying the other
reported variables have been dropped to prevent distortion of our estimates.
Variable Mean Median St. Dev. Min Max N. Obs.
Q 1.8187 1.4338 1.1261 0.5766 13.4921 21,950
m 0.0274 0.0038 0.0601 0.0000 0.4265 19,935
m2 0.0044 0.0000 0.0168 0.0000 0.1819 19,935
m1 0.0135 0.0038 0.0183 0.0000 0.0500 19,935
m2 0.0125 0.0000 0.0400 0.0000 0.2000 19,935
m3 0.0014 0.0000 0.0115 0.0000 0.1765 19,935
e 25.0304 0.7011 379.964 0.0000 21,262.0900 19,927
Size 7.0240 6.9362 1.5680 2.4650 11.4945 21,944
Size2 51.7957 48.1110 22.6631 6.0764 132.1227 21,944
Sigma 0.0217 0.0192 0.0103 0.0034 0.0754 22,390
SigmaDummy 0.9968 1.0000 0.0569 0.0000 1.0000 22,463
HardCapital 0.3911 0.1787 0.6905 0.0005 31.5207 20,747
HardCapital2 0.6297 0.0319 7.9362 0.0000 993.5573 20,747
MarketPower 0.1705 0.1496 0.2140 -7.0907 0.9678 20,871
R&D 0.0083 0.0000 0.0945 0.0000 4.1320 17,820
R&DDummy 0.8494 1.0000 0.3577 0.0000 1.0000 22,463
Advertising 0.3421 0.0821 1.1956 0.0000 29.7071 8,864
AdvertisingDummy 0.4019 0.0000 0.4901 0.0000 1.0000 22,463
InvestmentRate 0.2472 0.1987 0.1990 0.0000 9.3339 20,256
30
Table 3
(A) Determinants of total managerial ownership
The table below reports the estimated coefficients from regressing managerial ownership, defined as ln(�/(1-
�)), on a set of control variables. These variables are all described in detail in the Appendix. #�%��'����,
6&''���� and 9+��*��:�;%'���� are not reported because of collinearity. Industry fixed effects (3-digit
SIC code) are controlled for in column (2). Firm fixed effects are controlled for in column (3). Year fixed effects
are controlled for in columns (1)-(3). Intercept terms and year dummies are included for all regressions but not
reported. The same is true for fixed effects where appropriate. Standard errors are robust, clustered around firms
and reported in parentheses. The sample consists of all firms from Compustat’s ExecuComp data set of which
additional information on firm characteristics is available in the CRSP/Compustat Merged data set over the
period 2000—2010. For robustness, all underlying variables have been censored at the 1st and 99th percentiles.
The symbols *, ** and *** indicate statistical significance at the 10%, 5% and 1% level, respectively.
Variable (1) (2) (3)
Size 0.3605 -0.2437 0.4552 (0.3205) (0.3971) (0.5152) Size2 -0.0383* -0.0073 -0.0517 (0.0228) (0.0286) (0.0374) Sigma 2.1729 -1.9676 3.2985 (7.0600) (6.8083) (6.8164) HardCapital -0.1952 0.3033 0.1408 (0.4026) (0.5542) (0.6242) HardCapital2 0.1109 -0.2022 -0.1912 (0.1785) (0.1999) (0.2124) MarketPower -0.1210 0.2816 -0.0896 (0.2665) (0.2483) (0.2127) R&D -1.8512*** -1.2714*** -0.2289 (0.5308) (0.4239) (0.2968) Advertising -0.0003 0.0081 -0.0118 (0.0287) (0.0252) (0.0589) InvestmentRate -0.2937 -0.4245* -0.4044** (0.2607) (0.2549) (0.2015) Industry f.e. No Yes No Firm f.e. No No Yes Year f.e. Yes Yes Yes N. Obs. 19,935 19,935 19,935 R2 0.0415 0.2148 0.7712 St. Errors Cluster Firm Firm Firm
31
Table 3 – Continued
(B) Determinants of average equity ownership per manager
The table below reports the estimated coefficients from regressing average equity ownership per manager,
defined as ln(�) to allow for comparison with Himmelberg, Hubbard and Palia (1999), on a set of control
variables. These variables are all described in detail in the Appendix. #�%��'���� , 6&''���� and
9+��*��:�;%'���� are not reported because of collinearity. Industry fixed effects (3-digit SIC code) are
controlled for in column (2). Firm fixed effects are controlled for in column (3). Year fixed effects are controlled
for in columns (1)-(3). Intercept terms and year dummies are included for all regressions but not reported. The
same is true for fixed effects where appropriate. Standard errors are robust, clustered around firms and reported
in parentheses. The sample consists of all firms from Compustat’s ExecuComp data set of which additional
information on firm characteristics is available in the CRSP/Compustat Merged data set over the period 2000—
