Corporate governance and economic performance in Norwegian listed …home.bi.no/oyvind.bohren/work/mono/14-Corporate... · 2014-07-15 · Corporate governance and economic performance

Corporate governance and economic performance in Norwegian

listed firms

Øyvind Bøhren and Bernt Arne Ødegaard.1

November 26, 2001

1The Norwegian School of Management BI. We are grateful for financial support from the NorwegianResearch 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 ownershipmatters 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 thestate. 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 veryrobust in single–equation models and thereby suggest that our sample firms have suboptimal cor-porate governance mechanisms, the conclusions are quite sensitive to the choice of performancemeasure. Moreover, most of the significant relationships disappear in simultaneous equations mod-els, which may in principle handle both independence between governance mechanisms and reversecausality 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 drivenby weak instruments in the simultaneous system. Until we have a better theory of how corporategovernance and economic performance interact, the simultaneous equations approach may not havemuch to offer in terms of valid new insights.

Keywords: Corporate Governance, Economic Performance, Norwegian Equity Market, Own-ership Concentration, Inside Ownership, Simultaneous Equations.

JEL Codes: G3, L22

Contents

1 Introduction 1

2 Theoretical framework and existing evidence 4

2.1 Theory . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4

2.1.1 Corporate governance mechanisms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5

2.1.2 Interactions and causality . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10

2.2 Empirical evidence . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

2.3 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16

3 Descriptive statistics 18

3.1 Market place and institutional environment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18

3.2 Ownership structure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18

3.3 Board composition, security design, and financial policy . . . . . . . . . . . . . . . . . . . . . . . . . . 21

3.4 Controls . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21

3.5 Economic performance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22

3.6 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23

4 Univariate relationships 25

4.1 Overall pattern of univariate regressions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25

4.2 Ownership concentration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25

4.3 Owner type . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27

4.4 Insider ownership . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27

4.5 Board characteristics, security design, and financial policy . . . . . . . . . . . . . . . . . . . . . . . . . 27

4.6 Market competition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28

4.7 Controls . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29

4.8 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29

5 Ownership concentration 30

5.1 The Demsetz–Lehn approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30

5.2 Econometric issues . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31

5.3 Alternative functional specifications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34

5.3.1 Tobin’s Q as performance measure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34

5.3.2 Nonlinearity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34

5.4 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37

6 Insider ownership 39

6.1 The Morck–Shleifer–Vishny approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39

6.1.1 Extensions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41

6.2 The McConnell–Servaes framework . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42

6.3 Alternative insider definitions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46

6.4 The large insider . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46

6.5 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47

7 Owner type 48

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 design, and financial policy 53

8.1 Board characteristics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53

8.2 Security design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54

8.3 Financial policy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54

8.4 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55

9 A full multivariate model 569.1 Measuring performance with Tobin’s Q . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 569.2 Performance sensitivity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 619.3 Alternative performance measures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 639.4 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63

10 Explaining the corporate governance mechanisms 6510.1 The mechanisms one by one . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6510.2 Simultaneous equations modeling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6910.3 Examples of simultaneous systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7110.4 Ownership concentration and insider holdings as a simultaneous system . . . . . . . . . . . . . . . . . 7310.5 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79

11 Causation between corporate governance and economic performance 8011.1 Governance driving performance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8011.2 Two–way causation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8311.3 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 85

12 Conclusions 86

Appendix 89

A Data sources, variable definitions, and descriptive statistics 90A.1 Data sources . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 90A.2 List of variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 92A.3 Histograms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 97

A.3.1 Ownership concentration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 97A.3.2 Owner type . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 100A.3.3 Insider ownership . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 101A.3.4 Board characteristics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 102A.3.5 Security design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 102A.3.6 Financial policy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 102A.3.7 Controls . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 103A.3.8 Performance measures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 104

B Supplementary regressions 105B.1 Univariate relationships . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 105

B.1.1 Regressions underlying summary table . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 105B.1.2 Using voting rights instead of cash flow rights . . . . . . . . . . . . . . . . . . . . . . . . . . . . 110B.1.3 Plots of performance vs explanatory variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . 112

B.2 Ownership concentration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 118B.2.1 Year by year, GMM, and fixed effects regressions . . . . . . . . . . . . . . . . . . . . . . . . . . 118B.2.2 Alternative concentration measures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 122

B.3 Insider ownership . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 126B.3.1 Year by year, GMM, and fixed effects regressions . . . . . . . . . . . . . . . . . . . . . . . . . . 126B.3.2 Inside ownership without controls . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 132B.3.3 Alternative performance measure: RoA5. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 135B.3.4 Alternative insider definitions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 140B.3.5 Insider holdings and outside concentration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 147

B.4 Owner type . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 150B.4.1 Year by year, GMM, and fixed effects regressions . . . . . . . . . . . . . . . . . . . . . . . . . . 150B.4.2 Alternative performance measure: RoA5. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 155

B.5 Board characteristics, security design, and financial policy . . . . . . . . . . . . . . . . . . . . . . . . . 158B.5.1 Year by year, GMM, and fixed effects regressions . . . . . . . . . . . . . . . . . . . . . . . . . . 158B.5.2 Alternative performance measure: RoA5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 161

B.6 A full multivariate model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 164B.6.1 Year by year, GMM, and fixed effects regressions . . . . . . . . . . . . . . . . . . . . . . . . . . 164B.6.2 Alternative performance measure: RoA5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 167B.6.3 Alternative performance measure: RoA . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 169

iv

B.6.4 Alternative performance measure: RoS5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 171B.6.5 Alternative performance measure: RoS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 173B.6.6 Intercorporate ownership . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 175B.6.7 Outside (external) concentration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 177B.6.8 Voting rights . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 179

B.7 Explaining the corporate governance mechanisms with single equation models . . . . . . . . . . . . . . 181B.7.1 Single equation estimates of governance mechanism endogeneity, using aggregate ownership per

type as owner identity proxy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 181B.7.2 Single equation estimates of governance mechanism endogeneity, using type of largest owner as

owner type proxy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 187B.7.3 Outside (external) concentration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 190

B.8 Interactions between ownership concentration and insider holdings in a system of equations . . . . . . 192B.8.1 Only controls as additional explanatory variables . . . . . . . . . . . . . . . . . . . . . . . . . . 192B.8.2 Outside concentration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 195

B.9 Causation between corporate governance and economic performance, governance driving performance 198B.9.1 Regressions underlying summary table . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 198B.9.2 Controls, instruments and endogenous mechanisms only . . . . . . . . . . . . . . . . . . . . . . 202B.9.3 Outside (external) concentration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 205

B.10 Two-way causaution between corporate governance and economic performance . . . . . . . . . . . . . 209B.10.1 Regressions underlying summary table . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 209B.10.2 Controls, instruments and endogenous mechanisms, only . . . . . . . . . . . . . . . . . . . . . . 213B.10.3 Outside concentration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 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 Cadburyreport in 1992, the OECD has recently published overall corporate governance guidelines for itsmember states (OECD, 1999). Twelve EU countries have established national codes,1 and corporategovernance systems are being established for recently privatized firms in the ex–communist blockand in emerging economies in general (Shleifer and Vishny, 1997; Economist, 2001). This focus oncorporate governance is amplified by the growing fraction of the market portfolio held by mutualfunds and private pension funds in many countries, which raises the question of whether institutionalinvestors should maintain their classic role as passive owners who vote with their feet, or insteaduse their power to more actively monitor and discipline the managers of firms in which they haveinvested.

The intense public attention which is currently paid to corporate governance issues in Norwayreflects similar concerns. The privatization programs for the government’s telecom (Telenor) andpetroleum (Statoil and SDØE) firms are not motivated by the owner’s need for a more liquidor a more diversified portfolio, but rather by the desire to substitute state owners by privateones. The association of institutional investors (Eierforum) recently established ten principles forgood governance, and two pending court cases on minority freeze-outs (Norway Seafoods and AkerRGI) 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 proposingplans for fundamental restructuring (K. I. Røkke in Kværner). Finally, there are heated debates onwhether national ownership is worth protecting. This concern was triggered by the government’sdecision to hold a 1/3 blocking minority in the largest commercial bank (DnB) and by the recenttender offer from an international investor (the Finnish Sampo) for Norway’s largest insurancecompany (Storebrand).

The basic premise underlying all these cases is that governance matters. Economic performanceis thought to depend on corporate governance mechanisms, such as the overall legal protection ofstockholder rights, the firm’s competitive environment, the existence of large owners in the firm’sownership structure, the identity of such large owners, equity holdings by management, the designof the corporate charter, the decisions made at the stockholder meeting, the composition of theboard, the firm’s financial policy, and on the design of managements’ employment contracts.

Empirical research aimed at understanding the governance–performance interaction has so farbeen rather limited. This is partly because corporate governance is a novel academic field with anundeveloped theory foundation, and partly because high-quality data on these phenomena is quitedifficult to find. Existing research deals almost exclusively with a small subset of the governancemechanisms 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 corporategovernance system looks like.

Our study offers four new insights into the relationship between corporate governance and eco-nomic 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

1The 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 com-mon on the board. In contrast, our sample firms are on average much smaller, the legal regimebelongs to the Scandinavian version of the civil law tradition, hostile takeovers are very rare, thefirms are more closely held (i.e., ownership concentration is higher), performance–related pay isless common, and corporate boards have mostly none and never more than one inside director. Ac-cording to the principal–agent theory, all these governance mechanisms may matter for economicperformance (Agrawal and Knoeber, 1996; Shleifer and Vishny, 1997; Tirole, 2001). By testingthe 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 orjust implicit arguments about the functioning of corporate governance in general and ownershipstructure in particular, there is a national interest in knowing more precisely what these empiricalregularities really look like. Our study is an attempt at providing well–founded insights into thisissue, using a large sample, a wide set of governance mechanisms and performance measures, anddifferent econometric techniques.3

Second, because we have better ownership data than any existing study, we have the potentialof producing more reliable evidence. For instance, the analyses of ownership structure in theUS, Japan, the UK, and continental Europe are based on large holdings, only, as there is nolegal obligation to report other stakes (Barca and Becht, 2001). This means any holding below aminimum reporting threshold of 2–5% (depending on the country) cannot be observed, typicallyimplying 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 UKand 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 Exchangeover the period 1989–1997, involves a relatively long time series which suffers neither from the largeholdings bias nor the discrete thresholds problem.

Third, unlike most existing research, which have mostly focused on one or two ownership struc-ture variables (concentration and insider holdings) we also include many other corporate governancemechanisms, such as the identity of any owner in the firm (e.g, institutional, international, andpersonal 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 enablesus to use a comprehensive, multivariate approach and to contrast our findings with what we getusing more partial multivariate or the simplest univariate methods, which are much more commonin the literature.

Our fourth contribution is improved insight into endogeneity and causality, which are importantin practice and both difficult and underexplored in corporate governance research. Since some of

2The typical holding of the largest owner in a listed firm is 3% in the US (Barca and Becht, 2001), 45% incontinental Europe (Barca and Becht, 2001), and 30% in Norway (Bøhren and Ødegaard, 2001).

3The recent Norwegian studies by Mishra and Randøy (2000) and Roland et al. (2001) represent a more narrowapproach, 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 8.500largest (by number of employees) Norwegian firms which are majority–owned in 1996–1999. No other governancemechanism is considered, and no statistical tests are reported. Their findings suggest that the lowest and highestperformance 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 whichcaptures potential endogeneity of the mechanisms, e.g., the possibility that large external ownersand high insider stakes are substitute or complementary rather than independent ways of influencingeconomic performance. Because causality may not only run from governance to performance butalso the opposite way (like when insiders ask for stock bonus plans based on their private informationabout 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 inthe academic literature, using simultaneous system estimation techniques (Agrawal and Knoeber,1996; Cho, 1998; Demsetz and Villalonga, 2001). Since the findings in these papers differ quiteremarkably from those using standard uni– or multivariate regressions, we explore whether thisdifference is due to the underlying nature of the corporate governance problem or whether it isdriven by the difficulty of using simultaneous systems methodology in a setting where the theorywe try to test is loose and under–developed. We think our rich data set is particularly suitable forexploring this question.

Unlike most existing research, we find a negative and very significant relationship betweenownership concentration and economic performance. Insider ownership is value–creating up toa 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 stateintermediaries, and small boards create more value than large. Firms which issue shares withunequal voting rights tend to lose market value, but there is no sign that debt financing and dividendpayments are value–creating disciplining mechanisms. All these findings survive across a wide rangeof single–equation regression models, but most of them are reversed or become insignificant if weinstead use a simultaneous equation approach. Our analysis suggests that this may happen becausetheir is no reliable theory for generating the instruments. Until the theory of corporate governancecan handle not just each mechanism separately but also their endogeneous nature, we doubt whetherthe 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, start-ing 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, followedby a univariate analysis in chapter 4 which relates performance to one governance mechanism ata time. A multivariate approach is used in chapters 5 to 9, focusing particularly on how perfor-mance 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 chap-ter 8. On this background, a full multivariate model of the governance–performance interactionis constructed and tested in chapter 9. The endogeneous nature of the governance mechanismsis analyzed in chapter 10, and chapter 11 explores the direction of causation between governanceand performance. Chapter 12 summarizes our findings. Appendix A specifies the data sources,defines the variables used, and shows frequency plots for the performance measures, governancemechanisms, 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) anal-ysis of the separation between ownership and control in large corporations. Similar ideas werelater spelled out more formally by Jensen and Meckling (1976), which is currently the most citedpaper in the social sciences. Both articles address the principal–agent problem, which occurs whenmanagers 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 managersexert insufficient effort (by over-committing to external activities, accepting over-staffing, and byignoring internal control), collect excessive private benefits (by building unprofitable empires, pay-ing inflated transfer prices to affiliated entities, and by over–consuming perks), and when managersentrench 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-creatingtakeovers which threaten their position).

These examples illustrate that the separation between ownership and control may produce moralhazard and adverse selection problems. The resulting value loss is an agency cost, and corporategovernance can be thought of as a set of mechanisms which reduce such costs, i.e., a system forminimizing the value destruction caused by the agency problem. The challenge is to ensure thatthe firm is run by a competent management team which makes the same decisions that ownerswould have made themselves. This view is reflected in Shleifer and Vishny’s definition of corporategovernance as

...the ways in which the suppliers of finance to corporations assure themselves of gettinga return on their investment. How do the suppliers of finance get managers to returnsome of the profits to them? How do they make sure that managers do not steal thecapital they supply or invest it in bad projects? How do suppliers of finance controlmanagers? Shleifer and Vishny (1997).

In a recent presidential address to the Econometric Society, Tirole (2001) argues that the traditionalshareholder approach to corporate governance reflected in the above definition is too narrow for aneconomic analysis of whether firms should have social responsibility beyond maximizing the marketvalue of stockholders’ claims. In such a perspective, Tirole argues that the designer of a corporategovernance system must consider how all stakeholders (such as financiers, employees, suppliers, andcustomers) 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 “thedesign 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 cor-porate governance system aimed at maximizing shareholder wealth may not promote a stakeholdersociety. However, Tirole argues that an operational measure of aggregate stakeholder welfare isunattainable in practice, and that monitoring becomes much harder under multiple missions. He

1These authors were not the first to address the corporate governance problem. Adam Smith (1776) and ThorsteinVeblen (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 impacton 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 andbetween subgroups of owners, ignoring potential owner–creditor conflict. Although the resultingmenu of corporate governance mechanisms is rather extensive, the common denominator is alwaysincentives, power, competence, and monitoring. The next section briefly discusses the mechanismsone by one.

2.1.1 Corporate governance mechanisms

The corporate governance mechanisms discussed below are market competition, concentrated own-ership, owner types, insider ownership, board characteristics, security design, and financial policy.We also consider exogeneous variables that either shape the framework in which the governancemechanisms operate, or influence performance directly. These are called control variables. Finally,we consider the equilibrium condition, which predicts how any individual mechanism relates toperformance when all the mechanisms are used in an optimal way.

Market competition. The failure to maximize share value puts the firm at a competitive disad-vantage. In an agency context, this means that the stronger the competition in the firm’s outputmarket, 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 mayact as a disciplining device which reduces agency costs. The market for managerial talent plays acorresponding role, as the manager’s reputation for value-maximizing abilities may influences theaccess to attractive jobs in the future. Finally, competition in the market for corporate controlmay function as a governance mechanism, primarily by the threat of management displacement inhostile takeovers (Fama, 1980; Fama and Jensen, 1985; Stulz, 1988).

These arguments suggest that when products, labour, and takeover markets are fully compet-itive, a self–serving manager will find it optimal to maximize stockholders’ equity. Competitionwould be the only governance mechanism needed, and it works without owner interference. How-ever, since real–world markets are not fully competitive, this single disciplining device cannot beexpected to do the full job. The corporate governance mechanisms discussed in the following canbe thought of as additional disciplining devices which become relevant once we leave a world whereagency problems is the only market imperfection.2

Ownership concentration. When ownership is separated from control, agency theory argues thatif 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 carrymonitoring costs, and also the power to monitor effectively, he must hold a sufficiently large equitystake in the firm. If monitoring by owners improve the quality of managerial decisions, and if thereare no other effects of ownership concentration, performance and concentration will be positivelycorrelated (Shleifer and Vishny, 1986).

Ownership concentration may have several effects beyond the incentive and the power to mon-itor. 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 largebidders (Burkart, 1995). There are also several costs of holding a large stake. First, owners who

2Without the discipline from competitive markets, the agency problem may still be optimally solved by means ofcomplete 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 theoreticalframework assumes both imperfect markets and incomplete contracts.


cannot be large unless they invest most of their wealth in one firm end up with undiversified port-folios (Demsetz and Lehn, 1985). Second, if the stock’s market liquidity decreases with increasingconcentration, the information production ability of the stock price may suffer (Holmstrom andTirole, 1993).3 Third, minority shareholders suffer if large owners use the firm’s resources to bene-fit 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 modelof Burkhart et al. (1997), managers who expect owners to interfere will search less actively forprojects which bring private benefits to the owners (like a more stable firm cash flow), but whichalso increase market value. Consequently, the owners face a trade–off between the gains from moni-toring (less separation between ownership and control) and the opportunity loss caused by reducedmanagerial initiative. The higher the ownership concentration, the higher the expected opportunitycost. Finally, the implicit assumption behind any monitoring argument is that owners are compe-tent. 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 goalof equity value maximization. If this competence argument does not hold, ownership concentrationand economic performance may be inversely related.4

The relative impact of these benefits and costs at different concentration levels cannot be speci-fied ex ante. Therefore, agency theory cannot predict the relationship between concentrated owner-ship 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 ownersdo not arise until concentration is relatively high, the relationship between performance and con-centration 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 andability to create corporate value. A personal investor who votes at the stockholder meeting rep-resents 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 cashflow rights attached to the stakes they are voting for. In such settings, one agent monitors an-other agent, without direct interference from the ultimate principal. Generally, indirect monitoringthrough 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 bettermonitors than non–personal owners.

Institutional (financial) investors is a special case of non–personal owners. Since institutionsare holding a growing proportion of the equity market portfolio in most western countries, it is be-coming increasingly important to understand the governance activities of this investor type. Pound(1988) argues that institutional owners may influence performance in three ways. The efficient-monitoring hypothesis, which rests on the presumption that institutions are more competent thanother investors, predicts that institutions can monitor with higher quality at lower costs. Theconflict–of–interest hypothesis posits that when institutions have business relationships with firmsthey invest in (like an insurance company which both invests in and sells insurance to the samefirm), institutions may feel forced to protect the investee firm’s management. Finally, Pound’sstrategic-alignment hypothesis corresponds to our earlier argument that because the managers of

3Brennan and Subrahmanyam (1996) and Chordia et al. (2001) represent a growing literature on the relationshipbetween a stock’s returns and its market liquidity.

4We have only limited anecdotal evidence to substantiate this possibility. H. Brewster Atwater, who is the CEOof General Mills and also the head of the Business Roundtable’s corporate governance task force, recently expressedconcerns that institutional owners would take management hostage and force them to sacrifice long–term growthprospects 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 haveinsufficient value-maximization incentives. Thus, institutions will monitor with lower quality thanwould personal.

The efficient-monitoring hypothesis posits a positive performance effect of institutional owner-ship, whereas both the conflict-of-interest and the strategic-alignment arguments make the oppo-site prediction. Even if we add that more competitive products markets for institutional investorsreduces the relevance of the strategic-alignment hypothesis, we still cannot theoretically predictwhether institutional ownership has a positive, negative or no effect on economic performance.Like we concluded for ownership concentration, the question of whether institutional ownershipmatters for corporate performance can only be answered empirically.

Besides personal and institutional owners, state and international owners deserve special at-tention. As already mentioned, state owners are similar to most large corporate owners in thesense that both are represented at stockholder or board meetings by agents with negligible cashflow rights relative to the voting rights they exercise. This negative effect of misaligned incentivesis reinforced by the competence problem that state bureaucrats may lack experience with privatebusiness in general and corporate governance processes in particular. A state owner may also beinclined to ask the private firm to abstain from equity value maximization in order to achieve cer-tain social goals, such as higher local employment, reduced pollution, and a more level distributionof income between top management and other employees. Relative to private owners, one maytherefore 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 incorporate governance. They are normally at an informational disadvantage by knowing less aboutthe foreign country’s legal and institutional framework, the local competitive environment of thefirm’s industry, about the other large owners of the firm, and details of the firm’s strategy. This maybe one reason why we observe the universal home-bias phenomenon, by which investors allocate amuch higher proportion of their wealth to national equity securities than what a reasonable tradeoffbetween risk and return would prescribe.5 Thus, international investors may not invest in foreignfirms because they want to be active monitors, but simply to capture diversification benefits. Theywould rather vote with their feet (i.e., trade in the stock) than take corrective action by using theirvoting power. Like for state vs. private and non–personal vs. personal investors, we would expectthat because increased holdings by international investors reduces monitoring, firm performancewill be negatively affected.

Insider ownership. As insiders are owners of a particular kind, they might be considered justanother case of owner types discussed above. However, inside owners influence the agency problemin 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 themanagement 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 thenature of the principal–agent problem, which suggests that the interests of owners and managers arealigned when managers and board members (hereafter insiders) become owners as well. Based onthis convergence-of-interest hypothesis, Jensen and Meckling (1976) predict a positive relationshipbetween insider holdings and firm performance.

