Corporate governance and economic performance in Norwegian
listed firms
Øyvind Bøhren and Bernt Arne Ødegaard.1
November 26, 2001
1
The Norwegian School of Management BI. We are grateful for financial support from the Norwegian
Research Council (NFR).
Abstract
Using very rich and accurate data from all non–financial Oslo Stock Exchange firms in 1989–
1997, we find that ownership structure matters for economic performance, that insider ownership
matters the most and is almost always value–creating, that ownership concentration destroys value,
and that direct ownership is superior to investing through intermediaries like institutions and the
state. The value of the firm decreases with increasing board size, with the use of non–voting shares,
and when firms finance with more debt and pay higher dividends. Although these effects are very
robust in single–equation models and thereby suggest that our sample firms have suboptimal corporate governance mechanisms, the conclusions are quite sensitive to the choice of performance
measure. Moreover, most of the significant relationships disappear in simultaneous equations models, which may in principle handle both independence between governance mechanisms and reverse
causality between governance and performance, which both are ignored by single–equation models.
We suspect that this apparent evidence that real–world governance systems are optimal is driven
by weak instruments in the simultaneous system. Until we have a better theory of how corporate
governance and economic performance interact, the simultaneous equations approach may not have
much to offer in terms of valid new insights.
Keywords: Corporate Governance, Economic Performance, Norwegian Equity Market, Ownership Concentration, Inside Ownership, Simultaneous Equations.
JEL Codes: G3, L22
Contents
1 Introduction
1
2 Theoretical framework and existing evidence
2.1 Theory . . . . . . . . . . . . . . . . . . . . . . .
2.1.1 Corporate governance mechanisms . . .
2.1.2 Interactions and causality . . . . . . . .
2.2 Empirical evidence . . . . . . . . . . . . . . . .
2.3 Summary . . . . . . . . . . . . . . . . . . . . .
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3 Descriptive statistics
3.1 Market place and institutional environment . . . . . . .
3.2 Ownership structure . . . . . . . . . . . . . . . . . . . .
3.3 Board composition, security design, and financial policy
3.4 Controls . . . . . . . . . . . . . . . . . . . . . . . . . . .
3.5 Economic performance . . . . . . . . . . . . . . . . . . .
3.6 Summary . . . . . . . . . . . . . . . . . . . . . . . . . .
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4 Univariate relationships
4.1 Overall pattern of univariate regressions .
4.2 Ownership concentration . . . . . . . . . .
4.3 Owner type . . . . . . . . . . . . . . . . .
4.4 Insider ownership . . . . . . . . . . . . . .
4.5 Board characteristics, security design, and
4.6 Market competition . . . . . . . . . . . .
4.7 Controls . . . . . . . . . . . . . . . . . . .
4.8 Summary . . . . . . . . . . . . . . . . . .
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5 Ownership concentration
5.1 The Demsetz–Lehn approach . . . . . . .
5.2 Econometric issues . . . . . . . . . . . . .
5.3 Alternative functional specifications . . .
5.3.1 Tobin’s Q as performance measure
5.3.2 Nonlinearity . . . . . . . . . . . . .
5.4 Summary . . . . . . . . . . . . . . . . . .
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6 Insider ownership
6.1 The Morck–Shleifer–Vishny approach .
6.1.1 Extensions . . . . . . . . . . .
6.2 The McConnell–Servaes framework . .
6.3 Alternative insider definitions . . . . .
6.4 The large insider . . . . . . . . . . . .
6.5 Summary . . . . . . . . . . . . . . . .
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7 Owner type
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7.1 Aggregate holdings by owner type . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48
7.2 The type of the largest owner . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49
7.3 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52
8 Board characteristics, security
8.1 Board characteristics . . . . .
8.2 Security design . . . . . . . .
8.3 Financial policy . . . . . . . .
8.4 Summary . . . . . . . . . . .
design, and financial policy
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9 A full multivariate model
9.1 Measuring performance with Tobin’s Q .
9.2 Performance sensitivity . . . . . . . . .
9.3 Alternative performance measures . . .
9.4 Summary . . . . . . . . . . . . . . . . .
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10 Explaining the corporate governance mechanisms
10.1 The mechanisms one by one . . . . . . . . . . . . . . . . . . . . . . . . .
10.2 Simultaneous equations modeling . . . . . . . . . . . . . . . . . . . . . .
10.3 Examples of simultaneous systems . . . . . . . . . . . . . . . . . . . . .
10.4 Ownership concentration and insider holdings as a simultaneous system
10.5 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
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11 Causation between corporate governance and economic
11.1 Governance driving performance . . . . . . . . . . . . . .
11.2 Two–way causation . . . . . . . . . . . . . . . . . . . . . .
11.3 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . .
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performance
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12 Conclusions
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Appendix
89
A Data sources, variable definitions,
A.1 Data sources . . . . . . . . . . .
A.2 List of variables . . . . . . . . . .
A.3 Histograms . . . . . . . . . . . .
A.3.1 Ownership concentration
A.3.2 Owner type . . . . . . . .
A.3.3 Insider ownership . . . . .
A.3.4 Board characteristics . . .
A.3.5 Security design . . . . . .
A.3.6 Financial policy . . . . . .
A.3.7 Controls . . . . . . . . . .
A.3.8 Performance measures . .
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B Supplementary regressions
B.1 Univariate relationships . . . . . . . . . . . . . . . . . . .
B.1.1 Regressions underlying summary table . . . . . . .
B.1.2 Using voting rights instead of cash flow rights . . .
B.1.3 Plots of performance vs explanatory variables . . .
B.2 Ownership concentration . . . . . . . . . . . . . . . . . . .
B.2.1 Year by year, GMM, and fixed effects regressions .
B.2.2 Alternative concentration measures . . . . . . . . .
B.3 Insider ownership . . . . . . . . . . . . . . . . . . . . . . .
B.3.1 Year by year, GMM, and fixed effects regressions .
B.3.2 Inside ownership without controls . . . . . . . . . .
B.3.3 Alternative performance measure: RoA5 . . . . . .
B.3.4 Alternative insider definitions . . . . . . . . . . . .
B.3.5 Insider holdings and outside concentration . . . . .
B.4 Owner type . . . . . . . . . . . . . . . . . . . . . . . . . .
B.4.1 Year by year, GMM, and fixed effects regressions .
B.4.2 Alternative performance measure: RoA5 . . . . . .
B.5 Board characteristics, security design, and financial policy
B.5.1 Year by year, GMM, and fixed effects regressions .
B.5.2 Alternative performance measure: RoA5 . . . . . .
B.6 A full multivariate model . . . . . . . . . . . . . . . . . .
B.6.1 Year by year, GMM, and fixed effects regressions .
B.6.2 Alternative performance measure: RoA5 . . . . . .
B.6.3 Alternative performance measure: RoA . . . . . .
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105
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118
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126
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147
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158
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164
164
167
169
iv
B.7
B.8
B.9
B.10
B.6.4 Alternative performance measure: RoS5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
B.6.5 Alternative performance measure: RoS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
B.6.6 Intercorporate ownership . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
B.6.7 Outside (external) concentration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
B.6.8 Voting rights . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Explaining the corporate governance mechanisms with single equation models . . . . . . . . . . . . . .
B.7.1 Single equation estimates of governance mechanism endogeneity, using aggregate ownership per
type as owner identity proxy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
B.7.2 Single equation estimates of governance mechanism endogeneity, using type of largest owner as
owner type proxy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
B.7.3 Outside (external) concentration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Interactions between ownership concentration and insider holdings in a system of equations . . . . . .
B.8.1 Only controls as additional explanatory variables . . . . . . . . . . . . . . . . . . . . . . . . . .
B.8.2 Outside concentration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Causation between corporate governance and economic performance, governance driving performance
B.9.1 Regressions underlying summary table . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
B.9.2 Controls, instruments and endogenous mechanisms only . . . . . . . . . . . . . . . . . . . . . .
B.9.3 Outside (external) concentration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Two-way causaution between corporate governance and economic performance . . . . . . . . . . . . .
B.10.1 Regressions underlying summary table . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
B.10.2 Controls, instruments and endogenous mechanisms, only . . . . . . . . . . . . . . . . . . . . . .
B.10.3 Outside concentration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
171
173
175
177
179
181
181
187
190
192
192
195
198
198
202
205
209
209
213
216
List of Tables
220
List of Figures
225
Bibliography
227
Introduction
1
Chapter 1
Introduction
Corporate governance is currently a hot public issue around the world. Triggered by the Cadbury
report in 1992, the OECD has recently published overall corporate governance guidelines for its
member states (OECD, 1999). Twelve EU countries have established national codes,1 and corporate
governance systems are being established for recently privatized firms in the ex–communist block
and in emerging economies in general (Shleifer and Vishny, 1997; Economist, 2001). This focus on
corporate governance is amplified by the growing fraction of the market portfolio held by mutual
funds and private pension funds in many countries, which raises the question of whether institutional
investors should maintain their classic role as passive owners who vote with their feet, or instead
use their power to more actively monitor and discipline the managers of firms in which they have
invested.
The intense public attention which is currently paid to corporate governance issues in Norway
reflects similar concerns. The privatization programs for the government’s telecom (Telenor) and
petroleum (Statoil and SDØE) firms are not motivated by the owner’s need for a more liquid
or a more diversified portfolio, but rather by the desire to substitute state owners by private
ones. The association of institutional investors (Eierforum) recently established ten principles for
good governance, and two pending court cases on minority freeze-outs (Norway Seafoods and Aker
RGI) reflect a growing concern for the legal protection of minority stockholder rights. Similarly,
large personal investors have challenged existing management by demanding seats on the board
(C. Sveaas in Orkla) and by openly stating discontent with the current strategy and proposing
plans for fundamental restructuring (K. I. Røkke in Kværner). Finally, there are heated debates on
whether national ownership is worth protecting. This concern was triggered by the government’s
decision to hold a 1/3 blocking minority in the largest commercial bank (DnB) and by the recent
tender offer from an international investor (the Finnish Sampo) for Norway’s largest insurance
company (Storebrand).
The basic premise underlying all these cases is that governance matters. Economic performance
is thought to depend on corporate governance mechanisms, such as the overall legal protection of
stockholder rights, the firm’s competitive environment, the existence of large owners in the firm’s
ownership structure, the identity of such large owners, equity holdings by management, the design
of the corporate charter, the decisions made at the stockholder meeting, the composition of the
board, the firm’s financial policy, and on the design of managements’ employment contracts.
Empirical research aimed at understanding the governance–performance interaction has so far
been rather limited. This is partly because corporate governance is a novel academic field with an
undeveloped theory foundation, and partly because high-quality data on these phenomena is quite
difficult to find. Existing research deals almost exclusively with a small subset of the governance
mechanisms in very large US firms at a single point in time, and their findings are quite mixed.
Not surprisingly, therefore, we cannot yet convincingly specify what a value-maximizing corporate
governance system looks like.
Our study offers four new insights into the relationship between corporate governance and economic performance. First, by using data from Norwegian listed firms, we may clarify the context–
dependence of existing evidence from other countries. For instance, the typical US study deals
1
The codes can be downloaded at www.ecgn.org/ecgn/codes
2
Introduction
with very large firms in a so-called common law regime and an active market for corporate control,
including hostile takeovers. Ownership concentration is extremely low by international standards,2
high–powered incentive contracts for management is the rule, and inside directors are quite common on the board. In contrast, our sample firms are on average much smaller, the legal regime
belongs to the Scandinavian version of the civil law tradition, hostile takeovers are very rare, the
firms are more closely held (i.e., ownership concentration is higher), performance–related pay is
less common, and corporate boards have mostly none and never more than one inside director. According to the principal–agent theory, all these governance mechanisms may matter for economic
performance (Agrawal and Knoeber, 1996; Shleifer and Vishny, 1997; Tirole, 2001). By testing
the theory’s predictions on firms operating under a quite different corporate governance regime,
we can better judge the general validity of the agency approach to corporate governance research.
Also, since important political decisions in Norway are currently made based on quite general or
just implicit arguments about the functioning of corporate governance in general and ownership
structure in particular, there is a national interest in knowing more precisely what these empirical
regularities really look like. Our study is an attempt at providing well–founded insights into this
issue, using a large sample, a wide set of governance mechanisms and performance measures, and
different econometric techniques.3
Second, because we have better ownership data than any existing study, we have the potential
of producing more reliable evidence. For instance, the analyses of ownership structure in the
US, Japan, the UK, and continental Europe are based on large holdings, only, as there is no
legal obligation to report other stakes (Barca and Becht, 2001). This means any holding below a
minimum reporting threshold of 2–5% (depending on the country) cannot be observed, typically
implying that the owners of roughly one third to one half of outstanding equity must be ignored.
Moreover, as a large holding is only registered when it passes certain thresholds (like 10%, 20%
and 50% of outstanding equity), any stake in–between these discrete points is estimated with error,
and all stakes above the highest reporting threshold are underestimated. Also, except for the UK
and the US, the available international evidence refers to just one or two years in the mid 1990s.
In contrast, our data, which includes every owner of all firms listed on the Oslo Stock Exchange
over the period 1989–1997, involves a relatively long time series which suffers neither from the large
holdings bias nor the discrete thresholds problem.
Third, unlike most existing research, which have mostly focused on one or two ownership structure variables (concentration and insider holdings) we also include many other corporate governance
mechanisms, such as the identity of any owner in the firm (e.g, institutional, international, and
personal owner), the owner’s holding of both voting and non–voting shares, board characteristics
(e.g., number of directors), and financial policy (e.g, debt to equity ratio). This framework enables
us to use a comprehensive, multivariate approach and to contrast our findings with what we get
using more partial multivariate or the simplest univariate methods, which are much more common
in the literature.
Our fourth contribution is improved insight into endogeneity and causality, which are important
in practice and both difficult and underexplored in corporate governance research. Since some of
2
The typical holding of the largest owner in a listed firm is 3% in the US (Barca and Becht, 2001), 45% in
continental Europe (Barca and Becht, 2001), and 30% in Norway (Bøhren and Ødegaard, 2001).
3
The recent Norwegian studies by Mishra and Randøy (2000) and Roland et al. (2001) represent a more narrow
approach, using data for one or a few years, a smaller subset of governance mechanisms, and a simpler methodology.
For instance, Roland et al. (2001) relate average return on book equity to the identity of the largest owner for the 8.500
largest (by number of employees) Norwegian firms which are majority–owned in 1996–1999. No other governance
mechanism is considered, and no statistical tests are reported. Their findings suggest that the lowest and highest
performance is in firms with majority state owners and majority personal owners, respectively.
Introduction
3
the governance mechanisms may be internally related, we use an empirical methodology which
captures potential endogeneity of the mechanisms, e.g., the possibility that large external owners
and high insider stakes are substitute or complementary rather than independent ways of influencing
economic performance. Because causality may not only run from governance to performance but
also the opposite way (like when insiders ask for stock bonus plans based on their private information
about the firm’s future performance), we also use an approach which can handle reverse causation.
Such attempts at capturing endogeneity and two–way causality have only recently been made in
the academic literature, using simultaneous system estimation techniques (Agrawal and Knoeber,
1996; Cho, 1998; Demsetz and Villalonga, 2001). Since the findings in these papers differ quite
remarkably from those using standard uni– or multivariate regressions, we explore whether this
difference is due to the underlying nature of the corporate governance problem or whether it is
driven by the difficulty of using simultaneous systems methodology in a setting where the theory
we try to test is loose and under–developed. We think our rich data set is particularly suitable for
exploring this question.
Unlike most existing research, we find a negative and very significant relationship between
ownership concentration and economic performance. Insider ownership is value–creating up to
a stake of roughly 60%, which is far above the typical fraction in almost all our sample firms.
Individual (personal) owners outperform multiple–agent relationships through corporate or state
intermediaries, and small boards create more value than large. Firms which issue shares with
unequal voting rights tend to lose market value, but there is no sign that debt financing and dividend
payments are value–creating disciplining mechanisms. All these findings survive across a wide range
of single–equation regression models, but most of them are reversed or become insignificant if we
instead use a simultaneous equation approach. Our analysis suggests that this may happen because
their is no reliable theory for generating the instruments. Until the theory of corporate governance
can handle not just each mechanism separately but also their endogeneous nature, we doubt whether
the systems approach can offer deeper insight into the governance–performance relationship.
The exposition progresses roughly in the order of the intellectual development of the field, starting out by briefly presenting the theory and summarizing existing empirical findings in chapter 2.
We present the descriptive statistics of our governance and performance data in chapter 3, followed
by a univariate analysis in chapter 4 which relates performance to one governance mechanism at
a time. A multivariate approach is used in chapters 5 to 9, focusing particularly on how performance relates to ownership concentration in chapter 5, insider holdings in chapter 6, owner type
(identity) in chapter 7, and to security design, board characteristics, and financial policy in chapter 8. On this background, a full multivariate model of the governance–performance interaction
is constructed and tested in chapter 9. The endogeneous nature of the governance mechanisms
is analyzed in chapter 10, and chapter 11 explores the direction of causation between governance
and performance. Chapter 12 summarizes our findings. Appendix A specifies the data sources,
defines the variables used, and shows frequency plots for the performance measures, governance
mechanisms, and control variables. Supplementary regressions are reported in appendix B.
4
Theoretical framework and existing evidence
Chapter 2
Theoretical framework and existing evidence
2.1
Theory
One of the earliest academic papers on corporate governance is the Berle and Means (1932) analysis of the separation between ownership and control in large corporations. Similar ideas were
later spelled out more formally by Jensen and Meckling (1976), which is currently the most cited
paper in the social sciences. Both articles address the principal–agent problem, which occurs when
managers with private information have incentives to pursue their own interests at the owners’
expense.1 According to Tirole (2001), the principal–agent problem manifests itself when managers
exert insufficient effort (by over-committing to external activities, accepting over-staffing, and by
ignoring internal control), collect excessive private benefits (by building unprofitable empires, paying inflated transfer prices to affiliated entities, and by over–consuming perks), and when managers
entrench themselves (by investing in declining industries because that’s where their competence is,
diversifying across products markets to reduce unsystematic risk, and by resisting value-creating
takeovers which threaten their position).
These examples illustrate that the separation between ownership and control may produce moral
hazard and adverse selection problems. The resulting value loss is an agency cost, and corporate
governance can be thought of as a set of mechanisms which reduce such costs, i.e., a system for
minimizing the value destruction caused by the agency problem. The challenge is to ensure that
the firm is run by a competent management team which makes the same decisions that owners
would have made themselves. This view is reflected in Shleifer and Vishny’s definition of corporate
governance as
...the ways in which the suppliers of finance to corporations assure themselves of getting
a return on their investment. How do the suppliers of finance get managers to return
some of the profits to them? How do they make sure that managers do not steal the
capital they supply or invest it in bad projects? How do suppliers of finance control
managers?
Shleifer and Vishny (1997).
In a recent presidential address to the Econometric Society, Tirole (2001) argues that the traditional
shareholder approach to corporate governance reflected in the above definition is too narrow for an
economic analysis of whether firms should have social responsibility beyond maximizing the market
value of stockholders’ claims. In such a perspective, Tirole argues that the designer of a corporate
governance system must consider how all stakeholders (such as financiers, employees, suppliers, and
customers) are affected by the firm’s decisions rather than just the financiers (owners and creditors).
He extends the focus from shareholders to stakeholders by defining corporate governance as “the
design of institutions that induce or force management to internalize the welfare of stakeholders.”
Compared to the stockholder–based definition by Shleifer and Vishny (1997), it seems that a corporate governance system aimed at maximizing shareholder wealth may not promote a stakeholder
society. However, Tirole argues that an operational measure of aggregate stakeholder welfare is
unattainable in practice, and that monitoring becomes much harder under multiple missions. He
1
These authors were not the first to address the corporate governance problem. Adam Smith (1776) and Thorstein
Veblen (1924) both argued that when ownership concentration declines and non-owning managers increase their power,
the firm is less likely to make value–maximizing decisions.
2.1 Theory
5
concludes that because managers can rationalize almost any action by invoking its welfare impact
on one particular stakeholder, the stakeholder approach to corporate governance is questionable.
In the following, we focus on the principal–agent problem between managers and owners and
between subgroups of owners, ignoring potential owner–creditor conflict. Although the resulting
menu of corporate governance mechanisms is rather extensive, the common denominator is always
incentives, power, competence, and monitoring. The next section briefly discusses the mechanisms
one by one.
2.1.1
Corporate governance mechanisms
The corporate governance mechanisms discussed below are market competition, concentrated ownership, owner types, insider ownership, board characteristics, security design, and financial policy.
We also consider exogeneous variables that either shape the framework in which the governance
mechanisms operate, or influence performance directly. These are called control variables. Finally,
we consider the equilibrium condition, which predicts how any individual mechanism relates to
performance when all the mechanisms are used in an optimal way.
Market competition. The failure to maximize share value puts the firm at a competitive disadvantage. In an agency context, this means that the stronger the competition in the firm’s output
market, the less room managers have for wasting corporate resources. Since managers with firm–
specific human capital suffer a welfare loss in financial distress, product market competition may
act as a disciplining device which reduces agency costs. The market for managerial talent plays a
corresponding role, as the manager’s reputation for value-maximizing abilities may influences the
access to attractive jobs in the future. Finally, competition in the market for corporate control
may function as a governance mechanism, primarily by the threat of management displacement in
hostile takeovers (Fama, 1980; Fama and Jensen, 1985; Stulz, 1988).
These arguments suggest that when products, labour, and takeover markets are fully competitive, a self–serving manager will find it optimal to maximize stockholders’ equity. Competition
would be the only governance mechanism needed, and it works without owner interference. However, since real–world markets are not fully competitive, this single disciplining device cannot be
expected to do the full job. The corporate governance mechanisms discussed in the following can
be thought of as additional disciplining devices which become relevant once we leave a world where
agency problems is the only market imperfection.2
Ownership concentration. When ownership is separated from control, agency theory argues that
if the monitoring of management is weak, corporate value can be destroyed (Jensen and Meckling,
1976; Demsetz and Lehn, 1985). In order for an owner to have economic incentives to carry
monitoring costs, and also the power to monitor effectively, he must hold a sufficiently large equity
stake in the firm. If monitoring by owners improve the quality of managerial decisions, and if there
are no other effects of ownership concentration, performance and concentration will be positively
correlated (Shleifer and Vishny, 1986).
Ownership concentration may have several effects beyond the incentive and the power to monitor. On the benefit side, large shareholders may reduce the free–riding problem in takeovers
(Shleifer and Vishny, 1986) and increase the takeover premium by competing with other large
bidders (Burkart, 1995). There are also several costs of holding a large stake. First, owners who
2
Without the discipline from competitive markets, the agency problem may still be optimally solved by means of
complete contracts, i.e., a full specification of managers’ and owners’ duties and rights in every possible contingency.
As such contracts cannot be written in practice without excessive costs (Hart, 1995; Vives, 2000), our theoretical
framework assumes both imperfect markets and incomplete contracts.
6
Theoretical framework and existing evidence
cannot be large unless they invest most of their wealth in one firm end up with undiversified portfolios (Demsetz and Lehn, 1985). Second, if the stock’s market liquidity decreases with increasing
concentration, the information production ability of the stock price may suffer (Holmstrom and
Tirole, 1993).3 Third, minority shareholders suffer if large owners use the firm’s resources to benefit themselves at the minority’s expense (Shleifer and Vishny, 1997; Johnson et al., 2000). Fourth,
monitoring may reduce market value for at least two reasons. According to the theoretical model
of Burkhart et al. (1997), managers who expect owners to interfere will search less actively for
projects which bring private benefits to the owners (like a more stable firm cash flow), but which
also increase market value. Consequently, the owners face a trade–off between the gains from monitoring (less separation between ownership and control) and the opportunity loss caused by reduced
managerial initiative. The higher the ownership concentration, the higher the expected opportunity
cost. Finally, the implicit assumption behind any monitoring argument is that owners are competent. That is, they know better than managers how to run the firm in a value–maximizing way,
and the nature of monitoring is to guide and correct managerial decision–making towards the goal
of equity value maximization. If this competence argument does not hold, ownership concentration
and economic performance may be inversely related.4
The relative impact of these benefits and costs at different concentration levels cannot be specified ex ante. Therefore, agency theory cannot predict the relationship between concentrated ownership and firm performance. Admittedly, if the monitoring effect sets in at low concentration levels,
if this monitoring is beneficial rather than value–destroying, and if the non–private costs to owners
do not arise until concentration is relatively high, the relationship between performance and concentration would be positive at moderate concentration levels and negative thereafter. However,
only empirical evidence can give the definite answer.
Owner type. Two owners with identical equity fractions may differ both in their incentive and
ability to create corporate value. A personal investor who votes at the stockholder meeting represents a personal claim to the firm’s cash flow. Thus, the principal directly monitors the agent.
In contrast, representatives for the state or widely held corporations have minuscule personal cash
flow rights attached to the stakes they are voting for. In such settings, one agent monitors another agent, without direct interference from the ultimate principal. Generally, indirect monitoring
through layers of agents occurs when owners are non–personal, i.e., state or corporate investors.
Based on the resulting incentive differences, agency theory predicts that personal owners are better
monitors than non–personal owners.
Institutional (financial) investors is a special case of non–personal owners. Since institutions
are holding a growing proportion of the equity market portfolio in most western countries, it is becoming increasingly important to understand the governance activities of this investor type. Pound
(1988) argues that institutional owners may influence performance in three ways. The efficientmonitoring hypothesis, which rests on the presumption that institutions are more competent than
other investors, predicts that institutions can monitor with higher quality at lower costs. The
conflict–of–interest hypothesis posits that when institutions have business relationships with firms
they invest in (like an insurance company which both invests in and sells insurance to the same
firm), institutions may feel forced to protect the investee firm’s management. Finally, Pound’s
strategic-alignment hypothesis corresponds to our earlier argument that because the managers of
3
Brennan and Subrahmanyam (1996) and Chordia et al. (2001) represent a growing literature on the relationship
between a stock’s returns and its market liquidity.
4
We have only limited anecdotal evidence to substantiate this possibility. H. Brewster Atwater, who is the CEO
of General Mills and also the head of the Business Roundtable’s corporate governance task force, recently expressed
concerns that institutional owners would take management hostage and force them to sacrifice long–term growth
prospects in favor of fulfilling short–term goals which are not necessarily value–maximizing.
2.1 Theory
7
both investor and investee firms are agents acting on behalf of other principals, they both have
insufficient value-maximization incentives. Thus, institutions will monitor with lower quality than
would personal.
The efficient-monitoring hypothesis posits a positive performance effect of institutional ownership, whereas both the conflict-of-interest and the strategic-alignment arguments make the opposite prediction. Even if we add that more competitive products markets for institutional investors
reduces the relevance of the strategic-alignment hypothesis, we still cannot theoretically predict
whether institutional ownership has a positive, negative or no effect on economic performance.
Like we concluded for ownership concentration, the question of whether institutional ownership
matters for corporate performance can only be answered empirically.
Besides personal and institutional owners, state and international owners deserve special attention. As already mentioned, state owners are similar to most large corporate owners in the
sense that both are represented at stockholder or board meetings by agents with negligible cash
flow rights relative to the voting rights they exercise. This negative effect of misaligned incentives
is reinforced by the competence problem that state bureaucrats may lack experience with private
business in general and corporate governance processes in particular. A state owner may also be
inclined to ask the private firm to abstain from equity value maximization in order to achieve certain social goals, such as higher local employment, reduced pollution, and a more level distribution
of income between top management and other employees. Relative to private owners, one may
therefore expect that high state ownership has a negative effect on firm performance.
International (foreign) investors may be less inclined than national owners to be active in
corporate governance. They are normally at an informational disadvantage by knowing less about
the foreign country’s legal and institutional framework, the local competitive environment of the
firm’s industry, about the other large owners of the firm, and details of the firm’s strategy. This may
be one reason why we observe the universal home-bias phenomenon, by which investors allocate a
much higher proportion of their wealth to national equity securities than what a reasonable tradeoff
between risk and return would prescribe.5 Thus, international investors may not invest in foreign
firms because they want to be active monitors, but simply to capture diversification benefits. They
would rather vote with their feet (i.e., trade in the stock) than take corrective action by using their
voting power. Like for state vs. private and non–personal vs. personal investors, we would expect
that because increased holdings by international investors reduces monitoring, firm performance
will be negatively affected.
Insider ownership. As insiders are owners of a particular kind, they might be considered just
another case of owner types discussed above. However, inside owners influence the agency problem
in fundamentally different ways than outsiders, who are not involved in the management of the firm.
According to the agency logic, the key governance function of an outside owner is to monitor the
management team, and the incentive and power to do so increases with the outsider’s investment.
In contrast, increased insider stakes reduces the need for outside monitoring. This follows from the
nature of the principal–agent problem, which suggests that the interests of owners and managers are
aligned when managers and board members (hereafter insiders) become owners as well. Based on
this convergence-of-interest hypothesis, Jensen and Meckling (1976) predict a positive relationship
between insider holdings and firm performance.
Insider ownership may also destroy market value. Morck et al. (1988) argue that powerful
insiders may expropriate wealth from the outsiders in similar ways that majority shareholders
exploit the minority. This is the entrenchment hypothesis, which argues that owner–managers may
5
Kang and Stulz (1994), Brennan and Cao (1997) and de Santis and Gerard (1997) provide empirical evidence on
the home bias puzzle.
8
Theoretical framework and existing evidence
make value–reducing decisions in order to safeguard their position in the firm. Such entrenchment
occurs when management invests in their area of competence even though the industry is declining,
when they build conglomerates to reduce unsystematic risk, and when management resists valuecreating takeovers which threaten their position (Tirole, 2001).
Because insider voting power is not the only source of insider power, the entrenchment hypothesis has less clear-cut predictions than the convergence-of-interest hypothesis. For instance,
some managers may be entrenched at low stakes because they have a long tenure, are among the
founders, or simply because of a strong personality. Others may be unable to obtain similar control
unless they have considerably higher ownership stakes. Morck et al. (1988) think that although
more insider ownership allows deeper entrenchment in general, one cannot predict the level at
which diminishing returns sets in. Also, as insiders carry a larger fraction of the destructed market
value the higher their stake, the negative performance effect of entrenchment may disappear or
turn positive as the insider stake becomes sufficiently large.
The convergence-of-interest and the entrenchment hypotheses jointly imply that performance
first increases with insider holdings (convergence–of–interest dominates), then decreases (entrenchment dominates), and then becomes neutral or positive (convergence-of-interest dominates). Because the shape of the entrenchment function is unclear, the classic agency model cannot offer a
sharp prediction of the insider–performance relationship. A more accurate hypothesis is provided
by the takeover model developed by Stulz (1988), where a hostile bidder must pay a higher takeover
premium for the target firm the larger the fraction held by its entrenched management. This positive effect of increased insider holdings on firm value via the higher takeover premium is reduced by
a decreased takeover probability, which drops to zero once the insider fraction reaches 50%. These
two counteracting forces give rise to a curvilinear relationship, where firm value first increases and
then decreases with insider ownership, and where the minimum occurs at a 50% insider holding.
The Stulz model predicts that the relationship is curvilinear and that the value-minimizing insider
fraction is 50%, but it cannot specify the optimal insider holding.
Board characteristics. The stockholder meeting elects the board, which is the owners’ key formal vehicle for observing and influencing the quality of the management team. The two board
characteristics studied the most by finance researchers are independence and size. The argument
that economic performance increases with board independence rests on the agency idea that the
board’s primary function is to monitor management. Unless the board is independent of management, monitoring will be weak. The opposite hypothesis assumes that the board supplements the
management team by being a resource on strategic issues in particular. This extended management capacity is more valuable the more board members know about the firm and its environment,
suggesting that manager-dependent boards will outperform independent ones (Bhagat and Black,
1998a).
According to Jensen (1993), increased board size may destroy value because of the board’s
reduced ability to communicate, coordinate, and hence monitor. Jensen argues that for this reason, self–serving managers want to increase board size beyond its value-maximizing level. The
agency model predicts that because agents generate boards which are ineffective, board size and
performance are inversely related.
Security design. Equity securities come in different formats, such as equity with full ownership
rights (A shares), restricted voting rights (B shares), preferred stock, warrants, and stock options.
Non-voting (B) shares are particularly interesting, since this deviation from the one-share-onevote principle allows investors to separate voting rights from cash flow rights by holding unequal
proportions of A and B shares. As this means investors may vote for more or less than their cash
flow rights would dictate, firms issuing dual–class shares may create a conflict of interest between
2.1 Theory
9
groups of owners which resembles the one between majority and minority stockholders with full
voting rights. Consequently, the existence of equity securities with unequal ownership rights may
influence the firm’s value. In particular, the most common theories of pricing differences between
A and B shares assume a potential extraction of private rents for fully voting owners. If this is the
case, we would expect that according to performance measures which do not capture the private
benefits of control (like the market value of equity), firms would be less valuable the higher the
fraction of shares outstanding which is non–voting (Grossman and Hart, 1988; Harris and Raviv,
1988).
Financial policy. In addition to ownership structure, board composition, and security design, a
firm’s financial policy (choice of capital structure and dividend payout) can also influence its agency
costs. The idea is that management discretion is restricted if the firm finances with debt rather
than equity and pays earnings out as dividends rather than retains it (Jensen, 1986). Unlike equity
financing, debt ensures that most of the firm’s cash flow must be used to honor contracts with
creditors who can enforce bankruptcy if their claim is not met. Similarly, high dividend payout
ensures that most of the cash flow is handed over to owners, leaving correspondingly less resources
for management discretion to finance value-reducing investments. A higher dividend payout will
also force the firm more frequently to the market for new equity, where management must inform
the general public about future plans in order to attract funds for new investments (Easterbrook,
1984). Because this reduces liquidity and exposes the firm to more intense monitoring by existing
and prospective financiers, agency theory predicts that debt financing and dividend payments are
value-creating governance mechanisms.
Controls. Corporate governance mechanisms are not the only source of value creation in a firm.
A theoretical prediction of the governance–performance interaction should therefore include exogeneous variables in the environment that either influence the optimal governance mix or directly
affect performance without influencing governance. The problem is, however, that with imperfections like conflicts of interest between managers and owners, no existing valuation model can
specify the pricing–relevant characteristics. The standard solution to this problem is an ad–hoc
approach which uses not just the systematic risk factor from CAPM-type equilibrium models to
explain cross-sectional differences in returns. Factors which have been shown to have independent
explanatory power in empirical asset pricing research are also included, such as firm size, the book
to market ratio, the price to earnings ratio, price momentum, and seasonality.6
Controls can also be used to get rid of spurious correlation effects. This problem occurs when
what seems like a relationship between governance and performance is driven by a third, omitted
variable. For instance, suppose performance decreases with firm size due to diseconomies of scale
and that insider ownership decreases with firm size due to the cost of being undiversified. If
performance is regressed on insider holdings alone, an apparent negative relationship between the
two is caused by an underlying change in firm size. However, if the relationship persists after
the effect of size on performance has been properly controlled for, insider ownership does have a
separate (i.e., size–independent) effect on performance.
The equilibrium condition. Corporate governance mechanisms may be thought of as factors
of production. If applied in a value-maximizing way, they should all satisfy the zero marginal
value condition: Any mechanism should be used up to the point where a small change leaves firm
value practically unaltered. According to Demsetz (1983), this means that if firms are owned by
value-maximizing investors who understand how to select governance mechanisms which minimize
agency costs, corporate governance and firm performance will be unrelated.
This is far from saying that governance is irrelevant for performance. The equilibrium argument
6
Hawawini and Keim (2000) provides a survey of the empirical findings.
10
Theoretical framework and existing evidence
simply states what when the firm has chosen the optimal combination of mechanisms, a marginal
change of this set will have an insignificant value impact. Since two firms may have widely different
sets of optimal mechanisms (depending on their individual firm characteristics and the resulting
potential for generating agency costs), this implies that if we run a cross-sectional regression of performance on governance variables, the equilibrium hypothesis states that no governance variable will
be significantly related to performance. Conversely, a significant variable reflects a disequilibrium
case because the mechanism is not used at its value–maximizing level.
2.1.2
Interactions and causality
The theoretical literature on corporate governance and economic performance reviewed in section 2.1.1 has at least one of the following characteristics:
1. Univariate rather than multivariate relationships
2. Exogeneous rather than endogeneous mechanisms
3. One-way rather than two-way causation
The first characteristic is simply that predictions are stated for one mechanism, only, without
concern for the impact of the others. For instance, we first hypothesized the performance effect
of concentration, disregarding the impact of insider holdings. Next, we considered the impact of
insider holdings while disregarding ownership concentration. This way of stating the hypotheses
in univariate form reflects the nature of theory development in the area. For instance, Demsetz
and Lehn (1985) model the performance effect of ownership concentration, whereas Morck et al.
(1988) and Stulz (1988) focus on insider ownership.7 The problem is that because more than
one governance mechanism may influence performance, one may not capture the impact of one
mechanism unless one controls for the simultaneous impact of the others.
Even though we recognize this problem and move from a univariate to a multivariate approach,
the next question is how to model potential interactions between the mechanisms. This is the issue
of exogeneous vs. endogeneous mechanisms, which occurs because several of the mechanisms may
be internally dependent. They may be substitute or complementary ways of reducing agency costs,
such that the impact of one mechanism depends on the chosen level of the other. For instance, if a
certain combination of outside directors (monitoring) and insider holdings (incentives) has the best
aggregate impact on value creation, replacing inside directors by outsiders may reduce performance
when insider ownership is high (too much monitoring capacity on the board because managers are
also owners). Conversely, a more independent board may be value–increasing if the insider stakes
are low (too many uncritical directors facing non–owning managers).
These questions are seldom addressed in the literature. Although McConnell and Servaes (1990)
use a multivariate approach by considering both ownership concentration, insider holdings, and institutional ownership, they present no theory and do not use an empirical approach which may
capture the way the three mechanisms interact. This is a multivariate approach with exogeneous
mechanisms. The only paper we know which establishes a system of endogeneous, multiple governance mechanisms is Agrawal and Knoeber (1996), who argue theoretically (although rather
incompletely) why the mechanisms are modeled as functions of each other (like insider ownership
7
Not surprisingly, the mathematical models in the field are typically even more restrictive. For instance, Burkhart
et al. (1997) focus on ownership concentration alone, deriving the conditions for optimal concentration when there is
just one benefit (monitoring) and one cost (reduced management initiative).
2.2 Empirical evidence
11
partially driven by ownership concentration and vice versa) and of exogeneous variables (like insider
holdings partially determined by management tenure).
The third question triggered by existing research concerns the order of causation between governance and performance, where standard theory argues that the former causes the latter. However,
one can easily imagine the effect going in the opposite direction. Insiders may ask for stock bonus
plans when they expect performance improvements. The government may take over distressed
private firms in order to reduce negative externalities like unemployment and bank runs. In either
case, performance drives governance. The general point is that causation may run either way, and
that theory should recognize this possibility and model the corporate governance problem accordingly. Although this problem has been raised earlier (see e.g. McConnell and Servaes (1990)), it has
only very recently been analyzed (Agrawal and Knoeber, 1996; Cho, 1998; Demsetz and Villalonga,
2001). As far as we know, Cho (1998) is the only paper that addresses this point both at the
theoretical and the empirical level.
2.2
Empirical evidence
The empirical analyses of corporate governance and economic performance can be classified according to their choice of methodology and object of study:
1. International comparisons of different institutional environments
2. Event studies of a modified mechanism
3. Cross–sectional analyses of mechanisms in place
The first approach reflects a recent, popular trend of comparing corporate governance systems
across many nations (La Porta et al., 1998; Barca and Becht, 2001). This research finds that a
country’s legal and regulatory regime influences key characteristics of its security market, ownership
structures, and valuation processes. For instance, it seems that the weaker the protection of ownership rights in the corporate law, the less developed the equity market and the more concentrated
the ownership structure (La Porta et al., 1997, 1998, 1999, 2000).
The second and third approach use data from a single country. This means the institutional
environment for each firm is identical, and the empirical question is how governance relates to
performance under the given institutional framework. The second approach, which uses the event
study methodology, studies what happens to the firm’s stock price when a governance mechanism
is altered. If the modified mechanism triggers a significant stock price reaction, the mechanism
is considered relevant for economic performance. Examples include the adoption of anti takeover
charter amendments (Linn and McConnell, 1983; Jarrell and Poulsen, 1987), poison pills (Malatesta and Walkling, 1988), green-mail prohibitions (Eckbo, 1990), executive stock and option plans
(Bhagat et al., 1985; Brickley et al., 1985; DeFusco and Johnson, 1990), and golden parachutes
(Lambert and Larcker, 1985). This research tends to find a positive valuation effect when firms
get more exposed to the market for corporate control, when management’s incentive contracts are
strenghtened, and when the managerial outside options are improved. Karpoff et al. (2000) surveys this literature and concludes that the introduction of restrictive governance mechanisms is
considered bad news by investors.
An apparent advantage of the event study approach is that the researcher can directly observe
what happens to the market value of equity when a single mechanism is modified. This similarity
to a natural experiment also allows for the study of causality, as both the mechanism change and
the price reaction are dated events. The problem is that when other governance mechanisms are
12
Theoretical framework and existing evidence
not controlled for, one ignores the possibility that the performance impact of the mechanism in
question depends on the level of the others. To understand the value of the modified mechanism,
one would have to allow for endogeneity, as discussed in section 2.1.2. Moreover, large unexpected
changes in key governance mechanisms like ownership structure are rare events. When they do
occur, they often involve more than just changes in ownership, such as the transfer of large equity
blocks in a corporate control contest (Morck et al., 1988).
The third approach, which we use in this study, compares the performance of firms with different
governance structures in place. The analytical tool is some form of regression analysis, and the
sample is a cross-section of firms which are thought to represent a sufficiently rich variation in their
choice of mechanisms. The empirical findings of this research tradition can be classified according
to the governance mechanisms from section 2.1.1:
• Ownership concentration
• Insider ownership
• Owner types
• Security design
• Financial policy
• Market competition
• Board characteristics
The vast majority of empirical papers on corporate governance and economic performance use
one or more ownership characteristics as the object of study, i.e., ownership concentration, insider
holdings, and owner types. Ownership concentration has been analyzed the most. For instance,
among 33 empirical ownership–performance papers published over the 1932–1998 period surveyed
by Gugler (2001), 27 deal with ownership concentration and only 6 with insider holdings. The
aggregate evidence on concentration and performance suggests that in most cases, there is either
a positive effect or no effect. The estimated relationship is positive in 12 cases, neutral in 13, and
negative in the two remaining ones. In a recent paper, Lehmann and Weigand (2000) find that in
Germany, concentration and performance are inversely related.
Four of the six insider papers (Morck et al., 1988; McConnell and Servaes, 1990; Belkaoui and
Pavlik, 1992; Holderness et al., 1999) uncover a curvilinear relationship between insider holdings and
firm performance (first increasing at low insider stakes, then decreasing, then either still decreasing,
slightly increasing or neutral). The two other papers (Agrawal and Knoeber, 1996; Cho, 1998)
cannot detect any significant link.
No comprehensive study of owner identity has been made, and the evidence is rather mixed.
For instance, some find a significantly positive performance effect of family control (Jacquemin
and de Ghellinck, 1980; Mishra et al., 2000), of founder–insiders in young (but not in old) firms
(Morck et al., 1988), of private (as opposed to state) ownership (Boardman and Vining, 1989) and
of institutional (vs. all other) investors (McConnell and Servaes, 1990). Several authors cannot
detect a significant relationship, like Kole and Mulherin (1997) on state owners and Smith (1996)
on shareholder activism by institutional investors. According to Gugler (2001), the relationship
between owner identity and economic performance is a remarkably unexplored field of research.
This is particularly worrisome regarding institutional investors, since their aggregate share of the
equity market is everywhere large and increasing.
2.2 Empirical evidence
13
Security design, financial policy, and market competition are the mechanisms which have been
studied the least in the literature. The only analysis we know on the disciplining role of competitive
products markets is an early paper by Palmer (1973).8 Another reason this paper is special is that it
explicitly considers the interaction between alternative governance mechanisms. Palmer finds that
when firms operate in competitive products markets, ownership concentration and performance
are not significantly related. However, when barriers to entry are strong, owner-controlled firms
(firms with high ownership concentration) perform better than management-controlled firms. This
finding is consistent with the notion that market competition is a disciplining device, and that
owner monitoring and product market competition are substitute mechanisms.
To our knowledge, no paper has yet analyzed the empirical relationship between security design
and economic performance in a corporate governance setting. Moreover, except for Agrawal and
Knoeber (1996), who model the debt to equity ratio as one of seven governance mechanisms, existing
research only includes financial policy as a control variable.9 In these cases, financial policy is only
used to control for performance impacts which are unrelated to governance, such as the interest
tax shield caused by debt financing.
Although the empirical research on board characteristics and economic performance have produced mixed results (Bhagat and Black, 1998b), two findings are rather robust: Performance
decreases with increasing board size (e.g., Agrawal and Knoeber (1996)) and with an increasing
fraction of outside (management–independent) board members (e.g., Bhagat and Black (1998a)).
The evidence indicates that firms have too large boards to function optimally, and that the benefit
of having independent directors who monitor managers is more than offset by the cost of having
too few board members who really know the firm.
The empirical research summarized above can be characterized according to the data sets and
the methodologies used. In terms of data, the following pattern emerges:
• Mostly US firms. For instance, among the 28 studies of concentration and performance
discussed above, 18 use data from the US, 5 are based on British data, 2 are from Germany,
and the remaining 3 are using data from respectively Australia, France, and Japan. The 6
insider papers are all based on US data.
• Very large firms. For instance, Morck et al. (1988), Agrawal and Knoeber (1996) and Cho
(1998), who are among the most sophisticated and influential papers, are all sampling from
the Fortune 500 list, studying 371 such firms from 1980, 383 firms from 1987 and 326 firms
from 1991, respectively. One problem with these samples is that because the relationship
between a governance mechanism and performance may depend on firm size, a good sample
should be heterogeneous in terms of size. McConnell and Servaes (1990) are less vulnerable
to this criticism, as they sample randomly from the NYSE and Amex lists, using 1.173 firms
from 1976 and 1.093 firms from 1986.
• Blockholders only. Ownership concentration per firm is based on the aggregate fraction across
reported blocks, i.e., holdings above a certain limit (normally 5%). This is an arbitrary cutoff
point which is not dictated by theory, but by limited data availability.
• Biased insider holdings. The papers on insider ownership mostly use the aggregate stake
of the board members as their proxy. Since this measure ignores ownership by non–board
insiders (like officers who are not directors), it underestimates insider holdings. If the ratio
8
The managerial labour market and the market for corporate control, which were analyzed by Agrawal and
Knoeber (1996), will be discussed later.
9
Examples are Demsetz and Lehn (1985), Morck et al. (1988), McConnell and Servaes (1990), and Cho (1998).
14
Theoretical framework and existing evidence
Table 2.1 Mechanism interaction and mechanism–performance causality in empirical corporate
governance research
Causation
Mechanisms One-way Two-way
Exogeneous
1
3
Endogeneous
2
4
The table classifies the empirical approaches used by existing research on corporate governance and economic performance.
of board to non–board insider holdings differs systematically across firms, this approach may
fail to detect the true relationship between insider ownership and performance.
• Narrow set of owner types. Most studies do not consider owner identity, and those that
do use only two categories, such as institutional vs. non-institutional, state vs. private, or
personal vs. non–personal owners. Since the theory argues that several different owner types
have different roles to play when ownership is separated from control, a data set with a richer
classification of types has a better chance of capturing the relevance of owner identity.
• No time series. The McConnell and Servaes (1990) and Holderness et al. (1999) papers,
which use data from two different years and test the predictions on both sets, are exceptions
to the overall pattern of using a cross–section of firms at only one point in time. This
snapshot approach, which is probably due to limited data availability, cannot tell whether
the relationship between governance and performance is stable over time.
The theoretical discussion in section 2.1.2 established two dimensions in the modeling of corporate
governance and economic performance. The first is whether mechanisms are considered exogeneous
or endogeneous. That is, if they are modeled as given parameters or functions of other variables,
including one or more of the other mechanisms. The second dimension is whether causation is one–
way (from governance to performance) or two-way (governance and performance may be mutually
dependent).
Table 2.1, which reflects these two theoretical dimensions, can be used to classify the methodologies of existing cross–sectional analyses of governance and performance. Almost without exception,
all papers in this area belong in cell 1. The econometric approach takes the mechanisms as externally given, and causation is supposed to run from governance to performance, only. A single
regression equation is specified, typically containing one or two mechanisms and a number of controls. A sophisticated example of this approach is McConnell and Servaes (1990), who estimate the
dependence of Tobin’s Q on ownership concentration, insider ownership, and institutional holdings,
using proxies for financial leverage, growth potential, and firm size as controls.
A study that comes close to being in cell 2 is Himmelberg et al. (1999). Although they analyze
causation running from managerial ownership to performance, only, they argue that these stakes
are explained by key variables in the contracting environment. Estimating managerial ownership
from firm characteristics and firm fixed effects, but with no explicit modeling of the mechanism
interaction, they cannot reject the hypothesis that managerial ownership and firm performance are
independent.
Cell 3 is unfeasible, as one cannot model two-way causation without letting at least one mechanism be endogeneously related to performance. The studies in cell 1 are criticized by Agrawal and
Knoeber (1996) and Cho (1998), stating the arguments from our section 2.1.2 that the empirical
2.2 Empirical evidence
15
methodology should allow for the possibility that governance mechanisms are internally related and
also driven by performance. Using the approach in cell 1 first and then moving to cell 4 by estimating the governance mechanisms and performance as a system of simultaneous equations, most
of the significant results disappear. For instance, Agrawal and Knoeber (1996) find that if each of
their seven governance mechanisms are considered exogeneous and related to Q one by one, four of
them are significant (performance is positively related to insider holdings and negatively to board
independence, leverage, and corporate control activity). Keeping the exogeneity assumption, but
allowing for all the exogeneous mechanisms in one multivariate regression equation, insider holdings
are no longer significant. Finally, moving from cell 1 to cell 4 by estimating a simultaneous system,
the only significant mechanism is the fraction of outside directors on the board, which is inversely
related to performance. Whereas Agrawal and Knoeber (1996) do not report their findings on
causation, Cho (1998) concludes that causation runs from performance to insider holdings (his only
corporate governance variable) rather than the opposite way.
Our theoretical discussion in section 2.1.2 argued that because the governance–performance
relationship may involve both mechanism endogeneity and two–way causation, cell 4 represents
the proper approach. Moreover, the findings of Agrawal and Knoeber (1996) and Cho (1998) both
suggest that the empirical inference may critically depend on the chosen methodology. Nevertheless,
because the theory is underdeveloped, cell 4 is not necessarily the best choice. As we pointed out,
existing corporate governance theory is a collection of partial hypothesis stated variable by variable.
There is little concern for how the wide set of mechanisms interact, on what variables are a priori
relevant for two–way causation, and how the equilibrium would look in terms of an optimal set of
governance mechanisms for a given set of exogeneous variables.
This lack of clear theoretical predictions, which becomes particularly critical in cell 4, are well illustrated when Agrawal and Knoeber (1996) operationalize their model of endogeneous mechanisms
and two–way causation. To capture mechanism endogeneity, they use a system of six equations
where any equation relates a mechanism linearly to the five others (mostly without stating theoretical reasons) and to a set of exogeneous variables (like listing status and stock volatility). To
model two–way causation, they include Q as an independent variable in each governance equation,
and each mechanism is used as an independent variable in the Q equation (again mostly without
theoretical arguments). The resulting system of 7 equations and 15 exogeneous variables is estimated by the two–stage least squares method. As will be discussed in chapter 10, such a system of
equations must have certain properties in order to solve the socalled identification problem, which
is the impossibility of finding a set of unique estimates in an unrestricted system of equations.
Such restrictions should be based on the theory which is up for testing rather than be justified by
observed patterns in the data. As implemented by Agrawal and Knoeber (1996), the exclusion of
exogeneous variables from any single equation to identify the system is done in a rather ad-hoc,
theory-less fashion.
We conclude this discussion of table 2.1 by noting that although there is no well–specified theory
saying why and how, we cannot a priori preclude the possibility that the governance mechanisms
are internally related and that they are also driven by performance. Such arguments would make
us favour the empirical approach of cell 4 to that in cell 1. That is still just half the story. Due
to the auxiliary assumptions needed to operationalize an empirical investigation of cell 4, it is not
obvious that findings based on a cell 1 methodology are less reliable than those in cell 4. This is
particularly true if, as in Agrawal and Knoeber (1996), the number of ad–hoc assumptions is high
and the conclusions change substantially as we move from cell 1 to cell 4.
The weak theoretical foundation and the unclear a priori effects of methodological decisions are
the reasons why we choose a rather exploratory approach. We move from cell 1 to 4 in several
16
Theoretical framework and existing evidence
explicit and moderate steps, roughly using the chronological development of the field; from univariate regression analysis and one–way causation in cell 1 to a simultaneous systems approach which
allows for mechanism endogeneity and two–way causation in cell 4.
2.3
Summary
This chapter has shown that the set of corporate governance mechanisms is wide, as it includes
ownership structure characteristics like concentration, owner identity, and insider holdings. It also
incorporates financial policy, board composition, security design, and market competition. We find
that the theoretical predictions, which mostly come from agency theory, are always partial and
sometimes diffuse, as they deal with the performance impact of just one governance mechanism
and sometimes cannot specify the shape of the univariate governance–performance relationship.
The expected performance effect of higher ownership concentration is unclear, as it reflects
the net impact of benefits (valuable monitoring, higher takeover premia, less free-riding by small
shareholders) and costs (reduced market liquidity, lower diversification benefits, increased majority–
minority conflicts, reduced management initiative, incompetent owners). Similarly, compared to the
direct principal–agent relationship represented by a personal investor, the multiple–agent setting of
institutional ownership has an unpredictable value effect (the beneficial efficient-monitoring effect
vs. the costly conflict-of-interest and strategic-alignment effects), whereas international and state
ownership will be less value-creating. Like for ownership concentration, we cannot a priori specify
the shape of the insider–performance relation, as it reflects the net effect of beneficial alignment–of–
interest and the costs of entrenchment and diversification loss. Admittedly, if the monitoring effect
sets in at low concentration levels, if this monitoring is beneficial rather than value–destroying,
and if the non–private costs to owners do not arise until concentration is relatively high, the
relationship between performance and concentration would be positive at moderate concentration
levels and negative thereafter. However, only empirical evidence can give the definite answer.
Agency theory predicts that performance will decrease with increasing board size (inefficient
communication), that firms with dual–class shares will have a lower market value than others (private benefits), and that both dividends and financial leverage are value–creating (reduced free cash
flow). Competition in the firm’s product market, the managers’ labour market, and in the market
for corporate control will be value–increasing (reduced room for wasteful decisions). Finally, even if
the theory is not well–specified on how the different governance mechanisms interact (substitutes,
complements, or independent), the equilibrium condition posits that if the governance systems we
observe in practice are indeed the optimal ones for each individual firm, no mechanism will be
significantly related to performance in a cross-section.
The vast majority of empirical research singles out ownership structure as the object of study
and mostly analyzes just one ownership characteristic. Overall, the evidence suggests that the association between ownership concentration and performance is as often zero as positive, and seldom
negative. Insider ownership is mostly positively related to performance at moderate insider holdings and negatively related or unrelated at higher levels. The role of owner identity is unclear and
under-explored. The recent papers in this area find that whereas several governance–performance
relationships are significant in single–equation settings, very few survive under a simultaneous
equations approach, which tries to capture both endogeneity between the mechanisms and two–
way causation between mechanisms and performance.
There are two reasons why existing empirical research may not be telling the full story about
governance and performance. First, because data on ownership structure is hard to find, most
papers only consider very large firms in the US, have ownership data on the large owners (block-
2.3 Summary
17
holders), only, use one or just a few of the potentially performance–relevant owner types, disregard
important insider subcategories, and focus on a single point in time. The second problem with
existing research is methodological. It occurs because most papers treat the different mechanisms
as though they were independent of each other, and they implicitly assume that causation runs
from governance to performance and not the other way. Our study relates economic performance to
corporate governance mechanisms in a way which carefully and step by step addresses the problems
of one vs. several mechanisms, exogeneous vs. endogeneous mechanisms and one–way vs. two–
way causation. In doing this, we will also evaluate whether simultaneous equations econometrics
can really offer additional, reliable insight compared to simpler, single–equation approaches. The
problem here is that the implementation of the simultaneous equations approach requires a priori
restrictions on mechanism interaction which the current theory of corporate governance may be
unable to deliver.
18
Descriptive statistics
Chapter 3
Descriptive statistics
Because our sample consists of all firms listed at Oslo Stock Exchange (OSE), this chapter first
describes key characteristics of the OSE in section 3.1. We present summary statistics of the sample
firms’ ownership structure in section 3.2, their board composition, financial policy, and security
design in section 3.3, control variables in section 3.4, and economic performance in section 3.5.1
Although this chapter comments on the overall patterns, only, detailed descriptive tables can be
found in Bøhren and Ødegaard (2000).
3.1
Market place and institutional environment
The Oslo Stock Exchange (OSE) is medium–sized by European standards. It plays a modest but
increasingly important role in the national economy, and it has become considerably more liquid
over our sample period, which is from 1989 to 1997. As of year-end 1997, the 217 listed firms have
an aggregate market capitalization equivalent to 67 bill. USD, which ranks the OSE twelfth among
the twenty–one European stock exchanges for which comparable data is available. From 1989 to
1997, the number of firms listed jumped from 129 to 217, market capitalization grew by an annual
average of 7%, and market liquidity as measured by annual turnover (transaction value/average
market value) almost doubled from 52% in 1989 to 97% in 1997.
The market value of the OSE firms as a fraction of GDP grew steadily over the sample period,
and reached 43% in 1997. This ratio is below the international average of about 65%, but rather
close to the European median of 49%.2 The book value of Norwegian listed firms’ equity in 1994
was 17% of all private and state firms’ equity. Relative to all limited liability firms in 1996, listed
firms represent 21% of the book equity, 8% of sales and 8% of employment.
The regulatory framework of corporate governance in Norway is somewhat peculiar. Even
though the country belongs to the civil law tradition, which is generally considered less investor–
protective than the common law jurisdiction, Norway’s regulatory environment still seems to provide better protection of shareholder rights than in many common law countries (La Porta et al.,
2000). This may be one reason why except for the UK, Norway’s listed firms have a less concentrated ownership structure than any other European country (Barca and Becht, 2001; Bøhren
and Ødegaard, 2001). For instance, whereas the average largest owner in a European listed firm
(ex. Norway and the UK) holds close to 50% of the voting equity, the corresponding fraction is 30%
in Norway and 15% in the UK.3 Moreover, the other large owners are large relative to the largest
one both in Norway and the UK. For instance, the average ratio of the largest to the second largest
equity stake is 2.0 in the UK, 2.6 in Norway, and 5.6 in the rest of Europe.
3.2
Ownership structure
Table 3.1 summarizes the descriptive statistics for governance mechanisms, controls, and performance measures in our sample firms. All averages are equally weighted across firms and years, but
1
Appendix A specifies data sources, defines all variables used, and graphs the variables.
Source: International Federation of Stock Exchanges (www.fibv.com).
3
The US figure is 3%.
2
3.2 Ownership structure
19
we will also provide corresponding value–weighted averages to highlight certain points.4 The most
common concentration measure in the literature is the fraction of outstanding equity owned by the
n’th largest or the n largest shareholders, with n mostly varying between 1 and 5. The table reports
such fractions for n up to 20 and also the Herfindahl concentration index5 , the number of owners,
the median fraction, the mean fraction held, and the stake of the largest outside (i.e., non–insider)
owner. The table shows that on average, the median owner holds one tenth of a percent of the
firm’s equity, the largest holds 29%, the two largest make up a blocking minority against charter
amendments, the four largest produce a simple majority, and a coalition of the ten largest form a
super majority.6
The owner type may either be measured as the aggregate fraction in a firm held by a certain
type or by the identity of a particular owner, usually the largest one. Consistent with the arguments
in chapter 2, we initially classify investors into five types: state, individuals (persons), financials
(institutional owners), nonfinancials, and international. To capture a pure case of indirect ownership
between rather large, public firms with many owners, we also include intercorporate shareholdings
between publicly listed firms as a separate type. Such a holding is operationalized as a stake held
by a sample firm in another sample firm, i.e., equity investments between Oslo Stock Exchange
firms.
We show in Bøhren and Ødegaard (2000) that according to the value-weighted averages, international investors as a group is the largest owner type and hold almost one third of OSE market
cap over the sample period. Non-financial domestic firms own about one fourth, the state and
financial investors both own roughly one fifth, and individuals hold about one tenth. Financial
investors increase their share of OSE market cap almost every year due to the rapid growth of
mutual funds, and individuals gradually become less significant. Aggregate state ownership varies
considerably over time, primarily due to the state’s rescue of large commercial banks in the early
nineties. OSE firms hold 8% of the equity issued by other OSE firms. This fraction is decreasing
over time; from 14% in the beginning to 4% in the end of the sample period. International and
financial investors are relatively seldom the largest owner in the firm, whereas national corporations
are strongly overrepresented. Norwegian individual investors as a group own a lower fraction of the
equity value than in any other European country (8% vs. a European average of 28% in 1997).
Notice that the equally–weighted averages in table 3.1 differ substantially from their value–
weighted counterparts discussed above, suggesting that certain investor types gravitate towards
certain firm sizes. It turns out that international investors, the state, and non-bank financials
hold their largest aggregate stakes in large firms (the value–weighted averages exceed the equally–
weighted ones), whereas individuals and non-financial corporations tend to prefer smaller firms.
The largest outside (external) owner holds 26% on average. This figure reflects the stake of
the largest owner who is not also an insider. This definition of concentration solves the potential
problem that if an insider is also the largest owner, concentration (measured unconditionally as the
largest stake) and insider holdings will reflect one instead of two ownership dimensions.
By insiders we mean investors who are obliged by the securities law to report all their equity
trades in a firm to the OSE, regardless of whether or not they have private information. This
definition includes the board, the management team, the auditor, and their immediate family
4
Value-weighted averages for most of these variables can be found in Bøhren and Ødegaard (2000).
The Herfindahl index is the sum of squared ownership fractions across all the firm’s investors. This ratio has a
maximum of one (a single investor owns every share) and approaches its minimum of zero as the ownership structure
gets increasingly diffuse.
6
All findings in are based on cash flow rights (i.e., all equity issued) rather than voting rights (i.e., voting equity).
Appendix B.1.2 is the only exception, here we explicitly show that the univariate regression results are insensitive to
whether ownership structure characteristics are based on voting rights or cash flow rights.
5
20
Descriptive statistics
Table 3.1 Summary of descriptive statistics
Ownership concentration
Herfindahl index
Median owner
Mean owner
Largest owner
1-2 largest owners
1-3 largest owners
1-4 largest owners
1-5 largest owners
1-10 largest owners
1-20 largest owners
Number of owners
2nd largest owner
3rd largest owner
4th largest owner
5th largest owner
Largest outside owner
Insider ownership
All insiders
Board members
Management team
Primary insiders
Largest insider
Largest primary insider
Owner type
Aggregate state holdings
Aggregate international holdings
Aggregate individual holdings
Aggregate financial holdings
Aggregate nonfinancial holdings
Aggregate intercorporate holdings
Largest owner is state
Largest owner is international
Largest owner is individual
Largest owner is nonfinancial
Largest owner is financial
Largest owner is listed
Board characteristics
Board size
Security design
Fraction voting shares
Financial policy
Debt to assets
Dividends to earnings
Dividends to price
Controls
Investments over income
Stock volatility
Stock turnover
Stock beta
Firm value
Performance measures
Q
RoA
RoS
Mean
StDev
Q1
Median
Q3
n
0.2
0.0
0.2
29.0
40.1
47.0
52.0
55.9
67.5
77.4
4392.5
11.1
7.0
5.0
3.9
25.7
(0.2)
(0.0)
(0.3)
(19.2)
(20.2)
(20.0)
(19.6)
(19.1)
(16.9)
(14.0)
(9578.5)
(6.1)
(3.6)
(2.3)
(1.8)
(19.3)
0.0
0.0
0.0
14.3
23.6
30.3
35.8
40.6
54.7
67.6
691.0
6.9
4.7
3.5
2.7
11.0
0.1
0.0
0.1
23.2
36.3
44.2
50.5
55.0
68.4
79.5
1245.0
9.7
6.3
4.7
3.7
19.1
0.2
0.0
0.1
40.6
53.8
62.6
66.9
70.4
80.9
88.4
2938.0
13.8
8.8
6.3
4.9
35.6
1069
1069
1069
1069
1069
1069
1069
1069
1069
1069
1069
1069
1069
1069
1069
1069
19.9
7.8
4.2
8.2
10.9
5.5
(27.7)
(20.7)
(14.7)
(19.0)
(16.4)
(12.1)
0.5
0.0
0.0
0.0
0.2
0.0
6.3
0.1
0.0
0.4
3.0
0.4
29.7
2.5
0.7
4.5
14.9
4.5
1069
1069
1069
1069
1059
1062
5.1
22.1
17.8
16.6
39.0
9.0
8.6
13.2
10.4
54.9
7.8
12.9
(13.8)
(22.3)
(15.6)
(14.0)
(24.0)
(14.9)
(28.0)
(33.8)
(30.5)
(49.8)
(26.8)
(33.5)
0.0
4.6
6.5
5.5
17.5
0.3
0.0
0.0
0.0
0.0
0.0
0.0
0.0
14.8
12.4
14.2
37.5
3.0
0.0
0.0
0.0
100.0
0.0
0.0
3.8
32.8
25.2
23.7
58.7
10.7
0.0
0.0
0.0
100.0
0.0
0.0
1069
1069
1069
1069
1069
1067
1069
1069
1069
1069
1069
1069
6.6
(2.5)
5.0
6.0
8.0
964
96.8
(9.3)
100.0
100.0
100.0
1054
57.1
26.5
159.5
(19.4)
(68.1)
(333.4)
46.2
0.0
0.0
60.2
0.0
57.0
70.0
33.0
225.0
1058
1040
1069
60.2
54.2
59.4
0.9
1995.4
(283.7)
(28.7)
(65.3)
(0.6)
(6062.9)
3.2
33.7
13.4
0.5
168.6
8.1
46.3
40.3
0.8
480.8
30.4
65.3
79.0
1.2
1429.9
1006
949
1034
947
1069
1.5
5.0
33.1
(1.0)
(14.8)
(92.4)
1.0
3.2
-16.7
1.2
7.3
13.0
1.6
10.9
49.0
1068
1061
894
Firm value is in millions of constant 1997 NOK. The other values are in percent except for the Herfindahl index,
board size, stock beta and Q, which are in their natural units.Data for firms listed on the Oslo Stock Exchange,
1989-1997. Variable definitions are in Appendix A.2.
3.3 Board composition, security design, and financial policy
21
members. The table considers four groups of such insiders: All, the board (directors), members of
the management team (officers), and the primary insiders, which are the directors and officers. On
average over firms and years, all insiders hold 20% of the equity, directors have 8%, and management
owns 4%. Due to the overlap between directors and officers, who together constitute the primary
insiders, the average fraction held by primary insiders is 8%. Since the management team of OSE
firms are either not on the board or are represented by the CEO, these figures reflect that insider
ownership by management is very often just CEO holdings. The 11% held by the largest insider
and 6% held by the largest primary insider show that insider holdings are often concentrated.7
3.3
Board composition, security design, and financial policy
Norwegian boards are small by international standards. The average number of directors is 7
(median is 6), and 75% of the firms have 8 members or less. Since the boards of OSE firms never
have more than one officer, all boards in our sample are outsider–dominated according to the
standard definition.8
Non-voting equity (B shares) constitute on average 3% of outstanding equity (equally weighted).
As shown in Bøhren and Ødegaard (2000), B shares are issued by 14% of the firms, they constitute
10% of OSE market capitalization and 29% of the equity in dual-class firms, and the propensity to
issue B shares decreases over time. International investors, who hold 54% of non-voting shares, are
heavily over-represented in this security type, both before and after 1995, when the restriction on
international holdings of voting shares was lifted.9
The average leverage ratio (debt to total assets) for the sample firms is 57%. The average
payout ratio is 27% for all firms and 52% for firms that actually pay dividends, which is half the
firms. Restrictions in the corporate law made stock repurchases practically non–existent in the
sample period.10
3.4
Controls
As discussed in section 2.1.1, a test of the governance–performance interaction should also consider
exogeneous control variable that either influence the framework in which the governance mechanisms
operate, or which are driving performance directly. Our control variables are investments (measured
as accounting investments per unit of sales), stock volatility (total equity risk), stock liquidity
(proxied as annual turnover), stock beta (systematic equity risk), and firm size. We measure size
as the logarithm of firm value, which we estimate as the market value of equity plus the book value
of debt.
7
The large difference between the average stake of all insiders (19.9%) and of primary insiders (8.2%) is surprising,
considering that officers and directors are included in both categories. The reason is probably that when we manually
classify insiders from the insider registry of the OSE, we only assign a holding to the primary insider category if
the holder can be identified in our board and management data base. When this match is unsuccessful, the insider
remains in the All category, but is not included in the primary insider category. This identification problem makes
us underestimate the holdings by primary insiders, but we have no reason to suspect that it biases our tests of the
governance–performance relationship.
8
Even though a board member is formally external, he or she may still be closely related to management. This
would easily happen if the recruiting process for board members is heavily influenced by the CEO. As we lack
observations on how director candidates are generated, we must ignore this type of mechanism.
9
Until 1995, the aggregate fraction of voting shares in a firm held by international investors could not exceed one
third.
10
After restrictions were softened in 1999, a firm can repurchase up to 10% of its outstanding equity.
22
Descriptive statistics
Asset pricing theory predicts a positive relationship between the beta of a stock and its expected
returns. Performance will also be influenced by the stock’s market liquidity if investors must be
compensated for low liquidity in terms of higher expected returns. Demsetz and Lehn (1985)
argue that because the value of owner monitoring increases when the predictability of the firm’s
environment decreases, ownership concentration is positively related to the total risk of the firm’s
cash flow. We use stock price volatility to proxy for cash flow risk. Investments will be used to
control for potential noise in accounting–based performance measures (Demsetz and Lehn, 1985),
and firm size is included to capture the well–documented empirical association between size and
performance (Hawawini and Keim, 2000).
The table shows that the average OSE share is traded roughly every second year, and that the
average firm size is NOK 2 bill. in terms of firm value. At the end of the sample period, the average
market value per firm is about one fifth the average NYSE firm and roughly twice the average
NASDAQ firm.
3.5
Economic performance
Economists care about corporate governance because it may influence the economic value of society’s resources in a positive or negative way. As the performance measure is supposed to reflect this
creation or destruction of value, the list of potential candidates is long. For instance, in periods
with high unemployment and low growth, economic planners may focus on input measures like
the number of jobs created or the new investment in fixed assets. Private owners would be more
concerned with output measures of performance, and particularly those which reflect the impact
on their wealth, such as the level or the growth in the value of the firm’s total assets or equity
securities. We will focus on wealth–related performance measures.
The most commonly used proxies in the recent literature on governance and performance are
Tobin’s Q ratio (Q), the accounting (book) rate of return on assets (RoA), and the market return
on the stock (RoS). Q is the market value of assets divided by their replacement value, RoA is
profits after taxes plus interest payments after taxes divided by the book value of assets, and RoS
is the sum of the period’s capital gains and dividends divided by the market value of equity at the
beginning of the period.
Q and RoA measure performance relative to all financiers (i.e., owners and creditors as a group),
whereas RoS captures the effect on the owners’ wealth, only. In principle, Q, RoA, and RoS are
equivalent in the sense that if no conflict of interest exists between stockholders and creditors,
decisions which maximize firm (total) value will also maximize stockholder wealth and hence stock
returns. However, since corporate governance recognizes the possibility of such a conflict, and
because owners are considered the key to improved governance, total return and equity return
measures are not necessarily equivalent. The bottom of table 3.1 shows summary statistics of
the three estimated performance measures. Because we miss data on replacement values, Q is
operationalized as the market value to book value of assets. The mean (median) estimate is 1.5
(1.2) for Q, 5.0% (7.3%) for return on assets, and 33.1% (13.0%) for stock returns. The returns are
nominal.11
The degree of consistency between the three performance measures is indicated by table 3.2,
which shows the linear (Pearson; panel A) and non–linear (rank; panel B) coefficients of correlation
between Q, RoA, and RoS. To explore the effect of short–lived noise, we correlate the measures
using both annual values and five–year average values for RoA and RoS. The five-year averages are
denoted by the subscript 5. The table shows that the correlations are generally low. Since there
11
The annual inflation rate varied between 5% and 2% in the sample period.
3.6 Summary
23
is no reason to expect a linear co–movement, the fact that rank correlations are more consistently
positive than the Pearson correlations is somewhat reassuring. The question of consistency between
performance measures will be analyzed further in the regression models in subsequent chapters.
Table 3.2 Correlations between performance measures
Panel A: Pearson correlation
RoA
RoA5
RoS
RoS5
Q
0.06∗
0.32∗∗∗
0.27∗∗∗
0.34∗∗∗
RoA
RoA5
RoS
RoS5
Q
0.20∗∗∗
0.20∗∗∗
0.23∗∗∗
0.22∗∗∗
RoA
RoA5
RoS
0.20∗∗∗
0.09∗∗∗
−0.08∗∗
0.10∗∗∗
0.23∗∗∗
0.51∗∗∗
RoA
RoA5
RoS
0.38∗∗∗
0.17∗∗∗
0.11∗∗∗
0.05∗
0.11∗∗∗
0.24∗∗∗
Panel B: Rank correlation
Correlation between Tobin’s Q (Q), return on assets (RoA), and return on stock (RoS). Correlations based on
five-year average values are denoted by the subscript 5. The ∗ , ∗∗ , and ∗∗∗ means the relationship is significant
at the 5%, 2.5% and 1% level, respectively. Data for firms listed on the Oslo Stock Exchange, 1989-1997. Variable
definitions are in Appendix A.2.
3.6
Summary
Our sample is the population of firms listed on the Oslo Stock Exchange (OSE). The OSE is a
rather typical European exchange which experienced a rapid growth in our sample period 1989–
1997, both in terms of firms listed, market cap relative to GDP, and market liquidity. The average
OSE firm is about twice the size of a NASDAQ firm and roughly one fifth of a NYSE firm.
OSE firms have low ownership concentration by European standards, international owners hold
about one third of aggregate market capitalization, financial (institutional) investors steadily increase their share, and ownership by individuals (personal investors) is small and declining. Insiders
hold on average one fifth of a firm’s outstanding equity. Roughly half the insider stakes belong to
primary insiders (the firm’s managers and directors), and the CEO holds almost all the shares in
the officers category.
OSE firms have boards with seven members on average. About half the firms pay dividends,
and those that pay distribute 52% of their earnings. Debt financing is 60% of total capital, and
14% of the firms have issued both fully voting equity (A shares) and non–voting equity (B shares).
Although B shares constitute close to one third of total equity in dual–class firms, the propensity
to issue B shares decreases over time.
We measure performance by the Tobin’s Q ratio (Q), the accounting (book) rate of return on assets (RoA), and the market return on the stock (RoS). The correlation between these performance
measures is generally low.
Table 3.3 summarizes the variables which will be used in the regression models of the subsequent
chapters.
24
Descriptive statistics
Table 3.3 Governance variables, controls, and performance measures used in the regression models
Ownership concentration
Owner type
By aggregate holdings
By type of largest owner
Insider ownership
Board characteristics
Security design
Financial policy
Market competition
Controls
Performance measures
Herfindahl index
1-n largest owners
n’th largest owner
Aggregate state holdings
Aggregate international holdings
Aggregate individual holdings
Aggregate financial holdings
Aggregate nonfinancial holdings
Aggregate intercorporate holdings
Largest owner is state
Largest owner is international
Largest owner is individual
Largest owner is financial
Largest owner is nonfinancial
Largest owner is listed company
All insiders
Board members
Management team
Primary insiders
Board size
Fraction voting shares
Debt to assets
Dividends to earnings
Industrial
Transport/shipping
Offshore
Investments over income
Stock volatility
Stock turnover
Stock beta
Firm value
Q
RoA
RoS
Univariate relationships
25
Chapter 4
Univariate relationships
This chapter uses a univariate approach to the analysis of corporate governance and economic
performance, relating performance to just one governance mechanism at a time. According to the
classification in table 2.1, this approach belongs in cell 1 (exogeneous mechanisms and one-way
causation). Among the alternatives in cell 1, the univariate approach represents the most partial
methodology, as it ignores all other mechanisms when any one of them is being studied. To simplify
the exposition, we only report the estimated signs of the regression coefficients and their levels of
significance, leaving detailed results to appendix B.1. We will mostly just report the findings,
postponing the qualitative discussion to later chapters.
4.1
Overall pattern of univariate regressions
Table 4.1 summarizes the estimates from all the univariate regression models. For each model,
where we regress a performance measure on one independent variable, which is either a governance
mechanism or a control, the table shows the estimated sign of the coefficient of the independent
variable. Significance is indicated by ∗ , ∗∗ , and ∗∗∗ , which means the estimated coefficient is
significantly different from zero at the 5%, 2.5%, and 1% significance level, respectively.
The table reflects two patterns which are relevant for the choice of performance measure. First,
the strength of a relationship depends on the performance measure used. For instance, although
the holding size of the five largest owners varies inversely with every performance measure, the
relationship is insignificant for RoA and RoS, significant at the 2.5% level for RoS5 , and significant
at the 1% level for RoA5 and Q. Overall, the covariation is more often significant with Q than with
any other measure, more often with the five–year averages RoA5 and RoS5 than with the annual
RoA and RoS, and, for a given averaging period, more often for return measures based on total
assets (A) than on equity (S ).
Second, the consistency across performance measures is higher using RoA5 and RoS5 than with
RoA and RoS. This is particularly true for Tobin’s Q and RoA5 , which both measure total value
creation (i.e., for all financiers). For instance, the holdings of either one of the four insider categories
is never significantly related to RoA, and the estimated sign is the opposite of what we find using
Q for all categories except one (board). In contrast, RoA5 and Q produce the same estimated
sign (+) for every insider group, both suggest that the All insiders category is insignificant and
that holdings by the board as well as the primary insiders are highly significant (p < 1%). In the
following discussion of the findings from table 4.1, we will focus on Q and the five–year averages
RoA5 and RoS5 as our performance measures.1
4.2
Ownership concentration
Since the agency model does not specify one particular concentration measure as being theoretically
superior, the estimated relationship should not be based on just one concentration proxy. As shown
1
The use of five–year averages introduces a potential econometric problem in RoA5 and RoS5 , as there will be 80%
data overlap between consecutive performance measures for the same firm. Such a sampling method may produce
autocorrelated error terms in the regressions. Q does not suffer from this problem, since each value is sampled over
one year, only. This is one reason why we mostly use Q as the performance measure in the following.
26
Univariate relationships
Table 4.1 Summary of the univariate regressions relating performance to a governance mechanism
or a control
Ownership concentration
Herfindahl index
Largest owner
1-3 largest owners
1-5 largest owners
2nd largest owner
3rd largest owner
4th largest owner
5th largest owner
Owner type
Aggregate state holdings
Aggregate international holdings
Aggregate individual holdings
Aggregate financial holdings
Aggregate nonfinancial holdings
Aggregate intercorporate holdings
Largest owner is state
Largest owner is international
Largest owner is individual
Largest owner is financial
Largest owner is nonfinancial
Largest owner is listed
Insider ownership
All insiders
Board members
Management team
Primary insiders
Board characteristics
ln(Board size)
Security design
Fraction voting shares
Financial policy
Debt to assets
Dividends to price
Dividends to earnings
Market competition
Industrial
Transport/shipping
Offshore
Controls
ln(Firm value)
Investments over income
Stock volatility
Stock turnover
Stock beta
Q
RoA5
RoS5
RoA
RoS
−***
−***
−***
−***
−
+
+
+
−***
−***
−***
−***
−
−
+
+
−
−
−*
−**
−**
−
−
−
−
−
−
−
+
−
−
−
−
−
−
−
−
−
−
−**
−***
+
+***
+
−***
−***
−***
−
+***
−
−***
−*
−
−
+***
+
−*
−**
−
+
+**
−
−
−**
−*
+
+***
−*
−
−
−*
+
+***
−
−
−
−
−
−***
+***
+
+***
+
−
−*
+
+***
+
−
−
+***
+
−
+
−
+
+
−
−
+
+
+***
+
+***
+
+***
+**
+***
+
−
+***
+*
−
+
−
−
+
+
+
+
−
−
−***
+
−
+*
−
+*
−
+
−***
−***
−
−***
+***
+
−***
−
−
+***
+***
+***
−
−
+
+
−***
−*
−
−***
−***
+
−**
+
+
+
−
+
−
+
+***
−
−***
+***
+
−
−
−**
+
−
−
−
+***
+***
+***
+***
+
−***
−
−
+*
−
+
+***
+
The table summarizes the estimated sign of univariate relations between a performance measure (Q, RoA5 , RoS5 ,
RoA, and RoS) and an independent variable (governance mechanism or control variable). Statistical significance is
indicated with ∗ , ∗∗ , and ∗∗∗ , which means the relationship is significant at the 5%, 2.5% and 1% level, respectively.
Detailed results are in appendix B.1.
Data for firms listed on the Oslo Stock Exchange, 1989-1997. Variable
definitions are in Appendix A.2.
4.3 Owner type
27
in table 4.1, we measure concentration by single–investor stakes (such as fraction held by largest
owner), aggregate stakes (e.g., fraction held by the five largest owners), and a proxy which reflects
the full ownership structure (the Herfindahl index).
The table documents that without exception, the significant relationships between concentration
and performance are negative. It is almost never significant using annual stock return or total
return, whereas Q and RoA5 relate significantly to concentration (p < 1%) as measured by the
Herfindahl concentration index or by the holdings of the largest, three largest or five largest owners.
4.3
Owner type
The aggregate fraction in a firm held by a particular investor type is often significantly related to
performance. Focusing on Q, performance is lower the higher the aggregate fraction held by state,
non–financial, and intercorporate owners, whereas individual investors are associated with higher
performance. All these findings are significant at the 1% level.
The aggregate stake per owner type may be a noisy measure, as it is the product of the number
of such investors and the average stake per investor. Therefore, a 60% aggregate stake for individual
investors as a group may represent one 60% holding by a single investor (high power by one principal
who directly monitors the agent) or 60.000 stakes of 0,001% each (no power and no incentives for
any principal). Hence, the aggregate stake may mix concentration and identity characteristics in a
misleading way. It also rests on the implicit assumption that cooperation between investors within
the type is unproblematic regardless of the number of investors involved, and that cooperation is
harder across types than within a type. Neither assumption seems unproblematic. To avoid the
interpretation problems of the aggregate stake, we also consider the identity of large owners.
As seen in table 4.1, Q, RoA5 , and RoS5 are significantly higher (at the 1%, 2.5%, and 1%
levels, respectively) when the largest stake is held by an individual (personal) investor. Similarly,
but not as consistently across performance measures, performance is significantly lower when the
largest stake is held by the state, non–financials or another OSE firm. These findings correspond
roughly to those based on aggregate holdings.
4.4
Insider ownership
Unlike for other investor types in our sample, the aggregate stake of a firm’s insiders in a firm may
directly reflect how several owners jointly influence economic performance. The insider group is
much smaller than other investor categories, the information level is more homogeneous, and their
joint ability to affect important corporate decisions is better. Since the management team may
have other incentives than owner representatives on the board, we consider not just all insiders,
but also the aggregate holdings of three subgroups, i.e., directors, officers, and the primary insiders
(directors and officers).
With very few exceptions, insider ownership is positively correlated with every performance
measure, although the association is never statistically significant for all insiders as a group. The
positive relationship is significant (p < 1%) for the board and the primary insiders relative to Q
and RoA5 , and for management relative to RoS5 .
4.5
Board characteristics, security design, and financial policy
There is generally a negative relationship between board size and performance, but the link is
statistically weak except for RoS5 (p < 1%).
28
Univariate relationships
As shown by Ødegaard (2000), the fully voting A shares and the non–voting B shares are
differently priced at the OSE. This pricing discrepancy may reflect the value of the option to
use the voting right in order to extract private benefits. Table 4.1 indicates that consistent with
the conflict of interest idea, performance as measured by Q and RoS5 is positively related to the
fraction of voting shares outstanding (p < 5%). For both Q, RoA5 and RoS5 , there is a significantly
negative relation (p < 1%) between leverage and performance. The findings on dividends are quite
inconsistent across performance measures.
4.6
Market competition
The competitive setting of a firm is related to factors like its market share in the input and output
markets, the barriers to entry and exit, and the overall regulatory environment (Porter (1980)).
As we lack data at this level of detail, industry membership is used as a proxy. We recognize
that such measures are noisy, primarily because the industry may not reflect the individual firm’s
unique competitive position, such as market share and strategic assets, but rather the average
characteristics of all firms in the industry, like the overall protection due to entry barriers. This
problem is more serious the more heterogeneous the competitive position of each individual firm.
Because we want proxies which reflect competition–related differences across industries, we
classify the sample firms along product market lines, using a system developed by the OSE.2 Since
some of the industries in the OSE system contain very few firms (like real estate and utilities),
and because the IT industry has many firms with a very short listing period, our concern for data
availability and sample size forces us to reduce the classification into four groups. In the aggregate,
these groups contain the maximum number of firms, and they represent reasonably intuitive labels:
• General industry
• Transportation/shipping
• Offshore related
• Miscellaneous
The miscellaneous (misc.) category contains real estate, trade, IT and communications, media,
and unclassified firms. We will mostly use the misc. category as the base case in the regressions.3
The OSE is the world’s largest stock exchange for shipping firms, which have historically been
dominated by family-owned businesses operating in international product and capital markets.
Currently, about every fourth OSE firm is in shipping. The mean size of a shipping firm is close
to the market-wide OSE average of 2.1 bill. NOK, and they dominate the transportation/shipping
category. As the table shows, industry membership matters for performance. According to Q,
RoA5 and RoS5 , transport/shipping firms performed significantly worse than others in the sample
period.
2
Another alternative would have been the SIC system. Unfortunately, because many multi–product OSE firms
are SIC–classified as holding companies, only, this alternative tells nothing about the firm’s underlying operations.
3
In Bøhren and Ødegaard (2000) we stratified the sample into IPO firms (Norwegian: selskaper på SMB listen),
industrials, financials, and shipping firms. This classification is inappropriate in the present context, since it may
not properly account for product market differences. The major problem is the IPO category. These firms belong to
many different industries (like shipping and industrials), but they all end up as IPO firms in our classification only
because they are newly listed and hence young and small. Industrials and shipping correspond roughly to our first
two categories above (general industry and transportation/shipping, respectively).
4.7 Controls
29
The crudeness of the competition proxy becomes problematic when we want to interpret its
observed link to performance. Since differences in competitive environment may not have been a
major criterion when the OSE constructed the industry proxy, it may as well be considered a rough
industry classification system with questionable links to underlying differences in the competitive
environment. Thus, even if the proxy reflects variables which matter for performance, they are
not driving the design of corporate governance systems. Hence, the proxy picks up effects which
would otherwise be subsumed in the error term if we were to regress performance on corporate
governance mechanisms alone. One example is when the competition proxy reflects differences in
systematic risk rather than competition differences. Although risk differences influence the cost of
capital and hence the firm’s performance, they may be inconsequential for corporate governance.
For this reason, it is not obvious whether our market competition proxy should be considered a
governance mechanism or a control variable. We will mostly interpret it as a control variable in
the following.
4.7
Controls
The control variables, which will be used in the multivariate regressions in later chapters, are firm
size, investments, stock volatility, stock turnover, and stock beta. As shown in table 4.1, the firm
size results are somewhat inconsistent across performance measures, but the significant associations
are the positive ones. That is, larger firms tend to have higher performance.
Investment and performance show no convincing associations. The three stock characteristics (total risk, liquidity, and systematic risk) are all positively correlated with both stock-based
performance measures, and the correlations are significant at the 1% level for RoA5 .
4.8
Summary
The univariate approach used in this chapter relates performance to governance mechanisms and
controls one by one. In the language of table 2.1, this approach is the most partial version of
cell 1 methodologies, which all assume exogeneous mechanisms and one–way causation. Overall,
we observe the strongest association with corporate governance mechanisms when performance is
measured by Tobin’s Q. Consistency across performance measures is largest for Q and the five–year
average return on assets (RoA5 ).
Almost without exception, ownership concentration is inversely related to performance, and
the negative covariation is particularly strong for alliances of large investors, such as the three
largest owners as a group rather than the third largest alone. Holdings by individual investors
(both in the aggregate and as large separate owners), board members, and primary insiders covary
positively and significantly with performance, whereas the relationship is significantly negative for
non–financial and state owners. Firms with dual–class shares tend to have lower performance than
others. The univariate relationship between board size and performance is negative, but rather
weak.
Due the partial nature of the models used in this chapter, we choose to postpone interpretations
until we have estimated the more comprehensive models in later chapters.
30
Ownership concentration
Chapter 5
Ownership concentration
The single-equation, univariate approach used in chapter 4 is the simplest alternative in cell 1 of
table 2.1, (exogeneous mechanisms and one–way causation). In the following five chapters, we stay
in cell 1, but move to a multivariate approach. The focus of the current chapter is the relationship
between performance and concentration, but we also control for variables which are not governance
mechanisms, but still potentially relevant for the estimated relationship between concentration
and performance. Chapter 6 first explores the insider–performance interaction in a corresponding
way, subsequently extending the analysis to include more than one governance mechanism in the
regression. This approach reflects the chronological development in this field, from the simple
concentration paper of Demsetz and Lehn (1985) to the more comprehensive insider ownership
study of McConnell and Servaes (1990). In both chapters, we replicate the classic papers on our
sample and extend the analyses by exploiting the richness of our data set. To keep the exposition
reasonably compact, we show supplementary regressions in appendix B.2.
As discussed in chapter 2, agency theory argues that from a monitoring perspective, performance
improves with increasing concentration. However, as both reduced diversification benefits, lower
liquidity, reduced manager initiative, and increased majority–minority conflicts work in the opposite
direction, the theoretical prediction on the covariation between concentration and performance is
unclear. Also, the monitoring argument implicitly assumes that owners are sufficiently competent
to choose a monitoring approach which improves management’s ability to create economic value.
Existing empirical evidence is mixed, but most papers suggest that the estimated relationship is
either positive or insignificant.