2010. For robustness, all underlying variables have been censored at the 1st and 99th percentiles. The symbols *,
** and *** indicate statistical significance at the 10%, 5% and 1% level, respectively.
Variable (1) (2) (3)
Size -0.0055 -0.0495 0.6516 (0.3531) (0.4150) (0.5667) Size2 0.0329 0.0344 -0.0158 (0.0254) (0.0297) (0.0414) Sigma -52.7045*** -50.6563*** -25.3101*** (9.3972) (8.9869) (7.5880) HardCapital 0.4551 1.8469*** 0.7389 (0.4445) (0.5951) (0.7066) HardCapital2 -0.0953 -0.6583*** 0.3531 (0.1879) (0.2123) (0.2349) MarketPower 1.6281** 1.4479* 0.4257 (0.7410) (0.7508) (0.3948) R&D -0.8354*** -0.6792** 0.0201 (0.3060) (0.3285) (0.4169) Advertising -0.0025 0.0223 0.0010 (0.0364) (0.0329) (0.0693) InvestmentRate 1.3370*** 0.8156*** 0.1818 (0.2992) (0.2852) (0.2257) Industry f.e. No Yes No Firm f.e. No No Yes Year f.e. Yes Yes Yes N. Obs. 19,935 19,935 19,935 R2 0.1989 0.3481 0.8134 St. Errors Cluster Firm Firm Firm
32
Table 4
(A) Determinants of firm performance (Tobin’s Q), quadratic specification
The table below reports the estimated coefficients from regressing firm performance, � , on measures of
managerial ownership, � and ��, and a set of control variables. These variables are all described in detail in the
Appendix. #�%��'���� , 6&''���� and 9+��*��:�;%'���� are not reported because of collinearity.
Industry fixed effects (3-digit SIC code) are controlled for in columns (2), (5) and (8). Firm fixed effects are
controlled for in columns (3), (6) and (9). Year fixed effects are controlled for in columns (1)-(9). Intercept terms
and year dummies are included for all regressions but not reported. The same is true for fixed effects where
appropriate. Standard errors are robust, clustered around firms and reported in parentheses. The sample consists
of all firms from Compustat’s ExecuComp data set of which additional information on firm characteristics is
available in the CRSP/Compustat Merged data set over the period 2000—2010. The top and bottom 1% of
observations on � and variables underlying the independent variables have been dropped to prevent distortion of
our estimates. The symbols *, ** and *** indicate statistical significance at the 10%, 5% and 1% level,
respectively.