Insider ownership may also destroy market value. Morck et al. (1988) argue that powerfulinsiders may expropriate wealth from the outsiders in similar ways that majority shareholdersexploit the minority. This is the entrenchment hypothesis, which argues that owner–managers may

5Kang and Stulz (1994), Brennan and Cao (1997) and de Santis and Gerard (1997) provide empirical evidence onthe home bias puzzle.


make value–reducing decisions in order to safeguard their position in the firm. Such entrenchmentoccurs 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 value-creating takeovers which threaten their position (Tirole, 2001).

Because insider voting power is not the only source of insider power, the entrenchment hy-pothesis 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 thefounders, or simply because of a strong personality. Others may be unable to obtain similar controlunless they have considerably higher ownership stakes. Morck et al. (1988) think that althoughmore insider ownership allows deeper entrenchment in general, one cannot predict the level atwhich diminishing returns sets in. Also, as insiders carry a larger fraction of the destructed marketvalue the higher their stake, the negative performance effect of entrenchment may disappear orturn positive as the insider stake becomes sufficiently large.

The convergence-of-interest and the entrenchment hypotheses jointly imply that performancefirst increases with insider holdings (convergence–of–interest dominates), then decreases (entrench-ment dominates), and then becomes neutral or positive (convergence-of-interest dominates). Be-cause the shape of the entrenchment function is unclear, the classic agency model cannot offer asharp prediction of the insider–performance relationship. A more accurate hypothesis is providedby the takeover model developed by Stulz (1988), where a hostile bidder must pay a higher takeoverpremium for the target firm the larger the fraction held by its entrenched management. This posi-tive effect of increased insider holdings on firm value via the higher takeover premium is reduced bya decreased takeover probability, which drops to zero once the insider fraction reaches 50%. Thesetwo counteracting forces give rise to a curvilinear relationship, where firm value first increases andthen 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 insiderfraction is 50%, but it cannot specify the optimal insider holding.

Board characteristics. The stockholder meeting elects the board, which is the owners’ key for-mal vehicle for observing and influencing the quality of the management team. The two boardcharacteristics studied the most by finance researchers are independence and size. The argumentthat economic performance increases with board independence rests on the agency idea that theboard’s primary function is to monitor management. Unless the board is independent of manage-ment, monitoring will be weak. The opposite hypothesis assumes that the board supplements themanagement team by being a resource on strategic issues in particular. This extended manage-ment 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’sreduced ability to communicate, coordinate, and hence monitor. Jensen argues that for this rea-son, self–serving managers want to increase board size beyond its value-maximizing level. Theagency model predicts that because agents generate boards which are ineffective, board size andperformance are inversely related.

Security design. Equity securities come in different formats, such as equity with full ownershiprights (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-one-vote principle allows investors to separate voting rights from cash flow rights by holding unequalproportions of A and B shares. As this means investors may vote for more or less than their cashflow 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 fullvoting rights. Consequently, the existence of equity securities with unequal ownership rights mayinfluence the firm’s value. In particular, the most common theories of pricing differences betweenA and B shares assume a potential extraction of private rents for fully voting owners. If this is thecase, we would expect that according to performance measures which do not capture the privatebenefits of control (like the market value of equity), firms would be less valuable the higher thefraction 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, afirm’s financial policy (choice of capital structure and dividend payout) can also influence its agencycosts. The idea is that management discretion is restricted if the firm finances with debt ratherthan equity and pays earnings out as dividends rather than retains it (Jensen, 1986). Unlike equityfinancing, debt ensures that most of the firm’s cash flow must be used to honor contracts withcreditors who can enforce bankruptcy if their claim is not met. Similarly, high dividend payoutensures that most of the cash flow is handed over to owners, leaving correspondingly less resourcesfor management discretion to finance value-reducing investments. A higher dividend payout willalso force the firm more frequently to the market for new equity, where management must informthe 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 existingand prospective financiers, agency theory predicts that debt financing and dividend payments arevalue-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 exo-geneous variables in the environment that either influence the optimal governance mix or directlyaffect performance without influencing governance. The problem is, however, that with imper-fections like conflicts of interest between managers and owners, no existing valuation model canspecify the pricing–relevant characteristics. The standard solution to this problem is an ad–hocapproach which uses not just the systematic risk factor from CAPM-type equilibrium models toexplain cross-sectional differences in returns. Factors which have been shown to have independentexplanatory power in empirical asset pricing research are also included, such as firm size, the bookto 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 whenwhat seems like a relationship between governance and performance is driven by a third, omittedvariable. For instance, suppose performance decreases with firm size due to diseconomies of scaleand that insider ownership decreases with firm size due to the cost of being undiversified. Ifperformance is regressed on insider holdings alone, an apparent negative relationship between thetwo is caused by an underlying change in firm size. However, if the relationship persists afterthe effect of size on performance has been properly controlled for, insider ownership does have aseparate (i.e., size–independent) effect on performance.

The equilibrium condition. Corporate governance mechanisms may be thought of as factorsof production. If applied in a value-maximizing way, they should all satisfy the zero marginalvalue condition: Any mechanism should be used up to the point where a small change leaves firmvalue practically unaltered. According to Demsetz (1983), this means that if firms are owned byvalue-maximizing investors who understand how to select governance mechanisms which minimizeagency costs, corporate governance and firm performance will be unrelated.

This is far from saying that governance is irrelevant for performance. The equilibrium argument6Hawawini and Keim (2000) provides a survey of the empirical findings.


simply states what when the firm has chosen the optimal combination of mechanisms, a marginalchange of this set will have an insignificant value impact. Since two firms may have widely differentsets of optimal mechanisms (depending on their individual firm characteristics and the resultingpotential for generating agency costs), this implies that if we run a cross-sectional regression of per-formance on governance variables, the equilibrium hypothesis states that no governance variable willbe significantly related to performance. Conversely, a significant variable reflects a disequilibriumcase 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 sec-tion 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, withoutconcern for the impact of the others. For instance, we first hypothesized the performance effectof concentration, disregarding the impact of insider holdings. Next, we considered the impact ofinsider holdings while disregarding ownership concentration. This way of stating the hypothesesin univariate form reflects the nature of theory development in the area. For instance, Demsetzand 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 thanone governance mechanism may influence performance, one may not capture the impact of onemechanism 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 issueof exogeneous vs. endogeneous mechanisms, which occurs because several of the mechanisms maybe 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 acertain combination of outside directors (monitoring) and insider holdings (incentives) has the bestaggregate impact on value creation, replacing inside directors by outsiders may reduce performancewhen insider ownership is high (too much monitoring capacity on the board because managers arealso owners). Conversely, a more independent board may be value–increasing if the insider stakesare 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 in-stitutional ownership, they present no theory and do not use an empirical approach which maycapture the way the three mechanisms interact. This is a multivariate approach with exogeneousmechanisms. The only paper we know which establishes a system of endogeneous, multiple gov-ernance mechanisms is Agrawal and Knoeber (1996), who argue theoretically (although ratherincompletely) why the mechanisms are modeled as functions of each other (like insider ownership

7Not surprisingly, the mathematical models in the field are typically even more restrictive. For instance, Burkhartet al. (1997) focus on ownership concentration alone, deriving the conditions for optimal concentration when there isjust 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 insiderholdings partially determined by management tenure).

The third question triggered by existing research concerns the order of causation between gover-nance 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 bonusplans when they expect performance improvements. The government may take over distressedprivate firms in order to reduce negative externalities like unemployment and bank runs. In eithercase, performance drives governance. The general point is that causation may run either way, andthat theory should recognize this possibility and model the corporate governance problem accord-ingly. Although this problem has been raised earlier (see e.g. McConnell and Servaes (1990)), it hasonly 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 thetheoretical and the empirical level.

2.2 Empirical evidence

The empirical analyses of corporate governance and economic performance can be classified accord-ing 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 systemsacross many nations (La Porta et al., 1998; Barca and Becht, 2001). This research finds that acountry’s legal and regulatory regime influences key characteristics of its security market, ownershipstructures, and valuation processes. For instance, it seems that the weaker the protection of own-ership rights in the corporate law, the less developed the equity market and the more concentratedthe ownership structure (La Porta et al., 1997, 1998, 1999, 2000).

The second and third approach use data from a single country. This means the institutionalenvironment for each firm is identical, and the empirical question is how governance relates toperformance under the given institutional framework. The second approach, which uses the eventstudy methodology, studies what happens to the firm’s stock price when a governance mechanismis altered. If the modified mechanism triggers a significant stock price reaction, the mechanismis considered relevant for economic performance. Examples include the adoption of anti takeovercharter amendments (Linn and McConnell, 1983; Jarrell and Poulsen, 1987), poison pills (Malat-esta 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 firmsget more exposed to the market for corporate control, when management’s incentive contracts arestrenghtened, and when the managerial outside options are improved. Karpoff et al. (2000) sur-veys this literature and concludes that the introduction of restrictive governance mechanisms isconsidered bad news by investors.

An apparent advantage of the event study approach is that the researcher can directly observewhat happens to the market value of equity when a single mechanism is modified. This similarityto a natural experiment also allows for the study of causality, as both the mechanism change andthe price reaction are dated events. The problem is that when other governance mechanisms are


not controlled for, one ignores the possibility that the performance impact of the mechanism inquestion 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 unexpectedchanges in key governance mechanisms like ownership structure are rare events. When they dooccur, they often involve more than just changes in ownership, such as the transfer of large equityblocks in a corporate control contest (Morck et al., 1988).

The third approach, which we use in this study, compares the performance of firms with differentgovernance structures in place. The analytical tool is some form of regression analysis, and thesample is a cross-section of firms which are thought to represent a sufficiently rich variation in theirchoice of mechanisms. The empirical findings of this research tradition can be classified accordingto 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 useone or more ownership characteristics as the object of study, i.e., ownership concentration, insiderholdings, and owner types. Ownership concentration has been analyzed the most. For instance,among 33 empirical ownership–performance papers published over the 1932–1998 period surveyedby Gugler (2001), 27 deal with ownership concentration and only 6 with insider holdings. Theaggregate evidence on concentration and performance suggests that in most cases, there is eithera positive effect or no effect. The estimated relationship is positive in 12 cases, neutral in 13, andnegative in the two remaining ones. In a recent paper, Lehmann and Weigand (2000) find that inGermany, concentration and performance are inversely related.

Four of the six insider papers (Morck et al., 1988; McConnell and Servaes, 1990; Belkaoui andPavlik, 1992; Holderness et al., 1999) uncover a curvilinear relationship between insider holdings andfirm 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 (Jacqueminand 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) andof institutional (vs. all other) investors (McConnell and Servaes, 1990). Several authors cannotdetect 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 relationshipbetween owner identity and economic performance is a remarkably unexplored field of research.This is particularly worrisome regarding institutional investors, since their aggregate share of theequity market is everywhere large and increasing.


Security design, financial policy, and market competition are the mechanisms which have beenstudied the least in the literature. The only analysis we know on the disciplining role of competitiveproducts markets is an early paper by Palmer (1973).8 Another reason this paper is special is that itexplicitly considers the interaction between alternative governance mechanisms. Palmer finds thatwhen firms operate in competitive products markets, ownership concentration and performanceare not significantly related. However, when barriers to entry are strong, owner-controlled firms(firms with high ownership concentration) perform better than management-controlled firms. Thisfinding is consistent with the notion that market competition is a disciplining device, and thatowner monitoring and product market competition are substitute mechanisms.

To our knowledge, no paper has yet analyzed the empirical relationship between security designand economic performance in a corporate governance setting. Moreover, except for Agrawal andKnoeber (1996), who model the debt to equity ratio as one of seven governance mechanisms, existingresearch only includes financial policy as a control variable.9 In these cases, financial policy is onlyused to control for performance impacts which are unrelated to governance, such as the interesttax shield caused by debt financing.

Although the empirical research on board characteristics and economic performance have pro-duced mixed results (Bhagat and Black, 1998b), two findings are rather robust: Performancedecreases with increasing board size (e.g., Agrawal and Knoeber (1996)) and with an increasingfraction 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 benefitof having independent directors who monitor managers is more than offset by the cost of havingtoo few board members who really know the firm.

The empirical research summarized above can be characterized according to the data sets andthe methodologies used. In terms of data, the following pattern emerges:

• Mostly US firms. For instance, among the 28 studies of concentration and performancediscussed 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 6insider 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 fromthe Fortune 500 list, studying 371 such firms from 1980, 383 firms from 1987 and 326 firmsfrom 1991, respectively. One problem with these samples is that because the relationshipbetween a governance mechanism and performance may depend on firm size, a good sampleshould be heterogeneous in terms of size. McConnell and Servaes (1990) are less vulnerableto this criticism, as they sample randomly from the NYSE and Amex lists, using 1.173 firmsfrom 1976 and 1.093 firms from 1986.

• Blockholders only. Ownership concentration per firm is based on the aggregate fraction acrossreported blocks, i.e., holdings above a certain limit (normally 5%). This is an arbitrary cutoffpoint which is not dictated by theory, but by limited data availability.

• Biased insider holdings. The papers on insider ownership mostly use the aggregate stakeof the board members as their proxy. Since this measure ignores ownership by non–boardinsiders (like officers who are not directors), it underestimates insider holdings. If the ratio

8The managerial labour market and the market for corporate control, which were analyzed by Agrawal andKnoeber (1996), will be discussed later.

9Examples are Demsetz and Lehn (1985), Morck et al. (1988), McConnell and Servaes (1990), and Cho (1998).


Table 2.1 Mechanism interaction and mechanism–performance causality in empirical corporategovernance research

CausationMechanisms One-way Two-wayExogeneous 1 3Endogeneous 2 4

The table classifies the empirical approaches used by existing research on corporate governance and economic perfor-

mance.

of board to non–board insider holdings differs systematically across firms, this approach mayfail to detect the true relationship between insider ownership and performance.

• Narrow set of owner types. Most studies do not consider owner identity, and those thatdo use only two categories, such as institutional vs. non-institutional, state vs. private, orpersonal vs. non–personal owners. Since the theory argues that several different owner typeshave different roles to play when ownership is separated from control, a data set with a richerclassification 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 exceptionsto the overall pattern of using a cross–section of firms at only one point in time. Thissnapshot approach, which is probably due to limited data availability, cannot tell whetherthe relationship between governance and performance is stable over time.

The theoretical discussion in section 2.1.2 established two dimensions in the modeling of corporategovernance and economic performance. The first is whether mechanisms are considered exogeneousor 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 mutuallydependent).

Table 2.1, which reflects these two theoretical dimensions, can be used to classify the methodolo-gies 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 ex-ternally given, and causation is supposed to run from governance to performance, only. A singleregression equation is specified, typically containing one or two mechanisms and a number of con-trols. A sophisticated example of this approach is McConnell and Servaes (1990), who estimate thedependence 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 analyzecausation running from managerial ownership to performance, only, they argue that these stakesare explained by key variables in the contracting environment. Estimating managerial ownershipfrom firm characteristics and firm fixed effects, but with no explicit modeling of the mechanisminteraction, they cannot reject the hypothesis that managerial ownership and firm performance areindependent.

Cell 3 is unfeasible, as one cannot model two-way causation without letting at least one mecha-nism be endogeneously related to performance. The studies in cell 1 are criticized by Agrawal andKnoeber (1996) and Cho (1998), stating the arguments from our section 2.1.2 that the empirical


methodology should allow for the possibility that governance mechanisms are internally related andalso driven by performance. Using the approach in cell 1 first and then moving to cell 4 by esti-mating the governance mechanisms and performance as a system of simultaneous equations, mostof the significant results disappear. For instance, Agrawal and Knoeber (1996) find that if each oftheir seven governance mechanisms are considered exogeneous and related to Q one by one, four ofthem are significant (performance is positively related to insider holdings and negatively to boardindependence, leverage, and corporate control activity). Keeping the exogeneity assumption, butallowing for all the exogeneous mechanisms in one multivariate regression equation, insider holdingsare 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 inverselyrelated to performance. Whereas Agrawal and Knoeber (1996) do not report their findings oncausation, Cho (1998) concludes that causation runs from performance to insider holdings (his onlycorporate governance variable) rather than the opposite way.

Our theoretical discussion in section 2.1.2 argued that because the governance–performancerelationship may involve both mechanism endogeneity and two–way causation, cell 4 representsthe proper approach. Moreover, the findings of Agrawal and Knoeber (1996) and Cho (1998) bothsuggest 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 priorirelevant for two–way causation, and how the equilibrium would look in terms of an optimal set ofgovernance mechanisms for a given set of exogeneous variables.

This lack of clear theoretical predictions, which becomes particularly critical in cell 4, are well il-lustrated when Agrawal and Knoeber (1996) operationalize their model of endogeneous mechanismsand two–way causation. To capture mechanism endogeneity, they use a system of six equationswhere any equation relates a mechanism linearly to the five others (mostly without stating theo-retical reasons) and to a set of exogeneous variables (like listing status and stock volatility). Tomodel 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 withouttheoretical arguments). The resulting system of 7 equations and 15 exogeneous variables is esti-mated by the two–stage least squares method. As will be discussed in chapter 10, such a system ofequations must have certain properties in order to solve the socalled identification problem, whichis 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 byobserved patterns in the data. As implemented by Agrawal and Knoeber (1996), the exclusion ofexogeneous 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 theorysaying why and how, we cannot a priori preclude the possibility that the governance mechanismsare internally related and that they are also driven by performance. Such arguments would makeus favour the empirical approach of cell 4 to that in cell 1. That is still just half the story. Dueto the auxiliary assumptions needed to operationalize an empirical investigation of cell 4, it is notobvious that findings based on a cell 1 methodology are less reliable than those in cell 4. This isparticularly true if, as in Agrawal and Knoeber (1996), the number of ad–hoc assumptions is highand 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 arethe reasons why we choose a rather exploratory approach. We move from cell 1 to 4 in several


explicit and moderate steps, roughly using the chronological development of the field; from univari-ate regression analysis and one–way causation in cell 1 to a simultaneous systems approach whichallows 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 includesownership structure characteristics like concentration, owner identity, and insider holdings. It alsoincorporates financial policy, board composition, security design, and market competition. We findthat the theoretical predictions, which mostly come from agency theory, are always partial andsometimes diffuse, as they deal with the performance impact of just one governance mechanismand sometimes cannot specify the shape of the univariate governance–performance relationship.

The expected performance effect of higher ownership concentration is unclear, as it reflectsthe net impact of benefits (valuable monitoring, higher takeover premia, less free-riding by smallshareholders) and costs (reduced market liquidity, lower diversification benefits, increased majority–minority conflicts, reduced management initiative, incompetent owners). Similarly, compared to thedirect principal–agent relationship represented by a personal investor, the multiple–agent setting ofinstitutional ownership has an unpredictable value effect (the beneficial efficient-monitoring effectvs. the costly conflict-of-interest and strategic-alignment effects), whereas international and stateownership will be less value-creating. Like for ownership concentration, we cannot a priori specifythe 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 effectsets 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, therelationship between performance and concentration would be positive at moderate concentrationlevels and negative thereafter. However, only empirical evidence can give the definite answer.

Agency theory predicts that performance will decrease with increasing board size (inefficientcommunication), that firms with dual–class shares will have a lower market value than others (pri-vate benefits), and that both dividends and financial leverage are value–creating (reduced free cashflow). Competition in the firm’s product market, the managers’ labour market, and in the marketfor corporate control will be value–increasing (reduced room for wasteful decisions). Finally, even ifthe 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 weobserve in practice are indeed the optimal ones for each individual firm, no mechanism will besignificantly related to performance in a cross-section.

The vast majority of empirical research singles out ownership structure as the object of studyand mostly analyzes just one ownership characteristic. Overall, the evidence suggests that the as-sociation between ownership concentration and performance is as often zero as positive, and seldomnegative. Insider ownership is mostly positively related to performance at moderate insider hold-ings and negatively related or unrelated at higher levels. The role of owner identity is unclear andunder-explored. The recent papers in this area find that whereas several governance–performancerelationships are significant in single–equation settings, very few survive under a simultaneousequations 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 aboutgovernance and performance. First, because data on ownership structure is hard to find, mostpapers 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, disregardimportant insider subcategories, and focus on a single point in time. The second problem withexisting research is methodological. It occurs because most papers treat the different mechanismsas though they were independent of each other, and they implicitly assume that causation runsfrom governance to performance and not the other way. Our study relates economic performance tocorporate governance mechanisms in a way which carefully and step by step addresses the problemsof 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 econometricscan really offer additional, reliable insight compared to simpler, single–equation approaches. Theproblem here is that the implementation of the simultaneous equations approach requires a priorirestrictions on mechanism interaction which the current theory of corporate governance may beunable to deliver.

18 Descriptive statistics

Chapter 3

Descriptive statistics

Because our sample consists of all firms listed at Oslo Stock Exchange (OSE), this chapter firstdescribes key characteristics of the OSE in section 3.1. We present summary statistics of the samplefirms’ ownership structure in section 3.2, their board composition, financial policy, and securitydesign 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 befound 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 butincreasingly important role in the national economy, and it has become considerably more liquidover our sample period, which is from 1989 to 1997. As of year-end 1997, the 217 listed firms havean aggregate market capitalization equivalent to 67 bill. USD, which ranks the OSE twelfth amongthe twenty–one European stock exchanges for which comparable data is available. From 1989 to1997, the number of firms listed jumped from 129 to 217, market capitalization grew by an annualaverage of 7%, and market liquidity as measured by annual turnover (transaction value/averagemarket 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 ratherclose to the European median of 49%.2 The book value of Norwegian listed firms’ equity in 1994was 17% of all private and state firms’ equity. Relative to all limited liability firms in 1996, listedfirms represent 21% of the book equity, 8% of sales and 8% of employment.

The regulatory framework of corporate governance in Norway is somewhat peculiar. Eventhough 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 pro-vide 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 con-centrated ownership structure than any other European country (Barca and Becht, 2001; Bøhrenand 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 largestone both in Norway and the UK. For instance, the average ratio of the largest to the second largestequity 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 perfor-mance measures in our sample firms. All averages are equally weighted across firms and years, but

1Appendix A specifies data sources, defines all variables used, and graphs the variables.2Source: International Federation of Stock Exchanges (www.fibv.com).3The US figure is 3%.

3.2 Ownership structure 19

we will also provide corresponding value–weighted averages to highlight certain points.4 The mostcommon concentration measure in the literature is the fraction of outstanding equity owned by then’th largest or the n largest shareholders, with n mostly varying between 1 and 5. The table reportssuch 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 thefirm’s equity, the largest holds 29%, the two largest make up a blocking minority against charteramendments, the four largest produce a simple majority, and a coalition of the ten largest form asuper majority.6

The owner type may either be measured as the aggregate fraction in a firm held by a certaintype or by the identity of a particular owner, usually the largest one. Consistent with the argumentsin 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 ownershipbetween rather large, public firms with many owners, we also include intercorporate shareholdingsbetween publicly listed firms as a separate type. Such a holding is operationalized as a stake heldby a sample firm in another sample firm, i.e., equity investments between Oslo Stock Exchangefirms.