5.1
The Demsetz–Lehn approach
Demsetz and Lehn (1985) analyze 511 large US corporations in 1980, measuring performance as
RoA5 for the period 1976–1980. Alternative concentration proxies are the fraction held by the
five largest owners, by the twenty largest, and the Herfindahl concentration index. Their control
variables are industry dummies for utilities and financials (supposed to capture the effect of regulation), investments in real assets, R&D investments, advertising expenses, firm size, and stock
price volatility. Although the estimated relationship between concentration and performance is
negative, Demsetz and Lehn (1985) find that it is not significant at conventional levels (table 9 in
their paper). This result is inconsistent with the Berle and Means (1932) hypothesis. However,
the evidence is consistent with the equilibrium argument of Demsetz (1983) that because investors
choose value–maximizing governance systems for each firm, the empirically observed relationship
between concentration and performance will be insignificant.
We implement a corresponding analysis on our data set by using the same performance measure
(RoA5 ) and the same concentration proxies (fraction owned by the five largest, twenty largest, and
the Herfindahl index). Because our sample contains no financials and very few utilities, we use
the industry classification discussed in section 4.6, which assigns a firm into either the industrials,
shipping/transport, offshore, or misc. category. Since Norwegian accounting statements do not
specify R&D and advertising, these two items are ignored in our model. As a similar proxy we use
investment intensity, measured as investment over income.1
1
Although Demsetz and Lehn (1985) use their industry dummies as controls, we argued in section 2.1 that the
5.2 Econometric issues
31
Table 5.1 shows the results when using the fraction owned by the five largest owners as the
concentration measure, and estimating the model on data pooled for the period 1989 to 1997.2
Unlike what Demsetz and Lehn (1985) found for large US firms in 1980, estimating their model
with our Norwegian data strongly suggests that ownership concentration and performance are
indeed related. The regression shows a very significant, negative relationship between the two
(p < 1%).3 This means the univariate result from table 4.1 holds up after having controlled for
industry, size, investment, and stock price volatility. The other coefficients in the regression tally
with the DL results, as they have the same signs and similar significance levels.
Table 5.1 Multivariate regression relating performance (RoA5 ) to ownership concentration and
controls, following Demsetz and Lehn (1985)
Dependent variable: RoA5
Constant
lntrans(1-5 largest owners)
Industrial
Transport/shipping
Offshore
Investments over income
ln(Firm value)
Stock volatility
n
R2
Average (RoA5 )
coeff
14.41
-0.54
-1.97
-2.81
-3.93
-0.08
-0.11
-1.68
886
0.09
9.41
(stdev)
(2.67)
(0.18)
(0.38)
(0.40)
(0.61)
(0.05)
(0.12)
(0.66)
pvalue
0.00
0.00
0.00
0.00
0.00
0.12
0.37
0.01
Pooled OLS regression using data for all listed firms on the OSE in the period 1989 to 1997. Table 5.2 shows
complementary regressions which use the same set of independent variables. Variable definitions are in Appendix A.2.
In regressions using firm size across years the nominal values are adjusted to the 1997 general price level.
5.2
Econometric issues
The results in table 5.1 are estimated with one single OLS regression. Even disregarding the problems of simultaneity and reverse causation, which will be addressed in chapters 10 and 11, there
are other issues which may question the OLS approach. First, the estimation is carried out on
panel data, i.e., a pooled cross section–time series data set. Because this means the same firm
may appear numerous times in the sample, not all observations are independent, and the resulting
regression error terms may be serially correlated (autocorrelation). Second, the governance mechanisms may be systematically related to each other and to the controls (multicollinearity). Third,
industry may proxy for product market competition, which may be considered a separate governance mechanism.
The empirical evidence of Palmer (1973) does indeed suggest that product market competition and ownership concentration are substitute disciplining devices.
2
Two of the independent variables have been ln–transformed. We ln–transform the fractional holding of the five
largest owners in order to be consistent with Demsetz and Lehn (1985), who transformed it from a bounded to an
unbounded variable because it subsequently served as the dependent variable in a model relating concentration to
potential determinants. Because a few of our sample firms are very large relative to the others, we ln–transform firm
size in order to reduce the inflating effect of outliers on the standard errors of the estimated coefficients.
3
Appendix B.2.2 contains regressions using two alternative concentration measures: Herfindahl index and ownership by 20 largest owners. The results are very similar, in particular the negative relation between concentration and
performance is significant using these alternative concentration measures.
32
Ownership concentration
if the underlying structural relationship between the variables changes over the nine–year time period, a time–independent specification may not capture the true picture (instability). Fourth, the
functional relationship between performance, governance, and controls may be incorrectly modeled
(mis–specifications).
We use four different approaches to minimize these problems. First, we always run separate,
year–by–year OLS regressions in addition to the pooled ones. This methodology addresses the first
problem (autocorrelation) and the third one (instability). There is no time series correlation in
a single–year cross-section, and structural shifts will show up as systematic patterns in the time
series of estimated coefficients. However, these year–by–year regressions may suffer relative to the
pooled one due to a much smaller number of observations (on average 100 firms per year vs roughly
900 firm–years). We would therefore expect that as we move from the pooled to the year–by–
year regressions, the standard deviations (standard errors of the estimated coefficients) will grow.
Consequently, the p−values (the probability under the null hypothesis of observing the estimated
coefficient or a more extreme one) will increase. This will bias our test towards keeping the null
hypothesis that governance and performance are unrelated.
To avoid the small–sample problem and simultaneously address autocorrelation and instability,
we use two additional approaches which both utilize the full, pooled panel data set. Thus, our
second alternative estimation technique is GMM4 , which is used to estimate the model specified
for the OLS regressions with pooled data, such as the one in table 5.1. This approach produces
identical point estimates of the coefficients, but, unlike with OLS, any error term dependencies are
picked up by the estimated standard deviations and hence reflected in the p−values.
Our third alternative technique is to still use OLS, but to add indicator variables for each year.
The resulting fixed effects regression addresses at least certain forms of instability by allowing the
constant term to change from year to year. This may happen if the aggregate performance effect
of factors subsumed in the error term are changing over time, such as a market-wide upward or
downward revision in the market value of most firms due to changed risk premia or interest rates.
Such events will influence market–based performance measures (such as Q), but not necessarily
governance mechanisms or controls.5
Table 5.2 shows the results of applying the three alternative regression techniques to the basic
model of table 5.1. The major patterns from table 5.1 reappear in table 5.2. The inverse relation
between performance and concentration persists in 7 out of 9 years even in the year–by year
regressions (panel A), although it is only significant at conventional levels in one year. The GMM
and fixed effects regressions in panel B both find that the relationship is highly significant. Notice
that the GMM approach (left–hand side) mostly produces higher standard deviations than OLS,
which is as expected. However, the difference in p−values is never material. Finally, the fixed
effects regression (right–hand side) finds that the structural relationship is marginally different in
two out of the nine sample years. Otherwise, there are no striking contrasts to the findings in
table 5.1.
From now on, these robustness tests are put into appendix tables, and we only comment on
them when they produce conclusions which differ materially from those in the text.
4
General Method of Moments, see Ogaki (1993) or Hamilton (1994) for an overview.
For reasons of brevity we only report the OLS estimates of the fixed effects regression. Unlike the GMM, the
OLS has the added benefit of producing R2 estimates, which can be used to evaluate overall model fit.
5
5.2 Econometric issues
33
Table 5.2 Multivariate regression relating performance (RoA5 ) to ownership concentration and
controls according to Demsetz and Lehn (1985), but using year–by–year OLS, GMM, and fixed
effects OLS techniques
Panel A: Year by year OLS regressions
constant
lntrans(1-5 largest owners)
Industrial
Transport/shipping
Offshore
Investments over income
ln(Firm value)
Stock volatility
n
R2
Average (RoA5 )
1989
7.55
(0.27)
−0.41
(0.33)
−1.61
(0.11)
−0.80
(0.47)
−5.79
(0.00)
−0.05
(0.48)
0.23
(0.46)
−1.69
(0.34)
83
0.05
9.82
1990
−1.01
(0.89)
0.16
(0.74)
0.26
(0.80)
0.19
(0.86)
−1.66
(0.33)
−0.04
(0.75)
0.51
(0.13)
0.15
(0.94)
79
−0.04
9.33
1991
12.27
(0.16)
−0.23
(0.63)
0.09
(0.94)
−0.40
(0.72)
−2.98
(0.06)
0.05
(0.50)
0.05
(0.89)
−5.65
(0.01)
76
0.16
9.19
1992
14.21
(0.05)
−0.76
(0.21)
−0.47
(0.72)
−0.92
(0.46)
−2.68
(0.12)
−1.88
(0.27)
−0.04
(0.90)
−2.73
(0.12)
69
0.02
10.16
Year
1993
26.40
(0.00)
−0.85
(0.09)
−2.05
(0.08)
−2.38
(0.03)
−4.44
(0.00)
0.33
(0.67)
−0.58
(0.11)
−4.42
(0.01)
82
0.17
10.06
1994
23.86
(0.00)
−0.96
(0.02)
−2.22
(0.03)
−3.01
(0.00)
−4.27
(0.01)
−0.66
(0.15)
−0.58
(0.04)
−1.72
(0.36)
106
0.15
8.94
1995
2.50
(0.70)
−0.77
(0.11)
−2.37
(0.01)
−3.57
(0.00)
−3.42
(0.05)
−0.37
(0.11)
0.35
(0.25)
2.68
(0.09)
115
0.14
8.77
1996
22.35
(0.02)
−0.10
(0.87)
−2.53
(0.02)
−4.98
(0.00)
−3.42
(0.10)
−0.40
(0.25)
−0.45
(0.30)
−3.61
(0.12)
119
0.12
9.04
1997
33.31
(0.00)
−0.01
(0.99)
−3.38
(0.01)
−5.36
(0.00)
−4.03
(0.03)
−0.29
(0.27)
−0.86
(0.04)
−6.49
(0.03)
157
0.12
9.76
Panel B: Pooled GMM and fixed effects regressions
Dependent variable: RoA5
Dependent variable: RoA5
Constant
lntrans(1-5 largest owners)
Industrial
Transport/shipping
Offshore
Investments over income
ln(Firm value)
Stock volatility
n
Average (RoA5 )
coeff
14.41
-0.54
-1.97
-2.81
-3.93
-0.08
-0.11
-1.68
886
9.41
(stdev)
(3.82)
(0.17)
(0.40)
(0.47)
(0.55)
(0.04)
(0.16)
(0.97)
pvalue
0.00
0.00
0.00
0.00
0.00
0.04
0.49
0.09
Constant
lntrans(1-5 largest owners)
Industrial
Transport/shipping
Offshore
Investments over income
ln(Firm value)
Stock volatility
1990
1991
1992
1993
1994
1995
1996
1997
n
R2
Average (RoA5 )
coeff
16.49
-0.55
-2.03
-2.87
-4.12
-0.08
-0.16
-2.47
-0.47
-0.32
0.64
0.20
-1.33
-1.60
-1.40
-0.60
886
0.10
9.41
(stdev)
(2.72)
(0.18)
(0.38)
(0.40)
(0.61)
(0.05)
(0.12)
(0.68)
(0.70)
(0.71)
(0.74)
(0.70)
(0.66)
(0.65)
(0.65)
(0.62)
pvalue
0.00
0.00
0.00
0.00
0.00
0.10
0.20
0.00
0.50
0.66
0.39
0.78
0.05
0.01
0.03
0.33
This table complements the pooled OLS regression in table 5.1 by three additional regressions. Panel A shows OLS
estimates on a year by year basis. The left–hand side table in panel B lists pooled estimates, estimated with GMM.
The table on the right–hand side in panel B shows the results of a pooled OLS regression where dummy variables
for each year are used to capture fixed effects. 1989 is the base year in this regression. In regressions using firm size
across years the nominal values are adjusted to the 1997 general price level. Data for firms listed on the Oslo Stock
Exchange, 1989-1997. Variable definitions are in Appendix A.2.
34
Ownership concentration
5.3
Alternative functional specifications
In the preceding section, we listed four potential estimation problems and presented three techniques
for addressing two of them (autocorrelation and instability). OLS still produces unbiased coefficient
estimates under multicollinearity, but we bias the test towards keeping the null hypothesis because
multicollinearity inflates the estimated standard deviations compared to the uncorrelated case.
Since some of the independent variables may be correlated according to the theory (substitute or
complementary mechanisms), it would be wrong to just exclude a mechanism which is correlated
with another one. This is simply an important part of the theory we want to test. Thus, we will
gradually include new mechanisms based on the theory of corporate governance, independently of
multicollinearity concerns. Still, we indirectly address multicollinearity in chapter 10, which models
the interrelationships between the mechanisms.
The fourth econometric problem discussed above is a mis–specified functional form between
performance, governance, and controls. This section describes our methodology for handling this
problem. We use Q rather than RoA5 as an alternative performance in section 5.3.1. Nonlinear
relationships between concentration and performance in our regression models are analyzed in
section 5.3.2 .
5.3.1
Tobin’s Q as performance measure
Table 5.3 shows the regression which uses Q rather than RoA5 as the performance measure. The
inverse, very significant relationship between governance and performance persists. It turns out
later that in many cases, we either get this result or that the associations are more significant using
Q than RoA5 . Because most existing papers use Q and the inherent autocorrelation problem of
RoA5 discussed in footnote 1 of chapter 4, we use Q as our performance measure in most of the
following analyses.
The robustness tests reported in appendix table B.2 show that the corresponding relationship
between Q and concentration in the year–by–year regressions is more often significant than in the
RoA5 –based table 5.1.6 Using the fixed effects model, the estimated dummy variables for the two
final sample years (1996 and 1997) are always positive and very significant. The structural shift
happens because equity market values (and hence Q) rose very sharply in these two years. This
pattern will reappear in almost every single model in the following.7
5.3.2
Nonlinearity
The Demsetz and Lehn (1985) study has been criticized by later authors, particularly for their
choice of a simple linear function in the regression of performance on concentration. In fact, Morck
et al. (1988) argue that
“...the failure of Demsetz and Lehn to find a significant relationship between ownership concentration and profitability is probably due to their use of a linear specification
that does not capture an important nonmonotonicity”.
We therefore analyze the effect of allowing for nonlinearities in the functional specification.
6
The p−value is below 10% in five of the years and below 22% in the remaining four years.
In 1989–1995, average Q is 1.26, varying between a minimum of 1.05 and a maximum of 1.47. Subsequently, Q
rises to 1.98 in 1996 and to 2.01 in 1997.
7
5.3 Alternative functional specifications
35
Table 5.3 Multivariate regression relating performance to ownership concentration and controls,
using Q rather than RoA5 as performance measure in the Demsetz and Lehn (1985) approach
Dependent variable: Q
Constant
lntrans(1-5 largest owners)
Industrial
Transport/shipping
Offshore
Investments over income
ln(Firm value)
Stock volatility
n
R2
Average (Q)
coeff
0.32
-0.18
-0.44
-0.84
-0.74
-0.01
0.08
-0.04
905
0.14
1.53
(stdev)
(0.56)
(0.04)
(0.08)
(0.08)
(0.13)
(0.01)
(0.03)
(0.14)
pvalue
0.56
0.00
0.00
0.00
0.00
0.19
0.00
0.79
Pooled OLS regression using data for all listed firms on the OSE in the period 1989 to 1997. Table B.2 shows
complementary regressions which use the same set of independent variables. Variable definitions are in Appendix A.2.
In regressions using firm size across years the nominal values are adjusted to the 1997 general price level.
5.3.2.1
Piecewise linear model
As a first test of non–monotonicity between governance and performance, we replace the linear
specification with a piecewise linear one. Our pre–specified step points (5% and 25%) are identical
to those used by Morck et al. (1988) to analyze the insider–performance relationship, which we
will analyze in the next chapter. Using Q as the performance proxy and the fraction held by the
largest owner as the concentration measure,8 the result from estimating such a function without
controls is shown in figure 5.1. The figure suggests an inverse relationship between performance
and concentration for the pooled regression, but that the association may be positive if the largest
owner holds less than 5%.
We next add controls and estimate the model reported in table 5.4. Consistent with our findings
in the linear model without steps in table 5.3, all three coefficients on the largest owner in the pooled
regression are negative in table 5.4. The coefficient is insignificant for concentration levels up to
5%, and very significant thereafter. The year–by–year coefficients9 are mostly insignificant, but
still negative in most cases, in particular for the “5–25%” and “above 25%” cases.10
5.3.2.2
Quadratic approximation
As an alternative, more parsimonious nonlinear model, we consider the quadratic specification used
by McConnell and Servaes (1990). Unlike the piecewise linear approach, this model does not require
any prespecification of parameter values. Figure 5.2 fits a quadratic function to the relationship
between performance and the holding of the largest owner, using no controls. Judging from the
pooled regression and most of the year–by–year regressions, this univariate relationship appears
negative and possibly curvilinear.
8
We use the holding of the largest owner rather than the lntrans of the holding of the five largest owners because the
step points of Morck et al. (1988) are motivated by an individual owner’s concerns for flagging and voting thresholds.
9
Reported in appendix table B.3.
10
The lack of significance for the lowest “0–5%” interval may be due to the fact that our sample contains very few
cases where the largest stake is below 5%.
36
Ownership concentration
Figure 5.1 The relationship between performance (Q) and the holding of the largest owner in
Norwegian firms, using the piecewise linear function of Morck et al. (1988)
2.5
3
all years
1989
1990
1991
1992
1993
1994
1995
1996
1997
2.5
2
2
Q
Q
1.5
1.5
1
1
0.5
0.5
0
0
0
0.2
0.4
0.6
0.8
1
0
0.2
fraction owned
0.4
0.6
0.8
1
fraction owned
All years
Year by year
The figure shows the implied functional relationship from a piecewise linear regression with Q as the dependent
variable and the fraction held by the largest owner as the independent variable. The figure to the left pools data
for all years, while the figure to the right shows the results of year–by–year regressions. The underlying regressions,
which are detailed in appendix table B.5, includes no controls and no other governance mechanism beyond ownership
concentration. Data for firms listed on the Oslo Stock Exchange, 1989-1997. Variable definitions are in Appendix A.2.
Table 5.4 Multivariate regression relating performance (Q) to ownership concentration and controls, using the piecewise linear function of Morck et al. (1988)
Dependent variable: Q
Constant
Largest owner (0 to 5)
Largest owner (5 to 25)
Largest owner (25 to 100)
Industrial
Transport/shipping
Offshore
Investments over income
ln(Firm value)
Stock volatility
n
R2
Average (Q)
coeff
2.45
-38.62
-1.34
-0.76
-0.44
-0.86
-0.75
-0.02
0.09
-0.07
905
0.14
1.53
(stdev)
(2.17)
(42.76)
(0.60)
(0.28)
(0.08)
(0.08)
(0.13)
(0.01)
(0.03)
(0.14)
pvalue
0.26
0.37
0.03
0.01
0.00
0.00
0.00
0.17
0.00
0.63
Pooled OLS regression using data for all listed firms on the OSE in the period 1989 to 1997. Table B.3 shows
complementary regressions which use the same set of independent variables. Variable definitions are in Appendix A.2.
In regressions using firm size across years the nominal values are adjusted to the 1997 general price level.
5.4 Summary
37
Figure 5.2 The quadratic relationship between performance (Q) and the holdings of the largest
owner
2
3
all years
1989
1990
1991
1992
1993
1994
1995
1996
1997
1.8
2.5
1.6
1.4
2
1
Q
Q
1.2
1.5
0.8
1
0.6
0.4
0.5
0.2
0
0
0
0.2
0.4
0.6
fraction owned
All years
0.8
1
0
0.2
0.4
0.6
0.8
1
fraction owned
Year by year
The graphs show the implied functional relationship from estimating the regression
Qi = a + bxi + cx2i + εi ,
where xi is the holdings by the largest owner in firm i, εi is an error term, and a, b and c are constants to be
estimated. The graph to the left pools data for all years, and the right–hand side graph shows the results of year–
by–year estimation. The underlying regressions are detailed in appendix table B.6 Data for firms listed on the Oslo
Stock Exchange, 1989-1997. Variable definitions are in Appendix A.2.
Table 5.5, which also includes the control variables ignored by figure 5.2, addresses non–linearity
more formally by including a quadratic term in the multivariate regression. Once more, we find
an inverse, significant relationship between concentration and performance. The quadratic term
is insignificant at conventional levels, however, suggesting that the simple linear specification is
sufficient.
The shape of the graph in figure 5.2 and the numerical value of the estimated coefficient in
table 5.5 both indicate that the covariance between concentration and performance is far from negligible in economic terms. The estimated coefficient of −2.02 suggests that if all other independent
variables in the regression are kept constant, changing the holding of the largest owner by one unit
decreases performance by 2.02 units. To illustrate, consider a firm where market value is initially
1.53 times its book value (i.e., Q = 1.53), and where the largest owner holds 29% (0.29) of outstanding equity (these figures correspond to the average sample values). If concentration increases
by 10 units (i.e., from 0.29 to 0.39), the model of table 5.5 predicts that Q will drop from 1.53 to
1.33.11 That is, when the largest owner increases the stake from 29 to 39%, the market value of
the firm drops by 13%.
5.4
Summary
In this chapter we have moved from a univariate to a multivariate analysis of governance and
performance, focusing on ownership concentration. Unlike what Demsetz and Lehn (1985) find
for 511 large US firms in 1980, we conclude that for the population of Norwegian listed firms in
the 1989–1997 period, concentration and performance are strongly related. Economic performance
varies inversely and very significantly with the holdings of large owners. This is true both in a
11
Ignoring non–linearities and assuming that the other variables in the model remain at their initial levels
38
Ownership concentration
Table 5.5 Multivariate regression relating performance (Q) to ownership concentration and controls, using a quadratic function
Dependent variable: Q
Constant
Largest owner
Squared (Largest owner)
Industrial
Transport/shipping
Offshore
Investments over income
ln(Firm value)
Stock volatility
n
R2
Average (Q)
coeff
0.66
-1.96
1.37
-0.45
-0.86
-0.74
-0.01
0.09
-0.06
905
0.14
1.53
(stdev)
(0.56)
(0.65)
(0.84)
(0.08)
(0.08)
(0.13)
(0.01)
(0.03)
(0.14)
pvalue
0.24
0.00
0.10
0.00
0.00
0.00
0.17
0.00
0.65
Pooled OLS regression using data for all listed firms on the OSE in the period 1989 to 1997. Table B.4 shows
complementary regressions which use the same set of independent variables. Variable definitions are in Appendix A.2.
In regressions using firm size across years the nominal values are adjusted to the 1997 general price level.
statistical and an economic sense. The conclusion is valid under several alternative performance and
concentration measures, and regardless of whether the assumed functional form is linear, piecewise
linear or quadratic.
Insider ownership
39
Chapter 6
Insider ownership
The most remarkable finding in chapter 5 is the strongly inverse link between performance and
concentration. However, as we only studied large owners without concern for their identity, we
should not conclude that this result holds for large owners of any type. We argued in section 2.1
that owner identity may matter, and that inside owners may be fundamentally different from
outsider owners. Also, we found that the alignment–of–interest (Jensen and Meckling, 1976) and
the entrenchment (Morck et al., 1988) hypotheses jointly suggest that the relationship between
insider holdings and economic performance is curvilinear. If we assume that entrenched insiders
use their voting power to resist hostile takeovers (Stulz, 1988), the prediction becomes more precise,
as the curvilinear relationship has its minimum value at 50% insider holdings.
The two key empirical papers on insiders are Morck et al. (1988) and McConnell and Servaes
(1990). This chapter replicates and extends their analyses with our data set.
6.1
The Morck–Shleifer–Vishny approach
Morck et al. (1988) (hereafter MSV) analyze the relationship between Tobin’s Q and insider holdings
in 371 firms sampled from the Fortune 500 list in 1980. Insider ownership is operationalized as
the aggregate fraction held by the firm’s board members (directors).1 To account for the predicted
curvilinear relationship, MSV use a piecewise linear regression function with two step points. Their
theoretical argument is that 5% is a point of mandatory disclosure to the SEC, and that 20–30%
is by some considered an ownership range beyond which a hostile bid for the firm cannot succeed.
Admitting that these arguments are rather weak, they decide to choose step points of 5% and 25%
primarily because this combination maximized the R2 of their regressions.2
A summary of the MSV results is provided by figure 6.1. Performance increases with insider
holdings up to the pre–specified breakpoint of 5%, decreases as the stake grows further to the
second breakpoint of 25%, and increases again thereafter. In these three intervals, the estimated
coefficient of the insider stake is significant at the 1%, 5%, and 10% levels, respectively. This result
indicates that the alignment–of–interest effect dominates in the beginning, is dominated by the
entrenchment effect thereafter, and once more becomes the dominating force after 25%.
Using the same step points as MSV, figure 6.2 shows the corresponding graph for Norway.3
According to the pooled sample in the left graph, performance increases with higher insider holdings
up to 25%; and more so below 5% than above. After 25% is reached, performance declines with
increased insider stakes.4 Thus, our piecewise linear, univariate model suggests that insider holdings
are positively related to performance up to 25%, and negatively for stakes beyond this level. These
results differ from the findings of Morck et al. (1988) in large US firms, where the association is
positive for insider holdings below 5% and above 25%, and negative in the intermediate 5–25%
interval.
1
To be included in their insider proxy, an individual must hold at least 0.2% of the firm’s outstanding equity.
MSV’s theoretical argument about the 5% seems misplaced, as this rule applies to each separate insider fraction
and not to the aggregate stake of all the firm’s insiders.
3
Whereas MSV only include holdings by the board in their insider measure, we use primary insiders (board and
management) as our basic insider group. The graph in figure 6.2 is practically unchanged if primary insiders are
replaced by the board.
4
The three estimated coefficients have p values of 0%, 1%, and 0%, respectively.
2
40
Insider ownership
Figure 6.1 Relating performance (Q) to insider ownership using a piecewise linear function. US
data
The graph, which is copied from Morck et al. (1988), shows the implied functional relationship from a piecewise linear
regression with Q as the dependent variable and insider ownership (directors, only) as the independent variable. The
pre–specified steps in the underlying linear regression are at 5% and 25% insider ownership. The regression includes
no controls and no governance mechanism beyond insider ownership.
Figure 6.2 The piecewise linear relationship between performance (Q) and insider ownership in
Norwegian firms, following Morck et al. (1988)
2.5
3.5
all years
1989
1990
1991
1992
1993
1994
1995
1996
1997
3
2
2.5
1.5
Q
Q
2
1.5
1
1
0.5
0.5
0
0
0
0.2
0.4
0.6
fraction owned
All years
0.8
1
0
0.2
0.4
0.6
0.8
1
fraction owned
Year by year
The graphs show the implied functional relationship from a piecewise linear regression with Q as the dependent
variable and insider ownership as the independent variable. The underlying regression, which is detailed in appendix
table B.17, includes no controls and no other governance mechanism than insider ownership. Data for firms listed on
the Oslo Stock Exchange, 1989-1997. Variable definitions are in Appendix A.2.
6.1 The Morck–Shleifer–Vishny approach
41
Since the models underlying the figures 6.1 and 6.2 ignore the potential performance effect
of other governance mechanisms and controls, these patterns should be interpreted with caution.
To get an initial feeling for robustness, we follow Morck et al. (1988) and expand the model by
adding some control variables (but no governance mechanism). MSV include R&D and advertising
expenses to account for the impact on Q of cross-sectional differences in immaterial assets (reflected
in the market value, but not in the book value). Since we lack such data, these controls must be
ignored in our test. Like MSV, we include leverage to control for governance–independent effects
of financing on Q, such as the interest tax shield. For the same reasons as in chapter 5, we also
control for size and industry. Finally, for the reasons discussed in chapter 3, we use primary insiders
(officers and directors) as our basic insider definition.
Table 6.1 shows the findings from the pooled regressions. The estimated signs and the significance levels of the insider ownership variables are similar to those in figure 6.2, although the
p–values for the two upper size intervals increase from 1% to 2% and from 0% to 6%, respectively.5
Table 6.1 Multivariate regression relating performance (Q) to insider ownership and controls,
following Morck et al. (1988)
Dependent variable: Q
Constant
Primary insiders (0 to 5)
Primary insiders (5 to 25)
Primary insiders (25 to 100)
Industrial
Transport/shipping
Offshore
Debt to assets
ln(Firm value)
n
R2
Average (Q)
coeff
-0.00
7.74
1.85
-0.57
-0.28
-0.59
-0.58
-1.10
0.11
1057
0.20
1.47
(stdev)
(0.32)
(1.97)
(0.76)
(0.30)
(0.07)
(0.07)
(0.11)
(0.15)
(0.02)
pvalue
0.99
0.00
0.02
0.06
0.00
0.00
0.00
0.00
0.00
Pooled OLS regression using data for all listed firms on the OSE in the period 1989 to 1997. Table B.11 shows
complementary regressions which use the same set of independent variables. Variable definitions are in Appendix A.2.
In regressions using firm size across years the nominal values are adjusted to the 1997 general price level.
6.1.1
Extensions
So far, we have found that economic performance is inversely related to ownership concentration
in general (chapter 5), but that the story is quite different when we study one particular owner
type. There is a strong, positive relationship between Q and insider holdings up to 25%, and
a negative and less significant link thereafter. A natural question to ask is whether this result
is caused by fundamental differences between owner types or simply by different specifications
of the regression equations in the two tests. Table 6.2 explores this question by including both
ownership concentration and insider holdings in the same regression, keeping the piecewise linear
specification for the insider stake from table 6.1. The table shows that ownership concentration
5
Appendix B.3 shows that the coefficients in the year–by–year regressions are seldom significant. Also, the evidence
is weaker using GMM or fixed effects OLS, as the coefficients for the two upper size intervals become insignificant. If
we use RoA5 instead of Q, the overall shape of the relationship is maintained. Like for GMM and fixed effects OLS
under the Q measure, the coefficients for the two upper size intervals are insignificant.
42
Insider ownership
and insider holdings have separate roles to play. As in chapter 5, ownership concentration enters
with a significantly negative coefficient, whereas the insider stake works like in table 6.1. Notice
also that the expected change in performance is considerably stronger with corresponding changes
in insider holdings than in concentration, particularly at low insider levels. The absolute value of
the estimated coefficient is roughly eight times higher.
Table 6.2 Multivariate regression relating performance (Q) to insider holdings, ownership concentration and controls, using the piecewise linear function of Morck et al. (1988).
Dependent variable: Q
Constant
Primary insiders (0 to 5)
Primary insiders (5 to 25)
Primary insiders (25 to 100)
Largest owner
Industrial
Transport/shipping
Offshore
Debt to assets
ln(Firm value)
n
R2
Average (Q)
coeff
0.45
6.31
1.90
-0.42
-0.78
-0.26
-0.60
-0.59
-1.12
0.10
1057
0.22
1.47
(stdev)
(0.33)
(1.96)
(0.75)
(0.30)
(0.14)
(0.07)
(0.07)
(0.11)
(0.14)
(0.02)
pvalue
0.17
0.00
0.01
0.16
0.00
0.00
0.00
0.00
0.00
0.00
Pooled OLS regression using data for all listed firms on the OSE in the period 1989 to 1997. Table B.12 shows
complementary regressions which use the same set of independent variables. Variable definitions are in Appendix A.2.
In regressions using firm size across years the nominal values are adjusted to the 1997 general price level.
6.2
The McConnell–Servaes framework
The McConnell and Servaes (1990) paper (hereafter McS) differs from Morck et al. (1988) in several
ways. The number of firms is roughly twice as large, the sample is more heterogeneous in terms of
firm size, and the regression model is estimated using data for two different years (1976 and 1986).
Besides the aggregate insider holdings analyzed by MSV, McS include two other governance mechanisms, which are ownership concentration and the fraction held by institutional investors. Whereas
MSV define insiders as directors, only, McS include both officers and directors, which corresponds
to our primary insider category.6 Finally, instead of the rather ad–hoc linear approximation to a
possibly non-linear relationship used by MSV, McS argue that the estimation model should allow
for less restrictions and more smoothness in the insider–performance relationship. Therefore, they
choose a quadratic functional form rather than a piecewise linear approximation with pre–specified
breakpoints. Figure 6.3 shows the result from their two univariate regressions of performance on
insider holdings, only. There is a distinct quadratic relationship in both years, and the estimated
inflection points are at 38% in 1986 and 49% in 1976.7
The corresponding findings for Norway are shown in figure 6.4. The graph for the pooled
observations indicates an inflection point around 50%, which is roughly the case in the year–
6
7
As shown in the appendix, our results are rather insensitive to this distinction.
The inflection points are the inferred insider stakes at which Q is maximized.
6.2 The McConnell–Servaes framework
43
Figure 6.3 The quadratic relationship between performance (Q) and insider ownership in the US
The graph, which is copied from McConnell and Servaes (1990), shows the implied functional relationship from
estimating the regression
Qi = a + bxi + cx2i + ε,
where xi is the equity holdings of the primary insiders (officers and directors) in firm i, εi is an error term, and a, b
and c are constants to be estimated.
44
Insider ownership
by–year graphs as well.8 Table 6.3 shows that the quadratic shape persists after controlling for
leverage, size, and industry.9 Overall, the curvilinear specification seems to fit the data better than
the piecewise linear model of Morck et al. (1988).
Figure 6.4 The quadratic relationship between performance (Q) and insider ownership, following
McConnell and Servaes (1990)
2.5
3.5
all years
1989
1990
1991
1992
1993
1994
1995
1996
1997
3
2
2.5
1.5
Q
Q
2
1.5
1
1
0.5
0.5
0
0
0
0.2
0.4
0.6
fraction owned
All years
0.8
1
0
0.2
0.4
0.6
0.8
1
fraction owned
Year by year
The figure shows the implied functional relationship from estimating the regression
Q = a + bx + cx2 + ε,
where x is the holdings by primary insiders (officers and directors), ε is an error term, and a, b and c are constants
to be estimated. The figure on the left pools data for all years, the figure on the right shows the results of doing the
estimation year by year. The underlying regression is detailed in appendix table B.18. Data for firms listed on the
Oslo Stock Exchange, 1989-1997. Variable definitions are in Appendix A.2.
Table 6.4 takes the analysis one step further by including not just insider ownership, but also
the overall concentration as measured by the fraction held by the largest stockholder. Like we found
with the piecewise linear specification, the significant curvilinear relationship for insiders persists,
and the coefficient of the concentration variable is negative and highly significant. Once more, we
conclude that insider holdings and ownership concentration seem to capture separate governance
characteristics.10
The final step in our comparison with McConnell and Servaes (1990) is to add the aggregate
fraction held by institutional owners to the model in table 6.4. McS find a separate, positive
relationship with economic performance, which is consistent with the efficient monitoring hypothesis
of Pound (1988) discussed in section 2.1. Table 6.5 finds no convincing evidence of such an effect in
our sample. This may reflect that the positive monitoring effect is neutralized by the two negative
8
The insider fraction I which maximizes performance Q can be found by expressing Q as a quadratic function
of I, taking the partial derivative of Q with respect to I, setting this expression equal to zero, solving for I, and
plugging in the coefficient estimates from the tables. For instance, in table 6.3, the condition is
I = 2.81 − 2 · 2.64 · I = 0, i.e., I = 53%.
9
As shown in appendix table B.18 there is considerable consistency across individual years. The coefficient of
the linear term is always positive, with a pvalue below 5% in five out of nine cases. The squared term always has a
negative coefficient, and pvalue below 5% in four of the years.
10
One may worry that some of these results may be caused by an overlap between concentration and insider
holdings, since some of the large owners may also be insiders. As we show in appendix B.3.5, no conclusion changes
if we account for this overlap by removing the insiders from the concentration measure.
6.2 The McConnell–Servaes framework
45
Table 6.3 Multivariate regression relating performance (Q) to insider ownership and controls,
following McConnell and Servaes (1990)
Dependent variable: Q
Constant
Primary insiders
Squared (Primary insiders)
Industrial
Transport/shipping
Offshore
Debt to assets
ln(Firm value)
n
R2
Average (Q)
coeff
0.04
2.81
-2.64
-0.29
-0.59
-0.56
-1.15
0.11
1057
0.19
1.47
(stdev)
(0.32)
(0.42)
(0.52)
(0.07)
(0.08)
(0.11)
(0.15)
(0.02)
pvalue
0.90
0.00
0.00
0.00
0.00
0.00
0.00
0.00
Pooled OLS regression using data for all listed firms on the OSE in the period 1989 to 1997. Table B.13 shows
complementary regressions which use the same set of independent variables. Variable definitions are in Appendix A.2.
In regressions using firm size across years the nominal values are adjusted to the 1997 general price level.
Table 6.4 Multivariate regression relating performance (Q) to insider ownership, ownership concentration and controls, following McConnell and Servaes (1990)
Dependent variable: Q
Constant
Primary insiders
Squared (Primary insiders)
Largest owner
Industrial
Transport/shipping
Offshore
Debt to assets
ln(Firm value)
n
R2
Average (Q)
coeff
0.52
2.56
-2.31
-0.85
-0.26
-0.59
-0.58
-1.16
0.10
1057
0.22
1.47
(stdev)
(0.33)
(0.42)
(0.51)
(0.14)
(0.07)
(0.07)
(0.11)
(0.15)
(0.02)
pvalue
0.12
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
Pooled OLS regression using data for all listed firms on the OSE in the period 1989 to 1997. Table B.14 shows
complementary regressions which use the same set of independent variables. Variable definitions are in Appendix A.2.
In regressions using firm size across years the nominal values are adjusted to the 1997 general price level.
46
Insider ownership
effects, i.e., the conflict–of–interest and the strategic alignment costs.11 Finally, appendix B.3
documents that unlike for the MSV–type model, the McS model and its extensions produce the
same results for the governance mechanisms independently of whether we use pooled OLS, GMM,
or pooled OLS with fixed effects.
Table 6.5 Multivariate regression relating performance (Q) to insider holdings, ownership concentration, institutional ownership and controls, following McConnell and Servaes (1990)
Dependent variable: Q
Constant
Primary insiders
Squared (Primary insiders)
Largest owner
Aggregate financial holdings
Industrial
Transport/shipping
Offshore
Debt to assets
ln(Firm value)
n
R2
Average (Q)
coeff
0.47
2.56
-2.33
-0.89
-0.24
-0.26
-0.60
-0.58
-1.14
0.11
1057
0.22
1.47
(stdev)
(0.33)
(0.42)
(0.51)
(0.15)
(0.21)
(0.07)
(0.07)
(0.11)
(0.15)
(0.02)
pvalue
0.15
0.00
0.00
0.00
0.25
0.00
0.00
0.00
0.00
0.00
Pooled OLS regression using data for all listed firms on the OSE in the period 1989 to 1997. Table B.15 shows
complementary regressions which use the same set of independent variables. Variable definitions are in Appendix A.2.
In regressions using firm size across years the nominal values are adjusted to the 1997 general price level.
6.3
Alternative insider definitions
So far, we have used the holdings by primary insiders (officers and directors) to proxy for insider
ownership. To investigate the sensitivity to this definition, we re–estimate the models using three
alternative insider proxies: all insiders, officers, and directors. Appendix B.3.4 shows that using
board members as insiders produce even more significant results, with the same coefficient signs
as for primary insiders. Using all insiders or management holdings as the insider proxy produces
less significant results, but the curvilinear relationship between performance and insider ownership
persists.
6.4
The large insider
There is a possibility that the estimated curvilinear relationship is driven by the cases where one
specific insider owns a particularly large stake. If this insider uses the voting power to extract
private benefits at the expense of other owners, firm value may decrease with insider holdings even
if all primary insiders as a group hold rather moderate stakes. As shown in the histogram of insider
ownership in appendix A.3, the distribution of this variable is higly skewed, as there are only a
few cases with insider ownership above 10%. One possible explanation of our curvilinear results is
that it reflects these relatively few cases of a negative effect of one very large insider. To explore
11
As institutional investors is one of our five basic investor types, we will reconsider their role in chapter 7, which
explicitly analyzes the relationship between outside owner identity and economic performance.
6.5 Summary
47
this hypothesis, we disentangle the insider holdings by explicitly considering the size of the largest
primary insider. We add the the largest primary insider as an explanatory variable together with the
holdings by all primary insiders12 and ownership concentration. Table 6.6 shows the result. There
is a significantly negative coefficient on the ownership by the largest primary insider, indicating
that having particularly large insiders is negative for performance. Still, the curvilinear relation
between performance and aggregate insiders persists, as the linear and the sqared terms remain
significant.
Table 6.6 Multivariate regression relating performance (Q) to insider ownership, the holdings of
the largest insider, ownership concentration, and controls
Dependent variable: Q
Constant
Primary insiders
Squared (Primary insiders)
Largest primary insider
Largest outside owner
Industrial
Transport/shipping
Offshore
Debt to assets
Investments over income
ln(Firm value)
n
R2
Average (Q)
coeff
1.08
3.54
-3.35
-1.10
-0.73
-0.29
-0.60
-0.61
-1.51
-0.00
0.08
990
0.25
1.47
(stdev)
(0.35)
(0.50)
(0.56)
(0.34)
(0.14)
(0.07)
(0.07)
(0.11)
(0.16)
(0.01)
(0.02)
pvalue
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.72
0.00
Pooled OLS regression using data for all listed firms on the OSE in the period 1989 to 1997. Table B.16 shows
complementary regressions which use the same set of independent variables. Variable definitions are in Appendix A.2.
In regressions using firm size across years the nominal values are adjusted to the 1997 general price level.
6.5
Summary
The evidence in this chapter shows that while ownership concentration in general is inversely related
to performance across all concentration levels, holdings by corporate insiders play an independent
and different role. Performance is positively related to insider holdings up to roughly 50% and
negatively thereafter, reflecting a curvilinear variation which is better approximated by a quadratic
function than a piecewise linear one. This result, which corresponds quite well to what was found
in the US by McConnell and Servaes (1990), is robust to alternative insider specifications and to
potential overlap between the insider and the large owner categories. In regressions with independent variables representing both insider ownership, ownership concentration, industry membership
, financial leverage, and firm size, performance (as measured by Tobin’s Q) is roughly three times
more sensitive to insider holdings than to ownership concentration. The difference is larger at very
low or very high insider stakes.
12
There is an overlap between primary insider ownership and the owership by the largest inside owner. We keep
this overlap because we want to investigate the robustness of the conclusion about all primary insiders when we
account separately for the largest one.
48
Owner type
Chapter 7
Owner type
The two preceding chapters showed that although ownership concentration and economic performance are inversely related in general, a finer partition of the concentration variable shows a more
complex pattern. We found that although increased concentration of insider ownership reduces
performance if the insider stake is high (typically above 50%), the relationship is positive at lower
concentration levels. Thus, inside and outside concentration relate very differently to economic
performance in our sample. Along with the theoretical arguments in section 2.1, these findings suggest that to better understand the interaction between ownership and performance, we should focus
more closely on owner identity. This chapter analyzes how Tobin’s Q is linked to the aggregate holdings of different owner types in a firm and to the identity of its largest owner. Given the evidence
in the two previous chapters, the base–case regression model in this chapter includes ownership
concentration, insider holdings (quadratic specification), and controls (industry, leverage, and firm
size). It turns out that results are insensitive to whether we measure concentration by the holdings
of the largest owner, the two largest, three largest, four largest, five largest or by the Herfindahl
concentration index. Since the Herfindahl index has the theoretical advantage of accounting for
size heterogeneity across the largest holdings, we measure concentration by the Herfindahl index in
the following. The base–case model is augmented by owner identity characteristics as we go along.
7.1
Aggregate holdings by owner type
Except for state and possibly financial owners, insiders are an owner type where its aggregate stake
in a firm directly reflects the type’s power and incentives. Coordination is particularly costly for the
small and numerous individual owners, and correspondingly easier for the state and institutions,
who normally own larger stakes, are less numerous, and tend to own shares across many of the
same firms (i.e., large firms with liquid stocks).
Using our earlier classification system, we initially categorize the investors into the five basic
types of state, international, individual, financial, and nonfinancial owners. As we cannot argue
convincingly that any of these types coordinate their power and incentives in a systematic fashion,
we do not specify a priori how a type’s aggregate stake is related to performance.
We add the aggregate stake per investor type to the base–case regression model described above.
Because the five ownership fractions sum to unity per firm by construction, we avoid econometric
problems by excluding one type and interpreting this type as the reference case. Since we have
already explored the aggregate holdings of financials in section 6.2, we choose financials as the
reference group. Table 7.1 shows the results.
The table shows that compared to financial investors as a group, the relationship between owner
identity and performance is no different for state, international, financial, and non–financial owners.
The positive and significant coefficient for individual holdings reflects a tendency that the larger the
aggregate fraction of a firm’s equity which is owned directly rather than indirectly, the better the
firm’s performance. Thus, although performance correlates inversely with (outside) concentration
in general, the negative effect is less pronounced when individuals as a group hold large stakes.
An agency interpretation would be that this is because with personal owners, the agent is directly
monitored by the principal rather than by intermediate agents acting on the principal’s behalf.
This interpretation may still be too simple, as indirect ownership can also create benefits which
7.2 The type of the largest owner
49
Table 7.1 Multivariate regression relating performance (Q) to ownership concentration, insider
holdings, aggregate holdings per owner type, and controls
Dependent variable: Q
Constant
Herfindahl index
Primary insiders
Squared (Primary insiders)
Aggregate state holdings
Aggregate international holdings
Aggregate individual holdings
Aggregate nonfinancial holdings
Industrial
Transport/shipping
Offshore
Debt to assets
ln(Firm value)
n
R2
Average (Q)
coeff
-0.18
-0.59
2.05
-2.02
-0.40
0.02
0.97
-0.15
-0.19
-0.50
-0.52
-1.15
0.12
1057
0.23
1.47
(stdev)
(0.44)
(0.21)
(0.43)
(0.51)
(0.30)
(0.21)
(0.27)
(0.22)
(0.07)
(0.08)
(0.11)
(0.15)
(0.02)
pvalue
0.68
0.00
0.00
0.00
0.19
0.93
0.00
0.50
0.01
0.00
0.00
0.00
0.00
Pooled OLS regression using data for all listed firms on the OSE in the period 1989 to 1997. Table B.34 shows
complementary regressions which use the same set of independent variables. Variable definitions are in Appendix A.2.
In regressions using firm size across years the nominal values are adjusted to the 1997 general price level.
are neither captured by the agency model nor by the five basic owner types. Allen and Phillips
(2000) argue that non–financial firms in particular may create value by holding long–term equity
positions in other firms. This may happen when ownership acts as a mechanism for sharing jointly
produced profits or to reduce information asymmetries between separate firms participating in a
strategic alliance. Hence, because intercorporate ownership between large firms may involve both
a monitoring disadvantage and a strategic benefit, the net effect is unclear.
To analyze this possibility, we exploit the fact that our ownership data includes all equity stakes
by listed firms in other listed firms. This allows us to use intercorporate ownership between OSE
firms as a proxy for holdings between large firms with many owners.1 Table 7.2 shows that there
is a significantly (p < 2%) negative relationship between performance and the aggregate fraction
of an OSE firm’s equity held by other OSE firms. Thus, on average, the positive strategic effect of
intercorporate investments is more than offset by the negative monitoring effect hypothesized by
the agency model.
7.2
The type of the largest owner
In order to avoid the interpretation problems caused by the aggregate holding per owner type in
the preceding section, we focus instead on the identity of the largest owner. Building on the base–
case model, we add four indicator variables which equal unity if the largest owner is the state, an
individual, a non–financial corporation, or an international investor, respectively. The reference
case is when all indicator variables are zero, which happens when the largest owner is a financial
1
State, international, financial, and non–financial owner types represent much less homogeneous versions of indirect
ownership. For instance, an international investor may be a person, and a non–financial firm may be closely held by
its manager.
50
Owner type
Table 7.2 Multivariate regression relating performance (Q) to ownership concentration, insider
holdings, aggregate intercorporate holdings, and controls
Dependent variable: Q
Constant
Herfindahl index
Primary insiders
Squared (Primary insiders)
Aggregate intercorporate holdings
Industrial
Transport/shipping
Offshore
Debt to assets
ln(Firm value)
n
R2
Average (Q)
coeff
0.49
-0.98
2.50
-2.30
-0.38
-0.24
-0.59
-0.58
-1.20
0.10
1055
0.22
1.47
(stdev)
(0.33)
(0.18)
(0.42)
(0.51)
(0.19)
(0.07)
(0.07)
(0.11)
(0.15)
(0.02)
pvalue
0.14
0.00
0.00
0.00
0.05
0.00
0.00
0.00
0.00
0.00
Pooled OLS regression using data for all listed firms on the OSE in the period 1989 to 1997. Table B.35 shows
complementary regressions which use the same set of independent variables. Variable definitions are in Appendix A.2.
In regressions using firm size across years the nominal values are adjusted to the 1997 general price level.
institution. Table 7.3 reports our findings.
Table 7.3 Multivariate regression relating performance (Q) to ownership concentration, insider
holdings, largest owner identity, and controls
Dependent variable: Q
Constant
Herfindahl index
Primary insiders
Squared (Primary insiders)
Largest owner is state
Largest owner is individual
Largest owner is nonfinancial
Largest owner is international
Industrial
Transport/shipping
Offshore
Debt to assets
ln(Firm value)
n
R2
Average (Q)
coeff
0.60
-0.86
2.46
-2.25
-0.43
-0.16
-0.23
-0.28
-0.23
-0.57
-0.59
-1.20
0.10
1057
0.22
1.47
(stdev)
(0.35)
(0.18)
(0.44)
(0.52)
(0.12)
(0.12)
(0.09)
(0.11)
(0.07)
(0.08)
(0.11)
(0.15)
(0.02)
pvalue
0.08
0.00
0.00
0.00
0.00
0.18
0.01
0.01
0.00
0.00
0.00
0.00
0.00
Pooled OLS regression using data for all listed firms on the OSE in the period 1989 to 1997. Table B.36 shows
complementary regressions which use the same set of independent variables. Variable definitions are in Appendix A.2.
In regressions using firm size across years the nominal values are adjusted to the 1997 general price level.
The estimated sign of the indicator variable is negative and highly significant when the largest
owner is the state, a non–financial firm, or an international owner, but insignificant for an individual
7.2 The type of the largest owner
51
owner. This means that compared to the case where the largest owner is a financial or an individual,
performance is lower when the owner is the state, an international investor or a non–financial
national corporation. Once more, owner identity is seen to matter for the firm’s performance.
To complement our results for aggregate ownership by intercorporate investors from table 7.2,
we also consider the case where the largest owner is another listed firm. Table 7.4 shows that the
estimated coefficient is negative, but insignificantly different from zero.
Table 7.4 Multivariate regression relating performance (Q) to ownership concentration, insider
holdings, largest owner being listed, and controls
Dependent variable: Q
Constant
Herfindahl index
Primary insiders
Squared (Primary insiders)
Largest owner is listed
Industrial
Transport/shipping
Offshore
Debt to assets
ln(Firm value)
n
R2
Average (Q)
coeff
0.52
-1.02
2.52
-2.31
-0.10
-0.25
-0.59
-0.58
-1.20
0.10
1057
0.21
1.47
(stdev)
(0.33)
(0.18)
(0.42)
(0.51)
(0.08)
(0.07)
(0.07)
(0.11)
(0.15)
(0.02)
pvalue
0.12
0.00
0.00
0.00
0.23
0.00
0.00
0.00
0.00
0.00
Pooled OLS regression using data for all listed firms on the OSE in the period 1989 to 1997. Table B.37 shows
complementary regressions which use the same set of independent variables. Variable definitions are in Appendix A.2.
In regressions using firm size across years the nominal values are adjusted to the 1997 general price level.
52
7.3
Owner type
Summary
This chapter finds that even if we account for the identity of the five basic owner types, performance
still decreases monotonically with ownership concentration and increases with insider holdings up
to roughly 50% before declining. Beyond this insider/outsider dimension, our evidence shows that
the owner’s identity matters in the sense that when individual investors hold large aggregate stakes
or is the largest separate investor in a firm, the negative relationship between concentration and
performance is less pronounced than for other investor types.
Board characteristics, security design, and financial policy
53
Chapter 8
Board characteristics, security design, and financial policy
The three preceding chapters focused on ownership concentration, insider holdings, and outside
owner identity, respectively. Still using the single–equation approach of cell 1 in table 2.1, this
chapter analyzes the link between performance and the three remaining governance mechanisms,
i.e. board characteristics, security design, and financial policy.
Our starting point is the base-case regression model established at the beginning of chapter 7,
which includes ownership concentration (the Herfindahl index), insider holdings (with a quadratic
specification), and controls (industry, leverage, and firm size).1
8.1
Board characteristics
Norwegian boards often have no firm officers among its members, never have more than one officer,
and never have an officer as the chairman. As discussed in section 4.5, this means we ignore the
question of manager–board independence and focus only on the relationship between board size and
performance, which is the second board characteristic analyzed by existing finance–based research
on corporate governance.2
Table 8.1 Multivariate regression relating performance (Q) to ownership concentration, insider
ownership, board size, and controls
Dependent variable: Q
Constant
Herfindahl index
Primary insiders
Squared (Primary insiders)
ln(Board size)
Industrial
Transport/shipping
Offshore
Debt to assets
ln(Firm value)
n
R2
Average (Q)
coeff
0.63
-1.01
2.40
-2.15
-0.24
-0.25
-0.58
-0.62
-1.19
0.11
956
0.21
1.50
(stdev)
(0.35)
(0.20)
(0.45)
(0.56)
(0.08)
(0.07)
(0.08)
(0.12)
(0.16)
(0.02)
pvalue
0.07
0.00
0.00
0.00
0.01
0.00
0.00
0.00
0.00
0.00
Pooled OLS regression using data for all listed firms on the OSE in the period 1989 to 1997. Table B.41 shows
complementary regressions which use the same set of independent variables. Variable definitions are in Appendix A.2.
In regressions using firm size across years the nominal values are adjusted to the 1997 general price level.
The univariate analysis in section 4.5 found that although most performance measures are
inversely related to board size, the association was insignificant for Q, RoA5 , and RoS. Using Q as
1
We considered including owner identity variables in the base case model, such as the type of the largest owner
or the aggregate stake per owner type. For the sake of simplicity, we decided to postpone the introduction of this
ownership dimension until chapter 9.
2
The relationship between performance and stock ownership by directors was analyzed in chapter 6 on insider
ownership.
54
Board characteristics, security design, and financial policy
the performance measure and adding the two governance mechanisms and three controls of the base–
case model, the multivariate regression in table 8.1 finds a more clear–cut result which is consistent
with the findings from other countries. Performance is negatively and significantly (p < 1%) related
to board size in the pooled model. The earlier findings for concentration and insider holdings
persist.3
8.2
Security design
As discussed in section 2.1, observed price differences between voting (A) and non–voting (B) shares
have been explained by the extraction of private rents by voting shareholders. Because Q does not
reflect the value of these private benefits, we would expect firms with dual-class shares to be less
worth than others, and more so the lower the fraction of A shares outstanding.
This prediction is supported by the test in table 8.2, where the fraction of a firm’s equity which
is voting has a positive, significant (p < 1%) coefficient. Like for board size, this result is more
consistent with the theory than what we found using the univariate approach.4
Table 8.2 Multivariate regression relating performance (Q) to ownership concentration, insider
ownership, security design, and controls
Dependent variable: Q
Constant
Herfindahl index
Primary insiders
Squared (Primary insiders)
Fraction voting shares
Industrial
Transport/shipping
Offshore
Debt to assets
ln(Firm value)
n
R2
Average (Q)
coeff
-0.55
-1.08
2.52
-2.20
0.88
-0.24
-0.57
-0.56
-1.18
0.11
1042
0.22
1.48
(stdev)
(0.49)
(0.18)
(0.42)
(0.51)
(0.31)
(0.07)
(0.08)
(0.11)
(0.15)
(0.02)
pvalue
0.27
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
Pooled OLS regression using data for all listed firms on the OSE in the period 1989 to 1997. Table B.42 shows
complementary regressions which use the same set of independent variables. Variable definitions are in Appendix A.2.
In regressions using firm size across years the nominal values are adjusted to the 1997 general price level.
8.3
Financial policy
Since it is common in the empirical corporate governance literature to use financial leverage as a
control variable, most of our regressions so far have included the debt to assets ratio as a governance–
independent control. We argued in chapter 2, however, that both leverage and dividend payout
3
Table B.41 documents that the pooled GMM regression and the pooled OLS regression produce corresponding
evidence. As expected, the year–by–year regressions are weaker. For instance, the estimated coefficient for board size
is negative in six out of nine years, but is only significant in one of them (p < 1%).
4
Table B.42 shows that the pooled GMM regression and the pooled OLS regression with fixed effects produce
corresponding findings, although the p-value increases to 6% in the latter model. The estimated coefficient for board
size in the year–by–year regressions is negative in seven of the years, but significant (p < 1%) only in one of them.
8.4 Summary
55
may act as governance mechanisms. Agency theory predicts that due to the disciplining effect of
high debt and low retained earnings, firm value will be positively related to financial leverage and
dividend payout.
Table 8.3 Multivariate regression relating performance (Q) to ownership concentration, insider
ownership, financial policy, and controls
Dependent variable: Q
Constant
Herfindahl index
Primary insiders
Squared (Primary insiders)
Industrial
Transport/shipping
Offshore
Debt to assets
Dividends to earnings
ln(Firm value)
n
R2
Average (Q)
coeff
0.50
-1.02
2.61
-2.32
-0.25
-0.59
-0.59
-1.23
-0.06
0.10
1028
0.22
1.48
(stdev)
(0.34)
(0.18)
(0.43)
(0.52)
(0.07)
(0.08)
(0.11)
(0.15)
(0.04)
(0.02)
pvalue
0.14
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.14
0.00
Pooled OLS regression using data for all listed firms on the OSE in the period 1989 to 1997. Table B.43 shows
complementary regressions which use the same set of independent variables. Variable definitions are in Appendix A.2.
In regressions using firm size across years the nominal values are adjusted to the 1997 general price level.
We expand the base case model by these financial policy variables and obtain the estimates
reported in table 8.3. The evidence is inconsistent with an agency story, as the estimated sign is
negative in both cases. At conventional levels, the coefficient is significant (p < 1%) for leverage
and insignificant for dividend payout.5
8.4
Summary
This chapter explored whether performance is systematically related to security design, board composition, and financial policy. We find that when our base–case model (which includes ownership
concentration, insider holdings, and controls) is alternately expanded by these three mechanisms,
performance varies inversely with board size, the fraction of non–voting shares outstanding, financial leverage, and dividend payout. The estimated coefficients are very significant for all these
mechanisms except dividends. We conclude once more that the inclusion of additional governance
mechanisms does not affect how performance varies systematically with concentration and insider
holdings.
5
We show in table B.43 that the pooled GMM regression and the pooled OLS regression with fixed effects are
consistent with the pooled OLS regression in table 8.3. The year–by–year regressions mostly produce negative signs
for the coefficients of leverage and dividend payout, but the estimate is rarely significant.
56
A full multivariate model
Chapter 9
A full multivariate model
The four preceding chapters established and tested a series of multivariate single-equation models.
To explore whether the findings from these partial models of selected governance mechanisms hold in
a less restrictive context, we specify a multivariate model which captures the full set of mechanisms
discussed in chapter 2. We estimate two versions of the model which reflect the two alternative
ways of incorporating outside owner type discussed in chapter 7. One version uses type of the
largest owner as owner identity, and the other version uses aggregate holding per type. To cover
a wider range of performance measures, we start out with the standard Tobin’s Q in the first two
sections and reestimate the model using both return on assets and return in section 9.3.
9.1
Measuring performance with Tobin’s Q
The estimates from the full multivariate model when performance is measured by Q are shown in
tables 9.1 (type of largest owner) and 9.2 (aggregate holding per type). The evidence in the two
tables is very similar.1
Two general points are worth noticing before we start discussing the details. First, because
this is a multivariate relationship, the empirical evidence should be interpreted accordingly. For
instance, as both concentration, insider holdings, board size, and firm size are included in the full
model, the estimated coefficient of the Herfindahl index reflects the performance effect of changes
in ownership concentration, keeping insider holdings, board size, and firm size constant.
Second, most relationships have survived all the step–by–step analyses from the simplest to the
most comprehensive models. In particular, the very significant, negative link between concentration
and performance has consistently showed up all the way from the univariate analysis in chapter 4
through the various partial multivariate models in chapters 5–8 to the full versions in tables 9.1
and 9.2. This is also true for the inverse relationship between leverage and performance, for the
positive link between firm size and performance, and for the industry effects.
Insider ownership is in a similar position, but not quite. The univariate, linear model in chapter 4
suggests a positive relationship regardless of insider levels. All the subsequent models, i.e., from
the simplest regressions without controls graphed in figures 6.2 and 6.4 to the full multivariate
models in this chapter use a non–linear specification. The non–linear function always comes out
with significant coefficients for the linear and the quadratic terms, and it reaches a maximum
around an insider stake of 50–60%. Similarly, board characteristics and security design have kept
their estimated signs, but they have become more significant as we have built more comprehensive
1
Appendix tables B.47 and B.48 show the results of applying the three alternative regression techniques to the
models in tables 9.1 and 9.2, respectively. As usual, the year–by–year OLS regressions mostly produce the same
estimated signs for coefficients which are significant in tables 9.1 and 9.2, but the p–values are mostly much higher.
The OLS regressions with fixed annual effects are consistent with the pooled OLS except that the p–value for the
fraction of voting shares increases from 2% to 7% in the model using the identity of the largest owner, and international
investors join individuals in the group of superior investors in the model using the aggregate stake per owner type. As
always, the two final sample years come out with a very significant, positive coefficient, reflecting the strong growth
in market values in these two years. In the regression with GMM–based standard errors, the non–linear term keeps
its negative sign, but p increases to 10% when investor type is proxied by aggregate holdings per type and to 6%
using the type of the largest owner. The other estimates correspond to those in the pooled OLS regression without
fixed effects.
9.1 Measuring performance with Tobin’s Q
57
Table 9.1 Multivariate regression relating performance (Q) to ownership concentration, insider
holdings, owner type (identity of largest owner), board characteristics, security design, financial
policy, and controls
Dependent variable: Q
Constant
Herfindahl index
Primary insiders
Squared (Primary insiders)
Largest owner is state
Largest owner is international
Largest owner is individual
Largest owner is nonfinancial
ln(Board size)
Fraction voting shares
Debt to assets
Dividend payout ratio
Industrial
Transport/shipping
Offshore
Investments over income
ln(Firm value)
n
R2
Average (Q)
coeff
0.20
-1.00
2.04
-1.61
-0.42
-0.28
-0.16
-0.29
-0.22
0.89
-1.62
-0.10
-0.25
-0.55
-0.65
-0.00
0.12
868
0.28
1.52
(stdev)
(0.61)
(0.21)
(0.49)
(0.59)
(0.14)
(0.12)
(0.13)
(0.10)
(0.09)
(0.36)
(0.18)
(0.05)
(0.08)
(0.09)
(0.14)
(0.01)
(0.02)
pvalue
0.75
0.00
0.00
0.01
0.00
0.02
0.24
0.00
0.01
0.01
0.00
0.06
0.00
0.00
0.00
0.73
0.00
Pooled OLS regression using data for all listed firms on the OSE in the period 1989 to 1997. Table B.47 shows
complementary regressions which use the same set of independent variables. Variable definitions are in Appendix A.2.
In regressions using firm size across years the nominal values are adjusted to the 1997 general price level.
58
A full multivariate model
Table 9.2 Multivariate regression relating performance (Q) to ownership concentration, insider
holdings, owner type (aggregate holding per type) , board characteristics, security design, financial
policy, and controls
Dependent variable: Q
Constant
Herfindahl index
Primary insiders
Squared (Primary insiders)
Aggregate state holdings
Aggregate international holdings
Aggregate individual holdings
Aggregate nonfinancial holdings
ln(Board size)
Fraction voting shares
Debt to assets
Dividend payout ratio
Industrial
Transport/shipping
Offshore
Investments over income
ln(Firm value)
n
R2
Average (Q)
coeff
-0.94
-0.68
1.64
-1.37
-0.43
0.12
1.02
-0.23
-0.19
1.14
-1.54
-0.11
-0.19
-0.46
-0.57
-0.00
0.14
868
0.29
1.52
(stdev)
(0.69)
(0.25)
(0.47)
(0.58)
(0.35)
(0.25)
(0.30)
(0.25)
(0.09)
(0.36)
(0.18)
(0.05)
(0.08)
(0.09)
(0.14)
(0.01)
(0.02)
pvalue
0.17
0.01
0.00
0.02
0.21
0.64
0.00
0.36
0.03
0.00
0.00
0.04
0.01
0.00
0.00
0.98
0.00
Pooled OLS regression using data for all listed firms on the OSE in the period 1989 to 1997. Table B.48 shows
complementary regressions which use the same set of independent variables. Variable definitions are in Appendix A.2.
In regressions using firm size across years the nominal values are adjusted to the 1997 general price level.
9.1 Measuring performance with Tobin’s Q
59
models.
We interpret these patterns as saying that the sign (but not necessarily the strength) and
the statistical significance of a governance–performance link is rather independent of what model
specification we choose in cell 1 of table 2.1. Moreover, the finding that the significance of any
mechanism is quite independent of what other mechanisms are included in the regression equation
suggests that each mechanism has a separate, individual link to performance. It is not the result
of a spurious effect driven by other mechanisms or controls. Finally, the fact that the p–values
are robust to the introduction of additional variables indicates that the dominating pattern in our
sample is not that the mechanisms are used as substitutes and complements. We will analyze this
issue of mechanism interaction more closely in the next chapter.
Moving on from the comparison of successive models to the interpretation of the estimates of
the full multivariate model in tables 9.1 and 9.2, they reveal the following pattern:2
1. Ownership concentration and economic performance are inversely related.
2. Performance increases with insider ownership up to roughly 60% and then decreases.
3. Compared to a financial and particularly individual (personal) owners, the effect on performance is less favorable when the largest owner is the state, an international investor, a
nonfinancial corporation, or another listed firm.3
4. Performance is inversely related to board size, the fraction of non–voting shares outstanding,
and financial leverage.
5. Performance increases with firm size.
6. Industry membership and economic performance are systematically related.
7. Governance mechanisms and controls jointly explain about 30% of performance differences
across firms.
8. Performance is more sensitive to some governance mechanisms than to others.
We will now discuss these findings one by one and relate them to the theoretical predictions
from chapter 2. According to this theory, the expected performance effect of ownership concentration is unclear, as it reflects the net impact of benefits (valuable monitoring, higher takeover
premia, less free-riding) and costs (reduced market liquidity, lower diversification benefits, increased
majority–minority conflicts, reduced management initiative, incompetent monitoring). Our finding
that performance and concentration are inversely related over the entire concentration range is
an indication that monitoring by powerful owners with strong incentives either does not occur or
destroys value if it is carried out. It supports the theoretical argument by Burkhart et al. (1997)
that active monitoring by powerful investors can stifle managerial initiative and therefore reduce
corporate value. It also questions the agency idea that large owners are competent end hence beneficial for all stockholders, suggesting that firms do better if management is faced with small owners
who vote with their feet rather than powerful ones who interfere via the stockholder meeting or
through informal communication with management. The evidence differs from the mostly positive
or neutral effects reported in the literature, but is in line with recent evidence from Germany, where
2
3
Every relationship listed below has a p-value of 3% or less.
The evidence on intercorporate investments is in appendix B.6.
60
A full multivariate model
Lehmann and Weigand (2000) find a negative relation between ownership concentration and RoA
for 361 listed and unlisted firms from 1991 to 1996.
The second result that performance first increases and then decreases with insider holdings
demonstrates that although concentration in general destroys value, the effect is driven by the
majority–minority conflict and the various costs of outside rather than inside concentration. Inside
concentration benefits all stockholders unless the insiders become so powerful that their entrenchment hurts the remaining owners in the same way that large outside stockholders may do. Although
this is consistent with agency theory and the existence of diversification and liquidity costs, it puts
the focus on incentives for managers and directors rather than the power and monitoring activity
of owners. Also, the evidence supports the notion that minority shareholder protection is value–
creating. It is important to notice, however, that the maximum point of the insider–performance
relation occurs around 60%, that the average insider fraction in the sample firms is 8%, and that
3% of the firms have insider stakes above 60%. This means many sample firms are on the steep,
increasing section of the curve and that almost all of them are on the increasing part. Thus, although there are decreasing returns to insider holdings throughout the whole range, the marginal
return is almost always positive in our sample.
The finding on owner identity shows that individual (personal) investors are beneficial, supporting the idea that because such investors communicate directly with the firm they own rather than
indirectly through one or more layers of intermediate agents, they are better owners. The result
that financial owners are beneficial as well may be consistent with the efficient–monitoring argument of Pound (1988) that financial owners are more professional than others. It may also reflect
the fact that because regulation prevents financials from owning more than 10% in a firm, they are
never large owners, which we already know is negatively associated with performance. Moreover, it
may be driven by the disciplining effect of competitive pressure in the financials’ product market.
Because financial investors attract new funds from customers who partially base their investment
decisions on the funds’ performance record, competition forces financials to exert their ownership
rights with care. Finally, the inverse relationship between performance and large holdings by listed
owners may be because the benefit of strategic ownership among large firms is dominated by the
inherent cost of multiple–agent contexts.
The negative link between board size and performance supports the idea that small groups
communicate better than large, and that the efficiency loss sets in at a rather small group size. It
fits well with empirical findings in both small and large firms in other countries. The hypothesis that
non–voting shares enable some shareholders to extract wealth from other shareholders is consistent
with our finding that the higher the fraction of such shares outstanding, the lower the performance.
Moreover, the inverse interaction between leverage and performance does not support the notion
that debt disciplines management. If we, like most of the empirical governance literature, instead
consider leverage a control variable reflecting the value of the interest tax shield, the observed
pattern cannot be rationalized by such a theory either.
The significant industry effects is difficult to interpret because we do not know whether the
competition proxy is better described as a governance mechanism or a governance–independent industry effect.4 Anyway, the evidence does reflect a source of industry–wide performance differences
which are not picked up by the other variables in the regressions, and which would otherwise have
ended up in the error term. We choose not to conclude that the observed effect is driven by the
disciplining force of product market competition.
We interpret the very consistent, positive association between firm size and performance as a
governance–independent source of value creation, possibly due to factors like market power and the
4
See the discussion in section 4.6.
9.2 Performance sensitivity
61
economies of scale and scope. Finally, since the evidence shows that several mechanisms covary very
systematically with economic performance, we reject the hypothesis that the equilibrium condition
prevails. Performance is inferior because most firms operate with governance mechanisms which
are not value–maximizing.
The adjusted R2 , which expresses the fraction of performance variation which is explained
by the independent variables in the regression, has increased gradually as we have built more
comprehensive models. For instance, the typical R2 is barely 1% in the univariate regressions in
chapter 4, but close to 30% in the full multivariate models of this chapter.
9.2
Performance sensitivity
Even if the governance mechanisms as a group partly explain the performance differences, and
even if many of the separate mechanisms differ significantly from zero, their relative importance for
performance is not identical. To get a feeling for order of magnitude, we may compare the impact
on Q of changing key governance mechanisms by a standardized unit, such as one percent, one
percentage point or one standard deviation. Although we might use the two models in tables 9.1
and 9.2 for this purpose, they both include the Herfindahl index as the concentration measure.
Because this index has no obvious intuitive interpretation in terms of what happens to concentration
if the index changes by a percentage point, we instead use the holding of the largest owner as the
concentration proxy, keeping all the other model components from table 9.2. The regression results
are shown in table 9.3, which also include the mean sample values of the independent variables in
the rightmost column.
Table 9.3 Multivariate regression relating performance (Q) to ownership concentration, insider
holdings, owner type (aggregate holding per type) , board characteristics, security design, financial
policy, and controls
Dependent variable: Q
Constant
Largest owner
Primary insiders
Squared (Primary insiders)
Aggregate state holdings
Aggregate international holdings
Aggregate individual holdings
Aggregate nonfinancial holdings
ln(Board size)
Fraction voting shares
Debt to assets
Dividend payout ratio
Industrial
Transport/shipping
Offshore
Investments over income
ln(Firm value)
n
R2
Average (Q)
coeff
-1.04
-0.63
1.64
-1.34
-0.37
0.15
1.04
-0.17
-0.19
1.19
-1.51
-0.10
-0.20
-0.47
-0.56
-0.00
0.14
868
0.29
1.52
(stdev)
(0.69)
(0.19)
(0.47)
(0.58)
(0.34)
(0.25)
(0.30)
(0.26)
(0.09)
(0.36)
(0.18)
(0.05)
(0.08)
(0.09)
(0.14)
(0.01)
(0.02)
pvalue
0.13
0.00
0.00
0.02
0.29
0.54
0.00
0.52
0.03
0.00
0.00
0.05
0.01
0.00
0.00
0.98
0.00
mean
0.28
0.08
0.04
0.06
0.21
0.18
0.38
1.83
0.97
0.59
0.27
0.37
0.22
0.06
0.59
20.06
Pooled OLS regression using data for all listed firms on the OSE in the period 1989 to 1997. Variable definitions are
in Appendix A.2. In regressions using firm size across years the nominal values are adjusted to the 1997 general price
level.
To keep the analysis reasonably simple and focused, we concentrate on governance mechanisms,
only, ignore variables with insignificant (at the 3% level) coefficient estimates, and consider industry
62
A full multivariate model
membership a control variable rather than a governance mechanism. We exclude leverage from the
discussion because the significant, negative sign is inconsistent with both governance theory and the
tax shield hypothesis. This leaves us with ownership concentration, insider holdings, the individual
(direct) investor type, board size, and the fraction of voting shares outstanding.
It follows directly from the estimated coefficients in the table that Q decreases by 0.62 units when
concentration increases with one unit. The sensitivity is roughly twice as large to a corresponding
change in aggregate individual holdings (1.04) and the fraction of voting shares outstanding (1.19).
These effects may also be quantified as valuation changes for the average firm. Due to the two
non-linear relationships in the regression equation, however, we cannot simply start out with the
mean values of all the independent variables from table 9.3. Since we want to study the average
firm in our sample, we should use the square of the mean insider stake (0.082 , i.e., 0.01) rather
than the average of the squared stakes from the table (0.04). Similarly, whereas the table shows
the average of the ln(board size) variable, we should use the ln of average board size (1.89 instead
of 1.83). Because these two figures differ from the corresponding sample averages, the estimated
Q for the average firm is not the sample average of Q (1.520), but the expected Q of a firm where
every governance and control variable corresponds to the mean values of the sample (1.558).
Suppose the largest owner holds 28% of the equity in a firm with Q = 1.558. It this owner
decides to decrease the stake by ten percentage points to 18%, our model predicts that Q will
increase from 1.558 to 1.620. This means the market value of the firm grows by 0.4% for every
percentage point increase in concentration. Since the average firm value in the sample is NOK
2 bill., this corresponds to a value increase of NOK 8 mill. The value impact would be roughly
doubled (0.8% or 16 mill.) if there were a percentage point increase in either aggregate holdings
by individuals or the fraction of voting shares outstanding.5
Since Q is a quadratic function of insider holdings, we must consider both the positive linear
term and the negative quadratic term, and the non–linear relationship makes the sensitivity of Q
dependent on the level of insider holdings. We once more consider a firm where all the independent
variables (except the non–linear terms) are at their sample averages from table 9.3, which means
insiders own 8% of the firm’s equity. If this stake is increased by one percentage point to 9%, Q
grows from 1.558 to 1.572, which is 0.9% or NOK 18 mill. If the initial insider stake were 1%
instead of 8% , a one percentage point increase would push firm value up by 22 mill. instead of 19
mill. This difference reflects the effect of the concave relationship between performance and insider
holdings.
Because Q is specified as a logarithmic function of board size, the estimated coefficient expresses
the absolute change in Q per unit relative change in board size. Thus, if board size decreases with
one percent (i.e, the relative change is 0.01), Q will increase by 0.002 units; i.e, from 1.558 to 1.560.
This is 0.13% or 2.6 mill. If, more realistically, board size is decreased by one seat rather than one
percent, firm value would increase by 2% or by NOK 40 mill.
Overall, this analysis shows that for the average firm, the ownership characteristic with the
strongest impact on firm value is insider holdings. Our model predicts that an increase in insider
ownership by one percentage point increases firm value by 1%, which is roughly NOK 20 mill. The
value impact of a corresponding increase in the holdings by individual investors is 0.8%, and one
percentage point reduction in ownership concentration increases firm value by 0.4%. Increasing the
fraction of voting shares by one percentage point drives up firm value by 0.8%, and the average firm
will become about 2% more worth if board size is reduced by one member. Since equity constitutes
about 40% of total assets in the average sample firm, the relative impact on equity value will be
5
These absolute changes in Q are independent of the level of Q. The relative change in Q and the absolute change
in firm value both decrease with Q.
9.3 Alternative performance measures
63
higher, and more so the less the value of debt is influenced by altered governance mechanisms. If
the value of debt is unaffected, the relative change in equity value will be 2.5 times the relative
change in firm value.
9.3
Alternative performance measures
As shown in appendix B.6, the results are much weaker if we drop Q and instead use return on
assets or return on stock as performance measure. The appendix documents that regardless of
whether we use RoA5 or RoS5 , only a few relationships are significant. For instance, if the identity
of the largest owner is the investor type proxy and RoA5 is the performance measure, we get the
same, significant results as with Q regarding concentration, insiders, leverage, and industry, but
all the other variables become insignificant. If the performance measure is RoS5 , some coefficients
change sign (like a negative linear term for insiders), every ownership structure variable becomes
insignificant, and the only mechanisms which enter with a significant sign are board size (negative)
and leverage (negative).
As discussed earlier, we have several reasons to prefer Q to the other performance measures. Q is
by far the most commonly used proxy in the recent literature. Consecutive observations of RoA5 and
RoS5 are constructed from overlapping observations for four of the five years, potentially causing
the error terms to be autocorrelated. Also, because RoA and RoA5 are constructed exclusively
from book values, they may be far from market returns and may also be influenced by management
discretion.
9.4
Summary
Estimating our most comprehensive multivariate regression equation, we find that the relationship
between ownership concentration and economic performance as measured by Q is inverse and
very significant, even at low concentration levels. This result, which is atypical in the literature,
questions the basic agency hypothesis of Berle and Means (1932) and Jensen and Meckling (1976)
that managers who are not properly monitored by powerful owners will not fulfill their fiduciary
duty, and that powerful owners are beneficial because they discipline management towards making
value–maximizing decisions. However, there is support for agency–based ideas in our evidence that
performance correlates positively with insider holdings at almost any level, that direct ownership
is more value–creating than indirect, and that both non–voting shares and larger boards reduce
market value. Even though we find the relationship between insider holdings and performance to
be quadratic, there is almost never a firm in our sample where the marginal value effect of a higher
insider stake is negative. This suggests that the costs of higher insider stakes very seldom outweigh
the benefits. We also find that the choice of performance measure is important, as very few of these
relationships stay significant if we replace Q by return on assets or return on stock.
The ownership characteristic with the strongest impact on the average firm’s performance as
measured by Q is insider holdings, where a one percentage point higher stake increases firm value by
1% or by roughly NOK 20 mill. A corresponding increase in direct as opposed to indirect ownership
has a 0.8% effect, and one percentage point lower ownership concentration increases firm value by
0.4%. Stepping up the fraction of voting shares by one percentage point drives up firm value by
0.8%, and the average firm will be about 2% more valuable if board size is reduced by one member.
If the value of debt is unaffected by these changes in the corporate governance mechanisms, the
relative impact on equity value will be roughly 2.5 times larger than these relative changes in firm
value.
64
A full multivariate model
Most of the significant relationships in the full multivariate model have survived all the way from
the univariate analysis in chapter 4 through the various partial multivariate models in chapters 5–
8. This pattern indicates that the estimated sign and the statistical significance of a governance–
performance link is rather robust to what model specification we choose in cell 1 of table 2.1. It
also suggests that each governance mechanism has a separate, individual link to performance which
is not offset or driven by other mechanisms.
Explaining the corporate governance mechanisms
65
Chapter 10
Explaining the corporate governance mechanisms
The preceding chapters have taken the governance mechanisms as given (exogeneous) and also
ignored potential interactions between them.1 In order to address dependence and causality in
an explicit way, we will now use simultaneous equation econometrics, which has recently been
applied to corporate governance research by Agrawal and Knoeber (1996), Loderer and Martin
(1997), Cho (1998) and Demsetz and Villalonga (2001). To successfully implement this approach,
however, we need a corporate governance theory which puts a priori restrictions on the coefficient
estimates, such as a theoretical argument stating that board composition and insider ownership are
independent governance mechanisms. The problem is that such a theory very often does not exist.
Not surprisingly, therefore, the researchers have restricted the equation system in a rather informal
manner, often assuming endogeneity between two governance mechanisms, only, or between one
mechanism and the performance measure.
Using ad–hoc arguments which resemble the ones found in the literature, we will restrict the
equation system in several alternative ways. It turns out that that empirical conclusions are very
sensitive to the choice of restrictions. We conclude that due to the partial, incomplete nature of
corporate governance theory, simultaneous equations modeling is a questionable tool for analyzing
endogeneity and causality.
This chapter ignores the link to performance and focuses exclusively on interrelationships between the mechanisms. Section 10.1 presents single–equation estimations which model each governance mechanism at a time as a function of the other mechanisms and controls. We switch to
a simultaneous equations framework by presenting the general structure of such models in section
10.2. Specific examples from the governance literature are presented in section 10.3, and we use our
sample data to analyze the endogeneous nature of ownership concentration and insider holdings in
section 10.4.
10.1
The mechanisms one by one
This section involves a series of single–equation multiple regression models. In any model, the
dependent variable is one mechanism, and the independent variables are other mechanisms and
controls. We start by using the aggregate holdings per investor type as owner type proxy. Subsequently, the type of the largest owner is used as the alternative measure.
Our findings using aggregate holdings per investor type as owner type proxy are summarized
in table 10.1. The first column lists the independent (exogeneous) variables, and each of the
other columns holds a regression. Column titles specify the dependent (endogeneous) variable in
question. For instance, the second column shows the result of a regression where concentration
is the dependent variable and the other governance mechanisms and controls are the independent
variables. The table reports the estimated signs and the significance levels of the coefficients of the
independent variables. We concentrate on evidence which is significant at the 1% level, which is
denoted ∗∗∗ in the table.
1
Although we did not explicitly address interaction between the exogeneous mechanisms in the preceding chapters,
it is once more important to notice that multicollinearity (dependence between the independent variables) does not
invalidate the OLS or GMM estimates used so far. As discussed in section 5.2, multicollinearity will show up as
increased standard deviations of the estimated coefficients and hence increased p-values.
66
Explaining the corporate governance mechanisms
Table 10.1 Summary of the single–equation regressions for governance mechanism endogeneity,
using the aggregate holding per type as owner identity proxy
Independent variables
Herfindahl index
Primary insiders
Aggregate state holdings
Aggregate international holdings
Aggregate individual holdings
Aggregate nonfinancial holdings
Fraction voting shares
ln(Board size)
Debt to assets
Dividends to earnings
Industrial
Transport/shipping
Offshore
Investments over income
ln(Firm value)
Stock volatility
R2
Independent variables
Herfindahl index
Primary insiders
Aggregate state holdings
Aggregate international holdings
Aggregate individual holdings
Aggregate nonfinancial holdings
Fraction voting shares
ln(Board size)
Debt to assets
Dividends to earnings
Industrial
Transport/shipping
Offshore
Investments over income
ln(Firm value)
Stock volatility
R2
Herfindahl
index
+***
+***
+***
+***
+***
+
+*
-***
+
-*
+
0.25
Aggregate
nonfinancial
holdings
+***
-***
+
+
+***
+
-***
+
0.23
Dependent variables
Aggregate Aggregate
Primary state
international
insiders
holdings
holdings
+***
+***
+
-***
-**
+
+***
+***
+***
+
+
+
+***
-*
-***
-**
+*
+***
+***
+***
+*
+***
0.16
0.21
0.12
Dependent variables
Aggregate Fraction
ln(Board
voting
financial
holdings
shares
size)
-***
+***
-*
+
-***
-*
+
+
+***
+
+
+
+***
-***
+***
-***
-***
-***
-***
-*
+
-***
+***
-***
-**
+**
0.17
0.14
0.17
Aggregate
individual
holdings
-***
+***
Debt
to
assets
+
+***
-*
-***
-***
-***
+
-**
+
-***
-***
-***
-***
0.34
Dividends
to
earnings
+*
+
+
+
+
-
+
+***
+
+*
+***
+*
0.07
+
0.01
The table summarizes the signs and significance levels of eleven multivariate OLS regressions. Each column is a
regression, with the column title as the dependent variable. The row titles are the independent variables. The + or reflects the estimated sign of the coefficient. Statistical significance is indicated with ∗ , ∗∗ , and ∗∗∗ , which means the
relationship is significant at the 5%, 2.5% and 1% level, respectively. A sign without an asterisk means the relation is
not significant at the 5% level. An empty cell means that the variable does not enter the regression as an independent
variable. The detailed regressions are reported in appendix B.7. Variable definitions are in Appendix A.2. Data for
firms listed on the Oslo Stock Exchange, 1989-1997.
10.1 The mechanisms one by one
67
Ownership concentration is positively related to insider ownership. This complementary rather
than substitute relationship is supported by the corresponding result from the insider regression
in the next column. Concentration also increases with the fraction of voting shares outstanding.
Taken together, this strenghtens the earlier conclusion that concentration per se destroys value:
Despite the evidence that insider ownership and fraction voting shares are positively related to both
concentration and performance,2 concentration and performance still move inversely. This pattern
suggests that the least favorable combination of the three mechanisms is high concentration, low
insider holdings, and a low fraction of voting shares.
High aggregate stakes by state, international, and nonfinancial investors are associated with
high concentration. Inconsistent with the agency hypothesis that concentration and dividends
are substitute mechanisms, firms with high concentration (and a correspondingly limited need to
control free cash flow by financial policies) do not have significantly lower dividends than others.
The same independence holds for debt financing.
Insider ownership tends to be large when individuals as a group hold a relatively high fraction of
the firm’s equity. This is hardly surprising, since personal equity ownership by officers and directors
are included in both variables. However, this positive relationship stays significant at the 1% level
across all our insider categories (i.e, all, officers, directors, and primary insiders), and regardless of
whether we use aggregate holdings per type or the identity of the largest owner as investor identity
proxy. Thus, the finding that individual investors are large in firms with high insider stakes is a
robust one.
Insider ownership relates positively to financial leverage. Just like the way concentration is
positively associated with dividends, this is inconsistent with the agency prediction that leverage
and insider holdings are substitute disciplining mechanisms. An alternative explanation which fits
the data well is lost diversification benefits. When leverage is high, equity value is low, which means
a high ownership fraction can be acquired with a moderate investment. The diversification loss of
a given equity fraction in a firm is therefore smaller the higher the leverage. On the other hand,
this explanation seems inconsistent with the evidence that insider holdings increase with firm size.
To achieve the same insider fraction in a larger firm, a higher stake is required in monetary terms.
This additional portfolio concentration increases the portfolio’s unsystematic risk.
In any owner type regression, we exclude the aggregate holdings of the other types as explanatory
variables.3 The table shows that individuals as a group own higher stakes in firms with low
concentration and high insider holdings, and that the opposite is true for state and nonfinancial
owners. Since we know that performance is related negatively to concentration and positively to
insider holdings, this evidence partly explains why performance is better in firms where individuals
rather than state or nonfinancial investors hold large aggregate stakes. The former type of firms
have a more value-creating ownership structure than the other, i.e., lower concentration and higher
insider stakes. Notice also that financials tend to end up in firms where both concentration and
insiders holdings are low. Both findings may be driven by the 10% cap on a financial investor’s
equity stake.
Small firms with concentrated ownership use more voting stock than others.4 Notice, however,
our earlier findings that both higher concentration and lower firm size reduce performance. Still,
the use of voting shares has an independent, positive effect on performance in such firms. Moreover,
2
Insider ownership is only positively related to performance for holdings up to roughly 60%
This is because the fraction held by the four other types is sufficient information to exactly determine the fraction
owned by the type in question (the dependent variable).
4
A possible explanation dates back to the period before 1995, when only one third of a Norwegian firm’s voting
stock could be owned by international investors. There was no such limitation on non–voting (B) equity. If attracting
foreign capital was more of an issue for the larger firms, they would tend do issue relatively more B shares than others.
3
68
Explaining the corporate governance mechanisms
like for insider holdings and individual owners, this evidence once more tells us that concentration
per se has a negative effect on performance which is not offset by other mechanisms with a positive
impact.
Board size grows with firm size. This may reflect the attitude that large firms are more complicated to manage and monitor than small ones, and that this setting requires a more heterogeneous
and hence larger board. Given our earlier finding that board size and performance are inversely related (controlling for firm size, which benefits performance), this presumption seems unwarranted.
Notice also that board size is significantly lower in firms where individual owners are large.
The findings on financial policy suggest that compared to other investors, financial investors
hold more equity in firms with high leverage. Notice again that unlike what agency theory predicts,
there are no signs of a substitution between concentration, insider holdings, and financial policy.
We have so far proxied for owner type by the aggregate holding per type. Table 10.2 shows
the corresponding results using the identity of the largest owner as type proxy.5 The estimates in
tables 10.2 and 10.1 are quite consistent, although the coefficient of determination and the p-values
of the coefficient estimates are generally lower.
Table 10.2 Summary of the single–equation regressions for governance mechanism endogeneity,
using the type of the largest investor as owner type proxy
Independent variables
Herfindahl index
Primary insiders
Largest owner is state
Largest owner is international
Largest owner is individual
Largest owner is nonfinancial
Fraction voting shares
ln(Board size)
Debt to assets
Dividends to earnings
Industrial
Transport/shipping
Offshore
Investments over income
ln(Firm value)
Stock volatility
R2
Herfindahl
index
+
+***
+***
+**
+***
+***
+
+*
+
+
+
+***
0.09
Dependent variables
Fraction
ln(Board
Primary voting
insiders
shares
size)
+
+***
-***
+
+
+***
-***
-***
+
-***
-*
+***
+
+
+
-***
+***
-***
-***
-***
-***
-***
-*
+
-***
+***
+
-***
+***
0.16
0.14
0.17
Debt
to
assets
+
+***
+
-***
+
Dividends
to
earnings
+*
+
+
+
+
+
-
+
+***
+
+*
+***
+
+
0.05
+
0.02
The table summarizes the signs and significance levels of six multivariate OLS regressions. Each column is a regression,
with the column title as the dependent variable. The row titles are the independent variables. The sign of the
estimated relation is shown. Statistical significance is indicated with ∗ , ∗∗ , and ∗∗∗ , which means the relationship is
significant at the 5%, 2.5% and 1% level, respectively. A sign without an asterisk means the relation is not significant
at the 5% level. An empty cell means that the variable does not enter the regression as an independent variable. The
detailed regressions are reported in appendix B.7. Variable definitions are in Appendix A.2. Data for firms listed
on the Oslo Stock Exchange, 1989-1997.