Variable (1) (2) (3) (4) (5) (6) (7) (8) (9)
m 2.4347*** 1.0563* 0.7187 0.8301 1.6848* 0.9259 0.6875 0.4573 0.5850 (0.7170) (0.5920) (0.6432) (1.0214) (0.9619) (0.9882) (0.7163) (0.6109) (0.6547) m2 -5.6385** 2.6447 -2.4792 0.6451 -2.8260 -3.4325 -0.3467 -0.5475 -2.4737 (2.2831) (1.8994) (1.9544) (3.1377) (3.0052) (3.0070) (2.2343) (1.9138) (2.0195) Size - - - -
0.7658*** -
0.6362*** -0.7656** -
0.7547*** -
0.6302*** -0.7322***
(0.1505) (0.1661) (0.3309) (0.0994) (0.0944) (0.1844) Size2 - - - 0.0460*** 0.0425*** 0.0298 0.0456*** 0.0403*** 0.0320** (0.0100) (0.0108) (0.0231) (0.0066) (0.0062) (0.0130) Sigma - - - -11.4597* -0.7418 11.2422*** 6.1775** 4.6120* 17.6040*** (6.3174) (6.0578) (4.1438) (2.5927) (2.4949) (2.3230) HardCapital - - - -0.1311 -0.2222 -2.2074*** -
0.4463*** -
0.3093*** -0.4269***
(0.2283) (0.2781) (0.4658) (0.0543) (0.0754) (0.0774) HardCapital2 - - - -0.1463* -0.0595 0.6228*** 0.0437*** 0.0327** 0.0341*** (0.0852) (0.0985) (0.1509) (0.0136) (0.0160) (0.0114) MarketPower - - - 1.9168*** 1.7627** 0.9847 1.0315*** 1.2219*** 0.9176*** (0.7124) (0.7896) (0.7665) (0.2130) (0.2496) (0.2064) R&D - - - 0.3316 0.1317 0.2251 - - - (0.2701) (0.2719) (0.1790) Advertising - - - 0.0038 0.0118 -0.0153 - - - (0.0317) (0.0309) (0.0426) InvestmentRate - - - 1.1366*** 1.1176*** 0.6581*** - - - (0.3358) (0.3110) (0.2377) Industry f.e. No Yes No No Yes No No Yes No Firm f.e. No No Yes No No Yes No No Yes Year f.e. Yes Yes Yes Yes Yes Yes Yes Yes Yes N. Obs. 21,950 21,950 21,950 21,950 21,950 21,950 21,950 21,950 21,950 R2 0.1989 0.3481 0.8134 0.1903 0.3489 0.7293 0.1033 0.3170 0.6878 St. Errors Cluster
Firm Firm Firm Firm Firm Firm Firm Firm Firm
33
Table 4 – Continued
(B) Determinants of firm performance (Tobin’s Q), spline specification
The table below reports the estimated coefficients from regressing firm performance, � , on measures of
managerial ownership, �1, �2 and �3, and a set of control variables. These variables are all described in detail
in the Appendix. #�%��'���� , 6&''���� and 9+��*��:�;%'���� are not reported because of
collinearity. Industry fixed effects (3-digit SIC code) are controlled for in columns (2), (5) and (8). Firm fixed
effects are controlled for in columns (3), (6) and (9). Year fixed effects are controlled for in columns (1)-(9).
Intercept terms and year dummies are included for all regressions but not reported. The same is true for fixed
effects where appropriate. Standard errors are robust, clustered around firms and reported in parentheses. The
sample consists of all firms from Compustat’s ExecuComp data set of which additional information on firm
characteristics is available in the CRSP/Compustat Merged data set over the period 2000—2010. The top and
bottom 1% of observations on � and variables underlying the independent variables have been dropped to
prevent distortion of our estimates. The symbols *, ** and *** indicate statistical significance at the 10%, 5%
and 1% level, respectively.
Variable (1) (2) (3) (4) (5) (6) (7) (8) (9)
m1 2.7106** 1.1320 0.6733 0.6639 2.4993 0.8507 -0.8847 0.1194 0.4096 (1.1932) (0.9704) (1.0228) (1.7895) (1.6824) (1.6402) (1.2474) (1.0326) (1.0373) m2 0.8782 0.4496 0.1383 0.8913 0.7057 0.0089 1.6104** 0.6514 0.0903 (0.7068) (0.5918) (0.5205) (1.0409) (0.9009) (0.7996) (0.7027) (0.6024) (0.5361) m3 -1.7669 -1.4220 -1.2986 -0.5508 0.4612 -1.4136 -1.9351 -1.0404 -1.5977 (1.4348) (1.2451) 1.0453 (1.9947) (1.8487) (1.6833) (1.3727) (1.2437) (1.1198) Size - - - -
0.7653*** -
0.6317*** -0.7654** -
0.7576*** -
0.6305*** -0.7321***