We show in Bøhren and Ødegaard (2000) that according to the value-weighted averages, inter-national investors as a group is the largest owner type and hold almost one third of OSE marketcap over the sample period. Non-financial domestic firms own about one fourth, the state andfinancial investors both own roughly one fifth, and individuals hold about one tenth. Financialinvestors increase their share of OSE market cap almost every year due to the rapid growth ofmutual funds, and individuals gradually become less significant. Aggregate state ownership variesconsiderably over time, primarily due to the state’s rescue of large commercial banks in the earlynineties. OSE firms hold 8% of the equity issued by other OSE firms. This fraction is decreasingover time; from 14% in the beginning to 4% in the end of the sample period. International andfinancial investors are relatively seldom the largest owner in the firm, whereas national corporationsare strongly overrepresented. Norwegian individual investors as a group own a lower fraction of theequity 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 towardscertain firm sizes. It turns out that international investors, the state, and non-bank financialshold 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 ofthe largest owner who is not also an insider. This definition of concentration solves the potentialproblem that if an insider is also the largest owner, concentration (measured unconditionally as thelargest 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 equitytrades in a firm to the OSE, regardless of whether or not they have private information. Thisdefinition includes the board, the management team, the auditor, and their immediate family

4Value-weighted averages for most of these variables can be found in Bøhren and Ødegaard (2000).5The 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 structuregets increasingly diffuse.

6All 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 towhether ownership structure characteristics are based on voting rights or cash flow rights.


Table 3.1 Summary of descriptive statisticsMean StDev Q1 Median Q3 n

Ownership concentrationHerfindahl index 0.2 (0.2) 0.0 0.1 0.2 1069Median owner 0.0 (0.0) 0.0 0.0 0.0 1069Mean owner 0.2 (0.3) 0.0 0.1 0.1 1069Largest owner 29.0 (19.2) 14.3 23.2 40.6 10691-2 largest owners 40.1 (20.2) 23.6 36.3 53.8 10691-3 largest owners 47.0 (20.0) 30.3 44.2 62.6 10691-4 largest owners 52.0 (19.6) 35.8 50.5 66.9 10691-5 largest owners 55.9 (19.1) 40.6 55.0 70.4 10691-10 largest owners 67.5 (16.9) 54.7 68.4 80.9 10691-20 largest owners 77.4 (14.0) 67.6 79.5 88.4 1069Number of owners 4392.5 (9578.5) 691.0 1245.0 2938.0 10692nd largest owner 11.1 (6.1) 6.9 9.7 13.8 10693rd largest owner 7.0 (3.6) 4.7 6.3 8.8 10694th largest owner 5.0 (2.3) 3.5 4.7 6.3 10695th largest owner 3.9 (1.8) 2.7 3.7 4.9 1069Largest outside owner 25.7 (19.3) 11.0 19.1 35.6 1069

Insider ownershipAll insiders 19.9 (27.7) 0.5 6.3 29.7 1069Board members 7.8 (20.7) 0.0 0.1 2.5 1069Management team 4.2 (14.7) 0.0 0.0 0.7 1069Primary insiders 8.2 (19.0) 0.0 0.4 4.5 1069Largest insider 10.9 (16.4) 0.2 3.0 14.9 1059Largest primary insider 5.5 (12.1) 0.0 0.4 4.5 1062

Owner typeAggregate state holdings 5.1 (13.8) 0.0 0.0 3.8 1069Aggregate international holdings 22.1 (22.3) 4.6 14.8 32.8 1069Aggregate individual holdings 17.8 (15.6) 6.5 12.4 25.2 1069Aggregate financial holdings 16.6 (14.0) 5.5 14.2 23.7 1069Aggregate nonfinancial holdings 39.0 (24.0) 17.5 37.5 58.7 1069Aggregate intercorporate holdings 9.0 (14.9) 0.3 3.0 10.7 1067Largest owner is state 8.6 (28.0) 0.0 0.0 0.0 1069Largest owner is international 13.2 (33.8) 0.0 0.0 0.0 1069Largest owner is individual 10.4 (30.5) 0.0 0.0 0.0 1069Largest owner is nonfinancial 54.9 (49.8) 0.0 100.0 100.0 1069Largest owner is financial 7.8 (26.8) 0.0 0.0 0.0 1069Largest owner is listed 12.9 (33.5) 0.0 0.0 0.0 1069

Board characteristicsBoard size 6.6 (2.5) 5.0 6.0 8.0 964

Security designFraction voting shares 96.8 (9.3) 100.0 100.0 100.0 1054

Financial policyDebt to assets 57.1 (19.4) 46.2 60.2 70.0 1058Dividends to earnings 26.5 (68.1) 0.0 0.0 33.0 1040Dividends to price 159.5 (333.4) 0.0 57.0 225.0 1069

ControlsInvestments over income 60.2 (283.7) 3.2 8.1 30.4 1006Stock volatility 54.2 (28.7) 33.7 46.3 65.3 949Stock turnover 59.4 (65.3) 13.4 40.3 79.0 1034Stock beta 0.9 (0.6) 0.5 0.8 1.2 947Firm value 1995.4 (6062.9) 168.6 480.8 1429.9 1069

Performance measuresQ 1.5 (1.0) 1.0 1.2 1.6 1068RoA 5.0 (14.8) 3.2 7.3 10.9 1061RoS 33.1 (92.4) -16.7 13.0 49.0 894

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 ofthe management team (officers), and the primary insiders, which are the directors and officers. Onaverage over firms and years, all insiders hold 20% of the equity, directors have 8%, and managementowns 4%. Due to the overlap between directors and officers, who together constitute the primaryinsiders, the average fraction held by primary insiders is 8%. Since the management team of OSEfirms are either not on the board or are represented by the CEO, these figures reflect that insiderownership by management is very often just CEO holdings. The 11% held by the largest insiderand 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 neverhave more than one officer, all boards in our sample are outsider–dominated according to thestandard 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 constitute10% of OSE market capitalization and 29% of the equity in dual-class firms, and the propensity toissue B shares decreases over time. International investors, who hold 54% of non-voting shares, areheavily over-represented in this security type, both before and after 1995, when the restriction oninternational holdings of voting shares was lifted.9

The average leverage ratio (debt to total assets) for the sample firms is 57%. The averagepayout ratio is 27% for all firms and 52% for firms that actually pay dividends, which is half thefirms. Restrictions in the corporate law made stock repurchases practically non–existent in thesample period.10

3.4 Controls

As discussed in section 2.1.1, a test of the governance–performance interaction should also considerexogeneous control variable that either influence the framework in which the governance mechanismsoperate, or which are driving performance directly. Our control variables are investments (measuredas 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 sizeas the logarithm of firm value, which we estimate as the market value of equity plus the book valueof debt.

7The 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 manuallyclassify insiders from the insider registry of the OSE, we only assign a holding to the primary insider category ifthe holder can be identified in our board and management data base. When this match is unsuccessful, the insiderremains in the All category, but is not included in the primary insider category. This identification problem makesus underestimate the holdings by primary insiders, but we have no reason to suspect that it biases our tests of thegovernance–performance relationship.

8Even though a board member is formally external, he or she may still be closely related to management. Thiswould easily happen if the recruiting process for board members is heavily influenced by the CEO. As we lackobservations on how director candidates are generated, we must ignore this type of mechanism.

9Until 1995, the aggregate fraction of voting shares in a firm held by international investors could not exceed onethird.

10After restrictions were softened in 1999, a firm can repurchase up to 10% of its outstanding equity.


Asset pricing theory predicts a positive relationship between the beta of a stock and its expectedreturns. Performance will also be influenced by the stock’s market liquidity if investors must becompensated 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’senvironment decreases, ownership concentration is positively related to the total risk of the firm’scash flow. We use stock price volatility to proxy for cash flow risk. Investments will be used tocontrol 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 andperformance (Hawawini and Keim, 2000).

The table shows that the average OSE share is traded roughly every second year, and that theaverage firm size is NOK 2 bill. in terms of firm value. At the end of the sample period, the averagemarket value per firm is about one fifth the average NYSE firm and roughly twice the averageNASDAQ firm.

3.5 Economic performance

Economists care about corporate governance because it may influence the economic value of soci-ety’s resources in a positive or negative way. As the performance measure is supposed to reflect thiscreation or destruction of value, the list of potential candidates is long. For instance, in periodswith high unemployment and low growth, economic planners may focus on input measures likethe number of jobs created or the new investment in fixed assets. Private owners would be moreconcerned with output measures of performance, and particularly those which reflect the impacton their wealth, such as the level or the growth in the value of the firm’s total assets or equitysecurities. We will focus on wealth–related performance measures.

The most commonly used proxies in the recent literature on governance and performance areTobin’s Q ratio (Q), the accounting (book) rate of return on assets (RoA), and the market returnon the stock (RoS). Q is the market value of assets divided by their replacement value, RoA isprofits after taxes plus interest payments after taxes divided by the book value of assets, and RoSis the sum of the period’s capital gains and dividends divided by the market value of equity at thebeginning 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 areequivalent 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 stockreturns. However, since corporate governance recognizes the possibility of such a conflict, andbecause owners are considered the key to improved governance, total return and equity returnmeasures are not necessarily equivalent. The bottom of table 3.1 shows summary statistics ofthe three estimated performance measures. Because we miss data on replacement values, Q isoperationalized 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 arenominal.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 correlationbetween Q, RoA, and RoS. To explore the effect of short–lived noise, we correlate the measuresusing both annual values and five–year average values for RoA and RoS. The five-year averages aredenoted by the subscript 5. The table shows that the correlations are generally low. Since there

11The 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 consistentlypositive than the Pearson correlations is somewhat reassuring. The question of consistency betweenperformance measures will be analyzed further in the regression models in subsequent chapters.

Table 3.2 Correlations between performance measuresPanel A: Pearson correlation

Q RoA RoA5 RoSRoA 0.06∗

RoA5 0.32∗∗∗ 0.20∗∗∗

RoS 0.27∗∗∗ 0.09∗∗∗ 0.10∗∗∗

RoS5 0.34∗∗∗ −0.08∗∗ 0.23∗∗∗ 0.51∗∗∗

Panel B: Rank correlation

Q RoA RoA5 RoSRoA 0.20∗∗∗

RoA5 0.20∗∗∗ 0.38∗∗∗

RoS 0.23∗∗∗ 0.17∗∗∗ 0.05∗

RoS5 0.22∗∗∗ 0.11∗∗∗ 0.11∗∗∗ 0.24∗∗∗

Correlation between Tobin’s Q (Q), return on assets (RoA), and return on stock (RoS). Correlations based onfive-year average values are denoted by the subscript 5. The ∗, ∗∗, and ∗∗∗ means the relationship is significantat the 5%, 2.5% and 1% level, respectively. Data for firms listed on the Oslo Stock Exchange, 1989-1997. Variabledefinitions 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 arather 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 averageOSE 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 holdabout one third of aggregate market capitalization, financial (institutional) investors steadily in-crease their share, and ownership by individuals (personal investors) is small and declining. Insidershold on average one fifth of a firm’s outstanding equity. Roughly half the insider stakes belong toprimary insiders (the firm’s managers and directors), and the CEO holds almost all the shares inthe 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, and14% 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 propensityto issue B shares decreases over time.

We measure performance by the Tobin’s Q ratio (Q), the accounting (book) rate of return on as-sets (RoA), and the market return on the stock (RoS). The correlation between these performancemeasures is generally low.

Table 3.3 summarizes the variables which will be used in the regression models of the subsequentchapters.


Table 3.3 Governance variables, controls, and performance measures used in the regression modelsOwnership concentration Herfindahl index

1-n largest ownersn’th largest owner

Owner typeBy aggregate holdings Aggregate state holdings

Aggregate international holdingsAggregate individual holdingsAggregate financial holdingsAggregate nonfinancial holdingsAggregate intercorporate holdings

By type of largest owner Largest owner is stateLargest owner is internationalLargest owner is individualLargest owner is financialLargest owner is nonfinancialLargest owner is listed company

Insider ownership All insidersBoard membersManagement teamPrimary insiders

Board characteristics Board size

Security design Fraction voting shares

Financial policy Debt to assetsDividends to earnings

Market competition IndustrialTransport/shippingOffshore

Controls Investments over incomeStock volatilityStock turnoverStock betaFirm value

Performance measures QRoARoS

Univariate relationships 25

Chapter 4

Univariate relationships

This chapter uses a univariate approach to the analysis of corporate governance and economicperformance, relating performance to just one governance mechanism at a time. According to theclassification in table 2.1, this approach belongs in cell 1 (exogeneous mechanisms and one-waycausation). Among the alternatives in cell 1, the univariate approach represents the most partialmethodology, as it ignores all other mechanisms when any one of them is being studied. To simplifythe exposition, we only report the estimated signs of the regression coefficients and their levels ofsignificance, 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 governancemechanism or a control, the table shows the estimated sign of the coefficient of the independentvariable. Significance is indicated by ∗, ∗∗, and ∗∗∗, which means the estimated coefficient issignificantly 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, althoughthe holding size of the five largest owners varies inversely with every performance measure, therelationship is insignificant for RoA and RoS, significant at the 2.5% level for RoS5, and significantat the 1% level for RoA5 and Q. Overall, the covariation is more often significant with Q than withany other measure, more often with the five–year averages RoA5 and RoS5 than with the annualRoA and RoS, and, for a given averaging period, more often for return measures based on totalassets (A) than on equity (S ).

Second, the consistency across performance measures is higher using RoA5 and RoS5 than withRoA and RoS. This is particularly true for Tobin’s Q and RoA5, which both measure total valuecreation (i.e., for all financiers). For instance, the holdings of either one of the four insider categoriesis never significantly related to RoA, and the estimated sign is the opposite of what we find usingQ for all categories except one (board). In contrast, RoA5 and Q produce the same estimatedsign (+) for every insider group, both suggest that the All insiders category is insignificant andthat holdings by the board as well as the primary insiders are highly significant (p < 1%). In thefollowing discussion of the findings from table 4.1, we will focus on Q and the five–year averagesRoA5 and RoS5 as our performance measures.1

4.2 Ownership concentration

Since the agency model does not specify one particular concentration measure as being theoreticallysuperior, 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 produceautocorrelated error terms in the regressions. Q does not suffer from this problem, since each value is sampled overone 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 mechanismor a control

Q RoA5 RoS5 RoA RoS

Ownership concentrationHerfindahl index −*** −*** − − −Largest owner −*** −*** − − −1-3 largest owners −*** −*** −* − −1-5 largest owners −*** −*** −** − −2nd largest owner − − −** + −3rd largest owner + − − − −4th largest owner + + − − −5th largest owner + + − − −**

Owner typeAggregate 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 ownershipAll insiders + + + − +Board members +*** +*** − + +Management team + +** +*** − +Primary insiders +*** +*** +* − +

Board characteristicsln(Board size) − − −*** + −Security designFraction voting shares +* − +* − +

Financial policyDebt to assets −*** −*** −*** +*** −Dividends to price −*** +*** − +*** −Dividends to earnings − + − +*** +

Market competitionIndustrial + − + + +Transport/shipping −*** −*** −** + −Offshore −* −*** + − +

Controlsln(Firm value) +*** − − +*** +*Investments over income − − − + −Stock volatility −*** −** +*** −*** +Stock turnover +*** + +*** − +***Stock beta + − +*** − +

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 isindicated 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. Variabledefinitions 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 largestowner), aggregate stakes (e.g., fraction held by the five largest owners), and a proxy which reflectsthe full ownership structure (the Herfindahl index).

The table documents that without exception, the significant relationships between concentrationand performance are negative. It is almost never significant using annual stock return or totalreturn, whereas Q and RoA5 relate significantly to concentration (p < 1%) as measured by theHerfindahl 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 toperformance. 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 higherperformance. 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 numberof such investors and the average stake per investor. Therefore, a 60% aggregate stake for individualinvestors as a group may represent one 60% holding by a single investor (high power by one principalwho directly monitors the agent) or 60.000 stakes of 0,001% each (no power and no incentives forany principal). Hence, the aggregate stake may mix concentration and identity characteristics in amisleading way. It also rests on the implicit assumption that cooperation between investors withinthe type is unproblematic regardless of the number of investors involved, and that cooperation isharder across types than within a type. Neither assumption seems unproblematic. To avoid theinterpretation 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 thelargest stake is held by the state, non–financials or another OSE firm. These findings correspondroughly 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 maydirectly reflect how several owners jointly influence economic performance. The insider group ismuch smaller than other investor categories, the information level is more homogeneous, and theirjoint ability to affect important corporate decisions is better. Since the management team mayhave 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 performancemeasure, although the association is never statistically significant for all insiders as a group. Thepositive relationship is significant (p < 1%) for the board and the primary insiders relative to Qand 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 isstatistically 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 aredifferently priced at the OSE. This pricing discrepancy may reflect the value of the option touse the voting right in order to extract private benefits. Table 4.1 indicates that consistent withthe conflict of interest idea, performance as measured by Q and RoS5 is positively related to thefraction of voting shares outstanding (p < 5%). For both Q, RoA5 and RoS5, there is a significantlynegative relation (p < 1%) between leverage and performance. The findings on dividends are quiteinconsistent 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 outputmarkets, 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 recognizethat such measures are noisy, primarily because the industry may not reflect the individual firm’sunique competitive position, such as market share and strategic assets, but rather the averagecharacteristics of all firms in the industry, like the overall protection due to entry barriers. Thisproblem is more serious the more heterogeneous the competitive position of each individual firm.

Because we want proxies which reflect competition–related differences across industries, weclassify the sample firms along product market lines, using a system developed by the OSE.2 Sincesome 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 dataavailability 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 beendominated 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 closeto the market-wide OSE average of 2.1 bill. NOK, and they dominate the transportation/shippingcategory. 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 sampleperiod.

2Another alternative would have been the SIC system. Unfortunately, because many multi–product OSE firmsare SIC–classified as holding companies, only, this alternative tells nothing about the firm’s underlying operations.

3In Bøhren and Ødegaard (2000) we stratified the sample into IPO firms (Norwegian: selskaper pa SMB listen),industrials, financials, and shipping firms. This classification is inappropriate in the present context, since it maynot properly account for product market differences. The major problem is the IPO category. These firms belong tomany different industries (like shipping and industrials), but they all end up as IPO firms in our classification onlybecause they are newly listed and hence young and small. Industrials and shipping correspond roughly to our firsttwo 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 itsobserved link to performance. Since differences in competitive environment may not have been amajor criterion when the OSE constructed the industry proxy, it may as well be considered a roughindustry classification system with questionable links to underlying differences in the competitiveenvironment. Thus, even if the proxy reflects variables which matter for performance, they arenot driving the design of corporate governance systems. Hence, the proxy picks up effects whichwould otherwise be subsumed in the error term if we were to regress performance on corporategovernance mechanisms alone. One example is when the competition proxy reflects differences insystematic risk rather than competition differences. Although risk differences influence the cost ofcapital 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 agovernance mechanism or a control variable. We will mostly interpret it as a control variable inthe following.

4.7 Controls

The control variables, which will be used in the multivariate regressions in later chapters, are firmsize, investments, stock volatility, stock turnover, and stock beta. As shown in table 4.1, the firmsize results are somewhat inconsistent across performance measures, but the significant associationsare the positive ones. That is, larger firms tend to have higher performance.

Investment and performance show no convincing associations. The three stock characteris-tics (total risk, liquidity, and systematic risk) are all positively correlated with both stock-basedperformance 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 andcontrols one by one. In the language of table 2.1, this approach is the most partial version ofcell 1 methodologies, which all assume exogeneous mechanisms and one–way causation. Overall,we observe the strongest association with corporate governance mechanisms when performance ismeasured by Tobin’s Q. Consistency across performance measures is largest for Q and the five–yearaverage return on assets (RoA5).

Almost without exception, ownership concentration is inversely related to performance, andthe negative covariation is particularly strong for alliances of large investors, such as the threelargest 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 covarypositively and significantly with performance, whereas the relationship is significantly negative fornon–financial and state owners. Firms with dual–class shares tend to have lower performance thanothers. The univariate relationship between board size and performance is negative, but ratherweak.

Due the partial nature of the models used in this chapter, we choose to postpone interpretationsuntil 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 oftable 2.1, (exogeneous mechanisms and one–way causation). In the following five chapters, we stayin cell 1, but move to a multivariate approach. The focus of the current chapter is the relationshipbetween performance and concentration, but we also control for variables which are not governancemechanisms, but still potentially relevant for the estimated relationship between concentrationand performance. Chapter 6 first explores the insider–performance interaction in a correspondingway, subsequently extending the analysis to include more than one governance mechanism in theregression. This approach reflects the chronological development in this field, from the simpleconcentration paper of Demsetz and Lehn (1985) to the more comprehensive insider ownershipstudy of McConnell and Servaes (1990). In both chapters, we replicate the classic papers on oursample and extend the analyses by exploiting the richness of our data set. To keep the expositionreasonably compact, we show supplementary regressions in appendix B.2.

As discussed in chapter 2, agency theory argues that from a monitoring perspective, performanceimproves with increasing concentration. However, as both reduced diversification benefits, lowerliquidity, reduced manager initiative, and increased majority–minority conflicts work in the oppositedirection, the theoretical prediction on the covariation between concentration and performance isunclear. Also, the monitoring argument implicitly assumes that owners are sufficiently competentto 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 iseither positive or insignificant.

5.1 The Demsetz–Lehn approach

Demsetz and Lehn (1985) analyze 511 large US corporations in 1980, measuring performance asRoA5 for the period 1976–1980. Alternative concentration proxies are the fraction held by thefive largest owners, by the twenty largest, and the Herfindahl concentration index. Their controlvariables are industry dummies for utilities and financials (supposed to capture the effect of reg-ulation), investments in real assets, R&D investments, advertising expenses, firm size, and stockprice volatility. Although the estimated relationship between concentration and performance isnegative, Demsetz and Lehn (1985) find that it is not significant at conventional levels (table 9 intheir 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 investorschoose value–maximizing governance systems for each firm, the empirically observed relationshipbetween 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, andthe Herfindahl index). Because our sample contains no financials and very few utilities, we usethe 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 notspecify R&D and advertising, these two items are ignored in our model. As a similar proxy we useinvestment intensity, measured as investment over income.1

1Although 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 theconcentration 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 modelwith our Norwegian data strongly suggests that ownership concentration and performance areindeed 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 forindustry, size, investment, and stock price volatility. The other coefficients in the regression tallywith the DL results, as they have the same signs and similar significance levels.