To complete this single–equation approach, we consider the determinants of the largest owner’s
identity, which could not be addressed by OLS in table 10.2. Table 10.3 shows the output from a
5
As this variable is binary, we cannot run regressions where owner type is the dependent variable in table 10.2.
Instead, we analyze this relationship with a multinomial logit model in table 10.3.
10.2 Simultaneous equations modeling
69
multinominal logit regression, which estimates the determinants of the probability that the largest
owner is a certain type. When interpreting the results, note that because a financial owner is
the base case, the coefficients show the increased probability of observing the particular type for
a marginal, partial change of the independent variable in question. For example, the coefficient
estimate for primary insiders in the third regression (largest owner is individual) is 8.27. This
means that if insider holdings increase by one percentage point, the probability that the largest
owner is an individual rather than a financial increases by 8.27 percentage points.
The significant relations indicate that if the firm has a financial investor as its largest owner,
both concentration and insider holdings are smaller than in other firms. The board is comparatively
large in state–dominated firms and small in firms dominated by individuals. Both international
and state owners tend to be the largest investor in large firms. Overall, this evidence is consistent
with the alternative model in tables 10.1 and 10.2.
The evidence in the three tables shows that the estimated sign of a relation between two
variables is rather consistent across models, no matter which of the two is the dependent variable.
For example, in table 10.1 insiders enter with a positive and significant sign in the concentration
model (column 2), and concentration enters in the same way in the insider model (column 3).
However, even if the sign is identical, they are not always both significant. The relationship
between state holdings and board size in table 10.1 is such a case, as the positive coefficient is only
significant when board size is the independent variable. Finally, remember that the single–equation
approach in this section can only uncover covariation between variables; not the causation. The
next section establishes the analytical framework for determining causation.
10.2
Simultaneous equations modeling
We now leave the single-equation relationships in cell 1 and move to cell 2 of table 2.1, where we
account for the simultaneous nature of the governance mechanisms. The equilibrium condition
argues that the mechanisms are adjusted relative to each other until they produce an optimal set
for a given firm. This logic leads us to consider a system of equations where every relationship is
supposed to hold simultaneously.
To implement this idea, we use the econometric framework known as simultaneous equations
estimation. While this approach ideally allows us to analyze the joint nature of all the mechanisms,
the implementation in our setting is handicapped by the lack of a comprehensive theory of corporate
governance. The need for such a theory is driven by the so–called identification problem, which
becomes evident in the subsequent description of simultaneous equations econometrics. As we only
give a very brief sketch of key issues, we refer the interested reader to chapters 18–20 of Gujarati
(1995) and chapters 14–15 in Judge et al. (1985). More advanced references are Davidson and
MacKinnon (1993) and Greene (2000).
A system of simultaneous equations is usually compactly written as
YΓ + XB + E = 0
(10.1)
where Y is a set of jointly determined (endogeneous) variables, X is a set of predetermined (exogeneous) variables, E is a set of (mean zero) error terms, and Γ and B are parameters to be
estimated. If there are M endogeneous and K exogeneous variables, Γ will be a (M × M ) matrix
and B a (K × M ) matrix. With T observations, Y and E are (T × M ) matrices, X is a (T × K)
matrix and 0 is a (T × M ) matrix of zeros.
The general equation system defined in (10.1) has an infinite number of solutions (i.e, Γ and B
matrices) which are all consistent with the same set of observations (i.e, Y and X). This is the
70
Explaining the corporate governance mechanisms
Table 10.3 Estimating the determinants of the largest owner type using a multinomial logit model
Dependent variables
Largest owner is state
Independent variables
Herfindahl index
Primary insiders
ln(Board size)
Fraction voting shares
Debt to assets
Dividend payout ratio
Industrial
Transport/shipping
Investments over income
ln(Firm value)
Stock volatility
constant
Largest owner is international
Herfindahl index
Primary insiders
ln(Board size)
Fraction voting shares
Debt to assets
Dividend payout ratio
Industrial
Transport/shipping
Investments over income
ln(Firm value)
Stock volatility
constant
Largest owner is individual
Herfindahl index
Primary insiders
ln(Board size)
Fraction voting shares
Debt to assets
Dividend payout ratio
Industrial
Transport/shipping
Investments over income
ln(Firm value)
Stock volatility
constant
Largest owner is nonfinancial
Herfindahl index
Primary insiders
ln(Board size)
Fraction voting shares
Debt to assets
Dividend payout ratio
Industrial
Transport/shipping
Investments over income
ln(Firm value)
Stock volatility
constant
n = 815, Pseudo R2 = 0.1658
coeff
10.15
-4.54
1.66
-3.01
-3.09
0.37
-0.25
0.02
-0.49
0.45
6.70
-9.11
7.13
4.12
0.03
0.27
-2.17
-0.16
-1.23
0.41
0.02
0.34
6.84
-6.24
4.42
8.27
-1.31
-3.82
-4.94
0.16
-2.04
0.61
-0.15
0.21
-2.39
4.40
7.28
4.37
-0.50
-3.97
-2.92
0.18
-0.61
1.54
0.02
0.18
3.82
3.68
stdev
1.94
3.87
0.58
2.27
1.28
0.39
0.41
0.80
0.46
0.15
4.64
4.41
1.91
1.85
0.47
2.46
1.06
0.45
0.39
0.61
0.03
0.14
4.53
4.36
2.14
1.80
0.50
2.21
1.12
0.39
0.49
0.64
0.16
0.16
6.73
4.61
1.84
1.76
0.40
1.93
0.93
0.35
0.31
0.52
0.03
0.12
4.42
3.67
pvalue
0.00
0.24
0.00
0.18
0.02
0.34
0.54
0.98
0.29
0.00
0.15
0.04
0.00
0.03
0.94
0.91
0.04
0.72
0.00
0.50
0.56
0.01
0.13
0.15
0.04
0.00
0.01
0.09
0.00
0.68
0.00
0.35
0.36
0.20
0.72
0.34
0.00
0.01
0.21
0.04
0.00
0.61
0.05
0.00
0.61
0.14
0.39
0.32
The table reports the results from estimating four multinomial logit models, where the dependent variable is the
probability that a certain investor type is the largest owner. The base case is that the largest owner is a financial. For
each of the four owner type regressions, the coefficients express the increased probability of observing this particular
type for a marginal, partial change of the independent variable in question. Data for firms listed on the Oslo Stock
Exchange, 1989-1997. Variable definitions are in Appendix A.2.
10.3 Examples of simultaneous systems
71
identification problem. To perform a meaningful estimation, restrictions must be imposed on the
system. Intuitively, the need for restrictions is driven by the need for determining causality, i.e.,
does causation go only from variable A to variable B, only from B to A, or both ways? Unless
additional information is brought into the system of equations, it is impossible to distinguish these
three cases from each other. The typical solution to the identification problem in (10.1) is to
exclude some variables from some of the equations, or to introduce additional exogeneous variables
as so–called instruments. To help in identification, these instruments should only affect one or a
few of the endogeneous variables, but not all. To justify such restrictions, however, one cannot rely
on findings from single-equation estimations, such as the evidence in tables 10.1–10.3. Rather, the
restrictions should be rationalized by theoretical arguments about the phenomenon in question,
which in our case is corporate governance theory. This is a critical point because simultaneous
system estimates may be very sensitive to misspecified restrictions.
10.3
Examples of simultaneous systems
Since it may be easier to see the general nature of the problem through a specific example, we first
consider a simple simultaneous system used by Loderer and Martin (1997).6 The paper analyzes the
interaction between corporate value and insider ownership in a sample of firms which are involved
in mergers. Loderer and Martin (1997) specify the following system of equations:
Qi = γ12 OF F DIRi + β11 LSALESi + β12 ST KF INi + εi1
(10.2)
OF F DIRi = γ21 Qi + β21 LSALESi + β23 ST DDEVi + V ARi + εi2
(10.3)
where, for firm i, Qi is an estimate of Tobin’s Q, OF F DIRi is the holdings by officers and directors,
LSALESi is the size of the firm as measured by sales, ST KF INi reflects the financing of the
merger(stock vs. cash), and ST DDEVi and V ARi is the standard deviation and variance of the
underlying stock return, respectively.
The endogeneous variables in this system are Q and OF F DIR, i.e., performance and insider
holdings are assumed to be interrelated. The other variables are exogeneous. We now determine
the coefficient matrices Γ and B, starting out with the coefficient matrix Γ of the endogeneous
variables Qi and OF F DIRi :
Γ=
"
γ11 γ12
γ21 γ22
#
By specifying the equation system in (10.2) and (10.3), Loderer and Martin (1997) have restricted
this matrix into:
Γ=
"
−1 γ12
γ21 −1
#
This is a normalization of the endogeneous variables, which is standard, but which makes it harder
to interpret the coefficients.
Consider next the coefficient matrix B for the four exogeneous variables LSALESi , ST KF INi ,
ST DDEVi and V ARi . With two equations and four exogeneous variables, B is generally written
as
B=
6
"
β11 β12 β13 β14
β21 β22 β23 β24
#
This model is chosen both for pedagogical simplicity and because some of the findings are particularly relevant.
72
Explaining the corporate governance mechanisms
In the system specified in (10.2) and (10.3), B has been restricted into
B=
"
β11 β12 0
0
β21 0 β23 β24
#
Thus, three coefficients in this system have been restricted to equal zero. This approach exemplifies
the standard solution to the identification problem, which is to let an exogeneous variable affect
only one of the endogeneous ones.7 The theoretical rationale for such restrictions should specify
why an exogeneous variable drives the endogeneous variable in question. In addition, and equally
important, it should state why the variable is irrelevant for the remaining endogeneous variables.
The nature of the estimation problem is such that this rationale cannot come from sample information, but must be based on extra-sample information, preferably the economic theory for the
object of study.
The sample in the firms in the Loderer and Martin (1997) example consists of firms involved in
mergers, and the variables which may be interrelated (endogeneous) are firm performance (Q)and
insider ownership (OF F DIR). The identification assumptions are that the medium of exchange in
the merger (ST KF IN ) only affects performance, not insider ownership, and that stock variability,
measured by both the standard deviation (ST DEV ) and the variance of the stock (V AR), drives
performance, but not insider ownership.
To exemplify the typical discussion leading up to such exclusion restrictions, consider the assumption that stock variability affects insider ownership, but not performance. The insider argument is the Demsetz and Lehn (1985) hypothesis that increased variability in the firm’s environment
creates stronger incentives for outsiders to monitor closely because management quality matters
more for economic performance in risky environments. What is much harder to argue, and which
is not touched upon in the paper, is why the control potential is not reflected in the value of the
firm and thus in Q. This illustrates the general problem that it is often easier to argue why an
exogeneous variable drives one endogeneous variable than to argue why it is irrelevant for all the
others. Both rationales are needed in simultaneous equations estimation, and both must come from
extra-sample information.
There is one particularly notable finding in Loderer and Martin (1997). Whereas governance
theory argues that causation runs from governance to performance, Loderer and Martin (1997)
conclude that the causation is reversed. Insider holdings in their system do not enter significantly
in the performance equation in 10.2, but performance has a positive, significant coefficient in the
insider equation 10.3. We return to this finding in the next chapter.
Cho (1998) estimates the following system of equations:
Insider ownership = f (Market value of firm’s common equity, Corporate value,
Investment, Volatility of earnings, Liquidity, Industry)
Corporate value
= g(Insider ownership, Investment,
Financial leverage, Asset size, Industry)
Investment
= h(Insider ownership, Corporate value,
Volatility of earnings, Liquidity, Industry)
This system has three endogeneous variables, i.e., insider ownership, corporate value (proxied by
Q), and investment. The exogeneous variables are market value of firm’s common equity, earnings
volatility, asset liquidity, industry, financial leverage, and asset size.
7
This is not the only possible solution. Adding functional restrictions on the coefficients is another alternative,
such as setting some coefficients equal to each other.
10.4 Ownership concentration and insider holdings as a simultaneous system
73
Compared to Loderer and Martin (1997), the Cho model adds one equation which endogeneously
determines investments, but there is still just one endogeneous governance mechanism (insider
ownership). Cho argues that since the key firm decision involves investments, this variable should
enter the causal relationships endogeneously. The firm’s industry is a control variable, as it enters all
three equations. One example of an identification restriction is that earnings volatility is assumed to
only affect insider ownership and investment, not corporate value, which resembles the assumption
of Loderer and Martin (1997). Another example is that asset liquidity is assumed to be irrelevant for
firm value, only. Like in Loderer and Martin (1997), these identification restrictions are rationalized
rather informally. An important empirical result from the Cho (1998) paper is added support for
the Loderer and Martin (1997) finding that performance drives insider ownership, but not vice
versa.
Agrawal and Knoeber (1996) is a more comprehensive example, where the system includes
performance and six governance mechanisms as endogeneous variables. Examples of instruments
used to identify the system are the standard deviation of stock return, firm size, CEO tenure, and
acquisition probability in the industry. The use of the instruments are mostly justified by arguing
why some instruments are relevant to include in a given equation, with less emphasis on why they
are irrelevant in the remaining equations. Compared to their OLS model, they find less significant
results in the system. What is not pointed out in the paper is that their results are also consistent
with their instruments being weak or misspecified. As we will show later, this is a potentially large
problem.
10.4
Ownership concentration and insider holdings as a simultaneous system
We now return to our sample and define the estimation problem as a system of equations. Our
approach resembles existing research in the sense that several instruments lack a convincing theoretical foundation. We differ from earlier papers in the sense that we test out several sets of
instruments rather than just one.
The theoretical discussion of our problem in chapter 2 argued that there may in principle
be a relationship between any combination of governance mechanisms. Because the theory is
rather silent on simultaneity, we cannot validly restrict the coefficients in a comprehensive equation
system. Therefore, we choose to endogenize ownership concentration and insider holdings, which
have received the widest attention in the literature. Also, theoretical arguments suggest they have
a complementary role, but there is little theoretical backing for how they relate to the remaining
mechanisms. Consequently, this setup is quite well suited for illustrating how conclusions change as
we alter the restrictions by using three alternative sets of instruments. The problems we encounter
using this limited approach will show rather well what would happen if we tried to endogenize more
mechanisms than these two.
Agency theory argues that ownership concentration and insider holdings are the key governance
mechanisms, that they play different roles (external monitoring vs. internal incentives), and that
they represent alternative means for reducing agency costs. Since the single-equation estimates of
the previous section cannot uncover causality, we estimate a system which allows ownership concentration and insider holdings to be mutually dependent and simultaneously determined. This means
the two mechanisms are the elements of the endogeneous variables vector Y in equation (10.1).
The first model uses board size and stock volatility as instruments. Board size is assumed to
affect insider ownership, but not concentration. The argument is that the larger the board, the
higher the number of potential insiders and hence the higher the insider stake. Stock volatility is
assumed to affect concentration, but not insider ownership. One argument is the Demsetz and Lehn
74
Explaining the corporate governance mechanisms
(1985) idea that higher variability in the economic environment creates a larger value potential of
having external owners who actively monitor management.In fact, as discussed in section 10.3,
Loderer and Martin (1997) assumed that volatility drives insider ownership.
Table 10.4 Interactions between governance mechanisms modeled as a system of equations. Concentration and insider holdings are endogeneous variables. Stock volatility and board size are used
as instruments
Panel A. Regression results
Dep.variable
Herfindahl index
Primary insiders
Indep.variable
Primary insiders
Aggregate state holdings
Aggregate international holdings
Aggregate individual holdings
Aggregate nonfinancial holdings
Fraction voting shares
Debt to assets
Dividends to earnings
Industrial
Transport/shipping
Offshore
Investments over income
ln(Firm value)
Stock volatility
constant
Herfindahl index
Aggregate state holdings
Aggregate international holdings
Aggregate individual holdings
Aggregate nonfinancial holdings
Fraction voting shares
Debt to assets
Dividends to earnings
Industrial
Transport/shipping
Offshore
Investments over income
ln(Firm value)
ln(Board size)
constant
coeff
0.87
0.61
0.20
-0.34
0.28
0.27
-0.04
0.01
-0.00
-0.01
0.04
-0.00
-0.02
-0.01
0.03
1.50
-0.89
-0.31
0.38
-0.42
-0.38
0.03
-0.02
0.01
0.03
-0.05
0.00
0.02
0.01
0.04
(stdev)
(0.68)
(0.10)
(0.06)
(0.30)
(0.05)
(0.08)
(0.09)
(0.01)
(0.02)
(0.03)
(0.05)
(0.00)
(0.01)
(0.04)
(0.28)
(1.06)
(0.59)
(0.27)
(0.09)
(0.32)
(0.24)
(0.08)
(0.02)
(0.03)
(0.06)
(0.04)
(0.00)
(0.01)
(0.03)
(0.25)
pvalue
0.20
0.00
0.00
0.26
0.00
0.00
0.63
0.26
0.94
0.70
0.35
0.35
0.18
0.80
0.91
0.16
0.13
0.24
0.00
0.19
0.11
0.73
0.37
0.75
0.57
0.21
0.41
0.05
0.80
0.87
Panel B. Regression diagnostics
Equation
1
2
n
741
741
Parms
14
14
RMSE
0.17
0.22
R2
-0.78
-0.56
χ2
103.75
79.71
p
0.00
0.00
The tables shows 3SLS estimates of a system of simultanous equations. Panel A report system estimates. The
leftmost column is the dependent variable in that particular equation, which is a function of the variables listed in
the next column. Equations are separated by a line. Panel B holds diagnostics. For each equation the diagnostics
include n, the number of observations, Parms, the number of parameters, RMSE, the root mean squared error, R2 , a
“pseudo” R squared, χ2 , the chi squared of the equation, and p, a p-value for the fit of the equation. The estimates
are performed with Stata’s reg3 3SLS routine. Variable definitions are in Appendix A.2. Data for firms listed on
the Oslo Stock Exchange, 1989-1997. Appendix table B.82 estimates a similar system which only uses controls as
additional explanatory variables beyond the two endogeneous mechanisms and the two instruments.
The results using this set of instruments are shown in table 10.4.8 The table documents that
8
We use 3SLS with Stata as the estimation engine. As a practical matter 3SLS is chosen rather than 2SLS. The
two methods will give the same estimated coefficients in this case, but 3SLS produces a few additional diagnostics,
such as the pseudo-R2 statistic. Note that this statistic can be negative due to the fact that because the estimation
10.4 Ownership concentration and insider holdings as a simultaneous system
75
the significant determinants (p < 5%) of ownership concentration are the fraction of voting shares
outstanding and aggregate holdings by state, international and nonfinancial investors (all positive).
In the insider equation, aggregate individual holdings and firm value are significant (both positive).
Thus, there is no supporting evidence for the notion that insider holdings and concentration are
substitutes or complements. Referring back to the equation–by–equation estimates in table 10.1 of
section 10.1, we find that the sign of the estimated coefficients are often the same, but that most
system estimates are less significant. The concentration equation of table 10.4 has four coefficients
which are significant at the 5% level, whereas there are eight in the single-equation model of
table 10.1. The insider equation in table 10.4 has two significant coefficients, and table 10.1 has
eight. Despite this discrepancy, the signs of all significant coefficients are consistent across the two
models. This reduced significance in simultaneous vs. single–equation models was also observed by
Agrawal and Knoeber (1996).
The loss of significance may have various causes, but the primary suspect is weak or misspecified instruments. One way of analyzing the seriousness of specification errors is by introducing
alternative instruments in the model from table 10.4 and checking the impact on estimate stability.
Two alternative model specifications are analyzed below.
The model estimated in table 10.4 assumes that stock volatility (total risk) influences ownership concentration, but not insider holdings. This restriction may be misplaced, given our earlier
argument that owners who put a considerable part of their wealth in one firm carry more unsystematic risk than others. This point also applies to inside owners in general and to managers in
particular, since they also receive labor income from the firm’s cash flow. The more volatile this
cash flow and the more of their wealth invested in the firm’s equity, the higher the uncertainty in
both their labour income and their financial portfolio. Whereas this is an argument that insider
holdings should be low in high–volatility firms, the opposite conclusion follows from the idea that
the potential for value–creating managerial decisions may be higher the riskier the firm’s industry.
Thus, the incentive–optimal insider stake is higher the more volatile the firm’s cash flow. Although
the net effect of the diversification and the incentive arguments is unclear, it follows that insider
holdings and stock volatility may not be independent. In fact, as discussed in section 10.3, Loderer
and Martin (1997) assumed that volatility drives insider ownership.
Hence, we drop stock volatility as an instrument and instead use it as a regular exogeneous
variable in both equations. To identify the insider equation, we continue using board size as
the instrument. The ownership concentration equation is identified by using the liquidity of the
firm’s equity as an instrument, which we operationalize as stock turnover.9 Thus, turnover is
included in the concentration equation, but not in the insider equation. The rationale is based on
the assumption that the investment horizon (holding period) is longer for larger owners than for
others. Market microstructure theory argues that there is an extra cost to selling large blocks due
to price pressure. Large owners may hesitate more than others before liquidating a position. There
is also a higher chance that large owners have strategic reasons for their investments. In any case,
if larger holdings tend to be longer term, a smaller fraction of the firm’s equity will be available for
trading in a highly concentrated firm. As the free float is lower, equity turnover will be smaller.
We assume that a similar effect does not influence insider shareholdings, which are normally much
smaller than the largest outsider stake.
Table 10.5 shows what happens when we maintain the board size instrument for insider holdings, but replace stock volatility by stock turnover as the concentration instrument. Compared to
in the two stages is not nested, the sum of squares may be larger for the unrestricted case. A negative pseudo-R2
does not necessarily reflect a major problem with the system. (Greene, 2000).
9
The fraction of a firm’s equity which is traded during one year.
76
Explaining the corporate governance mechanisms
Table 10.5 Interactions between governance mechanisms modeled as a system of equations. Concentration and insider holdings are endogeneous variables. Stock turnover and board size are used
as instruments
Panel A. Regression results
Dep.variable
Herfindahl index
Primary insiders
Indep.variable
Primary insiders
Aggregate state holdings
Aggregate international holdings
Aggregate individual holdings
Aggregate nonfinancial holdings
Fraction voting shares
Debt to assets
Dividends to earnings
Industrial
Transport/shipping
Offshore
Investments over income
ln(Firm value)
Stock volatility
Stock turnover
constant
Herfindahl index
Aggregate state holdings
Aggregate international holdings
Aggregate individual holdings
Aggregate nonfinancial holdings
Fraction voting shares
Debt to assets
Dividends to earnings
Industrial
Transport/shipping
Offshore
Investments over income
ln(Firm value)
ln(Board size)
Stock volatility
constant
coeff
1.02
0.57
0.18
-0.41
0.23
0.30
-0.06
0.01
0.00
-0.01
0.05
-0.00
-0.02
-0.01
-0.03
0.07
0.28
-0.24
-0.03
0.41
-0.07
-0.12
0.10
0.00
-0.02
-0.03
-0.06
-0.00
0.02
-0.02
0.04
-0.23
(stdev)
(0.71)
(0.12)
(0.06)
(0.32)
(0.06)
(0.09)
(0.09)
(0.01)
(0.03)
(0.03)
(0.05)
(0.00)
(0.01)
(0.05)
(0.01)
(0.30)
(0.22)
(0.14)
(0.07)
(0.06)
(0.08)
(0.08)
(0.04)
(0.01)
(0.02)
(0.02)
(0.03)
(0.00)
(0.01)
(0.02)
(0.03)
(0.18)
pvalue
0.15
0.00
0.01
0.20
0.00
0.00
0.49
0.49
0.91
0.84
0.29
0.37
0.20
0.79
0.03
0.82
0.20
0.08
0.66
0.00
0.37
0.16
0.01
0.89
0.28
0.21
0.03
0.99
0.01
0.27
0.13
0.20
Panel B. Regression diagnostics
Equation
1
2
n
741
741
Parms
15
15
RMSE
0.19
0.16
R2
-1.16
0.17
χ2
100.48
151.07
p
0.00
0.00
The tables shows 3SLS estimates of a system of simultanous equations. Panel A report system estimates. The
leftmost column is the dependent variable in that particular equation, which is a function of the variables listed in
the next column. Equations are separated by a line. Panel B holds diagnostics. For each equation the diagnostics
include n, the number of observations, Parms, the number of parameters, RMSE, the root mean squared error, R2 , a
“pseudo” R squared, χ2 , the chi squared of the equation, and p, a p-value for the fit of the equation. The estimates
are performed with Stata’s reg3 3SLS routine. Variable definitions are in Appendix A.2. Data for firms listed on
the Oslo Stock Exchange, 1989-1997. Appendix table B.83 estimates a similar system which only uses controls as
additional explanatory variables beyond the two endogeneous mechanisms and the two instruments.
10.4 Ownership concentration and insider holdings as a simultaneous system
77
table 10.4, the new model produces very similar results. The significant coefficients in table 10.4
keep their signs as well as their significance levels in table 10.5. There is also still no indication that
the two endogeneous mechanisms are related. The only difference is that debt to assets becomes
significant in the revised insider equation.
This comparison may suggest that the model is insensitive to whether we use stock volatility or
stock turnover as an instrument for identifying the ownership concentration equation. Alternatively,
this may simply be because both instruments are weak. This interpretation is consistent with the
finding that their p–values are high in both models. Also, since we have no underlying theory, it is
not obvious that board size is a good instrument for the insider equation. Our third specification
uses instruments which have not been used in any of the two models analyzed so far.
The new instrument for ownership concentration is intercorporate shareholdings between listed
firms. This choice is based on the evidence that when intercorporate owners have nontrivial stakes,
these holdings tend to be rather large.10 Thus, there is reason to believe that intercorporate
investments and concentration are positively related. On the other hand, there are no obvious
reasons why intercorporate investments are systematically related to insider holdings. The new
insider instrument is debt, using the argument that the higher the debt, the less it takes to buy a
given fraction of equity. In this case, however, we cannot convincingly argue why this should not
apply to outside concentration as well. One possibility is that the lack of diversification by insiders
makes it more costly for them than for outside large owners to hold a large fraction in the firm.
Table 10.6 shows the results using the new set of instruments. Notice first that unlike in the
previous models, the two new instruments enter the regressions with very significant coefficients.
Although this tells nothing about whether or not an instrument for endogeneous mechanism A is
unrelated to endogeneous mechanism B (which it should in order to be a good instrument), there
is at least a close link to mechanism A. Second, the concentration equation shows that the positive
association between concentration and insiders now becomes significant at the 4% level. Third, and
more dramatically, the association between the two mechanisms is suddenly negative in the insider
equation, with a p–value of 10%.
Taken together, the new instruments have strenghtened the case for a relationship between concentration and insider holdings. However, the negative relation in the insider equation in table 10.6
is absent in the other two models, and this negative coefficient is not significantly different from
zero at conventional levels. Since the positive association in the concentration equation is stable
across the three models and also have lower p-values in table 10.6, a reasonable conclusion is that
concentration and insider holdings may be positively related, and that the order of causation goes
from insider holdings to concentration rather than the opposite way. That is high insider stakes
generate high concentration, but not vice versa.
We hesitate to make strong conclusions based on these system estimations. The findings are not
convincing in a statistical sense. Also, because the theory is often silent on how the mechanisms
interact with each other and with controls, we have no strong arguments for one set of instruments and hence one equation system being more reliable. Compared to he equation–by–equation
estimates summarized in table 10.1, the simultaneous systems approach does not seem to offer
additional, reliable insight.11
10
This pattern can be inferred from the information in appendix table 3.1 of Bøhren and Ødegaard (2000). The
mean intercorporate holding is 10% while the median is 3%. This can only be the case with a few large holdings and
many small.
11
Appendix B.8 provides further evidence along these lines. We estimate systems which resemble the ones in
tables 10.4–10.6, but which only include controls as additional independent variables beyond the two endogeneous
mechanisms and the two instruments. The estimated sign of the concentration coefficient in the insider equation is
negative in two of three cases, and one of these coefficients has p-value below 1%. Insider holdings have a positive,
78
Explaining the corporate governance mechanisms
Table 10.6 Interactions between governance mechanisms modeled as a system of equations. Concentration and insider holdings are endogeneous variables. Intercorporate investments and financial
leverage are used as instruments
Panel A. Regression results
Dep.variable
Herfindahl index
Primary insiders
Indep.variable
Primary insiders
Aggregate state holdings
Aggregate international holdings
Aggregate individual holdings
Aggregate nonfinancial holdings
Fraction voting shares
Dividends to earnings
ln(Board size)
Industrial
Transport/shipping
Offshore
Investments over income
ln(Firm value)
Aggregate intercorporate holdings
constant
Herfindahl index
Aggregate state holdings
Aggregate international holdings
Aggregate individual holdings
Aggregate nonfinancial holdings
ln(Board size)
Fraction voting shares
Dividends to earnings
Industrial
Transport/shipping
Offshore
Investments over income
ln(Firm value)
Debt to assets
constant
coeff
0.56
0.59
0.23
-0.17
0.27
0.25
0.01
-0.01
-0.01
-0.03
0.03
-0.00
-0.01
0.16
-0.07
-0.79
0.36
0.24
0.46
0.25
-0.04
0.09
0.02
-0.04
-0.08
-0.07
-0.00
0.01
0.17
-0.25
(stdev)
(0.27)
(0.06)
(0.04)
(0.12)
(0.04)
(0.06)
(0.01)
(0.02)
(0.01)
(0.02)
(0.03)
(0.00)
(0.00)
(0.05)
(0.13)
(0.49)
(0.28)
(0.13)
(0.07)
(0.15)
(0.02)
(0.13)
(0.02)
(0.02)
(0.03)
(0.03)
(0.00)
(0.01)
(0.05)
(0.19)
pvalue
0.04
0.00
0.00
0.14
0.00
0.00
0.11
0.48
0.38
0.15
0.35
0.18
0.03
0.00
0.57
0.10
0.20
0.07
0.00
0.10
0.08
0.47
0.16
0.05
0.01
0.03
0.30
0.42
0.00
0.20
Panel B. Regression diagnostics
Equation
1
2
n
741
741
Parms
14
14
RMSE
0.14
0.19
R2
-0.11
-0.17
χ2
172.67
106.12
p
0.00
0.00
The tables shows 3SLS estimates of a system of simultanous equations. Panel A report system estimates. The
leftmost column is the dependent variable in that particular equation, which is a function of the variables listed in
the next column. Equations are separated by a line. Panel B holds diagnostics. For each equation the diagnostics
include n, the number of observations, Parms, the number of parameters, RMSE, the root mean squared error, R2 , a
“pseudo” R squared, χ2 , the chi squared of the equation, and p, a p-value for the fit of the equation. The estimates
are performed with Stata’s reg3 3SLS routine. Variable definitions are in Appendix A.2. Data for firms listed on
the Oslo Stock Exchange, 1989-1997. Appendix table B.84 estimates a similar system which only uses controls as
additional explanatory variables beyond the two endogeneous mechanisms and the two instruments.
10.5 Summary
10.5
79
Summary
Ignoring the link to economic performance, this chapter has analyzed the determinants of corporate
governance mechanisms. We initially use an equation–by–equation approach which endogenizes one
mechanism at a time, assuming all the others remain exogeneous. We find that the major determinants are ownership concentration, insider holdings, industry membership, and firm size. Unlike
what agency theory predicts, the mechanisms are often complements or independent rather than
substitutes. For instance, since both leverage and dividend payments are high when concentration
and insider holdings are high, financial policy is apparently used to divert the free cash flow from
management’s discretion when the need to do so is particularly small.
Individual (personal) investors are special by being heavily invested in firms with low concentration, high insider stakes, and small boards. As we showed in chapter 9, these three ownership
characteristics are associated with high performance. Quite the opposite ownership characteristics
attract state owners. Consistent with the notion that boards are expanded to cope with increasing
scale and complexity in the firm’s operations, we find that the number of directors increases with
firm size and cash flow volatility.
We extended the equation–by–equation approach by simultaneous equations estimation, using
ownership concentration and insider ownership as the endogeneous mechanisms and three alternative sets of instruments to identify the two equations. This change of methodology substantially
reduces the number of significant links between the mechanisms, suggesting they are more independent than what we found with the equation–by–equation approach. We hesitate to make strong
conclusions from the equation systems estimation, since the results are sensitive to the instruments
used to identify the equations, and since the theoretical basis for specifying the instruments is weak.
Finally, notice that if it is true that performance influences the governance mechanisms, all the
models used in this chapter are misspecified, since they ignore performance as a determinant of the
mechanisms. We consider this potential problem in the next chapter.
insignificant sign in the concentration equation in all three cases. We also introduced outside (external) concentration
in order to control for potential overlap where insiders are among the largest owners. The results are consistent with
the regressions discussed in the main text.
80
Causation between corporate governance and economic performance
Chapter 11
Causation between corporate governance and economic
performance
This chapter moves the analysis into cell 4 of table 2.1, which involves endogeneous mechanisms and
two–way causation. The econometric tool is simultaneous equation modeling, which was presented
and used in chapter 10. As discussed there, this method requires restrictions on the coefficients
in order to solve the identification problem. The restrictions, which materialize themselves as
instruments, must be imposed prior to estimation and should come from corporate governance
theory. We found, however, that the theory is sometimes non–existent and always partial, and that
different instruments produce different conclusions on how governance mechanisms interact. This
problem occurred even though we modeled just two of the mechanisms (ownership concentration
and insider ownership) as endogeneous variables.
An alternative explanation of these results is that because any potential link to performance
was ruled out, the model is misspecified. If some of these interactions occur through links to
performance, like if managers tend to increase their holdings in well-performing firms, a model which
ignores performance as a determinant of mechanisms is simply wrong. In a correctly specified system
which also considers performance, the problem of unstable coefficient estimates for concentration
and insider holdings may disappear.
This chapter explores the merit of using simultaneous equations modeling to uncover causality
between mechanisms and performance. We do this for several reasons. First, the method has
recently been used by researchers who all argue that the systems approach is superior (Agrawal
and Knoeber, 1996; Cho, 1998; Demsetz and Villalonga, 2001). Second, since we have already
performed the single-equation estimation of the full multivariate model in chapter 9, the systems
approach is a natural extension. Third, we want to explore whether the instability problems of the
preceding chapter is case–specific. We limit ourselves to the two endogeneous mechanisms used
in section 10.4, i.e, concentration and insider holdings. This setup is expanded by incorporating
an equation where performance (Q) is modeled as a function of the governance mechanisms (both
endogeneous and exogeneous) and controls. We perform the analysis in two steps. In section 11.1
endogeneous mechanisms are allowed to influence performance, but not vice versa. Section 11.2
allows causation to be two–way, i.e., the endogeneous governance mechanisms may also be influenced
by performance.
11.1
Governance driving performance
This section allows governance mechanisms to interact and to influence performance, but not to
be influenced by performance. The starting point is the setup of section 10.4, which used three
alternative sets of instruments to estimate the determinants of ownership concentration and insider
holdings. To capture the link between these two mechanisms and simultaneously explore their
influence on performance, we include an equation with performance (Q) as the dependent variable
and the full set of independent variables from chapter 9. This performance equation is identical
to the full multivariate model estimated in table 9.2. Thus, while we add one more equation to
the system of section 10.4, we do not need additional instruments to identify the system. This
is because the new endogeneous variable does not enter any of the equations explaining the two
11.1 Governance driving performance
81
endogeneous governance mechanisms. That is, the equation for Q is included in the system, but Q
influences neither concentration nor insider holdings.
Table 11.1 summarizes the estimation results.1 The three models, indicated by (I), (II) and (III),
only differ in terms of instruments used for concentration and insiders, which are stock volatility
and board size in (I), stock turnover and board size in (II), and intercorporate investments and
financial leverage in (III).
Several patterns emerge. Considering first the two equations for the endogeneous mechanisms,
the picture is quite different from what we just observed in section 10.4. A basis for comparison
is tables 10.4 –10.6, which applies simultaneous equations to the same sets of mechanisms and
instruments as in table 11.1, but did not include the Q equation. First, the systems which include
Q have considerably more significant coefficients in the mechanisms equations. Every concentration
equation in table 11.1 have eight significant parameters, whereas the average is five in tables 10.4 –
10.6. Second, there is higher consistency across instruments in the equations including performance.
If we compare the regression results on the mechanisms in table 11.1 to each other, we find that
almost without exception, the same coefficients are significant and have the same sign in (I)–(III).
This differs from what we observed in tables 10.4–10.6. As the instruments are the same in the
two model sets, the finding indicates that the increased consistency and significance is due to the
inclusion of the performance equation in the system. This suggests that the system of chapter 10,
which did not include performance, is misspecified. If all the instruments we have used are valid,
there seems to be a complementary instead of a substitute role of these two governance mechanisms.
Considering next the estimated performance equation in table 11.1, notice first the general lack
of significance. The only case where any explanatory variable is significant is in model (II), where
stock turnover and board size are the instruments. Second, the number of significant associations
in this table is comparatively low. The single–equation estimates in table 9.2 had twelve significant
coefficients at the 5% level, and model (II) has only six. Third, whereas concentration enters with
the usual, negative and very significant (p < 1%) coefficient, model (II) looks very different from
what we are used to. Insider holdings are no longer significant, and the role of the owner type
variables have changed. Individual investors were associated with higher performance than any
other type in table 9.2, but state, international, and nonfinancial owners are the superior ones in
model (II) of table 11.1.2
Now, a change of sign relative to a single-equation regressions is not necessarily an indication
that the model is misspecified. It may merely reflect that when we properly account for causality in
a system of equations, we have a better model than in the single–equation case. But this is only true
if we have reasons to believe that the system is correctly specified, which is not the case here. For
one thing, entering the performance equation into a system containing governance mechanisms has
reduced the significance of the estimates in the performance equation compared to the stand–alone
case. A similar effect can be observed in table 3 of Agrawal and Knoeber (1996), where the p-values
increase when moving from OLS to 2SLS estimation of the system. This is typically the case when
the instruments are weak. Another argument for not trusting the estimated performance relation
is that the only case of significant relationships is in model (II). With the two other instrument
sets, the estimated coefficient has the opposite sign and is insignificant.
The findings of this section are encouraging in the sense that there are less inconsistencies across
the concentration and insider equations when the instrument set changes. This is particularly true
1
Detailed results are shown in appendix tables B.88–B.90.
Appendix B.9 shows in tables B.91–B.93 that if we only use controls and instruments as independent variables
in addition to the two endogeneous mechanisms, no coefficient is ever significant at the 5% level in the performance
equations.
2
82
Causation between corporate governance and economic performance
Table 11.1 Summary of estimations of the simultaneous determinants of economic performance
(Q), ownership concentration, and insider holdings, using three alternative sets of instruments.
Only the two governance mechanisms enter the system endogeneously.
Dep.variable
Q
Herfindahl index
Primary insiders
Indep.variable
Herfindahl index
Primary insiders
Squared (Primary insiders)
Aggregate state holdings
Aggregate international holdings
Aggregate individual holdings
Aggregate nonfinancial holdings
Fraction voting shares
ln(Board size)
Debt to assets
Dividends to earnings
Industrial
Transport/shipping
Offshore
Investments over income
ln(Firm value)
constant
Primary insiders
Aggregate state holdings
Aggregate international holdings
Aggregate individual holdings
Aggregate nonfinancial holdings
Aggregate intercorporate holdings
ln(Board size)
Fraction voting shares
Debt to assets
Dividends to earnings
Industrial
Transport/shipping
Offshore
Investments over income
ln(Firm value)
Stock volatility
Stock turnover
constant
Herfindahl index
Aggregate state holdings
Aggregate international holdings
Aggregate individual holdings
Aggregate nonfinancial holdings
ln(Board size)
Fraction voting shares
Debt to assets
Dividend to earnings
Industrial
Transport/shipping
Offshore
Investments over income
ln(Firm value)
Stock volatility
constant
(I)
+
−
+
−
−
+
−
−
−
−
+
+
−
+
+
−
+∗∗∗
+∗∗∗
+∗∗∗
−
+∗∗∗
+∗∗∗
+
+∗∗∗
−∗
−∗∗∗
+
−
−∗
+
−
+∗∗∗
−∗∗∗
−∗∗∗
+
−∗∗∗
+
−∗∗∗
−
−∗∗
+
+∗
−
+
+∗
+
(II)
−∗∗∗
+
−
+∗∗∗
+∗∗∗
−
+∗∗
+
−
−∗∗∗
+
−
−∗∗∗
−
−
−
−
+∗∗∗
+∗∗∗
+∗∗∗
−∗∗∗
+∗∗∗
+∗∗∗
+
+∗
−
−
+
−
−∗
+
−∗
−
+∗∗∗
−∗∗∗
−∗∗∗
+∗∗∗
−∗∗∗
−
−∗∗∗
−
−
+
+
−
+
+∗
−
+
(III)
+
+
−
−
−
−
−
−
+
−
+
+
+
+
−
+
+∗∗∗
+∗∗∗
+∗∗∗
−
+∗∗∗
+
−
+∗∗∗
+∗∗∗
−
−∗∗∗
+
−
−∗∗
−
+∗∗∗
−∗∗∗
−∗∗∗
+
−∗∗∗
+
−∗∗∗
+
−∗
+
+∗
−
+
+∗
+
The table summarizes estimations of three different simultaneous systems which differ across instruments used. The
instruments for ownership concentration and insider holdings are stock volatility and board size in model (I), stock
turnover and board size in model (II), and intercorporate shareholdings and debt to assets in model (III). A + or a −
sign means the coefficient is estimated with a positive or negative coefficient, respectively. Statistical significance is
indicated with ∗ , ∗∗ , and ∗∗∗ , which means the relationship is significant at the 5%, 2.5% and 1% level, respectively.
The underlying estimations are shown in appendix B.9.
11.2 Two–way causation
83
for the interaction between the two endogeneous mechanisms, where every coefficient reflects a
significant complementary relationship (< 1%). The performance equation is not as clean, but this
may be because performance is not allowed to influence the two governance mechanisms. The next
section opens up for this possibility.
11.2
Two–way causation
This section lets performance, concentration, and insider holdings be simultaneously determined
by each other, by the remaining (and exogeneous) governance mechanisms, and by controls. We
expand the models of the preceding section by also allowing performance (Q) to have a causal effect
on ownership concentration and insider ownership. As discussed earlier, both Loderer and Martin
(1997) and Cho (1998) find evidence for reverse causality, as performance drives insider holdings,
but not vice versa.
We use stock beta as the instrument for identifying Q because asset pricing theory shows
that systematic risk directly influences the value of the firm and hence Q. As usual, we cannot
convincingly argue why this risk measure does not influence concentration and insider holdings as
well. One possibility is an appeal to order of magnitude and the idea that although beta drives all
three variables, it has a stronger effect on Q than on the two others.
Table 11.2 summarizes the results.3 The performance relation contains even less significant
coefficients than in table 11.1, where Q was not allowed to determine the mechanisms. The only
significant variables are in model (B), where stock beta, stock turnover, and board size are the
instruments, and where we find the usual negative covariation (p = 2%) between performance and
concentration. As for the concentration and insider equations, the pattern is less consistent across
models than earlier. For instance, whereas every coefficient reflects a significant complementary
relationship (< 1%) between concentration and insider holdings in table 11.1, this is definitely not
the case in table 11.2. Thus, once more we find that the choice of instruments matters a lot for the
conclusions. Still, there is one result which is consistent across models: Insider ownership is never
significant in the performance equations, but performance always enters with a positive sign in the
insider equations. This is consistent with Loderer and Martin (1997) and Cho (1998), who both
find that performance is a significant determinant of insider holdings, but not vice versa.
Just like us, Agrawal and Knoeber (1996), Cho (1998) and Demsetz and Villalonga (2001) find
that the governance–performance relationship is considerably less significant with simultaneous
equation estimation than with single–equation models. Unlike us, they do not test for different
instruments and therefore do not explore the instrument quality question. Their interpretation of
the insignificance findings is that such evidence supports the equilibrium hypothesis of Demsetz
(1983). We are not convinced by this interpretation, which implicitly assumes that the system
is better specified than single–equation models. As there exists no proper theoretical basis for
establishing instruments, we test out three different instrument sets and find that the qualitative
conclusions are sensitive to the choice of instruments. In particular, the choice of instruments
decides whether or not our data supports the equilibrium argument. It also determines what to
conclude about mechanism interaction and reverse causation.
The instability of qualitative conclusions across instruments and the reduced significance in
systems may both be driven by the choice of instruments, which should have high correlation with
the variable it is supposed to identify and low correlation with the remaining endogeneous variables.
It is evident from table 11.2 that some of the instruments are quite weak. This is particularly true
in the three performance equations, where the instrument (stock beta) is never significantly related
3
The underlying estimations are shown in appendix tables B.97–B.99
84
Causation between corporate governance and economic performance
Table 11.2 Summary of estimations of the simultaneous determinants of economic performance
(Q), ownership concentration, and insider holdings, using three alternative sets of instruments.
Dep.variable
Q
Herfindahl index
Primary insiders
Indep.variable
Herfindahl index
Primary insiders
Squared (Primary insiders)
Aggregate state holdings
Aggregate international holdings
Aggregate individual holdings
Aggregate nonfinancial holdings
Fraction voting shares
ln(Board size)
Debt to assets
Dividends to earnings
Industrial
Transport/shipping
Offshore
Investments over income
ln(Firm value)
Stock beta
constant
Primary insiders
Q
Aggregate state holdings
Aggregate international holdings
Aggregate individual holdings
Aggregate nonfinancial holdings
Aggregate intercorporate holdings
ln(Board size)
Fraction voting shares
Debt to assets
Dividends to earnings
Industrial
Transport/shipping
Offshore
Investments over income
ln(Firm value)
Stock volatility
Stock turnover
constant
Herfindahl index
Q
Aggregate state holdings
Aggregate international holdings
Aggregate individual holdings
Aggregate nonfinancial holdings
ln(Board size)
Fraction voting shares
Debt to assets
Dividend to earnings
Industrial
Transport/shipping
Offshore
Investments over income
ln(Firm value)
Stock volatility
constant
(A)
+
−
+
−
−
+
−
−
−
−
−
+
−
+
+
−
−
+
+
+∗∗∗
+∗∗∗
−
+∗∗∗
+
+
+∗∗
−
−
+
−
−
+
−
+
+∗
+
−
−
+
+∗
−∗∗∗
+∗
+
+∗
+∗∗∗
+∗
+
−
+
(B)
−∗∗
+
−
+
+∗
+
+
+
−
−
−
−
−∗∗∗
−
−
+
+
−
−
+
+∗∗∗
+∗∗∗
−
+∗∗∗
+
+
+
+
+
+
−
−
+
−∗∗∗
+
+∗∗∗
+∗∗∗
−∗
−∗
−∗
−
+∗
−∗∗∗
+∗∗∗
+
+∗∗
+∗∗∗
+∗∗
+
−∗∗∗
−
+
(C)
+
+
−
−
−
−
−
−
+
−
+
+
+
+
−
+
+
+∗∗∗
−∗∗
+∗∗∗
+∗∗∗
+
+∗∗∗
+∗∗∗
−∗
+∗∗∗
+
−∗∗∗
−∗∗∗
−
−
−
−
+∗
+∗∗
−
−
−
−
+
−∗∗∗
+∗
+
+
+∗∗∗
+∗
+
−
+
The table summarizes estimations of three different simultaneous system which differ across instruments used. The
instruments for performance, ownership concentration, and insider holdings are stock beta, stock volatility, and
board size in model (A), stock beta, stock turnover, and board size in model (B), and stock beta, intercorporate
shareholdings, and debt to assets in model (C). A + or a − sign means the coefficient is estimated with a positive or
negative coefficient, respectively. Statistical significance is indicated with ∗ , ∗∗ , and ∗∗∗ , which means the relationship
is significant at the 5%, 2.5% and 1% level, respectively. The underlying regressions are shown in appendix B.10.
11.3 Summary
85
to Q (the p-value is 30%, 37%, and 77% in models (A), (B), and (C), respectively). This is less
of a problem in the mechanism equations, where the instrument is insignificant at the 5% level in
only one of the six cases (stock volatility in model (A)).
11.3
Summary
This chapter has analyzed the relationship between corporate governance and economic performance using simultaneous equations modeling. We specified ownership concentration and insider
ownership as endogeneous governance mechanisms, letting the other mechanisms remain exogeneous. Economic performance was added to this system by alternatively letting governance influence performance (cell 2 in table 2.1) and by allowing for two–way causation between governance
and performance (cell 4).
The overall conclusion is that the findings are sensitive to the choice of instruments. For
instance, just like Agrawal and Knoeber (1996), we find although the introduction of simultaneous
equation systems reduces the number of significant determinants of economic performance quite
dramatically. However, the inverse relationship between concentration and performance found in
every single–equation model comes up when one of the three instrument sets is used, but not
with the two others. Similarly, although we never find that concentration and insider holdings are
substitute mechanisms (i.e., they are always complements or independent), the conclusion on the
order of causation between them differs across instruments.
Several papers in this field have concluded that the lack of statistical significance in simultaneous
equations models supports the equilibrium hypothesis of Demsetz (1983) that when governance
mechanisms are optimally installed, no mechanism is significantly related to economic performance.
Based on our analysis, we would forward the alternative hypothesis that these results may as
well be driven by a model misspecification problem which is due to weak instruments. Until we
have stronger theoretical justifications for choosing the instruments which restrict the systems of
equations, we doubt whether the simultaneous system approach can offer deeper insight into the
determinants of corporate governance mechanisms beyond those obtained from the single–equation
multivariate analyses in chapters 5–9.
86
Conclusions
Chapter 12
Conclusions
The question of whether corporate governance matters for economic performance is getting increasing international attention from politicians, practitioners, and academic researchers alike. This report initially outlines the relevant theory and existing empirics, concluding that not surprisingly, the
theoretical foundation of this novel academic field is rather weak, and that the empirical evidence
is quite narrow and mixed. On this background, we explored how governance and performance
interact in all Norwegian listed firms except financials over the period 1989–1997.
We think our analysis improves the general insight into the governance–performance relationship
in several ways. First, unlike most existing research, which studies just one or two ownership structure variables (typically ownership concentration and insider holdings), we add a wide range of other
governance mechanisms which corporate governance theory specifies as determinants of economic
performance. We add the identity of outside owners (such as personal and institutional investors),
board characteristics (number of directors), security design (voting vs. non–voting equity), and
financial policy (capital structure and dividend policy). Second, instead of making the standard
assumption that governance mechanisms are internally independent and that causation runs from
governance to performance, only, we expand our single–equations models into simultaneous equations systems, which can handle both mechanism endogeneity and reverse causation. Moreover,
whereas existing research has focued heavily on very large US corporations, our Norwegian sample
firms are on average much smaller, they are exposed to civil law rather than common law, hostile
takeovers are very rare, the firms are closely rather than widely held, performance–related pay is
much less common, and corporate boards are owner–driven rather than manager–dominated. Finally, our unusually accurate and detailed ownership structure data produces more reliable evidence
than earlier studies.
Our sample is the population of non–financial firms listed on the Oslo Stock Exchange (OSE),
which is a rather typical European exchange in terms of size, liquidity, recent growth, and relative
importance in the overall economy. The average OSE firm is about twice the size of a NASDAQ
firm and roughly one fifth of a NYSE firm. OSE firms have low ownership concentration by European standards, international owners hold about one third of aggregate market capitalization,
financial (institutional) investors steadily increase their share, and ownership by individuals (personal investors) is small and declining. Insiders hold 7% of the market portfolio, roughly half the
insider stakes belong to primary insiders (officers and directors), and the CEO holds almost all the
shares in the officers category.
We have tested a large number of single–equation regressions; from the simplest univariate
models studying one mechanism at a time through partial multivariate models with two or more
mechanisms to a full multivariate model which includes every governance mechanism and control
variable in our data base. Estimating the full multivariate regression equation, we find that the
relationship between ownership concentration and economic performance as measured by Tobin’s Q
(operationalized as market value to book value of assets) is inverse and very significant. This result,
which is our very strongest finding, is atypical in the literature, and it questions the fundamental
agency hypothesis of Berle and Means (1932) and Jensen and Meckling (1976) that managers
who are not closely monitored by powerful owners will not fulfill their fiduciary duty, and that
powerful owners are beneficial because they discipline management towards making maximizing
market values. Unlike what agency theory predicts, the mechanisms are often complements or
Conclusions
87
independent rather than substitutes. For instance, because we find that both financial leverage
and dividend payments are high when concentration and insider holdings are high, financial policy
is apparently used to divert the free cash flow from management’s discretion when the need to do
so is particularly small.
In contrast, we find support for agency–based ideas in our evidence that performance correlates
positively with insider holdings at almost any level, that direct ownership is more value–creating
than indirect, and that both non–voting shares and larger boards reduce market value. Even though
we find the relationship between insider holdings and performance to be quadratic, there are very
few firms in our sample where the marginal value effect of a higher insider stake is negative. This
suggests that the costs of higher insider stakes almost never outweigh the benefits.
The ownership characteristic with the strongest impact on the average firm’s Q is insider holdings, where a one percentage point higher stake increases firm value by 1% or by roughly NOK 20
mill. A corresponding increase in direct as opposed to indirect ownership has a 0.8% effect, and
one percentage point lower ownership concentration increases firm value by 0.4%. Stepping up the
fraction of voting shares by one percentage point drives up firm value by 0.8%, and the average
firm will be about 2% more valuable if board size is reduced by one member. If the value of debt is
unaffected by these changes in the corporate governance mechanisms, the relative impact on equity
value will be roughly 2.5 times larger than these relative changes in firm value.
We find that the choice of performance measure matters a lot, as very few of the above findings
stay significant if we replace Tobin’s Q by return on assets or return on stock. Quite remarkably,
however, most of the significant relationships in the full multivariate model survived all the way
from the univariate analysis through the various partial multivariate models to the full multivariate
specification when Q is the performance measure. This suggests that the estimated sign and the
statistical significance of a governance–performance link is rather robust to the choice of single–
equation models. It also reflects that each governance mechanism has a separate link to performance
which is not offset or driven by other mechanisms.
We finally expand the equation–by–equation approach into simultaneous equations estimation.
This change of methodology substantially reduces the number of significant links between the
mechanisms, suggesting they are more independent than what we found with the equation–by–
equation approach. The same loss of significance occurs when we use simulataneous equations
to allow for both mechanism endogeneity and reverse causation. For instance, just like other
researchers, we observe that the introduction of simultaneous equation systems reduces the number
of significant determinants of economic performance quite dramatically. Moreover, the findings
are sensitive to what instruments we use to identify the simultaneous equations. For instance, the
inverse relationship between concentration and performance found in every single–equation model
comes up when one of the three instrument sets is used, but not with the two others. Similarly,
although we never find that concentration and insider holdings are substitute mechanisms (i.e.,
they are always complements or independent), the conclusion on the order of causation between
them differs across instruments. Since the results are sensitive to the instruments used, and since
the theoretical basis for specifying the instruments is weak, we cannot make strong conclusions
from the equation systems estimation.
Several papers in this field have concluded that the lack of statistical significance in simultaneous
equations models supports the equilibrium hypothesis of Demsetz (1983) that when governance
mechanisms are optimally installed, no mechanism is significantly related to economic performance.
Based on our analysis, we forward the alternative hypothesis that these results may as well be
due to model misspecifications driven by weak instruments. Until we have stronger theoretical
justifications for choosing the instruments, we doubt whether the simultaneous system approach
88
Conclusions
can offer deeper insight into the determinants of corporate governance mechanisms beyond those
obtained from the single–equation multivariate analyses.
Appendix
90
Data sources, variable definitions, and descriptive statistics
Appendix A
Data sources, variable definitions, and descriptive statistics
A.1
Data sources
The five basic owner types
Based on data in electronic form from the Norwegian Central Securities Depository (Verdipapirsentralen; VPS ) we have a complete database of year–end holdings of all equity owners for companies
listed at the Oslo Stock Exchange (OSE). This data is available from 1989 through 1997. The
data does not specify the owner’s name, but each owner still has a unique ID in our data base.
Each owner is classified into one of the five basic types, which are: state, international, individual,
financial, and nonfinancial owners.
Insider owners
We have data on legal insiders which by law must report their transactions to the stock exchange.
Legal insiders include members of the company’s management team, members of the company
board, the company’s auditors and their immediate families. Each insider must report his or her
transaction to the OSE by 10 am the day after the transaction. The OSE publishes this report,
which details the insider’s name, position, number of shares bought and sold, and the resulting
total holding.
Our insider data base is constructed by manually recording the transactions from the insiders’
reports. We infer a time series of total holdings for each insider, adjusting for stock splits.
Insiders who leave the firm have no obligation to report neither this event nor their subsequent
transactions in the firm’s stock. Consequently, our data base may overestimate the insider holdings.
To at least partially eliminate this problem, we intend to cross–check our insider data base with
board and CEO data which specifies the dates on which these corporate insiders leave the firm.
OSE–listed owners
According to corporate law, firms owning equity stakes in other firms must specify these holdings
as of year–end in their annual reports. We manually collect these data for OSE listed corporate
owners and use them to construct the intercorporate shareholdings between OSE listed firms.
Share prices, shares outstanding, and dividend payments
The data base on equity prices, shares outstanding, new equity issues, stock splits, and dividend
payments is constructed from data in electronic form provided by the OBI (Oslo Børs Informasjon),
which is a subsidiary of the OSE.
Accounting information
Accounting data is taken from the OBI electronic data base, which provides all the accounting
figures (except for the footnotes) from the annual reports of OSE listed firms. We have also used a
large number of annual reports in paper format to supplement the electronic records. For instance,
data on intercorporate shareholdings, which is provided in footnotes, must be collected manually.
A.1 Data sources
Abbreviations
The following data sources are referenced:
• BI – Norwegian School of Management BI
• OB (Oslo Børs), the Oslo Stock Exchange
• OBI (Oslo Børsinformasjon) – the Oslo Stock Exchange data services
• OSE – Oslo Stock Exchange
• VPS (Verdipapirsentralen) – Norwegian Securities Registry.
91
92
A.2
Data sources, variable definitions, and descriptive statistics
List of variables
1989: Indicator variable equal to one if the observation is in the year 1989
1990: Indicator variable equal to one if the observation is in the year 1990
1991: Indicator variable equal to one if the observation is in the year 1991
1992: Indicator variable equal to one if the observation is in the year 1992
1993: Indicator variable equal to one if the observation is in the year 1993
1994: Indicator variable equal to one if the observation is in the year 1994
1995: Indicator variable equal to one if the observation is in the year 1995
1996: Indicator variable equal to one if the observation is in the year 1996
1997: Indicator variable equal to one if the observation is in the year 1997
1-2 largest owners: The aggregate fraction of a company’s equity held by the 2 largest owners.
Equity includes both voting and nonvoting stock. Data source: VPS.
1-3 largest owners: The aggregate fraction of a company’s equity held by the 3 largest owners.
Equity includes both voting and nonvoting stock. Data source: VPS.
1-4 largest owners: The aggregate fraction of a company’s equity held by the 4 largest owners.
Equity includes both voting and nonvoting stock. Data source: VPS.
1-5 largest owners: The aggregate fraction of a company’s equity held by the 5 largest owners.
Equity includes both voting and nonvoting stock. Data source: VPS.
1-10 largest owners: The aggregate fraction of a company’s equity held by the 10 largest owners.
Equity includes both voting and nonvoting stock. Data source: VPS.
1-20 largest owners: The aggregate fraction of a company’s equity held by the 20 largest owners.
Equity includes both voting and nonvoting stock. Data source: VPS.
2nd largest owner: The fraction of a company’s equity held by the second largest owner. Equity
includes both voting and nonvoting stock. Data source: VPS
3rd largest owner: The fraction of a company’s equity held by the third largest owner. Equity
includes both voting and nonvoting stock. Data source: VPS
4th largest owner: The fraction of a company’s equity held by the fourth largest owner. Equity
includes both voting and nonvoting stock. Data source: VPS
5th largest owner: The fraction of a company’s equity held by the fifth largest owner. Equity
includes both voting and nonvoting stock. Data source: VPS
Aggregate individual holdings: The aggregate fraction of a company’s equity held by individual owners. The owners have sector codes: 790-889. Data source: VPS.
Aggregate intercorporate holdings: Aggregate fraction of a company’s equity held by other
firms listed at the Oslo Stock Exchange. Data source: Company Annual Reports.
A.2 List of variables
93
Aggregate international holdings: The aggregate fraction of a company’s equity held by international owners. The owners have sector codes: 900–1000. Data source: VPS
Aggregate financial holdings: The aggregate fraction of a company’s equity held by financial
owners. Financial owners are companies which are in a financial business (banks, insurance
companies, mutual funds, etc.) The owners have sector codes: 210–499. Data source: VPS.
Aggregate nonfinancial holdings: The aggregate fraction of a company’s equity held by nonfinancial owners. A non-financial owner is a corporation which is not a financial corporation.
The owners have sector codes: 710-789. Data source: VPS.
Aggregate state holdings: The aggregate fraction of a company’s equity held by state owners.
The owners have sector codes: 110–199 and 510–699. Data source: VPS.
All insiders: The aggregate fraction of a company’s equity held by legal insiders. The legal insiders
include members of the company’s management team, members of the company board, the
company’s auditors and their immediate families. Data sources: OB and BI.
Board size: The number of board members (not including substitutes). Data source: Brønnøysundregistrene and BI.
Board members: The aggregate fraction of a company’s equity held by legal insiders who are
board members. Data source: OB & BI.
Dividends to earnings: Dividends to earnings (Utdelingsforhold), calculated by OBI.
Dividends to price: Dividends to price (Direkt avk.), calculated by OBI.
Debt to assets: Book value of debt divided by book value of assets. Data source: OBI.
Equity value: Market value of equity, estimated as share price at yearend times number of shares
outstanding. Data source: OBI
Firm value: Total firm value estimated as the sum of market value of equity and book value of
debt. The calculation is done at yearend. Data source: OBI
Fraction voting shares: Fraction of a company’s outstanding equities which is voting. Data
source: OBI.
Fraction nonvoting shares: Fraction of a company’s outstanding equities which is nonvoting.
Data source: OBI.
Herfindahl index: Index of ownership concentration. Defined as the sum of squared ownership
fractions across all owners. Has a maximum of 1 with one owner, and a minimum of 1/n2 if
each of the n owners holds a fraction of 1/n each. Data source: VPS
Investments over income: Company total investments (totalinvesteringer) divided by operating
income (driftsinntekter). Data source: OBI.
Industrial: Indicator variable equal to one if the company is an industrial corporation. Industrials
explicitly excluded are offshore related and shipping related.
94
Data sources, variable definitions, and descriptive statistics
Largest outside owner: To estimate the size of the largest outside owner we look at the largest
owner in the insider data and the largest owner overall. If the largest insider has equal size
to the largest overall owner, the largest overall owner is removed and the size of the second
largest overall is used as the largest outside owner. On the other hand, if the size of largest
insider holding is less than the size of the overall largest, the largest outside owner is the same
as the overall largest owner.
Largest insider: The fraction of the company held by the largest insider owner.
Largest owner: The fraction of a company’s equity held by the largest owner. Equity includes
both voting and nonvoting stock. Data source: VPS
Largest owner is financial: The largest owner is a financial corporation. Data source: VPS.
Largest owner is individual: The largest owner is an individual. Data source: VPS.
Largest owner is international: The largest owner is an international investor. Data source:
VPS.
Largest owner is listed: The largest owner is a listed company. Data sources: VPS, OB & BI.
Largest owner is nonfinancial: The largest owner is a nonfinancial. Data source: VPS.
Largest owner is state: The largest owner is a state owner. Data source: VPS.
Management team: The aggregate fraction of company’s equity held by legal insiders who are
members of the management team. Data source: OB & BI.
Mean owner: The fraction of a company’s equity held by its average owner. Data source: VPS
Median owner: The fraction of a company’s equity held by its median owner. Data source: VPS
Number of owners: The total number of different owners who own equity in a given company.
Equity includes both voting and nonvoting stock. Data source: VPS.
Offshore: Indicator variable equal to one if the company is offshore related
Primary insiders: The aggregate fraction of a company’s equity held by primary insiders. Primary insiders are defined as those of the legal insiders which are board members or members
of the management team, i.e., the CEO and the firm’s directors. Data source: OB & BI
Q: Tobin’s Q ratio. The theoretical definition of the Q ratio is market value divided by replacement
value. We estimated Q as the sum of the market value of equity and the book value of debt
divided by the book value of assets. See Perfect and Wiles (1994). Data source: OBI.
RoA: Book return on assets. Data source: OBI.
RoA5 : Annual book return on assets, average over five previous years. Data source: OBI.
RoS: Annual percentage return on stock. Data source: OBI.
RoS5 : Annual percentage return on stock, average over five previous years. Data source: OBI.
Stock beta: Estimated beta value for the company’s equity. The beta is estimated with the OBX
index as the market index, using daily return data over the last two years. Data source: OBI.
A.2 List of variables
95
Stock volatility: Annualized volatility (standard deviation) of stock returns. Data source: OBI
Stock turnover: Annual stock turnover. Turnover is the number of shares traded divided by number of shares outstanding. Turnover is measured every day with trading and then aggregated
to estimate annual turnover. Data source: OBI.
Transport/shipping: Indicator variable equal to one if the company is either in transport or in
shipping.
Qualifications
Some of the definitions above are qualified, which means further restrictions are put on the variable.
• (voting rights) in parenthesis after a variable: Only voting stock is used to calculate the
variable in question.
• (constant ’97 term) in parenthesis after a variable: The variable is in constant December ’97
terms.
Transformations
In some cases the variables are transformed, usually by a simple mathematical procedure.
• lntrans(x). Defined as
x
lntrans(x) = ln
1 − 0.99x
This transformation was used by Demsetz and Lehn (1985) with the purpose of transforming
a variable between zero and one into an unrestricted one.
• ln(x). The natural logarithm (ln) of x.
• squared(x). (= x2 ) The variable x squared.
• Piecewise linear transformation.
In several regressions we apply a piecewise linear transformation, where the interval between
0 and 1 is split into the three regions, [0, 0.05], (0.05, 0.25] and (0.25, 1]. Regressions using
this transformation produce an estimated coefficient for each interval, which is indicated by
the following qualifications to a variable x:
– (x) 0 to 5.
=
(
x
0.05
if x < 0.05
if x ≥ 0.05
– (x) 5 to 25.
=


 0
x − 0.05

 0.2
if x ≤ 0.05
if 0.05 < x ≤ 0.25
if x > 0.25
96
Data sources, variable definitions, and descriptive statistics
– (x) 25 to 100
=
(
0
x − 0.25
if x < 0.25
if x ≥ 0.25
This linearization was used by Morck et al. (1988).
A.3 Histograms
A.3
97
Histograms
This appendix complements the descriptive statistics of chapter 3 with a histogram for each variable.
Note that each histogram uses the units used in the regressions, which may differ from those used
in the descriptive table.
A.3.1
Ownership concentration
Mean owner
Median owner
1000
1200
900
1000
800
700
800
600
500
600
400
400
300
200
200
100
0
0
0
0.01
0.02
0.03
0.04
0.05
0.06
0.07
0.08
0
0.001
Mean owner
0.002
0.003
0.004
0.005
0.006
0.007
0.008
Median owner
Number of owners
Herfindahl index
900
300
800
250
700
600
200
500
150
400
300
100
200
50
100
0
0
0
10000
20000
30000
40000
50000
Number of owners
60000
70000
80000
0
0.1
0.2
0.3
0.4
0.5
0.6
Herfindahl index
0.7
0.8
0.9
1
98
Data sources, variable definitions, and descriptive statistics
1-2 largest owners
Largest owner
140
180
160
120
140
100
120
100
80
80
60
60
40
40
20
20
0
0
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
0
1
0.1
Largest owner
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
0.7
0.8
0.9
1
Two largest owners
1-3 largest owners
1-4 largest owners
100
100
90
90
80
80
70
70
60
60
50
50
40
40
30
30
20
20
10
10
0
0
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
0
0.1
Three largest owners
0.2
0.3
0.4
0.5
0.6
Four largest owners
1-5 largest owners
1-10 largest owners
100
120
90
100
80
70
80
60
50
60
40
40
30
20
20
10
0
0
0.1
0.2
0.3
0.4
0.5
0.6
Five largest owners
0.7
0.8
0.9
1
0
0.1
0.2
0.3
0.4
0.5
0.6
10 largest owners
0.7
0.8
0.9
1
A.3 Histograms
99
2nd largest owner
1-20 largest owners
250
140
120
200
100
150
80
60
100
40
50
20
0
0.1
0
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
0
1
0.05
20 largest owners
0.1
0.15
0.2
0.25
0.3
0.35
0.4
0.45
2nd largest owner
3rd largest owner
4th largest owner
200
200
180
180
160
160
140
140
120
120
100
100
80
80
60
60
40
40
20
20
0
0
0
0.05
0.1
0.15
0.2
0.25
0
0.02
3rd largest owner
0.04
0.06
0.08
0.1
0.12
0.14
0.16
4th largest owner
5th largest owner
Largest outside owner
180
250
160
200
140
120
150
100
80
100
60
40
50
20
0
0
0
0.02
0.04
0.06
0.08
5th largest owner
0.1
0.12
0.14
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
Largest outside owner
0.8
0.9
1
100
Data sources, variable definitions, and descriptive statistics
A.3.2
Owner type
Aggregate state holdings
Aggregate international holdings
900
300
800
250
700
600
200
500
150
400
300
100
200
50
100
0
0
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
0
0.1
Aggregate state holdings
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
Aggregate international holdings
Aggregate individual holdings
Aggregate financial holdings
250
300
250
200
200
150
150
100
100
50
50
0
0
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
0
0.1
Aggregate individual holdings
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
Aggregate financial holdings
Aggregate nonfinancial holdings
Aggregate intercorporate holdings
90
700
80
600
70
500
60
50
400
40
300
30
200
20
100
10
0
0
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
Aggregate nonfinancial holdings
1
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
Aggregate intercorporate holdings
A.3 Histograms
A.3.3
101
Insider ownership
Primary insiders
All insiders
900
600
800
500
700
600
400
500
300
400
300
200
200
100
100
0
0
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
0
0.1
Primary insiders
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
0.7
0.8
0.9
1
All insiders
Board members
Management team
900
1000
800
900
800
700
700
600
600
500
500
400
400
300
300
200
200
100
100
0
0
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
0
0.1
Board members
0.2
0.3
0.4
0.5
0.6
Management team
Largest insider
Largest primary insider
600
800
700
500
600
400
500
300
400
300
200
200
100
100
0
0
0
0.1
0.2
0.3
0.4
0.5
Largest insider
0.6
0.7
0.8
0.9
0
0.1
0.2
0.3
0.4
0.5
0.6
Largest primary insider
0.7
0.8
102
Data sources, variable definitions, and descriptive statistics
A.3.4
Board characteristics
Board size
180
160
140
120
100
80
60
40
20
0
0
2
4
6
8
10
12
14
16
18
20
Board size
A.3.5
Security design
Fraction voting shares
900
800
700
600
500
400
300
200
100
0
0.4
0.5
0.6
0.7
0.8
0.9
1
Fraction voting
A.3.6
Financial policy
Debt to assets
Dividends to earnings
160
1000
900
140
800
120
700
100
600
80
500
400
60
300
40
200
20
100
0
0
0
0.1
0.2
0.3
0.4
0.5
Debt to assets
0.6
0.7
0.8
0.9
1
0
1
2
3
4
5
6
7
Dividends to earnings
8
9
10
11
A.3 Histograms
103
Dividends to price
1000
900
800
700
600
500
400
300
200
100
0
0
10
20
30
40
50
60
70
Dividends to price
A.3.7
Controls
Firm value
Investments over income
1000
1000
900
900
800
800
700
700
600
600
500
500
400
400
300
300
200
200
100
100
0
0
0
1e+10
2e+10
3e+10
4e+10
5e+10
6e+10
7e+10
8e+10
9e+10
0
5
10
Firm value
15
20
25
30
35
40
45
50
Investments over income
Stock volatility
Stock beta
140
250
120
200
100
150
80
60
100
40
50
20
0
0
0
0.2
0.4
0.6
0.8
Stock volatility
1
1.2
1.4
1.6
0
1
2
3
Stock beta
4
5
6
7
104
Data sources, variable definitions, and descriptive statistics
Stock turnover
400
350
300
250
200
150
100
50
0
0
1
2
3
4
5
6
Stock turnover
A.3.8
Performance measures
Q
RoA
450
700
400
600
350
500
300
250
400
200
300
150
200
100
100
50
0
0
1
2
3
4
5
6
7
8
9
Q (Tobin’s Q)
RoS
250
200
150
100
50
0
100
200
300
-150
-100
-50
RoA (Return on assets)
300
0
-100
0
-200
400
500
RoS (Return on Stock)
600
700
0
50
Supplementary regressions
105
Appendix B
Supplementary regressions
B.1
Univariate relationships
This appendix supplements the discussion in chapter 4 on univariate regressions of performance
on governance mechanisms and controls. Section B.1.1 gives detailed regression results for the
univariate regressions summarized in table 4.1. Section B.1.2 provides estimates for variables
which are different if we consider voting rights instead of cash flow rights. Finally, section B.1.3
illustrates selected univariate relations graphically, by plotting performance (Q) against governance
mechanisms and controls.
B.1.1
Regressions underlying summary table
Each regression estimates the coefficients a (intercept or constant) and b (slope) of the linear
regression equation
yi = a + bxi + εi
where yi is the dependent variable, xi is the explanatory variable and εi is an error term.
Every table has five columns, each of which contains a regression with one particular dependent
variable (performance measure): Q, RoA5 , RoS5 , RoA and RoS. We report estimated parameter
values, with estimated p-values in parenthesis. For each regression we also report n (the number
of observations) and the R2 (the coefficient of determination) for each regression.
B.1.1.1
Ownership concentration
constant
Herfindahl index
n
R2
constant
Largest owner
n
R2
constant
1-3 largest owners
n
R2
Q
1.65
(0.00)
-1.12
(0.00)
1068
0.03
Q
1.76
(0.00)
-0.97
(0.00)
1068
0.03
Q
1.93
(0.00)
-0.97
(0.00)
1068
0.04
RoA5
9.74
(0.00)
-2.65
(0.01)
1031
0.00
RoA5
9.95
(0.00)
-2.13
(0.01)
1031
0.00
RoA5
10.43
(0.00)
-2.32
(0.00)
1031
0.01
RoS5
44.29
(0.00)
-2.41
(0.86)
731
-0.00
RoS5
47.24
(0.00)
-11.88
(0.26)
731
-0.00
RoS5
53.19
(0.00)
-20.40
(0.04)
731
0.00
RoA
5.42
(0.00)
-2.62
(0.37)
1061
-0.00
RoA
5.34
(0.00)
-1.10
(0.64)
1061
-0.00
RoA
5.50
(0.00)
-1.03
(0.65)
1061
-0.00
RoS
33.56
(0.00)
-3.01
(0.88)
894
-0.00
RoS
36.17
(0.00)
-10.67
(0.50)
894
-0.00
RoS
41.63
(0.00)
-18.23
(0.23)
894
-0.00
106
Supplementary regressions
constant
1-5 largest owners
n
R2
B.1.1.2
Q
2.06
(0.00)
-1.04
(0.00)
1068
0.04
RoS5
57.48
(0.00)
-25.03
(0.02)
731
0.00
RoA
5.87
(0.00)
-1.52
(0.52)
1061
-0.00
RoS
47.89
(0.00)
-26.59
(0.10)
894
0.00
Owner type
constant
Aggregate state holdings
n
R2
constant
Aggregate individual holdings
n
R2
constant
Aggregate financial holdings
n
R2
constant
Aggregate nonfinancial holdings
n
R2
constant
Aggregate international holdings
n
R2
constant
Aggregate intercorporate holdings
n
R2
RoA5
10.68
(0.00)
-2.39
(0.00)
1031
0.01
Q
1.51
(0.00)
-0.61
(0.01)
1068
0.01
RoA5
9.34
(0.00)
-0.15
(0.91)
1031
-0.00
Q
1.22
(0.00)
1.45
(0.00)
1068
0.05
Q
1.43
(0.00)
0.26
(0.23)
1068
-0.00
RoA5
8.46
(0.00)
4.99
(0.00)
1031
0.02
RoA5
9.33
(0.00)
0.03
(0.98)
1031
-0.00
RoS5
45.52
(0.00)
-29.40
(0.05)
731
0.00
RoA
5.24
(0.00)
-4.30
(0.19)
1061
-0.00
RoS
33.37
(0.00)
-5.12
(0.82)
894
-0.00
RoS5
33.03
(0.00)
63.22
(0.00)
731
0.03
RoA
6.38
(0.00)
-7.62
(0.01)
1061
0.00
RoS
22.84
(0.00)
60.60
(0.00)
894
0.01
RoS5
49.29
(0.00)
-31.38
(0.05)
731
0.00
RoA
2.37
(0.00)
16.01
(0.00)
1061
0.02
RoS
32.31
(0.00)
4.85
(0.83)
894
-0.00
Q
1.76
(0.00)
-0.74
(0.00)
1068
0.03
RoA5
9.83
(0.00)
-1.26
(0.05)
1031
0.00
RoS5
49.48
(0.00)
-14.45
(0.09)
731
0.00
RoA
3.98
(0.00)
2.66
(0.16)
1061
-0.00
RoS
39.03
(0.00)
-15.08
(0.24)
894
-0.00
Q
1.42
(0.00)
0.24
(0.08)
1068
0.00
RoA5
9.57
(0.00)
-1.07
(0.12)
1031
0.00
RoS5
41.93
(0.00)
8.76
(0.34)
731
-0.00
RoA
5.84
(0.00)
-3.69
(0.07)
1061
0.00
RoS
34.92
(0.00)
-7.90
(0.56)
894
-0.00
RoS5
46.26
(0.00)
-24.48
(0.07)
730
0.00
RoA
4.29
(0.00)
8.13
(0.01)
1059
0.00
RoS
31.96
(0.00)
9.00
(0.66)
893
-0.00
Q
1.54
(0.00)
-0.74
(0.00)
1066
0.01
RoA5
9.56
(0.00)
-2.41
(0.02)
1029
0.00
B.1 Univariate relationships
107
Q
1.50
(0.00)
-0.30
(0.01)
1068
0.01
constant
Largest owner is state
n
R2
Q
1.43
(0.00)
0.43
(0.00)
1068
0.02
constant
Largest owner is individual
n
R2
Largest owner is financial
n
R2
constant
Largest owner is nonfinancial
n
R2
All insiders
n
R2
RoA
5.35
(0.00)
-3.13
(0.03)
1061
0.00
RoS
32.00
(0.00)
12.20
(0.26)
894
-0.00
RoS5
44.59
(0.00)
-9.85
(0.23)
731
-0.00
RoA
4.90
(0.00)
1.63
(0.34)
1061
-0.00
RoS
33.50
(0.00)
-5.18
(0.65)
894
-0.00
RoA
3.63
(0.00)
2.52
(0.01)
1061
0.01
RoS
35.59
(0.00)
-4.47
(0.47)
894
-0.00
Q
1.49
(0.00)
-0.09
(0.33)
1068
-0.00
RoA5
9.32
(0.00)
0.10
(0.82)
1031
-0.00
RoS5
43.10
(0.00)
6.27
(0.29)
731
-0.00
RoA
5.08
(0.00)
-0.46
(0.73)
1061
-0.00
RoS
31.91
(0.00)
8.74
(0.33)
894
-0.00
Q
1.50
(0.00)
-0.19
(0.03)
1068
0.00
n
R2
RoS5
41.86
(0.00)
21.17
(0.00)
731
0.01
RoS5
48.26
(0.00)
-7.84
(0.05)
731
0.00
n
R2
Largest owner is listed
RoS
33.89
(0.00)
-9.08
(0.41)
894
-0.00
RoA5
9.57
(0.00)
-0.42
(0.17)
1031
-0.00
Largest owner is international
constant
RoA5
9.36
(0.00)
-0.34
(0.55)
1031
-0.00
RoA
5.01
(0.00)
0.12
(0.94)
1061
-0.00
Q
1.58
(0.00)
-0.19
(0.00)
1068
0.01
constant
constant
RoS5
45.33
(0.00)
-14.85
(0.03)
731
0.00
RoA5
9.20
(0.00)
1.28
(0.01)
1031
0.00
Q
1.48
(0.00)
-0.03
(0.78)
1068
-0.00
constant
B.1.1.3
RoA5
9.37
(0.00)
-0.42
(0.45)
1031
-0.00
RoA5
9.47
(0.00)
-1.01
(0.02)
1031
0.00
RoS5
45.13
(0.00)
-9.21
(0.13)
731
0.00
RoA
4.81
(0.00)
1.62
(0.23)
1061
-0.00
RoS
32.50
(0.00)
4.63
(0.61)
894
-0.00
Insider ownership
Q
1.47
(0.00)
0.03
(0.81)
1068
-0.00
RoA5
9.26
(0.00)
0.38
(0.49)
1031
-0.00
RoS5
43.91
(0.00)
0.15
(0.98)
731
-0.00
RoA
5.16
(0.00)
-0.70
(0.67)
1061
-0.00
RoS
31.91
(0.00)
5.57
(0.61)
894
-0.00
108
Supplementary regressions
constant
Board members
n
R2
constant
Management team
n
R2
constant
Primary insiders
n
R2
B.1.1.4
ln(Board size)
n
R2
Q
1.64
(0.00)
-0.07
(0.38)
966
-0.00
RoA
5.01
(0.00)
0.06
(0.98)
1061
-0.00
RoA
5.14
(0.00)
-2.87
(0.35)
1061
-0.00
RoA
5.31
(0.00)
-3.57
(0.13)
1061
0.00
RoS
32.28
(0.00)
9.91
(0.49)
894
-0.00
RoS
32.15
(0.00)
21.46
(0.28)
894
-0.00
RoS
31.18
(0.00)
22.54
(0.15)
894
0.00
RoA5
10.71
(0.00)
-0.61
(0.13)
937
0.00
RoS5
74.30
(0.00)
-16.18
(0.01)
670
0.01
RoA
0.89
(0.69)
2.38
(0.05)
959
0.00
RoS
44.40
(0.00)
-6.24
(0.45)
812
-0.00
Q
0.77
(0.02)
0.74
(0.03)
1053
0.00
RoA5
9.48
(0.00)
-0.12
(0.94)
1016
-0.00
RoS5
5.46
(0.77)
40.16
(0.04)
731
0.00
RoA
12.18
(0.01)
-7.42
(0.13)
1046
0.00
RoS
-12.53
(0.68)
47.35
(0.14)
894
0.00
Q
1.51
(0.00)
-0.08
(0.08)
1039
0.00
Q
1.52
(0.00)
-0.03
(0.00)
1068
0.01
RoA5
9.23
(0.00)
0.34
(0.12)
1003
0.00
RoA5
8.95
(0.00)
0.25
(0.00)
1031
0.02
RoS5
45.72
(0.00)
-3.92
(0.15)
708
0.00
RoS5
44.88
(0.00)
-0.50
(0.35)
731
-0.00
RoA
4.40
(0.00)
2.50
(0.00)
1032
0.01
RoA
4.06
(0.00)
0.60
(0.00)
1061
0.02
RoS
33.07
(0.00)
2.42
(0.59)
867
-0.00
RoS
33.29
(0.00)
-0.11
(0.90)
894
-0.00
Fraction voting shares
n
R2
Financial policy
constant
Dividends to earnings
n
R2
constant
Dividends to price
n
R2
RoS5
44.14
(0.00)
-2.25
(0.81)
731
-0.00
RoS5
42.00
(0.00)
43.87
(0.00)
731
0.01
RoS5
42.03
(0.00)
21.80
(0.04)
731
0.00
Security design
constant
B.1.1.6
RoA5
9.11
(0.00)
2.86
(0.00)
1031
0.01
RoA5
9.23
(0.00)
2.41
(0.02)
1031
0.00
RoA5
9.11
(0.00)
2.74
(0.00)
1031
0.01
Board characteristics
constant
B.1.1.5
Q
1.42
(0.00)
0.65
(0.00)
1068
0.02
Q
1.46
(0.00)
0.25
(0.23)
1068
-0.00
Q
1.42
(0.00)
0.68
(0.00)
1068
0.01
B.1 Univariate relationships
B.1.1.7
Competitive markets
Q
1.47
(0.00)
0.02
(0.75)
1068
-0.00
Q
1.49
(0.00)
-0.22
(0.04)
1068
0.00
constant
Industrial
n
R2
constant
Offshore
n
R2
constant
Transport/shipping
n
R2
B.1.1.8
ln(Firm value)
n
R2
constant
Stock volatility
n
R2
constant
Stock turnover
constant
Stock beta
n
R2
RoA5
9.46
(0.00)
-0.38
(0.24)
1031
-0.00
RoA5
9.56
(0.00)
-2.66
(0.00)
1031
0.02
Q
1.61
(0.00)
-0.52
(0.00)
1068
0.05
RoA5
9.62
(0.00)
-1.10
(0.00)
1031
0.01
RoS5
41.59
(0.00)
6.44
(0.12)
731
0.00
RoS5
43.77
(0.00)
2.34
(0.76)
731
-0.00
RoS5
46.79
(0.00)
-10.43
(0.02)
731
0.00
RoA
4.93
(0.00)
0.28
(0.77)
1061
-0.00
RoA
5.03
(0.00)
-0.14
(0.93)
1061
-0.00
RoA
4.51
(0.00)
1.99
(0.06)
1061
0.00
RoS
31.00
(0.00)
6.14
(0.35)
894
-0.00
RoS
32.39
(0.00)
9.46
(0.42)
894
-0.00
RoS
35.68
(0.00)
-9.80
(0.16)
894
-0.00
RoS5
82.23
(0.00)
-1.88
(0.13)
731
0.00
RoS5
30.81
(0.00)
21.69
(0.00)
688
0.01
RoS5
24.83
(0.00)
27.66
(0.00)
728
0.12
RoS5
30.56
(0.00)
14.11
(0.00)
678
0.02
RoA
-37.90
(0.00)
2.15
(0.00)
1061
0.07
RoA
11.74
(0.00)
-10.30
(0.00)
944
0.06
RoA
5.73
(0.00)
-0.92
(0.16)
1027
-0.00
RoA
6.77
(0.00)
-1.42
(0.06)
940
0.00
RoS
-45.84
(0.21)
3.94
(0.03)
894
0.00
RoS
23.89
(0.00)
15.32
(0.13)
814
0.00
RoS
6.65
(0.09)
41.31
(0.00)
890
0.09
RoS
26.09
(0.00)
6.60
(0.22)
824
-0.00
Controls
constant
n
R2
109
Q
-0.05
(0.88)
0.08
(0.00)
1068
0.02
Q
1.73
(0.00)
-0.38
(0.00)
949
0.01
Q
1.28
(0.00)
0.35
(0.00)
1033
0.05
Q
1.43
(0.00)
0.09
(0.12)
947
0.00
RoA5
10.76
(0.00)
-0.07
(0.39)
1031
-0.00
RoA5
10.14
(0.00)
-1.42
(0.01)
921
0.00
RoA5
9.23
(0.00)
0.19
(0.44)
997
-0.00
RoA5
9.57
(0.00)
-0.32
(0.23)
920
-0.00
110
B.1.2
Supplementary regressions
Using voting rights instead of cash flow rights
We provide univariate results which show that the conclusions do not materially differ when we use
voting rights instead of cash flow rights in the calculations of ownership concentration. Table B.1
complements table 4.1 in the text by summarizing univariate regressions where the concentration
variables are in terms of voting rights instead of cash flow rights.
Table B.1 Summary of univariate regressions, voting rights.
Concentration (voting rights)
Herfindahl (voting rights)
Largest owner (voting rights)
1-3 largest owners (voting rights)
1-5 largest owner (voting rights)
The largest owner (voting rights)
Largest owner is state (voting rights)
Largest owner is individual (voting rights)
Largest owner is financial (voting rights)
Largest owner is nonfinancial (voting rights)
Largest owner is international (voting rights)
Q
RoA5
RoS5
RoA
RoS
−***
−***
−***
−***
−
−*
−**
−**
−
−
−**
−***
−
−
−
−
−
−
−
−
−***
+***
+
−***
−
−
+***
−
−
+
−**
+***
−
−
+
+
−*
+
+***
−
−
+
−
−
+
The table summarizes the estimated signs of univariate relations between a performance measure and an independent
variable (governance mechanism or control variable). Statistical significance is indicated with ∗ , ∗∗ , and ∗∗∗ , which
means the relationship is significant at the 5%, 2.5% and 1% level, respectively. Data for firms listed on the Oslo
Stock Exchange, 1989-1997. Variable definitions are in Appendix A.2.
The summary table above shows no differences regarding the significance levels of these regressions
for Q. Comparing the detailed regression results listed below to the corresponding univariate
regressions using cash flow rights in appendix section B.1.1 above, we observe that while some of
the coefficient estimates show minor numerical differences, the signs and levels of significance are
mostly consistent. We therefore do not make further use of the distinction between voting and cash
flow rights in the report until we get to the full multivarate model in chapter 9 and appendix B.6.