(0.1505) (0.1663) (0.3308) (0.0994) (0.0945) (0.1846) Size2 - - - 0.0459*** 0.0423*** 0.0298 0.0456*** 0.0403*** 0.0320** (0.0100) (0.0108) (0.0231) (0.0065) (0.0062) (0.0130) Sigma - - - -11.4130* -0.7168 11.2436*** 6.3346** 4.6378* 17.6271*** (6.3119) (6.0524) (4.1313) (2.5983) (2.500) (2.3169) HardCapital - - - -0.1304 -0.2248 -2.2052*** -
0.4483*** -
0.3100*** -0.4273***
(0.2283) (0.2780) (0.4652) (0.0545) (0.0754) (0.0773) HardCapital2 - - - -0.1463* -0.0581 0.6214*** 0.0439*** 0.0327** 0.0341*** (0.0852) (0.0987) (0.1503) (0.0137) (0.0160) (0.0114) MarketPower - - - 1.9161*** 1.7606** 0.9842 1.0330*** 1.2212*** 0.9172*** (0.7124) (0.7894) (0.7666) (0.2128) (0.2495) (0.2063) R&D - - - 0.3320 0.1355 0.2252 - - - (0.2703) (0.2725) (1.1790) Advertising - - - 0.0040 0.0118 -0.0152 - - - (0.0318) (0.0309) (0.0425) InvestmentRate - - - 1.1344*** 1.1178*** 0.6586*** - - - (0.3355) (0.3105) (0.2382) Industry f.e. No Yes No No Yes No No Yes No Firm f.e. No No Yes No No Yes No No Yes Year f.e. Yes Yes Yes Yes Yes Yes Yes Yes Yes N. Obs. 21,950 21,950 21,950 21,950 21,950 21,950 21,950 21,950 21,950 R2 0.0367 0.2941 0.6722 0.1905 0.3493 0.7293 0.1040 0.3172 0.6878 St. Errors Cluster
Firm Firm Firm Firm Firm Firm Firm Firm Firm
34
Table 5
Ownership-performance model with instrumental variables
The table below reports the estimated coefficients from regressing firm performance, � , on measures of
managerial ownership, � and �� , and a set of control variables. #�$�, #�$��, #�%�� and #�%��'���� are
used as instruments for � and ��. All these variables are described in detail in the Appendix. 6&''���� and
9+��*��:�;%'���� are not reported because of collinearity. Industry fixed effects (3-digit SIC code) are
controlled for in column (2). Firm fixed effects are controlled for in column (3). Year fixed effects are controlled
for in columns (1)-(3). Intercept terms and year dummies are included for all regressions but not reported. The
same is true for fixed effects where appropriate. Standard errors are robust, clustered around firms and reported
in parentheses. The sample consists of all firms from Compustat’s ExecuComp data set of which additional
information on firm characteristics is available in the CRSP/Compustat Merged data set over the period 2000—
2010. The top and bottom 1% of observations on � and variables underlying the independent variables have
been dropped to prevent distortion of our estimates. First-stage F reports the first-stage F statistic for tests of
under- and weak identification of the instruments. Hansen and pHansen report, respectively, the Hansen J
statistic and associated p-value for a test of overidentification of the instruments. The symbols *, ** and ***
indicate statistical significance at the 10%, 5% and 1% level, respectively.
Variable (1) (2) (3)
m 102.8004 69.9304 -420.0164 (92.1357) (44.9864) (713.6528) m2 -479.3124 -332.9519 1607.408 (450.9528) (211.1995) (2550.8060) HardCapital 0.09098 -0.2252 1.0911 (0.8881) (0.6572) (5.9288) HardCapital2 0.1001 -0.1336 0.1827 (0.5370) (0.3045) (1.4209) MarketPower 2.1177** 1.7800** 0.4416 (0.8699) (0.8216) (1.2046) R&D 0.3745 0.0816 0.2686 (0.4768) (0.3208) (0.5856) Advertising 0.0216 0.0137 -0.2422 (0.0571 (0.0512) (0.5064) InvestmentRate 0.4064 0.6336 0.7246 (0.8053) (0.4199) (1.8517) Industry f.e. No Yes No Firm f.e. No No Yes Year f.e. Yes Yes Yes N. Obs. 21,950 21,950 21,950 St. Errors Cluster Firm Firm Firm First-stage F on instruments of m
8.06 8.54 4.21
First-stage F on instruments of m2 5.61 5.35 3.93
Hansen 1.6550 0.0240 0.0650 pHansen 0.1983 0.8759 0.7993
35
Table 6
Ownership-performance model with sample splits
The table below reports the estimated coefficients from regressing firm performance, � , on measures of managerial
ownership, � and �� , and a set of control variables. All these variables are described in detail in the Appendix.