Table 5.1 Multivariate regression relating performance (RoA5) to ownership concentration andcontrols, following Demsetz and Lehn (1985)

Dependent variable: RoA5

coeff (stdev) pvalueConstant 14.41 (2.67) 0.00lntrans(1-5 largest owners) -0.54 (0.18) 0.00Industrial -1.97 (0.38) 0.00Transport/shipping -2.81 (0.40) 0.00Offshore -3.93 (0.61) 0.00Investments over income -0.08 (0.05) 0.12ln(Firm value) -0.11 (0.12) 0.37Stock volatility -1.68 (0.66) 0.01n 886R2 0.09Average (RoA5) 9.41

Pooled OLS regression using data for all listed firms on the OSE in the period 1989 to 1997. Table 5.2 showscomplementary 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 prob-lems of simultaneity and reverse causation, which will be addressed in chapters 10 and 11, thereare other issues which may question the OLS approach. First, the estimation is carried out onpanel data, i.e., a pooled cross section–time series data set. Because this means the same firmmay appear numerous times in the sample, not all observations are independent, and the resultingregression error terms may be serially correlated (autocorrelation). Second, the governance mech-anisms 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 con-centration are substitute disciplining devices.

2Two of the independent variables have been ln–transformed. We ln–transform the fractional holding of the fivelargest owners in order to be consistent with Demsetz and Lehn (1985), who transformed it from a bounded to anunbounded variable because it subsequently served as the dependent variable in a model relating concentration topotential determinants. Because a few of our sample firms are very large relative to the others, we ln–transform firmsize in order to reduce the inflating effect of outliers on the standard errors of the estimated coefficients.

3Appendix B.2.2 contains regressions using two alternative concentration measures: Herfindahl index and owner-ship by 20 largest owners. The results are very similar, in particular the negative relation between concentration andperformance is significant using these alternative concentration measures.


if the underlying structural relationship between the variables changes over the nine–year time pe-riod, a time–independent specification may not capture the true picture (instability). Fourth, thefunctional 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 firstproblem (autocorrelation) and the third one (instability). There is no time series correlation ina single–year cross-section, and structural shifts will show up as systematic patterns in the timeseries of estimated coefficients. However, these year–by–year regressions may suffer relative to thepooled one due to a much smaller number of observations (on average 100 firms per year vs roughly900 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 estimatedcoefficient or a more extreme one) will increase. This will bias our test towards keeping the nullhypothesis 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, oursecond alternative estimation technique is GMM4, which is used to estimate the model specifiedfor the OLS regressions with pooled data, such as the one in table 5.1. This approach producesidentical point estimates of the coefficients, but, unlike with OLS, any error term dependencies arepicked 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 theconstant term to change from year to year. This may happen if the aggregate performance effectof factors subsumed in the error term are changing over time, such as a market-wide upward ordownward 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 necessarilygovernance mechanisms or controls.5

Table 5.2 shows the results of applying the three alternative regression techniques to the basicmodel of table 5.1. The major patterns from table 5.1 reappear in table 5.2. The inverse relationbetween performance and concentration persists in 7 out of 9 years even in the year–by yearregressions (panel A), although it is only significant at conventional levels in one year. The GMMand fixed effects regressions in panel B both find that the relationship is highly significant. Noticethat 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 fixedeffects regression (right–hand side) finds that the structural relationship is marginally different intwo out of the nine sample years. Otherwise, there are no striking contrasts to the findings intable 5.1.

From now on, these robustness tests are put into appendix tables, and we only comment onthem when they produce conclusions which differ materially from those in the text.

4General Method of Moments, see Ogaki (1993) or Hamilton (1994) for an overview.5For 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.

5.2 Econometric issues 33

Table 5.2 Multivariate regression relating performance (RoA5) to ownership concentration andcontrols according to Demsetz and Lehn (1985), but using year–by–year OLS, GMM, and fixedeffects OLS techniquesPanel A: Year by year OLS regressions

Year1989 1990 1991 1992 1993 1994 1995 1996 1997

constant 7.55 −1.01 12.27 14.21 26.40 23.86 2.50 22.35 33.31(0.27) (0.89) (0.16) (0.05) (0.00) (0.00) (0.70) (0.02) (0.00)

lntrans(1-5 largest owners) −0.41 0.16 −0.23 −0.76 −0.85 −0.96 −0.77 −0.10 −0.01(0.33) (0.74) (0.63) (0.21) (0.09) (0.02) (0.11) (0.87) (0.99)

Industrial −1.61 0.26 0.09 −0.47 −2.05 −2.22 −2.37 −2.53 −3.38(0.11) (0.80) (0.94) (0.72) (0.08) (0.03) (0.01) (0.02) (0.01)

Transport/shipping −0.80 0.19 −0.40 −0.92 −2.38 −3.01 −3.57 −4.98 −5.36(0.47) (0.86) (0.72) (0.46) (0.03) (0.00) (0.00) (0.00) (0.00)

Offshore −5.79 −1.66 −2.98 −2.68 −4.44 −4.27 −3.42 −3.42 −4.03(0.00) (0.33) (0.06) (0.12) (0.00) (0.01) (0.05) (0.10) (0.03)

Investments over income −0.05 −0.04 0.05 −1.88 0.33 −0.66 −0.37 −0.40 −0.29(0.48) (0.75) (0.50) (0.27) (0.67) (0.15) (0.11) (0.25) (0.27)

ln(Firm value) 0.23 0.51 0.05 −0.04 −0.58 −0.58 0.35 −0.45 −0.86(0.46) (0.13) (0.89) (0.90) (0.11) (0.04) (0.25) (0.30) (0.04)

Stock volatility −1.69 0.15 −5.65 −2.73 −4.42 −1.72 2.68 −3.61 −6.49(0.34) (0.94) (0.01) (0.12) (0.01) (0.36) (0.09) (0.12) (0.03)

n 83 79 76 69 82 106 115 119 157

R2 0.05 −0.04 0.16 0.02 0.17 0.15 0.14 0.12 0.12Average (RoA5) 9.82 9.33 9.19 10.16 10.06 8.94 8.77 9.04 9.76

Panel B: Pooled GMM and fixed effects regressions

Dependent variable: RoA5coeff (stdev) pvalue

Constant 14.41 (3.82) 0.00lntrans(1-5 largest owners) -0.54 (0.17) 0.00Industrial -1.97 (0.40) 0.00Transport/shipping -2.81 (0.47) 0.00Offshore -3.93 (0.55) 0.00Investments over income -0.08 (0.04) 0.04ln(Firm value) -0.11 (0.16) 0.49Stock volatility -1.68 (0.97) 0.09n 886Average (RoA5) 9.41


Constant 16.49 (2.72) 0.00lntrans(1-5 largest owners) -0.55 (0.18) 0.00Industrial -2.03 (0.38) 0.00Transport/shipping -2.87 (0.40) 0.00Offshore -4.12 (0.61) 0.00Investments over income -0.08 (0.05) 0.10ln(Firm value) -0.16 (0.12) 0.20Stock volatility -2.47 (0.68) 0.001990 -0.47 (0.70) 0.501991 -0.32 (0.71) 0.661992 0.64 (0.74) 0.391993 0.20 (0.70) 0.781994 -1.33 (0.66) 0.051995 -1.60 (0.65) 0.011996 -1.40 (0.65) 0.031997 -0.60 (0.62) 0.33n 886

R2 0.10Average (RoA5) 9.41

This table complements the pooled OLS regression in table 5.1 by three additional regressions. Panel A shows OLSestimates 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 variablesfor each year are used to capture fixed effects. 1989 is the base year in this regression. In regressions using firm sizeacross years the nominal values are adjusted to the 1997 general price level. Data for firms listed on the Oslo StockExchange, 1989-1997. Variable definitions are in Appendix A.2.


5.3 Alternative functional specifications

In the preceding section, we listed four potential estimation problems and presented three techniquesfor addressing two of them (autocorrelation and instability). OLS still produces unbiased coefficientestimates under multicollinearity, but we bias the test towards keeping the null hypothesis becausemulticollinearity inflates the estimated standard deviations compared to the uncorrelated case.Since some of the independent variables may be correlated according to the theory (substitute orcomplementary mechanisms), it would be wrong to just exclude a mechanism which is correlatedwith another one. This is simply an important part of the theory we want to test. Thus, we willgradually include new mechanisms based on the theory of corporate governance, independently ofmulticollinearity concerns. Still, we indirectly address multicollinearity in chapter 10, which modelsthe interrelationships between the mechanisms.

The fourth econometric problem discussed above is a mis–specified functional form betweenperformance, governance, and controls. This section describes our methodology for handling thisproblem. We use Q rather than RoA5 as an alternative performance in section 5.3.1. Nonlinearrelationships between concentration and performance in our regression models are analyzed insection 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. Theinverse, very significant relationship between governance and performance persists. It turns outlater that in many cases, we either get this result or that the associations are more significant usingQ than RoA5. Because most existing papers use Q and the inherent autocorrelation problem ofRoA5 discussed in footnote 1 of chapter 4, we use Q as our performance measure in most of thefollowing analyses.

The robustness tests reported in appendix table B.2 show that the corresponding relationshipbetween Q and concentration in the year–by–year regressions is more often significant than in theRoA5–based table 5.1.6 Using the fixed effects model, the estimated dummy variables for the twofinal sample years (1996 and 1997) are always positive and very significant. The structural shifthappens because equity market values (and hence Q) rose very sharply in these two years. Thispattern 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 theirchoice of a simple linear function in the regression of performance on concentration. In fact, Morcket al. (1988) argue that

“...the failure of Demsetz and Lehn to find a significant relationship between owner-ship concentration and profitability is probably due to their use of a linear specificationthat does not capture an important nonmonotonicity”.

We therefore analyze the effect of allowing for nonlinearities in the functional specification.

6The p−value is below 10% in five of the years and below 22% in the remaining four years.7In 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.

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: Qcoeff (stdev) pvalue

Constant 0.32 (0.56) 0.56lntrans(1-5 largest owners) -0.18 (0.04) 0.00Industrial -0.44 (0.08) 0.00Transport/shipping -0.84 (0.08) 0.00Offshore -0.74 (0.13) 0.00Investments over income -0.01 (0.01) 0.19ln(Firm value) 0.08 (0.03) 0.00Stock volatility -0.04 (0.14) 0.79n 905R2 0.14Average (Q) 1.53

Pooled OLS regression using data for all listed firms on the OSE in the period 1989 to 1997. Table B.2 showscomplementary 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 linearspecification with a piecewise linear one. Our pre–specified step points (5% and 25%) are identicalto those used by Morck et al. (1988) to analyze the insider–performance relationship, which wewill analyze in the next chapter. Using Q as the performance proxy and the fraction held by thelargest owner as the concentration measure,8 the result from estimating such a function withoutcontrols is shown in figure 5.1. The figure suggests an inverse relationship between performanceand concentration for the pooled regression, but that the association may be positive if the largestowner holds less than 5%.

We next add controls and estimate the model reported in table 5.4. Consistent with our findingsin the linear model without steps in table 5.3, all three coefficients on the largest owner in the pooledregression are negative in table 5.4. The coefficient is insignificant for concentration levels up to5%, and very significant thereafter. The year–by–year coefficients9 are mostly insignificant, butstill 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 usedby McConnell and Servaes (1990). Unlike the piecewise linear approach, this model does not requireany prespecification of parameter values. Figure 5.2 fits a quadratic function to the relationshipbetween performance and the holding of the largest owner, using no controls. Judging from thepooled regression and most of the year–by–year regressions, this univariate relationship appearsnegative and possibly curvilinear.

8We use the holding of the largest owner rather than the lntrans of the holding of the five largest owners because thestep points of Morck et al. (1988) are motivated by an individual owner’s concerns for flagging and voting thresholds.

9Reported in appendix table B.3.10The 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%.


Figure 5.1 The relationship between performance (Q) and the holding of the largest owner inNorwegian firms, using the piecewise linear function of Morck et al. (1988)

All years

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The figure shows the implied functional relationship from a piecewise linear regression with Q as the dependentvariable and the fraction held by the largest owner as the independent variable. The figure to the left pools datafor 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 ownershipconcentration. 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 con-trols, using the piecewise linear function of Morck et al. (1988)


Constant 2.45 (2.17) 0.26Largest owner (0 to 5) -38.62 (42.76) 0.37Largest owner (5 to 25) -1.34 (0.60) 0.03Largest owner (25 to 100) -0.76 (0.28) 0.01Industrial -0.44 (0.08) 0.00Transport/shipping -0.86 (0.08) 0.00Offshore -0.75 (0.13) 0.00Investments over income -0.02 (0.01) 0.17ln(Firm value) 0.09 (0.03) 0.00Stock volatility -0.07 (0.14) 0.63n 905R2 0.14Average (Q) 1.53


5.4 Summary 37

Figure 5.2 The quadratic relationship between performance (Q) and the holdings of the largestowner

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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 beestimated. 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 OsloStock 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–linearitymore formally by including a quadratic term in the multivariate regression. Once more, we findan inverse, significant relationship between concentration and performance. The quadratic termis insignificant at conventional levels, however, suggesting that the simple linear specification issufficient.

The shape of the graph in figure 5.2 and the numerical value of the estimated coefficient intable 5.5 both indicate that the covariance between concentration and performance is far from neg-ligible in economic terms. The estimated coefficient of −2.02 suggests that if all other independentvariables in the regression are kept constant, changing the holding of the largest owner by one unitdecreases performance by 2.02 units. To illustrate, consider a firm where market value is initially1.53 times its book value (i.e., Q = 1.53), and where the largest owner holds 29% (0.29) of out-standing equity (these figures correspond to the average sample values). If concentration increasesby 10 units (i.e., from 0.29 to 0.39), the model of table 5.5 predicts that Q will drop from 1.53 to1.33.11 That is, when the largest owner increases the stake from 29 to 39%, the market value ofthe firm drops by 13%.

5.4 Summary

In this chapter we have moved from a univariate to a multivariate analysis of governance andperformance, focusing on ownership concentration. Unlike what Demsetz and Lehn (1985) findfor 511 large US firms in 1980, we conclude that for the population of Norwegian listed firms inthe 1989–1997 period, concentration and performance are strongly related. Economic performancevaries inversely and very significantly with the holdings of large owners. This is true both in a

11Ignoring non–linearities and assuming that the other variables in the model remain at their initial levels


Table 5.5 Multivariate regression relating performance (Q) to ownership concentration and con-trols, using a quadratic function


Constant 0.66 (0.56) 0.24Largest owner -1.96 (0.65) 0.00Squared (Largest owner) 1.37 (0.84) 0.10Industrial -0.45 (0.08) 0.00Transport/shipping -0.86 (0.08) 0.00Offshore -0.74 (0.13) 0.00Investments over income -0.01 (0.01) 0.17ln(Firm value) 0.09 (0.03) 0.00Stock volatility -0.06 (0.14) 0.65n 905R2 0.14Average (Q) 1.53


statistical and an economic sense. The conclusion is valid under several alternative performance andconcentration measures, and regardless of whether the assumed functional form is linear, piecewiselinear or quadratic.

Insider ownership 39

Chapter 6

Insider ownership

The most remarkable finding in chapter 5 is the strongly inverse link between performance andconcentration. However, as we only studied large owners without concern for their identity, weshould not conclude that this result holds for large owners of any type. We argued in section 2.1that owner identity may matter, and that inside owners may be fundamentally different fromoutsider owners. Also, we found that the alignment–of–interest (Jensen and Meckling, 1976) andthe entrenchment (Morck et al., 1988) hypotheses jointly suggest that the relationship betweeninsider holdings and economic performance is curvilinear. If we assume that entrenched insidersuse 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’sQ and insider holdingsin 371 firms sampled from the Fortune 500 list in 1980. Insider ownership is operationalized asthe aggregate fraction held by the firm’s board members (directors).1 To account for the predictedcurvilinear relationship, MSV use a piecewise linear regression function with two step points. Theirtheoretical 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 insiderholdings up to the pre–specified breakpoint of 5%, decreases as the stake grows further to thesecond breakpoint of 25%, and increases again thereafter. In these three intervals, the estimatedcoefficient of the insider stake is significant at the 1%, 5%, and 10% levels, respectively. This resultindicates that the alignment–of–interest effect dominates in the beginning, is dominated by theentrenchment 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 holdingsup to 25%; and more so below 5% than above. After 25% is reached, performance declines withincreased insider stakes.4 Thus, our piecewise linear, univariate model suggests that insider holdingsare positively related to performance up to 25%, and negatively for stakes beyond this level. Theseresults differ from the findings of Morck et al. (1988) in large US firms, where the association ispositive for insider holdings below 5% and above 25%, and negative in the intermediate 5–25%interval.

1To be included in their insider proxy, an individual must hold at least 0.2% of the firm’s outstanding equity.2MSV’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.3Whereas 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 arereplaced by the board.

4The three estimated coefficients have p values of 0%, 1%, and 0%, respectively.

40 Insider ownership

Figure 6.1 Relating performance (Q) to insider ownership using a piecewise linear function. USdata

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 inNorwegian firms, following Morck et al. (1988)

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The graphs show the implied functional relationship from a piecewise linear regression with Q as the dependentvariable and insider ownership as the independent variable. The underlying regression, which is detailed in appendixtable B.17, includes no controls and no other governance mechanism than insider ownership. Data for firms listed onthe 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 effectof 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 byadding some control variables (but no governance mechanism). MSV include R&D and advertisingexpenses to account for the impact on Q of cross-sectional differences in immaterial assets (reflectedin the market value, but not in the book value). Since we lack such data, these controls must beignored in our test. Like MSV, we include leverage to control for governance–independent effectsof financing on Q, such as the interest tax shield. For the same reasons as in chapter 5, we alsocontrol 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 sig-nificance levels of the insider ownership variables are similar to those in figure 6.2, although thep–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)


Constant -0.00 (0.32) 0.99Primary insiders (0 to 5) 7.74 (1.97) 0.00Primary insiders (5 to 25) 1.85 (0.76) 0.02Primary insiders (25 to 100) -0.57 (0.30) 0.06Industrial -0.28 (0.07) 0.00Transport/shipping -0.59 (0.07) 0.00Offshore -0.58 (0.11) 0.00Debt to assets -1.10 (0.15) 0.00ln(Firm value) 0.11 (0.02) 0.00n 1057R2 0.20Average (Q) 1.47


6.1.1 Extensions

So far, we have found that economic performance is inversely related to ownership concentrationin general (chapter 5), but that the story is quite different when we study one particular ownertype. There is a strong, positive relationship between Q and insider holdings up to 25%, anda negative and less significant link thereafter. A natural question to ask is whether this resultis caused by fundamental differences between owner types or simply by different specificationsof the regression equations in the two tests. Table 6.2 explores this question by including bothownership concentration and insider holdings in the same regression, keeping the piecewise linearspecification for the insider stake from table 6.1. The table shows that ownership concentration

5Appendix B.3 shows that the coefficients in the year–by–year regressions are seldom significant. Also, the evidenceis weaker using GMM or fixed effects OLS, as the coefficients for the two upper size intervals become insignificant. Ifwe use RoA5 instead of Q, the overall shape of the relationship is maintained. Like for GMM and fixed effects OLSunder the Q measure, the coefficients for the two upper size intervals are insignificant.


and insider holdings have separate roles to play. As in chapter 5, ownership concentration enterswith a significantly negative coefficient, whereas the insider stake works like in table 6.1. Noticealso that the expected change in performance is considerably stronger with corresponding changesin insider holdings than in concentration, particularly at low insider levels. The absolute value ofthe estimated coefficient is roughly eight times higher.

Table 6.2 Multivariate regression relating performance (Q) to insider holdings, ownership concen-tration and controls, using the piecewise linear function of Morck et al. (1988).


Constant 0.45 (0.33) 0.17Primary insiders (0 to 5) 6.31 (1.96) 0.00Primary insiders (5 to 25) 1.90 (0.75) 0.01Primary insiders (25 to 100) -0.42 (0.30) 0.16Largest owner -0.78 (0.14) 0.00Industrial -0.26 (0.07) 0.00Transport/shipping -0.60 (0.07) 0.00Offshore -0.59 (0.11) 0.00Debt to assets -1.12 (0.14) 0.00ln(Firm value) 0.10 (0.02) 0.00n 1057R2 0.22Average (Q) 1.47


6.2 The McConnell–Servaes framework

The McConnell and Servaes (1990) paper (hereafter McS) differs from Morck et al. (1988) in severalways. The number of firms is roughly twice as large, the sample is more heterogeneous in terms offirm 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 mecha-nisms, which are ownership concentration and the fraction held by institutional investors. WhereasMSV define insiders as directors, only, McS include both officers and directors, which correspondsto our primary insider category.6 Finally, instead of the rather ad–hoc linear approximation to apossibly non-linear relationship used by MSV, McS argue that the estimation model should allowfor less restrictions and more smoothness in the insider–performance relationship. Therefore, theychoose a quadratic functional form rather than a piecewise linear approximation with pre–specifiedbreakpoints. Figure 6.3 shows the result from their two univariate regressions of performance oninsider holdings, only. There is a distinct quadratic relationship in both years, and the estimatedinflection 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 pooledobservations indicates an inflection point around 50%, which is roughly the case in the year–

6As shown in the appendix, our results are rather insensitive to this distinction.7The 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 fromestimating 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.


by–year graphs as well.8 Table 6.3 shows that the quadratic shape persists after controlling forleverage, size, and industry.9 Overall, the curvilinear specification seems to fit the data better thanthe piecewise linear model of Morck et al. (1988).

Figure 6.4 The quadratic relationship between performance (Q) and insider ownership, followingMcConnell and Servaes (1990)

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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 constantsto be estimated. The figure on the left pools data for all years, the figure on the right shows the results of doing theestimation year by year. The underlying regression is detailed in appendix table B.18. Data for firms listed on theOslo 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 alsothe overall concentration as measured by the fraction held by the largest stockholder. Like we foundwith 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, weconclude that insider holdings and ownership concentration seem to capture separate governancecharacteristics.10

The final step in our comparison with McConnell and Servaes (1990) is to add the aggregatefraction held by institutional owners to the model in table 6.4. McS find a separate, positiverelationship with economic performance, which is consistent with the efficient monitoring hypothesisof Pound (1988) discussed in section 2.1. Table 6.5 finds no convincing evidence of such an effect inour sample. This may reflect that the positive monitoring effect is neutralized by the two negative

8The insider fraction I which maximizes performance Q can be found by expressing Q as a quadratic functionof I, taking the partial derivative of Q with respect to I, setting this expression equal to zero, solving for I, andplugging 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%.