The following tables shows the results of the regressions underlying table B.1
B.1.2.1
Concentration measures, voting rights.
constant
Herfindahl (voting rights)
n
R2
constant
Largest owner (voting rights)
n
R2
Q
1.64
(0.00)
-1.00
(0.00)
1062
0.03
Q
1.76
(0.00)
-0.94
(0.00)
1062
0.03
RoA5
9.62
(0.00)
-1.64
(0.07)
1025
0.00
RoA5
9.82
(0.00)
-1.56
(0.04)
1025
0.00
RoS5
44.59
(0.00)
-4.26
(0.74)
731
-0.00
RoS5
47.94
(0.00)
-13.90
(0.19)
731
-0.00
RoA
5.25
(0.00)
-1.46
(0.58)
1055
-0.00
RoS
34.10
(0.00)
-6.27
(0.75)
894
-0.00
RoA
5.12
(0.00)
-0.36
(0.87)
1055
-0.00
RoS
36.95
(0.00)
-13.01
(0.41)
894
-0.00
B.1 Univariate relationships
constant
1-3 largest owners (voting rights)
n
R2
constant
1-5 largest owner (voting rights)
n
R2
constant
Largest owner is state (voting rights)
n
R2
111
Q
1.96
(0.00)
-1.00
(0.00)
1062
0.04
Q
2.10
(0.00)
-1.09
(0.00)
1062
0.04
Q
1.50
(0.00)
-0.28
(0.01)
1068
0.00
constant
Largest owner is individual (voting rights)
n
R2
constant
Largest owner is financial (voting rights)
n
R2
constant
Largest owner is nonfinancial (voting rights)
n
R2
constant
Largest owner is international (voting rights)
n
R2
RoA5
10.24
(0.00)
-1.83
(0.02)
1025
0.00
RoA5
10.49
(0.00)
-1.98
(0.01)
1025
0.00
RoA5
9.35
(0.00)
-0.19
(0.73)
1031
-0.00
Q
RoA5
1.43
9.20
(0.00)
(0.00)
0.44
1.30
(0.00)
(0.01)
1068
1031
0.02
0.00
Q
RoA5
1.47
9.36
(0.00)
(0.00)
0.01
-0.41
(0.93)
(0.50)
1068
1031
-0.00
-0.00
Q
RoA5
1.58
9.58
(0.00)
(0.00)
-0.20
-0.43
(0.00)
(0.16)
1068
1031
0.01
-0.00
Q
RoA5
1.49
9.31
(0.00)
(0.00)
-0.09
0.21
(0.34)
(0.64)
1068
1031
-0.00
-0.00
RoS5
54.64
(0.00)
-22.96
(0.02)
731
0.00
RoS5
59.75
(0.00)
-28.56
(0.01)
731
0.01
RoS5
45.59
(0.00)
-16.48
(0.01)
731
0.01
RoS5
41.90
(0.00)
20.51
(0.00)
731
0.01
RoS5
44.50
(0.00)
-8.93
(0.29)
731
-0.00
RoS5
48.31
(0.00)
-7.90
(0.05)
731
0.00
RoS5
43.06
(0.00)
6.71
(0.26)
731
-0.00
RoA
5.24
(0.00)
-0.47
(0.83)
1055
-0.00
RoA
5.53
(0.00)
-0.90
(0.70)
1055
-0.00
RoA
4.99
(0.00)
0.36
(0.82)
1061
-0.00
RoA
5.34
(0.00)
-3.04
(0.04)
1061
0.00
RoA
4.91
(0.00)
1.65
(0.36)
1061
-0.00
RoA
3.63
(0.00)
2.51
(0.01)
1061
0.01
RoA
5.10
(0.00)
-0.66
(0.63)
1061
-0.00
RoS
43.64
(0.00)
-22.02
(0.15)
894
0.00
RoS
50.51
(0.00)
-30.69
(0.06)
894
0.00
RoS
33.98
(0.00)
-9.55
(0.37)
894
-0.00
RoS
32.02
(0.00)
11.78
(0.27)
894
-0.00
RoS
33.23
(0.00)
-1.70
(0.89)
894
-0.00
RoS
36.09
(0.00)
-5.32
(0.39)
894
-0.00
RoS
31.99
(0.00)
8.25
(0.36)
894
-0.00
112
Supplementary regressions
B.1.3
Plots of performance vs explanatory variables
A useful way of gaining some intuition about the univariate relations between performance and it
determinants is to plot one against the other. This appendix presents a number of plots which
relate performance (Q) to the various governance mechanisms and controls, one by one. To reduce
the visual noise in the pictures we plot grouped means rather than individual observations. The
companies are sorted into 20 groups based on the numerical value of the independent variable, and
we plot the mean performance for the group against the mean numerical value of the independent
variable (governance mechanism or control) for the group.
B.1.3.1
Ownership concentration
2.6
2.2
2.4
2
2.2
1.8
2
1.6
Q
Q
1.8
1.6
1.4
1.4
1.2
1.2
1
1
0.8
0.8
0
0.1
0.2
0.3
0.4
0.5
Herfindahl index
0.6
0.7
0.8
0.9
0
0.1
Herfindahl index
0.2
0.3
0.4
0.5
0.6
Largest owner
0.7
0.8
0.9
1
The largest owner
2.4
2.2
2.2
2
2
1.8
1.6
Q
Q
1.8
1.6
1.4
1.4
1.2
1.2
1
0.8
0.1
0.2
0.3
0.4
0.5
0.6
1-2 largest owners
1-2 largest owners
0.7
0.8
0.9
1
1
0.1
0.2
0.3
0.4
0.5
0.6
1-3 largest owners
1-3 largest owners
0.7
0.8
0.9
1
B.1 Univariate relationships
113
2.4
2.4
2.2
2.2
2
2
1.8
Q
Q
1.8
1.6
1.6
1.4
1.4
1.2
1.2
1
1
0.2
0.3
0.4
0.5
0.6
0.7
1-4 largest owners
0.8
0.9
0.8
0.2
1
0.3
1-4 largest owners
0.4
0.5
0.6
0.7
1-5 largest owners
0.8
0.9
1
1-5 largest owners
2.4
2.2
2.1
2.2
2
1.9
2
1.8
1.8
Q
Q
1.7
1.6
1.6
1.5
1.4
1.4
1.3
1.2
1.2
1
0.3
0.4
0.5
0.6
0.7
1-10 largest owners
0.8
0.9
1.1
0.4
1
0.5
1-10 largest owners
0.6
0.7
1-20 largest owners
0.8
0.9
1
1–20 largest owners
1.8
2.1
2
1.7
1.9
1.6
1.8
1.7
1.5
Q
Q
1.6
1.4
1.5
1.3
1.4
1.3
1.2
1.2
1.1
1.1
1
1
0
0.05
0.1
0.15
0.2
0.25
2nd largest owner
2nd largest owner
0.3
0.35
0.4
0
0.02
0.04
0.06
0.08
0.1
0.12
3rd largest owner
3rd largest owner
0.14
0.16
0.18
0.2
0.22
114
Supplementary regressions
2
1.9
1.9
1.8
1.7
1.8
1.6
1.7
1.5
Q
Q
1.6
1.4
1.5
1.3
1.4
1.2
1.3
1.1
1.2
1
0.9
1.1
0
0.02
0.04
0.06
0.08
4th largest owner
0.1
0.12
0.14
0
0.16
0.02
4th largest owner
B.1.3.2
0.04
0.06
5th largest owner
0.08
0.1
0.12
5th largest owner
Owner type
2
2
1.9
1.8
1.8
1.6
1.7
1.6
1.2
Q
Q
1.4
1.5
1.4
1
1.3
0.8
1.2
0.6
1.1
0.4
1
0
0.1
0.2
0.3
0.4
0.5
Aggregate state holdings
0.6
0.7
0.8
0.9
0
0.1
Aggregate state holdings
0.2
0.3
0.4
0.5
0.6
Aggregate international holdings
0.7
0.8
0.9
1
Aggregate international holdings
2.2
2
2.1
1.8
2
1.9
1.6
1.8
Q
Q
1.7
1.4
1.6
1.5
1.2
1.4
1.3
1
1.2
1.1
0.8
0
0.1
0.2
0.3
0.4
0.5
Aggregate individual holdings
0.6
0.7
Aggregate individual holdings
0.8
0
0.1
0.2
0.3
0.4
0.5
Aggregate financial holdings
0.6
0.7
Aggregate financial holdings
0.8
B.1 Univariate relationships
115
1.9
1.9
1.8
1.8
1.7
1.7
1.6
1.6
Q
Q
1.5
1.4
1.5
1.3
1.4
1.2
1.3
1.1
1.2
1
0
0.1
0.2
0.3
0.4
0.5
0.6
Aggregate nonfinancial holdings
0.7
0.8
0.9
0
1
Aggregate nonfinancial holdings
B.1.3.3
0.1
0.2
0.3
0.4
0.5
0.6
Aggregate intercorporate holdings
0.7
0.8
0.9
Aggregate intercorporate holdings
Insider ownership
2
1.9
1.9
1.8
1.8
1.7
1.7
1.6
Q
Q
1.6
1.5
1.5
1.4
1.4
1.3
1.3
1.2
1.2
1.1
1.1
0
0.2
0.4
0.6
0.8
1
0
0.2
All insiders
All insiders
0.4
0.6
Management team
0.8
1
0.8
1
Management team
2.2
2.1
2.1
2
2
1.9
1.9
1.8
1.8
1.7
Q
Q
1.7
1.6
1.6
1.5
1.5
1.4
1.4
1.3
1.3
1.2
1.2
1.1
1.1
0
0.2
0.4
0.6
Board members
Board members
0.8
1
0
0.2
0.4
0.6
Primary insiders
Primary insiders
116
Supplementary regressions
B.1.3.4
Board characteristics
1.65
1.6
Q
1.55
1.5
1.45
1.4
1.35
3
4
5
6
7
Board size
8
9
10
11
Board size
B.1.3.5
Financial policy
2.2
1.9
2.1
1.8
2
1.7
1.9
1.8
1.6
Q
Q
1.7
1.5
1.6
1.4
1.5
1.4
1.3
1.3
1.2
1.2
1.1
1.1
0
0.1
0.2
0.3
0.4
0.5
0.6
Debt to assets
0.7
0.8
0.9
1
0
0.5
Debt to assets
B.1.3.6
1
1.5
2
2.5
Dividends to earnings
3
3.5
4
4.5
Dividends to earnings
Controls
1.9
1.7
1.8
1.6
1.7
1.5
1.6
1.4
1.4
Q
Q
1.5
1.3
1.3
1.2
1.2
1.1
1.1
1
0.9
1
0
1e+10
2e+10
Firm value
3e+10
Firm value
4e+10
5e+10
6e+10
0
5
10
15
20
25
Investments over income
30
Investments over income
35
40
B.1 Univariate relationships
117
1.9
2.8
2.6
1.8
2.4
2.2
1.7
1.8
Q
Q
2
1.6
1.6
1.5
1.4
1.2
1.4
1
1.3
0.8
0
0.5
1
1.5
2
Stock turnover
2.5
3
3.5
Stock turnover
1.8
1.7
Q
1.6
1.5
1.4
1.3
1.2
1.1
0.2
0.4
0.6
0.8
Stock volatility
Stock volatility
0.5
1
1.5
2
Stock beta
1.9
0
0
1
1.2
1.4
1.6
2.5
Stock beta
3
3.5
4
4.5
5
118
B.2
Supplementary regressions
Ownership concentration
This appendix lists supplementary results to the analysis in chapter 5.
B.2.1
Year by year, GMM, and fixed effects regressions
This section lists tables which supplement the pooled OLS regression shown in the main text. Using
the same dependent and independent variables, we show OLS estimations on a year by year basis,
estimations using GMM, and we also control for systematic differences across years with indicator
variables for each year (fixed effects) in an OLS regression.
Table B.2 Multivariate regression relating performance (Q) to ownership concentration and controls, following Demsetz and Lehn (1985)
Panel A: Year by year OLS regressions
constant
lntrans(1-5 largest owners)
Industrial
Transport/shipping
Offshore
Investments over income
ln(Firm value)
Stock volatility
n
R2
Average (Q)
1989
−0.03
(0.97)
−0.08
(0.13)
−0.25
(0.03)
0.03
(0.82)
−0.10
(0.66)
−0.01
(0.37)
0.08
(0.04)
−0.16
(0.44)
83
0.12
1.28
1990
0.84
(0.28)
−0.07
(0.19)
−0.25
(0.02)
−0.28
(0.02)
−0.37
(0.04)
−0.01
(0.43)
0.03
(0.37)
−0.14
(0.49)
79
0.04
1.18
1991
1.53
(0.12)
−0.03
(0.64)
−0.06
(0.64)
−0.23
(0.07)
−0.43
(0.01)
0.00
(0.82)
0.00
(0.94)
−0.53
(0.03)
77
0.14
1.10
1992
−0.35
(0.69)
−0.03
(0.72)
−0.31
(0.05)
−0.42
(0.00)
−0.48
(0.02)
−0.12
(0.56)
0.09
(0.03)
−0.01
(0.95)
69
0.11
1.11
Year
1993
−0.12
(0.93)
−0.11
(0.20)
−0.32
(0.12)
−0.68
(0.00)
−0.71
(0.01)
−0.08
(0.58)
0.09
(0.18)
0.36
(0.19)
83
0.13
1.46
1994
0.60
(0.39)
−0.09
(0.05)
−0.23
(0.04)
−0.56
(0.00)
−0.48
(0.01)
−0.07
(0.21)
0.05
(0.12)
0.08
(0.67)
108
0.19
1.33
1995
−1.44
(0.26)
−0.15
(0.11)
−0.40
(0.02)
−0.84
(0.00)
−0.69
(0.03)
−0.04
(0.40)
0.15
(0.01)
0.79
(0.01)
117
0.19
1.48
1996
6.94
(0.01)
−0.21
(0.16)
−0.61
(0.04)
−1.49
(0.00)
−0.74
(0.17)
−0.19
(0.05)
−0.18
(0.11)
−0.95
(0.14)
126
0.17
2.02
1997
−2.04
(0.31)
−0.21
(0.09)
−0.45
(0.09)
−1.37
(0.00)
−0.92
(0.02)
0.01
(0.89)
0.19
(0.03)
1.32
(0.04)
163
0.13
2.04
Panel B: Pooled GMM and fixed effects regressions
Dependent variable: Q
Dependent variable: Q
Constant
lntrans(1-5 largest owners)
Industrial
Transport/shipping
Offshore
Investments over income
ln(Firm value)
Stock volatility
n
Average (Q)
coeff
0.32
-0.18
-0.44
-0.84
-0.74
-0.01
0.08
-0.04
905
1.53
(stdev)
(0.64)
(0.04)
(0.10)
(0.10)
(0.11)
(0.01)
(0.03)
(0.17)
pvalue
0.61
0.00
0.00
0.00
0.00
0.02
0.00
0.83
Constant
lntrans(1-5 largest owners)
Industrial
Transport/shipping
Offshore
Investments over income
ln(Firm value)
Stock volatility
1990
1991
1992
1993
1994
1995
1996
1997
n
R2
Average (Q)
coeff
0.11
-0.16
-0.38
-0.77
-0.68
-0.01
0.07
0.21
-0.12
-0.16
-0.16
0.13
0.05
0.15
0.65
0.66
905
0.22
1.53
(stdev)
(0.55)
(0.04)
(0.08)
(0.08)
(0.12)
(0.01)
(0.02)
(0.14)
(0.14)
(0.14)
(0.15)
(0.14)
(0.13)
(0.13)
(0.13)
(0.12)
pvalue
0.84
0.00
0.00
0.00
0.00
0.34
0.00
0.13
0.40
0.27
0.29
0.37
0.73
0.25
0.00
0.00
This table complements the pooled OLS regression in table 5.3 by three additional regressions. Panel A shows OLS
estimates on a year by year basis. The left–hand side table in panel B lists pooled estimates, estimated with GMM.
The table on the right–hand side in panel B shows the results of a pooled OLS regression where dummy variables
for each year are used to capture fixed effects. 1989 is the base year in this regression. In regressions using firm size
across years the nominal values are adjusted to the 1997 general price level. Data for firms listed on the Oslo Stock
Exchange, 1989-1997. Variable definitions are in Appendix A.2.
B.2 Ownership concentration
119
Table B.3 Multivariate regression relating performance (Q) to ownership concentration and controls, using the piecewise linear function of Morck et al. (1988)
Panel A: Year by year OLS regressions
constant
Largest owner (0 to 5)
Largest owner (5 to 25)
Largest owner (25 to 100)
Industrial
Transport/shipping
Offshore
Investments over income
ln(Firm value)
Stock volatility
n
R2
Average (Q)
1989
0.26
(1.00)
−1.42
(1.00)
−0.84
(0.39)
−0.44
(0.33)
−0.28
(0.02)
0.01
(0.92)
−0.12
(0.62)
−0.01
(0.41)
0.08
(0.04)
−0.21
(0.32)
83
0.09
1.28
1990
0.89
(1.00)
3.66
(1.00)
0.64
(0.48)
−0.90
(0.03)
−0.25
(0.04)
−0.30
(0.02)
−0.39
(0.04)
−0.01
(0.34)
0.03
(0.44)
−0.23
(0.30)
79
−0.10
1.18
1991
1.36
(1.00)
4.06
(1.00)
−0.12
(0.89)
−0.42
(0.30)
−0.06
(0.61)
−0.24
(0.06)
−0.45
(0.01)
0.00
(0.92)
0.00
(0.96)
−0.53
(0.03)
77
0.12
1.10
1992
−0.21
(1.00)
−1.96
(1.00)
0.40
(0.71)
−0.56
(0.30)
−0.33
(0.04)
−0.44
(0.00)
−0.51
(0.02)
−0.13
(0.53)
0.09
(0.03)
−0.04
(0.85)
69
0.10
1.11
Year
1993
−0.18
(1.00)
4.24
(1.00)
−0.65
(0.67)
−0.46
(0.51)
−0.32
(0.13)
−0.69
(0.00)
−0.76
(0.01)
−0.07
(0.62)
0.09
(0.18)
0.38
(0.19)
83
0.07
1.46
1994
0.48
(1.00)
5.93
(1.00)
0.98
(0.25)
−0.99
(0.00)
−0.19
(0.09)
−0.59
(0.00)
−0.46
(0.01)
−0.06
(0.22)
0.04
(0.20)
−0.00
(0.99)
108
0.22
1.33
1995
−0.45
(0.90)
−18.39
(0.78)
0.20
(0.89)
−1.02
(0.13)
−0.41
(0.02)
−0.89
(0.00)
−0.71
(0.04)
−0.04
(0.37)
0.15
(0.01)
0.77
(0.01)
117
0.18
1.48
1996
23.94
(0.01)
−348.98
(0.06)
−2.60
(0.23)
−0.80
(0.45)
−0.54
(0.06)
−1.45
(0.00)
−0.62
(0.24)
−0.18
(0.06)
−0.15
(0.19)
−0.81
(0.20)
126
0.20
2.02
1997
−8.00
(0.08)
139.68
(0.10)
−3.70
(0.06)
−1.00
(0.31)
−0.50
(0.06)
−1.38
(0.00)
−0.75
(0.06)
0.01
(0.88)
0.17
(0.06)
1.42
(0.03)
163
0.16
2.04
Panel B: Pooled GMM and fixed effects regressions
Dependent variable: Q
Dependent variable: Q
Constant
Largest owner (0 to 5)
Largest owner (5 to 25)
Largest owner (25 to 100)
Industrial
Transport/shipping
Offshore
Investments over income
ln(Firm value)
Stock volatility
n
Average (Q)
coeff
2.45
-38.62
-1.34
-0.76
-0.44
-0.86
-0.75
-0.02
0.09
-0.07
905
1.53
(stdev)
(3.17)
(59.04)
(0.72)
(0.20)
(0.10)
(0.10)
(0.12)
(0.01)
(0.03)
(0.17)
pvalue
0.44
0.51
0.06
0.00
0.00
0.00
0.00
0.04
0.00
0.70
Constant
Largest owner (0 to 5)
Largest owner (5 to 25)
Largest owner (25 to 100)
Industrial
Transport/shipping
Offshore
Investments over income
ln(Firm value)
Stock volatility
1990
1991
1992
1993
1994
1995
1996
1997
n
R2
Average (Q)
coeff
0.60
-5.40
-1.54
-0.67
-0.38
-0.78
-0.68
-0.01
0.08
0.19
-0.11
-0.15
-0.14
0.14
0.06
0.18
0.67
0.68
905
0.23
1.53
(stdev)
(2.07)
(40.75)
(0.57)
(0.27)
(0.08)
(0.08)
(0.12)
(0.01)
(0.02)
(0.14)
(0.14)
(0.14)
(0.15)
(0.14)
(0.13)
(0.13)
(0.13)
(0.12)
pvalue
0.77
0.89
0.01
0.01
0.00
0.00
0.00
0.32
0.00
0.15
0.44
0.30
0.35
0.31
0.66
0.17
0.00
0.00
This table complements the pooled OLS regression in table 5.4 by three additional regressions. Panel A shows OLS
estimates on a year by year basis. The left–hand side table in panel B lists pooled estimates, estimated with GMM.
The table on the right–hand side in panel B shows the results of a pooled OLS regression where dummy variables
for each year are used to capture fixed effects. 1989 is the base year in this regression. In regressions using firm size
across years the nominal values are adjusted to the 1997 general price level. Data for firms listed on the Oslo Stock
Exchange, 1989-1997. Variable definitions are in Appendix A.2.
120
Supplementary regressions
Table B.4 Multivariate regression relating performance (Q) to ownership concentration, using a
quadratic function
Panel A: Year by year OLS regressions
constant
Largest owner
Squared (Largest owner)
Industrial
Transport/shipping
Offshore
Investments over income
ln(Firm value)
Stock volatility
n
R2
Average (Q)
1989
0.14
(0.87)
−0.72
(0.57)
0.24
(0.89)
−0.27
(0.02)
0.01
(0.91)
−0.13
(0.58)
−0.01
(0.37)
0.08
(0.03)
−0.21
(0.30)
83
0.13
1.28
1990
0.92
(0.24)
0.76
(0.45)
−1.73
(0.21)
−0.25
(0.02)
−0.30
(0.01)
−0.39
(0.02)
−0.01
(0.30)
0.03
(0.41)
−0.20
(0.31)
79
0.08
1.18
1991
1.63
(0.10)
−0.33
(0.70)
−0.01
(0.99)
−0.06
(0.61)
−0.24
(0.05)
−0.45
(0.01)
0.00
(0.90)
0.00
(0.96)
−0.52
(0.03)
77
0.14
1.10
1992
−0.28
(0.75)
−0.47
(0.71)
0.27
(0.88)
−0.32
(0.04)
−0.44
(0.00)
−0.49
(0.02)
−0.13
(0.52)
0.09
(0.02)
0.01
(0.95)
69
0.11
1.11
Year
1993
0.05
(0.97)
−1.53
(0.31)
1.33
(0.49)
−0.35
(0.10)
−0.67
(0.00)
−0.75
(0.01)
−0.08
(0.58)
0.09
(0.16)
0.38
(0.17)
83
0.12
1.46
1994
0.77
(0.25)
0.69
(0.40)
−1.49
(0.12)
−0.20
(0.07)
−0.59
(0.00)
−0.48
(0.01)
−0.07
(0.18)
0.04
(0.21)
0.03
(0.87)
108
0.22
1.33
1995
−1.29
(0.32)
−0.08
(0.96)
−0.83
(0.69)
−0.40
(0.02)
−0.88
(0.00)
−0.67
(0.04)
−0.04
(0.38)
0.15
(0.01)
0.77
(0.01)
117
0.19
1.48
1996
7.64
(0.00)
−4.63
(0.07)
4.40
(0.20)
−0.61
(0.04)
−1.51
(0.00)
−0.65
(0.22)
−0.18
(0.06)
−0.18
(0.11)
−0.97
(0.12)
126
0.19
2.02
1997
−1.20
(0.57)
−3.47
(0.11)
2.56
(0.39)
−0.46
(0.08)
−1.36
(0.00)
−0.87
(0.03)
0.01
(0.85)
0.19
(0.04)
1.29
(0.05)
163
0.15
2.04
Panel B: Pooled GMM and fixed effects regressions
Dependent variable: Q
Dependent variable: Q
Constant
Largest owner
Squared (Largest owner)
Industrial
Transport/shipping
Offshore
Investments over income
ln(Firm value)
Stock volatility
n
Average (Q)
coeff
0.66
-1.96
1.37
-0.45
-0.86
-0.74
-0.01
0.09
-0.06
905
1.53
(stdev)
(0.63)
(0.69)
(0.77)
(0.10)
(0.10)
(0.11)
(0.01)
(0.03)
(0.17)
pvalue
0.29
0.00
0.07
0.00
0.00
0.00
0.04
0.00
0.71
Constant
Largest owner
Squared (Largest owner)
Industrial
Transport/shipping
Offshore
Investments over income
ln(Firm value)
Stock volatility
1990
1991
1992
1993
1994
1995
1996
1997
n
R2
Average (Q)
coeff
0.43
-1.95
1.39
-0.38
-0.78
-0.68
-0.01
0.08
0.19
-0.11
-0.16
-0.14
0.14
0.05
0.17
0.67
0.68
905
0.23
1.53
(stdev)
(0.55)
(0.62)
(0.80)
(0.08)
(0.08)
(0.12)
(0.01)
(0.02)
(0.14)
(0.14)
(0.14)
(0.15)
(0.14)
(0.13)
(0.13)
(0.13)
(0.12)
pvalue
0.44
0.00
0.08
0.00
0.00
0.00
0.32
0.00
0.15
0.43
0.28
0.34
0.34
0.72
0.19
0.00
0.00
This table complements the pooled OLS regression in table 5.5 by three additional regressions. Panel A shows OLS
estimates on a year by year basis. The left–hand side table in panel B lists pooled estimates, estimated with GMM.
The table on the right–hand side in panel B shows the results of a pooled OLS regression where dummy variables
for each year are used to capture fixed effects. 1989 is the base year in this regression. In regressions using firm size
across years the nominal values are adjusted to the 1997 general price level. Data for firms listed on the Oslo Stock
Exchange, 1989-1997. Variable definitions are in Appendix A.2.
B.2 Ownership concentration
121
Table B.5 Multivariate regression relating performance (Q) to ownership concentration, without
controls, using the piecewise linear formulation of Morck et al. (1988)
Panel A: Pooled OLS regression
Dependent variable: Q
Constant
Largest owner (0 to 5)
Largest owner (5 to 25)
Largest owner (25 to 100)
n
R2
Average (Q)
coeff
2.14
-6.54
-1.85
-0.68
1068
0.03
1.48
(stdev)
(1.46)
(29.43)
(0.56)
(0.24)
pvalue
0.14
0.82
0.00
0.00
Panel B: Year by year OLS regressions
constant
Largest owner (0 to 5)
Largest owner (5 to 25)
Largest owner (25 to 100)
n
R2
Average (Q)
1989
0.96
(1.00)
8.92
(1.00)
−0.27
(0.78)
−0.63
(0.09)
104
0.01
1.29
1990
0.95
(1.00)
3.74
(1.00)
0.61
(0.44)
−0.80
(0.02)
94
0.02
1.16
1991
0.95
(1.00)
4.98
(1.00)
−0.33
(0.69)
−0.48
(0.17)
90
−0.00
1.09
1992
0.73
(1.00)
7.06
(1.00)
0.18
(0.84)
−0.64
(0.09)
102
−0.01
1.06
Year
1993
0.84
(1.00)
14.10
(1.00)
−0.88
(0.48)
−0.60
(0.20)
107
0.00
1.38
1994
0.64
(1.00)
12.46
(1.00)
0.70
(0.44)
−0.81
(0.01)
120
0.01
1.32
1995
0.71
(0.83)
19.57
(0.77)
−1.50
(0.30)
−0.52
(0.40)
129
0.00
1.43
1996
2.36
(0.42)
4.75
(0.94)
−3.66
(0.11)
−1.11
(0.30)
141
0.02
1.98
1997
−3.46
(0.40)
125.71
(0.13)
−5.54
(0.00)
−0.53
(0.51)
181
0.06
1.97
Panel A shows results of a pooled regression using data for whole period. Panel B shows results of OLS regressions
on a year by year basis. Data for firms listed on the Oslo Stock Exchange, 1989-1997. Variable definitions are in
Appendix A.2. The tables shows the numbers underlying figure 5.1
Table B.6 The quadratic relationship between performance (Q) and the holdings of the largest
owner
Panel A: Pooled OLS regression
Dependent variable: Q
Constant
Largest owner
Squared (Largest owner)
n
R2
Average (Q)
coeff
1.88
-1.89
1.14
1068
0.03
1.48
(stdev)
(0.09)
(0.55)
(0.66)
pvalue
0.00
0.00
0.08
Panel B: Year by year OLS regressions
constant
Largest owner
Squared (Largest owner)
n
R2
Average (Q)
1989
1.39
(0.00)
−0.04
(0.97)
−0.66
(0.59)
104
0.02
1.29
1990
1.09
(0.00)
1.02
(0.21)
−1.94
(0.07)
94
0.05
1.16
1991
1.19
(0.00)
−0.23
(0.77)
−0.27
(0.78)
90
0.01
1.09
1992
1.12
(0.00)
0.01
(0.99)
−0.56
(0.59)
102
0.00
1.06
Year
1993
1.63
(0.00)
−1.05
(0.37)
0.46
(0.73)
107
0.02
1.38
1994
1.30
(0.00)
0.67
(0.42)
−1.35
(0.15)
120
0.03
1.32
1995
1.70
(0.00)
−1.21
(0.40)
0.58
(0.74)
129
0.01
1.43
1996
2.76
(0.00)
−4.06
(0.11)
3.01
(0.35)
141
0.03
1.98
1997
2.86
(0.00)
−4.75
(0.01)
3.78
(0.06)
181
0.06
1.97
Panel A shows results of a pooled regression using data for whole period. Panel B shows results of OLS regressions
on a year by year basis. Data for firms listed on the Oslo Stock Exchange, 1989-1997. Variable definitions are in
Appendix A.2. This regression underlies figure 5.2.
122
B.2.2
Supplementary regressions
Alternative concentration measures
This section uses two alternative concentration measures: The Herfindahl index of ownership concentration and the holdings by the 20 largest owners.
Table B.7 Multivariate regression relating performance (RoA5 ) to ownership concentration
(Herfindahl) and controls
Panel A: Pooled regressions (OLS and GMM)
Dependent variable: RoA5
Constant
lntrans(Herfindahl index)
Industrial
Transport/shipping
Offshore
Investments over income
ln(Firm value)
Stock volatility
n
R2
Average (RoA5 )
coeff
13.40
-0.46
-1.98
-2.84
-3.94
-0.08
-0.11
-1.76
886
0.09
9.41
(stdev)
(2.68)
(0.14)
(0.38)
(0.40)
(0.61)
(0.05)
(0.12)
(0.65)
pvalue
0.00
0.00
0.00
0.00
0.00
0.11
0.36
0.01
Dependent variable: RoA5
Constant
lntrans(Herfindahl index)
Industrial
Transport/shipping
Offshore
Investments over income
ln(Firm value)
Stock volatility
n
Average (RoA5 )
coeff
13.40
-0.46
-1.98
-2.84
-3.94
-0.08
-0.11
-1.76
886
9.41
(stdev)
(3.86)
(0.13)
(0.39)
(0.47)
(0.56)
(0.04)
(0.16)
(0.95)
pvalue
0.00
0.00
0.00
0.00
0.00
0.04
0.47
0.06
Panel B: Year by year OLS regressions
constant
lntrans(Herfindahl index)
Industrial
Transport/shipping
Offshore
Investments over income
ln(Firm value)
Stock volatility
n
R2
Average (RoA5 )
1989
6.83
(0.33)
−0.22
(0.59)
−1.60
(0.11)
−0.80
(0.47)
−5.71
(0.00)
−0.05
(0.49)
0.24
(0.45)
−1.89
(0.28)
83
0.04
9.82
1990
−0.80
(0.91)
0.02
(0.97)
0.24
(0.82)
0.17
(0.88)
−1.76
(0.30)
−0.04
(0.75)
0.50
(0.14)
0.23
(0.90)
79
−0.04
9.33
1991
11.38
(0.20)
−0.38
(0.34)
−0.00
(1.00)
−0.46
(0.68)
−3.09
(0.05)
0.05
(0.52)
0.05
(0.89)
−5.62
(0.01)
76
0.17
9.19
1992
12.50
(0.08)
−0.71
(0.15)
−0.60
(0.65)
−1.01
(0.41)
−2.77
(0.11)
−1.95
(0.25)
−0.04
(0.90)
−2.67
(0.13)
69
0.03
10.16
Year
1993
25.06
(0.00)
−0.63
(0.12)
−2.04
(0.08)
−2.39
(0.03)
−4.71
(0.00)
0.37
(0.63)
−0.59
(0.11)
−4.46
(0.01)
82
0.17
10.06
1994
22.23
(0.00)
−0.74
(0.02)
−2.19
(0.03)
−3.08
(0.00)
−4.55
(0.00)
−0.67
(0.14)
−0.59
(0.04)
−1.86
(0.32)
106
0.15
8.94
1995
1.67
(0.80)
−0.50
(0.16)
−2.41
(0.01)
−3.65
(0.00)
−3.39
(0.05)
−0.37
(0.11)
0.34
(0.27)
2.57
(0.10)
115
0.14
8.77
1996
22.07
(0.02)
−0.11
(0.81)
−2.52
(0.02)
−4.98
(0.00)
−3.40
(0.10)
−0.40
(0.25)
−0.45
(0.30)
−3.60
(0.12)
119
0.12
9.04
1997
32.88
(0.00)
−0.22
(0.62)
−3.37
(0.01)
−5.26
(0.00)
−3.98
(0.03)
−0.29
(0.27)
−0.87
(0.03)
−6.44
(0.03)
157
0.12
9.76
Panel C: OLS fixed (annual) effects regression
Dependent variable: RoA5
Constant
lntrans(Herfindahl index)
Industrial
Transport/shipping
Offshore
Investments over income
ln(Firm value)
Stock volatility
1990
1991
1992
1993
1994
1995
1996
1997
n
R2
Average (RoA5 )
coeff
15.44
-0.46
-2.04
-2.90
-4.13
-0.09
-0.16
-2.55
-0.44
-0.30
0.69
0.25
-1.28
-1.53
-1.34
-0.54
886
0.10
9.41
(stdev)
(2.74)
(0.14)
(0.38)
(0.40)
(0.61)
(0.05)
(0.12)
(0.68)
(0.70)
(0.71)
(0.74)
(0.70)
(0.66)
(0.65)
(0.65)
(0.61)
pvalue
0.00
0.00
0.00
0.00
0.00
0.10
0.20
0.00
0.53
0.68
0.35
0.72
0.05
0.02
0.04
0.38
Panel A shows results of pooled regressions using data for the whole period. The left hand table in panel A shows
result for a OLS regression. The right hand table in panel A is a GMM regression. Panel B shows estimates on
a year by year basis, and panel C is a (annual) fixed effects regression. In regressions using firm size across years
the nominal values are adjusted to the 1997 general price level. Data for firms listed on the Oslo Stock Exchange,
1989-1997. Variable definitions are in Appendix A.2.
B.2 Ownership concentration
123
Table B.8 Multivariate regression relating performance (RoA5 ) to ownership concentration (20
largest owners) and controls
Panel A: Pooled regressions (OLS and GMM)
Dependent variable: RoA5
Constant
lntrans(1-20 largest owners)
Industrial
Transport/shipping
Offshore
Investments over income
ln(Firm value)
Stock volatility
n
R2
Average (RoA5 )
coeff
14.87
-0.29
-2.00
-2.87
-3.94
-0.08
-0.11
-1.82
886
0.08
9.41
(stdev)
(2.69)
(0.18)
(0.39)
(0.40)
(0.61)
(0.05)
(0.12)
(0.66)
pvalue
0.00
0.11
0.00
0.00
0.00
0.13
0.35
0.01
Dependent variable: RoA5
Constant
lntrans(1-20 largest owners)
Industrial
Transport/shipping
Offshore
Investments over income
ln(Firm value)
Stock volatility
n
Average (RoA5 )
coeff
14.87
-0.29
-2.00
-2.87
-3.94
-0.08
-0.11
-1.82
886
9.41
(stdev)
(3.77)
(0.18)
(0.40)
(0.47)
(0.55)
(0.04)
(0.16)
(0.96)
pvalue
0.00
0.10
0.00
0.00
0.00
0.04
0.47
0.06
Panel B: Year by year OLS regressions
constant
lntrans(1-20 largest owners)
Industrial
Transport/shipping
Offshore
Investments over income
ln(Firm value)
Stock volatility
n
R2
Average (RoA5 )
1989
7.98
(0.25)
−0.40
(0.43)
−1.64
(0.11)
−0.87
(0.43)
−5.77
(0.00)
−0.04
(0.51)
0.23
(0.47)
−1.66
(0.35)
83
0.05
9.82
1990
−1.41
(0.85)
0.30
(0.59)
0.31
(0.77)
0.25
(0.82)
−1.57
(0.36)
−0.04
(0.70)
0.51
(0.13)
−0.05
(0.98)
79
−0.03
9.33
1991
12.83
(0.14)
0.08
(0.88)
0.19
(0.87)
−0.34
(0.76)
−2.89
(0.06)
0.05
(0.48)
0.03
(0.95)
−6.06
(0.01)
76
0.16
9.19
1992
14.21
(0.05)
−0.04
(0.94)
−0.22
(0.87)
−0.65
(0.61)
−2.27
(0.19)
−1.62
(0.34)
−0.05
(0.89)
−3.17
(0.07)
69
−0.00
10.16
Year
1993
27.78
(0.00)
−0.46
(0.38)
−2.14
(0.07)
−2.61
(0.02)
−4.69
(0.00)
0.51
(0.51)
−0.62
(0.10)
−4.68
(0.00)
82
0.15
10.06
1994
24.84
(0.00)
−0.40
(0.34)
−2.28
(0.03)
−3.08
(0.00)
−4.52
(0.01)
−0.64
(0.17)
−0.60
(0.04)
−2.22
(0.25)
106
0.12
8.94
1995
2.90
(0.66)
−0.23
(0.61)
−2.47
(0.01)
−3.73
(0.00)
−3.42
(0.05)
−0.35
(0.13)
0.35
(0.26)
2.52
(0.11)
115
0.12
8.77
1996
22.43
(0.02)
−0.19
(0.74)
−2.55
(0.02)
−4.96
(0.00)
−3.43
(0.09)
−0.40
(0.25)
−0.44
(0.31)
−3.54
(0.13)
119
0.12
9.04
1997
33.15
(0.00)
0.12
(0.83)
−3.37
(0.01)
−5.43
(0.00)
−4.02
(0.03)
−0.29
(0.26)
−0.86
(0.04)
−6.48
(0.03)
157
0.12
9.76
Panel C: OLS fixed (annual) effects regression
Dependent variable: RoA5
Constant
lntrans(1-20 largest owners)
Industrial
Transport/shipping
Offshore
Investments over income
ln(Firm value)
Stock volatility
1990
1991
1992
1993
1994
1995
1996
1997
n
R2
Average (RoA5 )
coeff
16.90
-0.29
-2.05
-2.93
-4.13
-0.08
-0.16
-2.62
-0.41
-0.23
0.73
0.30
-1.25
-1.51
-1.34
-0.52
886
0.10
9.41
(stdev)
(2.75)
(0.18)
(0.38)
(0.40)
(0.61)
(0.05)
(0.12)
(0.69)
(0.71)
(0.71)
(0.74)
(0.70)
(0.67)
(0.65)
(0.65)
(0.62)
pvalue
0.00
0.10
0.00
0.00
0.00
0.11
0.19
0.00
0.56
0.75
0.33
0.67
0.06
0.02
0.04
0.40
Panel A shows results of pooled regressions using data for the whole period. The left hand table in panel A shows
result for a OLS regression. The right hand table in panel A is a GMM regression. Panel B shows estimates on
a year by year basis, and panel C is a (annual) fixed effects regression. In regressions using firm size across years
the nominal values are adjusted to the 1997 general price level. Data for firms listed on the Oslo Stock Exchange,
1989-1997. Variable definitions are in Appendix A.2.
124
Supplementary regressions
Table B.9 Multivariate regression relating performance (Q) to ownership concentration (Herfindahl) and controls
Panel A: Pooled regressions (OLS and GMM)
Dependent variable: Q
Constant
lntrans(Herfindahl index)
Industrial
Transport/shipping
Offshore
Investments over income
ln(Firm value)
Stock volatility
n
R2
Average (Q)
coeff
-0.02
-0.16
-0.44
-0.85
-0.75
-0.02
0.08
-0.06
905
0.14
1.53
(stdev)
(0.56)
(0.03)
(0.08)
(0.08)
(0.13)
(0.01)
(0.03)
(0.14)
pvalue
0.97
0.00
0.00
0.00
0.00
0.17
0.00
0.67
Dependent variable: Q
Constant
lntrans(Herfindahl index)
Industrial
Transport/shipping
Offshore
Investments over income
ln(Firm value)
Stock volatility
n
Average (Q)
coeff
-0.02
-0.16
-0.44
-0.85
-0.75
-0.02
0.08
-0.06
905
1.53
(stdev)
(0.66)
(0.03)
(0.10)
(0.10)
(0.11)
(0.01)
(0.03)
(0.17)
pvalue
0.97
0.00
0.00
0.00
0.00
0.03
0.00
0.73
Panel B: Year by year OLS regressions
constant
lntrans(Herfindahl index)
Industrial
Transport/shipping
Offshore
Investments over income
ln(Firm value)
Stock volatility
n
R2
Average (Q)
1989
−0.26
(0.74)
−0.11
(0.02)
−0.28
(0.02)
0.02
(0.89)
−0.14
(0.54)
−0.01
(0.35)
0.08
(0.03)
−0.17
(0.38)
83
0.15
1.28
1990
0.66
(0.39)
−0.08
(0.06)
−0.26
(0.02)
−0.29
(0.01)
−0.37
(0.03)
−0.01
(0.41)
0.03
(0.35)
−0.16
(0.44)
79
0.07
1.18
1991
1.42
(0.15)
−0.05
(0.25)
−0.07
(0.59)
−0.24
(0.06)
−0.45
(0.01)
0.00
(0.88)
0.00
(0.95)
−0.51
(0.03)
77
0.15
1.10
1992
−0.42
(0.63)
−0.03
(0.64)
−0.32
(0.05)
−0.42
(0.00)
−0.48
(0.02)
−0.12
(0.55)
0.09
(0.02)
−0.01
(0.96)
69
0.11
1.11
Year
1993
−0.31
(0.83)
−0.09
(0.21)
−0.32
(0.12)
−0.68
(0.00)
−0.75
(0.01)
−0.07
(0.60)
0.09
(0.19)
0.36
(0.19)
83
0.12
1.46
1994
0.43
(0.54)
−0.08
(0.03)
−0.23
(0.04)
−0.56
(0.00)
−0.50
(0.00)
−0.07
(0.19)
0.05
(0.12)
0.07
(0.71)
108
0.20
1.33
1995
−1.60
(0.21)
−0.10
(0.14)
−0.41
(0.02)
−0.85
(0.00)
−0.68
(0.04)
−0.04
(0.39)
0.14
(0.01)
0.77
(0.01)
117
0.19
1.48
1996
6.34
(0.01)
−0.23
(0.05)
−0.59
(0.04)
−1.50
(0.00)
−0.71
(0.18)
−0.19
(0.05)
−0.18
(0.11)
−0.93
(0.14)
126
0.18
2.02
1997
−2.50
(0.21)
−0.23
(0.02)
−0.44
(0.09)
−1.36
(0.00)
−0.89
(0.02)
0.01
(0.90)
0.19
(0.03)
1.30
(0.04)
163
0.15
2.04
Panel C: OLS fixed (annual) effects regression
Dependent variable: Q
Constant
lntrans(Herfindahl index)
Industrial
Transport/shipping
Offshore
Investments over income
ln(Firm value)
Stock volatility
1990
1991
1992
1993
1994
1995
1996
1997
n
R2
Average (Q)
coeff
-0.23
-0.15
-0.38
-0.77
-0.69
-0.01
0.07
0.19
-0.11
-0.15
-0.15
0.14
0.06
0.17
0.66
0.67
905
0.23
1.53
(stdev)
(0.55)
(0.03)
(0.08)
(0.08)
(0.12)
(0.01)
(0.02)
(0.14)
(0.14)
(0.14)
(0.15)
(0.14)
(0.13)
(0.13)
(0.13)
(0.12)
pvalue
0.68
0.00
0.00
0.00
0.00
0.31
0.00
0.15
0.43
0.29
0.32
0.33
0.67
0.20
0.00
0.00
Panel A shows results of pooled regressions using data for the whole period. The left hand table in panel A shows
result for a OLS regression. The right hand table in panel A is a GMM regression. Panel B shows estimates on
a year by year basis, and panel C is a (annual) fixed effects regression. In regressions using firm size across years
the nominal values are adjusted to the 1997 general price level. Data for firms listed on the Oslo Stock Exchange,
1989-1997. Variable definitions are in Appendix A.2.
B.2 Ownership concentration
125
Table B.10 Multivariate regression relating performance (Q) to ownership concentration (20
largest owners) and controls
Panel A: Pooled regressions (OLS and GMM)
Dependent variable: Q
Constant
lntrans(1-20 largest owners)
Industrial
Transport/shipping
Offshore
Investments over income
ln(Firm value)
Stock volatility
n
R2
Average (Q)
coeff
0.53
-0.14
-0.45
-0.86
-0.75
-0.01
0.08
-0.06
905
0.13
1.53
(stdev)
(0.57)
(0.04)
(0.08)
(0.08)
(0.13)
(0.01)
(0.03)
(0.14)
pvalue
0.35
0.00
0.00
0.00
0.00
0.22
0.00
0.67
Dependent variable: Q
Constant
lntrans(1-20 largest owners)
Industrial
Transport/shipping
Offshore
Investments over income
ln(Firm value)
Stock volatility
n
Average (Q)
coeff
0.53
-0.14
-0.45
-0.86
-0.75
-0.01
0.08
-0.06
905
1.53
(stdev)
(0.64)
(0.04)
(0.10)
(0.10)
(0.11)
(0.01)
(0.03)
(0.17)
pvalue
0.41
0.00
0.00
0.00
0.00
0.03
0.00
0.73
Panel B: Year by year OLS regressions
constant
lntrans(1-20 largest owners)
Industrial
Transport/shipping
Offshore
Investments over income
ln(Firm value)
Stock volatility
n
R2
Average (Q)
1989
0.06
(0.94)
−0.08
(0.19)
−0.26
(0.03)
0.02
(0.91)
−0.10
(0.66)
−0.01
(0.42)
0.08
(0.04)
−0.15
(0.48)
83
0.11
1.28
1990
0.85
(0.29)
−0.05
(0.43)
−0.25
(0.03)
−0.28
(0.02)
−0.35
(0.05)
−0.01
(0.49)
0.03
(0.35)
−0.13
(0.53)
79
0.03
1.18
1991
1.60
(0.11)
0.02
(0.76)
−0.04
(0.72)
−0.22
(0.08)
−0.42
(0.02)
0.00
(0.79)
−0.00
(0.99)
−0.59
(0.02)
77
0.13
1.10
1992
−0.40
(0.65)
0.03
(0.72)
−0.29
(0.06)
−0.40
(0.01)
−0.45
(0.03)
−0.10
(0.61)
0.09
(0.02)
−0.04
(0.86)
69
0.11
1.11
Year
1993
0.06
(0.97)
−0.07
(0.46)
−0.33
(0.11)
−0.71
(0.00)
−0.74
(0.01)
−0.05
(0.70)
0.08
(0.21)
0.34
(0.22)
83
0.11
1.46
1994
0.61
(0.39)
−0.01
(0.90)
−0.23
(0.04)
−0.56
(0.00)
−0.51
(0.01)
−0.06
(0.25)
0.05
(0.13)
0.03
(0.88)
108
0.16
1.33
1995
−1.33
(0.30)
−0.05
(0.59)
−0.42
(0.02)
−0.87
(0.00)
−0.66
(0.04)
−0.04
(0.45)
0.15
(0.02)
0.76
(0.01)
117
0.17
1.48
1996
7.23
(0.00)
−0.15
(0.30)
−0.63
(0.03)
−1.52
(0.00)
−0.75
(0.16)
−0.20
(0.04)
−0.19
(0.10)
−0.99
(0.13)
126
0.16
2.02
1997
−1.87
(0.36)
−0.13
(0.29)
−0.45
(0.09)
−1.40
(0.00)
−0.94
(0.02)
0.01
(0.87)
0.19
(0.03)
1.27
(0.05)
163
0.12
2.04
Panel C: OLS fixed (annual) effects regression
Dependent variable: Q
Constant
lntrans(1-20 largest owners)
Industrial
Transport/shipping
Offshore
Investments over income
ln(Firm value)
Stock volatility
1990
1991
1992
1993
1994
1995
1996
1997
n
R2
Average (Q)
coeff
0.26
-0.11
-0.39
-0.78
-0.69
-0.01
0.07
0.18
-0.11
-0.14
-0.14
0.15
0.07
0.17
0.66
0.68
905
0.21
1.53
(stdev)
(0.56)
(0.04)
(0.08)
(0.08)
(0.12)
(0.01)
(0.02)
(0.14)
(0.14)
(0.15)
(0.15)
(0.14)
(0.14)
(0.13)
(0.13)
(0.13)
pvalue
0.64
0.00
0.00
0.00
0.00
0.38
0.00
0.19
0.46
0.35
0.35
0.29
0.62
0.20
0.00
0.00
Panel A shows results of pooled regressions using data for the whole period. The left hand table in panel A shows
result for a OLS regression. The right hand table in panel A is a GMM regression. Panel B shows estimates on
a year by year basis, and panel C is a (annual) fixed effects regression. In regressions using firm size across years
the nominal values are adjusted to the 1997 general price level. Data for firms listed on the Oslo Stock Exchange,
1989-1997. Variable definitions are in Appendix A.2.
126
B.3
Supplementary regressions
Insider ownership
This appendix presents supplementary results to the analyses in chapter 6.
B.3.1
Year by year, GMM, and fixed effects regressions
This section lists tables which supplement the pooled OLS regression shown in the main text. Using
the same dependent and independent variables, we show OLS estimations on a year by year basis,
estimations using GMM, and we also control for systematic differences across years with indicator
variables for each year (fixed effects) in an OLS regression.
Table B.11 Multivariate regression relating performance (Q) to insider ownership and controls,
following Morck et al. (1988)
Panel A: Year by year OLS regressions
constant
Primary insiders (0 to 5)
Primary insiders (5 to 25)
Primary insiders (25 to 100)
Industrial
Transport/shipping
Offshore
Debt to assets
ln(Firm value)
n
R2
Average (Q)
1989
0.35
(0.48)
4.51
(0.24)
−0.82
(0.72)
0.05
(0.94)
−0.28
(0.02)
−0.01
(0.95)
−0.04
(0.85)
0.05
(0.84)
0.05
(0.04)
102
0.01
1.29
1990
−0.02
(0.96)
7.83
(0.00)
−1.85
(0.10)
0.52
(0.20)
−0.19
(0.06)
−0.25
(0.02)
−0.16
(0.30)
−0.05
(0.83)
0.06
(0.01)
91
0.12
1.15
1991
−0.16
(0.75)
9.02
(0.00)
−2.16
(0.06)
0.18
(0.60)
−0.11
(0.29)
−0.24
(0.03)
−0.35
(0.03)
−0.29
(0.17)
0.07
(0.00)
90
0.14
1.09
1992
−0.73
(0.08)
1.69
(0.58)
1.45
(0.25)
−0.76
(0.24)
−0.13
(0.24)
−0.26
(0.03)
−0.33
(0.04)
0.17
(0.46)
0.09
(0.00)
99
0.19
1.03
Year
1993
0.48
(0.47)
0.71
(0.88)
1.40
(0.47)
−0.68
(0.41)
−0.28
(0.08)
−0.55
(0.00)
−0.64
(0.01)
−0.52
(0.19)
0.08
(0.02)
107
0.12
1.38
1994
0.29
(0.54)
5.82
(0.06)
0.84
(0.48)
−0.44
(0.38)
−0.21
(0.05)
−0.47
(0.00)
−0.57
(0.00)
−0.55
(0.02)
0.07
(0.00)
120
0.24
1.32
1995
−0.51
(0.54)
6.24
(0.18)
−0.26
(0.88)
1.86
(0.00)
−0.33
(0.04)
−0.66
(0.00)
−0.51
(0.05)
−0.26
(0.47)
0.11
(0.01)
128
0.27
1.42
1996
2.69
(0.10)
9.69
(0.22)
3.72
(0.21)
−1.77
(0.24)
−0.44
(0.13)
−1.05
(0.00)
−1.04
(0.02)
−2.26
(0.00)
0.04
(0.67)
140
0.24
1.98
1997
0.14
(0.91)
11.76
(0.07)
2.85
(0.21)
−0.79
(0.52)
−0.10
(0.67)
−0.84
(0.00)
−0.67
(0.04)
−1.99
(0.00)
0.14
(0.04)
180
0.25
1.95
Panel B: Pooled GMM and fixed effects regressions
Dependent variable: Q
Dependent variable: Q
Constant
Primary insiders (0 to 5)
Primary insiders (5 to 25)
Primary insiders (25 to 100)
Industrial
Transport/shipping
Offshore
Debt to assets
ln(Firm value)
n
Average (Q)
coeff
-0.00
7.74
1.85
-0.57
-0.28
-0.59
-0.58
-1.10
0.11
1057
1.47
(stdev)
(0.31)
(2.08)
(0.97)
(0.40)
(0.09)
(0.07)
(0.10)
(0.22)
(0.02)
pvalue
0.99
0.00
0.06
0.16
0.00
0.00
0.00
0.00
0.00
Constant
Primary insiders (0 to 5)
Primary insiders (5 to 25)
Primary insiders (25 to 100)
Industrial
Transport/shipping
Offshore
Debt to assets
ln(Firm value)
1990
1991
1992
1993
1994
1995
1996
1997
n
R2
Average (Q)
coeff
0.41
7.48
1.44
-0.39
-0.24
-0.56
-0.54
-0.91
0.08
-0.18
-0.21
-0.18
0.08
-0.04
0.06
0.52
0.44
1057
0.26
1.47
(stdev)
(0.32)
(1.90)
(0.74)
(0.29)
(0.07)
(0.07)
(0.10)
(0.14)
(0.02)
(0.12)
(0.12)
(0.12)
(0.12)
(0.12)
(0.11)
(0.11)
(0.11)
pvalue
0.21
0.00
0.05
0.19
0.00
0.00
0.00
0.00
0.00
0.14
0.09
0.13
0.50
0.75
0.62
0.00
0.00
This table complements the pooled OLS regression in table 6.1 by three additional regressions. Panel A shows OLS
estimates on a year by year basis. The left–hand side table in panel B lists pooled estimates, estimated with GMM.
The table on the right–hand side in panel B shows the results of a pooled OLS regression where dummy variables
for each year are used to capture fixed effects. 1989 is the base year in this regression. In regressions using firm size
across years the nominal values are adjusted to the 1997 general price level. Data for firms listed on the Oslo Stock
Exchange, 1989-1997. Variable definitions are in Appendix A.2.
B.3 Insider ownership
127
Table B.12 Multivariate regression relating performance (Q) to insider ownership, ownership
concentration and controls, using the piecewise linear function of Morck et al. (1988).
Panel A: Year by year OLS regressions
constant
Primary insiders (0 to 5)
Primary insiders (5 to 25)
Primary insiders (25 to 100)
Largest owner
Industrial
Transport/shipping
Offshore
Debt to assets
ln(Firm value)
n
R2
Average (Q)
1989
0.58
(0.25)
3.61
(0.34)
−0.53
(0.82)
0.07
(0.90)
−0.52
(0.04)
−0.28
(0.02)
−0.02
(0.90)
−0.09
(0.70)
0.04
(0.87)
0.05
(0.05)
102
0.05
1.29
1990
0.21
(0.68)
7.45
(0.00)
−1.87
(0.09)
0.62
(0.12)
−0.45
(0.03)
−0.19
(0.05)
−0.27
(0.01)
−0.17
(0.25)
−0.03
(0.90)
0.06
(0.02)
91
0.15
1.15
1991
0.08
(0.87)
8.40
(0.01)
−2.03
(0.08)
0.27
(0.43)
−0.38
(0.08)
−0.10
(0.35)
−0.24
(0.03)
−0.36
(0.02)
−0.31
(0.14)
0.07
(0.01)
90
0.16
1.09
1992
−0.53
(0.24)
1.24
(0.68)
1.60
(0.21)
−0.77
(0.24)
−0.24
(0.30)
−0.12
(0.29)
−0.26
(0.03)
−0.33
(0.03)
0.14
(0.55)
0.09
(0.00)
99
0.19
1.03
Year
1993
0.95
(0.16)
−0.86
(0.85)
1.68
(0.37)
−0.65
(0.42)
−0.67
(0.03)
−0.21
(0.18)
−0.53
(0.00)
−0.62
(0.01)
−0.64
(0.10)
0.06
(0.05)
107
0.16
1.38
1994
0.62
(0.20)
3.99
(0.20)
1.02
(0.38)
−0.34
(0.49)
−0.49
(0.02)
−0.22
(0.04)
−0.50
(0.00)
−0.54
(0.00)
−0.64
(0.01)
0.07
(0.00)
120
0.27
1.32
1995
−0.11
(0.89)
5.11
(0.27)
−0.19
(0.92)
1.96
(0.00)
−0.66
(0.05)
−0.32
(0.05)
−0.65
(0.00)
−0.53
(0.04)
−0.32
(0.38)
0.10
(0.01)
128
0.29
1.42
1996
3.10
(0.06)
7.90
(0.32)
3.65
(0.22)
−1.60
(0.29)
−1.02
(0.12)
−0.41
(0.16)
−1.04
(0.00)
−1.03
(0.02)
−2.17
(0.00)
0.03
(0.74)
140
0.25
1.98
1997
0.62
(0.64)
10.66
(0.10)
2.59
(0.25)
−0.62
(0.61)
−0.99
(0.04)
−0.10
(0.68)
−0.82
(0.00)
−0.66
(0.05)
−1.94
(0.00)
0.13
(0.06)
180
0.26
1.95
Panel B: Pooled GMM and fixed effects regressions
Dependent variable: Q
Dependent variable: Q
Constant
Primary insiders (0 to 5)
Primary insiders (5 to 25)
Primary insiders (25 to 100)
Largest owner
Industrial
Transport/shipping
Offshore
Debt to assets
ln(Firm value)
n
Average (Q)
coeff
0.45
6.31
1.90
-0.42
-0.78
-0.26
-0.60
-0.59
-1.12
0.10
1057
1.47
(stdev)
(0.30)
(2.10)
(0.96)
(0.40)
(0.14)
(0.09)
(0.07)
(0.10)
(0.22)
(0.01)
pvalue
0.14
0.00
0.05
0.29
0.00
0.00
0.00
0.00
0.00
0.00
Constant
Primary insiders (0 to 5)
Primary insiders (5 to 25)
Primary insiders (25 to 100)
Largest owner
Industrial
Transport/shipping
Offshore
Debt to assets
ln(Firm value)
1990
1991
1992
1993
1994
1995
1996
1997
n
R2
Average (Q)
coeff
0.85
6.08
1.49
-0.24
-0.77
-0.22
-0.57
-0.55
-0.93
0.07
-0.19
-0.22
-0.19
0.08
-0.03
0.05
0.50
0.43
1057
0.28
1.47
(stdev)
(0.33)
(1.89)
(0.73)
(0.29)
(0.14)
(0.07)
(0.07)
(0.10)
(0.14)
(0.02)
(0.12)
(0.12)
(0.12)
(0.12)
(0.11)
(0.11)
(0.11)
(0.11)
pvalue
0.01
0.00
0.04
0.41
0.00
0.00
0.00
0.00
0.00
0.00
0.12
0.07
0.12
0.50
0.78
0.68
0.00
0.00
This table complements the pooled OLS regression in table 6.2 by three additional regressions. Panel A shows OLS
estimates on a year by year basis. The left–hand side table in panel B lists pooled estimates, estimated with GMM.
The table on the right–hand side in panel B shows the results of a pooled OLS regression where dummy variables
for each year are used to capture fixed effects. 1989 is the base year in this regression. In regressions using firm size
across years the nominal values are adjusted to the 1997 general price level. Data for firms listed on the Oslo Stock
Exchange, 1989-1997. Variable definitions are in Appendix A.2.
128
Supplementary regressions
Table B.13 Multivariate regression relating performance (Q) to insider ownership and controls,
following McConnell and Servaes (1990)
Panel A: Year by year OLS regressions
constant
Primary insiders
Squared (Primary insiders)
Industrial
Transport/shipping
Offshore
Debt to assets
ln(Firm value)
n
R2
Average (Q)
1989
0.42
(0.39)
0.17
(0.87)
−0.06
(0.95)
−0.24
(0.05)
0.02
(0.86)
−0.01
(0.97)
0.01
(0.97)
0.05
(0.06)
102
0.01
1.29
1990
−0.05
(0.93)
0.36
(0.61)
−0.04
(0.96)
−0.18
(0.08)
−0.22
(0.06)
−0.12
(0.45)
−0.04
(0.85)
0.07
(0.01)
91
0.04
1.15
1991
−0.01
(0.98)
0.54
(0.41)
−0.49
(0.48)
−0.10
(0.38)
−0.24
(0.04)
−0.33
(0.05)
−0.29
(0.20)
0.07
(0.01)
90
0.07
1.09
1992
−0.72
(0.08)
1.70
(0.03)
−2.38
(0.05)
−0.13
(0.25)
−0.25
(0.03)
−0.32
(0.04)
0.16
(0.49)
0.09
(0.00)
99
0.20
1.03
Year
1993
0.53
(0.42)
1.30
(0.21)
−1.71
(0.22)
−0.28
(0.08)
−0.55
(0.00)
−0.65
(0.01)
−0.52
(0.18)
0.07
(0.03)
107
0.13
1.38
1994
0.38
(0.44)
1.80
(0.00)
−1.84
(0.02)
−0.23
(0.03)
−0.46
(0.00)
−0.50
(0.00)
−0.57
(0.02)
0.07
(0.00)
120
0.23
1.32
1995
−0.47
(0.56)
0.93
(0.36)
0.74
(0.51)
−0.38
(0.02)
−0.70
(0.00)
−0.50
(0.06)
−0.26
(0.47)
0.11
(0.01)
128
0.27
1.42
1996
2.91
(0.08)
5.01
(0.01)
−5.68
(0.03)
−0.44
(0.12)
−1.02
(0.00)
−1.04
(0.02)
−2.33
(0.00)
0.03
(0.72)
140
0.23
1.98
1997
0.22
(0.87)
5.09
(0.00)
−5.40
(0.01)
−0.10
(0.68)
−0.85
(0.00)
−0.69
(0.04)
−2.04
(0.00)
0.14
(0.04)
180
0.24
1.95
Panel B: Pooled GMM and fixed effects regressions
Dependent variable: Q
Dependent variable: Q
Constant
Primary insiders
Squared (Primary insiders)
Industrial
Transport/shipping
Offshore
Debt to assets
ln(Firm value)
n
Average (Q)
coeff
0.04
2.81
-2.64
-0.29
-0.59
-0.56
-1.15
0.11
1057
1.47
(stdev)
(0.31)
(0.63)
(0.75)
(0.09)
(0.08)
(0.10)
(0.23)
(0.02)
pvalue
0.90
0.00
0.00
0.00
0.00
0.00
0.00
0.00
Constant
Primary insiders
Squared (Primary insiders)
Industrial
Transport/shipping
Offshore
Debt to assets
ln(Firm value)
1990
1991
1992
1993
1994
1995
1996
1997
n
R2
Average (Q)
coeff
0.45
2.47
-2.24
-0.24
-0.56
-0.53
-0.95
0.08
-0.17
-0.20
-0.19
0.09
-0.03
0.07
0.53
0.45
1057
0.25
1.47
(stdev)
(0.33)
(0.41)
(0.50)
(0.07)
(0.07)
(0.11)
(0.14)
(0.02)
(0.13)
(0.13)
(0.12)
(0.12)
(0.12)
(0.12)
(0.11)
(0.11)
pvalue
0.17
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.17
0.11
0.13
0.47
0.81
0.55
0.00
0.00
This table complements the pooled OLS regression in table 6.3 by three additional regressions. Panel A shows OLS
estimates on a year by year basis. The left–hand side table in panel B lists pooled estimates, estimated with GMM.
The table on the right–hand side in panel B shows the results of a pooled OLS regression where dummy variables
for each year are used to capture fixed effects. 1989 is the base year in this regression. In regressions using firm size
across years the nominal values are adjusted to the 1997 general price level. Data for firms listed on the Oslo Stock
Exchange, 1989-1997. Variable definitions are in Appendix A.2.
B.3 Insider ownership
129
Table B.14 Multivariate regression relating performance (Q) to insider ownership, ownership
concentration and controls, following McConnell and Servaes (1990)
Panel A: Year by year OLS regressions
constant
Primary insiders
Squared (Primary insiders)
Largest owner
Industrial
Transport/shipping
Offshore
Debt to assets
ln(Firm value)
n
R2
Average (Q)
1989
0.65
(0.18)
0.11
(0.91)
0.04
(0.97)
−0.55
(0.03)
−0.25
(0.04)
0.01
(0.96)
−0.06
(0.78)
0.01
(0.97)
0.04
(0.06)
102
0.05
1.29
1990
0.20
(0.70)
0.35
(0.61)
0.04
(0.96)
−0.48
(0.03)
−0.18
(0.07)
−0.23
(0.04)
−0.14
(0.38)
−0.03
(0.91)
0.06
(0.02)
91
0.08
1.15
1991
0.26
(0.63)
0.49
(0.44)
−0.36
(0.60)
−0.45
(0.05)
−0.08
(0.44)
−0.24
(0.04)
−0.34
(0.04)
−0.31
(0.15)
0.06
(0.01)
90
0.10
1.09
1992
−0.52
(0.25)
1.73
(0.03)
−2.40
(0.05)
−0.25
(0.26)
−0.11
(0.31)
−0.25
(0.03)
−0.33
(0.03)
0.13
(0.57)
0.09
(0.00)
99
0.20
1.03
Year
1993
1.00
(0.14)
1.19
(0.24)
−1.57
(0.25)
−0.66
(0.03)
−0.21
(0.18)
−0.53
(0.00)
−0.64
(0.01)
−0.63
(0.10)
0.06
(0.06)
107
0.17
1.38
1994
0.72
(0.14)
1.56
(0.01)
−1.55
(0.04)
−0.54
(0.01)
−0.22
(0.03)
−0.49
(0.00)
−0.49
(0.00)
−0.67
(0.01)
0.07
(0.01)
120
0.27
1.32
1995
−0.06
(0.94)
0.80
(0.42)
0.90
(0.42)
−0.69
(0.04)
−0.35
(0.03)
−0.68
(0.00)
−0.52
(0.05)
−0.32
(0.38)
0.10
(0.01)
128
0.29
1.42
1996
3.35
(0.04)
4.58
(0.01)
−5.19
(0.04)
−1.15
(0.07)
−0.40
(0.16)
−1.02
(0.00)
−1.03
(0.02)
−2.21
(0.00)
0.02
(0.81)
140
0.24
1.98
1997
0.70
(0.60)
4.57
(0.00)
−4.75
(0.02)
−1.02
(0.03)
−0.09
(0.69)
−0.82
(0.00)
−0.68
(0.04)
−1.99
(0.00)
0.13
(0.05)
180
0.26
1.95
Panel B: Pooled GMM and fixed effects regressions
Dependent variable: Q
Dependent variable: Q
Constant
Primary insiders
Squared (Primary insiders)
Largest owner
Industrial
Transport/shipping
Offshore
Debt to assets
ln(Firm value)
n
Average (Q)
coeff
0.52
2.56
-2.31
-0.85
-0.26
-0.59
-0.58
-1.16
0.10
1057
1.47
(stdev)
(0.31)
(0.61)
(0.73)
(0.14)
(0.09)
(0.07)
(0.10)
(0.22)
(0.01)
pvalue
0.09
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
Constant
Primary insiders
Squared (Primary insiders)
Largest owner
Industrial
Transport/shipping
Offshore
Debt to assets
ln(Firm value)
1990
1991
1992
1993
1994
1995
1996
1997
n
R2
Average (Q)
coeff
0.92
2.22
-1.91
-0.83
-0.22
-0.56
-0.54
-0.96
0.07
-0.18
-0.21
-0.19
0.08
-0.02
0.05
0.52
0.44
1057
0.27
1.47
(stdev)
(0.33)
(0.41)
(0.50)
(0.14)
(0.07)
(0.07)
(0.10)
(0.14)
(0.02)
(0.12)
(0.12)
(0.12)
(0.12)
(0.12)
(0.11)
(0.11)
(0.11)
pvalue
0.01
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.14
0.09
0.11
0.48
0.84
0.63
0.00
0.00
This table complements the pooled OLS regression in table 6.4 by three additional regressions. Panel A shows OLS
estimates on a year by year basis. The left–hand side table in panel B lists pooled estimates, estimated with GMM.
The table on the right–hand side in panel B shows the results of a pooled OLS regression where dummy variables
for each year are used to capture fixed effects. 1989 is the base year in this regression. In regressions using firm size
across years the nominal values are adjusted to the 1997 general price level. Data for firms listed on the Oslo Stock
Exchange, 1989-1997. Variable definitions are in Appendix A.2.
130
Supplementary regressions
Table B.15 Multivariate regression relating performance (Q) to insider ownership, ownership
concentration, institutional ownership, and controls, following McConnell and Servaes (1990)
Panel A: Year by year OLS regressions
constant
Primary insiders
Squared (Primary insiders)
Largest owner
Aggregate financial holdings
Industrial
Transport/shipping
Offshore
Debt to assets
ln(Firm value)
n
R2
Average (Q)
1989
0.69
(0.15)
0.37
(0.71)
−0.29
(0.79)
−0.73
(0.00)
−1.00
(0.01)
−0.25
(0.03)
−0.04
(0.72)
−0.11
(0.62)
−0.03
(0.91)
0.05
(0.03)
102
0.10
1.29
1990
0.18
(0.74)
0.35
(0.61)
0.04
(0.96)
−0.51
(0.02)
−0.18
(0.64)
−0.17
(0.09)
−0.24
(0.04)
−0.14
(0.38)
−0.01
(0.97)
0.06
(0.02)
91
0.07
1.15
1991
0.30
(0.58)
0.48
(0.45)
−0.35
(0.61)
−0.41
(0.08)
0.24
(0.62)
−0.11
(0.37)
−0.25
(0.03)
−0.35
(0.03)
−0.32
(0.14)
0.06
(0.02)
90
0.09
1.09
1992
−0.51
(0.26)
1.72
(0.03)
−2.37
(0.06)
−0.23
(0.32)
0.12
(0.73)
−0.12
(0.29)
−0.26
(0.03)
−0.34
(0.03)
0.12
(0.60)
0.08
(0.00)
99
0.20
1.03
Year
1993
1.00
(0.14)
1.18
(0.25)
−1.57
(0.25)
−0.66
(0.03)
−0.02
(0.97)
−0.21
(0.18)
−0.54
(0.00)
−0.64
(0.01)
−0.63
(0.12)
0.06
(0.07)
107
0.16
1.38
1994
0.76
(0.10)
1.33
(0.02)
−1.37
(0.07)
−0.75
(0.00)
−0.87
(0.00)
−0.21
(0.03)
−0.55
(0.00)
−0.54
(0.00)
−0.47
(0.05)
0.07
(0.00)
120
0.32
1.32
1995
−0.09
(0.91)
0.62
(0.52)
0.82
(0.45)
−1.11
(0.00)
−1.76
(0.00)
−0.23
(0.14)
−0.74
(0.00)
−0.53
(0.04)
−0.03
(0.93)
0.12
(0.00)
128
0.33
1.42
1996
2.64
(0.11)
4.26
(0.02)
−5.13
(0.04)
−1.41
(0.03)
−1.97
(0.02)
−0.39
(0.17)
−1.15
(0.00)
−0.98
(0.03)
−1.91
(0.00)
0.07
(0.39)
140
0.27
1.98
1997
0.66
(0.63)
4.57
(0.00)
−4.75
(0.02)
−1.03
(0.03)
−0.10
(0.88)
−0.09
(0.69)
−0.83
(0.00)
−0.68
(0.04)
−1.98
(0.00)
0.13
(0.06)
180
0.26
1.95
Panel B: Pooled GMM and fixed effects regressions
Dependent variable: Q
Dependent variable: Q
Constant
Primary insiders
Squared (Primary insiders)
Largest owner
Aggregate financial holdings
Industrial
Transport/shipping
Offshore
Debt to assets
ln(Firm value)
n
Average (Q)
coeff
0.47
2.56
-2.33
-0.89
-0.24
-0.26
-0.60
-0.58
-1.14
0.11
1057
1.47
(stdev)
(0.32)
(0.61)
(0.72)
(0.15)
(0.21)
(0.08)
(0.08)
(0.10)
(0.23)
(0.02)
pvalue
0.14
0.00
0.00
0.00
0.24
0.00
0.00
0.00
0.00
0.00
Constant
Primary insiders
Squared (Primary insiders)
Largest owner
Aggregate financial holdings
Industrial
Transport/shipping
Offshore
Debt to assets
ln(Firm value)
1990
1991
1992
1993
1994
1995
1996
1997
n
R2
Average (Q)
coeff
0.79
2.18
-1.91
-0.93
-0.63
-0.20
-0.59
-0.54
-0.89
0.08
-0.16
-0.19
-0.16
0.14
0.03
0.10
0.57
0.52
1057
0.28
1.47
(stdev)
(0.33)
(0.41)
(0.50)
(0.14)
(0.21)
(0.07)
(0.07)
(0.10)
(0.14)
(0.02)
(0.12)
(0.12)
(0.12)
(0.12)
(0.12)
(0.11)
(0.11)
(0.11)
pvalue
0.02
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.18
0.12
0.20
0.25
0.80
0.39
0.00
0.00
This table complements the pooled OLS regression in table 6.5 by three additional regressions. Panel A shows OLS
estimates on a year by year basis. The left–hand side table in panel B lists pooled estimates, estimated with GMM.
The table on the right–hand side in panel B shows the results of a pooled OLS regression where dummy variables
for each year are used to capture fixed effects. 1989 is the base year in this regression. In regressions using firm size
across years the nominal values are adjusted to the 1997 general price level. Data for firms listed on the Oslo Stock
Exchange, 1989-1997. Variable definitions are in Appendix A.2.
B.3 Insider ownership
131
Table B.16 Multivariate regression relating performance (Q) to insider ownership, the largest
primary insider, external concentration, and controls
Panel A: Year by year OLS regressions
constant
Primary insiders
Squared (Primary insiders)
Largest primary insider
Largest outside owner
Industrial
Transport/shipping
Offshore
Debt to assets
Investments over income
ln(Firm value)
n
R2
Average (Q)
1989
0.91
(0.08)
−0.06
(0.96)
0.63
(0.61)
−0.42
(0.40)
−0.54
(0.05)
−0.27
(0.03)
0.04
(0.77)
−0.12
(0.63)
−0.26
(0.35)
−0.01
(0.42)
0.04
(0.10)
94
0.07
1.30
1990
0.53
(0.33)
0.16
(0.85)
0.48
(0.60)
−0.51
(0.25)
−0.49
(0.02)
−0.23
(0.02)
−0.25
(0.03)
−0.30
(0.08)
−0.20
(0.40)
−0.01
(0.61)
0.05
(0.04)
85
0.14
1.16
1991
0.45
(0.49)
0.22
(0.76)
−0.06
(0.93)
0.07
(0.92)
−0.47
(0.04)
−0.11
(0.36)
−0.27
(0.03)
−0.46
(0.01)
−0.51
(0.05)
0.00
(0.98)
0.06
(0.04)
83
0.12
1.10
1992
−0.50
(0.29)
4.15
(0.00)
−4.02
(0.00)
−2.38
(0.00)
−0.12
(0.60)
−0.12
(0.27)
−0.26
(0.02)
−0.38
(0.02)
0.11
(0.65)
−0.03
(0.81)
0.08
(0.00)
94
0.29
1.04
Year
1993
1.05
(0.20)
2.34
(0.09)
−2.36
(0.11)
−1.32
(0.24)
−0.58
(0.07)
−0.27
(0.10)
−0.59
(0.00)
−0.73
(0.00)
−0.89
(0.03)
−0.02
(0.72)
0.07
(0.08)
101
0.21
1.39
1994
0.63
(0.24)
2.25
(0.00)
−1.92
(0.01)
−0.96
(0.16)
−0.51
(0.01)
−0.20
(0.05)
−0.49
(0.00)
−0.47
(0.00)
−0.36
(0.16)
−0.05
(0.31)
0.06
(0.02)
115
0.27
1.30
1995
1.49
(0.06)
2.53
(0.02)
−2.25
(0.05)
−0.47
(0.53)
−0.58
(0.05)
−0.22
(0.12)
−0.58
(0.00)
−0.43
(0.08)
−0.64
(0.06)
−0.01
(0.84)
0.03
(0.43)
119
0.24
1.39
1996
4.24
(0.01)
6.64
(0.00)
−6.84
(0.01)
−1.66
(0.22)
−0.78
(0.20)
−0.39
(0.16)
−0.96
(0.00)
−0.70
(0.14)
−2.49
(0.00)
−0.05
(0.61)
−0.02
(0.78)
131
0.28
1.96
1997
0.98
(0.47)
5.42
(0.00)
−5.04
(0.02)
−1.71
(0.09)
−0.72
(0.18)
−0.12
(0.60)
−0.90
(0.00)
−0.65
(0.07)
−2.79
(0.00)
0.01
(0.79)
0.13
(0.05)
168
0.30
1.99
Panel B: Pooled GMM and fixed effects regressions
Dependent variable: Q
Dependent variable: Q
Constant
Primary insiders
Squared (Primary insiders)
Largest primary insider
Largest outside owner
Industrial
Transport/shipping
Offshore
Debt to assets
Investments over income
ln(Firm value)
n
Average (Q)
coeff
1.08
3.54
-3.35
-1.10
-0.73
-0.29
-0.60
-0.61
-1.51
-0.00
0.08
990
1.47
(stdev)
(0.33)
(0.78)
(0.73)
(0.38)
(0.13)
(0.09)
(0.07)
(0.10)
(0.27)
(0.01)
(0.01)
pvalue
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.49
0.00
Constant
Primary insiders
Squared (Primary insiders)
Largest primary insider
Largest outside owner
Industrial
Transport/shipping
Offshore
Debt to assets
Investments over income
ln(Firm value)
1990
1991
1992
1993
1994
1995
1996
1997
n
R2
Average (Q)
coeff
1.48
3.30
-3.09
-1.08
-0.70
-0.24
-0.56
-0.56
-1.31
-0.00
0.05
-0.20
-0.23
-0.22
0.05
-0.10
-0.02
0.46
0.42
990
0.31
1.47
(stdev)
(0.35)
(0.48)
(0.54)
(0.33)
(0.14)
(0.07)
(0.07)
(0.11)
(0.15)
(0.01)
(0.02)
(0.12)
(0.12)
(0.12)
(0.12)
(0.11)
(0.11)
(0.11)
(0.11)
pvalue
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.76
0.00
0.10
0.06
0.07
0.68
0.40
0.85
0.00
0.00
This table complements the pooled OLS regression in table 6.6 by three additional regressions. Panel A shows OLS
estimates on a year by year basis. The left–hand side table in panel B lists pooled estimates, estimated with GMM.
The table on the right–hand side in panel B shows the results of a pooled OLS regression where dummy variables
for each year are used to capture fixed effects. 1989 is the base year in this regression. In regressions using firm size
across years the nominal values are adjusted to the 1997 general price level. Data for firms listed on the Oslo Stock
Exchange, 1989-1997. Variable definitions are in Appendix A.2.
132
B.3.2
Supplementary regressions
Inside ownership without controls
This section complements figures 6.2 and 6.4 by first showing the underlying regressions, and then
showing similar figures and tables when RoA5 is the performance measure.
Table B.17 Multivariate regression relating performance (Q) to insider ownership, without controls, using the piecewise linear formulation of Morck et al. (1988)
Panel A: Pooled OLS regression
Dependent variable: Q
Constant
Primary insiders (0 to 5)
Primary insiders (5 to 25)
Primary insiders (25 to 100)
n
R2
Average (Q)
coeff
1.29
8.56
2.28
-0.99
1068
0.06
1.48
(stdev)
(0.04)
(2.13)
(0.83)
(0.32)
pvalue
0.00
0.00
0.01
0.00
Panel B: Year by year OLS regressions
constant
Primary insiders (0 to 5)
Primary insiders (5 to 25)
Primary insiders (25 to 100)
n
R2
Average (Q)
1989
1.26
(0.00)
2.70
(0.47)
−0.28
(0.90)
0.16
(0.80)
104
−0.03
1.29
1990
1.07
(0.00)
7.20
(0.01)
−1.57
(0.16)
0.44
(0.24)
94
0.06
1.16
1991
1.02
(0.00)
8.01
(0.01)
−2.06
(0.09)
0.19
(0.60)
90
0.02
1.09
1992
1.01
(0.00)
1.22
(0.73)
1.91
(0.19)
−1.05
(0.18)
102
0.00
1.06
Year
1993
1.34
(0.00)
−0.16
(0.97)
2.77
(0.17)
−1.17
(0.17)
107
−0.01
1.38
1994
1.22
(0.00)
4.14
(0.21)
1.63
(0.20)
−0.87
(0.11)
120
0.05
1.32
1995
1.22
(0.00)
8.62
(0.07)
−0.42
(0.83)
1.79
(0.01)
129
0.16
1.43
1996
1.65
(0.00)
15.26
(0.08)
3.55
(0.27)
−2.70
(0.10)
141
0.06
1.98
1997
1.58
(0.00)
14.77
(0.04)
4.02
(0.11)
−2.57
(0.05)
181
0.10
1.97
Panel A shows results of a pooled regression using data for whole period. Panel B shows results of OLS regressions
on a year by year basis. Data for firms listed on the Oslo Stock Exchange, 1989-1997. Variable definitions are in
Appendix A.2. The tables relate to figure 6.2 in the text.
Table B.18 Multivariate regression relating performance (Q) to insider ownership, without controls, using the quadratic specifiction of McConnell and Servaes (1990)
Panel A: Pooled OLS regression
Dependent variable: Q
Constant
Primary insiders
Squared (Primary insiders)
n
R2
Average (Q)
coeff
1.35
3.30
-3.37
1068
0.05
1.48
(stdev)
(0.03)
(0.46)
(0.55)
pvalue
0.00
0.00
0.00
Panel B: Year by year OLS regressions
constant
Primary insiders
Squared (Primary insiders)
n
R2
Average (Q)
1989
1.28
(0.00)
0.10
(0.93)
0.11
(0.92)
104
−0.02
1.29
1990
1.13
(0.00)
0.47
(0.50)
−0.18
(0.81)
94
−0.00
1.16
1991
1.07
(0.00)
0.36
(0.60)
−0.32
(0.66)
90
−0.03
1.09
1992
1.01
(0.00)
1.96
(0.03)
−2.85
(0.05)
102
0.02
1.06
Year
1993
1.32
(0.00)
2.11
(0.05)
−2.72
(0.06)
107
0.01
1.38
1994
1.24
(0.00)
2.12
(0.00)
−2.45
(0.00)
120
0.06
1.32
1995
1.29
(0.00)
1.58
(0.14)
0.03
(0.98)
129
0.15
1.43
1996
1.74
(0.00)
6.68
(0.00)
−8.35
(0.00)
141
0.06
1.98
1997
1.68
(0.00)
6.94
(0.00)
−8.74
(0.00)
181
0.09
1.97
Panel A shows results of a pooled regression using data for whole period. Panel B shows results of OLS regressions
on a year by year basis. Data for firms listed on the Oslo Stock Exchange, 1989-1997. Variable definitions are in
Appendix A.2. The tables relate to figure 6.4 in the text.
B.3 Insider ownership
133
Figure B.1 The relationship between performance (RoA5 ) and insider ownership in Norwegian
firms, following Morck et al. (1988)
11.5
11.5
all years
11
11
10.5
10.5
RoA5
RoA5
all years
10
10
9.5
9.5
9
9
8.5
8.5
0
0.2
0.4
0.6
0.8
1
0
0.2
fraction owned
0.4
0.6
0.8
1
fraction owned
All years
Year by year
The figure shows the implied functional relationship from a piecewise linear regression with RoA5 as the dependent
variable and insider ownership as the independent variable. The figure on the left pools data for all years, the figure
on the right shows the results of doing the estimation year by year. The underlying regression, which is detailed in
appendix table B.19, includes no controls and no other governance mechanism than insider ownership. Data for firms
listed on the Oslo Stock Exchange, 1989-1997. Variable definitions are in Appendix A.2.
Table B.19 Multivariate regression relating performance (RoA5 ) to insider ownership, without
controls, using the piecewise linear formulation of Morck et al. (1988)
Panel A: Pooled OLS regression
Dependent variable: RoA5
Constant
Primary insiders (0 to 5)
Primary insiders (5 to 25)
Primary insiders (25 to 100)
n
R2
Average (RoA5 )
coeff
8.65
37.07
3.02
-1.23
1031
0.03
9.34
(stdev)
(0.20)
(10.74)
(4.16)
(1.61)
pvalue
0.00
0.00
0.47
0.44
Panel B: Year by year OLS regressions
constant
Primary insiders (0 to 5)
Primary insiders (5 to 25)
Primary insiders (25 to 100)
n
R2
Average (RoA5 )
1989
9.40
(0.00)
−2.77
(0.93)
22.41
(0.28)
−5.08
(0.37)
102
−0.02
9.58
1990
8.84
(0.00)
36.78
(0.17)
−6.44
(0.56)
3.95
(0.29)
93
0.01
9.44
1991
8.67
(0.00)
69.40
(0.03)
−14.82
(0.21)
−0.17
(0.96)
89
0.01
9.34
1992
8.97
(0.00)
53.81
(0.13)
9.93
(0.49)
−12.90
(0.08)
99
0.03
9.71
Year
1993
9.49
(0.00)
23.35
(0.48)
16.89
(0.22)
−12.76
(0.03)
104
0.03
9.98
1994
8.39
(0.00)
22.90
(0.44)
2.35
(0.84)
8.57
(0.08)
117
0.06
9.16
1995
7.78
(0.00)
51.66
(0.05)
−1.12
(0.91)
0.98
(0.78)
125
0.04
8.70
1996
7.62
(0.00)
49.79
(0.10)
10.74
(0.35)
−6.91
(0.22)
130
0.05
8.72
1997
8.82
(0.00)
35.73
(0.30)
1.18
(0.92)
−0.29
(0.96)
172
−0.01
9.58
Panel A shows results of a pooled regression using data for whole period. Panel B shows results of OLS regressions
on a year by year basis. Data for firms listed on the Oslo Stock Exchange, 1989-1997. Variable definitions are in
Appendix A.2. The tables relate to figure B.1.
134
Supplementary regressions
Figure B.2 The quadratic relationship between performance (RoA5 ) and insider ownership for
Norwegian firms, without controls, following McConnell and Servaes (1990).
12
18
all years
1989
1990
1991
1992
1993
1994
1995
1996
1997
16
11.5
14
12
11
10
RoA
RoA
10.5
8
6
10
4
9.5
2
0
9
-2
8.5
-4
0
0.2
0.4
0.6
0.8
1
0
0.2
fraction owned
0.4
0.6
0.8
1
fraction owned
All years
Year by year
The figure shows the implied functional relationship from estimating the regression
RoA5,i = a + bxi + cx2i + εi ,
where xi is the equity holdings of the primary insiders (officers and directors) in firm i. The figure on the left pools
data for all years, the figure on the right shows the results of doing the estimation year by year. The underlying
regressions are detailed in appendix table B.20.
Data for firms listed on the Oslo Stock Exchange, 1989-1997.
Variable definitions are in Appendix A.2.
Table B.20 Multivariate regression relating performance (RoA5 ) to insider ownership, without
controls, using the quadratic specifiction of McConnell and Servaes (1990)
Panel A: Pooled OLS regression
Dependent variable: RoA5
Constant
Primary insiders
Squared (Primary insiders)
n
R2
Average (RoA5 )
coeff
8.90
10.48
-9.94
1031
0.02
9.34
(stdev)
(0.17)
(2.28)
(2.75)
pvalue
0.00
0.00
0.00
Panel B: Year by year OLS regressions
constant
Primary insiders
Squared (Primary insiders)
n
R2
Average (RoA5 )
1989
9.35
(0.00)
10.76
(0.27)
−10.18
(0.32)
102
−0.02
9.58
1990
9.16
(0.00)
3.51
(0.60)
−0.40
(0.96)
93
0.00
9.44
1991
9.16
(0.00)
6.00
(0.35)
−6.57
(0.34)
89
−0.02
9.34
1992
9.25
(0.00)
21.40
(0.02)
−33.40
(0.02)
99
0.03
9.71
Year
1993
9.62
(0.00)
16.08
(0.03)
−23.29
(0.02)
104
0.03
9.98
1994
8.55
(0.00)
6.57
(0.27)
1.42
(0.85)
117
0.07
9.16
1995
8.12
(0.00)
13.09
(0.02)
−11.31
(0.08)
125
0.03
8.70
1996
7.97
(0.00)
20.24
(0.00)
−23.83
(0.01)
130
0.04
8.72
1997
9.02
(0.00)
12.04
(0.11)
−13.71
(0.21)
172
−0.00
9.58
Panel A shows results of a pooled regression using data for whole period. Panel B shows results of OLS regressions
on a year by year basis. Data for firms listed on the Oslo Stock Exchange, 1989-1997. Variable definitions are in
Appendix A.2. The tables relate to figure B.2.
B.3 Insider ownership
B.3.3
135
Alternative performance measure: RoA5 .