6&''���� and 9+��*��:�;%'���� are not reported because of collinearity. Columns (1) and (2) report coefficients on
the sample split at the median share turnover which is computed for each sample year. Column (1) reports coefficients for
firms which have a share turnover above the median share turnover. Column (2) reports coefficients for firms which have a
share turnover below the median share turnover. Columns (3) and (4) report coefficients on the sample split at the median
bid-ask spread which is computed for each sample year. The bid-ask spread is calculated by averaging end of month bid-ask
spreads that are readily available from CRSP. Column (3) reports coefficients for firms which face bid-ask spreads below the
median bid-ask spread. Column (4) reports coefficients for firms which face bid-ask spreads above the median bid-ask spread
(and whose shares are thus more illiquid). Columns (5) and (6) are similar to (3) and (4), except now the split is done at the
median bid-ask spread that is constructed from daily differences between bid and ask prices. Firm fixed effects and year fixed
effects are controlled for in columns (1)-(6). Intercept terms and year dummies are included for all regressions but not
reported. The same is true for fixed effects where appropriate. Standard errors are robust, clustered around firms and reported
in parentheses. The sample consists of all firms from Compustat’s ExecuComp data set of which additional information on
firm characteristics is available in the CRSP/Compustat Merged data set over the period 2000—2010. The top and bottom
1% of observations on � and variables underlying the independent variables have been dropped to prevent distortion of our
estimates. pChow reports the p-value of a Chow test that tests for the difference between the managerial ownership variables.
The symbols *, ** and *** indicate statistical significance at the 10%, 5% and 1% level, respectively.
Variable (1) (2) (3) (4) (5) (6)
m 2.6021* -1.7582* 2.8138** -0.3719 1.9826 0.3891 (1.5547) (1.0055) (1.3445) (1.2717) (1.2576) (1.3034) m2 -6.9101 4.7170 -8.0263** 0.5970 -4.7941 -2.1111 (4.6846) (3.0212) (3.9501) (3.9367) (3.6238) (3.8739) Size -1.5551*** -0.2254 -1.1576** -0.3603 -1.2586*** -0.4079 (0.4713) (0.4877) (0.4571) (0.4854) (0.4563) (0.4943) Size2 0.0752** 0.0067 0.0408 0.0176 0.0500 0.0143 (0.0325) (0.0328) (0.0327) (0.0311) (0.0336) (0.0325) Sigma 14.9161*** 12.0239*** 11.9756*** 6.0953 14.5259*** 12.6071*** (4.0527) (4.0483) (4.3100) (3.7881) (3.9130) (3.8669) HardCapital -2.0992*** -1.5460*** -2.4249*** -1.8386*** -2.1528*** -1.8456*** (0.5491) (0.5588) (0.5914) (0.5796) (0.5748) (0.5729) HardCapital2 0.5990*** 0.4192** 0.8002*** 0.4513*** 0.7033*** 0.4809*** (0.1973) (0.1674) (0.2251) (0.1753) (0.2397) (0.1737) MarketPower 2.1390*** 2.4610*** 2.3875*** 0.1507 2.2329*** 3.8398*** (0.5703) (0.6900) (0.4345) (0.7190) (0.4065) (0.7545) R&D 0.2713 -1.1720* 0.2909 -0.1307 0.3060 0.0627 (0.1806) (0.6317) (0.2555) (0.1328) (0.2397) (0.3705) Advertising 0.0184 -0.0569 -0.0436 0.0336 0.0009 -0.0296 (0.0561) (0.0668) (0.0487) (0.0918) (0.0524) (0.0857) InvestmentRate 0.6415** 0.2660 0.9513*** 0.2017 1.0885*** 0.0178 (0.3111) (0.2281) (0.3006) (0.1670) (0.2449) (0.1733) Firm f.e. Yes Yes Yes Yes Yes Yes Year f.e. Yes Yes Yes Yes Yes Yes N. Obs. 21,950 21,950 21,950 21,950 21,950 21,950 St. Err. Cluster Firm Firm Firm Firm Firm Firm pChow on m 0.0182 0.0740 0.3731 pChow on m2 0.0355 0.1112 0.6128
36