9As shown in appendix table B.18 there is considerable consistency across individual years. The coefficient ofthe linear term is always positive, with a pvalue below 5% in five out of nine cases. The squared term always has anegative coefficient, and pvalue below 5% in four of the years.

10One may worry that some of these results may be caused by an overlap between concentration and insiderholdings, since some of the large owners may also be insiders. As we show in appendix B.3.5, no conclusion changesif 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)


Constant 0.04 (0.32) 0.90Primary insiders 2.81 (0.42) 0.00Squared (Primary insiders) -2.64 (0.52) 0.00Industrial -0.29 (0.07) 0.00Transport/shipping -0.59 (0.08) 0.00Offshore -0.56 (0.11) 0.00Debt to assets -1.15 (0.15) 0.00ln(Firm value) 0.11 (0.02) 0.00n 1057R2 0.19Average (Q) 1.47


Table 6.4 Multivariate regression relating performance (Q) to insider ownership, ownership con-centration and controls, following McConnell and Servaes (1990)


Constant 0.52 (0.33) 0.12Primary insiders 2.56 (0.42) 0.00Squared (Primary insiders) -2.31 (0.51) 0.00Largest owner -0.85 (0.14) 0.00Industrial -0.26 (0.07) 0.00Transport/shipping -0.59 (0.07) 0.00Offshore -0.58 (0.11) 0.00Debt to assets -1.16 (0.15) 0.00ln(Firm value) 0.10 (0.02) 0.00n 1057R2 0.22Average (Q) 1.47



effects, i.e., the conflict–of–interest and the strategic alignment costs.11 Finally, appendix B.3documents that unlike for the MSV–type model, the McS model and its extensions produce thesame 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 concen-tration, institutional ownership and controls, following McConnell and Servaes (1990)


Constant 0.47 (0.33) 0.15Primary insiders 2.56 (0.42) 0.00Squared (Primary insiders) -2.33 (0.51) 0.00Largest owner -0.89 (0.15) 0.00Aggregate financial holdings -0.24 (0.21) 0.25Industrial -0.26 (0.07) 0.00Transport/shipping -0.60 (0.07) 0.00Offshore -0.58 (0.11) 0.00Debt to assets -1.14 (0.15) 0.00ln(Firm value) 0.11 (0.02) 0.00n 1057R2 0.22Average (Q) 1.47


6.3 Alternative insider definitions

So far, we have used the holdings by primary insiders (officers and directors) to proxy for insiderownership. To investigate the sensitivity to this definition, we re–estimate the models using threealternative insider proxies: all insiders, officers, and directors. Appendix B.3.4 shows that usingboard members as insiders produce even more significant results, with the same coefficient signsas for primary insiders. Using all insiders or management holdings as the insider proxy producesless significant results, but the curvilinear relationship between performance and insider ownershippersists.

6.4 The large insider

There is a possibility that the estimated curvilinear relationship is driven by the cases where onespecific insider owns a particularly large stake. If this insider uses the voting power to extractprivate benefits at the expense of other owners, firm value may decrease with insider holdings evenif all primary insiders as a group hold rather moderate stakes. As shown in the histogram of insiderownership in appendix A.3, the distribution of this variable is higly skewed, as there are only afew cases with insider ownership above 10%. One possible explanation of our curvilinear results isthat it reflects these relatively few cases of a negative effect of one very large insider. To explore

11As institutional investors is one of our five basic investor types, we will reconsider their role in chapter 7, whichexplicitly 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 largestprimary insider. We add the the largest primary insider as an explanatory variable together with theholdings by all primary insiders12 and ownership concentration. Table 6.6 shows the result. Thereis a significantly negative coefficient on the ownership by the largest primary insider, indicatingthat having particularly large insiders is negative for performance. Still, the curvilinear relationbetween performance and aggregate insiders persists, as the linear and the sqared terms remainsignificant.

Table 6.6 Multivariate regression relating performance (Q) to insider ownership, the holdings ofthe largest insider, ownership concentration, and controls


Constant 1.08 (0.35) 0.00Primary insiders 3.54 (0.50) 0.00Squared (Primary insiders) -3.35 (0.56) 0.00Largest primary insider -1.10 (0.34) 0.00Largest outside owner -0.73 (0.14) 0.00Industrial -0.29 (0.07) 0.00Transport/shipping -0.60 (0.07) 0.00Offshore -0.61 (0.11) 0.00Debt to assets -1.51 (0.16) 0.00Investments over income -0.00 (0.01) 0.72ln(Firm value) 0.08 (0.02) 0.00n 990R2 0.25Average (Q) 1.47


6.5 Summary

The evidence in this chapter shows that while ownership concentration in general is inversely relatedto performance across all concentration levels, holdings by corporate insiders play an independentand different role. Performance is positively related to insider holdings up to roughly 50% andnegatively thereafter, reflecting a curvilinear variation which is better approximated by a quadraticfunction than a piecewise linear one. This result, which corresponds quite well to what was foundin the US by McConnell and Servaes (1990), is robust to alternative insider specifications and topotential overlap between the insider and the large owner categories. In regressions with indepen-dent variables representing both insider ownership, ownership concentration, industry membership, financial leverage, and firm size, performance (as measured by Tobin’s Q) is roughly three timesmore sensitive to insider holdings than to ownership concentration. The difference is larger at verylow or very high insider stakes.

12There is an overlap between primary insider ownership and the owership by the largest inside owner. We keepthis overlap because we want to investigate the robustness of the conclusion about all primary insiders when weaccount separately for the largest one.

48 Owner type

Chapter 7

Owner type

The two preceding chapters showed that although ownership concentration and economic perfor-mance are inversely related in general, a finer partition of the concentration variable shows a morecomplex pattern. We found that although increased concentration of insider ownership reducesperformance if the insider stake is high (typically above 50%), the relationship is positive at lowerconcentration levels. Thus, inside and outside concentration relate very differently to economicperformance in our sample. Along with the theoretical arguments in section 2.1, these findings sug-gest that to better understand the interaction between ownership and performance, we should focusmore closely on owner identity. This chapter analyzes how Tobin’s Q is linked to the aggregate hold-ings of different owner types in a firm and to the identity of its largest owner. Given the evidencein the two previous chapters, the base–case regression model in this chapter includes ownershipconcentration, insider holdings (quadratic specification), and controls (industry, leverage, and firmsize). It turns out that results are insensitive to whether we measure concentration by the holdingsof the largest owner, the two largest, three largest, four largest, five largest or by the Herfindahlconcentration index. Since the Herfindahl index has the theoretical advantage of accounting forsize heterogeneity across the largest holdings, we measure concentration by the Herfindahl index inthe 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 stakein a firm directly reflects the type’s power and incentives. Coordination is particularly costly for thesmall 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 thesame firms (i.e., large firms with liquid stocks).

Using our earlier classification system, we initially categorize the investors into the five basictypes of state, international, individual, financial, and nonfinancial owners. As we cannot argueconvincingly 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 econometricproblems by excluding one type and interpreting this type as the reference case. Since we havealready explored the aggregate holdings of financials in section 6.2, we choose financials as thereference group. Table 7.1 shows the results.

The table shows that compared to financial investors as a group, the relationship between owneridentity 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 theaggregate fraction of a firm’s equity which is owned directly rather than indirectly, the better thefirm’s performance. Thus, although performance correlates inversely with (outside) concentrationin 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 directlymonitored 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, insiderholdings, aggregate holdings per owner type, and controls


Constant -0.18 (0.44) 0.68Herfindahl index -0.59 (0.21) 0.00Primary insiders 2.05 (0.43) 0.00Squared (Primary insiders) -2.02 (0.51) 0.00Aggregate state holdings -0.40 (0.30) 0.19Aggregate international holdings 0.02 (0.21) 0.93Aggregate individual holdings 0.97 (0.27) 0.00Aggregate nonfinancial holdings -0.15 (0.22) 0.50Industrial -0.19 (0.07) 0.01Transport/shipping -0.50 (0.08) 0.00Offshore -0.52 (0.11) 0.00Debt to assets -1.15 (0.15) 0.00ln(Firm value) 0.12 (0.02) 0.00n 1057R2 0.23Average (Q) 1.47


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 equitypositions in other firms. This may happen when ownership acts as a mechanism for sharing jointlyproduced profits or to reduce information asymmetries between separate firms participating in astrategic alliance. Hence, because intercorporate ownership between large firms may involve botha 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 stakesby listed firms in other listed firms. This allows us to use intercorporate ownership between OSEfirms as a proxy for holdings between large firms with many owners.1 Table 7.2 shows that thereis a significantly (p < 2%) negative relationship between performance and the aggregate fractionof an OSE firm’s equity held by other OSE firms. Thus, on average, the positive strategic effect ofintercorporate investments is more than offset by the negative monitoring effect hypothesized bythe 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 inthe 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, anindividual, a non–financial corporation, or an international investor, respectively. The referencecase is when all indicator variables are zero, which happens when the largest owner is a financial

1State, international, financial, and non–financial owner types represent much less homogeneous versions of indirectownership. For instance, an international investor may be a person, and a non–financial firm may be closely held byits manager.

50 Owner type

Table 7.2 Multivariate regression relating performance (Q) to ownership concentration, insiderholdings, aggregate intercorporate holdings, and controls


Constant 0.49 (0.33) 0.14Herfindahl index -0.98 (0.18) 0.00Primary insiders 2.50 (0.42) 0.00Squared (Primary insiders) -2.30 (0.51) 0.00Aggregate intercorporate holdings -0.38 (0.19) 0.05Industrial -0.24 (0.07) 0.00Transport/shipping -0.59 (0.07) 0.00Offshore -0.58 (0.11) 0.00Debt to assets -1.20 (0.15) 0.00ln(Firm value) 0.10 (0.02) 0.00n 1055R2 0.22Average (Q) 1.47


institution. Table 7.3 reports our findings.

Table 7.3 Multivariate regression relating performance (Q) to ownership concentration, insiderholdings, largest owner identity, and controls


Constant 0.60 (0.35) 0.08Herfindahl index -0.86 (0.18) 0.00Primary insiders 2.46 (0.44) 0.00Squared (Primary insiders) -2.25 (0.52) 0.00Largest owner is state -0.43 (0.12) 0.00Largest owner is individual -0.16 (0.12) 0.18Largest owner is nonfinancial -0.23 (0.09) 0.01Largest owner is international -0.28 (0.11) 0.01Industrial -0.23 (0.07) 0.00Transport/shipping -0.57 (0.08) 0.00Offshore -0.59 (0.11) 0.00Debt to assets -1.20 (0.15) 0.00ln(Firm value) 0.10 (0.02) 0.00n 1057R2 0.22Average (Q) 1.47


The estimated sign of the indicator variable is negative and highly significant when the largestowner 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–financialnational 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 theestimated coefficient is negative, but insignificantly different from zero.

Table 7.4 Multivariate regression relating performance (Q) to ownership concentration, insiderholdings, largest owner being listed, and controls


Constant 0.52 (0.33) 0.12Herfindahl index -1.02 (0.18) 0.00Primary insiders 2.52 (0.42) 0.00Squared (Primary insiders) -2.31 (0.51) 0.00Largest owner is listed -0.10 (0.08) 0.23Industrial -0.25 (0.07) 0.00Transport/shipping -0.59 (0.07) 0.00Offshore -0.58 (0.11) 0.00Debt to assets -1.20 (0.15) 0.00ln(Firm value) 0.10 (0.02) 0.00n 1057R2 0.21Average (Q) 1.47


52 Owner type

7.3 Summary

This chapter finds that even if we account for the identity of the five basic owner types, performancestill decreases monotonically with ownership concentration and increases with insider holdings upto roughly 50% before declining. Beyond this insider/outsider dimension, our evidence shows thatthe owner’s identity matters in the sense that when individual investors hold large aggregate stakesor is the largest separate investor in a firm, the negative relationship between concentration andperformance 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 outsideowner identity, respectively. Still using the single–equation approach of cell 1 in table 2.1, thischapter 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 quadraticspecification), 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 thequestion of manager–board independence and focus only on the relationship between board size andperformance, which is the second board characteristic analyzed by existing finance–based researchon corporate governance.2

Table 8.1 Multivariate regression relating performance (Q) to ownership concentration, insiderownership, board size, and controls


Constant 0.63 (0.35) 0.07Herfindahl index -1.01 (0.20) 0.00Primary insiders 2.40 (0.45) 0.00Squared (Primary insiders) -2.15 (0.56) 0.00ln(Board size) -0.24 (0.08) 0.01Industrial -0.25 (0.07) 0.00Transport/shipping -0.58 (0.08) 0.00Offshore -0.62 (0.12) 0.00Debt to assets -1.19 (0.16) 0.00ln(Firm value) 0.11 (0.02) 0.00n 956R2 0.21Average (Q) 1.50


The univariate analysis in section 4.5 found that although most performance measures areinversely related to board size, the association was insignificant for Q, RoA5, and RoS. Using Q as

1We considered including owner identity variables in the base case model, such as the type of the largest owneror the aggregate stake per owner type. For the sake of simplicity, we decided to postpone the introduction of thisownership dimension until chapter 9.

2The relationship between performance and stock ownership by directors was analyzed in chapter 6 on insiderownership.

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 consistentwith the findings from other countries. Performance is negatively and significantly (p < 1%) relatedto board size in the pooled model. The earlier findings for concentration and insider holdingspersist.3

8.2 Security design

As discussed in section 2.1, observed price differences between voting (A) and non–voting (B) shareshave been explained by the extraction of private rents by voting shareholders. Because Q does notreflect the value of these private benefits, we would expect firms with dual-class shares to be lessworth 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 whichis voting has a positive, significant (p < 1%) coefficient. Like for board size, this result is moreconsistent with the theory than what we found using the univariate approach.4

Table 8.2 Multivariate regression relating performance (Q) to ownership concentration, insiderownership, security design, and controls


Constant -0.55 (0.49) 0.27Herfindahl index -1.08 (0.18) 0.00Primary insiders 2.52 (0.42) 0.00Squared (Primary insiders) -2.20 (0.51) 0.00Fraction voting shares 0.88 (0.31) 0.00Industrial -0.24 (0.07) 0.00Transport/shipping -0.57 (0.08) 0.00Offshore -0.56 (0.11) 0.00Debt to assets -1.18 (0.15) 0.00ln(Firm value) 0.11 (0.02) 0.00n 1042R2 0.22Average (Q) 1.48


8.3 Financial policy

Since it is common in the empirical corporate governance literature to use financial leverage as acontrol 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

3Table B.41 documents that the pooled GMM regression and the pooled OLS regression produce correspondingevidence. As expected, the year–by–year regressions are weaker. For instance, the estimated coefficient for board sizeis negative in six out of nine years, but is only significant in one of them (p < 1%).

4Table B.42 shows that the pooled GMM regression and the pooled OLS regression with fixed effects producecorresponding findings, although the p-value increases to 6% in the latter model. The estimated coefficient for boardsize 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 ofhigh debt and low retained earnings, firm value will be positively related to financial leverage anddividend payout.

Table 8.3 Multivariate regression relating performance (Q) to ownership concentration, insiderownership, financial policy, and controls


Constant 0.50 (0.34) 0.14Herfindahl index -1.02 (0.18) 0.00Primary insiders 2.61 (0.43) 0.00Squared (Primary insiders) -2.32 (0.52) 0.00Industrial -0.25 (0.07) 0.00Transport/shipping -0.59 (0.08) 0.00Offshore -0.59 (0.11) 0.00Debt to assets -1.23 (0.15) 0.00Dividends to earnings -0.06 (0.04) 0.14ln(Firm value) 0.10 (0.02) 0.00n 1028R2 0.22Average (Q) 1.48


We expand the base case model by these financial policy variables and obtain the estimatesreported in table 8.3. The evidence is inconsistent with an agency story, as the estimated sign isnegative in both cases. At conventional levels, the coefficient is significant (p < 1%) for leverageand insignificant for dividend payout.5

8.4 Summary

This chapter explored whether performance is systematically related to security design, board com-position, and financial policy. We find that when our base–case model (which includes ownershipconcentration, insider holdings, and controls) is alternately expanded by these three mechanisms,performance varies inversely with board size, the fraction of non–voting shares outstanding, finan-cial leverage, and dividend payout. The estimated coefficients are very significant for all thesemechanisms except dividends. We conclude once more that the inclusion of additional governancemechanisms does not affect how performance varies systematically with concentration and insiderholdings.

5We show in table B.43 that the pooled GMM regression and the pooled OLS regression with fixed effects areconsistent with the pooled OLS regression in table 8.3. The year–by–year regressions mostly produce negative signsfor 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 ina less restrictive context, we specify a multivariate model which captures the full set of mechanismsdiscussed in chapter 2. We estimate two versions of the model which reflect the two alternativeways of incorporating outside owner type discussed in chapter 7. One version uses type of thelargest owner as owner identity, and the other version uses aggregate holding per type. To covera wider range of performance measures, we start out with the standard Tobin’s Q in the first twosections 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 intables 9.1 (type of largest owner) and 9.2 (aggregate holding per type). The evidence in the twotables is very similar.1

Two general points are worth noticing before we start discussing the details. First, becausethis is a multivariate relationship, the empirical evidence should be interpreted accordingly. Forinstance, as both concentration, insider holdings, board size, and firm size are included in the fullmodel, the estimated coefficient of the Herfindahl index reflects the performance effect of changesin 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 themost comprehensive models. In particular, the very significant, negative link between concentrationand performance has consistently showed up all the way from the univariate analysis in chapter 4through the various partial multivariate models in chapters 5–8 to the full versions in tables 9.1and 9.2. This is also true for the inverse relationship between leverage and performance, for thepositive 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 4suggests a positive relationship regardless of insider levels. All the subsequent models, i.e., fromthe simplest regressions without controls graphed in figures 6.2 and 6.4 to the full multivariatemodels in this chapter use a non–linear specification. The non–linear function always comes outwith significant coefficients for the linear and the quadratic terms, and it reaches a maximumaround an insider stake of 50–60%. Similarly, board characteristics and security design have kepttheir estimated signs, but they have become more significant as we have built more comprehensive

1Appendix tables B.47 and B.48 show the results of applying the three alternative regression techniques to themodels in tables 9.1 and 9.2, respectively. As usual, the year–by–year OLS regressions mostly produce the sameestimated 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 thefraction of voting shares increases from 2% to 7% in the model using the identity of the largest owner, and internationalinvestors join individuals in the group of superior investors in the model using the aggregate stake per owner type. Asalways, the two final sample years come out with a very significant, positive coefficient, reflecting the strong growthin market values in these two years. In the regression with GMM–based standard errors, the non–linear term keepsits 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 withoutfixed effects.

9.1 Measuring performance with Tobin’s Q 57

Table 9.1 Multivariate regression relating performance (Q) to ownership concentration, insiderholdings, owner type (identity of largest owner), board characteristics, security design, financialpolicy, and controls


Constant 0.20 (0.61) 0.75Herfindahl index -1.00 (0.21) 0.00Primary insiders 2.04 (0.49) 0.00Squared (Primary insiders) -1.61 (0.59) 0.01Largest owner is state -0.42 (0.14) 0.00Largest owner is international -0.28 (0.12) 0.02Largest owner is individual -0.16 (0.13) 0.24Largest owner is nonfinancial -0.29 (0.10) 0.00ln(Board size) -0.22 (0.09) 0.01Fraction voting shares 0.89 (0.36) 0.01Debt to assets -1.62 (0.18) 0.00Dividend payout ratio -0.10 (0.05) 0.06Industrial -0.25 (0.08) 0.00Transport/shipping -0.55 (0.09) 0.00Offshore -0.65 (0.14) 0.00Investments over income -0.00 (0.01) 0.73ln(Firm value) 0.12 (0.02) 0.00n 868R2 0.28Average (Q) 1.52



Table 9.2 Multivariate regression relating performance (Q) to ownership concentration, insiderholdings, owner type (aggregate holding per type) , board characteristics, security design, financialpolicy, and controls


Constant -0.94 (0.69) 0.17Herfindahl index -0.68 (0.25) 0.01Primary insiders 1.64 (0.47) 0.00Squared (Primary insiders) -1.37 (0.58) 0.02Aggregate state holdings -0.43 (0.35) 0.21Aggregate international holdings 0.12 (0.25) 0.64Aggregate individual holdings 1.02 (0.30) 0.00Aggregate nonfinancial holdings -0.23 (0.25) 0.36ln(Board size) -0.19 (0.09) 0.03Fraction voting shares 1.14 (0.36) 0.00Debt to assets -1.54 (0.18) 0.00Dividend payout ratio -0.11 (0.05) 0.04Industrial -0.19 (0.08) 0.01Transport/shipping -0.46 (0.09) 0.00Offshore -0.57 (0.14) 0.00Investments over income -0.00 (0.01) 0.98ln(Firm value) 0.14 (0.02) 0.00n 868R2 0.29Average (Q) 1.52


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 modelspecification we choose in cell 1 of table 2.1. Moreover, the finding that the significance of anymechanism is quite independent of what other mechanisms are included in the regression equationsuggests that each mechanism has a separate, individual link to performance. It is not the resultof a spurious effect driven by other mechanisms or controls. Finally, the fact that the p–valuesare robust to the introduction of additional variables indicates that the dominating pattern in oursample is not that the mechanisms are used as substitutes and complements. We will analyze thisissue of mechanism interaction more closely in the next chapter.

Moving on from the comparison of successive models to the interpretation of the estimates ofthe 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 per-formance is less favorable when the largest owner is the state, an international investor, anonfinancial 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 differencesacross 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 predictionsfrom chapter 2. According to this theory, the expected performance effect of ownership concen-tration is unclear, as it reflects the net impact of benefits (valuable monitoring, higher takeoverpremia, less free-riding) and costs (reduced market liquidity, lower diversification benefits, increasedmajority–minority conflicts, reduced management initiative, incompetent monitoring). Our findingthat performance and concentration are inversely related over the entire concentration range isan indication that monitoring by powerful owners with strong incentives either does not occur ordestroys 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 reducecorporate value. It also questions the agency idea that large owners are competent end hence bene-ficial for all stockholders, suggesting that firms do better if management is faced with small ownerswho vote with their feet rather than powerful ones who interfere via the stockholder meeting orthrough informal communication with management. The evidence differs from the mostly positiveor neutral effects reported in the literature, but is in line with recent evidence from Germany, where

2Every relationship listed below has a p-value of 3% or less.3The evidence on intercorporate investments is in appendix B.6.