Table B.21 Multivariate regression relating performance (RoA5 ) to insider ownership and controls,
following Morck et al. (1988)
Panel A: Pooled regressions (OLS and GMM)
Dependent variable: RoA5
Constant
Primary insiders (0 to 5)
Primary insiders (5 to 25)
Primary insiders (25 to 100)
Industrial
Transport/shipping
Offshore
Debt to assets
ln(Firm value)
n
R2
Average (RoA5 )
coeff
11.57
35.07
0.89
-0.99
-1.44
-1.86
-3.92
-3.64
0.03
1022
0.09
9.36
(stdev)
(1.74)
(10.42)
(4.05)
(1.58)
(0.37)
(0.39)
(0.57)
(0.81)
(0.08)
Dependent variable: RoA5
pvalue
0.00
0.00
0.83
0.53
0.00
0.00
0.00
0.00
0.73
Constant
Primary insiders (0 to 5)
Primary insiders (5 to 25)
Primary insiders (25 to 100)
Industrial
Transport/shipping
Offshore
Debt to assets
ln(Firm value)
n
Average (RoA5 )
coeff
11.57
35.07
0.89
-0.99
-1.44
-1.86
-3.92
-3.64
0.03
1022
9.36
(stdev)
(2.01)
(9.20)
(4.01)
(1.75)
(0.38)
(0.47)
(0.62)
(1.29)
(0.08)
pvalue
0.00
0.00
0.82
0.57
0.00
0.00
0.00
0.00
0.72
Panel B: Year by year OLS regressions
constant
Primary insiders (0 to 5)
Primary insiders (5 to 25)
Primary insiders (25 to 100)
Industrial
Transport/shipping
Offshore
Debt to assets
ln(Firm value)
n
R2
Average (RoA5 )
1989
6.65
(0.12)
−7.31
(0.82)
21.44
(0.27)
−5.27
(0.31)
−1.13
(0.30)
−0.15
(0.89)
−4.87
(0.01)
−5.81
(0.01)
0.36
(0.09)
100
0.09
9.73
1990
9.69
(0.07)
38.31
(0.15)
−6.25
(0.59)
1.78
(0.67)
−0.06
(0.95)
−0.17
(0.88)
−3.23
(0.04)
−3.99
(0.07)
0.09
(0.72)
90
0.01
9.40
1991
4.77
(0.38)
76.29
(0.01)
−16.48
(0.16)
0.01
(1.00)
−0.62
(0.57)
−0.79
(0.49)
−3.06
(0.05)
−3.24
(0.14)
0.33
(0.20)
89
0.04
9.34
1992
9.40
(0.06)
49.11
(0.18)
13.87
(0.36)
−13.03
(0.09)
0.74
(0.56)
1.28
(0.35)
−1.74
(0.34)
−5.22
(0.06)
0.12
(0.61)
97
0.04
9.71
Year
1993
14.01
(0.00)
32.00
(0.34)
10.79
(0.44)
−13.06
(0.03)
−2.59
(0.03)
−1.17
(0.34)
−3.63
(0.03)
−0.53
(0.85)
−0.13
(0.57)
104
0.06
9.98
1994
18.40
(0.00)
33.54
(0.26)
−3.66
(0.75)
8.73
(0.07)
−1.52
(0.14)
−1.83
(0.08)
−3.93
(0.02)
−2.72
(0.26)
−0.36
(0.13)
117
0.13
9.16
1995
10.32
(0.03)
41.15
(0.12)
0.09
(0.99)
0.49
(0.89)
−1.42
(0.13)
−3.11
(0.00)
−3.10
(0.05)
−1.11
(0.61)
−0.01
(0.97)
124
0.10
8.73
1996
4.50
(0.48)
34.94
(0.24)
11.41
(0.30)
−6.34
(0.25)
−2.05
(0.06)
−3.85
(0.00)
−5.06
(0.00)
−0.99
(0.70)
0.29
(0.34)
130
0.13
8.72
1997
15.54
(0.02)
10.13
(0.75)
−1.56
(0.89)
4.41
(0.47)
−3.09
(0.01)
−4.89
(0.00)
−5.81
(0.00)
−6.05
(0.02)
−0.02
(0.96)
171
0.13
9.64
Panel C: OLS fixed (annual) effects regression
Dependent variable: RoA5
Constant
Primary insiders (0 to 5)
Primary insiders (5 to 25)
Primary insiders (25 to 100)
Industrial
Transport/shipping
Offshore
Debt to assets
ln(Firm value)
1990
1991
1992
1993
1994
1995
1996
1997
n
R2
Average (RoA5 )
coeff
11.04
36.60
1.00
-1.08
-1.51
-1.89
-4.03
-3.94
0.09
-0.38
-0.35
0.29
0.35
-0.76
-1.22
-1.33
-0.62
1022
0.10
9.36
(stdev)
(1.80)
(10.40)
(4.05)
(1.58)
(0.37)
(0.39)
(0.57)
(0.82)
(0.09)
(0.67)
(0.68)
(0.67)
(0.65)
(0.63)
(0.62)
(0.62)
(0.60)
pvalue
0.00
0.00
0.81
0.50
0.00
0.00
0.00
0.00
0.30
0.57
0.60
0.67
0.59
0.23
0.05
0.03
0.29
Panel A shows results of pooled regressions using data for the whole period. The left hand table in panel A shows
result for a OLS regression. The right hand table in panel A is a GMM regression. Panel B shows estimates on
a year by year basis, and panel C is a (annual) fixed effects regression. In regressions using firm size across years
the nominal values are adjusted to the 1997 general price level. Data for firms listed on the Oslo Stock Exchange,
1989-1997. Variable definitions are in Appendix A.2.
136
Supplementary regressions
Table B.22 Multivariate regression relating performance (RoA5 ) to insider ownership, ownership
concentration and controls, using the piecewise linear function of Morck et al. (1988).
Panel A: Pooled regressions (OLS and GMM)
Dependent variable: RoA5
Constant
Primary insiders (0 to 5)
Primary insiders (5 to 25)
Primary insiders (25 to 100)
Largest owner
Industrial
Transport/shipping
Offshore
Debt to assets
ln(Firm value)
n
R2
Average (RoA5 )
coeff
12.60
31.67
1.01
-0.60
-1.96
-1.42
-1.88
-3.94
-3.73
0.01
1022
0.10
9.36
(stdev)
(1.78)
(10.48)
(4.04)
(1.58)
(0.79)
(0.37)
(0.39)
(0.57)
(0.81)
(0.08)
Dependent variable: RoA5
pvalue
0.00
0.00
0.80
0.70
0.01
0.00
0.00
0.00
0.00
0.90
Constant
Primary insiders (0 to 5)
Primary insiders (5 to 25)
Primary insiders (25 to 100)
Largest owner
Industrial
Transport/shipping
Offshore
Debt to assets
ln(Firm value)
n
Average (RoA5 )
coeff
12.60
31.67
1.01
-0.60
-1.96
-1.42
-1.88
-3.94
-3.73
0.01
1022
9.36
(stdev)
(2.12)
(9.61)
(4.01)
(1.75)
(0.67)
(0.38)
(0.47)
(0.62)
(1.28)
(0.08)
pvalue
0.00
0.00
0.80
0.73
0.00
0.00
0.00
0.00
0.00
0.90
Panel B: Year by year OLS regressions
constant
Primary insiders (0 to 5)
Primary insiders (5 to 25)
Primary insiders (25 to 100)
Largest owner
Industrial
Transport/shipping
Offshore
Debt to assets
ln(Firm value)
n
R2
Average (RoA5 )
1989
7.12
(0.10)
−9.03
(0.78)
22.03
(0.26)
−5.20
(0.32)
−1.42
(0.53)
−1.18
(0.27)
−0.20
(0.86)
−5.01
(0.01)
−5.82
(0.01)
0.36
(0.09)
100
0.09
9.73
1990
9.40
(0.08)
38.86
(0.15)
−6.22
(0.59)
1.57
(0.71)
0.90
(0.70)
−0.03
(0.97)
−0.13
(0.91)
−3.19
(0.04)
−4.08
(0.07)
0.10
(0.72)
90
0.00
9.40
1991
5.35
(0.33)
74.68
(0.02)
−16.13
(0.17)
0.31
(0.93)
−1.29
(0.59)
−0.61
(0.58)
−0.81
(0.48)
−3.08
(0.05)
−3.25
(0.14)
0.32
(0.22)
89
0.03
9.34
1992
11.03
(0.04)
46.16
(0.21)
14.89
(0.33)
−12.96
(0.09)
−2.17
(0.43)
0.81
(0.53)
1.24
(0.36)
−1.85
(0.31)
−5.44
(0.05)
0.08
(0.75)
97
0.04
9.71
Year
1993
16.60
(0.00)
24.41
(0.47)
11.77
(0.39)
−12.78
(0.03)
−4.07
(0.07)
−2.23
(0.06)
−1.10
(0.36)
−3.58
(0.03)
−1.26
(0.66)
−0.18
(0.43)
104
0.08
9.98
1994
20.60
(0.00)
21.03
(0.49)
−2.33
(0.84)
9.36
(0.05)
−3.34
(0.10)
−1.57
(0.13)
−1.98
(0.06)
−3.74
(0.02)
−3.42
(0.16)
−0.39
(0.10)
117
0.14
9.16
1995
11.05
(0.02)
38.66
(0.15)
0.30
(0.98)
0.69
(0.84)
−1.26
(0.53)
−1.41
(0.13)
−3.10
(0.00)
−3.09
(0.05)
−1.27
(0.56)
−0.02
(0.93)
124
0.09
8.73
1996
4.32
(0.50)
35.94
(0.23)
11.45
(0.30)
−6.43
(0.24)
0.55
(0.82)
−2.05
(0.06)
−3.85
(0.00)
−5.06
(0.00)
−1.01
(0.70)
0.29
(0.34)
130
0.13
8.72
1997
16.85
(0.02)
6.86
(0.83)
−2.13
(0.85)
4.83
(0.43)
−2.42
(0.32)
−3.08
(0.01)
−4.85
(0.00)
−5.76
(0.00)
−6.02
(0.02)
−0.04
(0.90)
171
0.13
9.64
Panel C: OLS fixed (annual) effects regression
Dependent variable: RoA5
Constant
Primary insiders (0 to 5)
Primary insiders (5 to 25)
Primary insiders (25 to 100)
Largest owner
Industrial
Transport/shipping
Offshore
Debt to assets
ln(Firm value)
1990
1991
1992
1993
1994
1995
1996
1997
n
R2
Average (RoA5 )
coeff
12.09
33.14
1.11
-0.68
-2.00
-1.49
-1.91
-4.05
-4.02
0.07
-0.41
-0.38
0.28
0.35
-0.74
-1.24
-1.35
-0.62
1022
0.11
9.36
(stdev)
(1.84)
(10.46)
(4.04)
(1.59)
(0.79)
(0.37)
(0.39)
(0.57)
(0.82)
(0.09)
(0.67)
(0.67)
(0.66)
(0.65)
(0.63)
(0.62)
(0.62)
(0.59)
pvalue
0.00
0.00
0.78
0.67
0.01
0.00
0.00
0.00
0.00
0.41
0.55
0.57
0.68
0.59
0.24
0.05
0.03
0.30
Panel A shows results of pooled regressions using data for the whole period. The left hand table in panel A shows
result for a OLS regression. The right hand table in panel A is a GMM regression. Panel B shows estimates on
a year by year basis, and panel C is a (annual) fixed effects regression. In regressions using firm size across years
the nominal values are adjusted to the 1997 general price level. Data for firms listed on the Oslo Stock Exchange,
1989-1997. Variable definitions are in Appendix A.2.
B.3 Insider ownership
137
Table B.23 Multivariate regression relating performance (RoA5 ) to insider ownership, following McConnell and Servaes (1990)
Panel A: Pooled regressions (OLS and GMM)
Dependent variable: RoA5
Constant
Primary insiders
Squared (Primary insiders)
Industrial
Transport/shipping
Offshore
Debt to assets
ln(Firm value)
n
R2
Average (RoA5 )
coeff
11.79
8.61
-8.33
-1.47
-1.82
-3.82
-3.82
0.04
1022
0.09
9.36
(stdev)
(1.74)
(2.23)
(2.70)
(0.37)
(0.39)
(0.58)
(0.82)
(0.08)
Dependent variable: RoA5
pvalue
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.68
Constant
Primary insiders
Squared (Primary insiders)
Industrial
Transport/shipping
Offshore
Debt to assets
ln(Firm value)
n
Average (RoA5 )
coeff
11.79
8.61
-8.33
-1.47
-1.82
-3.82
-3.82
0.04
1022
9.36
(stdev)
(1.98)
(2.56)
(3.09)
(0.38)
(0.48)
(0.63)
(1.27)
(0.08)
pvalue
0.00
0.00
0.01
0.00
0.00
0.00
0.00
0.67
Panel B: Year by year OLS regressions
constant
Primary insiders
Squared (Primary insiders)
Industrial
Transport/shipping
Offshore
Debt to assets
ln(Firm value)
n
R2
Average (RoA5 )
1989
6.47
(0.13)
9.21
(0.30)
−9.19
(0.33)
−1.18
(0.25)
−0.19
(0.86)
−4.99
(0.01)
−5.73
(0.01)
0.37
(0.08)
100
0.10
9.73
1990
9.66
(0.07)
5.14
(0.46)
−3.82
(0.62)
0.02
(0.98)
0.06
(0.96)
−3.01
(0.05)
−4.07
(0.07)
0.11
(0.68)
90
0.00
9.40
1991
6.23
(0.26)
6.53
(0.31)
−7.01
(0.31)
−0.49
(0.66)
−0.78
(0.51)
−2.86
(0.08)
−3.25
(0.14)
0.28
(0.29)
89
−0.01
9.34
1992
9.48
(0.05)
23.97
(0.01)
−35.81
(0.01)
0.82
(0.52)
1.50
(0.26)
−1.52
(0.40)
−5.58
(0.04)
0.14
(0.57)
97
0.05
9.71
Year
1993
14.60
(0.00)
14.83
(0.04)
−23.62
(0.02)
−2.66
(0.02)
−1.06
(0.38)
−3.51
(0.04)
−0.77
(0.78)
−0.15
(0.53)
104
0.06
9.98
1994
18.64
(0.00)
4.51
(0.44)
2.81
(0.71)
−1.62
(0.12)
−1.85
(0.08)
−3.56
(0.03)
−2.73
(0.26)
−0.36
(0.14)
117
0.13
9.16
1995
10.52
(0.02)
10.18
(0.07)
−8.58
(0.18)
−1.60
(0.08)
−3.18
(0.00)
−2.88
(0.07)
−1.29
(0.55)
0.00
(0.99)
124
0.10
8.73
1996
5.41
(0.40)
15.59
(0.02)
−17.88
(0.06)
−2.13
(0.05)
−3.82
(0.00)
−5.08
(0.00)
−1.28
(0.62)
0.27
(0.38)
130
0.12
8.72
1997
15.74
(0.02)
2.78
(0.70)
−0.24
(0.98)
−3.08
(0.01)
−4.84
(0.00)
−5.82
(0.00)
−5.97
(0.02)
−0.03
(0.94)
171
0.13
9.64
Panel C: OLS fixed (annual) effects regression
Dependent variable: RoA5
Constant
Primary insiders
Squared (Primary insiders)
Industrial
Transport/shipping
Offshore
Debt to assets
ln(Firm value)
1990
1991
1992
1993
1994
1995
1996
1997
n
R2
Average (RoA5 )
coeff
11.30
8.92
-8.62
-1.53
-1.85
-3.92
-4.11
0.09
-0.33
-0.32
0.24
0.34
-0.74
-1.17
-1.29
-0.60
1022
0.09
9.36
(stdev)
(1.81)
(2.25)
(2.72)
(0.37)
(0.39)
(0.58)
(0.82)
(0.09)
(0.68)
(0.68)
(0.67)
(0.65)
(0.64)
(0.63)
(0.63)
(0.60)
pvalue
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.28
0.62
0.64
0.72
0.60
0.25
0.06
0.04
0.31
Panel A shows results of pooled regressions using data for the whole period. The left hand table in panel A shows
result for a OLS regression. The right hand table in panel A is a GMM regression. Panel B shows estimates on
a year by year basis, and panel C is a (annual) fixed effects regression. In regressions using firm size across years
the nominal values are adjusted to the 1997 general price level. Data for firms listed on the Oslo Stock Exchange,
1989-1997. Variable definitions are in Appendix A.2.
138
Supplementary regressions
Table B.24 Multivariate regression relating performance (RoA5 ) to insider ownership, ownership
concentration and controls, following McConnell and Servaes (1990)
Panel A: Pooled regressions (OLS and GMM)
Dependent variable: RoA5
Constant
Primary insiders
Squared (Primary insiders)
Largest owner
Industrial
Transport/shipping
Offshore
Debt to assets
ln(Firm value)
n
R2
Average (RoA5 )
coeff
12.94
7.99
-7.48
-2.24
-1.44
-1.85
-3.85
-3.90
0.01
1022
0.09
9.36
(stdev)
(1.78)
(2.23)
(2.70)
(0.78)
(0.37)
(0.39)
(0.57)
(0.81)
(0.08)
Dependent variable: RoA5
pvalue
0.00
0.00
0.01
0.00
0.00
0.00
0.00
0.00
0.87
Constant
Primary insiders
Squared (Primary insiders)
Largest owner
Industrial
Transport/shipping
Offshore
Debt to assets
ln(Firm value)
n
Average (RoA5 )
coeff
12.94
7.99
-7.48
-2.24
-1.44
-1.85
-3.85
-3.90
0.01
1022
9.36
(stdev)
(2.08)
(2.54)
(3.08)
(0.64)
(0.38)
(0.48)
(0.63)
(1.26)
(0.08)
pvalue
0.00
0.00
0.02
0.00
0.00
0.00
0.00
0.00
0.87
Panel B: Year by year OLS regressions
constant
Primary insiders
Squared (Primary insiders)
Largest owner
Industrial
Transport/shipping
Offshore
Debt to assets
ln(Firm value)
n
R2
Average (RoA5 )
1989
6.88
(0.11)
9.12
(0.31)
−9.01
(0.34)
−1.35
(0.55)
−1.25
(0.23)
−0.25
(0.82)
−5.14
(0.01)
−5.72
(0.01)
0.37
(0.08)
100
0.09
9.73
1990
9.44
(0.08)
5.13
(0.46)
−3.90
(0.62)
0.70
(0.77)
0.04
(0.97)
0.08
(0.94)
−2.99
(0.06)
−4.13
(0.06)
0.11
(0.68)
90
−0.01
9.40
1991
6.97
(0.21)
6.43
(0.32)
−6.59
(0.34)
−1.78
(0.46)
−0.48
(0.67)
−0.81
(0.49)
−2.91
(0.07)
−3.27
(0.14)
0.27
(0.31)
89
−0.01
9.34
1992
11.47
(0.03)
24.33
(0.01)
−36.10
(0.01)
−2.67
(0.33)
0.91
(0.48)
1.43
(0.29)
−1.67
(0.36)
−5.83
(0.03)
0.08
(0.74)
97
0.04
9.71
Year
1993
17.38
(0.00)
14.02
(0.05)
−22.65
(0.02)
−4.34
(0.05)
−2.27
(0.05)
−1.01
(0.40)
−3.51
(0.03)
−1.51
(0.59)
−0.21
(0.38)
104
0.09
9.98
1994
20.81
(0.00)
2.99
(0.61)
4.69
(0.53)
−3.57
(0.07)
−1.63
(0.11)
−2.02
(0.05)
−3.53
(0.03)
−3.43
(0.15)
−0.39
(0.10)
117
0.15
9.16
1995
11.45
(0.02)
9.86
(0.08)
−8.15
(0.20)
−1.66
(0.41)
−1.56
(0.09)
−3.15
(0.00)
−2.89
(0.07)
−1.48
(0.50)
−0.01
(0.95)
124
0.09
8.73
1996
5.45
(0.40)
15.54
(0.03)
−17.83
(0.06)
−0.13
(0.96)
−2.13
(0.05)
−3.82
(0.00)
−5.08
(0.00)
−1.27
(0.63)
0.27
(0.38)
130
0.12
8.72
1997
16.99
(0.01)
1.50
(0.84)
1.38
(0.90)
−2.35
(0.33)
−3.07
(0.01)
−4.80
(0.00)
−5.76
(0.00)
−5.93
(0.02)
−0.05
(0.87)
171
0.13
9.64
Panel C: OLS fixed (annual) effects regression
Dependent variable: RoA5
Constant
Primary insiders
Squared (Primary insiders)
Largest owner
Industrial
Transport/shipping
Offshore
Debt to assets
ln(Firm value)
1990
1991
1992
1993
1994
1995
1996
1997
n
R2
Average (RoA5 )
coeff
12.48
8.27
-7.73
-2.30
-1.51
-1.87
-3.96
-4.19
0.07
-0.37
-0.36
0.23
0.34
-0.73
-1.21
-1.32
-0.61
1022
0.10
9.36
(stdev)
(1.84)
(2.25)
(2.73)
(0.78)
(0.37)
(0.39)
(0.57)
(0.82)
(0.09)
(0.67)
(0.68)
(0.67)
(0.65)
(0.63)
(0.62)
(0.62)
(0.60)
pvalue
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.41
0.58
0.60
0.73
0.60
0.25
0.05
0.03
0.31
Panel A shows results of pooled regressions using data for the whole period. The left hand table in panel A shows
result for a OLS regression. The right hand table in panel A is a GMM regression. Panel B shows estimates on
a year by year basis, and panel C is a (annual) fixed effects regression. In regressions using firm size across years
the nominal values are adjusted to the 1997 general price level. Data for firms listed on the Oslo Stock Exchange,
1989-1997. Variable definitions are in Appendix A.2.
B.3 Insider ownership
139
Table B.25 Multivariate regression relating performance (RoA5 ) to insider ownership, ownership
concentration, institutional ownership and controls, following McConnell and Servaes (1990)
Panel A: Pooled regressions (OLS and GMM)
Dependent variable: RoA5
Constant
Primary insiders
Squared (Primary insiders)
Largest owner
Aggregate financial holdings
Industrial
Transport/shipping
Offshore
Debt to assets
ln(Firm value)
n
R2
Average (RoA5 )
coeff
12.97
7.98
-7.46
-2.21
0.18
-1.45
-1.84
-3.85
-3.92
0.01
1022
0.09
9.36
(stdev)
(1.79)
(2.23)
(2.71)
(0.80)
(1.13)
(0.37)
(0.40)
(0.57)
(0.82)
(0.09)
Dependent variable: RoA5
pvalue
0.00
0.00
0.01
0.01
0.87
0.00
0.00
0.00
0.00
0.91
Constant
Primary insiders
Squared (Primary insiders)
Largest owner
Aggregate financial holdings
Industrial
Transport/shipping
Offshore
Debt to assets
ln(Firm value)
n
Average (RoA5 )
coeff
12.97
7.98
-7.46
-2.21
0.18
-1.45
-1.84
-3.85
-3.92
0.01
1022
9.36
(stdev)
(2.06)
(2.54)
(3.07)
(0.67)
(1.05)
(0.37)
(0.48)
(0.63)
(1.26)
(0.08)
pvalue
0.00
0.00
0.02
0.00
0.86
0.00
0.00
0.00
0.00
0.90
Panel B: Year by year OLS regressions
constant
Primary insiders
Squared (Primary insiders)
Largest owner
Aggregate financial holdings
Industrial
Transport/shipping
Offshore
Debt to assets
ln(Firm value)
n
R2
Average (RoA5 )
1989
6.85
(0.11)
9.30
(0.31)
−9.24
(0.34)
−1.45
(0.54)
−0.66
(0.87)
−1.24
(0.24)
−0.27
(0.81)
−5.16
(0.01)
−5.69
(0.01)
0.37
(0.08)
100
0.08
9.73
1990
9.67
(0.08)
5.14
(0.47)
−3.91
(0.62)
1.00
(0.68)
2.20
(0.58)
−0.04
(0.97)
0.20
(0.87)
−2.98
(0.06)
−4.34
(0.06)
0.09
(0.75)
90
−0.02
9.40
1991
7.35
(0.20)
6.35
(0.33)
−6.52
(0.35)
−1.49
(0.56)
2.26
(0.65)
−0.73
(0.56)
−0.90
(0.45)
−3.04
(0.07)
−3.37
(0.14)
0.24
(0.38)
89
−0.02
9.34
1992
11.38
(0.03)
24.41
(0.01)
−36.26
(0.01)
−2.78
(0.32)
−0.78
(0.85)
0.99
(0.47)
1.45
(0.28)
−1.62
(0.38)
−5.75
(0.04)
0.09
(0.72)
97
0.03
9.71
Year
1993
17.17
(0.00)
13.90
(0.06)
−22.77
(0.02)
−4.60
(0.04)
−1.77
(0.57)
−2.26
(0.05)
−1.16
(0.34)
−3.61
(0.03)
−1.00
(0.73)
−0.19
(0.43)
104
0.08
9.98
1994
20.74
(0.00)
3.21
(0.59)
4.52
(0.55)
−3.38
(0.11)
0.78
(0.80)
−1.65
(0.11)
−1.97
(0.06)
−3.49
(0.03)
−3.60
(0.15)
−0.39
(0.10)
117
0.14
9.16
1995
11.50
(0.02)
10.21
(0.07)
−7.96
(0.21)
−0.86
(0.69)
3.60
(0.30)
−1.82
(0.06)
−3.04
(0.00)
−2.87
(0.07)
−2.13
(0.35)
−0.04
(0.87)
124
0.10
8.73
1996
5.59
(0.40)
15.59
(0.03)
−17.83
(0.06)
−0.08
(0.97)
0.37
(0.91)
−2.13
(0.05)
−3.79
(0.00)
−5.09
(0.00)
−1.34
(0.62)
0.26
(0.42)
130
0.11
8.72
1997
16.31
(0.02)
1.47
(0.84)
1.35
(0.90)
−2.49
(0.31)
−1.49
(0.65)
−3.05
(0.01)
−4.88
(0.00)
−5.84
(0.00)
−5.87
(0.02)
−0.00
(0.99)
171
0.13
9.64
Panel C: OLS fixed (annual) effects regression
Dependent variable: RoA5
Constant
Primary insiders
Squared (Primary insiders)
Largest owner
Aggregate financial holdings
Industrial
Transport/shipping
Offshore
Debt to assets
ln(Firm value)
1990
1991
1992
1993
1994
1995
1996
1997
n
R2
Average (RoA5 )
coeff
12.61
8.31
-7.73
-2.20
0.62
-1.53
-1.85
-3.95
-4.27
0.06
-0.39
-0.38
0.19
0.28
-0.78
-1.25
-1.38
-0.68
1022
0.10
9.36
(stdev)
(1.86)
(2.25)
(2.73)
(0.80)
(1.17)
(0.37)
(0.40)
(0.57)
(0.83)
(0.09)
(0.68)
(0.68)
(0.67)
(0.66)
(0.64)
(0.63)
(0.63)
(0.61)
pvalue
0.00
0.00
0.00
0.01
0.60
0.00
0.00
0.00
0.00
0.48
0.56
0.57
0.77
0.67
0.22
0.05
0.03
0.26
Panel A shows results of pooled regressions using data for the whole period. The left hand table in panel A shows
result for a OLS regression. The right hand table in panel A is a GMM regression. Panel B shows estimates on
a year by year basis, and panel C is a (annual) fixed effects regression. In regressions using firm size across years
the nominal values are adjusted to the 1997 general price level. Data for firms listed on the Oslo Stock Exchange,
1989-1997. Variable definitions are in Appendix A.2.
140
B.3.4
Supplementary regressions
Alternative insider definitions
We show selected regressions which are comparable to those in the text, but which use alternative
insider definitions. We define insiders as the alternative categories: All insiders, Board members,
and the Management team.
B.3.4.1
All insiders
B.3 Insider ownership
141
Table B.26 Multivariate regression relating performance (Q) to insider (all) ownership and controls, following Morck et al. (1988)
Panel A: Pooled regressions (OLS and GMM)
Dependent variable: Q
Constant
All insiders (0 to 5)
All insiders (5 to 25)
All insiders (25 to 100)
Industrial
Transport/shipping
Offshore
Debt to assets
ln(Firm value)
n
R2
Average (Q)
coeff
-0.12
3.95
0.52
-0.23
-0.34
-0.66
-0.67
-1.07
0.12
1057
0.16
1.47
(stdev)
(0.34)
(1.96)
(0.59)
(0.20)
(0.07)
(0.08)
(0.11)
(0.15)
(0.02)
Dependent variable: Q
pvalue
0.73
0.04
0.38
0.25
0.00
0.00
0.00
0.00
0.00
Constant
All insiders (0 to 5)
All insiders (5 to 25)
All insiders (25 to 100)
Industrial
Transport/shipping
Offshore
Debt to assets
ln(Firm value)
n
Average (Q)
coeff
-0.12
3.95
0.52
-0.23
-0.34
-0.66
-0.67
-1.07
0.12
1057
1.47
(stdev)
(0.34)
(1.74)
(0.56)
(0.19)
(0.09)
(0.08)
(0.10)
(0.23)
(0.02)
pvalue
0.72
0.02
0.36
0.23
0.00
0.00
0.00
0.00
0.00
Panel B: Year by year OLS regressions
constant
All insiders (0 to 5)
All insiders (5 to 25)
All insiders (25 to 100)
Industrial
Transport/shipping
Offshore
Debt to assets
ln(Firm value)
n
R2
Average (Q)
1989
0.26
(0.61)
2.18
(0.48)
0.34
(0.73)
−0.15
(0.61)
−0.26
(0.03)
−0.01
(0.97)
−0.06
(0.79)
−0.01
(0.96)
0.05
(0.03)
102
0.01
1.29
1990
−0.57
(0.28)
7.89
(0.00)
−0.83
(0.30)
0.25
(0.36)
−0.21
(0.03)
−0.26
(0.01)
−0.13
(0.37)
−0.12
(0.58)
0.09
(0.00)
91
0.14
1.15
1991
−0.40
(0.44)
9.80
(0.00)
−1.62
(0.04)
0.24
(0.32)
−0.09
(0.40)
−0.20
(0.07)
−0.30
(0.05)
−0.30
(0.15)
0.08
(0.00)
90
0.18
1.09
1992
−0.88
(0.05)
2.48
(0.41)
0.37
(0.67)
−0.30
(0.28)
−0.18
(0.08)
−0.29
(0.01)
−0.39
(0.01)
0.28
(0.23)
0.10
(0.00)
99
0.18
1.03
Year
1993
0.36
(0.60)
2.53
(0.56)
−0.85
(0.52)
0.18
(0.70)
−0.31
(0.06)
−0.57
(0.00)
−0.66
(0.01)
−0.52
(0.19)
0.08
(0.01)
107
0.11
1.38
1994
0.47
(0.35)
1.47
(0.63)
0.48
(0.60)
−0.25
(0.43)
−0.25
(0.02)
−0.50
(0.00)
−0.55
(0.00)
−0.50
(0.05)
0.07
(0.01)
120
0.17
1.32
1995
−0.56
(0.52)
−0.06
(0.99)
1.35
(0.33)
0.71
(0.11)
−0.39
(0.02)
−0.82
(0.00)
−0.55
(0.05)
0.31
(0.41)
0.10
(0.02)
128
0.19
1.42
1996
3.35
(0.05)
0.08
(0.99)
1.92
(0.45)
−1.20
(0.20)
−0.47
(0.11)
−1.12
(0.00)
−1.15
(0.01)
−2.34
(0.00)
0.02
(0.83)
140
0.19
1.98
1997
0.14
(0.92)
11.91
(0.08)
0.33
(0.88)
−0.87
(0.23)
−0.14
(0.56)
−0.90
(0.00)
−0.77
(0.02)
−2.01
(0.00)
0.14
(0.04)
180
0.20
1.95
Panel C: OLS fixed (annual) effects regression
Dependent variable: Q
Constant
All insiders (0 to 5)
All insiders (5 to 25)
All insiders (25 to 100)
Industrial
Transport/shipping
Offshore
Debt to assets
ln(Firm value)
1990
1991
1992
1993
1994
1995
1996
1997
n
R2
Average (Q)
coeff
0.27
4.70
0.39
-0.23
-0.29
-0.62
-0.62
-0.85
0.08
-0.16
-0.18
-0.16
0.11
0.02
0.11
0.58
0.53
1057
0.23
1.47
(stdev)
(0.34)
(1.88)
(0.56)
(0.19)
(0.07)
(0.07)
(0.11)
(0.15)
(0.02)
(0.13)
(0.13)
(0.12)
(0.12)
(0.12)
(0.12)
(0.12)
(0.11)
pvalue
0.43
0.01
0.49
0.21
0.00
0.00
0.00
0.00
0.00
0.21
0.16
0.21
0.35
0.89
0.37
0.00
0.00
Panel A shows results of pooled regressions using data for the whole period. The left hand table in panel A shows
result for a OLS regression. The right hand table in panel A is a GMM regression. Panel B shows estimates on
a year by year basis, and panel C is a (annual) fixed effects regression. In regressions using firm size across years
the nominal values are adjusted to the 1997 general price level. Data for firms listed on the Oslo Stock Exchange,
1989-1997. Variable definitions are in Appendix A.2.
142
Supplementary regressions
Table B.27 Multivariate regression relating performance (Q) to insider (all) ownership and controls, following McConnell and Servaes (1990)
Panel A: Pooled regressions (OLS and GMM)
Dependent variable: Q
Constant
All insiders
Squared (All insiders)
Industrial
Transport/shipping
Offshore
Debt to assets
ln(Firm value)
n
R2
Average (Q)
coeff
0.01
0.79
-0.73
-0.33
-0.66
-0.66
-1.08
0.12
1057
0.15
1.47
(stdev)
(0.34)
(0.32)
(0.37)
(0.07)
(0.08)
(0.11)
(0.15)
(0.02)
Dependent variable: Q
pvalue
0.99
0.01
0.05
0.00
0.00
0.00
0.00
0.00
Constant
All insiders
Squared (All insiders)
Industrial
Transport/shipping
Offshore
Debt to assets
ln(Firm value)
n
Average (Q)
coeff
0.01
0.79
-0.73
-0.33
-0.66
-0.66
-1.08
0.12
1057
1.47
(stdev)
(0.33)
(0.33)
(0.36)
(0.09)
(0.08)
(0.10)
(0.23)
(0.02)
pvalue
0.99
0.02
0.04
0.00
0.00
0.00
0.00
0.00
Panel B: Year by year OLS regressions
constant
All insiders
Squared (All insiders)
Industrial
Transport/shipping
Offshore
Debt to assets
ln(Firm value)
n
R2
Average (Q)
1989
0.35
(0.49)
0.24
(0.69)
−0.19
(0.76)
−0.25
(0.04)
0.02
(0.91)
−0.04
(0.87)
−0.01
(0.98)
0.05
(0.04)
102
0.01
1.29
1990
−0.24
(0.65)
0.52
(0.26)
−0.25
(0.64)
−0.19
(0.06)
−0.24
(0.03)
−0.18
(0.24)
−0.13
(0.56)
0.08
(0.00)
91
0.06
1.15
1991
−0.14
(0.79)
0.35
(0.45)
−0.21
(0.67)
−0.10
(0.36)
−0.25
(0.03)
−0.37
(0.02)
−0.28
(0.22)
0.08
(0.00)
90
0.08
1.09
1992
−0.72
(0.10)
0.21
(0.67)
−0.26
(0.66)
−0.19
(0.08)
−0.31
(0.01)
−0.37
(0.02)
0.22
(0.34)
0.09
(0.00)
99
0.16
1.03
Year
1993
0.40
(0.55)
−0.21
(0.79)
0.26
(0.78)
−0.30
(0.06)
−0.56
(0.00)
−0.66
(0.01)
−0.53
(0.17)
0.08
(0.01)
107
0.12
1.38
1994
0.53
(0.30)
0.43
(0.40)
−0.47
(0.42)
−0.25
(0.02)
−0.50
(0.00)
−0.53
(0.00)
−0.50
(0.05)
0.07
(0.01)
120
0.17
1.32
1995
−0.57
(0.51)
0.86
(0.25)
−0.02
(0.98)
−0.39
(0.02)
−0.82
(0.00)
−0.54
(0.05)
0.31
(0.41)
0.10
(0.02)
128
0.20
1.42
1996
3.32
(0.05)
0.48
(0.72)
−0.89
(0.59)
−0.47
(0.11)
−1.14
(0.00)
−1.16
(0.01)
−2.34
(0.00)
0.02
(0.80)
140
0.19
1.98
1997
0.50
(0.72)
2.23
(0.04)
−2.60
(0.04)
−0.10
(0.67)
−0.90
(0.00)
−0.78
(0.02)
−2.06
(0.00)
0.13
(0.06)
180
0.19
1.95
Panel C: OLS fixed (annual) effects regression
Dependent variable: Q
Constant
All insiders
Squared (All insiders)
Industrial
Transport/shipping
Offshore
Debt to assets
ln(Firm value)
1990
1991
1992
1993
1994
1995
1996
1997
n
R2
Average (Q)
coeff
0.41
0.78
-0.73
-0.29
-0.62
-0.62
-0.87
0.08
-0.15
-0.17
-0.15
0.12
0.02
0.10
0.58
0.52
1057
0.22
1.47
(stdev)
(0.34)
(0.31)
(0.35)
(0.07)
(0.07)
(0.11)
(0.15)
(0.02)
(0.13)
(0.13)
(0.13)
(0.12)
(0.12)
(0.12)
(0.12)
(0.11)
pvalue
0.23
0.01
0.04
0.00
0.00
0.00
0.00
0.00
0.23
0.19
0.22
0.34
0.88
0.38
0.00
0.00
Panel A shows results of pooled regressions using data for the whole period. The left hand table in panel A shows
result for a OLS regression. The right hand table in panel A is a GMM regression. Panel B shows estimates on
a year by year basis, and panel C is a (annual) fixed effects regression. In regressions using firm size across years
the nominal values are adjusted to the 1997 general price level. Data for firms listed on the Oslo Stock Exchange,
1989-1997. Variable definitions are in Appendix A.2.
B.3 Insider ownership
B.3.4.2
143
Board members
Table B.28 Multivariate regression relating performance (Q) to insider (board) ownership and
controls, following Morck et al. (1988)
Panel A: Pooled regressions (OLS and GMM)
Dependent variable: Q
Constant
Board members (0 to 5)
Board members (5 to 25)
Board members (25 to 100)
Industrial
Transport/shipping
Offshore
Debt to assets
ln(Firm value)
n
R2
Average (Q)
coeff
0.05
5.17
2.71
-0.67
-0.29
-0.61
-0.58
-1.09
0.11
1057
0.19
1.47
(stdev)
(0.32)
(2.21)
(0.93)
(0.29)
(0.07)
(0.08)
(0.11)
(0.15)
(0.02)
pvalue
0.87
0.02
0.00
0.02
0.00
0.00
0.00
0.00
0.00
Dependent variable: Q
Constant
Board members (0 to 5)
Board members (5 to 25)
Board members (25 to 100)
Industrial
Transport/shipping
Offshore
Debt to assets
ln(Firm value)
n
Average (Q)
coeff
0.05
5.17
2.71
-0.67
-0.29
-0.61
-0.58
-1.09
0.11
1057
1.47
(stdev)
(0.30)
(2.19)
(1.33)
(0.41)
(0.09)
(0.08)
(0.10)
(0.22)
(0.01)
pvalue
0.86
0.02
0.04
0.10
0.00
0.00
0.00
0.00
0.00
Panel B: Year by year OLS regressions
constant
Board members (0 to 5)
Board members (5 to 25)
Board members (25 to 100)
Industrial
Transport/shipping
Offshore
Debt to assets
ln(Firm value)
n
R2
Average (Q)
1989
0.42
(0.40)
3.76
(0.37)
0.00
(1.00)
0.04
(0.95)
−0.25
(0.05)
0.02
(0.89)
−0.00
(0.99)
0.03
(0.90)
0.05
(0.07)
102
0.02
1.29
1990
−0.10
(0.84)
10.27
(0.00)
−2.95
(0.02)
0.94
(0.03)
−0.20
(0.04)
−0.24
(0.03)
−0.10
(0.48)
0.01
(0.95)
0.06
(0.01)
91
0.18
1.15
1991
−0.17
(0.74)
10.15
(0.00)
−2.33
(0.09)
0.22
(0.56)
−0.11
(0.30)
−0.24
(0.03)
−0.29
(0.07)
−0.27
(0.19)
0.07
(0.00)
90
0.16
1.09
1992
−0.73
(0.08)
2.60
(0.45)
0.85
(0.58)
−0.25
(0.63)
−0.14
(0.22)
−0.27
(0.02)
−0.30
(0.06)
0.17
(0.45)
0.09
(0.00)
99
0.18
1.03
Year
1993
0.37
(0.57)
−0.27
(0.96)
0.40
(0.85)
0.20
(0.77)
−0.30
(0.07)
−0.54
(0.00)
−0.64
(0.01)
−0.55
(0.16)
0.08
(0.01)
107
0.12
1.38
1994
0.30
(0.52)
8.13
(0.01)
0.15
(0.91)
−0.29
(0.52)
−0.19
(0.07)
−0.50
(0.00)
−0.59
(0.00)
−0.54
(0.03)
0.07
(0.00)
120
0.25
1.32
1995
−0.38
(0.65)
0.26
(0.96)
4.41
(0.10)
0.21
(0.77)
−0.38
(0.02)
−0.72
(0.00)
−0.54
(0.05)
−0.08
(0.82)
0.10
(0.01)
128
0.23
1.42
1996
2.81
(0.09)
−2.71
(0.76)
9.39
(0.02)
−2.07
(0.09)
−0.53
(0.07)
−1.10
(0.00)
−1.17
(0.01)
−2.27
(0.00)
0.04
(0.63)
140
0.23
1.98
1997
0.27
(0.84)
10.19
(0.15)
2.94
(0.26)
−0.43
(0.64)
−0.09
(0.70)
−0.89
(0.00)
−0.73
(0.03)
−1.98
(0.00)
0.13
(0.04)
180
0.24
1.95
Panel C: OLS fixed (annual) effects regression
Dependent variable: Q
Constant
Board members (0 to 5)
Board members (5 to 25)
Board members (25 to 100)
Industrial
Transport/shipping
Offshore
Debt to assets
ln(Firm value)
1990
1991
1992
1993
1994
1995
1996
1997
n
R2
Average (Q)
coeff
0.46
5.00
2.45
-0.53
-0.24
-0.58
-0.54
-0.89
0.08
-0.17
-0.20
-0.16
0.10
-0.02
0.10
0.56
0.47
1057
0.25
1.47
(stdev)
(0.32)
(2.12)
(0.89)
(0.28)
(0.07)
(0.07)
(0.10)
(0.14)
(0.02)
(0.12)
(0.12)
(0.12)
(0.12)
(0.12)
(0.11)
(0.11)
(0.11)
pvalue
0.16
0.02
0.01
0.06
0.00
0.00
0.00
0.00
0.00
0.17
0.11
0.18
0.40
0.89
0.39
0.00
0.00
Panel A shows results of pooled regressions using data for the whole period. The left hand table in panel A shows
result for a OLS regression. The right hand table in panel A is a GMM regression. Panel B shows estimates on
a year by year basis, and panel C is a (annual) fixed effects regression. In regressions using firm size across years
the nominal values are adjusted to the 1997 general price level. Data for firms listed on the Oslo Stock Exchange,
1989-1997. Variable definitions are in Appendix A.2.
144
Supplementary regressions
Table B.29 Multivariate regression relating performance (Q) to insider (board) ownership and
controls, following McConnell and Servaes (1990)
Panel A: Pooled regressions (OLS and GMM)
Dependent variable: Q
Constant
Board members
Squared (Board members)
Industrial
Transport/shipping
Offshore
Debt to assets
ln(Firm value)
n
R2
Average (Q)
coeff
0.08
2.58
-2.31
-0.28
-0.61
-0.57
-1.12
0.11
1057
0.18
1.47
(stdev)
(0.33)
(0.46)
(0.52)
(0.07)
(0.08)
(0.11)
(0.15)
(0.02)
pvalue
0.80
0.00
0.00
0.00
0.00
0.00
0.00
0.00
Dependent variable: Q
Constant
Board members
Squared (Board members)
Industrial
Transport/shipping
Offshore
Debt to assets
ln(Firm value)
n
Average (Q)
coeff
0.08
2.58
-2.31
-0.28
-0.61
-0.57
-1.12
0.11
1057
1.47
(stdev)
(0.31)
(0.81)
(0.86)
(0.09)
(0.08)
(0.10)
(0.23)
(0.01)
pvalue
0.79
0.00
0.01
0.00
0.00
0.00
0.00
0.00
Panel B: Year by year OLS regressions
constant
Board members
Squared (Board members)
Industrial
Transport/shipping
Offshore
Debt to assets
ln(Firm value)
n
R2
Average (Q)
1989
0.47
(0.33)
0.38
(0.69)
−0.11
(0.91)
−0.22
(0.07)
0.05
(0.72)
0.02
(0.94)
−0.00
(0.99)
0.04
(0.08)
102
0.02
1.29
1990
−0.00
(0.99)
0.37
(0.59)
0.16
(0.84)
−0.17
(0.11)
−0.18
(0.11)
−0.10
(0.55)
−0.03
(0.90)
0.06
(0.02)
91
0.07
1.15
1991
−0.01
(0.99)
0.85
(0.23)
−0.73
(0.32)
−0.08
(0.48)
−0.23
(0.06)
−0.30
(0.07)
−0.30
(0.17)
0.07
(0.01)
90
0.08
1.09
1992
−0.71
(0.09)
0.73
(0.29)
−0.63
(0.46)
−0.15
(0.19)
−0.27
(0.02)
−0.32
(0.05)
0.16
(0.48)
0.09
(0.00)
99
0.18
1.03
Year
1993
0.38
(0.56)
0.46
(0.66)
−0.28
(0.81)
−0.29
(0.07)
−0.54
(0.00)
−0.64
(0.01)
−0.55
(0.16)
0.08
(0.01)
107
0.13
1.38
1994
0.41
(0.39)
1.32
(0.04)
−1.13
(0.14)
−0.22
(0.05)
−0.50
(0.00)
−0.49
(0.00)
−0.54
(0.03)
0.07
(0.00)
120
0.21
1.32
1995
−0.41
(0.63)
2.48
(0.05)
−1.39
(0.30)
−0.39
(0.02)
−0.73
(0.00)
−0.55
(0.04)
−0.08
(0.82)
0.11
(0.01)
128
0.23
1.42
1996
2.94
(0.08)
5.28
(0.01)
−5.02
(0.03)
−0.44
(0.12)
−1.09
(0.00)
−1.12
(0.01)
−2.31
(0.00)
0.03
(0.71)
140
0.22
1.98
1997
0.34
(0.80)
5.69
(0.00)
−5.24
(0.00)
−0.09
(0.70)
−0.87
(0.00)
−0.73
(0.03)
−1.99
(0.00)
0.13
(0.04)
180
0.25
1.95
Panel C: OLS fixed (annual) effects regression
Dependent variable: Q
Constant
Board members
Squared (Board members)
Industrial
Transport/shipping
Offshore
Debt to assets
ln(Firm value)
1990
1991
1992
1993
1994
1995
1996
1997
n
R2
Average (Q)
coeff
0.50
2.51
-2.21
-0.24
-0.57
-0.53
-0.91
0.08
-0.16
-0.19
-0.17
0.10
-0.01
0.10
0.58
0.49
1057
0.25
1.47
(stdev)
(0.33)
(0.45)
(0.50)
(0.07)
(0.07)
(0.11)
(0.14)
(0.02)
(0.13)
(0.13)
(0.12)
(0.12)
(0.12)
(0.12)
(0.11)
(0.11)
pvalue
0.13
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.20
0.14
0.16
0.41
0.92
0.37
0.00
0.00
Panel A shows results of pooled regressions using data for the whole period. The left hand table in panel A shows
result for a OLS regression. The right hand table in panel A is a GMM regression. Panel B shows estimates on
a year by year basis, and panel C is a (annual) fixed effects regression. In regressions using firm size across years
the nominal values are adjusted to the 1997 general price level. Data for firms listed on the Oslo Stock Exchange,
1989-1997. Variable definitions are in Appendix A.2.
B.3 Insider ownership
B.3.4.3
145
Management team
Table B.30 Multivariate regression relating performance (Q) to insider (management) ownership
and controls, following Morck et al. (1988)
Panel A: Pooled regressions (OLS and GMM)
Dependent variable: Q
Constant
Management team (0 to 5)
Management team (5 to 25)
Management team (25 to 100)
Industrial
Transport/shipping
Offshore
Debt to assets
ln(Firm value)
n
R2
Average (Q)
coeff
0.09
10.81
-0.32
-0.48
-0.29
-0.65
-0.63
-1.11
0.11
1057
0.17
1.47
(stdev)
(0.33)
(2.34)
(0.98)
(0.39)
(0.07)
(0.08)
(0.11)
(0.15)
(0.02)
pvalue
0.78
0.00
0.74
0.21
0.00
0.00
0.00
0.00
0.00
Dependent variable: Q
Constant
Management team (0 to 5)
Management team (5 to 25)
Management team (25 to 100)
Industrial
Transport/shipping
Offshore
Debt to assets
ln(Firm value)
n
Average (Q)
coeff
0.09
10.81
-0.32
-0.48
-0.29
-0.65
-0.63
-1.11
0.11
1057
1.47
(stdev)
(0.30)
(2.68)
(1.24)
(0.35)
(0.09)
(0.08)
(0.10)
(0.23)
(0.02)
pvalue
0.76
0.00
0.80
0.16
0.00
0.00
0.00
0.00
0.00
Panel B: Year by year OLS regressions
constant
Management team (0 to 5)
Management team (5 to 25)
Management team (25 to 100)
Industrial
Transport/shipping
Offshore
Debt to assets
ln(Firm value)
n
R2
Average (Q)
1989
0.40
(0.42)
2.62
(0.56)
−3.00
(0.49)
0.69
(0.55)
−0.26
(0.03)
0.00
(0.97)
−0.04
(0.87)
0.03
(0.90)
0.05
(0.05)
102
0.00
1.29
1990
−0.03
(0.96)
3.51
(0.30)
−0.84
(0.60)
0.36
(0.45)
−0.20
(0.06)
−0.26
(0.02)
−0.17
(0.28)
−0.07
(0.77)
0.07
(0.01)
91
0.03
1.15
1991
0.01
(0.98)
5.56
(0.17)
−1.65
(0.28)
0.28
(0.48)
−0.10
(0.34)
−0.25
(0.03)
−0.39
(0.02)
−0.32
(0.15)
0.07
(0.01)
90
0.07
1.09
1992
−0.51
(0.21)
12.77
(0.00)
−3.58
(0.02)
−0.12
(0.85)
−0.17
(0.10)
−0.33
(0.00)
−0.48
(0.00)
0.16
(0.45)
0.08
(0.00)
99
0.26
1.03
Year
1993
0.47
(0.46)
9.03
(0.08)
−2.93
(0.26)
−0.51
(0.78)
−0.30
(0.06)
−0.61
(0.00)
−0.76
(0.00)
−0.52
(0.18)
0.08
(0.02)
107
0.14
1.38
1994
0.35
(0.48)
7.86
(0.02)
−0.29
(0.85)
−0.68
(0.54)
−0.20
(0.07)
−0.48
(0.00)
−0.51
(0.00)
−0.55
(0.03)
0.07
(0.00)
120
0.21
1.32
1995
−0.22
(0.80)
11.80
(0.06)
−2.05
(0.40)
0.82
(0.43)
−0.32
(0.07)
−0.74
(0.00)
−0.52
(0.07)
0.12
(0.75)
0.09
(0.04)
128
0.15
1.42
1996
2.93
(0.08)
12.86
(0.19)
−0.34
(0.93)
−0.71
(0.90)
−0.39
(0.19)
−1.14
(0.00)
−1.05
(0.02)
−2.30
(0.00)
0.03
(0.71)
140
0.20
1.98
1997
0.34
(0.80)
11.04
(0.12)
3.02
(0.32)
−1.76
(0.24)
−0.04
(0.88)
−0.92
(0.00)
−0.71
(0.04)
−2.17
(0.00)
0.14
(0.04)
180
0.21
1.95
Panel C: OLS fixed (annual) effects regression
Dependent variable: Q
Constant
Management team (0 to 5)
Management team (5 to 25)
Management team (25 to 100)
Industrial
Transport/shipping
Offshore
Debt to assets
ln(Firm value)
1990
1991
1992
1993
1994
1995
1996
1997
n
R2
Average (Q)
coeff
0.48
9.98
-0.68
-0.19
-0.25
-0.61
-0.59
-0.90
0.08
-0.16
-0.17
-0.15
0.12
0.00
0.09
0.56
0.49
1057
0.24
1.47
(stdev)
(0.33)
(2.25)
(0.94)
(0.37)
(0.07)
(0.07)
(0.11)
(0.14)
(0.02)
(0.13)
(0.13)
(0.12)
(0.12)
(0.12)
(0.12)
(0.12)
(0.11)
pvalue
0.14
0.00
0.47
0.61
0.00
0.00
0.00
0.00
0.00
0.21
0.17
0.23
0.33
0.97
0.42
0.00
0.00
Panel A shows results of pooled regressions using data for the whole period. The left hand table in panel A shows
result for a OLS regression. The right hand table in panel A is a GMM regression. Panel B shows estimates on
a year by year basis, and panel C is a (annual) fixed effects regression. In regressions using firm size across years
the nominal values are adjusted to the 1997 general price level. Data for firms listed on the Oslo Stock Exchange,
1989-1997. Variable definitions are in Appendix A.2.
146
Supplementary regressions
Table B.31 Multivariate regression relating performance (Q) to insider (management) ownership
and controls, following McConnell and Servaes (1990)
Panel A: Pooled regressions (OLS and GMM)
Dependent variable: Q
Constant
Management team
Squared (Management team)
Industrial
Transport/shipping
Offshore
Debt to assets
ln(Firm value)
n
R2
Average (Q)
coeff
0.07
2.27
-2.30
-0.32
-0.65
-0.62
-1.10
0.12
1057
0.16
1.47
(stdev)
(0.33)
(0.59)
(0.69)
(0.07)
(0.08)
(0.11)
(0.15)
(0.02)
pvalue
0.84
0.00
0.00
0.00
0.00
0.00
0.00
0.00
Dependent variable: Q
Constant
Management team
Squared (Management team)
Industrial
Transport/shipping
Offshore
Debt to assets
ln(Firm value)
n
Average (Q)
coeff
0.07
2.27
-2.30
-0.32
-0.65
-0.62
-1.10
0.12
1057
1.47
(stdev)
(0.30)
(0.80)
(0.84)
(0.09)
(0.08)
(0.10)
(0.23)
(0.02)
pvalue
0.82
0.00
0.01
0.00
0.00
0.00
0.00
0.00
Panel B: Year by year OLS regressions
constant
Management team
Squared (Management team)
Industrial
Transport/shipping
Offshore
Debt to assets
ln(Firm value)
n
R2
Average (Q)
1989
0.40
(0.41)
−0.59
(0.72)
0.63
(0.71)
−0.25
(0.03)
0.01
(0.94)
−0.02
(0.92)
0.02
(0.93)
0.05
(0.05)
102
0.01
1.29
1990
−0.07
(0.90)
−0.05
(0.96)
0.34
(0.74)
−0.19
(0.06)
−0.25
(0.03)
−0.15
(0.35)
−0.05
(0.82)
0.07
(0.01)
91
0.03
1.15
1991
0.00
(1.00)
0.15
(0.88)
−0.02
(0.98)
−0.10
(0.36)
−0.24
(0.04)
−0.34
(0.04)
−0.28
(0.20)
0.07
(0.01)
90
0.07
1.09
1992
−0.67
(0.11)
0.57
(0.51)
−0.96
(0.39)
−0.20
(0.07)
−0.33
(0.00)
−0.39
(0.01)
0.21
(0.35)
0.09
(0.00)
99
0.17
1.03
Year
1993
0.40
(0.53)
1.39
(0.52)
−3.34
(0.39)
−0.30
(0.06)
−0.58
(0.00)
−0.69
(0.00)
−0.52
(0.18)
0.08
(0.01)
107
0.13
1.38
1994
0.35
(0.48)
2.91
(0.04)
−4.91
(0.06)
−0.21
(0.05)
−0.48
(0.00)
−0.49
(0.01)
−0.58
(0.02)
0.08
(0.00)
120
0.20
1.32
1995
−0.20
(0.82)
0.44
(0.77)
0.45
(0.81)
−0.42
(0.02)
−0.80
(0.00)
−0.57
(0.05)
0.15
(0.71)
0.10
(0.04)
128
0.13
1.42
1996
2.91
(0.08)
6.58
(0.12)
−13.81
(0.21)
−0.44
(0.13)
−1.16
(0.00)
−1.08
(0.02)
−2.33
(0.00)
0.04
(0.67)
140
0.20
1.98
1997
0.30
(0.82)
5.38
(0.01)
−6.21
(0.02)
−0.06
(0.81)
−0.96
(0.00)
−0.76
(0.03)
−2.15
(0.00)
0.14
(0.03)
180
0.21
1.95
Panel C: OLS fixed (annual) effects regression
Dependent variable: Q
Constant
Management team
Squared (Management team)
Industrial
Transport/shipping
Offshore
Debt to assets
ln(Firm value)
1990
1991
1992
1993
1994
1995
1996
1997
n
R2
Average (Q)
coeff
0.46
1.76
-1.64
-0.27
-0.62
-0.58
-0.89
0.08
-0.15
-0.17
-0.14
0.13
0.01
0.10
0.57
0.51
1057
0.22
1.47
(stdev)
(0.33)
(0.57)
(0.67)
(0.07)
(0.07)
(0.11)
(0.15)
(0.02)
(0.13)
(0.13)
(0.13)
(0.12)
(0.12)
(0.12)
(0.12)
(0.11)
pvalue
0.17
0.00
0.01
0.00
0.00
0.00
0.00
0.00
0.24
0.19
0.25
0.31
0.94
0.40
0.00
0.00
Panel A shows results of pooled regressions using data for the whole period. The left hand table in panel A shows
result for a OLS regression. The right hand table in panel A is a GMM regression. Panel B shows estimates on
a year by year basis, and panel C is a (annual) fixed effects regression. In regressions using firm size across years
the nominal values are adjusted to the 1997 general price level. Data for firms listed on the Oslo Stock Exchange,
1989-1997. Variable definitions are in Appendix A.2.
B.3 Insider ownership
B.3.5
147
Insider holdings and outside concentration
To account for the possibility that insiders and large shareholders are double-counted, we have
estimated the outside (external) concentration by the size of the largest owner who is not an
insider. That is, we remove the largest owner if the largest insider owner has the same size as the
largest owner. The results are shown in tables B.32 (MSV approach) and B.33 (McS approach).
148
Supplementary regressions
Table B.32 Multivariate regression relating performance (Q) to insider ownership, outside (external) concentration and controls, following Morck et al. (1988)
Panel A: Pooled regressions (OLS and GMM)
Dependent variable: Q
Constant
Primary insiders (0 to 5)
Primary insiders (5 to 25)
Primary insiders (25 to 100)
Largest outside owner
Industrial
Transport/shipping
Offshore
Debt to assets
ln(Firm value)
n
R2
Average (Q)
coeff
0.31
6.62
1.85
-0.62
-0.59
-0.25
-0.59
-0.57
-1.10
0.10
1057
0.21
1.47
(stdev)
(0.33)
(1.98)
(0.76)
(0.30)
(0.15)
(0.07)
(0.07)
(0.11)
(0.15)
(0.02)
pvalue
0.35
0.00
0.01
0.04
0.00
0.00
0.00
0.00
0.00
0.00
Dependent variable: Q
Constant
Primary insiders (0 to 5)
Primary insiders (5 to 25)
Primary insiders (25 to 100)
Largest outside owner
Industrial
Transport/shipping
Offshore
Debt to assets
ln(Firm value)
n
Average (Q)
coeff
0.31
6.62
1.85
-0.62
-0.59
-0.25
-0.59
-0.57
-1.10
0.10
1057
1.47
(stdev)
(0.32)
(2.10)
(0.97)
(0.41)
(0.12)
(0.09)
(0.07)
(0.10)
(0.22)
(0.01)
pvalue
0.33
0.00
0.06
0.13
0.00
0.00
0.00
0.00
0.00
0.00
Panel B: Year by year OLS regressions
constant
Primary insiders (0 to 5)
Primary insiders (5 to 25)
Primary insiders (25 to 100)
Largest outside owner
Industrial
Transport/shipping
Offshore
Debt to assets
ln(Firm value)
n
R2
Average (Q)
1989
0.45
(0.36)
3.67
(0.34)
−0.60
(0.79)
−0.07
(0.91)
−0.38
(0.15)
−0.28
(0.02)
−0.02
(0.89)
−0.08
(0.73)
0.02
(0.94)
0.05
(0.04)
102
0.03
1.29
1990
0.12
(0.81)
7.45
(0.00)
−1.88
(0.10)
0.52
(0.20)
−0.27
(0.21)
−0.18
(0.07)
−0.26
(0.02)
−0.17
(0.27)
−0.04
(0.86)
0.06
(0.02)
91
0.12
1.15
1991
−0.00
(1.00)
8.35
(0.01)
−2.00
(0.08)
0.12
(0.73)
−0.33
(0.14)
−0.10
(0.36)
−0.25
(0.03)
−0.36
(0.02)
−0.30
(0.16)
0.07
(0.00)
90
0.15
1.09
1992
−0.60
(0.18)
1.49
(0.63)
1.53
(0.23)
−0.81
(0.22)
−0.16
(0.51)
−0.11
(0.31)
−0.26
(0.03)
−0.33
(0.04)
0.15
(0.51)
0.09
(0.00)
99
0.18
1.03
Year
1993
1.02
(0.14)
−0.95
(0.83)
1.71
(0.36)
−0.88
(0.28)
−0.70
(0.02)
−0.19
(0.23)
−0.54
(0.00)
−0.62
(0.01)
−0.63
(0.10)
0.06
(0.07)
107
0.16
1.38
1994
0.60
(0.22)
4.24
(0.18)
0.95
(0.41)
−0.51
(0.31)
−0.42
(0.04)
−0.21
(0.04)
−0.49
(0.00)
−0.54
(0.00)
−0.62
(0.01)
0.07
(0.01)
120
0.26
1.32
1995
−0.22
(0.79)
5.62
(0.23)
−0.41
(0.82)
1.87
(0.00)
−0.58
(0.11)
−0.28
(0.09)
−0.64
(0.00)
−0.48
(0.07)
−0.20
(0.59)
0.10
(0.02)
128
0.28
1.42
1996
2.85
(0.09)
9.14
(0.25)
3.51
(0.24)
−1.76
(0.25)
−0.42
(0.53)
−0.41
(0.16)
−1.04
(0.00)
−1.02
(0.02)
−2.21
(0.00)
0.03
(0.70)
140
0.23
1.98
1997
0.38
(0.78)
10.98
(0.09)
2.76
(0.23)
−0.92
(0.46)
−0.56
(0.26)
−0.08
(0.73)
−0.82
(0.00)
−0.64
(0.05)
−1.96
(0.00)
0.13
(0.05)
180
0.25
1.95
Panel C: OLS fixed (annual) effects regression
Dependent variable: Q
Constant
Primary insiders (0 to 5)
Primary insiders (5 to 25)
Primary insiders (25 to 100)
Largest outside owner
Industrial
Transport/shipping
Offshore
Debt to assets
ln(Firm value)
1990
1991
1992
1993
1994
1995
1996
1997
n
R2
Average (Q)
coeff
0.70
6.41
1.44
-0.44
-0.57
-0.21
-0.56
-0.54
-0.91
0.07
-0.18
-0.21
-0.18
0.09
-0.02
0.05
0.51
0.44
1057
0.27
1.47
(stdev)
(0.33)
(1.90)
(0.73)
(0.29)
(0.14)
(0.07)
(0.07)
(0.10)
(0.14)
(0.02)
(0.12)
(0.12)
(0.12)
(0.12)
(0.12)
(0.11)
(0.11)
(0.11)
pvalue
0.03
0.00
0.05
0.13
0.00
0.00
0.00
0.00
0.00
0.00
0.14
0.09
0.13
0.46
0.84
0.64
0.00
0.00
Panel A shows results of pooled regressions using data for the whole period. The left hand table in panel A shows
result for a OLS regression. The right hand table in panel A is a GMM regression. Panel B shows estimates on
a year by year basis, and panel C is a (annual) fixed effects regression. In regressions using firm size across years
the nominal values are adjusted to the 1997 general price level. Data for firms listed on the Oslo Stock Exchange,
1989-1997. Variable definitions are in Appendix A.2.
B.3 Insider ownership
149
Table B.33 Multivariate regression relating performance (Q) to insider ownership, outside (external) concentration and controls, following McConnell and Servaes (1990)
Panel A: Pooled regressions (OLS and GMM)
Dependent variable: Q
Constant
Primary insiders
Squared (Primary insiders)
Largest outside owner
Industrial
Transport/shipping
Offshore
Debt to assets
ln(Firm value)
n
R2
Average (Q)
coeff
0.37
2.53
-2.43
-0.64
-0.25
-0.59
-0.56
-1.14
0.10
1057
0.20
1.47
(stdev)
(0.33)
(0.43)
(0.51)
(0.15)
(0.07)
(0.07)
(0.11)
(0.15)
(0.02)
pvalue
0.27
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
Dependent variable: Q
Constant
Primary insiders
Squared (Primary insiders)
Largest outside owner
Industrial
Transport/shipping
Offshore
Debt to assets
ln(Firm value)
n
Average (Q)
coeff
0.37
2.53
-2.43
-0.64
-0.25
-0.59
-0.56
-1.14
0.10
1057
1.47
(stdev)
(0.32)
(0.62)
(0.74)
(0.12)
(0.09)
(0.07)
(0.10)
(0.22)
(0.02)
pvalue
0.25
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
Panel B: Year by year OLS regressions
constant
Primary insiders
Squared (Primary insiders)
Largest outside owner
Industrial
Transport/shipping
Offshore
Debt to assets
ln(Firm value)
n
R2
Average (Q)
1989
0.52
(0.28)
0.01
(0.99)
0.02
(0.98)
−0.42
(0.11)
−0.25
(0.04)
0.01
(0.96)
−0.06
(0.81)
−0.02
(0.95)
0.05
(0.04)
102
0.02
1.29
1990
0.13
(0.81)
0.21
(0.76)
0.09
(0.91)
−0.33
(0.14)
−0.17
(0.09)
−0.23
(0.04)
−0.13
(0.39)
−0.03
(0.89)
0.06
(0.02)
91
0.05
1.15
1991
0.17
(0.75)
0.52
(0.41)
−0.53
(0.44)
−0.41
(0.07)
−0.08
(0.46)
−0.25
(0.03)
−0.34
(0.04)
−0.30
(0.18)
0.07
(0.01)
90
0.10
1.09
1992
−0.60
(0.18)
1.72
(0.03)
−2.43
(0.05)
−0.17
(0.48)
−0.11
(0.33)
−0.25
(0.03)
−0.33
(0.04)
0.14
(0.54)
0.09
(0.00)
99
0.20
1.03
Year
1993
1.05
(0.12)
1.10
(0.28)
−1.62
(0.23)
−0.68
(0.02)
−0.20
(0.22)
−0.55
(0.00)
−0.63
(0.01)
−0.62
(0.11)
0.06
(0.08)
107
0.17
1.38
1994
0.70
(0.16)
1.48
(0.02)
−1.60
(0.04)
−0.46
(0.02)
−0.22
(0.03)
−0.48
(0.00)
−0.48
(0.00)
−0.64
(0.01)
0.07
(0.01)
120
0.26
1.32
1995
−0.17
(0.84)
0.70
(0.49)
0.92
(0.41)
−0.60
(0.09)
−0.32
(0.05)
−0.67
(0.00)
−0.48
(0.07)
−0.19
(0.60)
0.10
(0.01)
128
0.28
1.42
1996
3.10
(0.06)
4.66
(0.01)
−5.37
(0.04)
−0.51
(0.44)
−0.41
(0.15)
−1.01
(0.00)
−1.02
(0.03)
−2.26
(0.00)
0.02
(0.77)
140
0.23
1.98
1997
0.46
(0.73)
4.80
(0.00)
−5.22
(0.01)
−0.58
(0.24)
−0.08
(0.74)
−0.82
(0.00)
−0.66
(0.05)
−2.01
(0.00)
0.13
(0.05)
180
0.25
1.95
Panel C: OLS fixed (annual) effects regression
Dependent variable: Q
Constant
Primary insiders
Squared (Primary insiders)
Largest outside owner
Industrial
Transport/shipping
Offshore
Debt to assets
ln(Firm value)
1990
1991
1992
1993
1994
1995
1996
1997
n
R2
Average (Q)
coeff
0.76
2.19
-2.03
-0.62
-0.21
-0.56
-0.52
-0.94
0.07
-0.17
-0.20
-0.18
0.09
-0.01
0.06
0.53
0.45
1057
0.26
1.47
(stdev)
(0.33)
(0.41)
(0.50)
(0.14)
(0.07)
(0.07)
(0.10)
(0.14)
(0.02)
(0.12)
(0.12)
(0.12)
(0.12)
(0.12)
(0.11)
(0.11)
(0.11)
pvalue
0.02
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.16
0.11
0.13
0.43
0.91
0.58
0.00
0.00
Panel A shows results of pooled regressions using data for the whole period. The left hand table in panel A shows
result for a OLS regression. The right hand table in panel A is a GMM regression. Panel B shows estimates on
a year by year basis, and panel C is a (annual) fixed effects regression. In regressions using firm size across years
the nominal values are adjusted to the 1997 general price level. Data for firms listed on the Oslo Stock Exchange,
1989-1997. Variable definitions are in Appendix A.2.
150
B.4
Supplementary regressions
Owner type
This appendix presents regressions which supplement the analysis in chapter 7.
B.4.1
Year by year, GMM, and fixed effects regressions
This section lists tables which supplement the pooled OLS regression shown in the main text. Using
the same dependent and independent variables, we show OLS estimations on a year by year basis,
estimations using GMM, and we also control for systematic differences across years with indicator
variables for each year (fixed effects) in an OLS regression.
B.4 Owner type
151
Table B.34 Multivariate regression relating performance (Q) to ownership concentration, insider
ownership, aggregate holdings per owner type, and controls
Panel A: Year by year OLS regressions
constant
Herfindahl index
Primary insiders
Squared (Primary insiders)
Aggregate state holdings
Aggregate international holdings
Aggregate individual holdings
Aggregate nonfinancial holdings
Industrial
Transport/shipping
Offshore
Debt to assets
ln(Firm value)
n
R2
Average (Q)
1989
−0.45
(0.46)
−0.70
(0.07)
−0.04
(0.97)
0.03
(0.98)
0.72
(0.21)
0.82
(0.05)
1.36
(0.01)
0.98
(0.01)
−0.23
(0.05)
−0.02
(0.87)
−0.10
(0.64)
−0.05
(0.83)
0.05
(0.03)
102
0.09
1.29
1990
−0.34
(0.63)
−0.61
(0.09)
0.06
(0.93)
0.17
(0.83)
−0.00
(0.99)
0.05
(0.90)
0.59
(0.25)
0.30
(0.45)
−0.15
(0.15)
−0.27
(0.03)
−0.16
(0.30)
−0.06
(0.78)
0.08
(0.01)
91
0.07
1.15
1991
−0.01
(0.99)
−0.47
(0.26)
0.50
(0.45)
−0.48
(0.50)
0.04
(0.95)
0.06
(0.90)
0.33
(0.60)
0.04
(0.93)
−0.07
(0.54)
−0.23
(0.08)
−0.34
(0.04)
−0.28
(0.23)
0.07
(0.03)
90
0.06
1.09
1992
−0.50
(0.43)
−0.33
(0.38)
1.54
(0.07)
−2.16
(0.09)
0.27
(0.57)
−0.27
(0.45)
0.22
(0.60)
−0.08
(0.83)
−0.12
(0.30)
−0.26
(0.03)
−0.31
(0.05)
0.13
(0.58)
0.09
(0.00)
99
0.20
1.03
Year
1993
1.31
(0.14)
−0.61
(0.15)
0.67
(0.53)
−1.08
(0.44)
−0.20
(0.72)
0.21
(0.68)
0.52
(0.40)
−0.51
(0.27)
−0.13
(0.42)
−0.40
(0.03)
−0.57
(0.02)
−0.87
(0.03)
0.05
(0.16)
107
0.18
1.38
1994
−0.37
(0.55)
−0.71
(0.01)
0.96
(0.12)
−1.06
(0.17)
0.36
(0.39)
0.51
(0.09)
1.15
(0.00)
0.56
(0.09)
−0.15
(0.15)
−0.48
(0.00)
−0.45
(0.01)
−0.59
(0.02)
0.09
(0.00)
120
0.32
1.32
1995
−0.77
(0.50)
−0.85
(0.09)
0.63
(0.52)
0.79
(0.47)
−0.01
(0.99)
1.00
(0.07)
1.19
(0.06)
0.30
(0.64)
−0.19
(0.24)
−0.62
(0.00)
−0.52
(0.04)
−0.30
(0.41)
0.10
(0.03)
128
0.34
1.42
1996
−1.18
(0.61)
−0.43
(0.64)
2.93
(0.11)
−3.89
(0.13)
−0.18
(0.89)
1.01
(0.24)
3.46
(0.00)
0.79
(0.41)
−0.22
(0.43)
−0.86
(0.01)
−0.99
(0.02)
−2.07
(0.00)
0.17
(0.09)
140
0.29
1.98
1997
0.34
(0.86)
−0.79
(0.21)
4.34
(0.00)
−4.50
(0.04)
−0.86
(0.43)
0.20
(0.78)
0.39
(0.64)
−0.29
(0.68)
−0.04
(0.87)
−0.76
(0.01)
−0.67
(0.05)
−1.98
(0.00)
0.14
(0.09)
180
0.25
1.95
Panel B: Pooled GMM and fixed effects regressions
Dependent variable: Q
Dependent variable: Q
Constant
Herfindahl index
Primary insiders
Squared (Primary insiders)
Aggregate state holdings
Aggregate international holdings
Aggregate individual holdings
Aggregate nonfinancial holdings
Industrial
Transport/shipping
Offshore
Debt to assets
ln(Firm value)
n
Average (Q)
coeff
-0.18
-0.59
2.05
-2.02
-0.40
0.02
0.97
-0.15
-0.19
-0.50
-0.52
-1.15
0.12
1057
1.47
(stdev)
(0.43)
(0.17)
(0.59)
(0.71)
(0.23)
(0.19)
(0.29)
(0.20)
(0.08)
(0.07)
(0.09)
(0.23)
(0.02)
pvalue
0.67
0.00
0.00
0.00
0.08
0.92
0.00
0.45
0.02
0.00
0.00
0.00
0.00
Constant
Herfindahl index
Primary insiders
Squared (Primary insiders)
Aggregate state holdings
Aggregate international holdings
Aggregate individual holdings
Aggregate nonfinancial holdings
Industrial
Transport/shipping
Offshore
Debt to assets
ln(Firm value)
1990
1991
1992
1993
1994
1995
1996
1997
n
R2
Average (Q)
coeff
0.03
-0.72
1.85
-1.74
0.00
0.39
1.05
0.18
-0.16
-0.51
-0.51
-0.93
0.09
-0.17
-0.18
-0.17
0.11
-0.02
0.04
0.51
0.46
1057
0.28
1.47
(stdev)
(0.44)
(0.20)
(0.42)
(0.50)
(0.29)
(0.21)
(0.26)
(0.22)
(0.07)
(0.08)
(0.10)
(0.14)
(0.02)
(0.12)
(0.12)
(0.12)
(0.12)
(0.12)
(0.11)
(0.11)
(0.11)
pvalue
0.95
0.00
0.00
0.00
0.99
0.06
0.00
0.41
0.02
0.00
0.00
0.00
0.00
0.17
0.13
0.15
0.37
0.86
0.74
0.00
0.00
This table complements the pooled OLS regression in table 7.1 by three additional regressions. Panel A shows OLS
estimates on a year by year basis. The left–hand side table in panel B lists pooled estimates, estimated with GMM.
The table on the right–hand side in panel B shows the results of a pooled OLS regression where dummy variables
for each year are used to capture fixed effects. 1989 is the base year in this regression. In regressions using firm size
across years the nominal values are adjusted to the 1997 general price level. Data for firms listed on the Oslo Stock
Exchange, 1989-1997. Variable definitions are in Appendix A.2.
152
Supplementary regressions
Table B.35 Multivariate regression relating performance (Q) to ownership concentration, insider
ownership, aggregate intercorporate holdings, and controls
Panel A: Year by year OLS regressions
constant
Herfindahl index
Primary insiders
Squared (Primary insiders)
Aggregate intercorporate holdings
Industrial
Transport/shipping
Offshore
Debt to assets
ln(Firm value)
n
R2
Average (Q)
1989
0.69
(0.17)
−0.77
(0.03)
0.11
(0.92)
0.04
(0.97)
0.19
(0.46)
−0.25
(0.04)
−0.00
(0.98)
−0.08
(0.73)
0.01
(0.97)
0.04
(0.12)
101
0.04
1.29
1990
0.20
(0.71)
−0.60
(0.05)
0.32
(0.65)
0.01
(0.98)
−0.15
(0.54)
−0.17
(0.10)
−0.23
(0.04)
−0.15
(0.32)
−0.07
(0.76)
0.06
(0.02)
91
0.08
1.15
1991
0.19
(0.72)
−0.49
(0.10)
0.45
(0.48)
−0.37
(0.59)
−0.30
(0.33)
−0.07
(0.50)
−0.23
(0.04)
−0.33
(0.04)
−0.36
(0.10)
0.07
(0.01)
90
0.10
1.09
1992
−0.59
(0.20)
−0.22
(0.45)
1.66
(0.04)
−2.34
(0.06)
−0.29
(0.30)
−0.10
(0.35)
−0.24
(0.04)
−0.30
(0.06)
0.11
(0.63)
0.09
(0.00)
99
0.20
1.03
Year
1993
1.07
(0.12)
−0.84
(0.02)
1.10
(0.28)
−1.52
(0.27)
−0.22
(0.56)
−0.19
(0.24)
−0.53
(0.00)
−0.64
(0.01)
−0.72
(0.07)
0.06
(0.08)
107
0.16
1.38
1994
0.81
(0.09)
−0.77
(0.00)
1.44
(0.02)
−1.47
(0.05)
−0.22
(0.43)
−0.20
(0.05)
−0.49
(0.00)
−0.48
(0.00)
−0.71
(0.00)
0.06
(0.01)
120
0.29
1.32
1995
−0.08
(0.92)
−0.85
(0.05)
0.82
(0.41)
0.83
(0.46)
−0.38
(0.47)
−0.34
(0.04)
−0.69
(0.00)
−0.53
(0.04)
−0.34
(0.36)
0.10
(0.01)
128
0.29
1.42
1996
3.35
(0.04)
−1.29
(0.13)
4.56
(0.01)
−5.26
(0.04)
−0.55
(0.60)
−0.39
(0.17)
−0.99
(0.00)
−1.07
(0.02)
−2.20
(0.00)
0.01
(0.86)
140
0.24
1.98
1997
0.49
(0.71)
−1.04
(0.07)
4.75
(0.00)
−4.82
(0.02)
1.17
(0.16)
−0.12
(0.61)
−0.90
(0.00)
−0.68
(0.04)
−2.08
(0.00)
0.13
(0.05)
179
0.26
1.96
Panel B: Pooled GMM and fixed effects regressions
Dependent variable: Q
Dependent variable: Q
Constant
Herfindahl index
Primary insiders
Squared (Primary insiders)
Aggregate intercorporate holdings
Industrial
Transport/shipping
Offshore
Debt to assets
ln(Firm value)
n
Average (Q)
coeff
0.49
-0.98
2.50
-2.30
-0.38
-0.24
-0.59
-0.58
-1.20
0.10
1055
1.47
(stdev)
(0.31)
(0.15)
(0.61)
(0.73)
(0.15)
(0.08)
(0.07)
(0.09)
(0.23)
(0.01)
pvalue
0.12
0.00
0.00
0.00
0.01
0.00
0.00
0.00
0.00
0.00
Constant
Herfindahl index
Primary insiders
Squared (Primary insiders)
Aggregate intercorporate holdings
Industrial
Transport/shipping
Offshore
Debt to assets
ln(Firm value)
1990
1991
1992
1993
1994
1995
1996
1997
n
R2
Average (Q)
coeff
0.89
-0.98
2.20
-1.93
-0.16
-0.21
-0.56
-0.55
-1.01
0.07
-0.18
-0.21
-0.19
0.10
-0.02
0.05
0.51
0.45
1055
0.27
1.47
(stdev)
(0.33)
(0.17)
(0.41)
(0.50)
(0.18)
(0.07)
(0.07)
(0.10)
(0.14)
(0.02)
(0.12)
(0.12)
(0.12)
(0.12)
(0.12)
(0.11)
(0.11)
(0.11)
pvalue
0.01
0.00
0.00
0.00
0.39
0.00
0.00
0.00
0.00
0.00
0.15
0.10
0.13
0.42
0.89
0.64
0.00
0.00
This table complements the pooled OLS regression in table 7.2 by three additional regressions. Panel A shows OLS
estimates on a year by year basis. The left–hand side table in panel B lists pooled estimates, estimated with GMM.
The table on the right–hand side in panel B shows the results of a pooled OLS regression where dummy variables
for each year are used to capture fixed effects. 1989 is the base year in this regression. In regressions using firm size
across years the nominal values are adjusted to the 1997 general price level. Data for firms listed on the Oslo Stock
Exchange, 1989-1997. Variable definitions are in Appendix A.2.
B.4 Owner type
153
Table B.36 Multivariate regression relating performance (Q) to ownership concentration, insider
ownership, largest owner identity, and controls
Panel A: Year by year OLS regressions
constant
Herfindahl index
Primary insiders
Squared (Primary insiders)
Largest owner is state
Largest owner is individual
Largest owner is nonfinancial
Largest owner is international
Industrial
Transport/shipping
Offshore
Debt to assets
ln(Firm value)
n
R2
Average (Q)
1989
0.29
(0.57)
−0.82
(0.02)
−0.21
(0.84)
0.29
(0.79)
0.37
(0.22)
0.52
(0.04)
0.47
(0.02)
0.39
(0.07)
−0.27
(0.03)
−0.06
(0.66)
−0.17
(0.47)
−0.06
(0.82)
0.04
(0.07)
102
0.06
1.29
1990
0.34
(0.53)
−0.62
(0.06)
0.61
(0.41)
−0.21
(0.79)
−0.22
(0.31)
−0.29
(0.17)
−0.13
(0.37)
−0.30
(0.05)
−0.16
(0.11)
−0.22
(0.07)
−0.16
(0.34)
−0.09
(0.68)
0.06
(0.02)
91
0.10
1.15
1991
1.07
(0.07)
−0.74
(0.02)
0.93
(0.14)
−0.62
(0.35)
−0.09
(0.63)
−0.60
(0.00)
−0.29
(0.05)
−0.37
(0.03)
−0.13
(0.22)
−0.22
(0.06)
−0.31
(0.06)
−0.53
(0.02)
0.04
(0.12)
90
0.17
1.09
1992
−0.35
(0.48)
−0.49
(0.11)
2.49
(0.00)
−3.31
(0.01)
0.38
(0.05)
−0.18
(0.41)
0.11
(0.44)
0.17
(0.35)
−0.13
(0.26)
−0.27
(0.02)
−0.33
(0.03)
0.08
(0.75)
0.07
(0.00)
99
0.23
1.03
Year
1993
0.97
(0.17)
−0.79
(0.03)
0.35
(0.77)
−0.68
(0.65)
−0.05
(0.85)
0.42
(0.24)
0.02
(0.90)
0.43
(0.09)
−0.16
(0.35)
−0.51
(0.00)
−0.60
(0.01)
−0.54
(0.19)
0.05
(0.12)
107
0.19
1.38
1994
0.68
(0.17)
−0.74
(0.00)
1.31
(0.05)
−1.34
(0.10)
−0.20
(0.27)
0.06
(0.77)
−0.00
(0.99)
0.03
(0.86)
−0.17
(0.12)
−0.49
(0.00)
−0.49
(0.00)
−0.69
(0.01)
0.07
(0.01)
120
0.28
1.32
1995
0.24
(0.79)
−0.78
(0.08)
0.64
(0.54)
0.93
(0.41)
−0.70
(0.01)
−0.16
(0.55)
−0.39
(0.07)
−0.24
(0.37)
−0.22
(0.19)
−0.63
(0.00)
−0.58
(0.03)
−0.48
(0.19)
0.10
(0.01)
128
0.31
1.42
1996
2.53
(0.17)
−1.12
(0.19)
3.08
(0.14)
−3.68
(0.18)
−0.53
(0.26)
0.58
(0.23)
0.07
(0.84)
−0.29
(0.53)
−0.34
(0.24)
−1.02
(0.00)
−1.15
(0.01)
−2.32
(0.00)
0.06
(0.52)
140
0.24
1.98
1997
1.01
(0.49)
−0.92
(0.12)
4.58
(0.00)
−4.73
(0.03)
−0.84
(0.05)
−0.54
(0.14)
−0.47
(0.10)
−0.41
(0.27)
−0.10
(0.65)
−0.83
(0.00)
−0.72
(0.03)
−1.87
(0.00)
0.12
(0.08)
180
0.26
1.95
Panel B: Pooled GMM and fixed effects regressions
Dependent variable: Q
Dependent variable: Q
Constant
Herfindahl index
Primary insiders
Squared (Primary insiders)
Largest owner is state
Largest owner is individual
Largest owner is nonfinancial
Largest owner is international
Industrial
Transport/shipping
Offshore
Debt to assets
ln(Firm value)
n
Average (Q)
coeff
0.60
-0.86
2.46
-2.25
-0.43
-0.16
-0.23
-0.28
-0.23
-0.57
-0.59
-1.20
0.10
1057
1.47
(stdev)
(0.32)
(0.14)
(0.63)
(0.73)
(0.11)
(0.16)
(0.11)
(0.13)
(0.09)
(0.07)
(0.10)
(0.23)
(0.01)
pvalue
0.06
0.00
0.00
0.00
0.00
0.32
0.03
0.02
0.01
0.00
0.00
0.00
0.00
Constant
Herfindahl index
Primary insiders
Squared (Primary insiders)
Largest owner is state
Largest owner is individual
Largest owner is nonfinancial
Largest owner is international
Industrial
Transport/shipping
Offshore
Debt to assets
ln(Firm value)
1990
1991
1992
1993
1994
1995
1996
1997
n
R2
Average (Q)
coeff
0.95
-0.87
2.15
-1.88
-0.35
-0.12
-0.17
-0.20
-0.19
-0.54
-0.55
-1.00
0.07
-0.19
-0.21
-0.19
0.09
-0.02
0.05
0.49
0.43
1057
0.28
1.47
(stdev)
(0.35)
(0.18)
(0.43)
(0.51)
(0.12)
(0.12)
(0.08)
(0.10)
(0.07)
(0.07)
(0.10)
(0.14)
(0.02)
(0.12)
(0.12)
(0.12)
(0.12)
(0.12)
(0.11)
(0.11)
(0.11)
pvalue
0.01
0.00
0.00
0.00
0.00
0.29
0.04
0.06
0.01
0.00
0.00
0.00
0.00
0.13
0.09
0.13
0.47
0.83
0.67
0.00
0.00
This table complements the pooled OLS regression in table 7.3 by three additional regressions. Panel A shows OLS
estimates on a year by year basis. The left–hand side table in panel B lists pooled estimates, estimated with GMM.
The table on the right–hand side in panel B shows the results of a pooled OLS regression where dummy variables
for each year are used to capture fixed effects. 1989 is the base year in this regression. In regressions using firm size
across years the nominal values are adjusted to the 1997 general price level. Data for firms listed on the Oslo Stock
Exchange, 1989-1997. Variable definitions are in Appendix A.2.
154
Supplementary regressions
Table B.37 Multivariate regression relating performance (Q) to ownership concentration, insider
ownership, largest owner being listed, and controls
Panel A: Year by year OLS regressions
constant
Herfindahl index
Primary insiders
Squared (Primary insiders)
Largest owner is listed
Industrial
Transport/shipping
Offshore
Debt to assets
ln(Firm value)
n
R2
Average (Q)
1989
0.65
(0.19)
−0.64
(0.05)
0.04
(0.97)
0.09
(0.93)
−0.02
(0.86)
−0.23
(0.05)
0.01
(0.94)
−0.07
(0.76)
−0.01
(0.98)
0.04
(0.09)
102
0.03
1.29
1990
0.21
(0.70)
−0.63
(0.03)
0.31
(0.65)
0.03
(0.97)
−0.07
(0.55)
−0.17
(0.09)
−0.24
(0.04)
−0.15
(0.34)
−0.06
(0.80)
0.06
(0.02)
91
0.08
1.15
1991
0.21
(0.70)
−0.55
(0.05)
0.44
(0.49)
−0.35
(0.61)
−0.14
(0.27)
−0.06
(0.57)
−0.23
(0.05)
−0.32
(0.05)
−0.34
(0.12)
0.06
(0.01)
90
0.10
1.09
1992
−0.59
(0.20)
−0.23
(0.42)
1.63
(0.04)
−2.30
(0.06)
−0.10
(0.41)
−0.11
(0.32)
−0.25
(0.03)
−0.31
(0.05)
0.14
(0.56)
0.09
(0.00)
99
0.20
1.03
Year
1993
1.10
(0.11)
−0.87
(0.02)
1.09
(0.29)
−1.50
(0.27)
−0.10
(0.51)
−0.19
(0.23)
−0.53
(0.00)
−0.64
(0.01)
−0.71
(0.07)
0.06
(0.09)
107
0.17
1.38
1994
0.82
(0.09)
−0.79
(0.00)
1.44
(0.02)
−1.46
(0.06)
−0.08
(0.50)
−0.21
(0.04)
−0.50
(0.00)
−0.48
(0.00)
−0.71
(0.00)
0.06
(0.01)
120
0.29
1.32
1995
−0.01
(0.99)
−0.89
(0.04)
0.79
(0.43)
0.87
(0.44)
−0.15
(0.48)
−0.35
(0.03)
−0.71
(0.00)
−0.54
(0.04)
−0.35
(0.34)
0.10
(0.02)
128
0.29
1.42
1996
3.28
(0.05)
−1.34
(0.11)
4.63
(0.01)
−5.25
(0.04)
0.10
(0.81)
−0.40
(0.17)
−1.02
(0.00)
−1.05
(0.02)
−2.25
(0.00)
0.02
(0.83)
140
0.23
1.98
1997
0.42
(0.75)
−1.01
(0.08)
4.82
(0.00)
−4.99
(0.02)
0.23
(0.50)
−0.09
(0.69)
−0.83
(0.00)
−0.67
(0.04)
−2.04
(0.00)
0.13
(0.04)
180
0.25
1.95
Panel B: Pooled GMM and fixed effects regressions
Dependent variable: Q
Dependent variable: Q
Constant
Herfindahl index
Primary insiders
Squared (Primary insiders)
Largest owner is listed
Industrial
Transport/shipping
Offshore
Debt to assets
ln(Firm value)
n
Average (Q)
coeff
0.52
-1.02
2.52
-2.31
-0.10
-0.25
-0.59
-0.58
-1.20
0.10
1057
1.47
(stdev)
(0.31)
(0.15)
(0.62)
(0.74)
(0.07)
(0.09)
(0.07)
(0.10)
(0.23)
(0.01)
pvalue
0.10
0.00
0.00
0.00
0.17
0.00
0.00
0.00
0.00
0.00
Constant
Herfindahl index
Primary insiders
Squared (Primary insiders)
Largest owner is listed
Industrial
Transport/shipping
Offshore
Debt to assets
ln(Firm value)
1990
1991
1992
1993
1994
1995
1996
1997
n
R2
Average (Q)
coeff
0.91
-0.99
2.21
-1.94
-0.05
-0.21
-0.56
-0.54
-1.00
0.07
-0.19
-0.22
-0.20
0.09
-0.03
0.05
0.51
0.44
1057
0.27
1.47
(stdev)
(0.33)
(0.17)
(0.41)
(0.50)
(0.08)
(0.07)
(0.07)
(0.10)
(0.14)
(0.02)
(0.12)
(0.12)
(0.12)
(0.12)
(0.12)
(0.11)
(0.11)
(0.11)
pvalue
0.01
0.00
0.00
0.00
0.55
0.00
0.00
0.00
0.00
0.00
0.13
0.08
0.11
0.47
0.83
0.69
0.00
0.00
This table complements the pooled OLS regression in table 7.4 by three additional regressions. Panel A shows OLS
estimates on a year by year basis. The left–hand side table in panel B lists pooled estimates, estimated with GMM.
The table on the right–hand side in panel B shows the results of a pooled OLS regression where dummy variables
for each year are used to capture fixed effects. 1989 is the base year in this regression. In regressions using firm size
across years the nominal values are adjusted to the 1997 general price level. Data for firms listed on the Oslo Stock
Exchange, 1989-1997. Variable definitions are in Appendix A.2.
B.4 Owner type
B.4.2
155
Alternative performance measure: RoA5 .
Table B.38 Multivariate regression relating performance (RoA5 ) to ownership concentration, insider ownership, aggregate holdings per owner type, and controls
Panel A: Pooled OLS regression
Dependent variable: RoA5
Constant
Herfindahl index
Primary insiders
Squared (Primary insiders)
Aggregate state holdings
Aggregate international holdings
Aggregate individual holdings
Aggregate nonfinancial holdings
Industrial
Transport/shipping
Offshore
Debt to assets
ln(Firm value)
n
R2
Average (RoA5 )
coeff
12.82
-2.74
7.28
-7.00
1.29
-0.90
1.33
-0.49
-1.41
-1.68
-3.64
-4.11
0.02
1022
0.10
9.36
(stdev)
(2.42)
(1.11)
(2.31)
(2.73)
(1.73)
(1.13)
(1.45)
(1.18)
(0.38)
(0.41)
(0.58)
(0.82)
(0.10)
pvalue
0.00
0.01
0.00
0.01
0.46
0.43
0.36
0.68
0.00
0.00
0.00
0.00
0.86
Panel B: Year by year OLS regressions
constant
Herfindahl index
Primary insiders
Squared (Primary insiders)
Aggregate state holdings
Aggregate international holdings
Aggregate individual holdings
Aggregate nonfinancial holdings
Industrial
Transport/shipping
Offshore
Debt to assets
ln(Firm value)
n
R2
Average (RoA5 )
1989
5.93
(0.32)
−1.08
(0.76)
9.09
(0.33)
−9.76
(0.32)
3.17
(0.59)
3.18
(0.43)
3.71
(0.45)
0.13
(0.97)
−1.09
(0.30)
0.28
(0.81)
−4.94
(0.01)
−4.98
(0.03)
0.29
(0.19)
100
0.08
9.73
1990
10.58
(0.16)
−1.55
(0.68)
5.49
(0.46)
−3.85
(0.63)
1.00
(0.86)
−3.51
(0.40)
−2.05
(0.70)
0.05
(0.99)
−0.25
(0.82)
−0.51
(0.68)
−3.17
(0.05)
−4.61
(0.04)
0.16
(0.60)
90
−0.02
9.40
1991
3.90
(0.66)
−6.25
(0.14)
6.15
(0.35)
−5.07
(0.48)
5.38
(0.37)
−0.71
(0.88)
2.31
(0.71)
4.02
(0.42)
−0.36
(0.76)
−1.41
(0.27)
−2.87
(0.08)
−3.42
(0.15)
0.34
(0.29)
89
−0.01
9.34
1992
8.46
(0.28)
−3.14
(0.48)
21.02
(0.04)
−32.61
(0.03)
4.34
(0.47)
2.59
(0.53)
5.89
(0.25)
1.51
(0.72)
1.17
(0.38)
1.67
(0.23)
−1.17
(0.54)
−5.53
(0.05)
0.08
(0.79)
97
0.02
9.71
Year
1993
17.86
(0.01)
−6.03
(0.05)
14.05
(0.07)
−22.65
(0.02)
4.55
(0.30)
5.33
(0.14)
1.62
(0.72)
1.35
(0.68)
−2.30
(0.06)
−0.98
(0.46)
−3.87
(0.03)
−1.87
(0.54)
−0.34
(0.20)
104
0.09
9.98
1994
23.22
(0.00)
−4.13
(0.14)
3.15
(0.62)
4.33
(0.58)
0.81
(0.86)
−1.08
(0.73)
−0.92
(0.82)
−1.79
(0.60)
−1.64
(0.14)
−1.80
(0.13)
−3.28
(0.06)
−4.09
(0.11)
−0.46
(0.10)
117
0.12
9.16
1995
18.49
(0.01)
−0.03
(0.99)
9.73
(0.10)
−7.42
(0.25)
−6.21
(0.24)
−5.70
(0.08)
−4.43
(0.24)
−7.62
(0.05)
−1.69
(0.08)
−2.43
(0.02)
−2.55
(0.12)
−3.18
(0.18)
−0.09
(0.76)
124
0.10
8.73
1996
3.16
(0.73)
−0.02
(0.99)
15.27
(0.04)
−17.57
(0.08)
−0.97
(0.86)
−1.92
(0.57)
0.01
(1.00)
0.05
(0.99)
−2.12
(0.06)
−3.67
(0.00)
−5.08
(0.00)
−1.73
(0.53)
0.41
(0.30)
130
0.09
8.72
1997
11.80
(0.22)
−1.87
(0.55)
1.38
(0.85)
1.37
(0.90)
2.33
(0.67)
−1.24
(0.72)
3.47
(0.41)
0.01
(1.00)
−3.15
(0.01)
−4.43
(0.00)
−5.47
(0.00)
−6.17
(0.02)
0.16
(0.71)
171
0.12
9.64
Panel A shows results of a pooled regression using data for whole period. Panel B shows results of OLS regressions
on a year by year basis. Data for firms listed on the Oslo Stock Exchange, 1989-1997. Variable definitions are in
Appendix A.2.
156
Supplementary regressions
Table B.39 Multivariate regression relating performance (RoA5 ) to ownership concentration, insider ownership, aggregate intercorporate holdings, and controls
Panel A: Pooled OLS regression
Dependent variable: RoA5
Constant
Herfindahl index
Primary insiders
Squared (Primary insiders)
Aggregate intercorporate holdings
Industrial
Transport/shipping
Offshore
Debt to assets
ln(Firm value)
n
R2
Average (RoA5 )
coeff
13.00
-2.93
7.75
-7.38
-1.01
-1.39
-1.83
-3.88
-4.02
0.01
1020
0.10
9.36
(stdev)
(1.78)
(1.01)
(2.24)
(2.70)
(0.99)
(0.37)
(0.40)
(0.57)
(0.82)
(0.09)
pvalue
0.00
0.00
0.00
0.01
0.31
0.00
0.00
0.00
0.00
0.92
Panel B: Year by year OLS regressions
constant
Herfindahl index
Primary insiders
Squared (Primary insiders)
Aggregate intercorporate holdings
Industrial
Transport/shipping
Offshore
Debt to assets
ln(Firm value)
n
R2
Average (RoA5 )
1989
6.73
(0.12)
−1.49
(0.66)
8.45
(0.35)
−8.45
(0.38)
−1.46
(0.52)
−1.12
(0.29)
−0.24
(0.83)
−5.17
(0.01)
−5.97
(0.01)
0.38
(0.08)
99
0.09
9.75
1990
8.87
(0.10)
2.63
(0.47)
4.08
(0.56)
−3.23
(0.68)
−3.84
(0.14)
0.25
(0.81)
0.08
(0.94)
−2.93
(0.06)
−4.88
(0.03)
0.17
(0.52)
90
0.01
9.40
1991
6.97
(0.21)
−2.75
(0.42)
6.32
(0.33)
−6.57
(0.35)
−1.00
(0.75)
−0.44
(0.70)
−0.79
(0.50)
−2.93
(0.08)
−3.48
(0.13)
0.27
(0.31)
89
−0.02
9.34
1992
11.59
(0.03)
−3.56
(0.33)
23.77
(0.01)
−35.59
(0.01)
−1.77
(0.59)
1.01
(0.44)
1.51
(0.27)
−1.51
(0.42)
−6.12
(0.03)
0.08
(0.75)
97
0.04
9.71
Year
1993
17.64
(0.00)
−5.28
(0.05)
13.09
(0.07)
−22.14
(0.02)
−3.82
(0.16)
−2.06
(0.08)
−0.86
(0.47)
−3.52
(0.03)
−2.08
(0.46)
−0.20
(0.39)
104
0.10
9.98
1994
20.92
(0.00)
−4.17
(0.08)
2.61
(0.66)
4.75
(0.53)
−1.17
(0.66)
−1.52
(0.15)
−2.00
(0.06)
−3.46
(0.03)
−3.50
(0.15)
−0.40
(0.09)
117
0.14
9.16
1995
11.42
(0.02)
−2.22
(0.41)
9.92
(0.08)
−8.32
(0.19)
−0.64
(0.83)
−1.53
(0.10)
−3.17
(0.00)
−2.92
(0.07)
−1.51
(0.49)
−0.02
(0.94)
124
0.09
8.73
1996
5.36
(0.41)
−0.19
(0.95)
15.64
(0.03)
−17.86
(0.06)
0.71
(0.85)
−2.13
(0.05)
−3.84
(0.00)
−5.06
(0.00)
−1.34
(0.61)
0.27
(0.38)
130
0.11
8.72
1997
17.15
(0.01)
−2.86
(0.32)
1.12
(0.88)
1.46
(0.89)
−3.14
(0.45)
−3.03
(0.01)
−4.68
(0.00)
−5.84
(0.00)
−5.94
(0.02)
−0.06
(0.85)
170
0.13
9.65
Panel A shows results of a pooled regression using data for whole period. Panel B shows results of OLS regressions
on a year by year basis. Data for firms listed on the Oslo Stock Exchange, 1989-1997. Variable definitions are in
Appendix A.2.
B.4 Owner type
157
Table B.40 Multivariate regression relating performance (RoA5 ) to ownership concentration, insider ownership, largest owner identity, and controls
Panel A: Pooled OLS regression
Dependent variable: RoA5
Constant
Herfindahl index
Primary insiders
Squared (Primary insiders)
Largest owner is state
Largest owner is individual
Largest owner is nonfinancial
Largest owner is international
Industrial
Transport/shipping
Offshore
Debt to assets
ln(Firm value)
n
R2
Average (RoA5 )
coeff
13.07
-3.15
8.39
-7.89
0.19
-0.20
0.06
0.16
-1.45
-1.87
-3.89
-4.04
-0.00
1022
0.09
9.36
(stdev)
(1.90)
(1.01)
(2.37)
(2.78)
(0.68)
(0.66)
(0.47)
(0.58)
(0.38)
(0.40)
(0.58)
(0.82)
(0.09)
pvalue
0.00
0.00
0.00
0.00
0.78
0.76
0.90
0.79
0.00
0.00
0.00
0.00
1.00
Panel B: Year by year OLS regressions
constant
Herfindahl index
Primary insiders
Squared (Primary insiders)
Largest owner is state
Largest owner is individual
Largest owner is nonfinancial
Largest owner is international
Industrial
Transport/shipping
Offshore
Debt to assets
ln(Firm value)
n
R2
Average (RoA5 )
1989
6.34
(0.18)
−2.54
(0.42)
7.49
(0.41)
−7.58
(0.43)
1.83
(0.50)
1.46
(0.53)
0.76
(0.68)
2.36
(0.22)
−1.19
(0.27)
−0.13
(0.91)
−4.87
(0.02)
−5.49
(0.01)
0.33
(0.13)
100
0.08
9.73
1990
11.83
(0.04)
−0.38
(0.92)
4.15
(0.58)
−3.33
(0.68)
−0.27
(0.91)
−0.12
(0.95)
−0.86
(0.55)
−2.62
(0.09)
−0.01
(0.99)
−0.06
(0.96)
−3.34
(0.05)
−4.19
(0.06)
0.06
(0.83)
90
−0.01
9.40
1991
11.30
(0.07)
−4.97
(0.15)
7.69
(0.24)
−6.59
(0.34)
1.00
(0.63)
−1.87
(0.37)
−0.10
(0.95)
−2.52
(0.15)
−0.62
(0.58)
−1.03
(0.39)
−3.34
(0.05)
−4.36
(0.07)
0.12
(0.68)
89
0.00
9.34
1992
11.22
(0.06)
−4.10
(0.27)
27.00
(0.01)
−39.70
(0.01)
1.76
(0.45)
0.29
(0.91)
0.63
(0.72)
3.05
(0.14)
0.81
(0.55)
1.41
(0.30)
−1.33
(0.47)
−5.93
(0.04)
0.04
(0.89)
97
0.04
9.71
Year
1993
17.30
(0.00)
−4.83
(0.08)
19.89
(0.02)
−28.09
(0.01)
−0.13
(0.95)
−3.27
(0.21)
−0.65
(0.64)
2.66
(0.16)
−2.85
(0.02)
−1.33
(0.28)
−3.88
(0.02)
−2.12
(0.48)
−0.19
(0.44)
104
0.11
9.98
1994
20.30
(0.00)
−4.33
(0.08)
5.53
(0.41)
2.17
(0.79)
0.41
(0.82)
−1.23
(0.54)
0.78
(0.54)
1.18
(0.44)
−1.79
(0.10)
−2.26
(0.04)
−3.81
(0.02)
−2.79
(0.27)
−0.42
(0.08)
117
0.13
9.16
1995
10.78
(0.04)
−2.08
(0.47)
9.69
(0.12)
−8.16
(0.22)
−0.43
(0.79)
0.24
(0.88)
0.26
(0.83)
−0.14
(0.93)
−1.49
(0.13)
−3.24
(0.00)
−2.89
(0.07)
−1.45
(0.52)
0.01
(0.98)
124
0.07
8.73
1996
5.46
(0.46)
0.69
(0.84)
16.21
(0.04)
−18.84
(0.07)
−1.52
(0.40)
−0.99
(0.59)
0.08
(0.95)
−2.38
(0.19)
−2.13
(0.05)
−3.88
(0.00)
−4.91
(0.01)
−1.71
(0.52)
0.29
(0.39)
130
0.11
8.72
1997
16.70
(0.03)
−3.05
(0.31)
1.63
(0.83)
1.33
(0.90)
0.60
(0.79)
0.32
(0.87)
0.32
(0.83)
0.96
(0.62)
−3.01
(0.01)
−4.81
(0.00)
−5.81
(0.00)
−6.18
(0.02)
−0.06
(0.86)
171
0.11
9.64
Panel A shows results of a pooled regression using data for whole period. Panel B shows results of OLS regressions
on a year by year basis. Data for firms listed on the Oslo Stock Exchange, 1989-1997. Variable definitions are in
Appendix A.2.
158
B.5
Supplementary regressions
Board characteristics, security design, and financial policy
This appendix supplements chapter 8.
B.5.1
Year by year, GMM, and fixed effects regressions
This section lists tables which supplement the pooled OLS regression shown in the main text. Using
the same dependent and independent variables, we show OLS estimations on a year by year basis,
estimations using GMM, and we also control for systematic differences across years with indicator
variables for each year (fixed effects) in an OLS regression.
Table B.41 Multivariate regression relating performance (Q) to ownership concentration, insider
ownership, board size, and controls
Panel A: Year by year OLS regressions
constant
Herfindahl index
Primary insiders
Squared (Primary insiders)
ln(Board size)
Industrial
Transport/shipping
Offshore
Debt to assets
ln(Firm value)
n
R2
Average (Q)
1989
1.47
(0.01)
−0.72
(0.04)
0.16
(0.88)
0.02
(0.98)
−0.81
(0.00)
−0.08
(0.51)
−0.05
(0.69)
−0.28
(0.40)
−0.07
(0.79)
0.08
(0.00)
89
0.18
1.30
1990
0.60
(0.32)
−0.66
(0.04)
0.09
(0.90)
0.57
(0.51)
−0.33
(0.07)
−0.10
(0.37)
−0.22
(0.09)
−0.12
(0.55)
−0.08
(0.76)
0.07
(0.02)
80
0.12
1.17
1991
0.59
(0.31)
−0.75
(0.04)
0.18
(0.82)
0.04
(0.96)
−0.24
(0.18)
−0.05
(0.71)
−0.27
(0.05)
−0.35
(0.09)
−0.45
(0.06)
0.07
(0.02)
79
0.10
1.12
1992
−0.59
(0.23)
−0.22
(0.49)
2.11
(0.02)
−3.23
(0.04)
0.05
(0.68)
−0.12
(0.32)
−0.26
(0.04)
−0.31
(0.08)
0.15
(0.55)
0.08
(0.00)
92
0.18
1.05
Year
1993
1.07
(0.13)
−0.92
(0.02)
1.16
(0.26)
−1.66
(0.24)
−0.00
(1.00)
−0.22
(0.19)
−0.62
(0.00)
−0.64
(0.01)
−0.62
(0.12)
0.06
(0.12)
100
0.17
1.39
1994
0.71
(0.16)
−0.77
(0.00)
1.62
(0.02)
−1.81
(0.06)
0.02
(0.88)
−0.26
(0.02)
−0.49
(0.00)
−0.48
(0.01)
−0.89
(0.00)
0.07
(0.01)
106
0.29
1.35
1995
−0.18
(0.84)
−0.76
(0.12)
0.73
(0.48)
1.00
(0.39)
0.17
(0.39)
−0.39
(0.02)
−0.72
(0.00)
−0.53
(0.07)
−0.38
(0.34)
0.09
(0.04)
117
0.27
1.47
1996
3.24
(0.07)
−1.24
(0.16)
4.21
(0.03)
−4.80
(0.08)
−0.46
(0.28)
−0.34
(0.28)
−0.91
(0.01)
−1.11
(0.03)
−2.28
(0.00)
0.06
(0.52)
129
0.22
2.03
1997
0.26
(0.86)
−0.71
(0.29)
4.38
(0.00)
−4.55
(0.04)
−0.33
(0.18)
−0.06
(0.80)
−0.71
(0.02)
−0.82
(0.03)
−2.03
(0.00)
0.17
(0.03)
164
0.23
1.96
Panel B: Pooled GMM and fixed effects regressions
Dependent variable: Q
Dependent variable: Q
Constant
Herfindahl index
Primary insiders
Squared (Primary insiders)
ln(Board size)
Industrial
Transport/shipping
Offshore
Debt to assets
ln(Firm value)
n
Average (Q)
coeff
0.63
-1.01
2.40
-2.15
-0.24
-0.25
-0.58
-0.62
-1.19
0.11
956
1.50
(stdev)
(0.34)
(0.16)
(0.63)
(0.80)
(0.07)
(0.09)
(0.08)
(0.11)
(0.23)
(0.02)
pvalue
0.06
0.00
0.00
0.01
0.00
0.00
0.00
0.00
0.00
0.00
Constant
Herfindahl index
Primary insiders
Squared (Primary insiders)
ln(Board size)
Industrial
Transport/shipping
Offshore
Debt to assets
ln(Firm value)
1990
1991
1992
1993
1994
1995
1996
1997
n
R2
Average (Q)
coeff
0.96
-0.98
2.00
-1.70
-0.25
-0.20
-0.55
-0.61
-1.02
0.09
-0.18
-0.19
-0.15
0.09
0.00
0.07
0.56
0.48
956
0.27
1.50
(stdev)
(0.35)
(0.19)
(0.44)
(0.54)
(0.08)
(0.07)
(0.08)
(0.12)
(0.16)
(0.02)
(0.13)
(0.14)
(0.13)
(0.13)
(0.13)
(0.12)
(0.12)
(0.12)
pvalue
0.01
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.19
0.16
0.24
0.47
1.00
0.59
0.00
0.00
This table complements the pooled OLS regression in table 8.1 by three additional regressions. Panel A shows OLS
estimates on a year by year basis. The left–hand side table in panel B lists pooled estimates, estimated with GMM.
The table on the right–hand side in panel B shows the results of a pooled OLS regression where dummy variables
for each year are used to capture fixed effects. 1989 is the base year in this regression. In regressions using firm size
across years the nominal values are adjusted to the 1997 general price level. Data for firms listed on the Oslo Stock
Exchange, 1989-1997. Variable definitions are in Appendix A.2.
B.5 Board characteristics, security design, and financial policy
159
Table B.42 Multivariate regression relating performance (Q) to ownership concentration, insider
ownership, security design, and controls
Panel A: Year by year OLS regressions
constant
Herfindahl index
Primary insiders
Squared (Primary insiders)
Fraction voting shares
Industrial
Transport/shipping
Offshore
Debt to assets
ln(Firm value)
n
R2
Average (Q)
1989
0.56
(0.51)
−0.63
(0.05)
0.02
(0.99)
0.11
(0.92)
0.04
(0.94)
−0.24
(0.05)
0.03
(0.83)
−0.07
(0.77)
−0.00
(1.00)
0.04
(0.08)
101
0.03
1.29
1990
0.02
(0.97)
−0.67
(0.03)
0.37
(0.59)
−0.01
(0.99)
0.18
(0.66)
−0.16
(0.12)
−0.21
(0.08)
−0.14
(0.36)
−0.03
(0.87)
0.06
(0.03)
90
0.07
1.16
1991
−0.19
(0.81)
−0.61
(0.04)
0.61
(0.35)
−0.46
(0.50)
0.38
(0.39)
−0.05
(0.64)
−0.21
(0.08)
−0.32
(0.05)
−0.32
(0.15)
0.06
(0.01)
89
0.09
1.09
1992
−0.34
(0.61)
−0.23
(0.44)
1.71
(0.03)
−2.43
(0.05)
−0.19
(0.63)
−0.12
(0.29)
−0.25
(0.03)
−0.33
(0.03)
0.11
(0.63)
0.08
(0.00)
98
0.19
1.03
Year
1993
1.17
(0.27)
−0.83
(0.02)
1.15
(0.26)
−1.56
(0.26)
−0.09
(0.89)
−0.20
(0.23)
−0.53
(0.00)
−0.64
(0.01)
−0.71
(0.07)
0.06
(0.10)
106
0.16
1.38
1994
0.66
(0.38)
−0.78
(0.00)
1.42
(0.02)
−1.41
(0.07)
0.07
(0.88)
−0.24
(0.02)
−0.52
(0.00)
−0.51
(0.00)
−0.75
(0.00)
0.07
(0.01)
118
0.30
1.32
1995
−2.47
(0.04)
−1.03
(0.02)
1.02
(0.30)
0.84
(0.45)
1.99
(0.01)
−0.34
(0.03)
−0.64
(0.00)
−0.46
(0.07)
−0.42
(0.25)
0.13
(0.00)
125
0.32
1.43
1996
3.08
(0.22)
−1.29
(0.14)
4.51
(0.02)
−5.11
(0.07)
0.14
(0.92)
−0.42
(0.15)
−1.03
(0.00)
−1.07
(0.02)
−2.23
(0.00)
0.02
(0.80)
137
0.23
2.00
1997
−0.64
(0.76)
−1.09
(0.06)
4.46
(0.00)
−4.41
(0.05)
0.91
(0.48)
−0.08
(0.72)
−0.82
(0.00)
−0.68
(0.04)
−2.03
(0.00)
0.14
(0.04)
178
0.25
1.96
Panel B: Pooled GMM and fixed effects regressions
Dependent variable: Q
Dependent variable: Q
Constant
Herfindahl index
Primary insiders
Squared (Primary insiders)
Fraction voting shares
Industrial
Transport/shipping
Offshore
Debt to assets
ln(Firm value)
n
Average (Q)
coeff
-0.55
-1.08
2.52
-2.20
0.88
-0.24
-0.57
-0.56
-1.18
0.11
1042
1.48
(stdev)
(0.43)
(0.16)
(0.60)
(0.72)
(0.22)
(0.09)
(0.07)
(0.10)
(0.22)
(0.02)
pvalue
0.20
0.00
0.00
0.00
0.00
0.01
0.00
0.00
0.00
0.00
Constant
Herfindahl index
Primary insiders
Squared (Primary insiders)
Fraction voting shares
Industrial
Transport/shipping
Offshore
Debt to assets
ln(Firm value)
1990
1991
1992
1993
1994
1995
1996
1997
n
R2
Average (Q)
coeff
0.19
-1.03
2.18
-1.84
0.57
-0.21
-0.55
-0.53
-0.99
0.07
-0.18
-0.20
-0.18
0.09
-0.01
0.05
0.52
0.44
1042
0.27
1.48
(stdev)
(0.49)
(0.18)
(0.41)
(0.50)
(0.30)
(0.07)
(0.07)
(0.10)
(0.14)
(0.02)
(0.12)
(0.12)
(0.12)
(0.12)
(0.12)
(0.12)
(0.11)
(0.11)
pvalue
0.69
0.00
0.00
0.00
0.06
0.00
0.00
0.00
0.00
0.00
0.16
0.10
0.14
0.44
0.90
0.66
0.00
0.00
This table complements the pooled OLS regression in table 8.2 by three additional regressions. Panel A shows OLS
estimates on a year by year basis. The left–hand side table in panel B lists pooled estimates, estimated with GMM.
The table on the right–hand side in panel B shows the results of a pooled OLS regression where dummy variables
for each year are used to capture fixed effects. 1989 is the base year in this regression. In regressions using firm size
across years the nominal values are adjusted to the 1997 general price level. Data for firms listed on the Oslo Stock
Exchange, 1989-1997. Variable definitions are in Appendix A.2.
160
Supplementary regressions
Table B.43 Multivariate regression relating performance (Q) to ownership concentration, insider
ownership, financial policy and controls
Panel A: Year by year OLS regressions
constant
Herfindahl index
Primary insiders
Squared (Primary insiders)
Industrial
Transport/shipping
Offshore
Debt to assets
Dividends to earnings
ln(Firm value)
n
R2
Average (Q)
1989
0.55
(0.27)
−0.60
(0.07)
0.54
(0.63)
−0.26
(0.83)
−0.23
(0.06)
0.02
(0.87)
−0.05
(0.81)
0.03
(0.89)
−0.04
(0.41)
0.05
(0.07)
99
0.05
1.30
1990
0.30
(0.58)
−0.65
(0.03)
0.26
(0.71)
0.20
(0.81)
−0.16
(0.13)
−0.20
(0.10)
−0.15
(0.35)
−0.03
(0.89)
−0.03
(0.44)
0.05
(0.04)
89
0.07
1.16
1991
0.36
(0.58)
−0.67
(0.02)
0.81
(0.24)
−0.69
(0.34)
−0.03
(0.78)
−0.30
(0.02)
−0.38
(0.02)
−0.45
(0.07)
0.09
(0.20)
0.06
(0.04)
81
0.15
1.09
1992
−0.60
(0.22)
−0.25
(0.40)
1.75
(0.04)
−2.39
(0.06)
−0.12
(0.28)
−0.26
(0.03)
−0.33
(0.05)
0.11
(0.64)
−0.04
(0.54)
0.09
(0.00)
95
0.19
1.04
Year
1993
0.98
(0.17)
−0.87
(0.02)
1.28
(0.23)
−1.63
(0.25)
−0.20
(0.24)
−0.53
(0.00)
−0.65
(0.01)
−0.70
(0.08)
−0.06
(0.45)
0.06
(0.07)
103
0.17
1.39
1994
0.81
(0.09)
−0.77
(0.00)
1.46
(0.02)
−1.46
(0.06)
−0.21
(0.05)
−0.49
(0.00)
−0.47
(0.00)
−0.73
(0.00)
0.05
(0.46)
0.06
(0.01)
118
0.28
1.31
1995
−0.14
(0.87)
−0.90
(0.04)
0.90
(0.37)
0.77
(0.50)
−0.35
(0.03)
−0.68
(0.00)
−0.54
(0.04)
−0.37
(0.31)
−0.15
(0.53)
0.11
(0.01)
127
0.28
1.42
1996
3.03
(0.08)
−1.27
(0.14)
4.41
(0.02)
−4.97
(0.06)
−0.40
(0.17)
−1.05
(0.00)
−1.09
(0.02)
−2.24
(0.00)
−0.19
(0.51)
0.03
(0.70)
138
0.24
1.99
1997
0.43
(0.75)
−0.93
(0.11)
4.69
(0.00)
−4.91
(0.02)
−0.08
(0.72)
−0.78
(0.00)
−0.73
(0.03)
−2.14
(0.00)
−0.32
(0.25)
0.14
(0.05)
178
0.26
1.95
Panel B: Pooled GMM and fixed effects regressions
Dependent variable: Q
Dependent variable: Q
Constant
Herfindahl index
Primary insiders
Squared (Primary insiders)
Industrial
Transport/shipping
Offshore
Debt to assets
Dividends to earnings
ln(Firm value)
n
Average (Q)
coeff
0.50
-1.02
2.61
-2.32
-0.25
-0.59
-0.59
-1.23
-0.06
0.10
1028
1.48
(stdev)
(0.32)
(0.15)
(0.62)
(0.75)
(0.09)
(0.07)
(0.10)
(0.23)
(0.03)
(0.02)
pvalue
0.12
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.02
0.00
Constant
Herfindahl index
Primary insiders
Squared (Primary insiders)
Industrial
Transport/shipping
Offshore
Debt to assets
Dividends to earnings
ln(Firm value)
1990
1991
1992
1993
1994
1995
1996
1997
n
R2
Average (Q)
coeff
0.92
-1.00
2.29
-1.96
-0.21
-0.57
-0.55
-1.03
-0.04
0.07
-0.20
-0.21
-0.20
0.08
-0.04
0.04
0.50
0.42
1028
0.27
1.48
(stdev)
(0.34)
(0.17)
(0.42)
(0.51)
(0.07)
(0.07)
(0.11)
(0.14)
(0.04)
(0.02)
(0.13)
(0.13)
(0.12)
(0.12)
(0.12)
(0.12)
(0.12)
(0.11)
pvalue
0.01
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.35
0.00
0.12
0.10
0.11
0.48
0.75
0.75
0.00
0.00
This table complements the pooled OLS regression in table 8.3 by three additional regressions. Panel A shows OLS
estimates on a year by year basis. The left–hand side table in panel B lists pooled estimates, estimated with GMM.
The table on the right–hand side in panel B shows the results of a pooled OLS regression where dummy variables
for each year are used to capture fixed effects. 1989 is the base year in this regression. In regressions using firm size
across years the nominal values are adjusted to the 1997 general price level. Data for firms listed on the Oslo Stock
Exchange, 1989-1997. Variable definitions are in Appendix A.2.
B.5 Board characteristics, security design, and financial policy
B.5.2
161
Alternative performance measure: RoA5
Table B.44 Multivariate regression relating performance (RoA5 ) to ownership concentration, insider ownership, board size, and controls
Panel A: Pooled OLS regression
Dependent variable: RoA5
Constant
Herfindahl index
Primary insiders
Squared (Primary insiders)
ln(Board size)
Industrial
Transport/shipping
Offshore
Debt to assets
ln(Firm value)
n
R2
Average (RoA5 )
coeff
12.94
-2.56
7.84
-7.26
-0.45
-1.29
-1.42
-3.49
-4.19
0.05
929
0.08
9.62
(stdev)
(1.85)
(1.10)
(2.35)
(2.90)
(0.44)
(0.39)
(0.43)
(0.64)
(0.86)
(0.09)
pvalue
0.00
0.02
0.00
0.01
0.30
0.00
0.00
0.00
0.00
0.60
Panel B: Year by year OLS regressions
constant
Herfindahl index
Primary insiders
Squared (Primary insiders)
ln(Board size)
Industrial
Transport/shipping
Offshore
Debt to assets
ln(Firm value)
n
R2
Average (RoA5 )
1989
7.77
(0.12)
−3.37
(0.34)
9.78
(0.31)
−10.88
(0.29)
−1.05
(0.58)
−1.31
(0.27)
−0.26
(0.83)
−3.57
(0.23)
−5.92
(0.01)
0.44
(0.07)
87
0.04
10.09
1990
4.73
(0.43)
0.15
(0.97)
5.32
(0.46)
−3.23
(0.70)
0.57
(0.74)
0.20
(0.86)
0.68
(0.58)
−1.21
(0.53)
−1.07
(0.66)
0.20
(0.49)
79
−0.09
9.57
1991
8.32
(0.14)
−1.19
(0.77)
10.88
(0.14)
−10.50
(0.18)
−0.53
(0.75)
0.52
(0.66)
0.01
(1.00)
−1.15
(0.56)
−2.90
(0.20)
0.18
(0.53)
78
−0.06
9.50
1992
10.27
(0.07)
−1.35
(0.73)
26.09
(0.01)
−37.37
(0.03)
1.83
(0.17)
1.48
(0.27)
2.29
(0.11)
−0.17
(0.93)
−6.59
(0.02)
−0.06
(0.80)
90
0.04
9.90
Year
1993
17.66
(0.00)
−5.32
(0.06)
13.01
(0.08)
−20.32
(0.04)
−0.47
(0.68)
−1.98
(0.10)
−0.69
(0.59)
−2.62
(0.15)
−1.81
(0.54)
−0.20
(0.44)
97
0.05
10.20
1994
18.57
(0.00)
−3.91
(0.11)
−2.96
(0.63)
19.84
(0.02)
−0.88
(0.41)
−1.71
(0.08)
−1.29
(0.22)
−3.02
(0.07)
−3.74
(0.11)
−0.20
(0.41)
104
0.25
9.67
1995
11.11
(0.02)
−1.99
(0.49)
8.54
(0.13)
−6.87
(0.28)
0.20
(0.85)
−1.71
(0.07)
−3.00
(0.00)
−3.13
(0.05)
−2.31
(0.31)
0.01
(0.95)
114
0.07
9.07
1996
4.38
(0.51)
0.70
(0.84)
14.65
(0.04)
−16.86
(0.09)
0.35
(0.82)
−2.41
(0.03)
−3.81
(0.00)
−5.06
(0.01)
−1.62
(0.56)
0.30
(0.38)
121
0.10
8.98
1997
15.10
(0.04)
−1.74
(0.60)
0.28
(0.97)
2.80
(0.80)
−1.59
(0.20)
−2.77
(0.02)
−4.74
(0.00)
−6.71
(0.00)
−5.95
(0.04)
0.17
(0.67)
159
0.13
9.79
Panel A shows results of a pooled regression using data for whole period. Panel B shows results of OLS regressions
on a year by year basis. Data for firms listed on the Oslo Stock Exchange, 1989-1997. Variable definitions are in
Appendix A.2.
162
Supplementary regressions
Table B.45 Multivariate regression relating performance (RoA5 ) to ownership concentration, insider ownership, security design, and controls
Panel A: Pooled OLS regression
Dependent variable: RoA5
Constant
Herfindahl index
Primary insiders
Squared (Primary insiders)
Fraction voting shares
Industrial
Transport/shipping
Offshore
Debt to assets
ln(Firm value)
n
R2
Average (RoA5 )
coeff
14.92
-2.88
7.86
-7.62
-1.73
-1.53
-1.98
-4.01
-4.02
-0.00
1007
0.10
9.39
(stdev)
(2.62)
(1.01)
(2.24)
(2.72)
(1.64)
(0.38)
(0.40)
(0.58)
(0.82)
(0.09)
pvalue
0.00
0.00
0.00
0.01
0.29
0.00
0.00
0.00
0.00
0.96
Panel B: Year by year OLS regressions
constant
Herfindahl index
Primary insiders
Squared (Primary insiders)
Fraction voting shares
Industrial
Transport/shipping
Offshore
Debt to assets
ln(Firm value)
n
R2
Average (RoA5 )
1989
3.17
(0.67)
−2.93
(0.34)
8.67
(0.34)
−8.61
(0.37)
3.61
(0.51)
−1.13
(0.29)
−0.25
(0.82)
−5.09
(0.01)
−5.83
(0.01)
0.38
(0.08)
99
0.09
9.71
1990
6.16
(0.40)
−0.19
(0.96)
5.13
(0.47)
−3.54
(0.66)
2.96
(0.48)
0.19
(0.86)
0.19
(0.88)
−2.87
(0.07)
−4.07
(0.07)
0.14
(0.62)
89
−0.01
9.39
1991
4.07
(0.60)
−3.61
(0.27)
7.27
(0.27)
−7.11
(0.31)
2.65
(0.56)
−0.30
(0.80)
−0.70
(0.57)
−2.82
(0.09)
−3.33
(0.14)
0.28
(0.30)
88
−0.02
9.33
1992
12.97
(0.09)
−3.70
(0.31)
24.09
(0.01)
−36.12
(0.01)
−1.03
(0.82)
0.92
(0.48)
1.43
(0.30)
−1.75
(0.34)
−6.11
(0.03)
0.05
(0.84)
96
0.04
9.72
Year
1993
20.71
(0.01)
−5.04
(0.07)
13.91
(0.06)
−23.01
(0.02)
−2.66
(0.58)
−2.22
(0.06)
−1.00
(0.41)
−3.61
(0.03)
−1.87
(0.51)
−0.26
(0.30)
103
0.08
10.01
1994
23.98
(0.00)
−4.02
(0.10)
2.86
(0.63)
4.14
(0.59)
−2.79
(0.56)
−1.75
(0.09)
−2.15
(0.04)
−3.70
(0.02)
−3.79
(0.12)
−0.41
(0.10)
115
0.14
9.21
1995
15.66
(0.03)
−1.87
(0.50)
8.94
(0.12)
−7.71
(0.23)
−3.79
(0.39)
−1.79
(0.06)
−3.55
(0.00)
−3.25
(0.04)
−1.23
(0.58)
−0.04
(0.86)
121
0.10
8.79
1996
11.96
(0.20)
0.96
(0.78)
17.03
(0.02)
−21.08
(0.04)
−5.06
(0.32)
−2.34
(0.03)
−4.19
(0.00)
−5.48
(0.00)
−1.07
(0.68)
0.19
(0.56)
127
0.12
8.79
1997
19.86
(0.06)
−2.64
(0.37)
2.34
(0.75)
−0.08
(0.99)
−2.42
(0.71)
−3.05
(0.01)
−4.88
(0.00)
−5.76
(0.00)
−6.04
(0.02)
−0.09
(0.79)
169
0.13
9.66
Panel A shows results of a pooled regression using data for whole period. Panel B shows results of OLS regressions
on a year by year basis. Data for firms listed on the Oslo Stock Exchange, 1989-1997. Variable definitions are in
Appendix A.2.
B.5 Board characteristics, security design, and financial policy
163
Table B.46 Multivariate regression relating performance (RoA5 ) to ownership concentration, insider ownership, financial policy and controls
Panel A: Pooled OLS regression
Dependent variable: RoA5
Constant
Herfindahl index
Primary insiders
Squared (Primary insiders)
Industrial
Transport/shipping
Offshore
Debt to assets
Dividends to earnings
ln(Firm value)
n
R2
Average (RoA5 )
coeff
13.62
-3.26
8.16
-7.98
-1.40
-1.90
-3.84
-4.09
0.45
-0.03
994
0.10
9.35
(stdev)
(1.81)
(1.00)
(2.26)
(2.75)
(0.38)
(0.40)
(0.58)
(0.82)
(0.22)
(0.09)
pvalue
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.04
0.73
Panel B: Year by year OLS regressions
constant
Herfindahl index
Primary insiders
Squared (Primary insiders)
Industrial
Transport/shipping
Offshore
Debt to assets
Dividends to earnings
ln(Firm value)
n
R2
Average (RoA5 )
1989
7.72
(0.08)
−2.79
(0.37)
10.67
(0.27)
−11.20
(0.27)
−1.27
(0.24)
−0.27
(0.81)
−5.43
(0.01)
−5.49
(0.01)
0.38
(0.38)
0.32
(0.14)
97
0.08
9.80
1990
9.03
(0.10)
0.77
(0.82)
5.34
(0.45)
−3.52
(0.67)
−0.04
(0.97)
−0.03
(0.98)
−2.97
(0.06)
−4.31
(0.06)
−0.13
(0.76)
0.14
(0.61)
88
−0.01
9.33
1991
11.60
(0.07)
−4.55
(0.16)
8.61
(0.21)
−8.61
(0.24)
−0.40
(0.73)
−1.34
(0.28)
−3.47
(0.04)
−5.30
(0.03)
1.29
(0.05)
0.10
(0.75)
80
0.06
9.33
1992
11.90
(0.04)
−3.79
(0.31)
24.85
(0.01)
−36.65
(0.01)
0.91
(0.50)
1.47
(0.30)
−1.79
(0.36)
−6.20
(0.03)
−0.12
(0.87)
0.06
(0.82)
93
0.03
9.73
Year
1993
16.86
(0.00)
−5.31
(0.05)
14.22
(0.06)
−22.89
(0.02)
−2.27
(0.06)
−0.92
(0.47)
−3.57
(0.03)
−1.61
(0.58)
−0.11
(0.85)
−0.20
(0.42)
100
0.08
9.99
1994
21.38
(0.00)
−4.01
(0.08)
2.16
(0.70)
5.58
(0.43)
−1.31
(0.18)
−1.81
(0.08)
−3.20
(0.04)
−3.71
(0.11)
1.16
(0.06)
−0.45
(0.05)
115
0.17
9.13
1995
11.42
(0.01)
−1.79
(0.49)
9.04
(0.10)
−7.08
(0.25)
−1.30
(0.14)
−3.18
(0.00)
−2.47
(0.10)
−0.95
(0.66)
1.89
(0.15)
−0.07
(0.76)
123
0.09
8.62
1996
5.02
(0.45)
−0.64
(0.85)
15.69
(0.03)
−18.11
(0.06)
−2.33
(0.04)
−4.01
(0.00)
−5.22
(0.00)
−1.27
(0.63)
0.55
(0.61)
0.29
(0.36)
128
0.12
8.75
1997
17.97
(0.01)
−3.04
(0.30)
2.18
(0.76)
0.72
(0.95)
−3.11
(0.01)
−4.92
(0.00)
−5.42
(0.00)
−5.73
(0.02)
1.41
(0.31)
−0.13
(0.70)
170
0.13
9.66
Panel A shows results of a pooled regression using data for whole period. Panel B shows results of OLS regressions
on a year by year basis. Data for firms listed on the Oslo Stock Exchange, 1989-1997. Variable definitions are in
Appendix A.2.
164
B.6
Supplementary regressions
A full multivariate model
This appendix supplements chapter 9. Subsection B.6.1 shows the usual year by year, GMM, and
fixed effects regressions. The next four subsections consider the alternative performance measures:
RoA5 , RoA, RoS5 and RoS. Subsection B.6.6 includes intercorporate ownership as an explanatory
variable. Subsection B.6.7 considers outside concentration, and subsection B.6.8 considers voting
rights.
B.6.1
Year by year, GMM, and fixed effects regressions
This section lists tables which supplement the pooled OLS regression shown in the main text. Using
the same dependent and independent variables, we show OLS estimations on a year by year basis,
estimations using GMM, and we also control for systematic differences across years with indicator
variables for each year (fixed effects) in an OLS regression.
B.6 A full multivariate model
165
Table B.47 Multivariate regression relating performance (Q) to ownership concentration, insider
ownership, owner type (largest owner), board characteristics, security design, financial policy, and
controls (full multivariate model)
Panel A: Year by year OLS regressions
constant
Herfindahl index
Primary insiders
Squared (Primary insiders)
Largest owner is state
Largest owner is international
Largest owner is individual
Largest owner is nonfinancial
ln(Board size)
Fraction voting shares
Debt to assets
Dividend payout ratio
Industrial
Transport/shipping
Offshore
Investments over income
ln(Firm value)
n
R2
Average (Q)
1989
1.14
(0.26)
−1.21
(0.00)
0.26
(0.82)
0.36
(0.78)
0.43
(0.21)
0.20
(0.39)
0.11
(0.69)
0.24
(0.27)
−0.73
(0.00)
0.42
(0.56)
−0.35
(0.25)
0.06
(0.39)
−0.22
(0.15)
−0.13
(0.41)
−0.55
(0.24)
0.00
(0.92)
0.07
(0.01)
81
0.35
1.32
1990
0.89
(0.37)
−0.80
(0.03)
−0.00
(1.00)
0.63
(0.50)
−0.12
(0.63)
−0.23
(0.17)
−0.25
(0.31)
−0.14
(0.37)
−0.33
(0.09)
0.15
(0.81)
−0.26
(0.37)
0.03
(0.86)
−0.15
(0.22)
−0.25
(0.08)
−0.33
(0.21)
−0.01
(0.33)
0.06
(0.04)
73
0.31
1.18
1991
1.88
(0.17)
−1.34
(0.01)
0.77
(0.40)
−0.13
(0.89)
0.14
(0.63)
−0.36
(0.08)
−0.66
(0.02)
−0.34
(0.08)
−0.41
(0.06)
0.16
(0.83)
−0.86
(0.01)
0.04
(0.65)
−0.11
(0.44)
−0.36
(0.02)
−0.45
(0.07)
−0.00
(0.81)
0.05
(0.26)
64
0.45
1.13
1992
0.11
(0.91)
−0.61
(0.10)
3.14
(0.00)
−4.52
(0.01)
0.54
(0.02)
0.18
(0.35)
−0.17
(0.53)
0.11
(0.50)
−0.04
(0.79)
−0.31
(0.54)
0.10
(0.74)
−0.06
(0.34)
−0.18
(0.16)
−0.29
(0.03)
−0.38
(0.08)
−0.20
(0.15)
0.07
(0.01)
83
0.36
1.07
Year
1993
1.03
(0.45)
−0.85
(0.05)
0.46
(0.72)
−0.91
(0.57)
0.02
(0.95)
0.61
(0.02)
0.66
(0.12)
0.05
(0.81)
−0.03
(0.85)
−0.06
(0.94)
−0.64
(0.18)
−0.10
(0.26)
−0.24
(0.18)
−0.65
(0.00)
−0.73
(0.01)
−0.00
(0.96)
0.06
(0.17)
90
0.39
1.41
1994
−0.11
(0.92)
−0.77
(0.00)
1.10
(0.17)
−1.43
(0.17)
−0.26
(0.18)
0.06
(0.75)
0.24
(0.31)
−0.10
(0.49)
0.03
(0.82)
0.64
(0.36)
−0.58
(0.05)
0.05
(0.45)
−0.15
(0.19)
−0.44
(0.00)
−0.43
(0.05)
−0.08
(0.16)
0.07
(0.03)
98
0.40
1.34
1995
−2.25
(0.17)
−0.96
(0.06)
0.43
(0.70)
1.52
(0.21)
−0.69
(0.02)
−0.05
(0.86)
−0.03
(0.92)
−0.31
(0.18)
0.26
(0.22)
2.48
(0.01)
−1.29
(0.01)
−0.27
(0.33)
−0.26
(0.15)
−0.49
(0.02)
−0.47
(0.16)
−0.00
(0.98)
0.11
(0.03)
108
0.47
1.51
1996
4.70
(0.10)
−0.94
(0.30)
3.18
(0.15)
−3.69
(0.22)
−0.80
(0.12)
−0.45
(0.41)
0.19
(0.72)
−0.38
(0.30)
−0.49
(0.26)
−0.33
(0.83)
−2.70
(0.00)
−0.12
(0.72)
−0.30
(0.33)
−0.85
(0.02)
−0.80
(0.16)
−0.05
(0.60)
0.04
(0.72)
118
0.36
2.04
1997
0.62
(0.79)
−0.53
(0.48)
3.37
(0.03)
−2.99
(0.20)
−0.85
(0.07)
−0.66
(0.14)
−0.90
(0.02)
−0.66
(0.05)
−0.33
(0.19)
0.78
(0.55)
−3.16
(0.00)
−0.51
(0.09)
−0.16
(0.51)
−0.55
(0.08)
−0.80
(0.05)
−0.02
(0.73)
0.19
(0.03)
153
0.38
2.00
Panel B: Pooled GMM and fixed effects regressions
Dependent variable: Q
Dependent variable: Q
Constant
Herfindahl index
Primary insiders
Squared (Primary insiders)
Largest owner is state
Largest owner is international
Largest owner is individual
Largest owner is nonfinancial
ln(Board size)
Fraction voting shares
Debt to assets
Dividend payout ratio
Industrial
Transport/shipping
Offshore
Investments over income
ln(Firm value)
n
Average (Q)
coeff
0.20
-1.00
2.04
-1.61
-0.42
-0.28
-0.16
-0.29
-0.22
0.89
-1.62
-0.10
-0.25
-0.55
-0.65
-0.00
0.12
868
1.52
(stdev)
(0.52)
(0.16)
(0.69)
(0.87)
(0.12)
(0.14)
(0.19)
(0.12)
(0.08)
(0.25)
(0.29)
(0.03)
(0.08)
(0.08)
(0.12)
(0.01)
(0.02)
pvalue
0.70
0.00
0.00
0.06
0.00
0.04
0.39
0.01
0.01
0.00
0.00
0.00
0.00
0.00
0.00
0.48
0.00
Constant
Herfindahl index
Primary insiders
Squared (Primary insiders)
Largest owner is state
Largest owner is international
Largest owner is individual
Largest owner is nonfinancial
ln(Board size)
Fraction voting shares
Debt to assets
Dividend payout ratio
Industrial
Transport/shipping
Offshore
Investments over income
ln(Firm value)
1990
1991
1992
1993
1994
1995
1996
1997
n
R2
Average (Q)
coeff
0.88
-0.95
1.73
-1.27
-0.41
-0.21
-0.18
-0.30
-0.25
0.64
-1.48
-0.07
-0.21
-0.52
-0.66
-0.00
0.09
-0.23
-0.23
-0.13
0.07
-0.09
0.04
0.49
0.44
868
0.34
1.52
(stdev)
(0.60)
(0.20)
(0.47)
(0.58)
(0.13)
(0.12)
(0.13)
(0.09)
(0.08)
(0.35)
(0.17)
(0.05)
(0.07)
(0.08)
(0.13)
(0.01)
(0.02)
(0.14)
(0.14)
(0.13)
(0.13)
(0.13)
(0.13)
(0.12)
(0.12)
pvalue
0.14
0.00
0.00
0.03
0.00
0.06
0.17
0.00
0.00
0.07
0.00
0.15
0.00
0.00
0.00
0.77
0.00
0.09
0.11
0.32
0.59
0.51
0.73
0.00
0.00
This table complements the pooled OLS regression in table 9.1 by three additional regressions. Panel A shows OLS
estimates on a year by year basis. The left–hand side table in panel B lists pooled estimates, estimated with GMM.
The table on the right–hand side in panel B shows the results of a pooled OLS regression where dummy variables
for each year are used to capture fixed effects. 1989 is the base year in this regression. In regressions using firm size
across years the nominal values are adjusted to the 1997 general price level. Data for firms listed on the Oslo Stock
Exchange, 1989-1997. Variable definitions are in Appendix A.2.
166
Supplementary regressions
Table B.48 Multivariate regression relating performance (Q) to ownership concentration, insider
ownership, owner type (aggregate holdings), board characteristics, security design, financial policy,
and controls (full multivariate model)
Panel A: Year by year OLS regressions
constant
Herfindahl index
Primary insiders
Squared (Primary insiders)
Aggregate state holdings
Aggregate international holdings
Aggregate individual holdings
Aggregate nonfinancial holdings
ln(Board size)
Fraction voting shares
Debt to assets
Dividend payout ratio
Industrial
Transport/shipping
Offshore
Investments over income
ln(Firm value)
n
R2
Average (Q)
1989
1.01
(0.34)
−1.27
(0.01)
0.54
(0.65)
0.02
(0.99)
0.85
(0.21)
0.31
(0.53)
0.42
(0.51)
0.49
(0.32)
−0.78
(0.00)
0.30
(0.67)
−0.29
(0.34)
0.07
(0.32)
−0.18
(0.21)
−0.13
(0.42)
−0.48
(0.30)
0.00
(0.90)
0.08
(0.01)
81
0.35
1.32
1990
0.01
(1.00)
−0.81
(0.05)
−0.52
(0.52)
1.04
(0.25)
0.19
(0.73)
0.23
(0.61)
0.67
(0.25)
0.35
(0.43)
−0.29
(0.14)
0.31
(0.58)
−0.28
(0.35)
0.01
(0.93)
−0.13
(0.32)
−0.30
(0.04)
−0.39
(0.12)
−0.01
(0.62)
0.08
(0.03)
73
0.30
1.18
1991
−0.86
(0.64)
−1.39
(0.03)
0.06
(0.95)
0.28
(0.78)
1.00
(0.23)
0.57
(0.34)
0.84
(0.35)
0.47
(0.48)
−0.29
(0.22)
0.87
(0.25)
−0.74
(0.03)
0.05
(0.56)
0.01
(0.97)
−0.36
(0.05)
−0.54
(0.04)
−0.00
(0.95)
0.10
(0.06)
64
0.37
1.13
1992
−0.19
(0.85)
−0.47
(0.31)
2.00
(0.06)
−2.99
(0.09)
0.56
(0.32)
−0.26
(0.52)
0.47
(0.37)
−0.05
(0.91)
−0.07
(0.65)
−0.11
(0.83)
0.05
(0.86)
−0.07
(0.32)
−0.15
(0.25)
−0.27
(0.06)
−0.34
(0.13)
−0.09
(0.53)
0.08
(0.01)
83
0.34
1.07
Year
1993
0.51
(0.75)
−0.64
(0.24)
0.91
(0.45)
−1.44
(0.36)
−0.16
(0.82)
0.14
(0.81)
0.76
(0.31)
−0.50
(0.33)
−0.05
(0.78)
0.49
(0.56)
−0.98
(0.04)
−0.10
(0.26)
−0.20
(0.27)
−0.52
(0.02)
−0.69
(0.02)
−0.00
(0.99)
0.08
(0.14)
90
0.34
1.41
1994
−0.96
(0.40)
−0.81
(0.01)
1.16
(0.12)
−1.53
(0.13)
0.45
(0.38)
0.78
(0.04)
1.17
(0.01)
0.48
(0.22)
−0.05
(0.69)
0.77
(0.26)
−0.45
(0.13)
0.03
(0.65)
−0.15
(0.19)
−0.43
(0.00)
−0.43
(0.05)
−0.06
(0.25)
0.08
(0.03)
98
0.43
1.34
1995
−3.49
(0.04)
−0.98
(0.08)
0.41
(0.69)
1.31
(0.24)
0.05
(0.96)
1.71
(0.01)
1.62
(0.03)
0.57
(0.44)
0.42
(0.03)
2.65
(0.00)
−1.03
(0.02)
−0.12
(0.63)
−0.21
(0.23)
−0.38
(0.08)
−0.35
(0.27)
−0.01
(0.70)
0.08
(0.14)
108
0.53
1.51
1996
0.34
(0.92)
−0.52
(0.60)
2.66
(0.20)
−3.29
(0.25)
−0.43
(0.77)
1.62
(0.15)
2.87
(0.02)
0.64
(0.60)
−0.25
(0.55)
0.59
(0.70)
−2.59
(0.00)
−0.10
(0.75)
−0.14
(0.63)
−0.63
(0.11)
−0.75
(0.18)
−0.02
(0.87)
0.11
(0.35)
118
0.40
2.04
1997
−0.64
(0.82)
−0.42
(0.62)
2.88
(0.07)
−2.41
(0.31)
−1.17
(0.36)
0.04
(0.96)
0.10
(0.92)
−0.52
(0.56)
−0.23
(0.38)
1.27
(0.35)
−3.28
(0.00)
−0.46
(0.15)
−0.07
(0.77)
−0.44
(0.19)
−0.71
(0.09)
−0.01
(0.84)
0.20
(0.05)
153
0.36
2.00
Panel B: Pooled GMM and fixed effects regressions
Dependent variable: Q
Dependent variable: Q
Constant
Herfindahl index
Primary insiders
Squared (Primary insiders)
Aggregate state holdings
Aggregate international holdings
Aggregate individual holdings
Aggregate nonfinancial holdings
ln(Board size)
Fraction voting shares
Debt to assets
Dividend payout ratio
Industrial
Transport/shipping
Offshore
Investments over income
ln(Firm value)
n
Average (Q)
coeff
-0.94
-0.68
1.64
-1.37
-0.43
0.12
1.02
-0.23
-0.19
1.14
-1.54
-0.11
-0.19
-0.46
-0.57
-0.00
0.14
868
1.52
(stdev)
(0.58)
(0.20)
(0.62)
(0.84)
(0.26)
(0.24)
(0.32)
(0.21)
(0.07)
(0.26)
(0.29)
(0.03)
(0.09)
(0.07)
(0.11)
(0.00)
(0.02)
pvalue
0.10
0.00
0.01
0.10
0.10
0.63
0.00
0.25
0.01
0.00
0.00
0.00
0.02
0.00
0.00
0.95
0.00
Constant
Herfindahl index
Primary insiders
Squared (Primary insiders)
Aggregate state holdings
Aggregate international holdings
Aggregate individual holdings
Aggregate nonfinancial holdings
ln(Board size)
Fraction voting shares
Debt to assets
Dividend payout ratio
Industrial
Transport/shipping
Offshore
Investments over income
ln(Firm value)
1990
1991
1992
1993
1994
1995
1996
1997
n
R2
Average (Q)
coeff
-0.42
-0.82
1.43
-1.15
0.01
0.61
1.10
0.10
-0.23
0.93
-1.36
-0.08
-0.16
-0.45
-0.60
-0.00
0.10
-0.20
-0.20
-0.11
0.11
-0.05
0.06
0.52
0.49
868
0.35
1.52
(stdev)
(0.68)
(0.24)
(0.46)
(0.56)
(0.34)
(0.25)
(0.29)
(0.25)
(0.08)
(0.35)
(0.17)
(0.05)
(0.07)
(0.09)
(0.13)
(0.01)
(0.02)
(0.14)
(0.14)
(0.13)
(0.13)
(0.13)
(0.13)
(0.13)
(0.12)
pvalue
0.54
0.00
0.00
0.04
0.99
0.01
0.00
0.68
0.01
0.01
0.00
0.13
0.03
0.00
0.00
0.86
0.00
0.15
0.17
0.42
0.41
0.68
0.64
0.00
0.00
This table complements the pooled OLS regression in table 9.2 by three additional regressions. Panel A shows OLS
estimates on a year by year basis. The left–hand side table in panel B lists pooled estimates, estimated with GMM.
The table on the right–hand side in panel B shows the results of a pooled OLS regression where dummy variables
for each year are used to capture fixed effects. 1989 is the base year in this regression. In regressions using firm size
across years the nominal values are adjusted to the 1997 general price level. Data for firms listed on the Oslo Stock
Exchange, 1989-1997. Variable definitions are in Appendix A.2.
B.6 A full multivariate model
B.6.2
167
Alternative performance measure: RoA5
Table B.49 Multivariate regression relating performance (RoA5 ) to ownership concentration, insider ownership, type of largest owner, board characteristics, security design, financial policy, and
controls (full multivariate model)
Panel A: Pooled OLS regression
Dependent variable: RoA5
Constant
Herfindahl index
Primary insiders
Squared (Primary insiders)
Largest owner is state
Largest owner is international
Largest owner is individual
Largest owner is nonfinancial
ln(Board size)
Fraction voting shares
Debt to assets
Dividend payout ratio
Industrial
Transport/shipping
Offshore
Investments over income
ln(Firm value)
n
R2
Average (RoA5 )
coeff
16.54
-2.22
7.80
-7.07
-0.21
0.53
-0.26
-0.30
-0.75
-1.21
-5.98
0.35
-1.39
-1.33
-3.06
-0.08
0.01
851
0.12
9.68
(stdev)
(3.22)
(1.17)
(2.56)
(3.11)
(0.73)
(0.63)
(0.71)
(0.50)
(0.45)
(1.89)
(0.95)
(0.27)
(0.40)
(0.45)
(0.71)
(0.06)
(0.10)
pvalue
0.00
0.06
0.00
0.02
0.78
0.40
0.71
0.55
0.10
0.52
0.00
0.20
0.00
0.00
0.00
0.16
0.89
Panel B: Year by year OLS regressions
constant
Herfindahl index
Primary insiders
Squared (Primary insiders)
Largest owner is state
Largest owner is international
Largest owner is individual
Largest owner is nonfinancial
ln(Board size)
Fraction voting shares
Debt to assets
Dividend payout ratio
Industrial
Transport/shipping
Offshore
Investments over income
ln(Firm value)
n
R2
Average (RoA5 )
1989
−2.34
(0.78)
−2.99
(0.43)
10.00
(0.28)
−13.92
(0.17)
2.04
(0.47)
2.36
(0.21)
2.52
(0.26)
1.05
(0.55)
−0.91
(0.63)
7.19
(0.21)
−3.98
(0.11)
0.09
(0.87)
−1.22
(0.32)
0.10
(0.94)
−1.08
(0.77)
−0.08
(0.36)
0.46
(0.05)
80
0.20
10.08
1990
3.12
(0.75)
−0.11
(0.98)
5.33
(0.51)
−3.97
(0.66)
−1.34
(0.59)
−2.51
(0.12)
−0.64
(0.79)
−1.15
(0.45)
1.70
(0.37)
2.17
(0.71)
−3.10
(0.28)
0.91
(0.56)
−0.18
(0.88)
0.43
(0.76)
0.97
(0.70)
−0.03
(0.84)
0.19
(0.55)
72
0.15
9.67
1991
−0.57
(0.97)
−4.32
(0.39)
12.02
(0.16)
−9.14
(0.31)
−0.05
(0.98)
−2.10
(0.29)
−1.70
(0.52)
−0.64
(0.73)
1.06
(0.61)
2.20
(0.75)
−4.59
(0.12)
1.29
(0.07)
0.79
(0.56)
−0.26
(0.86)
−0.98
(0.67)
0.05
(0.56)
0.46
(0.28)
63
0.29
9.30
1992
11.55
(0.27)
−0.08
(0.99)
32.60
(0.01)
−46.47
(0.02)
1.37
(0.61)
2.97
(0.18)
−0.98
(0.75)
0.64
(0.73)
1.82
(0.25)
−3.75
(0.52)
−6.26
(0.06)
−0.16
(0.83)
1.12
(0.44)
2.46
(0.12)
−0.94
(0.70)
−0.80
(0.62)
−0.00
(0.99)
82
0.20
9.90
Year
1993
28.55
(0.00)
−2.11
(0.52)
22.95
(0.01)
−29.78
(0.01)
−0.49
(0.82)
2.49
(0.21)
−4.03
(0.19)
−1.11
(0.47)
−0.36
(0.77)
−9.10
(0.12)
−2.78
(0.43)
−0.12
(0.85)
−2.94
(0.03)
−0.70
(0.63)
−4.28
(0.04)
0.23
(0.57)
−0.27
(0.41)
88
0.24
10.17
1994
29.49
(0.00)
−3.63
(0.14)
−2.93
(0.68)
19.31
(0.03)
0.26
(0.88)
1.73
(0.25)
−0.34
(0.88)
0.57
(0.64)
−1.62
(0.16)
−7.35
(0.22)
−2.61
(0.31)
0.50
(0.42)
−1.84
(0.07)
−1.19
(0.28)
−4.64
(0.01)
−0.71
(0.15)
−0.37
(0.18)
96
0.43
9.60
1995
2.07
(0.81)
−4.39
(0.14)
7.14
(0.23)
−5.09
(0.43)
−0.55
(0.73)
0.35
(0.83)
1.80
(0.24)
0.29
(0.81)
0.05
(0.97)
6.66
(0.19)
−2.41
(0.34)
1.85
(0.21)
−0.99
(0.31)
−2.24
(0.06)
−1.96
(0.27)
−0.34
(0.11)
0.11
(0.68)
106
0.24
8.99
1996
19.32
(0.11)
2.36
(0.54)
16.23
(0.06)
−19.70
(0.09)
−2.25
(0.27)
−2.36
(0.28)
−2.04
(0.34)
−0.99
(0.50)
−0.76
(0.64)
−5.50
(0.36)
−4.22
(0.21)
−0.30
(0.82)
−2.49
(0.04)
−4.15
(0.00)
−3.86
(0.07)
−0.23
(0.57)
0.08
(0.85)
113
0.21
9.28
1997
25.96
(0.03)
−0.99
(0.80)
−0.91
(0.91)
4.54
(0.70)
1.34
(0.57)
2.10
(0.35)
−0.46
(0.82)
0.16
(0.92)
−2.34
(0.07)
−2.44
(0.71)
−10.20
(0.00)
0.54
(0.72)
−2.76
(0.02)
−4.24
(0.01)
−4.82
(0.02)
−0.29
(0.27)
−0.07
(0.87)
151
0.24
10.08
Panel A shows results of a pooled regression using data for whole period. Panel B shows results of OLS regressions
on a year by year basis. Data for firms listed on the Oslo Stock Exchange, 1989-1997. Variable definitions are in
Appendix A.2.
168
Supplementary regressions
Table B.50 Multivariate regression relating performance (RoA5 ) to ownership concentration, insider ownership, owner type (aggregate holdings), board characteristics, security design, financial
policy, and controls (full multivariate model)
Panel A: Pooled OLS regression
Dependent variable: RoA5
Constant
Herfindahl index
Primary insiders
Squared (Primary insiders)
Aggregate state holdings
Aggregate international holdings
Aggregate individual holdings
Aggregate nonfinancial holdings
ln(Board size)
Fraction voting shares
Debt to assets
Dividend payout ratio
Industrial
Transport/shipping
Offshore
Investments over income
ln(Firm value)
n
R2
Average (RoA5 )
coeff
17.14
-1.99
7.44
-6.92
0.92
0.70
0.53
-0.97
-0.75
-0.75
-5.93
0.32
-1.37
-1.09
-2.97
-0.07
-0.04
851
0.12
9.68
(stdev)
(3.67)
(1.31)
(2.49)
(3.06)
(1.97)
(1.30)
(1.60)
(1.33)
(0.45)
(1.88)
(0.96)
(0.27)
(0.40)
(0.47)
(0.72)
(0.06)
(0.11)
pvalue
0.00
0.13
0.00
0.02
0.64
0.59
0.74
0.47
0.09
0.69
0.00
0.24
0.00
0.02
0.00
0.19
0.69
Panel B: Year by year OLS regressions
constant
Herfindahl index
Primary insiders
Squared (Primary insiders)
Aggregate state holdings
Aggregate international holdings
Aggregate individual holdings
Aggregate nonfinancial holdings
ln(Board size)
Fraction voting shares
Debt to assets
Dividend payout ratio
Industrial
Transport/shipping
Offshore
Investments over income
ln(Firm value)
n
R2
Average (RoA5 )
1989
−0.64
(0.94)
0.10
(0.98)
11.26
(0.23)
−15.76
(0.12)
0.60
(0.92)
1.92
(0.63)
2.37
(0.64)
−2.36
(0.54)
−1.13
(0.56)
7.47
(0.18)
−3.61
(0.14)
0.00
(1.00)
−0.95
(0.41)
0.95
(0.48)
−1.21
(0.74)
−0.08
(0.33)
0.41
(0.08)
80
0.22
10.08
1990
5.45
(0.63)
−1.25
(0.76)
6.98
(0.38)
−5.45
(0.54)
−1.50
(0.81)
−2.00
(0.65)
−3.02
(0.59)
−0.46
(0.92)
1.26
(0.51)
0.92
(0.87)
−3.58
(0.23)
1.35
(0.40)
−0.50
(0.69)
−0.20
(0.90)
1.06
(0.66)
−0.02
(0.88)
0.20
(0.56)
72
0.12
9.67
1991
−5.90
(0.72)
−5.66
(0.34)
10.00
(0.22)
−7.09
(0.43)
2.10
(0.78)
−1.21
(0.82)
0.35
(0.96)
1.83
(0.76)
1.09
(0.60)
2.48
(0.71)
−4.50
(0.13)
1.32
(0.07)
0.98
(0.49)
−0.81
(0.62)
−0.99
(0.66)
0.06
(0.49)
0.64
(0.16)
63
0.28
9.30
1992
7.00
(0.56)
0.80
(0.88)
25.29
(0.04)
−37.32
(0.06)
3.29
(0.63)
3.22
(0.48)
5.56
(0.34)
1.56
(0.74)
1.68
(0.31)
−1.39
(0.81)
−6.02
(0.08)
−0.32
(0.67)
1.56
(0.29)
2.74
(0.09)
−0.63
(0.80)
−0.49
(0.75)
0.03
(0.93)
82
0.18
9.90
Year
1993
36.15
(0.00)
−6.04
(0.11)
18.89
(0.03)
−27.55
(0.01)
8.31
(0.11)
8.89
(0.03)
−0.28
(0.96)
1.96
(0.59)
−0.89
(0.49)
−8.89
(0.13)
−1.51
(0.66)
−0.18
(0.78)
−2.66
(0.04)
−0.94
(0.53)
−4.86
(0.02)
0.21
(0.62)
−0.78
(0.04)
88
0.25
10.17
1994
36.62
(0.00)
−4.26
(0.12)
−0.87
(0.89)
16.43
(0.06)
0.54
(0.91)
1.08
(0.74)
−3.96
(0.32)
−2.80
(0.41)
−1.42
(0.22)
−6.23
(0.29)
−3.50
(0.17)
0.63
(0.30)
−2.22
(0.03)
−0.98
(0.38)
−5.24
(0.00)
−0.70
(0.12)
−0.67
(0.04)
96
0.44
9.60
1995
9.38
(0.34)
−3.34
(0.31)
10.00
(0.08)
−7.36
(0.24)
−2.60
(0.64)
−1.24
(0.74)
−2.43
(0.57)
−4.60
(0.28)
−0.28
(0.80)
7.25
(0.15)
−3.47
(0.18)
1.69
(0.25)
−1.33
(0.17)
−1.67
(0.17)
−2.26
(0.21)
−0.32
(0.14)
−0.09
(0.77)
106
0.25
8.99
1996
25.31
(0.08)
1.40
(0.73)
16.13
(0.05)
−18.77
(0.11)
−2.94
(0.66)
−2.02
(0.65)
−5.24
(0.29)
−2.29
(0.63)
−0.64
(0.70)
−5.82
(0.33)
−4.71
(0.18)
−0.48
(0.70)
−2.51
(0.04)
−4.21
(0.01)
−3.88
(0.07)
−0.25
(0.54)
−0.13
(0.79)
113
0.21
9.28
1997
24.14
(0.09)
−0.21
(0.96)
−1.48
(0.85)
5.01
(0.67)
2.24
(0.73)
1.07
(0.82)
0.80
(0.87)
−0.34
(0.94)
−2.27
(0.08)
−2.20
(0.74)
−9.52
(0.00)
0.59
(0.71)
−2.83
(0.02)
−4.14
(0.01)
−4.65
(0.03)
−0.26
(0.32)
−0.02
(0.96)
151
0.23
10.08
Panel A shows results of a pooled regression using data for whole period. Panel B shows results of OLS regressions
on a year by year basis. Data for firms listed on the Oslo Stock Exchange, 1989-1997. Variable definitions are in
Appendix A.2.
B.6 A full multivariate model
B.6.3
169
Alternative performance measure: RoA
Table B.51 Multivariate regression relating performance (RoA) to ownership concentration, insider
ownership, owner type (largest owner), board characteristics, security design, financial policy, and
controls (full multivariate model)
Panel A: Pooled OLS regression
Dependent variable: RoA
Constant
Herfindahl index
Primary insiders
Squared (Primary insiders)
Largest owner is state
Largest owner is international
Largest owner is individual
Largest owner is nonfinancial
ln(Board size)
Fraction voting shares
Debt to assets
Dividend payout ratio
Industrial
Transport/shipping
Offshore
Investments over income
ln(Firm value)
n
R2
Average (RoA)
coeff
-29.34
-0.20
23.00
-35.65
-1.56
-0.34
-2.75
2.25
-0.27
-1.52
6.08
2.12
-1.66
-1.50
-1.27
0.02
1.65
869
0.10
5.99
(stdev)
(8.78)
(3.05)
(7.02)
(8.57)
(1.98)
(1.72)
(1.94)
(1.38)
(1.25)
(5.25)
(2.55)
(0.75)
(1.09)
(1.24)
(1.98)
(0.16)
(0.28)
pvalue
0.00
0.95
0.00
0.00
0.43
0.85
0.16
0.10
0.83
0.77
0.02
0.00
0.13
0.23
0.52
0.90
0.00
Panel B: Year by year OLS regressions
constant
Herfindahl index
Primary insiders
Squared (Primary insiders)
Largest owner is state
Largest owner is international
Largest owner is individual
Largest owner is nonfinancial
ln(Board size)
Fraction voting shares
Debt to assets
Dividend payout ratio
Industrial
Transport/shipping
Offshore
Investments over income
ln(Firm value)
n
R2
Average (RoA)
1989
−30.37
(0.39)
−5.76
(0.70)
48.54
(0.23)
−39.31
(0.36)
−4.45
(0.71)
−3.71
(0.65)
−19.75
(0.04)
−0.60
(0.94)
−15.38
(0.06)
−1.39
(0.96)
25.22
(0.02)
1.85
(0.44)
4.89
(0.34)
4.29
(0.43)
4.27
(0.79)
−0.02
(0.95)
2.62
(0.01)
81
0.34
7.94
1990
7.90
(0.68)
−17.79
(0.01)
4.53
(0.78)
−1.15
(0.95)
−1.01
(0.84)
−4.59
(0.16)
−2.71
(0.57)
−0.15
(0.96)
2.79
(0.47)
−4.97
(0.67)
3.44
(0.54)
8.67
(0.00)
−1.46
(0.56)
0.20
(0.94)
−0.31
(0.95)
−0.14
(0.61)
0.01
(0.99)
73
0.31
7.97
1991
−65.37
(0.04)
−21.05
(0.07)
5.75
(0.79)
−16.74
(0.46)
4.22
(0.53)
6.70
(0.17)
13.07
(0.05)
9.07
(0.05)
−1.99
(0.70)
33.29
(0.05)
−14.59
(0.05)
2.04
(0.26)
4.03
(0.23)
−4.23
(0.26)
−4.60
(0.43)
0.13
(0.55)
2.28
(0.02)
64
0.39
4.66
1992
−4.00
(0.83)
−17.59
(0.02)
34.64
(0.12)
−33.76
(0.35)
0.46
(0.92)
−5.98
(0.14)
−3.65
(0.52)
−0.68
(0.84)
1.21
(0.68)
−4.27
(0.69)
−5.11
(0.41)
0.84
(0.54)
−2.48
(0.35)
−1.56
(0.59)
−3.27
(0.47)
−7.54
(0.01)
0.98
(0.09)
83
0.34
4.06
Year
1993
17.19
(0.22)
−12.55
(0.01)
14.51
(0.28)
−9.15
(0.58)
−3.73
(0.22)
−5.78
(0.04)
−2.33
(0.60)
0.09
(0.97)
−3.58
(0.04)
−10.83
(0.19)
−4.04
(0.42)
0.21
(0.82)
−0.31
(0.87)
1.15
(0.57)
−2.97
(0.30)
−0.22
(0.71)
0.59
(0.20)
90
0.35
6.87
1994
−21.59
(0.41)
14.78
(0.02)
110.33
(0.00)
−195.65
(0.00)
−0.97
(0.84)
−2.41
(0.57)
−13.33
(0.02)
−0.25
(0.94)
−3.28
(0.32)
1.62
(0.93)
9.70
(0.18)
0.62
(0.72)
−5.49
(0.05)
−4.84
(0.12)
−6.55
(0.21)
0.75
(0.60)
1.35
(0.08)
98
0.58
5.77
1995
−2.67
(0.89)
−2.81
(0.66)
17.41
(0.22)
−37.65
(0.01)
−0.52
(0.89)
5.58
(0.15)
6.05
(0.09)
5.07
(0.08)
3.20
(0.22)
−11.36
(0.34)
2.33
(0.68)
7.35
(0.03)
−0.50
(0.82)
−3.57
(0.18)
−1.20
(0.77)
−0.22
(0.66)
0.50
(0.42)
109
0.30
7.01
1996
−57.22
(0.03)
−0.21
(0.98)
−4.64
(0.82)
−10.85
(0.69)
−2.01
(0.67)
4.34
(0.38)
7.29
(0.13)
4.21
(0.21)
1.02
(0.80)
3.91
(0.78)
9.48
(0.18)
3.97
(0.18)
−4.15
(0.14)
−3.71
(0.28)
−5.90
(0.26)
0.29
(0.76)
2.50
(0.01)
118
0.15
6.08
1997
−43.57
(0.24)
14.54
(0.22)
15.19
(0.54)
−12.15
(0.74)
−1.62
(0.83)
0.22
(0.98)
−5.06
(0.42)
−2.56
(0.63)
1.54
(0.70)
−2.41
(0.91)
5.67
(0.58)
5.88
(0.22)
−2.32
(0.54)
0.28
(0.95)
3.76
(0.56)
−0.12
(0.88)
2.09
(0.12)
153
0.09
4.46
Panel A shows results of a pooled regression using data for whole period. Panel B shows results of OLS regressions
on a year by year basis. Data for firms listed on the Oslo Stock Exchange, 1989-1997. Variable definitions are in
Appendix A.2.
170
Supplementary regressions
Table B.52 Multivariate regression relating performance (RoA) to ownership concentration, insider
ownership, aggregate holdings per owner types, board characteristics, security design, financial
policy, and controls (full multivariate model)
Panel A: Pooled OLS regression
Dependent variable: RoA
Constant
Herfindahl index
Primary insiders
Squared (Primary insiders)
Aggregate state holdings
Aggregate international holdings
Aggregate individual holdings
Aggregate nonfinancial holdings
ln(Board size)
Fraction voting shares
Debt to assets
Dividend payout ratio
Industrial
Transport/shipping
Offshore
Investments over income
ln(Firm value)
n
R2
Average (RoA)
coeff
-26.44
4.80
15.81
-28.46
-17.32
-10.73
-5.35
-3.14
0.12
-3.49
5.51
1.97
-1.34
-1.47
-1.05
0.05
1.86
869
0.11
5.99
(stdev)
(9.98)
(3.55)
(6.82)
(8.41)
(5.02)
(3.59)
(4.39)
(3.68)
(1.24)
(5.20)
(2.56)
(0.74)
(1.09)
(1.29)
(1.98)
(0.16)
(0.31)
pvalue
0.01
0.18
0.02
0.00
0.00
0.00
0.22
0.39
0.92
0.50
0.03
0.01
0.22
0.25
0.60
0.73
0.00
Panel B: Year by year OLS regressions
constant
Herfindahl index
Primary insiders
Squared (Primary insiders)
Aggregate state holdings
Aggregate international holdings
Aggregate individual holdings
Aggregate nonfinancial holdings
ln(Board size)
Fraction voting shares
Debt to assets
Dividend payout ratio
Industrial
Transport/shipping
Offshore
Investments over income
ln(Firm value)
n
R2
Average (RoA)
1989
−35.36
(0.35)
−2.34
(0.90)
27.84
(0.51)
−23.18
(0.61)
−19.88
(0.42)
−14.35
(0.43)
−21.30
(0.35)
−13.87
(0.43)
−16.62
(0.06)
13.52
(0.59)
30.92
(0.00)
0.91
(0.72)
7.98
(0.12)
6.39
(0.29)
6.80
(0.68)
−0.02
(0.96)
2.57
(0.01)
81
0.28
7.94
1990
10.17
(0.63)
−16.26
(0.03)
−1.77
(0.91)
4.27
(0.80)
−12.22
(0.24)
−14.54
(0.09)
−5.69
(0.60)
−2.21
(0.79)
2.20
(0.55)
−6.63
(0.53)
0.23
(0.97)
7.70
(0.01)
−1.33
(0.58)
−1.21
(0.67)
−0.78
(0.87)
−0.11
(0.69)
0.38
(0.54)
73
0.36
7.97
1991
−26.88
(0.51)
−11.66
(0.42)
15.65
(0.44)
−24.80
(0.27)
−19.06
(0.30)
−19.72
(0.14)
−5.09
(0.80)
−5.68
(0.70)
−4.28
(0.40)
18.41
(0.27)
−16.47
(0.03)
1.86
(0.31)
2.35
(0.50)
−5.39
(0.17)
−2.01
(0.72)
0.18
(0.40)
2.12
(0.05)
64
0.39
4.66
1992
−5.31
(0.81)
−8.49
(0.37)
27.95
(0.21)
−27.10
(0.46)
−14.45
(0.22)
−8.85
(0.29)
−3.12
(0.77)
−7.39
(0.38)
1.38
(0.65)
−5.09
(0.63)
−5.02
(0.42)
1.32
(0.33)
−2.49
(0.36)
−1.41
(0.64)
−2.57
(0.58)
−6.69
(0.02)
1.24
(0.05)
83
0.32
4.06
Year
1993
9.84
(0.55)
−9.37
(0.09)
11.87
(0.33)
−5.70
(0.72)
−9.79
(0.17)
−7.93
(0.19)
−0.57
(0.94)
0.98
(0.85)
−3.43
(0.07)
−11.08
(0.20)
−3.13
(0.52)
0.07
(0.94)
−0.63
(0.74)
0.49
(0.82)
−2.20
(0.48)
−0.15
(0.81)
0.95
(0.08)
90
0.32
6.87
1994
−22.62
(0.44)
17.04
(0.04)
85.98
(0.00)
−177.30
(0.00)
−11.65
(0.38)
−4.16
(0.67)
−1.56
(0.89)
−4.03
(0.69)
−3.02
(0.38)
1.87
(0.92)
9.86
(0.19)
0.62
(0.73)
−3.95
(0.18)
−3.38
(0.31)
−5.26
(0.34)
−0.63
(0.64)
1.45
(0.13)
98
0.55
5.77
1995
6.15
(0.79)
7.49
(0.32)
13.21
(0.33)
−33.97
(0.02)
−22.07
(0.07)
−12.26
(0.16)
0.61
(0.95)
−11.70
(0.24)
1.37
(0.60)
−14.18
(0.23)
−0.94
(0.87)
5.97
(0.08)
−0.37
(0.87)
−1.51
(0.59)
−1.92
(0.65)
−0.07
(0.89)
0.97
(0.19)
109
0.30
7.01
1996
−28.36
(0.37)
8.21
(0.37)
5.28
(0.78)
−18.05
(0.50)
−28.58
(0.04)
−20.63
(0.05)
−14.47
(0.22)
−13.96
(0.21)
−0.59
(0.88)
−1.82
(0.90)
6.58
(0.36)
2.55
(0.39)
−4.66
(0.09)
−3.18
(0.38)
−5.15
(0.32)
0.26
(0.78)
2.34
(0.03)
118
0.17
6.08
1997
−58.26
(0.19)
19.46
(0.14)
12.25
(0.62)
−9.15
(0.80)
−23.22
(0.24)
−19.43
(0.18)
−8.76
(0.56)
−6.80
(0.62)
1.85
(0.64)
−2.72
(0.90)
4.14
(0.68)
5.61
(0.25)
−1.98
(0.60)
−0.69
(0.89)
2.91
(0.65)
−0.17
(0.83)
3.12
(0.04)
153
0.11
4.46
Panel A shows results of a pooled regression using data for whole period. Panel B shows results of OLS regressions
on a year by year basis. Data for firms listed on the Oslo Stock Exchange, 1989-1997. Variable definitions are in
Appendix A.2.
B.6 A full multivariate model
B.6.4
171
Alternative performance measure: RoS5
Table B.53 Multivariate regression relating performance (RoS5 ) to ownership concentration, insider ownership, owner type (largest owner), board characteristics, security design, financial policy,
and controls (full multivariate model)
Panel A: Pooled OLS regression
Dependent variable: RoS5
Constant
Herfindahl index
Primary insiders
Squared (Primary insiders)
Largest owner is state
Largest owner is international
Largest owner is individual
Largest owner is nonfinancial
ln(Board size)
Fraction voting shares
Debt to assets
Dividend payout ratio
Industrial
Transport/shipping
Offshore
Investments over income
ln(Firm value)
n
R2
Average (RoS5 )
coeff
99.37
-16.19
-24.18
76.91
-15.47
0.62
10.70
-11.53
-17.84
39.27
-63.78
-4.60
13.08
2.73
7.32
-0.64
-0.90
621
0.11
44.67
(stdev)
(44.29)
(15.38)
(35.03)
(43.24)
(9.40)
(8.50)
(9.96)
(6.94)
(6.68)
(24.28)
(13.17)
(3.44)
(5.62)
(6.29)
(10.36)
(0.92)
(1.48)
pvalue
0.02
0.29
0.49
0.08
0.10
0.94
0.28
0.10
0.01
0.11
0.00
0.18
0.02
0.66
0.48
0.49
0.54
Panel B: Year by year OLS regressions
constant
Herfindahl index
Primary insiders
Squared (Primary insiders)
Largest owner is state
Largest owner is international
Largest owner is individual
Largest owner is nonfinancial
ln(Board size)
Fraction voting shares
Debt to assets
Dividend payout ratio
Industrial
Transport/shipping
Offshore
Investments over income
ln(Firm value)
n
R2
Average (RoS5 )
1989
−18.08
(0.82)
−3.71
(0.94)
−61.86
(0.43)
43.19
(0.64)
22.98
(0.39)
18.00
(0.38)
6.89
(0.76)
16.02
(0.42)
−23.08
(0.23)
19.45
(0.68)
−36.69
(0.19)
3.90
(0.43)
−11.46
(0.33)
12.75
(0.32)
−22.59
(0.45)
−0.95
(0.18)
4.47
(0.12)
58
0.35
36.85
1990
−71.39
(0.13)
8.58
(0.64)
−14.46
(0.74)
15.12
(0.78)
−2.53
(0.85)
1.21
(0.90)
3.87
(0.75)
3.44
(0.69)
−2.89
(0.78)
9.53
(0.71)
−7.19
(0.62)
−1.74
(0.81)
−1.86
(0.77)
14.94
(0.03)
14.70
(0.45)
−0.71
(0.27)
4.45
(0.01)
54
0.40
23.70
1991
−115.70
(0.02)
−16.40
(0.38)
−40.84
(0.18)
22.53
(0.52)
−18.08
(0.10)
8.50
(0.31)
12.99
(0.25)
10.82
(0.16)
−1.84
(0.84)
8.22
(0.75)
−32.24
(0.01)
0.90
(0.71)
−0.37
(0.95)
6.22
(0.29)
−5.62
(0.72)
−3.53
(0.06)
7.44
(0.00)
43
0.71
22.90
1992
−42.80
(0.64)
17.79
(0.52)
−56.55
(0.63)
94.57
(0.78)
−14.28
(0.42)
7.55
(0.61)
2.88
(0.88)
0.63
(0.97)
10.96
(0.49)
−13.89
(0.76)
5.44
(0.81)
1.84
(0.73)
−16.16
(0.16)
9.21
(0.46)
−5.15
(0.80)
−21.80
(0.32)
3.16
(0.25)
40
0.37
27.29
Year
1993
413.49
(0.04)
8.23
(0.90)
83.26
(0.74)
−198.71
(0.63)
−8.14
(0.84)
93.05
(0.02)
17.63
(0.77)
3.35
(0.92)
−47.69
(0.17)
−122.65
(0.27)
20.05
(0.78)
0.78
(0.95)
3.83
(0.89)
−18.72
(0.54)
−20.69
(0.62)
−1.87
(0.81)
−8.66
(0.15)
72
0.23
53.27
1994
262.02
(0.03)
9.75
(0.79)
−17.16
(0.87)
73.44
(0.55)
−41.91
(0.04)
−34.05
(0.11)
−71.01
(0.04)
−46.46
(0.01)
−6.04
(0.73)
−38.63
(0.62)
−100.25
(0.01)
0.55
(0.94)
−25.31
(0.11)
−24.71
(0.14)
−28.03
(0.28)
−8.66
(0.59)
−2.94
(0.42)
71
0.34
38.54
1995
115.46
(0.48)
−54.20
(0.25)
−257.01
(0.03)
390.64
(0.00)
−38.61
(0.21)
29.95
(0.34)
92.20
(0.00)
−20.07
(0.38)
−17.57
(0.48)
113.76
(0.20)
−96.92
(0.04)
−36.99
(0.18)
56.26
(0.00)
9.80
(0.68)
24.02
(0.47)
17.83
(0.01)
−4.57
(0.37)
82
0.53
54.21
1996
389.92
(0.00)
−35.16
(0.40)
99.16
(0.34)
−117.45
(0.39)
−12.22
(0.59)
−6.63
(0.79)
8.08
(0.76)
−11.16
(0.50)
−14.79
(0.45)
−14.13
(0.83)
−131.90
(0.00)
−3.25
(0.84)
28.52
(0.05)
−3.14
(0.86)
12.25
(0.64)
4.72
(0.33)
−10.69
(0.02)
96
0.31
56.86
1997
173.87
(0.06)
−4.27
(0.88)
81.54
(0.22)
−80.63
(0.39)
1.73
(0.92)
−13.01
(0.46)
9.85
(0.57)
−9.21
(0.48)
−6.91
(0.52)
1.88
(0.97)
−105.48
(0.00)
−16.20
(0.18)
28.79
(0.01)
11.02
(0.39)
13.59
(0.47)
18.16
(0.05)
−2.94
(0.38)
105
0.34
54.95
Panel A shows results of a pooled regression using data for whole period. Panel B shows results of OLS regressions
on a year by year basis. Data for firms listed on the Oslo Stock Exchange, 1989-1997. Variable definitions are in
Appendix A.2.