Lehmann and Weigand (2000) find a negative relation between ownership concentration and RoAfor 361 listed and unlisted firms from 1991 to 1996.

The second result that performance first increases and then decreases with insider holdingsdemonstrates that although concentration in general destroys value, the effect is driven by themajority–minority conflict and the various costs of outside rather than inside concentration. Insideconcentration benefits all stockholders unless the insiders become so powerful that their entrench-ment hurts the remaining owners in the same way that large outside stockholders may do. Althoughthis is consistent with agency theory and the existence of diversification and liquidity costs, it putsthe focus on incentives for managers and directors rather than the power and monitoring activityof 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–performancerelation occurs around 60%, that the average insider fraction in the sample firms is 8%, and that3% 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, al-though there are decreasing returns to insider holdings throughout the whole range, the marginalreturn is almost always positive in our sample.

The finding on owner identity shows that individual (personal) investors are beneficial, support-ing the idea that because such investors communicate directly with the firm they own rather thanindirectly through one or more layers of intermediate agents, they are better owners. The resultthat financial owners are beneficial as well may be consistent with the efficient–monitoring argu-ment of Pound (1988) that financial owners are more professional than others. It may also reflectthe fact that because regulation prevents financials from owning more than 10% in a firm, they arenever large owners, which we already know is negatively associated with performance. Moreover, itmay 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 investmentdecisions on the funds’ performance record, competition forces financials to exert their ownershiprights with care. Finally, the inverse relationship between performance and large holdings by listedowners may be because the benefit of strategic ownership among large firms is dominated by theinherent cost of multiple–agent contexts.

The negative link between board size and performance supports the idea that small groupscommunicate better than large, and that the efficiency loss sets in at a rather small group size. Itfits well with empirical findings in both small and large firms in other countries. The hypothesis thatnon–voting shares enable some shareholders to extract wealth from other shareholders is consistentwith 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 notionthat debt disciplines management. If we, like most of the empirical governance literature, insteadconsider leverage a control variable reflecting the value of the interest tax shield, the observedpattern cannot be rationalized by such a theory either.

The significant industry effects is difficult to interpret because we do not know whether thecompetition proxy is better described as a governance mechanism or a governance–independent in-dustry effect.4 Anyway, the evidence does reflect a source of industry–wide performance differenceswhich are not picked up by the other variables in the regressions, and which would otherwise haveended up in the error term. We choose not to conclude that the observed effect is driven by thedisciplining force of product market competition.

We interpret the very consistent, positive association between firm size and performance as agovernance–independent source of value creation, possibly due to factors like market power and the

4See 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 verysystematically with economic performance, we reject the hypothesis that the equilibrium conditionprevails. Performance is inferior because most firms operate with governance mechanisms whichare not value–maximizing.

The adjusted R2, which expresses the fraction of performance variation which is explainedby the independent variables in the regression, has increased gradually as we have built morecomprehensive models. For instance, the typical R2 is barely 1% in the univariate regressions inchapter 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, andeven if many of the separate mechanisms differ significantly from zero, their relative importance forperformance is not identical. To get a feeling for order of magnitude, we may compare the impacton Q of changing key governance mechanisms by a standardized unit, such as one percent, onepercentage point or one standard deviation. Although we might use the two models in tables 9.1and 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 concentrationif the index changes by a percentage point, we instead use the holding of the largest owner as theconcentration proxy, keeping all the other model components from table 9.2. The regression resultsare shown in table 9.3, which also include the mean sample values of the independent variables inthe rightmost column.

Table 9.3 Multivariate regression relating performance (Q) to ownership concentration, insiderholdings, owner type (aggregate holding per type) , board characteristics, security design, financialpolicy, and controls

Dependent variable: Qcoeff (stdev) pvalue mean

Constant -1.04 (0.69) 0.13Largest owner -0.63 (0.19) 0.00 0.28Primary insiders 1.64 (0.47) 0.00 0.08Squared (Primary insiders) -1.34 (0.58) 0.02 0.04Aggregate state holdings -0.37 (0.34) 0.29 0.06Aggregate international holdings 0.15 (0.25) 0.54 0.21Aggregate individual holdings 1.04 (0.30) 0.00 0.18Aggregate nonfinancial holdings -0.17 (0.26) 0.52 0.38ln(Board size) -0.19 (0.09) 0.03 1.83Fraction voting shares 1.19 (0.36) 0.00 0.97Debt to assets -1.51 (0.18) 0.00 0.59Dividend payout ratio -0.10 (0.05) 0.05 0.27Industrial -0.20 (0.08) 0.01 0.37Transport/shipping -0.47 (0.09) 0.00 0.22Offshore -0.56 (0.14) 0.00 0.06Investments over income -0.00 (0.01) 0.98 0.59ln(Firm value) 0.14 (0.02) 0.00 20.06n 868R2 0.29Average (Q) 1.52

Pooled OLS regression using data for all listed firms on the OSE in the period 1989 to 1997. Variable definitions arein Appendix A.2. In regressions using firm size across years the nominal values are adjusted to the 1997 general pricelevel.

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


membership a control variable rather than a governance mechanism. We exclude leverage from thediscussion because the significant, negative sign is inconsistent with both governance theory and thetax 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 thatQ decreases by 0.62 units whenconcentration increases with one unit. The sensitivity is roughly twice as large to a correspondingchange 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 twonon-linear relationships in the regression equation, however, we cannot simply start out with themean values of all the independent variables from table 9.3. Since we want to study the averagefirm in our sample, we should use the square of the mean insider stake (0.082, i.e., 0.01) ratherthan the average of the squared stakes from the table (0.04). Similarly, whereas the table showsthe average of the ln(board size) variable, we should use the ln of average board size (1.89 insteadof 1.83). Because these two figures differ from the corresponding sample averages, the estimatedQ for the average firm is not the sample average of Q (1.520), but the expected Q of a firm whereevery 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 ownerdecides to decrease the stake by ten percentage points to 18%, our model predicts that Q willincrease from 1.558 to 1.620. This means the market value of the firm grows by 0.4% for everypercentage point increase in concentration. Since the average firm value in the sample is NOK2 bill., this corresponds to a value increase of NOK 8 mill. The value impact would be roughlydoubled (0.8% or 16 mill.) if there were a percentage point increase in either aggregate holdingsby individuals or the fraction of voting shares outstanding.5

Since Q is a quadratic function of insider holdings, we must consider both the positive linearterm and the negative quadratic term, and the non–linear relationship makes the sensitivity of Qdependent on the level of insider holdings. We once more consider a firm where all the independentvariables (except the non–linear terms) are at their sample averages from table 9.3, which meansinsiders own 8% of the firm’s equity. If this stake is increased by one percentage point to 9%, Qgrows 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 19mill. This difference reflects the effect of the concave relationship between performance and insiderholdings.

Because Q is specified as a logarithmic function of board size, the estimated coefficient expressesthe absolute change in Q per unit relative change in board size. Thus, if board size decreases withone 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 onepercent, firm value would increase by 2% or by NOK 40 mill.

Overall, this analysis shows that for the average firm, the ownership characteristic with thestrongest impact on firm value is insider holdings. Our model predicts that an increase in insiderownership by one percentage point increases firm value by 1%, which is roughly NOK 20 mill. Thevalue impact of a corresponding increase in the holdings by individual investors is 0.8%, and onepercentage point reduction in ownership concentration increases firm value by 0.4%. Increasing thefraction of voting shares by one percentage point drives up firm value by 0.8%, and the average firmwill become about 2% more worth if board size is reduced by one member. Since equity constitutesabout 40% of total assets in the average sample firm, the relative impact on equity value will be

5These absolute changes in Q are independent of the level of Q. The relative change in Q and the absolute changein 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. Ifthe value of debt is unaffected, the relative change in equity value will be 2.5 times the relativechange 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 onassets or return on stock as performance measure. The appendix documents that regardless ofwhether we use RoA5 or RoS5, only a few relationships are significant. For instance, if the identityof the largest owner is the investor type proxy and RoA5 is the performance measure, we get thesame, significant results as with Q regarding concentration, insiders, leverage, and industry, butall the other variables become insignificant. If the performance measure is RoS5, some coefficientschange sign (like a negative linear term for insiders), every ownership structure variable becomesinsignificant, 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 isby far the most commonly used proxy in the recent literature. Consecutive observations ofRoA5 andRoS5 are constructed from overlapping observations for four of the five years, potentially causingthe error terms to be autocorrelated. Also, because RoA and RoA5 are constructed exclusivelyfrom book values, they may be far from market returns and may also be influenced by managementdiscretion.

9.4 Summary

Estimating our most comprehensive multivariate regression equation, we find that the relationshipbetween ownership concentration and economic performance as measured by Q is inverse andvery 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 fiduciaryduty, and that powerful owners are beneficial because they discipline management towards makingvalue–maximizing decisions. However, there is support for agency–based ideas in our evidence thatperformance correlates positively with insider holdings at almost any level, that direct ownershipis more value–creating than indirect, and that both non–voting shares and larger boards reducemarket value. Even though we find the relationship between insider holdings and performance tobe quadratic, there is almost never a firm in our sample where the marginal value effect of a higherinsider stake is negative. This suggests that the costs of higher insider stakes very seldom outweighthe benefits. We also find that the choice of performance measure is important, as very few of theserelationships 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 asmeasured by Q is insider holdings, where a one percentage point higher stake increases firm value by1% or by roughly NOK 20 mill. A corresponding increase in direct as opposed to indirect ownershiphas a 0.8% effect, and one percentage point lower ownership concentration increases firm value by0.4%. Stepping up the fraction of voting shares by one percentage point drives up firm value by0.8%, 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, therelative impact on equity value will be roughly 2.5 times larger than these relative changes in firmvalue.


Most of the significant relationships in the full multivariate model have survived all the way fromthe 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. Italso suggests that each governance mechanism has a separate, individual link to performance whichis 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 alsoignored potential interactions between them.1 In order to address dependence and causality inan explicit way, we will now use simultaneous equation econometrics, which has recently beenapplied 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 coefficientestimates, such as a theoretical argument stating that board composition and insider ownership areindependent 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 informalmanner, often assuming endogeneity between two governance mechanisms, only, or between onemechanism and the performance measure.

Using ad–hoc arguments which resemble the ones found in the literature, we will restrict theequation system in several alternative ways. It turns out that that empirical conclusions are verysensitive to the choice of restrictions. We conclude that due to the partial, incomplete nature ofcorporate governance theory, simultaneous equations modeling is a questionable tool for analyzingendogeneity and causality.

This chapter ignores the link to performance and focuses exclusively on interrelationships be-tween the mechanisms. Section 10.1 presents single–equation estimations which model each gov-ernance mechanism at a time as a function of the other mechanisms and controls. We switch toa simultaneous equations framework by presenting the general structure of such models in section10.2. Specific examples from the governance literature are presented in section 10.3, and we use oursample data to analyze the endogeneous nature of ownership concentration and insider holdings insection 10.4.

10.1 The mechanisms one by one

This section involves a series of single–equation multiple regression models. In any model, thedependent variable is one mechanism, and the independent variables are other mechanisms andcontrols. We start by using the aggregate holdings per investor type as owner type proxy. Subse-quently, 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 summarizedin table 10.1. The first column lists the independent (exogeneous) variables, and each of theother columns holds a regression. Column titles specify the dependent (endogeneous) variable inquestion. For instance, the second column shows the result of a regression where concentrationis the dependent variable and the other governance mechanisms and controls are the independentvariables. The table reports the estimated signs and the significance levels of the coefficients of theindependent variables. We concentrate on evidence which is significant at the 1% level, which isdenoted ∗∗∗ in the table.

1Although 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 notinvalidate the OLS or GMM estimates used so far. As discussed in section 5.2, multicollinearity will show up asincreased 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

Dependent variables

Independent variablesHerfindahlindex

Primaryinsiders

Aggregatestateholdings

Aggregateinternationalholdings

Aggregateindividualholdings

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 0.25 0.16 0.21 0.12 0.34

Dependent variables

Independent variables

Aggregatenonfinancialholdings

Aggregatefinancialholdings

Fractionvotingshares

ln(Board

size)

Debttoassets

Dividendstoearnings

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 0.23 0.17 0.14 0.17 0.07 0.01

The table summarizes the signs and significance levels of eleven multivariate OLS regressions. Each column is aregression, 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 therelationship is significant at the 5%, 2.5% and 1% level, respectively. A sign without an asterisk means the relation isnot significant at the 5% level. An empty cell means that the variable does not enter the regression as an independentvariable. The detailed regressions are reported in appendix B.7. Variable definitions are in Appendix A.2. Data forfirms 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 ratherthan substitute relationship is supported by the corresponding result from the insider regressionin 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 bothconcentration and performance,2 concentration and performance still move inversely. This patternsuggests that the least favorable combination of the three mechanisms is high concentration, lowinsider holdings, and a low fraction of voting shares.

High aggregate stakes by state, international, and nonfinancial investors are associated withhigh concentration. Inconsistent with the agency hypothesis that concentration and dividendsare substitute mechanisms, firms with high concentration (and a correspondingly limited need tocontrol 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 ofthe firm’s equity. This is hardly surprising, since personal equity ownership by officers and directorsare included in both variables. However, this positive relationship stays significant at the 1% levelacross all our insider categories (i.e, all, officers, directors, and primary insiders), and regardless ofwhether we use aggregate holdings per type or the identity of the largest owner as investor identityproxy. Thus, the finding that individual investors are large in firms with high insider stakes is arobust one.

Insider ownership relates positively to financial leverage. Just like the way concentration ispositively associated with dividends, this is inconsistent with the agency prediction that leverageand insider holdings are substitute disciplining mechanisms. An alternative explanation which fitsthe data well is lost diversification benefits. When leverage is high, equity value is low, which meansa high ownership fraction can be acquired with a moderate investment. The diversification loss ofa 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 explanatoryvariables.3 The table shows that individuals as a group own higher stakes in firms with lowconcentration and high insider holdings, and that the opposite is true for state and nonfinancialowners. Since we know that performance is related negatively to concentration and positively toinsider holdings, this evidence partly explains why performance is better in firms where individualsrather than state or nonfinancial investors hold large aggregate stakes. The former type of firmshave a more value-creating ownership structure than the other, i.e., lower concentration and higherinsider stakes. Notice also that financials tend to end up in firms where both concentration andinsiders holdings are low. Both findings may be driven by the 10% cap on a financial investor’sequity 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,

2Insider ownership is only positively related to performance for holdings up to roughly 60%3This 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).4A 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 attractingforeign capital was more of an issue for the larger firms, they would tend do issue relatively more B shares than others.


like for insider holdings and individual owners, this evidence once more tells us that concentrationper se has a negative effect on performance which is not offset by other mechanisms with a positiveimpact.

Board size grows with firm size. This may reflect the attitude that large firms are more compli-cated to manage and monitor than small ones, and that this setting requires a more heterogeneousand hence larger board. Given our earlier finding that board size and performance are inversely re-lated (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 investorshold 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 showsthe corresponding results using the identity of the largest owner as type proxy.5 The estimates intables 10.2 and 10.1 are quite consistent, although the coefficient of determination and the p-valuesof 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

Dependent variables

Independent variablesHerfindahlindex

Primaryinsiders

Fractionvotingshares

ln(Board

size)

Debttoassets

Dividendstoearnings

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 0.09 0.16 0.14 0.17 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 theestimated relation is shown. Statistical significance is indicated with ∗, ∗∗, and ∗∗∗, which means the relationship issignificant at the 5%, 2.5% and 1% level, respectively. A sign without an asterisk means the relation is not significantat the 5% level. An empty cell means that the variable does not enter the regression as an independent variable. Thedetailed regressions are reported in appendix B.7. Variable definitions are in Appendix A.2. Data for firms listedon the Oslo Stock Exchange, 1989-1997.

To complete this single–equation approach, we consider the determinants of the largest owner’sidentity, which could not be addressed by OLS in table 10.2. Table 10.3 shows the output from a

5As 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 largestowner is a certain type. When interpreting the results, note that because a financial owner isthe base case, the coefficients show the increased probability of observing the particular type fora marginal, partial change of the independent variable in question. For example, the coefficientestimate for primary insiders in the third regression (largest owner is individual) is 8.27. Thismeans that if insider holdings increase by one percentage point, the probability that the largestowner 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 comparativelylarge in state–dominated firms and small in firms dominated by individuals. Both internationaland state owners tend to be the largest investor in large firms. Overall, this evidence is consistentwith 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 twovariables 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 concentrationmodel (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 relationshipbetween state holdings and board size in table 10.1 is such a case, as the positive coefficient is onlysignificant when board size is the independent variable. Finally, remember that the single–equationapproach in this section can only uncover covariation between variables; not the causation. Thenext 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 weaccount for the simultaneous nature of the governance mechanisms. The equilibrium conditionargues that the mechanisms are adjusted relative to each other until they produce an optimal setfor a given firm. This logic leads us to consider a system of equations where every relationship issupposed to hold simultaneously.

To implement this idea, we use the econometric framework known as simultaneous equationsestimation. 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 corporategovernance. The need for such a theory is driven by the so–called identification problem, whichbecomes evident in the subsequent description of simultaneous equations econometrics. As we onlygive 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 andMacKinnon (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 (ex-ogeneous) variables, E is a set of (mean zero) error terms, and Γ and B are parameters to beestimated. If there are M endogeneous and K exogeneous variables, Γ will be a (M ×M) matrixand 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 Bmatrices) which are all consistent with the same set of observations (i.e, Y and X). This is the


Table 10.3 Estimating the determinants of the largest owner type using a multinomial logit modelDependent variables Independent variables coeff stdev pvalueLargest owner is state Herfindahl index 10.15 1.94 0.00

Primary insiders -4.54 3.87 0.24ln(Board size) 1.66 0.58 0.00Fraction voting shares -3.01 2.27 0.18Debt to assets -3.09 1.28 0.02Dividend payout ratio 0.37 0.39 0.34Industrial -0.25 0.41 0.54Transport/shipping 0.02 0.80 0.98Investments over income -0.49 0.46 0.29ln(Firm value) 0.45 0.15 0.00Stock volatility 6.70 4.64 0.15constant -9.11 4.41 0.04

Largest owner is international Herfindahl index 7.13 1.91 0.00Primary insiders 4.12 1.85 0.03ln(Board size) 0.03 0.47 0.94Fraction voting shares 0.27 2.46 0.91Debt to assets -2.17 1.06 0.04Dividend payout ratio -0.16 0.45 0.72Industrial -1.23 0.39 0.00Transport/shipping 0.41 0.61 0.50Investments over income 0.02 0.03 0.56ln(Firm value) 0.34 0.14 0.01Stock volatility 6.84 4.53 0.13constant -6.24 4.36 0.15

Largest owner is individual Herfindahl index 4.42 2.14 0.04Primary insiders 8.27 1.80 0.00ln(Board size) -1.31 0.50 0.01Fraction voting shares -3.82 2.21 0.09Debt to assets -4.94 1.12 0.00Dividend payout ratio 0.16 0.39 0.68Industrial -2.04 0.49 0.00Transport/shipping 0.61 0.64 0.35Investments over income -0.15 0.16 0.36ln(Firm value) 0.21 0.16 0.20Stock volatility -2.39 6.73 0.72constant 4.40 4.61 0.34

Largest owner is nonfinancial Herfindahl index 7.28 1.84 0.00Primary insiders 4.37 1.76 0.01ln(Board size) -0.50 0.40 0.21Fraction voting shares -3.97 1.93 0.04Debt to assets -2.92 0.93 0.00Dividend payout ratio 0.18 0.35 0.61Industrial -0.61 0.31 0.05Transport/shipping 1.54 0.52 0.00Investments over income 0.02 0.03 0.61ln(Firm value) 0.18 0.12 0.14Stock volatility 3.82 4.42 0.39constant 3.68 3.67 0.32

n = 815, Pseudo R2 = 0.1658

The table reports the results from estimating four multinomial logit models, where the dependent variable is theprobability that a certain investor type is the largest owner. The base case is that the largest owner is a financial. Foreach of the four owner type regressions, the coefficients express the increased probability of observing this particulartype for a marginal, partial change of the independent variable in question. Data for firms listed on the Oslo StockExchange, 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 thesystem. 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? Unlessadditional information is brought into the system of equations, it is impossible to distinguish thesethree cases from each other. The typical solution to the identification problem in (10.1) is toexclude some variables from some of the equations, or to introduce additional exogeneous variablesas so–called instruments. To help in identification, these instruments should only affect one or afew of the endogeneous variables, but not all. To justify such restrictions, however, one cannot relyon findings from single-equation estimations, such as the evidence in tables 10.1–10.3. Rather, therestrictions 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 simultaneoussystem 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 firstconsider a simple simultaneous system used by Loderer and Martin (1997).6 The paper analyzes theinteraction between corporate value and insider ownership in a sample of firms which are involvedin mergers. Loderer and Martin (1997) specify the following system of equations:

Qi = γ12OFFDIRi + β11LSALESi + β12STKFINi + εi1 (10.2)

OFFDIRi = γ21Qi + β21LSALESi + β23STDDEVi + V ARi + εi2 (10.3)

where, for firm i, Qi is an estimate of Tobin’s Q, OFFDIRi is the holdings by officers and directors,LSALESi is the size of the firm as measured by sales, STKFINi reflects the financing of themerger(stock vs. cash), and STDDEVi and V ARi is the standard deviation and variance of theunderlying stock return, respectively.

The endogeneous variables in this system are Q and OFFDIR, i.e., performance and insiderholdings are assumed to be interrelated. The other variables are exogeneous. We now determinethe coefficient matrices Γ and B, starting out with the coefficient matrix Γ of the endogeneousvariables Qi and OFFDIRi:

Γ =

[γ11 γ12

γ21 γ22

]By specifying the equation system in (10.2) and (10.3), Loderer and Martin (1997) have restrictedthis matrix into:

Γ =

[−1 γ12

γ21 −1

]This is a normalization of the endogeneous variables, which is standard, but which makes it harderto interpret the coefficients.

Consider next the coefficient matrix B for the four exogeneous variables LSALESi, STKFINi,STDDEVi and V ARi. With two equations and four exogeneous variables, B is generally writtenas

B =

[β11 β12 β13 β14

β21 β22 β23 β24

]6This model is chosen both for pedagogical simplicity and because some of the findings are particularly relevant.


In the system specified in (10.2) and (10.3), B has been restricted into

B =

[β11 β12 0 0β21 0 β23 β24

]

Thus, three coefficients in this system have been restricted to equal zero. This approach exemplifiesthe standard solution to the identification problem, which is to let an exogeneous variable affectonly one of the endogeneous ones.7 The theoretical rationale for such restrictions should specifywhy an exogeneous variable drives the endogeneous variable in question. In addition, and equallyimportant, 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 infor-mation, but must be based on extra-sample information, preferably the economic theory for theobject of study.

The sample in the firms in the Loderer and Martin (1997) example consists of firms involved inmergers, and the variables which may be interrelated (endogeneous) are firm performance (Q)andinsider ownership (OFFDIR). The identification assumptions are that the medium of exchange inthe merger (STKFIN) only affects performance, not insider ownership, and that stock variability,measured by both the standard deviation (STDEV ) and the variance of the stock (V AR), drivesperformance, but not insider ownership.

To exemplify the typical discussion leading up to such exclusion restrictions, consider the as-sumption that stock variability affects insider ownership, but not performance. The insider argu-ment is the Demsetz and Lehn (1985) hypothesis that increased variability in the firm’s environmentcreates stronger incentives for outsiders to monitor closely because management quality mattersmore for economic performance in risky environments. What is much harder to argue, and whichis not touched upon in the paper, is why the control potential is not reflected in the value of thefirm and thus in Q. This illustrates the general problem that it is often easier to argue why anexogeneous variable drives one endogeneous variable than to argue why it is irrelevant for all theothers. Both rationales are needed in simultaneous equations estimation, and both must come fromextra-sample information.

There is one particularly notable finding in Loderer and Martin (1997). Whereas governancetheory 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 significantlyin the performance equation in 10.2, but performance has a positive, significant coefficient in theinsider 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 byQ), and investment. The exogeneous variables are market value of firm’s common equity, earningsvolatility, asset liquidity, industry, financial leverage, and asset size.

7This 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 endogeneouslydetermines investments, but there is still just one endogeneous governance mechanism (insiderownership). Cho argues that since the key firm decision involves investments, this variable shouldenter the causal relationships endogeneously. The firm’s industry is a control variable, as it enters allthree equations. One example of an identification restriction is that earnings volatility is assumed toonly affect insider ownership and investment, not corporate value, which resembles the assumptionof Loderer and Martin (1997). Another example is that asset liquidity is assumed to be irrelevant forfirm value, only. Like in Loderer and Martin (1997), these identification restrictions are rationalizedrather informally. An important empirical result from the Cho (1998) paper is added support forthe Loderer and Martin (1997) finding that performance drives insider ownership, but not viceversa.

Agrawal and Knoeber (1996) is a more comprehensive example, where the system includesperformance and six governance mechanisms as endogeneous variables. Examples of instrumentsused to identify the system are the standard deviation of stock return, firm size, CEO tenure, andacquisition probability in the industry. The use of the instruments are mostly justified by arguingwhy some instruments are relevant to include in a given equation, with less emphasis on why theyare irrelevant in the remaining equations. Compared to their OLS model, they find less significantresults in the system. What is not pointed out in the paper is that their results are also consistentwith their instruments being weak or misspecified. As we will show later, this is a potentially largeproblem.

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. Ourapproach resembles existing research in the sense that several instruments lack a convincing the-oretical foundation. We differ from earlier papers in the sense that we test out several sets ofinstruments rather than just one.

The theoretical discussion of our problem in chapter 2 argued that there may in principlebe a relationship between any combination of governance mechanisms. Because the theory israther silent on simultaneity, we cannot validly restrict the coefficients in a comprehensive equationsystem. Therefore, we choose to endogenize ownership concentration and insider holdings, whichhave received the widest attention in the literature. Also, theoretical arguments suggest they havea complementary role, but there is little theoretical backing for how they relate to the remainingmechanisms. Consequently, this setup is quite well suited for illustrating how conclusions change aswe alter the restrictions by using three alternative sets of instruments. The problems we encounterusing this limited approach will show rather well what would happen if we tried to endogenize moremechanisms than these two.

Agency theory argues that ownership concentration and insider holdings are the key governancemechanisms, that they play different roles (external monitoring vs. internal incentives), and thatthey represent alternative means for reducing agency costs. Since the single-equation estimates ofthe previous section cannot uncover causality, we estimate a system which allows ownership concen-tration and insider holdings to be mutually dependent and simultaneously determined. This meansthe 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 toaffect insider ownership, but not concentration. The argument is that the larger the board, thehigher the number of potential insiders and hence the higher the insider stake. Stock volatility isassumed to affect concentration, but not insider ownership. One argument is the Demsetz and Lehn


(1985) idea that higher variability in the economic environment creates a larger value potential ofhaving 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. Con-centration and insider holdings are endogeneous variables. Stock volatility and board size are usedas instrumentsPanel A. Regression results

Dep.variable Indep.variable coeff (stdev) pvalueHerfindahl index Primary insiders 0.87 (0.68) 0.20

Aggregate state holdings 0.61 (0.10) 0.00Aggregate international holdings 0.20 (0.06) 0.00Aggregate individual holdings -0.34 (0.30) 0.26Aggregate nonfinancial holdings 0.28 (0.05) 0.00Fraction voting shares 0.27 (0.08) 0.00Debt to assets -0.04 (0.09) 0.63Dividends to earnings 0.01 (0.01) 0.26Industrial -0.00 (0.02) 0.94Transport/shipping -0.01 (0.03) 0.70Offshore 0.04 (0.05) 0.35Investments over income -0.00 (0.00) 0.35ln(Firm value) -0.02 (0.01) 0.18Stock volatility -0.01 (0.04) 0.80constant 0.03 (0.28) 0.91

Primary insiders Herfindahl index 1.50 (1.06) 0.16Aggregate state holdings -0.89 (0.59) 0.13Aggregate international holdings -0.31 (0.27) 0.24Aggregate individual holdings 0.38 (0.09) 0.00Aggregate nonfinancial holdings -0.42 (0.32) 0.19Fraction voting shares -0.38 (0.24) 0.11Debt to assets 0.03 (0.08) 0.73Dividends to earnings -0.02 (0.02) 0.37Industrial 0.01 (0.03) 0.75Transport/shipping 0.03 (0.06) 0.57Offshore -0.05 (0.04) 0.21Investments over income 0.00 (0.00) 0.41ln(Firm value) 0.02 (0.01) 0.05ln(Board size) 0.01 (0.03) 0.80constant 0.04 (0.25) 0.87

Panel B. Regression diagnostics

Equation n Parms RMSE R2 χ2 p1 741 14 0.17 -0.78 103.75 0.002 741 14 0.22 -0.56 79.71 0.00

The tables shows 3SLS estimates of a system of simultanous equations. Panel A report system estimates. Theleftmost column is the dependent variable in that particular equation, which is a function of the variables listed inthe next column. Equations are separated by a line. Panel B holds diagnostics. For each equation the diagnosticsinclude 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 estimatesare performed with Stata’s reg3 3SLS routine. Variable definitions are in Appendix A.2. Data for firms listed onthe Oslo Stock Exchange, 1989-1997. Appendix table B.82 estimates a similar system which only uses controls asadditional 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

8We use 3SLS with Stata as the estimation engine. As a practical matter 3SLS is chosen rather than 2SLS. Thetwo 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


the significant determinants (p < 5%) of ownership concentration are the fraction of voting sharesoutstanding 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 aresubstitutes or complements. Referring back to the equation–by–equation estimates in table 10.1 ofsection 10.1, we find that the sign of the estimated coefficients are often the same, but that mostsystem estimates are less significant. The concentration equation of table 10.4 has four coefficientswhich are significant at the 5% level, whereas there are eight in the single-equation model oftable 10.1. The insider equation in table 10.4 has two significant coefficients, and table 10.1 haseight. Despite this discrepancy, the signs of all significant coefficients are consistent across the twomodels. This reduced significance in simultaneous vs. single–equation models was also observed byAgrawal and Knoeber (1996).

The loss of significance may have various causes, but the primary suspect is weak or misspec-ified instruments. One way of analyzing the seriousness of specification errors is by introducingalternative 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 owner-ship concentration, but not insider holdings. This restriction may be misplaced, given our earlierargument that owners who put a considerable part of their wealth in one firm carry more unsys-tematic risk than others. This point also applies to inside owners in general and to managers inparticular, since they also receive labor income from the firm’s cash flow. The more volatile thiscash flow and the more of their wealth invested in the firm’s equity, the higher the uncertainty inboth their labour income and their financial portfolio. Whereas this is an argument that insiderholdings should be low in high–volatility firms, the opposite conclusion follows from the idea thatthe 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. Althoughthe net effect of the diversification and the incentive arguments is unclear, it follows that insiderholdings and stock volatility may not be independent. In fact, as discussed in section 10.3, Lodererand Martin (1997) assumed that volatility drives insider ownership.

Hence, we drop stock volatility as an instrument and instead use it as a regular exogeneousvariable in both equations. To identify the insider equation, we continue using board size asthe instrument. The ownership concentration equation is identified by using the liquidity of thefirm’s equity as an instrument, which we operationalize as stock turnover.9 Thus, turnover isincluded in the concentration equation, but not in the insider equation. The rationale is based onthe assumption that the investment horizon (holding period) is longer for larger owners than forothers. Market microstructure theory argues that there is an extra cost to selling large blocks dueto price pressure. Large owners may hesitate more than others before liquidating a position. Thereis 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 fortrading 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 muchsmaller than the largest outsider stake.

Table 10.5 shows what happens when we maintain the board size instrument for insider hold-ings, 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).9The fraction of a firm’s equity which is traded during one year.


Table 10.5 Interactions between governance mechanisms modeled as a system of equations. Con-centration and insider holdings are endogeneous variables. Stock turnover and board size are usedas instrumentsPanel A. Regression results


Aggregate state holdings 0.57 (0.12) 0.00Aggregate international holdings 0.18 (0.06) 0.01Aggregate individual holdings -0.41 (0.32) 0.20Aggregate nonfinancial holdings 0.23 (0.06) 0.00Fraction voting shares 0.30 (0.09) 0.00Debt to assets -0.06 (0.09) 0.49Dividends to earnings 0.01 (0.01) 0.49Industrial 0.00 (0.03) 0.91Transport/shipping -0.01 (0.03) 0.84Offshore 0.05 (0.05) 0.29Investments over income -0.00 (0.00) 0.37ln(Firm value) -0.02 (0.01) 0.20Stock volatility -0.01 (0.05) 0.79Stock turnover -0.03 (0.01) 0.03constant 0.07 (0.30) 0.82

Primary insiders Herfindahl index 0.28 (0.22) 0.20Aggregate state holdings -0.24 (0.14) 0.08Aggregate international holdings -0.03 (0.07) 0.66Aggregate individual holdings 0.41 (0.06) 0.00Aggregate nonfinancial holdings -0.07 (0.08) 0.37Fraction voting shares -0.12 (0.08) 0.16Debt to assets 0.10 (0.04) 0.01Dividends to earnings 0.00 (0.01) 0.89Industrial -0.02 (0.02) 0.28Transport/shipping -0.03 (0.02) 0.21Offshore -0.06 (0.03) 0.03Investments over income -0.00 (0.00) 0.99ln(Firm value) 0.02 (0.01) 0.01ln(Board size) -0.02 (0.02) 0.27Stock volatility 0.04 (0.03) 0.13constant -0.23 (0.18) 0.20


Equation n Parms RMSE R2 χ2 p1 741 15 0.19 -1.16 100.48 0.002 741 15 0.16 0.17 151.07 0.00



table 10.4, the new model produces very similar results. The significant coefficients in table 10.4keep their signs as well as their significance levels in table 10.5. There is also still no indication thatthe two endogeneous mechanisms are related. The only difference is that debt to assets becomessignificant in the revised insider equation.

This comparison may suggest that the model is insensitive to whether we use stock volatility orstock 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 thefinding that their p–values are high in both models. Also, since we have no underlying theory, it isnot obvious that board size is a good instrument for the insider equation. Our third specificationuses 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 listedfirms. 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 intercorporateinvestments and concentration are positively related. On the other hand, there are no obviousreasons why intercorporate investments are systematically related to insider holdings. The newinsider instrument is debt, using the argument that the higher the debt, the less it takes to buy agiven fraction of equity. In this case, however, we cannot convincingly argue why this should notapply to outside concentration as well. One possibility is that the lack of diversification by insidersmakes 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 theprevious 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 isunrelated to endogeneous mechanism B (which it should in order to be a good instrument), thereis at least a close link to mechanism A. Second, the concentration equation shows that the positiveassociation between concentration and insiders now becomes significant at the 4% level. Third, andmore dramatically, the association between the two mechanisms is suddenly negative in the insiderequation, with a p–value of 10%.

Taken together, the new instruments have strenghtened the case for a relationship between con-centration and insider holdings. However, the negative relation in the insider equation in table 10.6is absent in the other two models, and this negative coefficient is not significantly different fromzero at conventional levels. Since the positive association in the concentration equation is stableacross the three models and also have lower p-values in table 10.6, a reasonable conclusion is thatconcentration and insider holdings may be positively related, and that the order of causation goesfrom insider holdings to concentration rather than the opposite way. That is high insider stakesgenerate high concentration, but not vice versa.

We hesitate to make strong conclusions based on these system estimations. The findings are notconvincing in a statistical sense. Also, because the theory is often silent on how the mechanismsinteract with each other and with controls, we have no strong arguments for one set of instru-ments and hence one equation system being more reliable. Compared to he equation–by–equationestimates summarized in table 10.1, the simultaneous systems approach does not seem to offeradditional, reliable insight.11

10This pattern can be inferred from the information in appendix table 3.1 of Bøhren and Ødegaard (2000). Themean intercorporate holding is 10% while the median is 3%. This can only be the case with a few large holdings andmany small.

11Appendix B.8 provides further evidence along these lines. We estimate systems which resemble the ones intables 10.4–10.6, but which only include controls as additional independent variables beyond the two endogeneousmechanisms and the two instruments. The estimated sign of the concentration coefficient in the insider equation isnegative in two of three cases, and one of these coefficients has p-value below 1%. Insider holdings have a positive,


Table 10.6 Interactions between governance mechanisms modeled as a system of equations. Con-centration and insider holdings are endogeneous variables. Intercorporate investments and financialleverage are used as instrumentsPanel A. Regression results


Aggregate state holdings 0.59 (0.06) 0.00Aggregate international holdings 0.23 (0.04) 0.00Aggregate individual holdings -0.17 (0.12) 0.14Aggregate nonfinancial holdings 0.27 (0.04) 0.00Fraction voting shares 0.25 (0.06) 0.00Dividends to earnings 0.01 (0.01) 0.11ln(Board size) -0.01 (0.02) 0.48Industrial -0.01 (0.01) 0.38Transport/shipping -0.03 (0.02) 0.15Offshore 0.03 (0.03) 0.35Investments over income -0.00 (0.00) 0.18ln(Firm value) -0.01 (0.00) 0.03Aggregate intercorporate holdings 0.16 (0.05) 0.00constant -0.07 (0.13) 0.57

Primary insiders Herfindahl index -0.79 (0.49) 0.10Aggregate state holdings 0.36 (0.28) 0.20Aggregate international holdings 0.24 (0.13) 0.07Aggregate individual holdings 0.46 (0.07) 0.00Aggregate nonfinancial holdings 0.25 (0.15) 0.10ln(Board size) -0.04 (0.02) 0.08Fraction voting shares 0.09 (0.13) 0.47Dividends to earnings 0.02 (0.02) 0.16Industrial -0.04 (0.02) 0.05Transport/shipping -0.08 (0.03) 0.01Offshore -0.07 (0.03) 0.03Investments over income -0.00 (0.00) 0.30ln(Firm value) 0.01 (0.01) 0.42Debt to assets 0.17 (0.05) 0.00constant -0.25 (0.19) 0.20




10.5 Summary 79

10.5 Summary

Ignoring the link to economic performance, this chapter has analyzed the determinants of corporategovernance mechanisms. We initially use an equation–by–equation approach which endogenizes onemechanism at a time, assuming all the others remain exogeneous. We find that the major deter-minants are ownership concentration, insider holdings, industry membership, and firm size. Unlikewhat agency theory predicts, the mechanisms are often complements or independent rather thansubstitutes. For instance, since both leverage and dividend payments are high when concentrationand insider holdings are high, financial policy is apparently used to divert the free cash flow frommanagement’s discretion when the need to do so is particularly small.

Individual (personal) investors are special by being heavily invested in firms with low concen-tration, high insider stakes, and small boards. As we showed in chapter 9, these three ownershipcharacteristics are associated with high performance. Quite the opposite ownership characteristicsattract state owners. Consistent with the notion that boards are expanded to cope with increasingscale and complexity in the firm’s operations, we find that the number of directors increases withfirm size and cash flow volatility.

We extended the equation–by–equation approach by simultaneous equations estimation, usingownership concentration and insider ownership as the endogeneous mechanisms and three alterna-tive sets of instruments to identify the two equations. This change of methodology substantiallyreduces the number of significant links between the mechanisms, suggesting they are more inde-pendent than what we found with the equation–by–equation approach. We hesitate to make strongconclusions from the equation systems estimation, since the results are sensitive to the instrumentsused 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 themodels used in this chapter are misspecified, since they ignore performance as a determinant of themechanisms. We consider this potential problem in the next chapter.

insignificant sign in the concentration equation in all three cases. We also introduced outside (external) concentrationin order to control for potential overlap where insiders are among the largest owners. The results are consistent withthe regressions discussed in the main text.

80 Causation between corporate governance and economic performance

Chapter 11

Causation between corporate governance and economicperformance

This chapter moves the analysis into cell 4 of table 2.1, which involves endogeneous mechanisms andtwo–way causation. The econometric tool is simultaneous equation modeling, which was presentedand used in chapter 10. As discussed there, this method requires restrictions on the coefficientsin order to solve the identification problem. The restrictions, which materialize themselves asinstruments, must be imposed prior to estimation and should come from corporate governancetheory. We found, however, that the theory is sometimes non–existent and always partial, and thatdifferent instruments produce different conclusions on how governance mechanisms interact. Thisproblem occurred even though we modeled just two of the mechanisms (ownership concentrationand insider ownership) as endogeneous variables.

An alternative explanation of these results is that because any potential link to performancewas ruled out, the model is misspecified. If some of these interactions occur through links toperformance, like if managers tend to increase their holdings in well-performing firms, a model whichignores performance as a determinant of mechanisms is simply wrong. In a correctly specified systemwhich also considers performance, the problem of unstable coefficient estimates for concentrationand insider holdings may disappear.

This chapter explores the merit of using simultaneous equations modeling to uncover causalitybetween mechanisms and performance. We do this for several reasons. First, the method hasrecently been used by researchers who all argue that the systems approach is superior (Agrawaland Knoeber, 1996; Cho, 1998; Demsetz and Villalonga, 2001). Second, since we have alreadyperformed the single-equation estimation of the full multivariate model in chapter 9, the systemsapproach is a natural extension. Third, we want to explore whether the instability problems of thepreceding chapter is case–specific. We limit ourselves to the two endogeneous mechanisms usedin section 10.4, i.e, concentration and insider holdings. This setup is expanded by incorporatingan equation where performance (Q) is modeled as a function of the governance mechanisms (bothendogeneous and exogeneous) and controls. We perform the analysis in two steps. In section 11.1endogeneous mechanisms are allowed to influence performance, but not vice versa. Section 11.2allows causation to be two–way, i.e., the endogeneous governance mechanisms may also be influencedby performance.

11.1 Governance driving performance

This section allows governance mechanisms to interact and to influence performance, but not tobe influenced by performance. The starting point is the setup of section 10.4, which used threealternative sets of instruments to estimate the determinants of ownership concentration and insiderholdings. To capture the link between these two mechanisms and simultaneously explore theirinfluence on performance, we include an equation with performance (Q) as the dependent variableand the full set of independent variables from chapter 9. This performance equation is identicalto the full multivariate model estimated in table 9.2. Thus, while we add one more equation tothe system of section 10.4, we do not need additional instruments to identify the system. Thisis 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 Qinfluences 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 volatilityand board size in (I), stock turnover and board size in (II), and intercorporate investments andfinancial 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 comparisonis tables 10.4 –10.6, which applies simultaneous equations to the same sets of mechanisms andinstruments as in table 11.1, but did not include the Q equation. First, the systems which includeQ have considerably more significant coefficients in the mechanisms equations. Every concentrationequation 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 thatalmost 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 thetwo model sets, the finding indicates that the increased consistency and significance is due to theinclusion 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 lackof significance. The only case where any explanatory variable is significant is in model (II), wherestock turnover and board size are the instruments. Second, the number of significant associationsin this table is comparatively low. The single–equation estimates in table 9.2 had twelve significantcoefficients at the 5% level, and model (II) has only six. Third, whereas concentration enters withthe usual, negative and very significant (p < 1%) coefficient, model (II) looks very different fromwhat we are used to. Insider holdings are no longer significant, and the role of the owner typevariables have changed. Individual investors were associated with higher performance than anyother type in table 9.2, but state, international, and nonfinancial owners are the superior ones inmodel (II) of table 11.1.2

Now, a change of sign relative to a single-equation regressions is not necessarily an indicationthat the model is misspecified. It may merely reflect that when we properly account for causality ina system of equations, we have a better model than in the single–equation case. But this is only trueif we have reasons to believe that the system is correctly specified, which is not the case here. Forone thing, entering the performance equation into a system containing governance mechanisms hasreduced the significance of the estimates in the performance equation compared to the stand–alonecase. A similar effect can be observed in table 3 of Agrawal and Knoeber (1996), where the p-valuesincrease when moving from OLS to 2SLS estimation of the system. This is typically the case whenthe instruments are weak. Another argument for not trusting the estimated performance relationis that the only case of significant relationships is in model (II). With the two other instrumentsets, 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 acrossthe concentration and insider equations when the instrument set changes. This is particularly true

1Detailed results are shown in appendix tables B.88–B.90.2Appendix 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 performanceequations.


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 Indep.variable (I) (II) (III)

Q 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 − − +

Herfindahl index 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 − − −

Primary insiders 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 + + +

The table summarizes estimations of three different simultaneous systems which differ across instruments used. Theinstruments for ownership concentration and insider holdings are stock volatility and board size in model (I), stockturnover 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 isindicated 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 asignificant complementary relationship (< 1%). The performance equation is not as clean, but thismay be because performance is not allowed to influence the two governance mechanisms. The nextsection opens up for this possibility.