172
Supplementary regressions
Table B.54 Multivariate regression relating performance (RoS5 ) to ownership concentration, insider ownership, aggregate holdings per owner type, board characteristics, security design, financial
policy, and controls (full multivariate model)
Panel A: Pooled OLS regression
Dependent variable: RoS5
Constant
Herfindahl index
Primary insiders
Squared (Primary insiders)
Aggregate state holdings
Aggregate international holdings
Aggregate individual holdings
Aggregate nonfinancial holdings
ln(Board size)
Fraction voting shares
Debt to assets
Dividend payout ratio
Industrial
Transport/shipping
Offshore
Investments over income
ln(Firm value)
n
R2
Average (RoS5 )
coeff
41.38
2.69
-23.81
70.17
-17.52
18.53
63.68
-11.12
-16.01
51.64
-63.63
-4.97
15.27
8.32
9.42
-0.44
0.13
621
0.12
44.67
(stdev)
(52.42)
(17.67)
(34.06)
(42.65)
(25.33)
(18.78)
(23.56)
(19.40)
(6.64)
(23.94)
(13.21)
(3.42)
(5.66)
(6.57)
(10.45)
(0.92)
(1.68)
pvalue
0.43
0.88
0.48
0.10
0.49
0.32
0.01
0.57
0.02
0.03
0.00
0.15
0.01
0.20
0.37
0.63
0.94
Panel B: Year by year OLS regressions
constant
Herfindahl index
Primary insiders
Squared (Primary insiders)
Aggregate state holdings
Aggregate international holdings
Aggregate individual holdings
Aggregate nonfinancial holdings
ln(Board size)
Fraction voting shares
Debt to assets
Dividend payout ratio
Industrial
Transport/shipping
Offshore
Investments over income
ln(Firm value)
n
R2
Average (RoS5 )
1989
42.63
(0.65)
−10.28
(0.85)
−48.43
(0.54)
48.59
(0.60)
14.77
(0.82)
−0.70
(0.99)
−56.11
(0.29)
−11.68
(0.81)
−26.35
(0.18)
21.57
(0.63)
−31.98
(0.24)
3.75
(0.47)
−8.57
(0.43)
15.28
(0.24)
−23.76
(0.42)
−1.03
(0.14)
2.90
(0.34)
58
0.36
36.85
1990
−78.54
(0.14)
6.37
(0.74)
3.49
(0.93)
3.86
(0.94)
11.82
(0.68)
31.24
(0.22)
−0.36
(0.99)
26.48
(0.30)
−3.00
(0.76)
3.81
(0.87)
−3.39
(0.81)
−0.23
(0.97)
−2.75
(0.66)
12.26
(0.08)
17.74
(0.28)
−0.83
(0.17)
4.14
(0.02)
54
0.44
23.70
1991
−72.72
(0.24)
−22.28
(0.34)
−28.21
(0.36)
26.93
(0.47)
−24.83
(0.45)
24.45
(0.30)
−19.92
(0.54)
15.52
(0.55)
3.46
(0.74)
−8.25
(0.73)
−29.01
(0.02)
1.25
(0.63)
−4.65
(0.42)
8.57
(0.20)
0.36
(0.98)
−4.60
(0.02)
5.51
(0.00)
43
0.70
22.90
1992
−130.27
(0.23)
49.22
(0.16)
−6.79
(0.95)
−91.22
(0.77)
−34.06
(0.36)
44.67
(0.14)
43.87
(0.35)
5.28
(0.86)
21.29
(0.17)
−8.42
(0.82)
16.61
(0.43)
−0.88
(0.85)
−25.32
(0.02)
12.86
(0.31)
−8.82
(0.63)
−44.69
(0.03)
5.20
(0.08)
40
0.48
27.29
Year
1993
465.42
(0.06)
2.31
(0.98)
95.38
(0.70)
−217.86
(0.59)
−9.15
(0.93)
149.66
(0.10)
−13.87
(0.90)
18.94
(0.83)
−47.17
(0.18)
−95.81
(0.42)
8.87
(0.90)
2.08
(0.88)
−5.30
(0.86)
−24.35
(0.47)
−50.33
(0.27)
−1.70
(0.83)
−12.81
(0.08)
72
0.17
53.27
1994
251.83
(0.07)
−29.02
(0.50)
−100.72
(0.28)
136.65
(0.25)
65.12
(0.31)
79.60
(0.10)
40.95
(0.51)
31.43
(0.52)
−4.95
(0.79)
−22.83
(0.77)
−56.36
(0.18)
−0.81
(0.92)
−20.86
(0.21)
−30.42
(0.10)
−26.11
(0.35)
−5.81
(0.74)
−8.04
(0.09)
71
0.28
38.54
1995
−94.91
(0.64)
−10.60
(0.86)
−146.78
(0.20)
285.26
(0.02)
−1.32
(0.99)
81.16
(0.30)
217.96
(0.03)
0.29
(1.00)
−13.34
(0.61)
160.75
(0.08)
−85.78
(0.11)
−34.46
(0.24)
56.69
(0.01)
27.98
(0.27)
32.27
(0.37)
17.12
(0.03)
−1.02
(0.87)
82
0.47
54.21
1996
274.99
(0.08)
−18.10
(0.67)
66.56
(0.47)
−87.83
(0.48)
−6.82
(0.92)
63.12
(0.22)
115.84
(0.06)
−16.54
(0.77)
−9.13
(0.62)
18.07
(0.77)
−128.69
(0.00)
10.07
(0.53)
35.02
(0.01)
12.56
(0.47)
12.83
(0.61)
6.98
(0.14)
−9.49
(0.06)
96
0.39
56.86
1997
86.46
(0.42)
32.80
(0.28)
74.67
(0.23)
−61.59
(0.49)
−38.06
(0.40)
−58.28
(0.10)
67.07
(0.10)
−70.22
(0.04)
0.33
(0.97)
32.70
(0.49)
−111.89
(0.00)
−14.70
(0.20)
31.21
(0.00)
23.64
(0.06)
26.64
(0.13)
20.06
(0.02)
−0.18
(0.96)
105
0.42
54.95
Panel A shows results of a pooled regression using data for whole period. Panel B shows results of OLS regressions
on a year by year basis. Data for firms listed on the Oslo Stock Exchange, 1989-1997. Variable definitions are in
Appendix A.2.
B.6 A full multivariate model
B.6.5
173
Alternative performance measure: RoS
Table B.55 Multivariate regression relating performance (RoS) to ownership concentration, insider
ownership, owner type (largest owner), board characteristics, security design, financial policy, and
controls (full multivariate model)
Panel A: Pooled OLS regression
Dependent variable: RoS
Constant
Herfindahl index
Primary insiders
Squared (Primary insiders)
Largest owner is state
Largest owner is international
Largest owner is individual
Largest owner is nonfinancial
ln(Board size)
Fraction voting shares
Debt to assets
Dividend payout ratio
Industrial
Transport/shipping
Offshore
Investments over income
ln(Firm value)
n
R2
Average (RoS)
coeff
-90.02
-0.15
46.70
-13.85
-9.37
4.88
11.69
-3.28
-13.37
57.83
-23.93
1.12
6.32
-2.87
18.20
-0.22
5.08
743
0.03
34.55
(stdev)
(66.23)
(22.53)
(52.58)
(62.82)
(14.11)
(12.49)
(15.07)
(10.04)
(9.51)
(37.67)
(19.07)
(5.45)
(8.16)
(9.15)
(15.19)
(1.13)
(2.18)
pvalue
0.17
0.99
0.37
0.83
0.51
0.70
0.44
0.74
0.16
0.12
0.21
0.84
0.44
0.75
0.23
0.84
0.02
Panel B: Year by year OLS regressions
constant
Herfindahl index
Primary insiders
Squared (Primary insiders)
Largest owner is state
Largest owner is international
Largest owner is individual
Largest owner is nonfinancial
ln(Board size)
Fraction voting shares
Debt to assets
Dividend payout ratio
Industrial
Transport/shipping
Offshore
Investments over income
ln(Firm value)
n
R2
Average (RoS)
1989
−15.25
(0.93)
−62.70
(0.34)
59.59
(0.72)
−74.24
(0.68)
5.49
(0.91)
21.04
(0.53)
−8.31
(0.84)
20.40
(0.52)
−45.60
(0.20)
−30.31
(0.77)
23.61
(0.63)
10.96
(0.28)
7.00
(0.75)
65.79
(0.01)
−6.58
(0.92)
−1.44
(0.35)
7.25
(0.17)
75
0.32
54.57
1990
100.34
(0.24)
4.30
(0.90)
79.27
(0.30)
−109.00
(0.20)
−6.25
(0.79)
−29.29
(0.04)
−24.82
(0.27)
−16.05
(0.23)
24.65
(0.20)
13.80
(0.79)
−52.96
(0.04)
3.94
(0.78)
−4.33
(0.69)
−20.43
(0.11)
73.40
(0.05)
−0.05
(0.97)
−5.52
(0.07)
66
0.38
−0.64
1991
−128.33
(0.30)
80.29
(0.07)
−96.13
(0.24)
109.76
(0.20)
−27.60
(0.28)
27.11
(0.14)
7.41
(0.77)
1.31
(0.94)
−23.04
(0.28)
−18.27
(0.78)
−67.80
(0.02)
7.85
(0.25)
9.17
(0.47)
−7.33
(0.61)
−38.16
(0.08)
−0.88
(0.27)
10.25
(0.01)
62
0.44
−13.98
1992
−394.82
(0.01)
−17.89
(0.73)
435.98
(0.00)
−597.12
(0.01)
48.97
(0.13)
18.67
(0.48)
14.77
(0.69)
23.00
(0.31)
17.95
(0.38)
123.29
(0.13)
26.20
(0.50)
−12.14
(0.28)
4.96
(0.78)
−6.45
(0.74)
4.07
(0.89)
−42.91
(0.05)
9.54
(0.04)
68
0.31
−19.73
Year
1993
450.75
(0.10)
−90.72
(0.31)
120.19
(0.71)
−142.68
(0.79)
−43.34
(0.44)
65.20
(0.23)
−30.25
(0.71)
−5.24
(0.91)
−26.78
(0.49)
−76.73
(0.61)
101.74
(0.27)
4.04
(0.83)
−42.09
(0.26)
−109.86
(0.01)
−10.80
(0.85)
−0.44
(0.97)
−10.40
(0.22)
77
0.23
122.05
1994
18.99
(0.90)
77.34
(0.07)
93.85
(0.47)
−166.60
(0.29)
25.98
(0.35)
6.42
(0.82)
−37.56
(0.41)
−7.07
(0.74)
−3.91
(0.86)
−10.31
(0.92)
0.87
(0.99)
−0.97
(0.93)
2.45
(0.89)
−2.11
(0.92)
2.61
(0.93)
−0.38
(0.99)
−0.20
(0.97)
86
0.13
10.33
1995
−211.82
(0.31)
−125.92
(0.04)
−67.82
(0.64)
210.06
(0.16)
−51.46
(0.18)
−14.41
(0.72)
92.63
(0.01)
−36.23
(0.20)
1.26
(0.96)
278.31
(0.02)
−208.93
(0.00)
−62.90
(0.06)
34.43
(0.15)
29.43
(0.28)
13.35
(0.74)
5.04
(0.29)
6.19
(0.33)
94
0.45
45.52
1996
203.03
(0.38)
−56.97
(0.45)
−30.32
(0.87)
115.76
(0.64)
−11.11
(0.78)
27.27
(0.53)
1.51
(0.97)
22.49
(0.44)
−55.19
(0.11)
33.54
(0.78)
−65.96
(0.31)
−14.58
(0.62)
31.07
(0.22)
−10.30
(0.73)
35.55
(0.45)
2.79
(0.73)
−2.68
(0.74)
102
0.12
56.85
1997
−236.83
(0.10)
47.08
(0.28)
125.56
(0.21)
−133.19
(0.35)
5.79
(0.83)
9.92
(0.71)
16.02
(0.53)
−21.67
(0.28)
14.30
(0.37)
94.76
(0.22)
−21.88
(0.61)
12.58
(0.49)
−10.30
(0.51)
24.86
(0.19)
51.64
(0.07)
35.91
(0.01)
6.53
(0.20)
113
0.22
30.64
Panel A shows results of a pooled regression using data for whole period. Panel B shows results of OLS regressions
on a year by year basis. Data for firms listed on the Oslo Stock Exchange, 1989-1997. Variable definitions are in
Appendix A.2.
174
Supplementary regressions
Table B.56 Multivariate regression relating performance (RoS) to ownership concentration, insider ownership, aggregate holdings per owner type, board characteristics, security design, financial
policy, and controls (full multivariate model)
Panel A: Pooled OLS regression
Dependent variable: RoS
Constant
Herfindahl index
Primary insiders
Squared (Primary insiders)
Aggregate state holdings
Aggregate international holdings
Aggregate individual holdings
Aggregate nonfinancial holdings
ln(Board size)
Fraction voting shares
Debt to assets
Dividend payout ratio
Industrial
Transport/shipping
Offshore
Investments over income
ln(Firm value)
n
R2
Average (RoS)
coeff
-183.51
25.84
23.45
-4.42
-2.36
-0.55
108.24
-2.52
-12.19
71.19
-19.86
-0.26
10.77
4.12
26.83
0.15
7.65
743
0.05
34.55
(stdev)
(75.64)
(26.02)
(50.54)
(61.14)
(36.00)
(26.15)
(34.41)
(26.96)
(9.43)
(37.07)
(19.09)
(5.41)
(8.14)
(9.48)
(15.23)
(1.13)
(2.38)
pvalue
0.02
0.32
0.64
0.94
0.95
0.98
0.00
0.93
0.20
0.05
0.30
0.96
0.19
0.66
0.08
0.89
0.00
Panel B: Year by year OLS regressions
constant
Herfindahl index
Primary insiders
Squared (Primary insiders)
Aggregate state holdings
Aggregate international holdings
Aggregate individual holdings
Aggregate nonfinancial holdings
ln(Board size)
Fraction voting shares
Debt to assets
Dividend payout ratio
Industrial
Transport/shipping
Offshore
Investments over income
ln(Firm value)
n
R2
Average (RoS)
1989
−81.27
(0.63)
−130.76
(0.08)
137.22
(0.41)
−148.92
(0.40)
168.03
(0.08)
107.81
(0.13)
61.27
(0.50)
122.22
(0.08)
−50.68
(0.16)
−25.72
(0.80)
39.67
(0.39)
14.54
(0.15)
7.46
(0.72)
62.57
(0.01)
−4.98
(0.94)
−1.57
(0.30)
6.59
(0.21)
75
0.35
54.57
1990
185.58
(0.04)
−17.99
(0.61)
66.34
(0.34)
−94.91
(0.22)
−10.38
(0.83)
−88.79
(0.02)
−90.52
(0.06)
−68.00
(0.06)
14.65
(0.42)
11.23
(0.80)
−57.58
(0.02)
7.58
(0.57)
−11.08
(0.29)
−25.59
(0.04)
70.14
(0.03)
−0.20
(0.86)
−5.76
(0.05)
66
0.42
−0.64
1991
−87.36
(0.57)
126.79
(0.02)
−87.01
(0.27)
84.41
(0.32)
−112.84
(0.11)
−3.01
(0.95)
−2.88
(0.97)
−39.72
(0.48)
−15.98
(0.45)
−11.24
(0.86)
−66.06
(0.02)
6.65
(0.34)
6.87
(0.60)
0.64
(0.97)
−42.38
(0.05)
−0.96
(0.24)
8.19
(0.05)
62
0.44
−13.98
1992
−512.03
(0.00)
9.14
(0.89)
329.52
(0.02)
−463.41
(0.04)
84.43
(0.26)
11.91
(0.82)
161.58
(0.02)
39.22
(0.47)
19.51
(0.33)
136.83
(0.06)
21.05
(0.56)
−14.64
(0.16)
8.93
(0.60)
1.19
(0.95)
16.25
(0.57)
−27.93
(0.19)
13.22
(0.00)
68
0.38
−19.73
Year
1993
710.04
(0.03)
−132.58
(0.21)
126.77
(0.68)
−111.16
(0.83)
−77.26
(0.56)
84.85
(0.45)
−209.46
(0.15)
−28.06
(0.77)
−24.58
(0.54)
−94.21
(0.54)
86.59
(0.34)
6.00
(0.74)
−59.48
(0.11)
−120.38
(0.00)
−47.26
(0.44)
−0.29
(0.98)
−19.63
(0.05)
77
0.24
122.05
1994
4.87
(0.98)
10.09
(0.83)
16.06
(0.88)
−99.02
(0.48)
226.08
(0.00)
72.92
(0.19)
117.57
(0.10)
71.05
(0.20)
−16.16
(0.43)
−7.09
(0.94)
5.26
(0.90)
−3.15
(0.74)
12.07
(0.48)
0.78
(0.97)
23.19
(0.44)
−2.16
(0.91)
−2.17
(0.69)
86
0.25
10.33
1995
−378.77
(0.10)
−64.80
(0.38)
50.35
(0.71)
104.46
(0.46)
−81.39
(0.49)
42.03
(0.63)
209.29
(0.05)
−83.25
(0.41)
13.22
(0.62)
338.62
(0.00)
−218.71
(0.00)
−55.59
(0.10)
40.03
(0.10)
56.99
(0.05)
30.70
(0.45)
4.48
(0.36)
8.03
(0.28)
94
0.45
45.52
1996
−44.53
(0.87)
−22.70
(0.78)
−115.03
(0.50)
173.73
(0.46)
−16.73
(0.89)
74.29
(0.41)
178.63
(0.09)
97.57
(0.32)
−44.17
(0.20)
40.08
(0.73)
−45.68
(0.49)
−15.45
(0.61)
37.74
(0.13)
−6.01
(0.85)
32.89
(0.48)
4.18
(0.60)
4.04
(0.66)
102
0.15
56.85
1997
−329.12
(0.05)
76.85
(0.10)
154.21
(0.11)
−155.10
(0.26)
41.37
(0.55)
−80.73
(0.13)
55.14
(0.39)
−86.64
(0.10)
21.57
(0.17)
132.55
(0.08)
−44.28
(0.28)
1.88
(0.92)
−11.47
(0.46)
41.36
(0.03)
65.58
(0.02)
39.70
(0.00)
9.99
(0.07)
113
0.27
30.64
Panel A shows results of a pooled regression using data for whole period. Panel B shows results of OLS regressions
on a year by year basis. Data for firms listed on the Oslo Stock Exchange, 1989-1997. Variable definitions are in
Appendix A.2.
B.6 A full multivariate model
B.6.6
175
Intercorporate ownership
Table B.57 Multivariate regression relating performance (Q) to ownership concentration, insider
ownership, aggregate intercorporate ownership by listed firms, board characteristics, security design, financial policy, and controls (full multivariate model)
Panel A: Pooled OLS regression
Dependent variable: Q
Constant
Herfindahl index
Primary insiders
Squared (Primary insiders)
Aggregate intercorporate holdings
ln(Board size)
Fraction voting shares
Debt to assets
Dividend payout ratio
Industrial
Transport/shipping
Offshore
Investments over income
ln(Firm value)
n
R2
Average (Q)
coeff
-0.20
-1.15
2.15
-1.73
-0.41
-0.21
1.05
-1.62
-0.10
-0.25
-0.57
-0.64
-0.00
0.12
866
0.27
1.52
(stdev)
(0.59)
(0.21)
(0.46)
(0.58)
(0.21)
(0.09)
(0.36)
(0.18)
(0.05)
(0.07)
(0.09)
(0.14)
(0.01)
(0.02)
pvalue
0.74
0.00
0.00
0.00
0.05
0.01
0.00
0.00
0.06
0.00
0.00
0.00
0.71
0.00
Panel B: Year by year OLS regressions
constant
Herfindahl index
Primary insiders
Squared (Primary insiders)
Aggregate intercorporate holdings
ln(Board size)
Fraction voting shares
Debt to assets
Dividend payout ratio
Industrial
Transport/shipping
Offshore
Investments over income
ln(Firm value)
n
R2
Average (Q)
1989
1.48
(0.11)
−1.10
(0.00)
0.34
(0.76)
0.28
(0.82)
0.21
(0.46)
−0.79
(0.00)
0.31
(0.64)
−0.29
(0.32)
0.06
(0.38)
−0.15
(0.28)
−0.13
(0.42)
−0.49
(0.28)
0.00
(0.84)
0.07
(0.02)
80
0.35
1.32
1990
0.62
(0.51)
−0.79
(0.02)
−0.30
(0.68)
0.88
(0.31)
−0.16
(0.56)
−0.32
(0.09)
0.25
(0.65)
−0.28
(0.33)
0.05
(0.76)
−0.16
(0.20)
−0.28
(0.03)
−0.39
(0.11)
−0.01
(0.44)
0.07
(0.03)
73
0.29
1.18
1991
0.19
(0.89)
−0.93
(0.04)
−0.04
(0.97)
0.42
(0.67)
−0.31
(0.47)
−0.26
(0.24)
0.58
(0.42)
−0.85
(0.01)
0.04
(0.64)
−0.02
(0.91)
−0.39
(0.02)
−0.56
(0.03)
−0.00
(1.00)
0.08
(0.05)
64
0.35
1.13
1992
−0.39
(0.66)
−0.22
(0.53)
2.23
(0.03)
−3.36
(0.05)
−0.28
(0.38)
0.05
(0.71)
−0.12
(0.81)
0.07
(0.82)
−0.03
(0.60)
−0.13
(0.31)
−0.25
(0.09)
−0.35
(0.12)
−0.09
(0.52)
0.08
(0.00)
83
0.29
1.07
Year
1993
0.68
(0.62)
−0.87
(0.04)
1.44
(0.20)
−1.92
(0.21)
−0.36
(0.44)
−0.02
(0.92)
0.23
(0.78)
−0.91
(0.05)
−0.06
(0.47)
−0.27
(0.13)
−0.61
(0.00)
−0.76
(0.01)
−0.01
(0.91)
0.08
(0.08)
90
0.31
1.41
1994
−0.34
(0.74)
−0.82
(0.00)
1.58
(0.03)
−1.74
(0.08)
−0.38
(0.23)
0.02
(0.88)
0.86
(0.22)
−0.57
(0.05)
0.05
(0.52)
−0.20
(0.08)
−0.46
(0.00)
−0.39
(0.07)
−0.05
(0.35)
0.07
(0.02)
98
0.37
1.34
1995
−3.10
(0.04)
−1.00
(0.05)
0.84
(0.43)
1.21
(0.30)
−0.53
(0.34)
0.27
(0.19)
2.89
(0.00)
−1.08
(0.02)
−0.29
(0.29)
−0.40
(0.03)
−0.53
(0.01)
−0.41
(0.22)
−0.01
(0.87)
0.12
(0.02)
108
0.43
1.51
1996
4.47
(0.09)
−1.20
(0.17)
4.37
(0.03)
−4.94
(0.08)
−0.29
(0.80)
−0.44
(0.32)
−0.09
(0.95)
−2.78
(0.00)
−0.20
(0.54)
−0.30
(0.33)
−0.81
(0.03)
−0.67
(0.23)
−0.06
(0.53)
0.02
(0.83)
118
0.34
2.04
1997
−0.40
(0.86)
−0.87
(0.23)
3.39
(0.03)
−2.86
(0.21)
2.21
(0.04)
−0.33
(0.19)
0.92
(0.48)
−3.37
(0.00)
−0.56
(0.06)
−0.13
(0.58)
−0.64
(0.04)
−0.77
(0.05)
−0.01
(0.89)
0.20
(0.01)
152
0.37
2.01
Panel A shows results of a pooled regression using data for whole period. Panel B shows results of OLS regressions
on a year by year basis. Data for firms listed on the Oslo Stock Exchange, 1989-1997. Variable definitions are in
Appendix A.2.
176
Supplementary regressions
Table B.58 Multivariate regression relating performance (RoA5 ) to ownership concentration, insider ownership, the largest owner being listed, board characteristics, security design, financial
policy, and controls (full multivariate model)
Panel A: Pooled OLS regression
Dependent variable: Q
Constant
Herfindahl index
Primary insiders
Squared (Primary insiders)
Largest owner is listed
ln(Board size)
Fraction voting shares
Debt to assets
Dividend payout ratio
Industrial
Transport/shipping
Offshore
Investments over income
ln(Firm value)
n
R2
Average (Q)
coeff
-0.15
-1.19
2.19
-1.75
-0.07
-0.22
1.04
-1.60
-0.10
-0.26
-0.58
-0.64
-0.00
0.12
868
0.27
1.52
(stdev)
(0.59)
(0.21)
(0.46)
(0.58)
(0.09)
(0.09)
(0.36)
(0.18)
(0.05)
(0.07)
(0.09)
(0.14)
(0.01)
(0.02)
pvalue
0.80
0.00
0.00
0.00
0.42
0.01
0.00
0.00
0.05
0.00
0.00
0.00
0.68
0.00
Panel B: Year by year OLS regressions
constant
Herfindahl index
Primary insiders
Squared (Primary insiders)
Largest owner is listed
ln(Board size)
Fraction voting shares
Debt to assets
Dividend payout ratio
Industrial
Transport/shipping
Offshore
Investments over income
ln(Firm value)
n
R2
Average (Q)
1989
1.26
(0.18)
−0.94
(0.01)
0.20
(0.86)
0.38
(0.76)
−0.05
(0.70)
−0.76
(0.00)
0.40
(0.56)
−0.30
(0.31)
0.05
(0.48)
−0.14
(0.32)
−0.08
(0.58)
−0.48
(0.30)
0.00
(0.91)
0.07
(0.01)
81
0.33
1.32
1990
0.60
(0.52)
−0.81
(0.01)
−0.33
(0.65)
0.92
(0.29)
−0.11
(0.45)
−0.34
(0.08)
0.28
(0.61)
−0.27
(0.34)
0.05
(0.74)
−0.15
(0.21)
−0.30
(0.03)
−0.40
(0.10)
−0.01
(0.40)
0.07
(0.03)
73
0.29
1.18
1991
0.10
(0.94)
−1.00
(0.02)
−0.03
(0.98)
0.42
(0.67)
−0.18
(0.33)
−0.27
(0.22)
0.70
(0.34)
−0.81
(0.01)
0.04
(0.65)
0.01
(0.95)
−0.38
(0.02)
−0.54
(0.03)
−0.00
(0.93)
0.08
(0.06)
64
0.35
1.13
1992
−0.36
(0.68)
−0.24
(0.50)
2.20
(0.04)
−3.31
(0.06)
−0.09
(0.54)
0.05
(0.72)
−0.14
(0.77)
0.09
(0.76)
−0.03
(0.62)
−0.14
(0.28)
−0.26
(0.07)
−0.37
(0.10)
−0.09
(0.53)
0.08
(0.00)
83
0.28
1.07
Year
1993
0.72
(0.60)
−0.91
(0.03)
1.43
(0.21)
−1.91
(0.21)
−0.14
(0.41)
−0.02
(0.91)
0.24
(0.77)
−0.90
(0.05)
−0.07
(0.43)
−0.28
(0.12)
−0.62
(0.00)
−0.77
(0.01)
−0.01
(0.92)
0.08
(0.09)
90
0.31
1.41
1994
−0.35
(0.74)
−0.84
(0.00)
1.57
(0.03)
−1.70
(0.09)
−0.12
(0.38)
−0.01
(0.92)
0.88
(0.21)
−0.57
(0.05)
0.04
(0.60)
−0.20
(0.08)
−0.47
(0.00)
−0.41
(0.05)
−0.05
(0.33)
0.07
(0.02)
98
0.37
1.34
1995
−3.15
(0.04)
−1.06
(0.04)
0.86
(0.42)
1.21
(0.30)
−0.07
(0.77)
0.25
(0.22)
2.90
(0.00)
−1.08
(0.02)
−0.32
(0.24)
−0.41
(0.02)
−0.53
(0.01)
−0.41
(0.22)
−0.01
(0.87)
0.12
(0.02)
108
0.42
1.51
1996
4.43
(0.10)
−1.22
(0.17)
4.40
(0.03)
−4.91
(0.08)
0.15
(0.74)
−0.51
(0.24)
−0.14
(0.93)
−2.80
(0.00)
−0.20
(0.53)
−0.29
(0.35)
−0.83
(0.02)
−0.66
(0.24)
−0.06
(0.55)
0.03
(0.76)
118
0.34
2.04
1997
−0.76
(0.73)
−0.75
(0.30)
3.57
(0.02)
−3.18
(0.16)
0.68
(0.08)
−0.32
(0.21)
0.96
(0.46)
−3.38
(0.00)
−0.53
(0.08)
−0.11
(0.64)
−0.52
(0.09)
−0.75
(0.06)
−0.01
(0.84)
0.22
(0.01)
153
0.36
2.00
Panel A shows results of a pooled regression using data for whole period. Panel B shows results of OLS regressions
on a year by year basis. Data for firms listed on the Oslo Stock Exchange, 1989-1997. Variable definitions are in
Appendix A.2.
B.6 A full multivariate model
B.6.7
177
Outside (external) concentration
Table B.59 Multivariate regression relating performance (RoA5 ) to outside (external) ownership
concentration, insider ownership, the type of the largest owner, board characteristics, security
design, financial policy, and controls (full multivariate model)
Panel A: Pooled OLS regression
Dependent variable: Q
Constant
Largest outside owner
Primary insiders
Squared (Primary insiders)
Largest owner is state
Largest owner is international
Largest owner is individual
Largest owner is nonfinancial
ln(Board size)
Fraction voting shares
Debt to assets
Dividend payout ratio
Industrial
Transport/shipping
Offshore
Investments over income
ln(Firm value)
n
R2
Average (Q)
coeff
0.12
-0.55
2.02
-1.69
-0.47
-0.30
-0.19
-0.30
-0.20
0.81
-1.56
-0.10
-0.25
-0.55
-0.64
-0.00
0.12
868
0.27
1.52
(stdev)
(0.61)
(0.17)
(0.49)
(0.60)
(0.14)
(0.12)
(0.14)
(0.10)
(0.09)
(0.37)
(0.18)
(0.05)
(0.08)
(0.09)
(0.14)
(0.01)
(0.02)
pvalue
0.84
0.00
0.00
0.00
0.00
0.01
0.16
0.00
0.02
0.03
0.00
0.05
0.00
0.00
0.00
0.80
0.00
Panel B: Year by year OLS regressions
constant
Largest outside owner
Primary insiders
Squared (Primary insiders)
Largest owner is state
Largest owner is international
Largest owner is individual
Largest owner is nonfinancial
ln(Board size)
Fraction voting shares
Debt to assets
Dividend payout ratio
Industrial
Transport/shipping
Offshore
Investments over income
ln(Firm value)
n
R2
Average (Q)
1989
1.01
(0.33)
−0.69
(0.05)
0.04
(0.98)
0.47
(0.71)
0.30
(0.40)
0.19
(0.43)
0.03
(0.93)
0.20
(0.37)
−0.70
(0.00)
0.31
(0.68)
−0.31
(0.32)
0.05
(0.50)
−0.19
(0.21)
−0.11
(0.49)
−0.53
(0.28)
0.00
(0.80)
0.08
(0.01)
81
0.31
1.32
1990
0.87
(0.39)
−0.42
(0.15)
−0.10
(0.90)
0.75
(0.43)
−0.20
(0.42)
−0.24
(0.16)
−0.34
(0.16)
−0.17
(0.29)
−0.31
(0.13)
0.01
(0.99)
−0.24
(0.42)
0.01
(0.96)
−0.15
(0.25)
−0.24
(0.10)
−0.26
(0.32)
−0.01
(0.35)
0.07
(0.03)
73
0.28
1.18
1991
1.74
(0.22)
−0.85
(0.03)
1.01
(0.28)
−0.60
(0.55)
0.07
(0.82)
−0.33
(0.13)
−0.71
(0.01)
−0.32
(0.12)
−0.41
(0.07)
0.09
(0.90)
−0.75
(0.02)
0.05
(0.50)
−0.12
(0.43)
−0.41
(0.01)
−0.43
(0.08)
−0.00
(0.85)
0.06
(0.19)
64
0.42
1.13
1992
0.02
(0.99)
−0.46
(0.12)
3.27
(0.00)
−4.81
(0.01)
0.53
(0.02)
0.18
(0.35)
−0.16
(0.56)
0.11
(0.51)
−0.03
(0.84)
−0.31
(0.54)
0.16
(0.58)
−0.06
(0.33)
−0.16
(0.22)
−0.28
(0.04)
−0.37
(0.09)
−0.20
(0.16)
0.07
(0.01)
83
0.36
1.07
Year
1993
0.88
(0.52)
−0.65
(0.08)
0.52
(0.69)
−1.08
(0.50)
0.01
(0.96)
0.63
(0.02)
0.62
(0.14)
0.08
(0.70)
−0.01
(0.97)
−0.03
(0.97)
−0.55
(0.25)
−0.12
(0.19)
−0.26
(0.15)
−0.67
(0.00)
−0.74
(0.01)
−0.01
(0.92)
0.06
(0.15)
90
0.38
1.41
1994
−0.14
(0.90)
−0.48
(0.04)
1.11
(0.18)
−1.55
(0.14)
−0.29
(0.15)
0.05
(0.77)
0.21
(0.38)
−0.10
(0.50)
0.05
(0.70)
0.57
(0.43)
−0.49
(0.10)
0.07
(0.36)
−0.16
(0.18)
−0.45
(0.00)
−0.45
(0.04)
−0.08
(0.16)
0.07
(0.03)
98
0.37
1.34
1995
−2.14
(0.19)
−0.78
(0.05)
0.24
(0.83)
1.66
(0.17)
−0.67
(0.03)
−0.03
(0.92)
−0.07
(0.81)
−0.29
(0.21)
0.28
(0.19)
2.46
(0.01)
−1.16
(0.01)
−0.27
(0.33)
−0.25
(0.17)
−0.49
(0.02)
−0.43
(0.20)
−0.00
(0.96)
0.10
(0.04)
108
0.47
1.51
1996
4.94
(0.09)
−0.20
(0.79)
3.38
(0.13)
−4.01
(0.18)
−0.85
(0.10)
−0.55
(0.32)
0.14
(0.80)
−0.42
(0.26)
−0.49
(0.26)
−0.66
(0.67)
−2.71
(0.00)
−0.18
(0.57)
−0.32
(0.30)
−0.87
(0.02)
−0.81
(0.16)
−0.05
(0.60)
0.04
(0.70)
118
0.36
2.04
1997
0.65
(0.78)
−0.11
(0.85)
3.45
(0.03)
−3.17
(0.17)
−0.89
(0.06)
−0.72
(0.10)
−0.92
(0.02)
−0.70
(0.04)
−0.34
(0.18)
0.66
(0.62)
−3.16
(0.00)
−0.54
(0.07)
−0.17
(0.49)
−0.55
(0.08)
−0.80
(0.05)
−0.02
(0.72)
0.19
(0.03)
153
0.38
2.00
Panel A shows results of a pooled regression using data for whole period. Panel B shows results of OLS regressions
on a year by year basis. Data for firms listed on the Oslo Stock Exchange, 1989-1997. Variable definitions are in
Appendix A.2.
178
Supplementary regressions
Table B.60 Multivariate regression relating performance (Q) to (outside) ownership concentration,
insider ownership, aggregate holdings by owner type, board characteristics, security design, financial
policy, and controls (full multivariate model)
Panel A: Pooled OLS regression
Dependent variable: Q
Constant
Largest outside owner
Primary insiders
Squared (Primary insiders)
Aggregate state holdings
Aggregate international holdings
Aggregate individual holdings
Aggregate nonfinancial holdings
ln(Board size)
Fraction voting shares
Debt to assets
Dividend payout ratio
Industrial
Transport/shipping
Offshore
Investments over income
ln(Firm value)
n
R2
Average (Q)
coeff
-0.99
-0.19
1.58
-1.37
-0.75
0.00
1.02
-0.38
-0.17
1.06
-1.52
-0.11
-0.19
-0.44
-0.56
0.00
0.14
868
0.28
1.52
(stdev)
(0.69)
(0.20)
(0.47)
(0.58)
(0.34)
(0.25)
(0.31)
(0.26)
(0.09)
(0.36)
(0.18)
(0.05)
(0.08)
(0.09)
(0.14)
(0.01)
(0.02)
pvalue
0.15
0.33
0.00
0.02
0.03
0.99
0.00
0.13
0.05
0.00
0.00
0.03
0.01
0.00
0.00
0.92
0.00
Panel B: Year by year OLS regressions
constant
Largest outside owner
Primary insiders
Squared (Primary insiders)
Aggregate state holdings
Aggregate international holdings
Aggregate individual holdings
Aggregate nonfinancial holdings
ln(Board size)
Fraction voting shares
Debt to assets
Dividend payout ratio
Industrial
Transport/shipping
Offshore
Investments over income
ln(Firm value)
n
R2
Average (Q)
1989
0.80
(0.46)
−0.59
(0.15)
0.18
(0.88)
0.25
(0.85)
0.44
(0.52)
0.31
(0.56)
0.39
(0.55)
0.32
(0.52)
−0.72
(0.00)
0.28
(0.70)
−0.20
(0.51)
0.04
(0.54)
−0.14
(0.34)
−0.08
(0.65)
−0.46
(0.34)
0.00
(0.83)
0.08
(0.01)
81
0.30
1.32
1990
−0.00
(1.00)
−0.30
(0.37)
−0.71
(0.39)
1.24
(0.18)
−0.14
(0.79)
0.13
(0.77)
0.58
(0.35)
0.20
(0.66)
−0.26
(0.21)
0.23
(0.70)
−0.26
(0.39)
−0.01
(0.94)
−0.11
(0.39)
−0.27
(0.08)
−0.36
(0.16)
−0.01
(0.68)
0.08
(0.02)
73
0.26
1.18
1991
−1.00
(0.60)
−0.65
(0.19)
0.18
(0.85)
−0.10
(0.93)
0.52
(0.52)
0.45
(0.47)
0.80
(0.39)
0.32
(0.64)
−0.27
(0.27)
0.83
(0.29)
−0.62
(0.07)
0.06
(0.48)
0.00
(0.98)
−0.38
(0.05)
−0.54
(0.04)
0.00
(0.97)
0.10
(0.04)
64
0.33
1.13
1992
−0.23
(0.83)
−0.34
(0.33)
2.07
(0.06)
−3.17
(0.07)
0.50
(0.36)
−0.27
(0.49)
0.47
(0.36)
−0.10
(0.80)
−0.06
(0.66)
−0.11
(0.83)
0.09
(0.75)
−0.07
(0.32)
−0.13
(0.30)
−0.26
(0.07)
−0.33
(0.13)
−0.08
(0.55)
0.09
(0.00)
83
0.34
1.07
Year
1993
0.40
(0.81)
−0.42
(0.36)
0.86
(0.47)
−1.45
(0.36)
−0.27
(0.70)
0.14
(0.82)
0.74
(0.33)
−0.50
(0.34)
−0.02
(0.90)
0.50
(0.55)
−0.93
(0.05)
−0.12
(0.20)
−0.22
(0.24)
−0.53
(0.01)
−0.70
(0.02)
−0.00
(0.99)
0.08
(0.13)
90
0.34
1.41
1994
−1.04
(0.37)
−0.44
(0.12)
1.10
(0.15)
−1.57
(0.13)
0.22
(0.68)
0.66
(0.10)
1.12
(0.01)
0.38
(0.36)
−0.02
(0.87)
0.71
(0.31)
−0.38
(0.21)
0.04
(0.53)
−0.15
(0.21)
−0.43
(0.00)
−0.44
(0.05)
−0.07
(0.23)
0.09
(0.02)
98
0.40
1.34
1995
−3.18
(0.06)
−0.86
(0.06)
0.19
(0.85)
1.47
(0.18)
0.14
(0.88)
1.78
(0.01)
1.51
(0.04)
0.65
(0.39)
0.44
(0.03)
2.61
(0.00)
−0.88
(0.06)
−0.12
(0.64)
−0.19
(0.26)
−0.39
(0.07)
−0.32
(0.31)
−0.02
(0.67)
0.06
(0.25)
108
0.53
1.51
1996
0.30
(0.93)
0.35
(0.65)
2.69
(0.19)
−3.27
(0.26)
−1.05
(0.48)
1.34
(0.22)
2.88
(0.02)
0.25
(0.83)
−0.24
(0.57)
0.41
(0.78)
−2.69
(0.00)
−0.15
(0.64)
−0.15
(0.62)
−0.58
(0.14)
−0.80
(0.15)
−0.01
(0.94)
0.12
(0.29)
118
0.40
2.04
1997
−0.66
(0.82)
0.10
(0.88)
2.89
(0.07)
−2.42
(0.31)
−1.48
(0.25)
−0.12
(0.90)
0.09
(0.93)
−0.73
(0.41)
−0.23
(0.37)
1.19
(0.38)
−3.32
(0.00)
−0.47
(0.13)
−0.08
(0.75)
−0.42
(0.21)
−0.71
(0.09)
−0.01
(0.83)
0.20
(0.04)
153
0.36
2.00
Panel A shows results of a pooled regression using data for whole period. Panel B shows results of OLS regressions
on a year by year basis. Data for firms listed on the Oslo Stock Exchange, 1989-1997. Variable definitions are in
Appendix A.2.
B.6 A full multivariate model
B.6.8
179
Voting rights
Table B.61 Multivariate regression relating performance (Q) to ownership concentration (voting
rights), insider ownership, the type of the largest owner, board characteristics, security design,
financial policy, and controls (full multivariate model)
Panel A: Pooled OLS regression
Dependent variable: Q
Constant
Herfindahl (voting rights)
Primary insiders
Squared (Primary insiders)
Largest owner is state
Largest owner is international
Largest owner is individual
Largest owner is nonfinancial
ln(Board size)
Fraction voting shares
Debt to assets
Dividends to earnings
Industrial
Transport/shipping
Offshore
Investments over income
ln(Firm value)
n
R2
Average (Q)
coeff
0.38
-0.92
2.06
-1.64
-0.44
-0.29
-0.16
-0.29
-0.22
0.69
-1.62
-0.10
-0.25
-0.55
-0.66
-0.00
0.12
868
0.26
1.52
(stdev)
(0.61)
(0.21)
(0.49)
(0.60)
(0.14)
(0.12)
(0.13)
(0.10)
(0.09)
(0.36)
(0.18)
(0.05)
(0.08)
(0.09)
(0.14)
(0.01)
(0.02)
pvalue
0.53
0.00
0.00
0.01
0.00
0.02
0.22
0.00
0.01
0.06
0.00
0.06
0.00
0.00
0.00
0.73
0.00
Panel B: Year by year OLS regressions
constant
Herfindahl (voting rights)
Primary insiders
Squared (Primary insiders)
Largest owner is state
Largest owner is international
Largest owner is individual
Largest owner is nonfinancial
ln(Board size)
Fraction voting shares
Debt to assets
Dividends to earnings
Industrial
Transport/shipping
Offshore
Investments over income
ln(Firm value)
n
R2
Average (Q)
1989
1.20
(0.23)
−1.26
(0.00)
0.26
(0.82)
0.36
(0.77)
0.44
(0.19)
0.20
(0.38)
0.12
(0.67)
0.25
(0.25)
−0.74
(0.00)
0.36
(0.61)
−0.36
(0.24)
0.06
(0.37)
−0.21
(0.15)
−0.13
(0.40)
−0.55
(0.23)
0.00
(0.91)
0.07
(0.01)
81
0.18
1.32
1990
0.98
(0.32)
−0.81
(0.03)
−0.01
(0.99)
0.63
(0.50)
−0.12
(0.63)
−0.23
(0.17)
−0.24
(0.32)
−0.14
(0.38)
−0.34
(0.09)
0.08
(0.90)
−0.26
(0.37)
0.02
(0.88)
−0.15
(0.23)
−0.25
(0.08)
−0.33
(0.21)
−0.01
(0.33)
0.06
(0.04)
73
0.10
1.18
1991
2.08
(0.13)
−1.34
(0.01)
0.81
(0.38)
−0.17
(0.86)
0.13
(0.64)
−0.36
(0.09)
−0.66
(0.02)
−0.33
(0.09)
−0.41
(0.06)
−0.02
(0.98)
−0.86
(0.01)
0.04
(0.65)
−0.11
(0.44)
−0.36
(0.02)
−0.44
(0.07)
−0.00
(0.81)
0.05
(0.27)
64
0.24
1.13
1992
0.18
(0.85)
−0.57
(0.13)
3.12
(0.00)
−4.49
(0.01)
0.53
(0.02)
0.18
(0.36)
−0.18
(0.51)
0.11
(0.52)
−0.03
(0.81)
−0.40
(0.42)
0.10
(0.75)
−0.06
(0.34)
−0.19
(0.14)
−0.30
(0.03)
−0.39
(0.07)
−0.21
(0.14)
0.07
(0.01)
83
0.19
1.07
Year
1993
1.19
(0.39)
−0.75
(0.07)
0.47
(0.72)
−0.93
(0.56)
−0.00
(0.99)
0.61
(0.02)
0.65
(0.12)
0.05
(0.80)
−0.02
(0.89)
−0.26
(0.74)
−0.61
(0.20)
−0.10
(0.25)
−0.26
(0.15)
−0.66
(0.00)
−0.75
(0.01)
−0.00
(0.95)
0.06
(0.16)
90
0.24
1.41
1994
−0.08
(0.94)
−0.76
(0.00)
1.12
(0.16)
−1.46
(0.16)
−0.26
(0.18)
0.06
(0.75)
0.24
(0.31)
−0.10
(0.49)
0.03
(0.83)
0.60
(0.40)
−0.59
(0.05)
0.05
(0.45)
−0.15
(0.19)
−0.44
(0.00)
−0.43
(0.04)
−0.08
(0.15)
0.07
(0.02)
98
0.27
1.34
1995
−2.12
(0.19)
−0.89
(0.08)
0.43
(0.71)
1.51
(0.21)
−0.70
(0.02)
−0.06
(0.84)
−0.03
(0.92)
−0.31
(0.18)
0.26
(0.22)
2.32
(0.01)
−1.29
(0.01)
−0.27
(0.32)
−0.27
(0.14)
−0.49
(0.03)
−0.47
(0.16)
−0.00
(0.98)
0.11
(0.03)
108
0.36
1.51
1996
5.11
(0.08)
−0.87
(0.31)
3.27
(0.14)
−3.88
(0.19)
−0.81
(0.12)
−0.47
(0.39)
0.18
(0.73)
−0.38
(0.30)
−0.49
(0.25)
−0.69
(0.64)
−2.69
(0.00)
−0.14
(0.67)
−0.31
(0.31)
−0.87
(0.02)
−0.83
(0.14)
−0.06
(0.58)
0.03
(0.73)
118
0.26
2.04
1997
0.83
(0.73)
−0.51
(0.48)
3.41
(0.03)
−3.09
(0.18)
−0.85
(0.07)
−0.66
(0.13)
−0.91
(0.02)
−0.66
(0.05)
−0.33
(0.19)
0.59
(0.65)
−3.16
(0.00)
−0.51
(0.09)
−0.16
(0.50)
−0.56
(0.08)
−0.80
(0.05)
−0.02
(0.73)
0.19
(0.03)
153
0.30
2.00
Panel A shows results of a pooled regression using data for whole period. Panel B shows results of OLS regressions
on a year by year basis. Data for firms listed on the Oslo Stock Exchange, 1989-1997. Variable definitions are in
Appendix A.2.
180
Supplementary regressions
Table B.62 Multivariate regression relating performance (Q) to ownership concentration (voting
rights), insider ownership, type of owner, board characteristics, security design, financial policy,
and controls (full multivariate model)
Panel A: Pooled OLS regression
Dependent variable: Q
Constant
Herfindahl (voting rights)
Primary insiders
Squared (Primary insiders)
Aggregate state holdings
Aggregate international holdings
Aggregate individual holdings
Aggregate nonfinancial holdings
ln(Board size)
Fraction voting shares
Debt to assets
Dividends to earnings
Industrial
Transport/shipping
Offshore
Investments over income
ln(Firm value)
n
R2
Average (Q)
coeff
-0.83
-0.60
1.64
-1.38
-0.49
0.09
1.02
-0.26
-0.19
1.01
-1.54
-0.11
-0.20
-0.46
-0.57
-0.00
0.14
868
0.27
1.52
(stdev)
(0.69)
(0.24)
(0.47)
(0.58)
(0.34)
(0.25)
(0.30)
(0.25)
(0.09)
(0.36)
(0.18)
(0.05)
(0.08)
(0.09)
(0.14)
(0.01)
(0.02)
pvalue
0.23
0.01
0.00
0.02
0.16
0.72
0.00
0.30
0.03
0.00
0.00
0.03
0.01
0.00
0.00
0.99
0.00
Panel B: Year by year OLS regressions
constant
Herfindahl (voting rights)
Primary insiders
Squared (Primary insiders)
Aggregate state holdings
Aggregate international holdings
Aggregate individual holdings
Aggregate nonfinancial holdings
ln(Board size)
Fraction voting shares
Debt to assets
Dividends to earnings
Industrial
Transport/shipping
Offshore
Investments over income
ln(Firm value)
n
R2
Average (Q)
1989
1.07
(0.31)
−1.37
(0.01)
0.56
(0.63)
−0.00
(1.00)
0.93
(0.17)
0.32
(0.52)
0.43
(0.49)
0.52
(0.28)
−0.80
(0.00)
0.22
(0.75)
−0.30
(0.32)
0.08
(0.28)
−0.18
(0.20)
−0.14
(0.39)
−0.49
(0.29)
0.00
(0.89)
0.08
(0.01)
81
0.18
1.32
1990
0.09
(0.93)
−0.82
(0.04)
−0.52
(0.51)
1.04
(0.25)
0.20
(0.71)
0.24
(0.60)
0.68
(0.24)
0.36
(0.42)
−0.30
(0.13)
0.24
(0.67)
−0.28
(0.34)
0.01
(0.94)
−0.13
(0.32)
−0.31
(0.04)
−0.40
(0.11)
−0.01
(0.62)
0.08
(0.03)
73
0.09
1.18
1991
−0.63
(0.73)
−1.38
(0.03)
0.10
(0.92)
0.25
(0.80)
0.98
(0.23)
0.56
(0.35)
0.81
(0.37)
0.45
(0.49)
−0.29
(0.21)
0.70
(0.36)
−0.74
(0.03)
0.05
(0.56)
0.01
(0.97)
−0.36
(0.05)
−0.54
(0.04)
−0.00
(0.95)
0.09
(0.06)
64
0.14
1.13
1992
−0.14
(0.89)
−0.43
(0.34)
1.96
(0.07)
−2.94
(0.09)
0.53
(0.34)
−0.27
(0.50)
0.48
(0.35)
−0.06
(0.88)
−0.07
(0.65)
−0.17
(0.73)
0.05
(0.87)
−0.07
(0.32)
−0.15
(0.24)
−0.27
(0.06)
−0.34
(0.12)
−0.09
(0.52)
0.09
(0.00)
83
0.16
1.07
Year
1993
0.51
(0.76)
−0.48
(0.34)
0.87
(0.47)
−1.40
(0.37)
−0.25
(0.72)
0.12
(0.84)
0.79
(0.29)
−0.51
(0.33)
−0.04
(0.83)
0.37
(0.66)
−0.96
(0.05)
−0.10
(0.25)
−0.22
(0.24)
−0.52
(0.01)
−0.70
(0.02)
0.00
(0.99)
0.08
(0.12)
90
0.18
1.41
1994
−0.93
(0.41)
−0.80
(0.01)
1.18
(0.11)
−1.55
(0.12)
0.44
(0.39)
0.77
(0.04)
1.16
(0.01)
0.47
(0.23)
−0.05
(0.69)
0.72
(0.29)
−0.45
(0.12)
0.03
(0.65)
−0.15
(0.19)
−0.43
(0.00)
−0.43
(0.04)
−0.06
(0.25)
0.08
(0.03)
98
0.30
1.34
1995
−3.31
(0.05)
−0.88
(0.11)
0.40
(0.70)
1.32
(0.24)
−0.05
(0.96)
1.66
(0.01)
1.59
(0.03)
0.51
(0.49)
0.42
(0.04)
2.49
(0.00)
−1.04
(0.02)
−0.13
(0.61)
−0.21
(0.22)
−0.37
(0.08)
−0.35
(0.26)
−0.01
(0.72)
0.08
(0.13)
108
0.44
1.51
1996
0.54
(0.88)
−0.41
(0.66)
2.69
(0.19)
−3.37
(0.24)
−0.51
(0.73)
1.58
(0.15)
2.86
(0.02)
0.60
(0.62)
−0.25
(0.56)
0.40
(0.79)
−2.59
(0.00)
−0.11
(0.72)
−0.15
(0.62)
−0.63
(0.11)
−0.76
(0.16)
−0.02
(0.86)
0.11
(0.35)
118
0.30
2.04
1997
−0.48
(0.87)
−0.36
(0.66)
2.91
(0.07)
−2.48
(0.29)
−1.21
(0.34)
0.02
(0.98)
0.09
(0.93)
−0.54
(0.54)
−0.23
(0.37)
1.12
(0.41)
−3.29
(0.00)
−0.46
(0.15)
−0.07
(0.76)
−0.44
(0.19)
−0.71
(0.09)
−0.01
(0.84)
0.19
(0.05)
153
0.28
2.00
Panel A shows results of a pooled regression using data for whole period. Panel B shows results of OLS regressions
on a year by year basis. Data for firms listed on the Oslo Stock Exchange, 1989-1997. Variable definitions are in
Appendix A.2.
B.7 Explaining the corporate governance mechanisms with single equation models
B.7
181
Explaining the corporate governance mechanisms with single equation models
This appendix complements section 10.1 of chapter 10. It contains detailed regression results for
each of the summary tables in the text.
B.7.1
Single equation estimates of governance mechanism endogeneity, using aggregate ownership per type as owner identity proxy
This section contains the results summarized in table 10.1.
Table B.63 Multivariate regression relating concentration (Herfindahl index) to other mechanisms
and controls, using aggregate ownership per type as owner identity proxy
Dependent variable: Herfindahl index
Constant
Primary insiders
Aggregate state holdings
Aggregate international holdings
Aggregate individual holdings
Aggregate nonfinancial holdings
Fraction voting shares
ln(Board size)
Debt to assets
Dividends to earnings
Industrial
Transport/shipping
Offshore
Investments over income
ln(Firm value)
Stock volatility
n
R2
Average (Herfindahl index)
coeff
-0.09
0.08
0.57
0.24
-0.02
0.30
0.22
-0.02
0.02
0.01
-0.01
-0.04
0.00
-0.00
-0.01
0.02
796
0.25
0.14
(stdev)
(0.11)
(0.03)
(0.05)
(0.03)
(0.04)
(0.03)
(0.05)
(0.01)
(0.03)
(0.01)
(0.01)
(0.01)
(0.02)
(0.00)
(0.00)
(0.02)
pvalue
0.43
0.00
0.00
0.00
0.62
0.00
0.00
0.17
0.40
0.03
0.29
0.00
0.89
0.08
0.04
0.22
Pooled OLS regression using data for all listed firms on the OSE in the period 1989 to 1997. Variable definitions are
in Appendix A.2. In regressions using firm size across years the nominal values are adjusted to the 1997 general price
level.
182
Supplementary regressions
Table B.64 Multivariate regression relating primary insider holdings to other mechanisms and
controls, using aggregate ownership per type as owner identity proxy
Dependent variable: Primary insiders
Constant
Herfindahl index
Aggregate state holdings
Aggregate international holdings
Aggregate individual holdings
Aggregate nonfinancial holdings
ln(Board size)
Fraction voting shares
Debt to assets
Dividends to earnings
Industrial
Transport/shipping
Offshore
Investments over income
ln(Firm value)
Stock volatility
n
R2
Average (Primary insiders)
coeff
-0.31
0.16
-0.17
0.01
0.43
-0.01
-0.03
-0.09
0.10
0.00
-0.01
-0.04
-0.06
-0.00
0.02
0.05
796
0.16
0.08
(stdev)
(0.16)
(0.05)
(0.07)
(0.05)
(0.06)
(0.05)
(0.02)
(0.07)
(0.04)
(0.01)
(0.01)
(0.02)
(0.03)
(0.00)
(0.01)
(0.03)
pvalue
0.05
0.00
0.02
0.83
0.00
0.79
0.10
0.20
0.00
0.84
0.42
0.03
0.02
0.85
0.00
0.03
Pooled OLS regression using data for all listed firms on the OSE in the period 1989 to 1997. Variable definitions are
in Appendix A.2. In regressions using firm size across years the nominal values are adjusted to the 1997 general price
level.
Table B.65 Multivariate regression relating aggregate state holdings to other mechanisms and
controls, using aggregate ownership per type as owner identity proxy
Dependent variable: Aggregate state holdings
Constant
Herfindahl index
Primary insiders
ln(Board size)
Fraction voting shares
Debt to assets
Dividends to earnings
Industrial
Transport/shipping
Offshore
Investments over income
ln(Firm value)
Stock volatility
n
R2
Average (Aggregate state holdings)
coeff
-0.41
0.25
-0.08
0.03
-0.02
0.03
0.00
0.03
-0.02
-0.04
-0.00
0.02
0.00
796
0.21
0.05
(stdev)
(0.09)
(0.03)
(0.02)
(0.01)
(0.05)
(0.02)
(0.01)
(0.01)
(0.01)
(0.02)
(0.00)
(0.00)
(0.02)
pvalue
0.00
0.00
0.00
0.00
0.65
0.18
0.45
0.00
0.04
0.01
0.90
0.00
0.99
Pooled OLS regression using data for all listed firms on the OSE in the period 1989 to 1997. Variable definitions are
in Appendix A.2. In regressions using firm size across years the nominal values are adjusted to the 1997 general price
level.
B.7 Explaining the corporate governance mechanisms with single equation models
183
Table B.66 Multivariate regression relating aggregate international holdings to other mechanisms
and controls, using aggregate ownership per type as owner identity proxy
Dependent variable: Aggregate international holdings
Constant
Herfindahl index
Primary insiders
ln(Board size)
Fraction voting shares
Debt to assets
Dividends to earnings
Industrial
Transport/shipping
Offshore
Investments over income
ln(Firm value)
Stock volatility
n
R2
Average (Aggregate international holdings)
coeff
-0.74
0.02
-0.01
-0.01
-0.02
-0.05
-0.02
-0.00
-0.07
0.06
0.01
0.05
0.08
796
0.12
0.21
(stdev)
(0.17)
(0.05)
(0.04)
(0.02)
(0.08)
(0.04)
(0.01)
(0.02)
(0.02)
(0.03)
(0.00)
(0.01)
(0.03)
pvalue
0.00
0.77
0.74
0.58
0.83
0.21
0.19
0.87
0.00
0.05
0.03
0.00
0.01
Pooled OLS regression using data for all listed firms on the OSE in the period 1989 to 1997. Variable definitions are
in Appendix A.2. In regressions using firm size across years the nominal values are adjusted to the 1997 general price
level.
Table B.67 Multivariate regression relating aggregate individual holdings to other mechanisms
and controls, using aggregate ownership per type as owner identity proxy
Dependent variable: Aggregate individual holdings
Constant
Herfindahl index
Primary insiders
ln(Board size)
Fraction voting shares
Debt to assets
Dividends to earnings
Industrial
Transport/shipping
Offshore
Investments over income
ln(Firm value)
Stock volatility
n
R2
Average (Aggregate individual holdings)
coeff
1.21
-0.27
0.25
-0.02
-0.15
-0.05
0.00
-0.05
-0.08
-0.09
-0.00
-0.04
-0.03
796
0.36
0.18
(stdev)
(0.11)
(0.03)
(0.03)
(0.01)
(0.05)
(0.03)
(0.01)
(0.01)
(0.01)
(0.02)
(0.00)
(0.00)
(0.02)
pvalue
0.00
0.00
0.00
0.06
0.00
0.07
0.62
0.00
0.00
0.00
0.00
0.00
0.18
Pooled OLS regression using data for all listed firms on the OSE in the period 1989 to 1997. Variable definitions are
in Appendix A.2. In regressions using firm size across years the nominal values are adjusted to the 1997 general price
level.
184
Supplementary regressions
Table B.68 Multivariate regression relating aggregate financial holdings to other mechanisms and
controls, using aggregate ownership per type as owner identity proxy
Dependent variable: Aggregate financial holdings
Constant
Herfindahl index
Primary insiders
ln(Board size)
Fraction voting shares
Debt to assets
Dividends to earnings
Industrial
Transport/shipping
Offshore
Investments over income
ln(Firm value)
Stock volatility
n
R2
Average (Aggregate financial holdings)
coeff
0.08
-0.23
-0.05
0.02
0.07
0.14
0.00
0.03
-0.03
0.01
-0.00
0.00
-0.11
796
0.17
0.18
(stdev)
(0.11)
(0.03)
(0.03)
(0.01)
(0.05)
(0.03)
(0.01)
(0.01)
(0.01)
(0.02)
(0.00)
(0.00)
(0.02)
pvalue
0.46
0.00
0.06
0.11
0.16
0.00
0.60
0.00
0.00
0.51
0.21
0.98
0.00
Pooled OLS regression using data for all listed firms on the OSE in the period 1989 to 1997. Variable definitions are
in Appendix A.2. In regressions using firm size across years the nominal values are adjusted to the 1997 general price
level.
Table B.69 Multivariate regression relating aggregate nonfinancial holdings to other mechanisms
and controls, using aggregate ownership per type as owner identity proxy
Dependent variable: Aggregate nonfinancial holdings
Constant
Herfindahl index
Primary insiders
ln(Board size)
Fraction voting shares
Debt to assets
Dividends to earnings
Industrial
Transport/shipping
Offshore
Investments over income
ln(Firm value)
Stock volatility
n
R2
Average (Aggregate nonfinancial holdings)
coeff
0.96
0.24
-0.10
-0.02
0.08
-0.06
0.00
0.01
0.21
0.06
0.00
-0.03
0.05
796
0.24
0.38
(stdev)
(0.17)
(0.05)
(0.04)
(0.02)
(0.09)
(0.04)
(0.01)
(0.02)
(0.02)
(0.03)
(0.00)
(0.01)
(0.03)
pvalue
0.00
0.00
0.01
0.26
0.36
0.16
0.84
0.76
0.00
0.06
0.72
0.00
0.12
Pooled OLS regression using data for all listed firms on the OSE in the period 1989 to 1997. Variable definitions are
in Appendix A.2. In regressions using firm size across years the nominal values are adjusted to the 1997 general price
level.
B.7 Explaining the corporate governance mechanisms with single equation models
185
Table B.70 Multivariate regression relating board size to other mechanisms and controls, using
aggregate ownership per type as owner identity proxy
Dependent variable: ln(Board size)
Constant
Herfindahl index
Aggregate state holdings
Aggregate international holdings
Aggregate individual holdings
Aggregate nonfinancial holdings
Primary insiders
Fraction voting shares
Debt to assets
Dividends to earnings
Industrial
Transport/shipping
Offshore
Investments over income
ln(Firm value)
Stock volatility
n
R2
Average (ln(Board size))
coeff
0.88
-0.15
0.21
-0.12
-0.28
-0.14
-0.13
-0.24
0.06
0.02
0.11
-0.05
-0.19
-0.01
0.06
0.14
796
0.17
1.84
(stdev)
(0.34)
(0.11)
(0.15)
(0.11)
(0.13)
(0.11)
(0.08)
(0.15)
(0.08)
(0.02)
(0.03)
(0.04)
(0.06)
(0.00)
(0.01)
(0.06)
pvalue
0.01
0.17
0.17
0.25
0.03
0.18
0.10
0.11
0.46
0.40
0.00
0.18
0.00
0.04
0.00
0.01
Pooled OLS regression using data for all listed firms on the OSE in the period 1989 to 1997. Variable definitions are
in Appendix A.2. In regressions using firm size across years the nominal values are adjusted to the 1997 general price
level.
Table B.71 Multivariate regression relating fraction voting shares to other mechanisms and controls, using aggregate ownership per type as owner identity proxy
Dependent variable: Fraction voting shares
Constant
Herfindahl index
Aggregate state holdings
Aggregate international holdings
Aggregate individual holdings
Aggregate nonfinancial holdings
Primary insiders
ln(Board size)
Debt to assets
Dividends to earnings
Industrial
Transport/shipping
Investments over income
ln(Firm value)
Stock volatility
n
R2
Average (Fraction voting shares)
coeff
1.44
0.11
-0.04
-0.04
-0.09
-0.03
-0.02
-0.01
-0.01
-0.01
-0.02
-0.02
-0.00
-0.02
-0.03
796
0.12
0.97
(stdev)
(0.06)
(0.03)
(0.04)
(0.03)
(0.03)
(0.03)
(0.02)
(0.01)
(0.02)
(0.01)
(0.01)
(0.01)
(0.00)
(0.00)
(0.01)
pvalue
0.00
0.00
0.24
0.10
0.00
0.18
0.32
0.25
0.47
0.22
0.03
0.01
0.53
0.00
0.01
Pooled OLS regression using data for all listed firms on the OSE in the period 1989 to 1997. Variable definitions are
in Appendix A.2. In regressions using firm size across years the nominal values are adjusted to the 1997 general price
level.
186
Supplementary regressions
Table B.72 Multivariate regression relating debt to assets to other mechanisms and controls, using
aggregate ownership per type as owner identity proxy
Dependent variable: Debt to assets
Constant
Herfindahl index
Aggregate state holdings
Aggregate international holdings
Aggregate individual holdings
Aggregate nonfinancial holdings
Primary insiders
ln(Board size)
Fraction voting shares
Industrial
Transport/shipping
Offshore
Investments over income
ln(Firm value)
Stock volatility
n
R2
Average (Debt to assets)
coeff
0.72
0.04
-0.14
-0.20
-0.24
-0.21
0.10
0.01
-0.04
0.03
0.09
0.01
0.01
-0.00
0.05
817
0.08
0.59
(stdev)
(0.15)
(0.05)
(0.07)
(0.05)
(0.06)
(0.05)
(0.03)
(0.02)
(0.07)
(0.01)
(0.02)
(0.03)
(0.00)
(0.01)
(0.02)
pvalue
0.00
0.42
0.05
0.00
0.00
0.00
0.00
0.44
0.55
0.05
0.00
0.79
0.00
0.78
0.03
Pooled OLS regression using data for all listed firms on the OSE in the period 1989 to 1997. Variable definitions are
in Appendix A.2. In regressions using firm size across years the nominal values are adjusted to the 1997 general price
level.
Table B.73 Multivariate regression relating dividends to earnings to other mechanisms and controls, using aggregate ownership per type as owner identity proxy
Dependent variable: Dividends to earnings
Constant
Herfindahl index
Aggregate state holdings
Aggregate international holdings
Aggregate individual holdings
Aggregate nonfinancial holdings
Primary insiders
ln(Board size)
Fraction voting shares
Industrial
Transport/shipping
Offshore
Investments over income
ln(Firm value)
Stock volatility
n
R2
Average (Dividends to earnings)
coeff
0.04
0.39
0.07
-0.15
0.04
-0.03
0.02
0.05
-0.36
-0.00
0.10
-0.12
-0.01
0.03
-0.13
800
0.02
0.29
(stdev)
(0.57)
(0.18)
(0.27)
(0.18)
(0.22)
(0.18)
(0.13)
(0.06)
(0.26)
(0.05)
(0.06)
(0.10)
(0.01)
(0.02)
(0.09)
pvalue
0.94
0.03
0.78
0.40
0.84
0.85
0.88
0.44
0.16
0.95
0.12
0.21
0.42
0.19
0.17
Pooled OLS regression using data for all listed firms on the OSE in the period 1989 to 1997. Variable definitions are
in Appendix A.2. In regressions using firm size across years the nominal values are adjusted to the 1997 general price
level.
B.7 Explaining the corporate governance mechanisms with single equation models
B.7.2
187
Single equation estimates of governance mechanism endogeneity, using type of
largest owner as owner type proxy
This section contains the results summarized in table 10.2.
Table B.74 Multivariate regression relating concentration (Herfindahl index) to other mechanisms
and controls, using type of largest owner as owner type proxy
Dependent variable: Herfindahl index
Constant
Largest owner is state
Largest owner is international
Largest owner is individual
Largest owner is nonfinancial
Primary insiders
ln(Board size)
Fraction voting shares
Debt to assets
Dividends to earnings
Industrial
Transport/shipping
Offshore
Investments over income
ln(Firm value)
Stock volatility
n
R2
Average (Herfindahl index)
coeff
-0.36
0.13
0.08
0.05
0.07
0.02
-0.01
0.31
0.01
0.02
0.00
-0.01
0.02
-0.00
0.00
0.07
796
0.09
0.14
(stdev)
(0.11)
(0.02)
(0.02)
(0.02)
(0.01)
(0.03)
(0.01)
(0.05)
(0.03)
(0.01)
(0.01)
(0.01)
(0.02)
(0.00)
(0.00)
(0.02)
pvalue
0.00
0.00
0.00
0.02
0.00
0.50
0.48
0.00
0.68
0.04
0.72
0.50
0.30
0.39
0.24
0.00
Pooled OLS regression using data for all listed firms on the OSE in the period 1989 to 1997. Variable definitions are
in Appendix A.2. In regressions using firm size across years the nominal values are adjusted to the 1997 general price
level.
Table B.75 Multivariate regression relating primary insider holdings to other mechanisms and
controls, using type of largest owner as owner type proxy
Dependent variable: Primary insiders
Constant
Herfindahl index
Largest owner is state
Largest owner is international
Largest owner is individual
Largest owner is nonfinancial
ln(Board size)
Fraction voting shares
Debt to assets
Industrial
Transport/shipping
Offshore
Investments over income
ln(Firm value)
Stock volatility
n
R2
Average (Primary insiders)
coeff
0.05
0.03
-0.05
0.00
0.21
0.02
-0.02
-0.10
0.11
-0.02
-0.06
-0.08
0.00
0.00
0.04
817
0.16
0.08
(stdev)
(0.14)
(0.04)
(0.03)
(0.02)
(0.02)
(0.02)
(0.02)
(0.07)
(0.04)
(0.01)
(0.02)
(0.03)
(0.00)
(0.00)
(0.02)
pvalue
0.71
0.51
0.08
0.96
0.00
0.38
0.21
0.11
0.00
0.28
0.00
0.00
0.37
0.46
0.09
Pooled OLS regression using data for all listed firms on the OSE in the period 1989 to 1997. Variable definitions are
in Appendix A.2. In regressions using firm size across years the nominal values are adjusted to the 1997 general price
level.
188
Supplementary regressions
Table B.76 Multivariate regression relating board size to other mechanisms and controls, using
type of largest owner as owner type proxy
Dependent variable: ln(Board size)
Constant
Herfindahl index
Largest owner is state
Largest owner is international
Largest owner is individual
Largest owner is nonfinancial
Primary insiders
Fraction voting shares
Debt to assets
Dividends to earnings
Industrial
Transport/shipping
Offshore
Investments over income
ln(Firm value)
Stock volatility
n
R2
Average (ln(Board size))
coeff
0.71
-0.07
0.08
-0.04
-0.17
-0.09
-0.10
-0.25
0.06
0.02
0.10
-0.04
-0.18
-0.01
0.07
0.13
796
0.18
1.84
(stdev)
(0.31)
(0.10)
(0.06)
(0.05)
(0.05)
(0.04)
(0.08)
(0.15)
(0.08)
(0.02)
(0.03)
(0.04)
(0.06)
(0.00)
(0.01)
(0.05)
pvalue
0.02
0.48
0.15
0.43
0.00
0.02
0.17
0.10
0.44
0.38
0.00
0.23
0.00
0.04
0.00
0.01
Pooled OLS regression using data for all listed firms on the OSE in the period 1989 to 1997. Variable definitions are
in Appendix A.2. In regressions using firm size across years the nominal values are adjusted to the 1997 general price
level.
Table B.77 Multivariate regression relating fraction voting to other mechanisms and controls,
using type of largest owner as owner type proxy
Dependent variable: Fraction voting shares
Constant
Herfindahl index
Largest owner is state
Largest owner is international
Largest owner is individual
Largest owner is nonfinancial
Primary insiders
ln(Board size)
Debt to assets
Dividends to earnings
Industrial
Transport/shipping
Investments over income
ln(Firm value)
Stock volatility
n
R2
Average (Fraction voting shares)
coeff
1.40
0.13
-0.03
-0.01
-0.03
-0.03
-0.03
-0.01
-0.01
-0.01
-0.01
-0.02
-0.00
-0.02
-0.04
796
0.13
0.97
(stdev)
(0.05)
(0.02)
(0.01)
(0.01)
(0.01)
(0.01)
(0.02)
(0.01)
(0.02)
(0.00)
(0.01)
(0.01)
(0.00)
(0.00)
(0.01)
pvalue
0.00
0.00
0.01
0.29
0.01
0.00
0.15
0.21
0.57
0.31
0.06
0.06
0.52
0.00
0.01
Pooled OLS regression using data for all listed firms on the OSE in the period 1989 to 1997. Variable definitions are
in Appendix A.2. In regressions using firm size across years the nominal values are adjusted to the 1997 general price
level.
B.7 Explaining the corporate governance mechanisms with single equation models
189
Table B.78 Multivariate regression relating debt to assets to other mechanisms and controls, using
type of largest owner as owner type proxy
Dependent variable: Debt to assets
Constant
Herfindahl index
Largest owner is state
Largest owner is international
Largest owner is individual
Largest owner is nonfinancial
Primary insiders
ln(Board size)
Fraction voting shares
Industrial
Transport/shipping
Offshore
Investments over income
ln(Firm value)
Stock volatility
n
R2
Average (Debt to assets)
coeff
0.54
0.02
0.00
-0.01
-0.06
-0.03
0.11
0.01
-0.03
0.03
0.09
0.01
0.01
0.00
0.03
817
0.06
0.59
(stdev)
(0.14)
(0.04)
(0.03)
(0.02)
(0.03)
(0.02)
(0.04)
(0.02)
(0.07)
(0.01)
(0.02)
(0.03)
(0.00)
(0.00)
(0.02)
pvalue
0.00
0.70
0.96
0.59
0.01
0.14
0.00
0.41
0.65
0.04
0.00
0.77
0.00
0.93
0.17
Pooled OLS regression using data for all listed firms on the OSE in the period 1989 to 1997. Variable definitions are
in Appendix A.2. In regressions using firm size across years the nominal values are adjusted to the 1997 general price
level.
Table B.79 Multivariate regression relating dividends to earnings to other mechanisms and controls, using type of largest owner as owner type proxy
Dependent variable: Dividends to earnings
Constant
Herfindahl index
Largest owner is state
Largest owner is international
Largest owner is individual
Largest owner is nonfinancial
Primary insiders
ln(Board size)
Fraction voting shares
Industrial
Transport/shipping
Offshore
Investments over income
ln(Firm value)
Stock volatility
n
R2
Average (Dividends to earnings)
coeff
0.11
0.34
0.16
-0.02
0.08
0.05
0.02
0.05
-0.31
-0.01
0.10
-0.13
-0.01
0.02
-0.14
800
0.02
0.29
(stdev)
(0.52)
(0.17)
(0.10)
(0.08)
(0.09)
(0.07)
(0.13)
(0.06)
(0.26)
(0.05)
(0.06)
(0.10)
(0.01)
(0.02)
(0.09)
pvalue
0.84
0.04
0.10
0.80
0.40
0.42
0.87
0.42
0.23
0.87
0.11
0.19
0.39
0.34
0.13
Pooled OLS regression using data for all listed firms on the OSE in the period 1989 to 1997. Variable definitions are
in Appendix A.2. In regressions using firm size across years the nominal values are adjusted to the 1997 general price
level.
190
B.7.3
Supplementary regressions
Outside (external) concentration
To check on the potential problem of double counting of concentration and insiders we estimate the
outside (external) concentration as the fraction of the company owned by the largest owner who
is not an insider, and use this concentration measure instead. As shown in tables B.80 and B.81,
the overlap can partly explain the positive relation between the two as the estimated sign of the
concentration - insider relationship has a negative (but not very significant) sign when using such
an adjusted concentration measure.
Table B.80 Multivariate regression relating outside concentration (Largest outside owner) to other
mechanisms and controls
Dependent variable: Largest outside owner
Constant
Primary insiders
Aggregate state holdings
Aggregate international holdings
Aggregate individual holdings
Aggregate nonfinancial holdings
Fraction voting shares
ln(Board size)
Debt to assets
Dividends to earnings
Industrial
Transport/shipping
Offshore
Investments over income
ln(Firm value)
Stock volatility
n
R2
Average (Largest outside owner)
coeff
0.01
-0.06
0.73
0.30
-0.11
0.38
0.24
0.00
0.10
0.01
-0.01
-0.06
0.03
-0.00
-0.01
0.02
796
0.31
0.24
(stdev)
(0.15)
(0.03)
(0.06)
(0.04)
(0.05)
(0.04)
(0.06)
(0.02)
(0.03)
(0.01)
(0.01)
(0.02)
(0.02)
(0.00)
(0.01)
(0.02)
pvalue
0.92
0.07
0.00
0.00
0.05
0.00
0.00
0.96
0.00
0.21
0.70
0.00
0.25
0.20
0.00
0.34
Pooled OLS regression using data for all listed firms on the OSE in the period 1989 to 1997. Variable definitions are
in Appendix A.2. In regressions using firm size across years the nominal values are adjusted to the 1997 general price
level.
B.7 Explaining the corporate governance mechanisms with single equation models
191
Table B.81 Multivariate regression relating primary insider holdings to other mechanisms and
controls, using largest outside owner as concentration measure
Dependent variable: Primary insiders
Constant
Largest outside owner
Aggregate state holdings
Aggregate international holdings
Aggregate individual holdings
Aggregate nonfinancial holdings
ln(Board size)
Fraction voting shares
Debt to assets
Industrial
Transport/shipping
Offshore
Investments over income
ln(Firm value)
Stock volatility
n
R2
Average (Primary insiders)
coeff
-0.37
-0.08
-0.01
0.07
0.43
0.07
-0.03
-0.01
0.12
-0.01
-0.05
-0.06
0.00
0.02
0.05
817
0.15
0.08
(stdev)
(0.16)
(0.04)
(0.07)
(0.05)
(0.06)
(0.05)
(0.02)
(0.07)
(0.04)
(0.01)
(0.02)
(0.03)
(0.00)
(0.01)
(0.03)
pvalue
0.02
0.05
0.86
0.14
0.00
0.13
0.09
0.92
0.00
0.39
0.00
0.03
0.36
0.00
0.04
Pooled OLS regression using data for all listed firms on the OSE in the period 1989 to 1997. Variable definitions are
in Appendix A.2. In regressions using firm size across years the nominal values are adjusted to the 1997 general price
level.
192
Supplementary regressions
B.8
Interactions between ownership concentration and insider holdings in a
system of equations
This appendix complements the results of section 10.2. Section B.8.1 contains similar regressions to
the ones in the text with fewer explanatory (exogenous) variables. Section B.8.2 considers outside
(external) concentration.
B.8.1
Only controls as additional explanatory variables
Table B.82 Interactions between governance mechanisms modeled as system of equations. Concentration and insider holdings are endogenous variables. Controls only as additional explanatory
variables. Board size and stock volatility are instruments.
Panel A. Regression results
Dep.variable
Herfindahl index
Primary insiders
Indep.variable
Primary insiders
Industrial
Transport/shipping
Offshore
ln(Firm value)
Stock volatility
constant
Herfindahl index
ln(Board size)
Industrial
Transport/shipping
Offshore
ln(Firm value)
constant
coeff
0.29
0.01
0.01
0.02
0.01
0.04
-0.02
0.94
-0.03
-0.04
-0.06
-0.10
-0.00
0.10
(stdev)
(0.34)
(0.02)
(0.03)
(0.04)
(0.00)
(0.03)
(0.10)
(0.63)
(0.02)
(0.02)
(0.02)
(0.03)
(0.01)
(0.15)
R2
-0.16
-0.44
χ2
7.24
24.93
pvalue
0.39
0.72
0.80
0.57
0.23
0.14
0.82
0.14
0.17
0.03
0.01
0.01
0.66
0.48
Panel B. Regression diagnostics
Equation
1
2
n
741
741
Parms
6
6
RMSE
0.14
0.22
p
0.30
0.00
The table complements table 10.4 in the text. It shows results with the same set of endogenous variables, but a
smaller set of explanatory variables: Only the controls. The tables shows 3SLS estimates of a system of simultanous
equations. Panel A report system estimates. The leftmost column is the dependent variable in that particular
equation, which is a function of the variables listed in the next column. Equations are separated by a line. Panel
B holds diagnostics. For each equation the diagnostics include n, the number of observations, Parms, the number
of parameters, RMSE, the root mean squared error, R2 , a “pseudo” R squared, χ2 , the chi squared of the equation,
and p, a p-value for the fit of the equation. The estimates are performed with Stata’s reg3 3SLS routine. Variable
definitions are in Appendix A.2. Data for firms listed on the Oslo Stock Exchange, 1989-1997.
B.8 Interactions between ownership concentration and insider holdings in a system of
equations
193
Table B.83 Interactions between governance mechanisms modeled as system of equations. Concentration and insider holdings are endogenous variables. Controls only as additional explanatory
variables. Board size and stock turnover are instruments.
Panel A. Regression results
Dep.variable
Herfindahl index
Primary insiders
Indep.variable
Primary insiders
Industrial
Transport/shipping
Offshore
ln(Firm value)
Stock volatility
Stock turnover
constant
Herfindahl index
Industrial
Transport/shipping
Offshore
ln(Firm value)
Stock volatility
ln(Board size)
constant
coeff
0.54
0.02
0.01
0.04
0.01
0.03
-0.07
-0.02
-0.06
-0.05
-0.08
-0.10
0.00
0.06
-0.05
0.11
(stdev)
(0.38)
(0.03)
(0.03)
(0.04)
(0.00)
(0.03)
(0.01)
(0.12)
(0.15)
(0.02)
(0.02)
(0.03)
(0.01)
(0.03)
(0.02)
(0.12)
R2
-0.45
0.05
χ2
58.53
38.00
pvalue
0.16
0.48
0.74
0.34
0.23
0.30
0.00
0.89
0.72
0.00
0.00
0.00
0.54
0.06
0.02
0.33
Panel B. Regression diagnostics
Equation
1
2
n
741
741
Parms
7
7
RMSE
0.16
0.17
p
0.00
0.00
The table complements table 10.5 in the text. It shows results with the same set of endogenous variables, but a
smaller set of explanatory variables: Only the controls. The tables shows 3SLS estimates of a system of simultanous
equations. Panel A report system estimates. The leftmost column is the dependent variable in that particular
equation, which is a function of the variables listed in the next column. Equations are separated by a line. Panel
B holds diagnostics. For each equation the diagnostics include n, the number of observations, Parms, the number
of parameters, RMSE, the root mean squared error, R2 , a “pseudo” R squared, χ2 , the chi squared of the equation,
and p, a p-value for the fit of the equation. The estimates are performed with Stata’s reg3 3SLS routine. Variable
definitions are in Appendix A.2. Data for firms listed on the Oslo Stock Exchange, 1989-1997.
194
Supplementary regressions
Table B.84 Interactions between governance mechanisms modelled as system of equations. Concentration and insider holdings are endogenous variables. Controls only as additional explanatory
variables. Aggregate intercorporate holdings and debt to assets are instruments.
Panel A. Regression results
Dep.variable
Herfindahl index
Primary insiders
Indep.variable
Primary insiders
Industrial
Transport/shipping
Offshore
ln(Firm value)
Aggregate intercorporate holdings
constant
Herfindahl index
Industrial
Transport/shipping
Offshore
ln(Firm value)
Debt to assets
constant
coeff
0.48
0.01
0.01
0.04
0.00
0.26
0.02
-1.12
-0.07
-0.10
-0.10
-0.01
0.14
0.33
(stdev)
(0.37)
(0.02)
(0.03)
(0.04)
(0.00)
(0.08)
(0.12)
(0.35)
(0.02)
(0.02)
(0.04)
(0.01)
(0.05)
(0.13)
R2
-0.36
-0.57
χ2
19.62
31.58
p
0.00
0.00
pvalue
0.20
0.68
0.69
0.38
0.66
0.00
0.87
0.00
0.00
0.00
0.01
0.28
0.01
0.01
Panel B. Regression diagnostics
Equation
1
2
n
741
741
Parms
6
6
RMSE
0.15
0.22
The table complements table 10.6 in the text. It shows results with the same set of endogenous variables, but a
smaller set of explanatory variables: Only the controls. The tables shows 3SLS estimates of a system of simultanous
equations. Panel A report system estimates. The leftmost column is the dependent variable in that particular
equation, which is a function of the variables listed in the next column. Equations are separated by a line. Panel
B holds diagnostics. For each equation the diagnostics include n, the number of observations, Parms, the number
of parameters, RMSE, the root mean squared error, R2 , a “pseudo” R squared, χ2 , the chi squared of the equation,
and p, a p-value for the fit of the equation. The estimates are performed with Stata’s reg3 3SLS routine. Variable
definitions are in Appendix A.2. Data for firms listed on the Oslo Stock Exchange, 1989-1997.
B.8 Interactions between ownership concentration and insider holdings in a system of
equations
B.8.2
195
Outside concentration
This appendix replaces the Herfindahl index used in the main text by the holdings of the largest
outside owner. We estimate outside (external) concentration by removing the largest owner if it
has the same holdings as the largest insider owner.
Table B.85 Interactions between governance mechanisms modeled as system of equations. Concentration and insider holdings are endogeneous variables. Adjusting for overlap between insiders
and large owners. Board size and stock volatility are instruments.
Panel A. Regression results
Dep.variable
Largest outside owner
Primary insiders
Indep.variable
Primary insiders
Aggregate state holdings
Aggregate international holdings
Aggregate individual holdings
Aggregate nonfinancial holdings
Fraction voting shares
Debt to assets
Dividends to earnings
Industrial
Transport/shipping
Offshore
Investments over income
ln(Firm value)
Stock volatility
constant
Largest outside owner
Aggregate state holdings
Aggregate international holdings
Aggregate individual holdings
Aggregate nonfinancial holdings
Fraction voting shares
Debt to assets
Dividends to earnings
Industrial
Transport/shipping
Offshore
Investments over income
ln(Firm value)
ln(Board size)
constant
coeff
0.08
0.73
0.28
-0.17
0.37
0.25
0.11
0.01
-0.01
-0.06
0.03
-0.00
-0.01
0.04
-0.04
1.29
-1.02
-0.33
0.59
-0.47
-0.38
-0.04
-0.01
-0.01
0.04
-0.09
0.00
0.03
-0.02
-0.21
(stdev)
(0.58)
(0.08)
(0.05)
(0.26)
(0.05)
(0.07)
(0.07)
(0.01)
(0.02)
(0.03)
(0.04)
(0.00)
(0.01)
(0.04)
(0.24)
(1.04)
(0.78)
(0.32)
(0.16)
(0.41)
(0.27)
(0.15)
(0.02)
(0.03)
(0.07)
(0.05)
(0.00)
(0.02)
(0.03)
(0.25)
pvalue
0.89
0.00
0.00
0.52
0.00
0.00
0.13
0.18
0.67
0.03
0.47
0.12
0.21
0.33
0.86
0.22
0.19
0.30
0.00
0.25
0.16
0.77
0.60
0.71
0.56
0.06
0.49
0.10
0.38
0.40
Panel B. Regression diagnostics
Equation
1
2
n
741
741
Parms
14
14
RMSE
0.15
0.26
R2
0.31
-1.05
χ2
345.75
60.75
p
0.00
0.00
The tables shows 3SLS estimates of a system of simultanous equations. Panel A report system estimates. The
leftmost column is the dependent variable in that particular equation, which is a function of the variables listed in
the next column. Equations are separated by a line. Panel B holds diagnostics. For each equation the diagnostics
include n, the number of observations, Parms, the number of parameters, RMSE, the root mean squared error, R2 , a
“pseudo” R squared, χ2 , the chi squared of the equation, and p, a p-value for the fit of the equation. The estimates
are performed with Stata’s reg3 3SLS routine. Variable definitions are in Appendix A.2. Data for firms listed on the
Oslo Stock Exchange, 1989-1997.
196
Supplementary regressions
Table B.86 Interactions between governance mechanisms modeled as system of equations. Concentration and insider holdings are endogeneous variables. Adjusting for overlap between insiders
and large owners. Board size and stock turnover are instruments.
Panel A. Regression results
Dep.variable
Largest outside owner
Primary insiders
Indep.variable
Primary insiders
Aggregate state holdings
Aggregate international holdings
Aggregate individual holdings
Aggregate nonfinancial holdings
Fraction voting shares
Debt to assets
Dividends to earnings
Industrial
Transport/shipping
Offshore
Investments over income
ln(Firm value)
Stock volatility
Stock turnover
constant
Largest outside owner
Aggregate state holdings
Aggregate international holdings
Aggregate individual holdings
Aggregate nonfinancial holdings
Fraction voting shares
Debt to assets
Dividends to earnings
Industrial
Transport/shipping
Offshore
Investments over income
ln(Firm value)
ln(Board size)
Stock volatility
constant
coeff
0.26
0.68
0.25
-0.25
0.32
0.30
0.09
0.01
-0.00
-0.05
0.04
-0.00
-0.01
0.04
-0.04
0.00
0.30
-0.31
-0.05
0.46
-0.10
-0.13
0.08
0.00
-0.02
-0.02
-0.07
0.00
0.02
-0.03
0.04
-0.27
(stdev)
(0.57)
(0.09)
(0.05)
(0.25)
(0.05)
(0.07)
(0.07)
(0.01)
(0.02)
(0.03)
(0.04)
(0.00)
(0.01)
(0.04)
(0.01)
(0.24)
(0.24)
(0.19)
(0.09)
(0.07)
(0.11)
(0.09)
(0.05)
(0.01)
(0.02)
(0.03)
(0.03)
(0.00)
(0.01)
(0.02)
(0.03)
(0.18)
pvalue
0.65
0.00
0.00
0.33
0.00
0.00
0.23
0.43
0.88
0.05
0.33
0.11
0.19
0.37
0.00
1.00
0.22
0.11
0.56
0.00
0.33
0.16
0.12
0.81
0.21
0.40
0.02
0.97
0.01
0.15
0.18
0.13
Panel B. Regression diagnostics
Equation
1
2
n
741
741
Parms
15
15
RMSE
0.15
0.17
R2
0.26
0.08
χ2
343.25
137.02
p
0.00
0.00
The tables shows 3SLS estimates of a system of simultanous equations. Panel A report system estimates. The
leftmost column is the dependent variable in that particular equation, which is a function of the variables listed in
the next column. Equations are separated by a line. Panel B holds diagnostics. For each equation the diagnostics
include n, the number of observations, Parms, the number of parameters, RMSE, the root mean squared error, R2 , a
“pseudo” R squared, χ2 , the chi squared of the equation, and p, a p-value for the fit of the equation. The estimates
are performed with Stata’s reg3 3SLS routine. Variable definitions are in Appendix A.2. Data for firms listed on the
Oslo Stock Exchange, 1989-1997.
B.8 Interactions between ownership concentration and insider holdings in a system of
equations
197
Table B.87 Interactions between governance mechanisms modelled as system of equations. Concentration and insider holdings are endogeneous variables. Adjusting for overlap between insiders
and large owners. Aggregate intercorporate holdings and debt to assets are instruments.
Panel A. Regression results
Dep.variable
Largest outside owner
Primary insiders
Indep.variable
Primary insiders
Aggregate state holdings
Aggregate international holdings
Aggregate individual holdings
Aggregate nonfinancial holdings
Fraction voting shares
Dividends to earnings
ln(Board size)
Industrial
Transport/shipping
Offshore
Investments over income
ln(Firm value)
Aggregate intercorporate holdings
constant
Largest outside owner
Aggregate state holdings
Aggregate international holdings
Aggregate individual holdings
Aggregate nonfinancial holdings
ln(Board size)
Fraction voting shares
Dividends to earnings
Industrial
Transport/shipping
Offshore
Investments over income
ln(Firm value)
Debt to assets
constant
coeff
1.13
0.82
0.27
-0.56
0.35
0.32
0.00
0.02
0.01
-0.02
0.10
-0.00
-0.03
0.23
0.18
-0.66
0.42
0.25
0.35
0.27
-0.02
0.09
0.02
-0.03
-0.08
-0.05
-0.00
-0.00
0.21
-0.12
(stdev)
(0.47)
(0.11)
(0.07)
(0.20)
(0.07)
(0.11)
(0.02)
(0.03)
(0.02)
(0.03)
(0.05)
(0.00)
(0.01)
(0.08)
(0.22)
(0.39)
(0.30)
(0.13)
(0.08)
(0.16)
(0.02)
(0.12)
(0.01)
(0.02)
(0.03)
(0.03)
(0.00)
(0.01)
(0.07)
(0.17)
pvalue
0.02
0.00
0.00
0.01
0.00
0.00
0.77
0.40
0.63
0.53
0.05
0.49
0.00
0.00
0.43
0.09
0.16
0.05
0.00
0.09
0.23
0.47
0.23
0.09
0.01
0.15
0.31
0.94
0.00
0.50
Panel B. Regression diagnostics
Equation
1
2
n
741
741
Parms
14
14
RMSE
0.24
0.19
R2
-0.84
-0.07
χ2
132.71
116.22
p
0.00
0.00
The tables shows 3SLS estimates of a system of simultanous equations. Panel A report system estimates. The
leftmost column is the dependent variable in that particular equation, which is a function of the variables listed in
the next column. Equations are separated by a line. Panel B holds diagnostics. For each equation the diagnostics
include n, the number of observations, Parms, the number of parameters, RMSE, the root mean squared error, R2 , a
“pseudo” R squared, χ2 , the chi squared of the equation, and p, a p-value for the fit of the equation. The estimates
are performed with Stata’s reg3 3SLS routine. Variable definitions are in Appendix A.2. Data for firms listed on the
Oslo Stock Exchange, 1989-1997.
198
Supplementary regressions
B.9
Causation between corporate governance and economic performance, governance driving performance
This appendix lists complementary estimations to those in section 11.1. Section B.9.1 gives the
estimations underlying summary table 11.1 in the text. Section B.9.2 contains similar regressions to
the ones in the text with fewer explanatory (exogenous) variables. Section B.9.3 considers outside
concentration.
B.9.1
Regressions underlying summary table
This section gives the estimations underlying summary table 11.1 in the text.
B.9 Causation between corporate governance and economic performance, governance driving
performance
199
Table B.88 Simultaneous systems estimation of the determinants of ownership concentration,
insider holdings, and economic performance (Q). The two endogeneous governance mechanisms
are independent of performance. Stock volatility and board size are instruments (Model (I) in
summary table 11.1)
Panel A. Regression results
Dep.variable
Q
Herfindahl index
Primary insiders
Indep.variable
Herfindahl index
Primary insiders
Squared (Primary insiders)
Aggregate state holdings
Aggregate international holdings
Aggregate individual holdings
Aggregate nonfinancial holdings
Fraction voting shares
Debt to assets
Dividends to earnings
Industrial
Transport/shipping
Offshore
Investments over income
ln(Firm value)
constant
Primary insiders
Aggregate state holdings
Aggregate international holdings
Aggregate individual holdings
Aggregate nonfinancial holdings
Fraction voting shares
Debt to assets
Dividends to earnings
Industrial
Transport/shipping
Offshore
Investments over income
ln(Firm value)
Stock volatility
constant
Herfindahl index
Aggregate state holdings
Aggregate international holdings
Aggregate individual holdings
Aggregate nonfinancial holdings
Fraction voting shares
Debt to assets
Dividends to earnings
Industrial
Transport/shipping
Offshore
Investments over income
ln(Firm value)
ln(Board size)
constant
coeff
29.28
-31.81
34.69
-20.35
-8.54
3.40
-11.09
-3.19
-3.90
-0.72
0.16
0.52
-1.05
0.09
0.55
-0.41
0.11
0.55
0.24
-0.01
0.29
0.22
0.05
0.02
-0.02
-0.04
0.00
-0.00
-0.01
0.00
-0.13
9.41
-5.19
-2.24
0.06
-2.75
-2.07
-0.45
-0.17
0.21
0.39
-0.01
0.02
0.07
0.00
1.26
(stdev)
(18.46)
(62.64)
(71.83)
(16.67)
(7.37)
(4.48)
(9.49)
(2.22)
(2.12)
(0.49)
(0.40)
(0.49)
(1.11)
(0.08)
(0.48)
(4.55)
(0.02)
(0.05)
(0.03)
(0.04)
(0.03)
(0.05)
(0.03)
(0.01)
(0.01)
(0.01)
(0.02)
(0.00)
(0.00)
(0.01)
(0.11)
(2.75)
(1.57)
(0.74)
(0.40)
(0.87)
(0.72)
(0.30)
(0.08)
(0.12)
(0.18)
(0.17)
(0.02)
(0.04)
(0.06)
(1.04)
pvalue
0.11
0.61
0.63
0.22
0.25
0.45
0.24
0.15
0.07
0.15
0.69
0.29
0.34
0.26
0.26
0.93
0.00
0.00
0.00
0.88
0.00
0.00
0.07
0.01
0.04
0.00
0.94
0.07
0.03
0.99
0.21
0.00
0.00
0.00
0.88
0.00
0.00
0.13
0.04
0.08
0.03
0.94
0.12
0.05
0.99
0.23
Panel B. Regression diagnostics
Equation
1
2
3
n
741
741
741
Parms
15
14
14
RMSE
4.07
0.11
1.06
R2
-13.54
0.25
-34.05
χ2
35.93
680.02
402.45
p
0.00
0.00
0.00
The tables shows 3SLS estimates of a system of simultanous equations. Panel A report system estimates. The
leftmost column is the dependent variable in that particular equation, which is a function of the variables listed in
the next column. Equations are separated by a line. Panel B holds diagnostics. For each equation the diagnostics
include n, the number of observations, Parms, the number of parameters, RMSE, the root mean squared error, R2 , a
“pseudo” R squared, χ2 , the chi squared of the equation, and p, a p-value for the fit of the equation. The estimates
are performed with Stata’s reg3 3SLS routine. Variable definitions are in Appendix A.2. Data for firms listed on
the Oslo Stock Exchange, 1989-1997. Appendix table B.91 estimates a similar system which only uses controls as
additional explanatory variables beyond the two endogeneous mechanisms and the two instruments.
200
Supplementary regressions
Table B.89 Simultaneous systems estimation of the determinants of ownership concentration,
insider holdings, and economic performance (Q). The two endogeneous governance mechanisms
are independent of performance. Stock turnover and board size are instruments (Model (II) in
summary table 11.1)
Panel A. Regression results
Dep.variable
Q
Herfindahl index
Primary insiders
Indep.variable
Herfindahl index
Primary insiders
Squared (Primary insiders)
Aggregate state holdings
Aggregate international holdings
Aggregate individual holdings
Aggregate nonfinancial holdings
Fraction voting shares
ln(Board size)
Debt to assets
Dividends to earnings
Industrial
Transport/shipping
Offshore
Investments over income
ln(Firm value)
constant
Primary insiders
Aggregate state holdings
Aggregate international holdings
Aggregate individual holdings
Aggregate nonfinancial holdings
Fraction voting shares
Debt to assets
Dividends to earnings
Industrial
Transport/shipping
Offshore
Investments over income
ln(Firm value)
Stock volatility
Stock turnover
constant
Herfindahl index
Aggregate state holdings
Aggregate international holdings
Aggregate individual holdings
Aggregate nonfinancial holdings
Fraction voting shares
Debt to assets
Dividends to earnings
Industrial
Transport/shipping
Offshore
Investments over income
ln(Firm value)
ln(Board size)
Stock volatility
constant
coeff
-8.77
19.43
-19.39
5.98
2.80
-0.71
3.21
2.11
-0.21
-1.34
0.06
-0.17
-0.61
-0.22
-0.03
-0.00
-0.65
0.36
0.54
0.21
-0.12
0.27
0.25
0.02
0.02
-0.02
-0.03
0.02
-0.00
-0.01
0.02
-0.01
-0.13
2.11
-1.22
-0.46
0.35
-0.60
-0.52
-0.01
-0.03
0.03
0.06
-0.05
0.01
0.02
-0.01
-0.02
0.20
(stdev)
(1.70)
(16.55)
(19.42)
(2.24)
(1.04)
(1.26)
(1.37)
(1.24)
(0.14)
(0.44)
(0.09)
(0.20)
(0.23)
(0.35)
(0.02)
(0.11)
(1.94)
(0.02)
(0.05)
(0.03)
(0.04)
(0.03)
(0.05)
(0.03)
(0.01)
(0.01)
(0.01)
(0.02)
(0.00)
(0.00)
(0.02)
(0.01)
(0.11)
(0.35)
(0.22)
(0.12)
(0.10)
(0.13)
(0.14)
(0.07)
(0.02)
(0.03)
(0.04)
(0.05)
(0.00)
(0.01)
(0.02)
(0.05)
(0.30)
pvalue
0.00
0.24
0.32
0.01
0.01
0.57
0.02
0.09
0.13
0.00
0.51
0.41
0.01
0.54
0.15
0.98
0.74
0.00
0.00
0.00
0.00
0.00
0.00
0.55
0.03
0.14
0.01
0.38
0.07
0.05
0.37
0.04
0.24
0.00
0.00
0.00
0.00
0.00
0.00
0.92
0.07
0.31
0.10
0.28
0.18
0.03
0.58
0.73
0.49
Panel B. Regression diagnostics
Equation
1
2
3
n
741
741
741
Parms
16
15
15
RMSE
1.71
0.12
0.28
R2
-1.57
0.14
-1.38
χ2
228.60
775.44
106.54
p
0.00
0.00
0.00
The tables shows 3SLS estimates of a system of simultanous equations. Panel A report system estimates. The
leftmost column is the dependent variable in that particular equation, which is a function of the variables listed in
the next column. Equations are separated by a line. Panel B holds diagnostics. For each equation the diagnostics
include n, the number of observations, Parms, the number of parameters, RMSE, the root mean squared error, R2 , a
“pseudo” R squared, χ2 , the chi squared of the equation, and p, a p-value for the fit of the equation. The estimates
are performed with Stata’s reg3 3SLS routine. Variable definitions are in Appendix A.2. Data for firms listed on
the Oslo Stock Exchange, 1989-1997. Appendix table B.92 estimates a similar system which only uses controls as
additional explanatory variables beyond the two endogeneous mechanisms and the two instruments.
B.9 Causation between corporate governance and economic performance, governance driving
performance
201
Table B.90 Simultaneous systems estimation of the determinants of ownership concentration,
insider holdings, and economic performance (Q). The two endogeneous governance mechanisms are
independent of performance. Debt to assets and intercorporate investments and are instruments
(Model (III) in summary table 11.1)
Panel A. Regression results
Dep.variable
Q
Herfindahl index
Primary insiders
Indep.variable
Herfindahl index
Primary insiders
Squared (Primary insiders)
Aggregate state holdings
Aggregate international holdings
Aggregate individual holdings
Aggregate nonfinancial holdings
Fraction voting shares
Dividends to earnings
ln(Board size)
Industrial
Transport/shipping
Offshore
Investments over income
ln(Firm value)
constant
Primary insiders
Aggregate state holdings
Aggregate international holdings
Aggregate individual holdings
Aggregate nonfinancial holdings
Fraction voting shares
Dividends to earnings
ln(Board size)
Industrial
Transport/shipping
Offshore
Investments over income
ln(Firm value)
Aggregate intercorporate holdings
constant
Herfindahl index
Aggregate state holdings
Aggregate international holdings
Aggregate individual holdings
Aggregate nonfinancial holdings
ln(Board size)
Fraction voting shares
Dividends to earnings
Industrial
Transport/shipping
Offshore
Investments over income
ln(Firm value)
Debt to assets
constant
coeff
81.38
178.99
-223.64
-24.54
-9.10
-9.42
-10.15
-28.95
-1.12
1.82
3.17
4.68
2.68
0.13
-0.34
22.92
0.21
0.56
0.23
-0.06
0.28
0.22
0.02
-0.02
-0.02
-0.04
0.00
-0.00
-0.01
0.01
-0.06
4.63
-2.59
-1.04
0.31
-1.30
0.08
-1.02
-0.08
0.08
0.17
-0.02
0.01
0.04
0.01
0.27
(stdev)
(182.84)
(670.37)
(803.40)
(30.46)
(12.83)
(49.22)
(12.15)
(84.11)
(2.12)
(5.88)
(10.67)
(15.26)
(12.83)
(0.32)
(2.93)
(88.37)
(0.02)
(0.05)
(0.03)
(0.04)
(0.03)
(0.05)
(0.01)
(0.01)
(0.01)
(0.01)
(0.02)
(0.00)
(0.00)
(0.03)
(0.10)
(1.01)
(0.60)
(0.29)
(0.20)
(0.34)
(0.06)
(0.31)
(0.04)
(0.06)
(0.08)
(0.09)
(0.01)
(0.02)
(0.06)
(0.51)
pvalue
0.66
0.79
0.78
0.42
0.48
0.85
0.40
0.73
0.60
0.76
0.77
0.76
0.83
0.70
0.91
0.80
0.00
0.00
0.00
0.14
0.00
0.00
0.01
0.17
0.12
0.00
0.85
0.09
0.02
0.75
0.54
0.00
0.00
0.00
0.12
0.00
0.22
0.00
0.04
0.18
0.03
0.83
0.14
0.04
0.89
0.59
Panel B. Regression diagnostics
Equation
1
2
3
n
741
741
741
Parms
15
14
14
RMSE
14.27
0.11
0.53
R2
-177.98
0.22
-7.87
χ2
2.87
894.95
115.91
p
1.00
0.00
0.00
The tables shows 3SLS estimates of a system of simultanous equations. Panel A report system estimates. The
leftmost column is the dependent variable in that particular equation, which is a function of the variables listed in
the next column. Equations are separated by a line. Panel B holds diagnostics. For each equation the diagnostics
include n, the number of observations, Parms, the number of parameters, RMSE, the root mean squared error, R2 , a
“pseudo” R squared, χ2 , the chi squared of the equation, and p, a p-value for the fit of the equation. The estimates
are performed with Stata’s reg3 3SLS routine. Variable definitions are in Appendix A.2. Data for firms listed on
the Oslo Stock Exchange, 1989-1997. Appendix table B.93 estimates a similar system which only uses controls as
additional explanatory variables beyond the two endogeneous mechanisms and the two instruments.
202
B.9.2
Supplementary regressions
Controls, instruments and endogenous mechanisms only
Table B.91 Simultaneous systems estimation of the determinants of ownership concentration,
insider holdings, and economic performance (Q). The two endogeneous governance mechanisms
are independent of performance. Concentration and insider holdings are endogeneous variables.
Board size and stock volatility are instruments. Only controls as additional explanatory variables.
Panel A. Regression results
Dep.variable
Q
Herfindahl index
Primary insiders
Indep.variable
Herfindahl index
Primary insiders
Squared (Primary insiders)
Industrial
Transport/shipping
Offshore
ln(Firm value)
constant
Primary insiders
Industrial
Transport/shipping
Offshore
ln(Firm value)
Stock volatility
constant
Herfindahl index
ln(Board size)
Industrial
Transport/shipping
Offshore
ln(Firm value)
constant
coeff
-7.90
36.96
-41.96
0.34
0.22
0.41
0.07
-0.41
0.23
0.01
0.00
0.02
0.00
0.01
0.09
4.01
-0.01
-0.03
-0.02
-0.08
-0.00
-0.39
(stdev)
(7.27)
(28.43)
(34.01)
(0.65)
(0.88)
(0.91)
(0.07)
(1.77)
(0.01)
(0.01)
(0.01)
(0.02)
(0.00)
(0.02)
(0.09)
(1.56)
(0.03)
(0.05)
(0.06)
(0.09)
(0.01)
(0.38)
pvalue
0.28
0.19
0.22
0.60
0.81
0.66
0.29
0.82
0.00
0.66
0.77
0.42
0.80
0.78
0.32
0.01
0.83
0.61
0.69
0.39
0.90
0.31
Panel B. Regression diagnostics
Equation
1
2
3
n
741
741
741
Parms
7
6
6
RMSE
2.70
0.14
0.55
R2
-5.39
-0.10
-8.53
χ2
29.17
606.61
18.60
p
0.00
0.00
0.00
The table complements table B.88 by using only controls as additional exogenous variables to the instruments in the
estimation. The tables shows 3SLS estimates of a system of simultanous equations. Panel A report system estimates.
The leftmost column is the dependent variable in that particular equation, which is a function of the variables listed
in the next column. Equations are separated by a line. Panel B holds diagnostics. For each equation the diagnostics
include n, the number of observations, Parms, the number of parameters, RMSE, the root mean squared error, R2 , a
“pseudo” R squared, χ2 , the chi squared of the equation, and p, a p-value for the fit of the equation. The estimates
are performed with Stata’s reg3 3SLS routine. Variable definitions are in Appendix A.2. Data for firms listed on the
Oslo Stock Exchange, 1989-1997.
B.9 Causation between corporate governance and economic performance, governance driving
performance
203
Table B.92 Simultaneous systems estimation of the determinants of ownership concentration,
insider holdings, and economic performance (Q). The two endogeneous governance mechanisms
are independent of performance. Concentration and insider holdings are endogeneous variables.
Board size and stock turnover are instruments. Only controls as additional explanatory variables.
Panel A. Regression results
Dep.variable
Q
Herfindahl index
Primary insiders
Indep.variable
Herfindahl index
Primary insiders
Squared (Primary insiders)
Industrial
Transport/shipping
Offshore
ln(Firm value)
constant
Primary insiders
Industrial
Transport/shipping
Offshore
ln(Firm value)
Stock volatility
Stock turnover
constant
Herfindahl index
Industrial
Transport/shipping
Offshore
ln(Firm value)
Stock volatility
ln(Board size)
constant
coeff
-3.39
16.62
-14.88
-0.00
-0.26
-0.02
0.11
-0.91
0.12
-0.01
-0.02
0.00
0.01
0.05
-0.06
0.03
0.15
-0.05
-0.07
-0.10
0.00
0.04
-0.05
0.12
(stdev)
(2.99)
(17.33)
(20.85)
(0.43)
(0.59)
(0.60)
(0.05)
(1.42)
(0.03)
(0.01)
(0.01)
(0.02)
(0.00)
(0.02)
(0.01)
(0.08)
(0.15)
(0.02)
(0.02)
(0.03)
(0.01)
(0.03)
(0.02)
(0.12)
pvalue
0.26
0.34
0.47
1.00
0.66
0.98
0.04
0.52
0.00
0.62
0.11
0.93
0.15
0.01
0.00
0.70
0.34
0.00
0.00
0.00
0.69
0.16
0.01
0.29
Panel B. Regression diagnostics
Equation
1
2
3
n
741
741
741
Parms
7
7
7
RMSE
1.51
0.12
0.18
R2
-1.00
0.08
0.03
χ2
136.11
106.89
39.18
p
0.00
0.00
0.00
The table complements table B.89 by using only controls as additional exogenous variables to the instruments in the
estimation. The tables shows 3SLS estimates of a system of simultanous equations. Panel A report system estimates.
The leftmost column is the dependent variable in that particular equation, which is a function of the variables listed
in the next column. Equations are separated by a line. Panel B holds diagnostics. For each equation the diagnostics
include n, the number of observations, Parms, the number of parameters, RMSE, the root mean squared error, R2 , a
“pseudo” R squared, χ2 , the chi squared of the equation, and p, a p-value for the fit of the equation. The estimates
are performed with Stata’s reg3 3SLS routine. Variable definitions are in Appendix A.2. Data for firms listed on the
Oslo Stock Exchange, 1989-1997.
204
Supplementary regressions
Table B.93 Simultaneous systems estimation of the determinants of ownership concentration, insider holdings, and economic performance (Q). The two endogeneous governance mechanisms are
independent of performance. Concentration and insider holdings are endogeneous variables. Aggregate intercorporate shareholdings and debt to assets are instruments. Only controls as additional
explanatory variables.
Panel A. Regression results
Dep.variable
Q
Herfindahl index
Primary insiders
Indep.variable
Herfindahl index
Primary insiders
Squared (Primary insiders)
Industrial
Transport/shipping
Offshore
ln(Firm value)
constant
Primary insiders
Industrial
Transport/shipping
Offshore
ln(Firm value)
Aggregate intercorporate holdings
constant
Herfindahl index
Industrial
Transport/shipping
Offshore
ln(Firm value)
Debt to assets
constant
coeff
54.48
204.63
-239.81
4.52
6.17
5.79
-0.01
-16.41
0.35
0.01
0.01
0.03
0.00
0.14
0.06
0.70
-0.05
-0.07
-0.09
-0.00
0.08
0.08
(stdev)
(114.75)
(339.38)
(410.28)
(8.21)
(11.65)
(10.54)
(0.43)
(29.65)
(0.03)
(0.01)
(0.01)
(0.02)
(0.00)
(0.03)
(0.07)
(0.30)
(0.02)
(0.02)
(0.03)
(0.01)
(0.04)
(0.11)
pvalue
0.64
0.55
0.56
0.58
0.60
0.58
0.99
0.58
0.00
0.56
0.57
0.19
0.71
0.00
0.35
0.02
0.00
0.00
0.00
0.34
0.06
0.46
Panel B. Regression diagnostics
Equation
1
2
3
n
741
741
741
Parms
7
6
6
RMSE
14.20
0.14
0.20
R2
-176.26
-0.19
-0.23
χ2
4.70
188.30
35.40
p
0.70
0.00
0.00
The table complements table B.90 in the text by only using controls as additional exogenous variables to the instruments in the estimation. The tables shows 3SLS estimates of a system of simultanous equations. Panel A report
system estimates. The leftmost column is the dependent variable in that particular equation, which is a function
of the variables listed in the next column. Equations are separated by a line. Panel B holds diagnostics. For each
equation the diagnostics include n, the number of observations, Parms, the number of parameters, RMSE, the root
mean squared error, R2 , a “pseudo” R squared, χ2 , the chi squared of the equation, and p, a p-value for the fit of the
equation. The estimates are performed with Stata’s reg3 3SLS routine. Variable definitions are in Appendix A.2.
Data for firms listed on the Oslo Stock Exchange, 1989-1997.
B.9 Causation between corporate governance and economic performance, governance driving
performance
205
B.9.3
Outside (external) concentration
This appendix replaces the Herfindahl index used in the main text by the holdings of the largest
outside owner. To estimate outside concentration we remove the largest owner if it has the same
holdings as the largest insider.
206
Supplementary regressions
Table B.94 Simultaneous systems estimation of the determinants of ownership concentration,
insider holdings, and economic performance (Q). The two endogeneous governance mechanisms
are independent of performance. Concentration and insider holdings are endogeneous variables.
Adjusting for overlap between insiders and large owners. Board size and stock volatility are instruments.
Panel A. Regression results
Dep.variable
Q
Largest outside owner
Primary insiders
Indep.variable
Largest outside owner
Primary insiders
Squared (Primary insiders)
Aggregate state holdings
Aggregate international holdings
Aggregate individual holdings
Aggregate nonfinancial holdings
Fraction voting shares
Debt to assets
Dividends to earnings
Industrial
Transport/shipping
Offshore
ln(Firm value)
constant
Primary insiders
Aggregate state holdings
Aggregate international holdings
Aggregate individual holdings
Aggregate nonfinancial holdings
Fraction voting shares
Debt to assets
Dividends to earnings
Industrial
Transport/shipping
Offshore
Investments over income
ln(Firm value)
Stock volatility
constant
Largest outside owner
Aggregate state holdings
Aggregate international holdings
Aggregate individual holdings
Aggregate nonfinancial holdings
Fraction voting shares
Debt to assets
Dividends to earnings
Industrial
Transport/shipping
Offshore
Investments over income
ln(Firm value)
ln(Board size)
constant
coeff
-1.25
9.30
-10.48
1.04
0.79
0.52
0.66
0.95
-1.56
-0.09
-0.12
-0.44
-0.37
0.09
-0.71
-0.15
0.73
0.30
-0.06
0.39
0.23
0.15
0.01
-0.01
-0.07
0.02
-0.00
-0.01
0.00
0.01
-6.40
4.66
1.96
-0.36
2.48
1.46
0.94
0.09
-0.09
-0.45
0.10
-0.02
-0.09
-0.00
0.12
(stdev)
(3.37)
(13.14)
(15.48)
(3.37)
(1.42)
(1.20)
(1.87)
(1.05)
(0.61)
(0.09)
(0.16)
(0.24)
(0.31)
(0.11)
(1.60)
(0.02)
(0.06)
(0.04)
(0.05)
(0.05)
(0.06)
(0.03)
(0.01)
(0.01)
(0.02)
(0.02)
(0.00)
(0.01)
(0.02)
(0.15)
(3.29)
(2.47)
(1.02)
(0.55)
(1.29)
(0.88)
(0.48)
(0.07)
(0.10)
(0.24)
(0.17)
(0.02)
(0.06)
(0.02)
(0.91)
pvalue
0.71
0.48
0.50
0.76
0.58
0.67
0.73
0.36
0.01
0.36
0.45
0.07
0.24
0.40
0.66
0.00
0.00
0.00
0.28
0.00
0.00
0.00
0.11
0.29
0.00
0.51
0.11
0.01
0.94
0.93
0.05
0.06
0.06
0.51
0.06
0.10
0.05
0.21
0.33
0.06
0.57
0.22
0.12
0.84
0.90
Panel B. Regression diagnostics
Equation
1
2
3
n
741
741
741
Parms
14
14
14
RMSE
1.04
0.15
0.94
R2
0.05
0.31
-26.66
χ2
190.72
997.74
9.20
p
0.00
0.00
0.82
The tables shows 3SLS estimates of a system of simultanous equations. Panel A report system estimates. The
leftmost column is the dependent variable in that particular equation, which is a function of the variables listed in
the next column. Equations are separated by a line. Panel B holds diagnostics. For each equation the diagnostics
include n, the number of observations, Parms, the number of parameters, RMSE, the root mean squared error, R2 , a
“pseudo” R squared, χ2 , the chi squared of the equation, and p, a p-value for the fit of the equation. The estimates
are performed with Stata’s reg3 3SLS routine. Variable definitions are in Appendix A.2. Data for firms listed on the
Oslo Stock Exchange, 1989-1997.
B.9 Causation between corporate governance and economic performance, governance driving
performance
207
Table B.95 Simultaneous systems estimation of the determinants of ownership concentration,
insider holdings, and economic performance (Q). The two endogeneous governance mechanisms
are independent of performance. Concentration and insider holdings are endogeneous variables.
Adjusting for overlap between insiders and large owners. Board size and stock turnover are instruments.
Panel A. Regression results
Dep.variable
Q
Largest outside owner
Primary insiders
Indep.variable
Largest outside owner
Primary insiders
Squared (Primary insiders)
Aggregate state holdings
Aggregate international holdings
Aggregate individual holdings
Aggregate nonfinancial holdings
Fraction voting shares
ln(Board size)
Debt to assets
Dividends to earnings
Industrial
Transport/shipping
Offshore
Investments over income
ln(Firm value)
constant
Primary insiders
Aggregate state holdings
Aggregate international holdings
Aggregate individual holdings
Aggregate nonfinancial holdings
Fraction voting shares
Debt to assets
Dividends to earnings
Industrial
Transport/shipping
Offshore
Investments over income
ln(Firm value)
Stock volatility
Stock turnover
constant
Largest outside owner
Aggregate state holdings
Aggregate international holdings
Aggregate individual holdings
Aggregate nonfinancial holdings
Fraction voting shares
Debt to assets
Dividends to earnings
Industrial
Transport/shipping
Offshore
Investments over income
ln(Firm value)
ln(Board size)
Stock volatility
constant
coeff
-5.78
25.04
-30.28
6.23
3.05
-0.79
3.60
0.81
-0.07
-0.52
0.02
-0.03
-0.61
-0.06
-0.03
-0.06
1.01
-0.47
0.65
0.29
0.07
0.35
0.24
0.17
0.01
-0.02
-0.08
-0.00
-0.00
-0.00
0.07
-0.02
-0.22
-1.21
0.79
0.38
0.27
0.46
0.25
0.26
0.02
-0.04
-0.12
-0.03
-0.00
0.00
-0.01
0.10
-0.39
(stdev)
(2.08)
(20.17)
(23.70)
(3.05)
(1.38)
(1.60)
(1.83)
(1.48)
(0.16)
(0.62)
(0.11)
(0.24)
(0.28)
(0.44)
(0.02)
(0.14)
(2.37)
(0.02)
(0.06)
(0.04)
(0.05)
(0.05)
(0.06)
(0.03)
(0.01)
(0.01)
(0.02)
(0.02)
(0.00)
(0.01)
(0.02)
(0.01)
(0.15)
(0.32)
(0.25)
(0.11)
(0.10)
(0.14)
(0.13)
(0.07)
(0.01)
(0.02)
(0.03)
(0.04)
(0.00)
(0.01)
(0.02)
(0.04)
(0.24)
pvalue
0.01
0.21
0.20
0.04
0.03
0.62
0.05
0.59
0.68
0.40
0.85
0.90
0.03
0.89
0.24
0.64
0.67
0.00
0.00
0.00
0.20
0.00
0.00
0.00
0.10
0.09
0.00
0.94
0.06
0.48
0.01
0.02
0.14
0.00
0.00
0.00
0.01
0.00
0.05
0.00
0.12
0.08
0.00
0.46
0.15
0.89
0.57
0.01
0.11
Panel B. Regression diagnostics
Equation
1
2
3
n
741
741
741
Parms
16
15
15
RMSE
1.94
0.16
0.23
R2
-2.32
0.20
-0.69
χ2
103.62
725.65
88.47
p
0.00
0.00
0.00
The tables shows 3SLS estimates of a system of simultanous equations. Panel A report system estimates. The
leftmost column is the dependent variable in that particular equation, which is a function of the variables listed in
the next column. Equations are separated by a line. Panel B holds diagnostics. For each equation the diagnostics
include n, the number of observations, Parms, the number of parameters, RMSE, the root mean squared error, R2 , a
“pseudo” R squared, χ2 , the chi squared of the equation, and p, a p-value for the fit of the equation. The estimates
are performed with Stata’s reg3 3SLS routine. Variable definitions are in Appendix A.2. Data for firms listed on the
Oslo Stock Exchange, 1989-1997.
208
Supplementary regressions
Table B.96 Simultaneous systems estimation of the determinants of ownership concentration,
insider holdings, and economic performance (Q). The two endogeneous governance mechanisms
are independent of performance. Concentration and insider holdings are endogeneous variables.
Adjusting for overlap between insiders and large owners. Aggregate intercorporate shareholdings
and debt to assets are instruments.
Panel A. Regression results
Dep.variable
Q
Largest outside owner
Primary insiders
Indep.variable
Largest outside owner
Primary insiders
Squared (Primary insiders)
Aggregate state holdings
Aggregate international holdings
Aggregate individual holdings
Aggregate nonfinancial holdings
Fraction voting shares
Dividends to earnings
ln(Board size)
Industrial
Transport/shipping
Offshore
Investments over income
ln(Firm value)
constant
Primary insiders
Aggregate state holdings
Aggregate international holdings
Aggregate individual holdings
Aggregate nonfinancial holdings
Fraction voting shares
Dividends to earnings
ln(Board size)
Industrial
Transport/shipping
Offshore
Investments over income
ln(Firm value)
Aggregate intercorporate holdings
constant
Largest outside owner
Aggregate state holdings
Aggregate international holdings
Aggregate individual holdings
Aggregate nonfinancial holdings
ln(Board size)
Fraction voting shares
Dividends to earnings
Industrial
Transport/shipping
Offshore
Investments over income
ln(Firm value)
Debt to assets
constant
coeff
-40.64
6.38
-24.85
29.98
12.93
0.19
15.92
8.10
0.53
-0.46
-0.57
-3.02
-0.17
-0.11
-0.50
5.47
-0.33
0.70
0.29
-0.01
0.36
0.21
0.01
-0.01
-0.01
-0.06
0.00
-0.00
-0.01
0.01
0.16
-2.80
1.96
0.81
0.01
1.02
-0.02
0.58
0.04
-0.04
-0.19
0.01
-0.01
-0.04
0.07
0.37
(stdev)
(73.65)
(435.39)
(508.45)
(12.41)
(5.34)
(22.83)
(7.10)
(45.99)
(0.74)
(1.75)
(5.11)
(9.06)
(6.08)
(0.16)
(1.45)
(37.13)
(0.02)
(0.06)
(0.04)
(0.06)
(0.04)
(0.06)
(0.01)
(0.02)
(0.01)
(0.02)
(0.02)
(0.00)
(0.00)
(0.04)
(0.13)
(0.81)
(0.62)
(0.27)
(0.18)
(0.33)
(0.04)
(0.26)
(0.03)
(0.04)
(0.07)
(0.07)
(0.01)
(0.02)
(0.12)
(0.37)
pvalue
0.58
0.99
0.96
0.02
0.01
0.99
0.03
0.86
0.47
0.79
0.91
0.74
0.98
0.48
0.73
0.88
0.00
0.00
0.00
0.84
0.00
0.00
0.11
0.67
0.36
0.00
0.87
0.23
0.00
0.86
0.25
0.00
0.00
0.00
0.97
0.00
0.63
0.03
0.12
0.31
0.01
0.92
0.24
0.03
0.56
0.31
Panel B. Regression diagnostics
Equation
1
2
3
n
741
741
741
Parms
15
14
14
RMSE
6.46
0.16
0.44
R2
-35.73
0.23
-4.98
χ2
47.11
768.84
95.77
p
0.00
0.00
0.00
The tables shows 3SLS estimates of a system of simultanous equations. Panel A report system estimates. The
leftmost column is the dependent variable in that particular equation, which is a function of the variables listed in
the next column. Equations are separated by a line. Panel B holds diagnostics. For each equation the diagnostics
include n, the number of observations, Parms, the number of parameters, RMSE, the root mean squared error, R2 , a
“pseudo” R squared, χ2 , the chi squared of the equation, and p, a p-value for the fit of the equation. The estimates
are performed with Stata’s reg3 3SLS routine. Variable definitions are in Appendix A.2. Data for firms listed on the
Oslo Stock Exchange, 1989-1997.
B.10 Two-way causaution between corporate governance and economic performance
B.10
209
Two-way causaution between corporate governance and economic performance
This appendix lists complementary estimations to those in section 11.2. Section B.10.1 gives the
estimations underlying summary table 11.2 in the text. Section B.10.2 contains similar regressions
to the ones in the text with fewer explanatory (exogenous) variables. Section B.10.3 considers
outside concentration.
B.10.1
Regressions underlying summary table
This section gives the estimations underlying summary table 11.1 in the text.
210
Supplementary regressions
Table B.97 Simultaneous systems estimation of the determinants of ownership concentration,
insider holdings, and economic performance (Q). Board size, stock beta and stock volatility are
instruments. (Model (A) in table 11.1)
Panel A. Regression results
Dep.variable
Q
Herfindahl index
Primary insiders
Indep.variable
Herfindahl index
Primary insiders
Squared (Primary insiders)
Aggregate state holdings
Aggregate international holdings
Aggregate individual holdings
Aggregate nonfinancial holdings
Fraction voting shares
Debt to assets
Dividends to earnings
Industrial
Transport/shipping
Offshore
Investments over income
ln(Firm value)
Stock beta
constant
Primary insiders
Q
Aggregate state holdings
Aggregate international holdings
Aggregate individual holdings
Aggregate nonfinancial holdings
Fraction voting shares
Debt to assets
Dividends to earnings
Industrial
Transport/shipping
Offshore
Investments over income
ln(Firm value)
Stock volatility
constant
Herfindahl index
Q
Aggregate state holdings
Aggregate international holdings
Aggregate individual holdings
Aggregate nonfinancial holdings
Fraction voting shares
Debt to assets
Dividends to earnings
Industrial
Transport/shipping
Offshore
Investments over income
ln(Firm value)
ln(Board size)
constant
coeff
29.77
-65.74
74.74
-24.59
-10.36
5.98
-13.60
-0.96
-4.59
-0.83
-0.11
0.31
-1.52
0.11
0.78
-0.41
-4.16
0.05
0.06
0.59
0.23
-0.07
0.31
0.15
0.16
0.03
-0.01
-0.02
0.03
-0.00
-0.01
0.03
-0.13
1.70
1.64
0.45
-0.32
-1.91
0.29
-2.32
3.02
0.17
0.33
0.82
0.96
0.00
-0.27
0.38
2.36
(stdev)
(24.39)
(74.55)
(85.20)
(21.39)
(9.37)
(5.46)
(12.03)
(2.31)
(2.62)
(0.63)
(0.37)
(0.59)
(1.22)
(0.10)
(0.62)
(0.39)
(5.25)
(0.05)
(0.06)
(0.08)
(0.04)
(0.09)
(0.05)
(0.10)
(0.13)
(0.01)
(0.02)
(0.03)
(0.04)
(0.00)
(0.01)
(0.02)
(0.17)
(3.64)
(0.74)
(2.50)
(0.97)
(1.07)
(1.36)
(0.89)
(1.52)
(0.16)
(0.17)
(0.31)
(0.48)
(0.02)
(0.15)
(0.18)
(1.46)
pvalue
0.22
0.38
0.38
0.25
0.27
0.27
0.26
0.68
0.08
0.19
0.76
0.60
0.21
0.28
0.21
0.30
0.43
0.26
0.37
0.00
0.00
0.44
0.00
0.12
0.21
0.02
0.51
0.58
0.43
0.08
0.24
0.15
0.44
0.64
0.03
0.86
0.74
0.07
0.83
0.01
0.05
0.29
0.05
0.01
0.04
0.95
0.07
0.04
0.11
Panel B. Regression diagnostics
Equation
1
2
3
n
741
741
741
Parms
16
15
15
RMSE
5.49
0.13
1.48
R2
-25.49
0.03
-67.53
χ2
35.55
199.70
12.68
p
0.00
0.00
0.63
The tables shows 3SLS estimates of a system of simultanous equations. Panel A report system estimates. The
leftmost column is the dependent variable in that particular equation, which is a function of the variables listed in
the next column. Equations are separated by a line. Panel B holds diagnostics. For each equation the diagnostics
include n, the number of observations, Parms, the number of parameters, RMSE, the root mean squared error, R2 , a
“pseudo” R squared, χ2 , the chi squared of the equation, and p, a p-value for the fit of the equation. The estimates
are performed with Stata’s reg3 3SLS routine. Variable definitions are in Appendix A.2. Data for firms listed on the
Oslo Stock Exchange, 1989-1997. Appendix table B.100 shows the results from estimating a similar system with
only controls as additional explanatory variables.
B.10 Two-way causaution between corporate governance and economic performance
211
Table B.98 Simultaneous systems estimation of the determinants of economic performance (Q),
ownership concentration, and insider holdings. Board size, stock beta and stock turnover are
instruments. (Model (B) in table 11.1)
Panel A. Regression results
Dep.variable
Q
Herfindahl index
Primary insiders
Indep.variable
Herfindahl index
Primary insiders
Squared (Primary insiders)
Aggregate state holdings
Aggregate international holdings
Aggregate individual holdings
Aggregate nonfinancial holdings
Fraction voting shares
ln(Board size)
Debt to assets
Dividends to earnings
Industrial
Transport/shipping
Offshore
Investments over income
ln(Firm value)
Stock beta
constant
Primary insiders
Q
Aggregate state holdings
Aggregate international holdings
Aggregate individual holdings
Aggregate nonfinancial holdings
Fraction voting shares
Debt to assets
Dividends to earnings
Industrial
Transport/shipping
Offshore
Investments over income
ln(Firm value)
Stock volatility
Stock turnover
constant
Herfindahl index
Q
Aggregate state holdings
Aggregate international holdings
Aggregate individual holdings
Aggregate nonfinancial holdings
Fraction voting shares
Debt to assets
Dividends to earnings
Industrial
Transport/shipping
Offshore
Investments over income
ln(Firm value)
ln(Board size)
Stock volatility
constant
coeff
-3.90
11.38
-12.35
2.53
1.40
0.35
1.39
1.32
-0.23
-1.50
-0.02
-0.17
-0.54
-0.44
-0.01
0.07
0.11
-0.66
-0.02
0.16
0.55
0.19
-0.20
0.26
0.09
0.34
0.03
0.01
0.03
0.09
-0.00
-0.03
0.04
-0.08
0.01
5.43
1.15
-1.96
-1.17
-1.32
-0.97
-2.58
1.92
0.04
0.32
0.77
0.70
0.01
-0.19
0.35
-0.22
2.76
(stdev)
(1.63)
(11.18)
(13.11)
(1.44)
(0.68)
(0.97)
(0.87)
(1.02)
(0.14)
(0.33)
(0.08)
(0.15)
(0.18)
(0.26)
(0.02)
(0.08)
(0.12)
(1.66)
(0.08)
(0.12)
(0.10)
(0.06)
(0.16)
(0.06)
(0.15)
(0.22)
(0.02)
(0.03)
(0.05)
(0.07)
(0.00)
(0.02)
(0.03)
(0.03)
(0.26)
(1.79)
(0.36)
(0.96)
(0.54)
(0.68)
(0.56)
(0.87)
(0.66)
(0.08)
(0.15)
(0.26)
(0.30)
(0.02)
(0.08)
(0.17)
(0.21)
(1.49)
pvalue
0.02
0.31
0.35
0.08
0.04
0.72
0.11
0.20
0.10
0.00
0.77
0.26
0.00
0.09
0.44
0.39
0.37
0.69
0.82
0.17
0.00
0.00
0.21
0.00
0.54
0.12
0.07
0.72
0.63
0.21
0.12
0.16
0.19
0.00
0.97
0.00
0.00
0.04
0.03
0.05
0.08
0.00
0.00
0.62
0.03
0.00
0.02
0.42
0.01
0.04
0.30
0.06
Panel B. Regression diagnostics
Equation
1
2
3
n
741
741
741
Parms
17
16
16
RMSE
1.14
0.18
1.17
R2
-0.14
-1.00
-41.28
χ2
181.71
112.00
15.51
p
0.00
0.00
0.49
The tables shows 3SLS estimates of a system of simultanous equations. Panel A report system estimates. The
leftmost column is the dependent variable in that particular equation, which is a function of the variables listed in
the next column. Equations are separated by a line. Panel B holds diagnostics. For each equation the diagnostics
include n, the number of observations, Parms, the number of parameters, RMSE, the root mean squared error, R2 , a
“pseudo” R squared, χ2 , the chi squared of the equation, and p, a p-value for the fit of the equation. The estimates
are performed with Stata’s reg3 3SLS routine. Variable definitions are in Appendix A.2. Data for firms listed on the
Oslo Stock Exchange, 1989-1997. Appendix table B.98 shows the results from estimating a similar system with only
controls as additional explanatory variables.
212
Supplementary regressions
Table B.99 Simultaneous systems estimation of the determinants of economic performance (Q),
ownership concentration, and insider holdings. Debt to assets, intercorporate shareholdings and
stock beta are instruments. (Model (C) in table 11.1)
Panel A. Regression results
Dep.variable
Q
Herfindahl index
Primary insiders
Indep.variable
Herfindahl index
Primary insiders
Squared (Primary insiders)
Aggregate state holdings
Aggregate international holdings
Aggregate individual holdings
Aggregate nonfinancial holdings
Fraction voting shares
Dividends to earnings
ln(Board size)
Industrial
Transport/shipping
Offshore
Investments over income
ln(Firm value)
Stock beta
constant
Primary insiders
Q
Aggregate state holdings
Aggregate international holdings
Aggregate individual holdings
Aggregate nonfinancial holdings
Fraction voting shares
Dividends to earnings
ln(Board size)
Industrial
Transport/shipping
Offshore
Investments over income
ln(Firm value)
Aggregate intercorporate holdings
constant
Herfindahl index
Q
Aggregate state holdings
Aggregate international holdings
Aggregate individual holdings
Aggregate nonfinancial holdings
ln(Board size)
Fraction voting shares
Dividends to earnings
Industrial
Transport/shipping
Offshore
Investments over income
ln(Firm value)
Debt to assets
constant
coeff
99.97
401.13
-478.09
-8.92
-3.55
-30.25
-1.14
-47.09
-0.83
3.49
5.33
7.28
6.19
0.13
-2.01
4.10
53.68
0.10
-0.03
0.53
0.25
0.06
0.27
0.25
0.01
-0.03
-0.03
-0.06
-0.02
-0.00
-0.00
0.10
-0.18
4.76
1.20
-1.61
-1.08
-1.41
-0.81
0.33
-2.45
0.06
0.32
0.76
0.72
0.01
-0.17
2.00
2.18
(stdev)
(307.90)
(1113.58)
(1329.20)
(45.48)
(21.09)
(90.44)
(18.07)
(140.72)
(2.86)
(11.46)
(15.89)
(22.79)
(19.03)
(0.56)
(6.24)
(13.82)
(164.21)
(0.03)
(0.01)
(0.05)
(0.03)
(0.05)
(0.03)
(0.05)
(0.01)
(0.01)
(0.01)
(0.02)
(0.02)
(0.00)
(0.00)
(0.03)
(0.11)
(2.20)
(0.50)
(1.39)
(0.65)
(0.84)
(0.79)
(0.18)
(0.91)
(0.11)
(0.16)
(0.30)
(0.37)
(0.02)
(0.10)
(0.97)
(1.41)
pvalue
0.74
0.72
0.72
0.84
0.87
0.74
0.95
0.74
0.77
0.76
0.74
0.75
0.74
0.82
0.75
0.77
0.74
0.00
0.02
0.00
0.00
0.25
0.00
0.00
0.06
0.03
0.01
0.00
0.29
0.07
0.85
0.00
0.11
0.03
0.02
0.25
0.10
0.10
0.30
0.07
0.01
0.57
0.05
0.01
0.05
0.55
0.07
0.04
0.12
Panel B. Regression diagnostics
Equation
1
2
3
n
741
741
741
Parms
16
15
15
RMSE
26.87
0.11
1.18
R2
-633.85
0.25
-41.97
χ2
0.77
264.75
12.89
p
1.00
0.00
0.61
The tables shows 3SLS estimates of a system of simultanous equations. Panel A report system estimates. The
leftmost column is the dependent variable in that particular equation, which is a function of the variables listed in
the next column. Equations are separated by a line. Panel B holds diagnostics. For each equation the diagnostics
include n, the number of observations, Parms, the number of parameters, RMSE, the root mean squared error, R2 , a
“pseudo” R squared, χ2 , the chi squared of the equation, and p, a p-value for the fit of the equation. The estimates
are performed with Stata’s reg3 3SLS routine. Variable definitions are in Appendix A.2. Data for firms listed on the
Oslo Stock Exchange, 1989-1997. Appendix table B.102 shows the results from estimating a similar system with
only controls as additional explanatory variables.
B.10 Two-way causaution between corporate governance and economic performance
B.10.2
213
Controls, instruments and endogenous mechanisms, only
This section complements the analysis of section 11.2, (detailed in appendix section B.10.1), using
estimation models with smaller number of explanatory (exogenous) variables.
Table B.100 Simultaneous systems estimation of the determinants of ownership concentration,
insider holdings, and economic performance (Q). Controls only as additional explanatory variables.
Board size, stock beta and stock volatility are instruments.
Panel A. Regression results
Dep.variable
Q
Herfindahl index
Primary insiders
Indep.variable
Herfindahl index
Primary insiders
Squared (Primary insiders)
Industrial
Transport/shipping
Offshore
ln(Firm value)
Stock beta
constant
Primary insiders
Q
Industrial
Transport/shipping
Offshore
ln(Firm value)
Stock volatility
constant
Herfindahl index
Q
ln(Board size)
Industrial
Transport/shipping
Offshore
ln(Firm value)
constant
coeff
-6.13
20.25
-23.31
-0.06
-0.34
-0.17
0.07
0.13
0.20
0.06
-0.05
-0.03
-0.06
-0.04
0.01
0.03
0.06
9.00
1.25
0.26
0.57
1.17
0.96
-0.14
-1.24
(stdev)
(6.17)
(26.74)
(31.97)
(0.61)
(0.82)
(0.85)
(0.07)
(0.20)
(1.52)
(0.04)
(0.04)
(0.02)
(0.03)
(0.03)
(0.01)
(0.02)
(0.08)
(3.30)
(0.63)
(0.28)
(0.32)
(0.62)
(0.59)
(0.09)
(1.02)
pvalue
0.32
0.45
0.47
0.92
0.68
0.84
0.28
0.53
0.90
0.10
0.17
0.15
0.11
0.24
0.13
0.11
0.46
0.01
0.05
0.35
0.08
0.06
0.11
0.11
0.23
Panel B. Regression diagnostics
Equation
1
2
3
n
741
741
741
Parms
8
7
7
RMSE
1.70
0.13
1.58
R2
-1.54
-0.04
-76.13
χ2
22.04
7.85
10.72
p
0.00
0.35
0.15
The table complements table B.97. It shows results with the same set of endogenous variables, but a smaller set of
explanatory variables: Only the controls. The tables shows 3SLS estimates of a system of simultanous equations.
Panel A report system estimates. The leftmost column is the dependent variable in that particular equation, which
is a function of the variables listed in the next column. Equations are separated by a line. Panel B holds diagnostics.
For each equation the diagnostics include n, the number of observations, Parms, the number of parameters, RMSE,
the root mean squared error, R2 , a “pseudo” R squared, χ2 , the chi squared of the equation, and p, a p-value for
the fit of the equation. The estimates are performed with Stata’s reg3 3SLS routine. Variable definitions are in
Appendix A.2. Data for firms listed on the Oslo Stock Exchange, 1989-1997.
214
Supplementary regressions
Table B.101 Simultaneous systems estimation of the determinants of ownership concentration,
insider holdings, and economic performance (Q). Controls only as additional explanatory variables.
Board size, stock turnover and stock volatility are instruments.
Panel A. Regression results
Dep.variable
Q
Herfindahl index
Primary insiders
Indep.variable
Herfindahl index
Primary insiders
Squared (Primary insiders)
Industrial
Transport/shipping
Offshore
ln(Firm value)
Stock beta
constant
Primary insiders
Q
Industrial
Transport/shipping
Offshore
ln(Firm value)
Stock volatility
Stock turnover
constant
Herfindahl index
Q
Industrial
Transport/shipping
Offshore
ln(Firm value)
Stock volatility
ln(Board size)
constant
coeff
-0.43
23.36
-27.27
0.04
-0.19
-0.07
0.07
0.19
-0.73
-0.04
0.10
0.03
0.05
0.06
-0.00
0.07
-0.10
0.04
7.73
1.63
0.70
1.52
1.29
-0.23
-0.30
0.56
-0.32
(stdev)
(2.48)
(12.00)
(14.42)
(0.31)
(0.41)
(0.43)
(0.05)
(0.14)
(1.09)
(0.05)
(0.07)
(0.03)
(0.05)
(0.05)
(0.01)
(0.03)
(0.02)
(0.11)
(3.38)
(0.65)
(0.34)
(0.66)
(0.63)
(0.11)
(0.30)
(0.31)
(1.18)
pvalue
0.86
0.05
0.06
0.91
0.64
0.87
0.14
0.16
0.51
0.47
0.16
0.36
0.40
0.28
0.72
0.01
0.00
0.70
0.02
0.01
0.04
0.02
0.04
0.03
0.32
0.08
0.79
Panel B. Regression diagnostics
Equation
1
2
3
n
741
741
741
Parms
8
8
8
RMSE
1.65
0.16
1.77
R2
-1.38
-0.51
-95.86
χ2
50.59
58.34
6.72
p
0.00
0.00
0.57
The table complements table B.98 in the text. It shows results with the same set of endogenous variables, but a
smaller set of explanatory variables: Only the controls. The tables shows 3SLS estimates of a system of simultanous
equations. Panel A report system estimates. The leftmost column is the dependent variable in that particular
equation, which is a function of the variables listed in the next column. Equations are separated by a line. Panel
B holds diagnostics. For each equation the diagnostics include n, the number of observations, Parms, the number
of parameters, RMSE, the root mean squared error, R2 , a “pseudo” R squared, χ2 , the chi squared of the equation,
and p, a p-value for the fit of the equation. The estimates are performed with Stata’s reg3 3SLS routine. Variable
definitions are in Appendix A.2. Data for firms listed on the Oslo Stock Exchange, 1989-1997.
B.10 Two-way causaution between corporate governance and economic performance
215
Table B.102 Simultaneous systems estimation of the determinants of ownership concentration,
insider holdings, and economic performance (Q). Controls only as additional explanatory variables.
Aggregate intercorporate holdings, debt to assets and stock beta are instruments.
Panel A. Regression results
Dep.variable
Q
Herfindahl index
Primary insiders
Indep.variable
Herfindahl index
Primary insiders
Squared (Primary insiders)
Industrial
Transport/shipping
Offshore
ln(Firm value)
Stock beta
constant
Primary insiders
Q
Industrial
Transport/shipping
Offshore
ln(Firm value)
Aggregate intercorporate holdings
constant
Herfindahl index
Q
Industrial
Transport/shipping
Offshore
ln(Firm value)
Debt to assets
constant
coeff
112.73
340.87
-410.85
7.49
10.28
9.17
-0.33
2.94
-26.93
0.06
-0.03
-0.02
-0.04
-0.02
0.00
0.13
0.13
6.25
1.08
0.42
0.83
0.76
-0.10
1.78
-1.92
(stdev)
(140.11)
(398.51)
(480.75)
(9.36)
(13.01)
(11.66)
(0.70)
(4.78)
(32.72)
(0.03)
(0.01)
(0.01)
(0.02)
(0.02)
(0.00)
(0.03)
(0.07)
(1.95)
(0.31)
(0.17)
(0.27)
(0.30)
(0.04)
(0.59)
(0.85)
pvalue
0.42
0.39
0.39
0.42
0.43
0.43
0.64
0.54
0.41
0.04
0.04
0.08
0.02
0.29
0.49
0.00
0.04
0.00
0.00
0.01
0.00
0.01
0.01
0.00
0.03
Panel B. Regression diagnostics
Equation
1
2
3
n
741
741
741
Parms
8
7
7
RMSE
24.79
0.13
1.21
R2
-539.10
0.04
-44.60
χ2
2.30
28.38
14.80
p
0.97
0.00
0.04
The table complements table B.99 in the text. It shows results with the same set of endogenous variables, but a
smaller set of explanatory variables: Only the controls. The tables shows 3SLS estimates of a system of simultanous
equations. Panel A report system estimates. The leftmost column is the dependent variable in that particular
equation, which is a function of the variables listed in the next column. Equations are separated by a line. Panel
B holds diagnostics. For each equation the diagnostics include n, the number of observations, Parms, the number
of parameters, RMSE, the root mean squared error, R2 , a “pseudo” R squared, χ2 , the chi squared of the equation,
and p, a p-value for the fit of the equation. The estimates are performed with Stata’s reg3 3SLS routine. Variable
definitions are in Appendix A.2. Data for firms listed on the Oslo Stock Exchange, 1989-1997.
216
B.10.3
Supplementary regressions
Outside concentration
This appendix replaces the Herfindahl index used in the main text as a concentration measure
by the ownership by the largest outside owner. To estimate outside concentration we remove the
largest owner if it has the same holdings as the largest insider owner.
B.10 Two-way causaution between corporate governance and economic performance
217
Table B.103 Simultaneous systems estimation of the determinants of ownership concentration,
insider holdings, and economic performance (Q). Adjusting for overlap between insiders and large
owners. Board size, stock beta and stock volatility are instruments.
Panel A. Regression results
Dep.variable
Q
Largest outside owner
Primary insiders
Indep.variable
Largest outside owner
Primary insiders
Squared (Primary insiders)
Aggregate state holdings
Aggregate international holdings
Aggregate individual holdings
Aggregate nonfinancial holdings
Fraction voting shares
Debt to assets
Dividends to earnings
Industrial
Transport/shipping
Offshore
Investments over income
ln(Firm value)
Stock beta
constant
Primary insiders
Q
Aggregate state holdings
Aggregate international holdings
Aggregate individual holdings
Aggregate nonfinancial holdings
Fraction voting shares
Debt to assets
Dividends to earnings
Industrial
Transport/shipping
Offshore
Investments over income
ln(Firm value)
Stock volatility
constant
Largest outside owner
Q
Aggregate state holdings
Aggregate international holdings
Aggregate individual holdings
Aggregate nonfinancial holdings
Fraction voting shares
Debt to assets
Dividends to earnings
Industrial
Transport/shipping
Offshore
Investments over income
ln(Firm value)
ln(Board size)
constant
coeff
-16.92
50.34
-58.94
17.44
7.52
-4.58
9.53
1.86
1.25
0.24
0.05
-1.17
0.58
-0.07
-0.44
0.49
4.67
-0.05
0.02
0.74
0.29
-0.14
0.38
0.23
0.16
0.02
-0.01
-0.06
0.03
-0.00
-0.01
0.04
-0.05
-2.16
1.67
3.00
0.73
-2.17
1.62
-1.49
3.45
0.24
0.27
0.61
1.01
-0.01
-0.32
0.32
2.31
(stdev)
(42.83)
(110.48)
(130.84)
(43.92)
(18.12)
(13.33)
(23.84)
(4.50)
(7.63)
(0.91)
(0.68)
(2.10)
(2.66)
(0.18)
(1.38)
(1.04)
(14.33)
(0.05)
(0.07)
(0.09)
(0.04)
(0.11)
(0.06)
(0.11)
(0.15)
(0.01)
(0.02)
(0.04)
(0.05)
(0.00)
(0.01)
(0.02)
(0.19)
(5.70)
(0.75)
(4.09)
(1.75)
(1.19)
(2.15)
(1.96)
(1.36)
(0.14)
(0.23)
(0.62)
(0.50)
(0.03)
(0.14)
(0.23)
(1.77)
R2
-13.81
0.32
-73.58
χ2
14.02
353.09
8.04
p
0.60
0.00
0.92
pvalue
0.69
0.65
0.65
0.69
0.68
0.73
0.69
0.68
0.87
0.79
0.94
0.58
0.83
0.71
0.75
0.64
0.74
0.36
0.81
0.00
0.00
0.21
0.00
0.04
0.27
0.21
0.66
0.10
0.50
0.10
0.30
0.09
0.79
0.70
0.03
0.46
0.68
0.07
0.45
0.45
0.01
0.08
0.23
0.32
0.04
0.70
0.02
0.15
0.19
Panel B. Regression diagnostics
Equation
1
2
3
n
741
741
741
Parms
16
15
15
RMSE
4.11
0.15
1.55
The tables shows 3SLS estimates of a system of simultanous equations. Panel A report system estimates. The
leftmost column is the dependent variable in that particular equation, which is a function of the variables listed in
the next column. Equations are separated by a line. Panel B holds diagnostics. For each equation the diagnostics
include n, the number of observations, Parms, the number of parameters, RMSE, the root mean squared error, R2 , a
“pseudo” R squared, χ2 , the chi squared of the equation, and p, a p-value for the fit of the equation. The estimates
are performed with Stata’s reg3 3SLS routine. Variable definitions are in Appendix A.2. Data for firms listed on the
Oslo Stock Exchange, 1989-1997.
218
Supplementary regressions
Table B.104 Simultaneous systems estimation of the determinants of ownership concentration,
insider holdings, and economic performance (Q). Adjusting for overlap between insiders and large
owners. Board size, stock turnover and stock volatility are instruments.
Panel A. Regression results
Dep.variable
Q
Herfindahl index
Primary insiders
Indep.variable
Herfindahl index
Primary insiders
Squared (Primary insiders)
Aggregate state holdings
Aggregate international holdings
Aggregate individual holdings
Aggregate nonfinancial holdings
Fraction voting shares
ln(Board size)
Debt to assets
Dividends to earnings
Industrial
Transport/shipping
Offshore
Investments over income
ln(Firm value)
Stock beta
constant
Primary insiders
Q
Aggregate state holdings
Aggregate international holdings
Aggregate individual holdings
Aggregate nonfinancial holdings
Fraction voting shares
Debt to assets
Dividends to earnings
Industrial
Transport/shipping
Offshore
Investments over income
ln(Firm value)
Stock volatility
Stock turnover
constant
Herfindahl index
Q
Aggregate state holdings
Aggregate international holdings
Aggregate individual holdings
Aggregate nonfinancial holdings
Fraction voting shares
Debt to assets
Dividends to earnings
Industrial
Transport/shipping
Offshore
Investments over income
ln(Firm value)
ln(Board size)
Stock volatility
constant
coeff
-3.90
11.38
-12.35
2.53
1.40
0.35
1.39
1.32
-0.23
-1.50
-0.02
-0.17
-0.54
-0.44
-0.01
0.07
0.11
-0.66
-0.02
0.16
0.55
0.19
-0.20
0.26
0.09
0.34
0.03
0.01
0.03
0.09
-0.00
-0.03
0.04
-0.08
0.01
5.43
1.15
-1.96
-1.17
-1.32
-0.97
-2.58
1.92
0.04
0.32
0.77
0.70
0.01
-0.19
0.35
-0.22
2.76
(stdev)
(1.63)
(11.18)
(13.11)
(1.44)
(0.68)
(0.97)
(0.87)
(1.02)
(0.14)
(0.33)
(0.08)
(0.15)
(0.18)
(0.26)
(0.02)
(0.08)
(0.12)
(1.66)
(0.08)
(0.12)
(0.10)
(0.06)
(0.16)
(0.06)
(0.15)
(0.22)
(0.02)
(0.03)
(0.05)
(0.07)
(0.00)
(0.02)
(0.03)
(0.03)
(0.26)
(1.79)
(0.36)
(0.96)
(0.54)
(0.68)
(0.56)
(0.87)
(0.66)
(0.08)
(0.15)
(0.26)
(0.30)
(0.02)
(0.08)
(0.17)
(0.21)
(1.49)
pvalue
0.02
0.31
0.35
0.08
0.04
0.72
0.11
0.20
0.10
0.00
0.77
0.26
0.00
0.09
0.44
0.39
0.37
0.69
0.82
0.17
0.00
0.00
0.21
0.00
0.54
0.12
0.07
0.72
0.63
0.21
0.12
0.16
0.19
0.00
0.97
0.00
0.00
0.04
0.03
0.05
0.08
0.00
0.00
0.62
0.03
0.00
0.02
0.42
0.01
0.04
0.30
0.06
Panel B. Regression diagnostics
Equation
1
2
3
n
741
741
741
Parms
17
16
16
RMSE
1.14
0.18
1.17
R2
-0.14
-1.00
-41.28
χ2
181.71
112.00
15.51
p
0.00
0.00
0.49
The tables shows 3SLS estimates of a system of simultanous equations. Panel A report system estimates. The
leftmost column is the dependent variable in that particular equation, which is a function of the variables listed in
the next column. Equations are separated by a line. Panel B holds diagnostics. For each equation the diagnostics
include n, the number of observations, Parms, the number of parameters, RMSE, the root mean squared error, R2 , a
“pseudo” R squared, χ2 , the chi squared of the equation, and p, a p-value for the fit of the equation. The estimates
are performed with Stata’s reg3 3SLS routine. Variable definitions are in Appendix A.2. Data for firms listed on the
Oslo Stock Exchange, 1989-1997.
B.10 Two-way causaution between corporate governance and economic performance
219
Table B.105 Simultaneous systems estimation of the determinants of ownership concentration,
insider holdings, and economic performance (Q). Adjusting for overlap between insiders and large
owners. Aggregate intercorporate holdings, debt to assets and stock beta are instruments.
Panel A. Regression results
Dep.variable
Q
Largest outside owner
Primary insiders
Indep.variable
Largest outside owner
Primary insiders
Squared (Primary insiders)
Aggregate state holdings
Aggregate international holdings
Aggregate individual holdings
Aggregate nonfinancial holdings
Fraction voting shares
Dividends to earnings
ln(Board size)
Industrial
Transport/shipping
Offshore
Investments over income
ln(Firm value)
Stock beta
constant
Primary insiders
Q
Aggregate state holdings
Aggregate international holdings
Aggregate individual holdings
Aggregate nonfinancial holdings
Fraction voting shares
Dividends to earnings
ln(Board size)
Industrial
Transport/shipping
Offshore
Investments over income
ln(Firm value)
Aggregate intercorporate holdings
constant
Largest outside owner
Q
Aggregate state holdings
Aggregate international holdings
Aggregate individual holdings
Aggregate nonfinancial holdings
ln(Board size)
Fraction voting shares
Dividends to earnings
Industrial
Transport/shipping
Offshore
Investments over income
ln(Firm value)
Debt to assets
constant
coeff
-35.95
-156.93
183.12
7.61
2.88
9.11
2.43
20.06
-0.06
-0.83
-1.92
-4.09
-2.21
-0.05
0.67
-1.43
-16.22
0.02
-0.07
0.68
0.31
-0.01
0.34
0.32
0.00
-0.02
-0.03
-0.10
-0.02
-0.00
-0.00
0.11
-0.07
4.29
1.94
-1.53
-1.18
-1.74
-0.73
0.38
-3.34
0.18
0.39
1.15
1.02
0.01
-0.26
3.11
2.39
(stdev)
(58.94)
(337.46)
(392.92)
(11.28)
(4.77)
(19.45)
(5.99)
(36.08)
(0.66)
(1.72)
(3.61)
(6.56)
(4.33)
(0.14)
(1.41)
(2.95)
(32.52)
(0.04)
(0.02)
(0.07)
(0.05)
(0.07)
(0.05)
(0.07)
(0.01)
(0.02)
(0.02)
(0.02)
(0.03)
(0.00)
(0.01)
(0.04)
(0.16)
(5.16)
(0.98)
(3.40)
(1.62)
(1.28)
(1.81)
(0.29)
(2.30)
(0.15)
(0.29)
(0.76)
(0.61)
(0.03)
(0.15)
(1.50)
(2.18)
R2
-101.72
0.20
-106.51
χ2
2.39
307.36
5.39
p
1.00
0.00
0.99
pvalue
0.54
0.64
0.64
0.50
0.55
0.64
0.69
0.58
0.93
0.63
0.59
0.53
0.61
0.72
0.63
0.63
0.62
0.62
0.00
0.00
0.00
0.92
0.00
0.00
0.70
0.36
0.10
0.00
0.54
0.17
0.60
0.01
0.65
0.41
0.05
0.65
0.47
0.17
0.69
0.19
0.15
0.22
0.18
0.13
0.10
0.76
0.07
0.04
0.27
Panel B. Regression diagnostics
Equation
1
2
3
n
741
741
741
Parms
16
15
15
RMSE
10.81
0.16
1.86
The tables shows 3SLS estimates of a system of simultanous equations. Panel A report system estimates. The
leftmost column is the dependent variable in that particular equation, which is a function of the variables listed in
the next column. Equations are separated by a line. Panel B holds diagnostics. For each equation the diagnostics
include n, the number of observations, Parms, the number of parameters, RMSE, the root mean squared error, R2 , a
“pseudo” R squared, χ2 , the chi squared of the equation, and p, a p-value for the fit of the equation. The estimates
are performed with Stata’s reg3 3SLS routine. Variable definitions are in Appendix A.2. Data for firms listed on the
Oslo Stock Exchange, 1989-1997.
220
LIST OF TABLES
List of Tables
2.1
Mechanism interaction and mechanism–performance causality in empirical corporate governance research 14
3.1
3.2
3.3
Summary of descriptive statistics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Correlations between performance measures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Governance variables, controls, and performance measures used in the regression models . . . . . . . .
20
23
24
4.1
Summary of the univariate regressions relating performance to a governance mechanism or a control .
26
5.1
Multivariate regression relating performance (RoA5 ) to ownership concentration and controls, following
Demsetz and Lehn (1985) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Multivariate regression relating performance (RoA5 ) to ownership concentration and controls according
to Demsetz and Lehn (1985), but using year–by–year OLS, GMM, and fixed effects OLS techniques .
Multivariate regression relating performance to ownership concentration and controls, using Q rather
than RoA5 as performance measure in the Demsetz and Lehn (1985) approach . . . . . . . . . . . . .
Multivariate regression relating performance (Q) to ownership concentration and controls, using the
piecewise linear function of Morck et al. (1988) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Multivariate regression relating performance (Q) to ownership concentration and controls, using a
quadratic function . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
5.2
5.3
5.4
5.5
6.1
6.2
6.3
6.4
6.5
6.6
7.1
7.2
7.3
7.4
8.1
8.2
8.3
9.1
9.2
9.3
Multivariate regression relating performance (Q) to insider ownership and controls, following Morck
et al. (1988) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Multivariate regression relating performance (Q) to insider holdings, ownership concentration and
controls, using the piecewise linear function of Morck et al. (1988). . . . . . . . . . . . . . . . . . . . .
Multivariate regression relating performance (Q) to insider ownership and controls, following McConnell and Servaes (1990) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Multivariate regression relating performance (Q) to insider ownership, ownership concentration and
controls, following McConnell and Servaes (1990) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Multivariate regression relating performance (Q) to insider holdings, ownership concentration, institutional ownership and controls, following McConnell and Servaes (1990) . . . . . . . . . . . . . . . . . .
Multivariate regression relating performance (Q) to insider ownership, the holdings of the largest
insider, ownership concentration, and controls . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Multivariate regression relating performance (Q) to ownership concentration, insider holdings, aggregate holdings per owner type, and controls . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Multivariate regression relating performance (Q) to ownership concentration, insider holdings, aggregate intercorporate holdings, and controls . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Multivariate regression relating performance (Q) to ownership concentration, insider holdings, largest
owner identity, and controls . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Multivariate regression relating performance (Q) to ownership concentration, insider holdings, largest
owner being listed, and controls . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Multivariate regression relating performance (Q) to ownership concentration, insider ownership, board
size, and controls . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Multivariate regression relating performance (Q) to ownership concentration, insider ownership, security design, and controls . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Multivariate regression relating performance (Q) to ownership concentration, insider ownership, financial policy, and controls . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Multivariate regression relating performance (Q) to ownership concentration, insider holdings, owner
type (identity of largest owner), board characteristics, security design, financial policy, and controls . .
Multivariate regression relating performance (Q) to ownership concentration, insider holdings, owner
type (aggregate holding per type) , board characteristics, security design, financial policy, and controls
Multivariate regression relating performance (Q) to ownership concentration, insider holdings, owner
type (aggregate holding per type) , board characteristics, security design, financial policy, and controls
31
33
35
36
38
41
42
45
45
46
47
49
50
50
51
53
54
55
57
58
61
LIST OF TABLES
10.1 Summary of the single–equation regressions for governance mechanism endogeneity, using the aggregate
holding per type as owner identity proxy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
10.2 Summary of the single–equation regressions for governance mechanism endogeneity, using the type of
the largest investor as owner type proxy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
10.3 Estimating the determinants of the largest owner type using a multinomial logit model . . . . . . . .
10.4 Interactions between governance mechanisms modeled as a system of equations. Concentration and
insider holdings are endogeneous variables. Stock volatility and board size are used as instruments . .
10.5 Interactions between governance mechanisms modeled as a system of equations. Concentration and
insider holdings are endogeneous variables. Stock turnover and board size are used as instruments . .
10.6 Interactions between governance mechanisms modeled as a system of equations. Concentration and
insider holdings are endogeneous variables. Intercorporate investments and financial leverage are used
as instruments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
11.1 Summary of estimations of the simultaneous determinants of economic performance (Q), ownership
concentration, and insider holdings, using three alternative sets of instruments. Only the two governance mechanisms enter the system endogeneously. . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
11.2 Summary of estimations of the simultaneous determinants of economic performance (Q), ownership
concentration, and insider holdings, using three alternative sets of instruments. . . . . . . . . . . . . .
221
66
68
70
74
76
78
82
84
B.1 Summary of univariate regressions, voting rights. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 110
B.2 Multivariate regression relating performance (Q) to ownership concentration and controls, following
Demsetz and Lehn (1985) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 118
B.3 Multivariate regression relating performance (Q) to ownership concentration and controls, using the
piecewise linear function of Morck et al. (1988) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 119
B.4 Multivariate regression relating performance (Q) to ownership concentration, using a quadratic function120
B.5 Multivariate regression relating performance (Q) to ownership concentration, without controls, using
the piecewise linear formulation of Morck et al. (1988) . . . . . . . . . . . . . . . . . . . . . . . . . . . 121
B.6 The quadratic relationship between performance (Q) and the holdings of the largest owner . . . . . . 121
B.7 Multivariate regression relating performance (RoA5 ) to ownership concentration (Herfindahl) and controls . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 122
B.8 Multivariate regression relating performance (RoA5 ) to ownership concentration (20 largest owners)
and controls . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 123
B.9 Multivariate regression relating performance (Q) to ownership concentration (Herfindahl) and controls 124
B.10 Multivariate regression relating performance (Q) to ownership concentration (20 largest owners) and
controls . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 125
B.11 Multivariate regression relating performance (Q) to insider ownership and controls, following Morck
et al. (1988) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 126
B.12 Multivariate regression relating performance (Q) to insider ownership, ownership concentration and
controls, using the piecewise linear function of Morck et al. (1988). . . . . . . . . . . . . . . . . . . . . 127
B.13 Multivariate regression relating performance (Q) to insider ownership and controls, following McConnell and Servaes (1990) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 128
B.14 Multivariate regression relating performance (Q) to insider ownership, ownership concentration and
controls, following McConnell and Servaes (1990) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 129
B.15 Multivariate regression relating performance (Q) to insider ownership, ownership concentration, institutional ownership, and controls, following McConnell and Servaes (1990) . . . . . . . . . . . . . . . . 130
B.16 Multivariate regression relating performance (Q) to insider ownership, the largest primary insider,
external concentration, and controls . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 131
B.17 Multivariate regression relating performance (Q) to insider ownership, without controls, using the
piecewise linear formulation of Morck et al. (1988) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 132
B.18 Multivariate regression relating performance (Q) to insider ownership, without controls, using the
quadratic specifiction of McConnell and Servaes (1990) . . . . . . . . . . . . . . . . . . . . . . . . . . . 132
B.19 Multivariate regression relating performance (RoA5 ) to insider ownership, without controls, using the
piecewise linear formulation of Morck et al. (1988) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 133
B.20 Multivariate regression relating performance (RoA5 ) to insider ownership, without controls, using the
quadratic specifiction of McConnell and Servaes (1990) . . . . . . . . . . . . . . . . . . . . . . . . . . . 134
B.21 Multivariate regression relating performance (RoA5 ) to insider ownership and controls, following Morck
et al. (1988) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 135
222
LIST OF TABLES
B.22 Multivariate regression relating performance (RoA5 ) to insider ownership, ownership concentration
and controls, using the piecewise linear function of Morck et al. (1988). . . . . . . . . . . . . . . . . . .
B.23 Multivariate regression relating performance (RoA5 ) to insider ownership, following McConnell and
Servaes (1990) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
B.24 Multivariate regression relating performance (RoA5 ) to insider ownership, ownership concentration
and controls, following McConnell and Servaes (1990) . . . . . . . . . . . . . . . . . . . . . . . . . . .
B.25 Multivariate regression relating performance (RoA5 ) to insider ownership, ownership concentration,
institutional ownership and controls, following McConnell and Servaes (1990) . . . . . . . . . . . . . .
B.26 Multivariate regression relating performance (Q) to insider (all) ownership and controls, following
Morck et al. (1988) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
B.27 Multivariate regression relating performance (Q) to insider (all) ownership and controls, following McConnell and Servaes (1990) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
B.28 Multivariate regression relating performance (Q) to insider (board) ownership and controls, following
Morck et al. (1988) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
B.29 Multivariate regression relating performance (Q) to insider (board) ownership and controls, following McConnell and Servaes (1990) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
B.30 Multivariate regression relating performance (Q) to insider (management) ownership and controls,
following Morck et al. (1988) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
B.31 Multivariate regression relating performance (Q) to insider (management) ownership and controls,
following McConnell and Servaes (1990) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
B.32 Multivariate regression relating performance (Q) to insider ownership, outside (external) concentration
and controls, following Morck et al. (1988) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
B.33 Multivariate regression relating performance (Q) to insider ownership, outside (external) concentration
and controls, following McConnell and Servaes (1990) . . . . . . . . . . . . . . . . . . . . . . . . . . .
B.34 Multivariate regression relating performance (Q) to ownership concentration, insider ownership, aggregate holdings per owner type, and controls . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
B.35 Multivariate regression relating performance (Q) to ownership concentration, insider ownership, aggregate intercorporate holdings, and controls . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
B.36 Multivariate regression relating performance (Q) to ownership concentration, insider ownership, largest
owner identity, and controls . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
B.37 Multivariate regression relating performance (Q) to ownership concentration, insider ownership, largest
owner being listed, and controls . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
B.38 Multivariate regression relating performance (RoA5 ) to ownership concentration, insider ownership,
aggregate holdings per owner type, and controls . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
B.39 Multivariate regression relating performance (RoA5 ) to ownership concentration, insider ownership,
aggregate intercorporate holdings, and controls . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
B.40 Multivariate regression relating performance (RoA5 ) to ownership concentration, insider ownership,
largest owner identity, and controls . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
B.41 Multivariate regression relating performance (Q) to ownership concentration, insider ownership, board
size, and controls . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
B.42 Multivariate regression relating performance (Q) to ownership concentration, insider ownership, security design, and controls . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
B.43 Multivariate regression relating performance (Q) to ownership concentration, insider ownership, financial policy and controls . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
B.44 Multivariate regression relating performance (RoA5 ) to ownership concentration, insider ownership,
board size, and controls . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
B.45 Multivariate regression relating performance (RoA5 ) to ownership concentration, insider ownership,
security design, and controls . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
B.46 Multivariate regression relating performance (RoA5 ) to ownership concentration, insider ownership,
financial policy and controls . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
B.47 Multivariate regression relating performance (Q) to ownership concentration, insider ownership, owner
type (largest owner), board characteristics, security design, financial policy, and controls (full multivariate model) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
B.48 Multivariate regression relating performance (Q) to ownership concentration, insider ownership, owner
type (aggregate holdings), board characteristics, security design, financial policy, and controls (full
multivariate model) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
136
137
138
139
141
142
143
144
145
146
148
149
151
152
153
154
155
156
157
158
159
160
161
162
163
165
166
LIST OF TABLES
B.49 Multivariate regression relating performance (RoA5 ) to ownership concentration, insider ownership,
type of largest owner, board characteristics, security design, financial policy, and controls (full multivariate model) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
B.50 Multivariate regression relating performance (RoA5 ) to ownership concentration, insider ownership,
owner type (aggregate holdings), board characteristics, security design, financial policy, and controls
(full multivariate model) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
B.51 Multivariate regression relating performance (RoA) to ownership concentration, insider ownership,
owner type (largest owner), board characteristics, security design, financial policy, and controls (full
multivariate model) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
B.52 Multivariate regression relating performance (RoA) to ownership concentration, insider ownership,
aggregate holdings per owner types, board characteristics, security design, financial policy, and controls
(full multivariate model) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
B.53 Multivariate regression relating performance (RoS5 ) to ownership concentration, insider ownership,
owner type (largest owner), board characteristics, security design, financial policy, and controls (full
multivariate model) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
B.54 Multivariate regression relating performance (RoS5 ) to ownership concentration, insider ownership,
aggregate holdings per owner type, board characteristics, security design, financial policy, and controls
(full multivariate model) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
B.55 Multivariate regression relating performance (RoS) to ownership concentration, insider ownership,
owner type (largest owner), board characteristics, security design, financial policy, and controls (full
multivariate model) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
B.56 Multivariate regression relating performance (RoS) to ownership concentration, insider ownership,
aggregate holdings per owner type, board characteristics, security design, financial policy, and controls
(full multivariate model) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
B.57 Multivariate regression relating performance (Q) to ownership concentration, insider ownership, aggregate intercorporate ownership by listed firms, board characteristics, security design, financial policy,
and controls (full multivariate model) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
B.58 Multivariate regression relating performance (RoA5 ) to ownership concentration, insider ownership,
the largest owner being listed, board characteristics, security design, financial policy, and controls (full
multivariate model) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
B.59 Multivariate regression relating performance (RoA5 ) to outside (external) ownership concentration,
insider ownership, the type of the largest owner, board characteristics, security design, financial policy,
and controls (full multivariate model) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
B.60 Multivariate regression relating performance (Q) to (outside) ownership concentration, insider ownership, aggregate holdings by owner type, board characteristics, security design, financial policy, and
controls (full multivariate model) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
B.61 Multivariate regression relating performance (Q) to ownership concentration (voting rights), insider
ownership, the type of the largest owner, board characteristics, security design, financial policy, and
controls (full multivariate model) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
B.62 Multivariate regression relating performance (Q) to ownership concentration (voting rights), insider
ownership, type of owner, board characteristics, security design, financial policy, and controls (full
multivariate model) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
B.63 Multivariate regression relating concentration (Herfindahl index) to other mechanisms and controls,
using aggregate ownership per type as owner identity proxy . . . . . . . . . . . . . . . . . . . . . . . .
B.64 Multivariate regression relating primary insider holdings to other mechanisms and controls, using
aggregate ownership per type as owner identity proxy . . . . . . . . . . . . . . . . . . . . . . . . . . .
B.65 Multivariate regression relating aggregate state holdings to other mechanisms and controls, using
aggregate ownership per type as owner identity proxy . . . . . . . . . . . . . . . . . . . . . . . . . . .
B.66 Multivariate regression relating aggregate international holdings to other mechanisms and controls,
using aggregate ownership per type as owner identity proxy . . . . . . . . . . . . . . . . . . . . . . . .
B.67 Multivariate regression relating aggregate individual holdings to other mechanisms and controls, using
aggregate ownership per type as owner identity proxy . . . . . . . . . . . . . . . . . . . . . . . . . . .
B.68 Multivariate regression relating aggregate financial holdings to other mechanisms and controls, using
aggregate ownership per type as owner identity proxy . . . . . . . . . . . . . . . . . . . . . . . . . . .
B.69 Multivariate regression relating aggregate nonfinancial holdings to other mechanisms and controls,
using aggregate ownership per type as owner identity proxy . . . . . . . . . . . . . . . . . . . . . . . .
B.70 Multivariate regression relating board size to other mechanisms and controls, using aggregate ownership
per type as owner identity proxy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
223
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
182
183
183
184
184
185
224
LIST OF TABLES
B.71 Multivariate regression relating fraction voting shares to other mechanisms and controls, using aggregate ownership per type as owner identity proxy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
B.72 Multivariate regression relating debt to assets to other mechanisms and controls, using aggregate
ownership per type as owner identity proxy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
B.73 Multivariate regression relating dividends to earnings to other mechanisms and controls, using aggregate ownership per type as owner identity proxy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
B.74 Multivariate regression relating concentration (Herfindahl index) to other mechanisms and controls,
using type of largest owner as owner type proxy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
B.75 Multivariate regression relating primary insider holdings to other mechanisms and controls, using type
of largest owner as owner type proxy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
B.76 Multivariate regression relating board size to other mechanisms and controls, using type of largest
owner as owner type proxy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
B.77 Multivariate regression relating fraction voting to other mechanisms and controls, using type of largest
owner as owner type proxy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
B.78 Multivariate regression relating debt to assets to other mechanisms and controls, using type of largest
owner as owner type proxy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
B.79 Multivariate regression relating dividends to earnings to other mechanisms and controls, using type of
largest owner as owner type proxy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
B.80 Multivariate regression relating outside concentration (Largest outside owner) to other mechanisms
and controls . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
B.81 Multivariate regression relating primary insider holdings to other mechanisms and controls, using
largest outside owner as concentration measure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
B.82 Interactions between governance mechanisms modeled as system of equations. Concentration and
insider holdings are endogenous variables. Controls only as additional explanatory variables. Board
size and stock volatility are instruments. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
B.83 Interactions between governance mechanisms modeled as system of equations. Concentration and
insider holdings are endogenous variables. Controls only as additional explanatory variables. Board
size and stock turnover are instruments. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
B.84 Interactions between governance mechanisms modelled as system of equations. Concentration and
insider holdings are endogenous variables. Controls only as additional explanatory variables. Aggregate
intercorporate holdings and debt to assets are instruments. . . . . . . . . . . . . . . . . . . . . . . . .
B.85 Interactions between governance mechanisms modeled as system of equations. Concentration and
insider holdings are endogeneous variables. Adjusting for overlap between insiders and large owners.
Board size and stock volatility are instruments. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
B.86 Interactions between governance mechanisms modeled as system of equations. Concentration and
insider holdings are endogeneous variables. Adjusting for overlap between insiders and large owners.
Board size and stock turnover are instruments. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
B.87 Interactions between governance mechanisms modelled as system of equations. Concentration and
insider holdings are endogeneous variables. Adjusting for overlap between insiders and large owners.
Aggregate intercorporate holdings and debt to assets are instruments. . . . . . . . . . . . . . . . . . .
B.88 Simultaneous systems estimation of the determinants of ownership concentration, insider holdings,
and economic performance (Q). The two endogeneous governance mechanisms are independent of
performance. Stock volatility and board size are instruments (Model (I) in summary table 11.1) . . . .
B.89 Simultaneous systems estimation of the determinants of ownership concentration, insider holdings,
and economic performance (Q). The two endogeneous governance mechanisms are independent of
performance. Stock turnover and board size are instruments (Model (II) in summary table 11.1) . . .
B.90 Simultaneous systems estimation of the determinants of ownership concentration, insider holdings,
and economic performance (Q). The two endogeneous governance mechanisms are independent of
performance. Debt to assets and intercorporate investments and are instruments (Model (III) in
summary table 11.1) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
B.91 Simultaneous systems estimation of the determinants of ownership concentration, insider holdings,
and economic performance (Q). The two endogeneous governance mechanisms are independent of
performance. Concentration and insider holdings are endogeneous variables. Board size and stock
volatility are instruments. Only controls as additional explanatory variables. . . . . . . . . . . . . . .
B.92 Simultaneous systems estimation of the determinants of ownership concentration, insider holdings,
and economic performance (Q). The two endogeneous governance mechanisms are independent of
performance. Concentration and insider holdings are endogeneous variables. Board size and stock
turnover are instruments. Only controls as additional explanatory variables. . . . . . . . . . . . . . . .
185
186
186
187
187
188
188
189
189
190
191
192
193
194
195
196
197
199
200
201
202
203
LIST OF TABLES
B.93 Simultaneous systems estimation of the determinants of ownership concentration, insider holdings,
and economic performance (Q). The two endogeneous governance mechanisms are independent of
performance. Concentration and insider holdings are endogeneous variables. Aggregate intercorporate
shareholdings and debt to assets are instruments. Only controls as additional explanatory variables. .
B.94 Simultaneous systems estimation of the determinants of ownership concentration, insider holdings,
and economic performance (Q). The two endogeneous governance mechanisms are independent of
performance. Concentration and insider holdings are endogeneous variables. Adjusting for overlap
between insiders and large owners. Board size and stock volatility are instruments. . . . . . . . . . .
B.95 Simultaneous systems estimation of the determinants of ownership concentration, insider holdings,
and economic performance (Q). The two endogeneous governance mechanisms are independent of
performance. Concentration and insider holdings are endogeneous variables. Adjusting for overlap
between insiders and large owners. Board size and stock turnover are instruments. . . . . . . . . . . .
B.96 Simultaneous systems estimation of the determinants of ownership concentration, insider holdings,
and economic performance (Q). The two endogeneous governance mechanisms are independent of
performance. Concentration and insider holdings are endogeneous variables. Adjusting for overlap
between insiders and large owners. Aggregate intercorporate shareholdings and debt to assets are
instruments. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
B.97 Simultaneous systems estimation of the determinants of ownership concentration, insider holdings, and
economic performance (Q). Board size, stock beta and stock volatility are instruments. (Model (A) in
table 11.1) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
B.98 Simultaneous systems estimation of the determinants of economic performance (Q), ownership concentration, and insider holdings. Board size, stock beta and stock turnover are instruments. (Model
(B) in table 11.1) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
B.99 Simultaneous systems estimation of the determinants of economic performance (Q), ownership concentration, and insider holdings. Debt to assets, intercorporate shareholdings and stock beta are
instruments. (Model (C) in table 11.1) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
B.100Simultaneous systems estimation of the determinants of ownership concentration, insider holdings, and
economic performance (Q). Controls only as additional explanatory variables. Board size, stock beta
and stock volatility are instruments. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
B.101Simultaneous systems estimation of the determinants of ownership concentration, insider holdings,
and economic performance (Q). Controls only as additional explanatory variables. Board size, stock
turnover and stock volatility are instruments. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
B.102Simultaneous systems estimation of the determinants of ownership concentration, insider holdings, and
economic performance (Q). Controls only as additional explanatory variables. Aggregate intercorporate holdings, debt to assets and stock beta are instruments. . . . . . . . . . . . . . . . . . . . . . . .
B.103Simultaneous systems estimation of the determinants of ownership concentration, insider holdings, and
economic performance (Q). Adjusting for overlap between insiders and large owners. Board size, stock
beta and stock volatility are instruments. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
B.104Simultaneous systems estimation of the determinants of ownership concentration, insider holdings, and
economic performance (Q). Adjusting for overlap between insiders and large owners. Board size, stock
turnover and stock volatility are instruments. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
B.105Simultaneous systems estimation of the determinants of ownership concentration, insider holdings,
and economic performance (Q). Adjusting for overlap between insiders and large owners. Aggregate
intercorporate holdings, debt to assets and stock beta are instruments. . . . . . . . . . . . . . . . . .
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207
208
210
211
212
213
214
215
217
218
219
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LIST OF FIGURES
List of Figures
5.1
5.2
6.1
6.2
6.3
6.4
The relationship between performance (Q) and the holding of the largest owner in Norwegian firms,
using the piecewise linear function of Morck et al. (1988) . . . . . . . . . . . . . . . . . . . . . . . . . .
The quadratic relationship between performance (Q) and the holdings of the largest owner . . . . . .
Relating performance (Q) to insider ownership using a piecewise linear function. US data . . . . . . .
The piecewise linear relationship between performance (Q) and insider ownership in Norwegian firms,
following Morck et al. (1988) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
The quadratic relationship between performance (Q) and insider ownership in the US . . . . . . . . .
The quadratic relationship between performance (Q) and insider ownership, following McConnell and
Servaes (1990) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
36
37
40
40
43
44
B.1 The relationship between performance (RoA5 ) and insider ownership in Norwegian firms, following
Morck et al. (1988) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 133
B.2 The quadratic relationship between performance (RoA5 ) and insider ownership for Norwegian firms,
without controls, following McConnell and Servaes (1990). . . . . . . . . . . . . . . . . . . . . . . . . . 134
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