11.2 Two–way causation

This section lets performance, concentration, and insider holdings be simultaneously determinedby each other, by the remaining (and exogeneous) governance mechanisms, and by controls. Weexpand the models of the preceding section by also allowing performance (Q) to have a causal effecton 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 showsthat systematic risk directly influences the value of the firm and hence Q. As usual, we cannotconvincingly argue why this risk measure does not influence concentration and insider holdings aswell. One possibility is an appeal to order of magnitude and the idea that although beta drives allthree 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 significantcoefficients than in table 11.1, where Q was not allowed to determine the mechanisms. The onlysignificant variables are in model (B), where stock beta, stock turnover, and board size are theinstruments, and where we find the usual negative covariation (p = 2%) between performance andconcentration. As for the concentration and insider equations, the pattern is less consistent acrossmodels than earlier. For instance, whereas every coefficient reflects a significant complementaryrelationship (< 1%) between concentration and insider holdings in table 11.1, this is definitely notthe case in table 11.2. Thus, once more we find that the choice of instruments matters a lot for theconclusions. Still, there is one result which is consistent across models: Insider ownership is neversignificant in the performance equations, but performance always enters with a positive sign in theinsider equations. This is consistent with Loderer and Martin (1997) and Cho (1998), who bothfind 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) findthat the governance–performance relationship is considerably less significant with simultaneousequation estimation than with single–equation models. Unlike us, they do not test for differentinstruments and therefore do not explore the instrument quality question. Their interpretation ofthe 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 systemis better specified than single–equation models. As there exists no proper theoretical basis forestablishing instruments, we test out three different instrument sets and find that the qualitativeconclusions are sensitive to the choice of instruments. In particular, the choice of instrumentsdecides whether or not our data supports the equilibrium argument. It also determines what toconclude about mechanism interaction and reverse causation.

The instability of qualitative conclusions across instruments and the reduced significance insystems may both be driven by the choice of instruments, which should have high correlation withthe 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 truein the three performance equations, where the instrument (stock beta) is never significantly related

3The underlying estimations are shown in appendix tables B.97–B.99


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 Indep.variable (A) (B) (C)

Q 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 − − +

Herfindahl index 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 − + −

Primary insiders 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 + + +

The table summarizes estimations of three different simultaneous system which differ across instruments used. Theinstruments for performance, ownership concentration, and insider holdings are stock beta, stock volatility, andboard size in model (A), stock beta, stock turnover, and board size in model (B), and stock beta, intercorporateshareholdings, and debt to assets in model (C). A + or a − sign means the coefficient is estimated with a positive ornegative coefficient, respectively. Statistical significance is indicated with ∗, ∗∗, and ∗∗∗, which means the relationshipis 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 lessof a problem in the mechanism equations, where the instrument is insignificant at the 5% level inonly one of the six cases (stock volatility in model (A)).

11.3 Summary

This chapter has analyzed the relationship between corporate governance and economic perfor-mance using simultaneous equations modeling. We specified ownership concentration and insiderownership as endogeneous governance mechanisms, letting the other mechanisms remain exoge-neous. Economic performance was added to this system by alternatively letting governance influ-ence performance (cell 2 in table 2.1) and by allowing for two–way causation between governanceand performance (cell 4).

The overall conclusion is that the findings are sensitive to the choice of instruments. Forinstance, just like Agrawal and Knoeber (1996), we find although the introduction of simultaneousequation systems reduces the number of significant determinants of economic performance quitedramatically. However, the inverse relationship between concentration and performance found inevery single–equation model comes up when one of the three instrument sets is used, but notwith the two others. Similarly, although we never find that concentration and insider holdings aresubstitute mechanisms (i.e., they are always complements or independent), the conclusion on theorder of causation between them differs across instruments.

Several papers in this field have concluded that the lack of statistical significance in simultaneousequations models supports the equilibrium hypothesis of Demsetz (1983) that when governancemechanisms 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 aswell be driven by a model misspecification problem which is due to weak instruments. Until wehave stronger theoretical justifications for choosing the instruments which restrict the systems ofequations, we doubt whether the simultaneous system approach can offer deeper insight into thedeterminants of corporate governance mechanisms beyond those obtained from the single–equationmultivariate analyses in chapters 5–9.

86 Conclusions

Chapter 12

Conclusions

The question of whether corporate governance matters for economic performance is getting increas-ing international attention from politicians, practitioners, and academic researchers alike. This re-port initially outlines the relevant theory and existing empirics, concluding that not surprisingly, thetheoretical foundation of this novel academic field is rather weak, and that the empirical evidenceis quite narrow and mixed. On this background, we explored how governance and performanceinteract in all Norwegian listed firms except financials over the period 1989–1997.

We think our analysis improves the general insight into the governance–performance relationshipin several ways. First, unlike most existing research, which studies just one or two ownership struc-ture variables (typically ownership concentration and insider holdings), we add a wide range of othergovernance mechanisms which corporate governance theory specifies as determinants of economicperformance. 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), andfinancial policy (capital structure and dividend policy). Second, instead of making the standardassumption that governance mechanisms are internally independent and that causation runs fromgovernance to performance, only, we expand our single–equations models into simultaneous equa-tions systems, which can handle both mechanism endogeneity and reverse causation. Moreover,whereas existing research has focued heavily on very large US corporations, our Norwegian samplefirms are on average much smaller, they are exposed to civil law rather than common law, hostiletakeovers are very rare, the firms are closely rather than widely held, performance–related pay ismuch less common, and corporate boards are owner–driven rather than manager–dominated. Fi-nally, our unusually accurate and detailed ownership structure data produces more reliable evidencethan 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 relativeimportance in the overall economy. The average OSE firm is about twice the size of a NASDAQfirm and roughly one fifth of a NYSE firm. OSE firms have low ownership concentration by Eu-ropean standards, international owners hold about one third of aggregate market capitalization,financial (institutional) investors steadily increase their share, and ownership by individuals (per-sonal investors) is small and declining. Insiders hold 7% of the market portfolio, roughly half theinsider stakes belong to primary insiders (officers and directors), and the CEO holds almost all theshares in the officers category.

We have tested a large number of single–equation regressions; from the simplest univariatemodels studying one mechanism at a time through partial multivariate models with two or moremechanisms to a full multivariate model which includes every governance mechanism and controlvariable in our data base. Estimating the full multivariate regression equation, we find that therelationship 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 fundamentalagency hypothesis of Berle and Means (1932) and Jensen and Meckling (1976) that managerswho are not closely monitored by powerful owners will not fulfill their fiduciary duty, and thatpowerful owners are beneficial because they discipline management towards making maximizingmarket 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 leverageand dividend payments are high when concentration and insider holdings are high, financial policyis apparently used to divert the free cash flow from management’s discretion when the need to doso is particularly small.

In contrast, we find support for agency–based ideas in our evidence that performance correlatespositively with insider holdings at almost any level, that direct ownership is more value–creatingthan indirect, and that both non–voting shares and larger boards reduce market value. Even thoughwe find the relationship between insider holdings and performance to be quadratic, there are veryfew firms in our sample where the marginal value effect of a higher insider stake is negative. Thissuggests 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 hold-ings, where a one percentage point higher stake increases firm value by 1% or by roughly NOK 20mill. A corresponding increase in direct as opposed to indirect ownership has a 0.8% effect, andone percentage point lower ownership concentration increases firm value by 0.4%. Stepping up thefraction of voting shares by one percentage point drives up firm value by 0.8%, and the averagefirm will be about 2% more valuable if board size is reduced by one member. If the value of debt isunaffected by these changes in the corporate governance mechanisms, the relative impact on equityvalue 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 findingsstay 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 wayfrom the univariate analysis through the various partial multivariate models to the full multivariatespecification when Q is the performance measure. This suggests that the estimated sign and thestatistical 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 performancewhich 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 themechanisms, 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 equationsto allow for both mechanism endogeneity and reverse causation. For instance, just like otherresearchers, we observe that the introduction of simultaneous equation systems reduces the numberof significant determinants of economic performance quite dramatically. Moreover, the findingsare sensitive to what instruments we use to identify the simultaneous equations. For instance, theinverse relationship between concentration and performance found in every single–equation modelcomes 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 betweenthem differs across instruments. Since the results are sensitive to the instruments used, and sincethe theoretical basis for specifying the instruments is weak, we cannot make strong conclusionsfrom the equation systems estimation.

Several papers in this field have concluded that the lack of statistical significance in simultaneousequations models supports the equilibrium hypothesis of Demsetz (1983) that when governancemechanisms 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 bedue to model misspecifications driven by weak instruments. Until we have stronger theoreticaljustifications 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 thoseobtained 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 (Verdipapirsen-tralen; VPS ) we have a complete database of year–end holdings of all equity owners for companieslisted at the Oslo Stock Exchange (OSE). This data is available from 1989 through 1997. Thedata 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 companyboard, the company’s auditors and their immediate families. Each insider must report his or hertransaction 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 resultingtotal 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 subsequenttransactions 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 withboard 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 holdingsas of year–end in their annual reports. We manually collect these data for OSE listed corporateowners 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 dividendpayments 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 accountingfigures (except for the footnotes) from the annual reports of OSE listed firms. We have also used alarge 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 91

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.


A.2 List of variables

1989: Indicator variable equal to one if the observation is in the year 1989









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.






2nd largest owner: The fraction of a company’s equity held by the second largest owner. Equityincludes both voting and nonvoting stock. Data source: VPS

3rd largest owner: The fraction of a company’s equity held by the third largest owner. Equityincludes both voting and nonvoting stock. Data source: VPS

4th largest owner: The fraction of a company’s equity held by the fourth largest owner. Equityincludes both voting and nonvoting stock. Data source: VPS

5th largest owner: The fraction of a company’s equity held by the fifth largest owner. Equityincludes both voting and nonvoting stock. Data source: VPS

Aggregate individual holdings: The aggregate fraction of a company’s equity held by individ-ual owners. The owners have sector codes: 790-889. Data source: VPS.

Aggregate intercorporate holdings: Aggregate fraction of a company’s equity held by otherfirms 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 inter-national owners. The owners have sector codes: 900–1000. Data source: VPS

Aggregate financial holdings: The aggregate fraction of a company’s equity held by financialowners. Financial owners are companies which are in a financial business (banks, insurancecompanies, 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 nonfi-nancial 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 insidersinclude members of the company’s management team, members of the company board, thecompany’s auditors and their immediate families. Data sources: OB and BI.

Board size: The number of board members (not including substitutes). Data source: Brønn-øysundregistrene and BI.

Board members: The aggregate fraction of a company’s equity held by legal insiders who areboard 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 sharesoutstanding. Data source: OBI

Firm value: Total firm value estimated as the sum of market value of equity and book value ofdebt. The calculation is done at yearend. Data source: OBI

Fraction voting shares: Fraction of a company’s outstanding equities which is voting. Datasource: 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 ownershipfractions across all owners. Has a maximum of 1 with one owner, and a minimum of 1/n2 ifeach of the n owners holds a fraction of 1/n each. Data source: VPS

Investments over income: Company total investments (totalinvesteringer) divided by operatingincome (driftsinntekter). Data source: OBI.

Industrial: Indicator variable equal to one if the company is an industrial corporation. Industrialsexplicitly excluded are offshore related and shipping related.


Largest outside owner: To estimate the size of the largest outside owner we look at the largestowner in the insider data and the largest owner overall. If the largest insider has equal sizeto the largest overall owner, the largest overall owner is removed and the size of the secondlargest overall is used as the largest outside owner. On the other hand, if the size of largestinsider holding is less than the size of the overall largest, the largest outside owner is the sameas 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 includesboth 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 aremembers 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. Pri-mary insiders are defined as those of the legal insiders which are board members or membersof 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 replacementvalue. We estimated Q as the sum of the market value of equity and the book value of debtdivided 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 OBXindex 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 num-ber of shares outstanding. Turnover is measured every day with trading and then aggregatedto estimate annual turnover. Data source: OBI.

Transport/shipping: Indicator variable equal to one if the company is either in transport or inshipping.

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 thevariable in question.

• (constant ’97 term) in parenthesis after a variable: The variable is in constant December ’97terms.

Transformations

In some cases the variables are transformed, usually by a simple mathematical procedure.

• lntrans(x). Defined as

lntrans(x) = ln(

x

1− 0.99x

)

This transformation was used by Demsetz and Lehn (1985) with the purpose of transforminga 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 between0 and 1 is split into the three regions, [0, 0.05], (0.05, 0.25] and (0.25, 1]. Regressions usingthis transformation produce an estimated coefficient for each interval, which is indicated bythe following qualifications to a variable x:

– (x) 0 to 5.

=

{x if x < 0.050.05 if x ≥ 0.05

– (x) 5 to 25.

=

0 if x ≤ 0.05x− 0.05 if 0.05 < x ≤ 0.250.2 if x > 0.25


– (x) 25 to 100

=

{0 if x < 0.25x− 0.25 if x ≥ 0.25

This linearization was used by Morck et al. (1988).

A.3 Histograms 97

A.3 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 usedin the descriptive table.

A.3.1 Ownership concentration

0

100

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0 0.01 0.02 0.03 0.04 0.05 0.06 0.07 0.08

Mean owner

Mean owner

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0 0.001 0.002 0.003 0.004 0.005 0.006 0.007 0.008

Median owner

Median owner

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0 10000 20000 30000 40000 50000 60000 70000 80000

Number of owners

Number of owners

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0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

Herfindahl index

Herfindahl index


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0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

Largest owner

Largest owner

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0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

1-2 largest owners

Two largest owners

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0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

1-3 largest owners

Three largest owners

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0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

1-4 largest owners

Four largest owners

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0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

1-5 largest owners

Five largest owners

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120

0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

1-10 largest owners

10 largest owners

A.3 Histograms 99

0

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0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

1-20 largest owners

20 largest owners

0

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0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45

2nd largest owner

2nd largest owner

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0 0.05 0.1 0.15 0.2 0.25

3rd largest owner

3rd largest owner

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0 0.02 0.04 0.06 0.08 0.1 0.12 0.14 0.16

4th largest owner

4th largest owner

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0 0.02 0.04 0.06 0.08 0.1 0.12 0.14

5th largest owner

5th largest owner

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0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

Largest outside owner

Largest outside owner


A.3.2 Owner type

0

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0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9

Aggregate state holdings


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0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

Aggregate international holdings


0

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0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9

Aggregate individual holdings


0

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0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

Aggregate financial holdings


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0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

Aggregate nonfinancial holdings


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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 101

A.3.3 Insider ownership

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0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

Primary insiders

Primary insiders

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0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

All insiders

All insiders

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0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

Board members

Board members

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0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

Management team

Management team

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0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9

Largest insider

Largest insider

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0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8

Largest primary insider

Largest primary insider


A.3.4 Board characteristics

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0 2 4 6 8 10 12 14 16 18 20

Board size

Board size

A.3.5 Security design

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0.4 0.5 0.6 0.7 0.8 0.9 1

Fraction voting shares

Fraction voting

A.3.6 Financial policy

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0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

Debt to assets

Debt to assets

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0 1 2 3 4 5 6 7 8 9 10 11

Dividends to earnings


A.3 Histograms 103

0

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Dividends to price

Dividends to price

A.3.7 Controls

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0 1e+10 2e+10 3e+10 4e+10 5e+10 6e+10 7e+10 8e+10 9e+10

Firm value

Firm value

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0 5 10 15 20 25 30 35 40 45 50

Investments over income


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0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6

Stock volatility

Stock volatility

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0 1 2 3 4 5 6 7

Stock beta

Stock beta


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0 1 2 3 4 5 6

Stock turnover

Stock turnover

A.3.8 Performance measures

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0 1 2 3 4 5 6 7 8 9

Q

Q (Tobin’s Q)

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-200 -150 -100 -50 0 50

RoA

RoA (Return on assets)

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-100 0 100 200 300 400 500 600 700

RoS

RoS (Return on Stock)

Supplementary regressions 105

Appendix B

Supplementary regressions

B.1 Univariate relationships

This appendix supplements the discussion in chapter 4 on univariate regressions of performanceon governance mechanisms and controls. Section B.1.1 gives detailed regression results for theunivariate regressions summarized in table 4.1. Section B.1.2 provides estimates for variableswhich are different if we consider voting rights instead of cash flow rights. Finally, section B.1.3illustrates selected univariate relations graphically, by plotting performance (Q) against governancemechanisms and controls.

B.1.1 Regressions underlying summary table

Each regression estimates the coefficients a (intercept or constant) and b (slope) of the linearregression 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 dependentvariable (performance measure): Q, RoA5, RoS5, RoA and RoS. We report estimated parametervalues, with estimated p-values in parenthesis. For each regression we also report n (the numberof observations) and the R2 (the coefficient of determination) for each regression.

B.1.1.1 Ownership concentration

Q RoA5 RoS5 RoA RoS

constant 1.65 9.74 44.29 5.42 33.56(0.00) (0.00) (0.00) (0.00) (0.00)

Herfindahl index -1.12 -2.65 -2.41 -2.62 -3.01(0.00) (0.01) (0.86) (0.37) (0.88)

n 1068 1031 731 1061 894R2 0.03 0.00 -0.00 -0.00 -0.00

Q RoA5 RoS5 RoA RoS

constant 1.76 9.95 47.24 5.34 36.17(0.00) (0.00) (0.00) (0.00) (0.00)

Largest owner -0.97 -2.13 -11.88 -1.10 -10.67(0.00) (0.01) (0.26) (0.64) (0.50)

n 1068 1031 731 1061 894R2 0.03 0.00 -0.00 -0.00 -0.00

Q RoA5 RoS5 RoA RoS

constant 1.93 10.43 53.19 5.50 41.63(0.00) (0.00) (0.00) (0.00) (0.00)

1-3 largest owners -0.97 -2.32 -20.40 -1.03 -18.23(0.00) (0.00) (0.04) (0.65) (0.23)

n 1068 1031 731 1061 894R2 0.04 0.01 0.00 -0.00 -0.00

106 Supplementary regressions

Q RoA5 RoS5 RoA RoS

constant 2.06 10.68 57.48 5.87 47.89(0.00) (0.00) (0.00) (0.00) (0.00)

1-5 largest owners -1.04 -2.39 -25.03 -1.52 -26.59(0.00) (0.00) (0.02) (0.52) (0.10)

n 1068 1031 731 1061 894R2 0.04 0.01 0.00 -0.00 0.00

B.1.1.2 Owner type

Q RoA5 RoS5 RoA RoS

constant 1.51 9.34 45.52 5.24 33.37(0.00) (0.00) (0.00) (0.00) (0.00)

Aggregate state holdings -0.61 -0.15 -29.40 -4.30 -5.12(0.01) (0.91) (0.05) (0.19) (0.82)

n 1068 1031 731 1061 894R2 0.01 -0.00 0.00 -0.00 -0.00

Q RoA5 RoS5 RoA RoS

constant 1.22 8.46 33.03 6.38 22.84(0.00) (0.00) (0.00) (0.00) (0.00)

Aggregate individual holdings 1.45 4.99 63.22 -7.62 60.60(0.00) (0.00) (0.00) (0.01) (0.00)

n 1068 1031 731 1061 894R2 0.05 0.02 0.03 0.00 0.01

Q RoA5 RoS5 RoA RoS

constant 1.43 9.33 49.29 2.37 32.31(0.00) (0.00) (0.00) (0.00) (0.00)

Aggregate financial holdings 0.26 0.03 -31.38 16.01 4.85(0.23) (0.98) (0.05) (0.00) (0.83)

n 1068 1031 731 1061 894R2 -0.00 -0.00 0.00 0.02 -0.00

Q RoA5 RoS5 RoA RoS

constant 1.76 9.83 49.48 3.98 39.03(0.00) (0.00) (0.00) (0.00) (0.00)

Aggregate nonfinancial holdings -0.74 -1.26 -14.45 2.66 -15.08(0.00) (0.05) (0.09) (0.16) (0.24)

n 1068 1031 731 1061 894R2 0.03 0.00 0.00 -0.00 -0.00

Q RoA5 RoS5 RoA RoS

constant 1.42 9.57 41.93 5.84 34.92(0.00) (0.00) (0.00) (0.00) (0.00)

Aggregate international holdings 0.24 -1.07 8.76 -3.69 -7.90(0.08) (0.12) (0.34) (0.07) (0.56)

n 1068 1031 731 1061 894R2 0.00 0.00 -0.00 0.00 -0.00

Q RoA5 RoS5 RoA RoS

constant 1.54 9.56 46.26 4.29 31.96(0.00) (0.00) (0.00) (0.00) (0.00)

Aggregate intercorporate holdings -0.74 -2.41 -24.48 8.13 9.00(0.00) (0.02) (0.07) (0.01) (0.66)

n 1066 1029 730 1059 893R2 0.01 0.00 0.00 0.00 -0.00

B.1 Univariate relationships 107

Q RoA5 RoS5 RoA RoS

constant 1.50 9.37 45.33 5.01 33.89(0.00) (0.00) (0.00) (0.00) (0.00)

Largest owner is state -0.30 -0.42 -14.85 0.12 -9.08(0.01) (0.45) (0.03) (0.94) (0.41)

n 1068 1031 731 1061 894R2 0.01 -0.00 0.00 -0.00 -0.00

Q RoA5 RoS5 RoA RoS

constant 1.43 9.20 41.86 5.35 32.00(0.00) (0.00) (0.00) (0.00) (0.00)

Largest owner is individual 0.43 1.28 21.17 -3.13 12.20(0.00) (0.01) (0.00) (0.03) (0.26)

n 1068 1031 731 1061 894R2 0.02 0.00 0.01 0.00 -0.00

Q RoA5 RoS5 RoA RoS

constant 1.48 9.36 44.59 4.90 33.50(0.00) (0.00) (0.00) (0.00) (0.00)

Largest owner is financial -0.03 -0.34 -9.85 1.63 -5.18(0.78) (0.55) (0.23) (0.34) (0.65)

n 1068 1031 731 1061 894R2 -0.00 -0.00 -0.00 -0.00 -0.00

Q RoA5 RoS5 RoA RoS

constant 1.58 9.57 48.26 3.63 35.59(0.00) (0.00) (0.00) (0.00) (0.00)

Largest owner is nonfinancial -0.19 -0.42 -7.84 2.52 -4.47(0.00) (0.17) (0.05) (0.01) (0.47)

n 1068 1031 731 1061 894R2 0.01 -0.00 0.00 0.01 -0.00

Q RoA5 RoS5 RoA RoS

constant 1.49 9.32 43.10 5.08 31.91(0.00) (0.00) (0.00) (0.00) (0.00)

Largest owner is international -0.09 0.10 6.27 -0.46 8.74(0.33) (0.82) (0.29) (0.73) (0.33)

n 1068 1031 731 1061 894R2 -0.00 -0.00 -0.00 -0.00 -0.00

Q RoA5 RoS5 RoA RoS

constant 1.50 9.47 45.13 4.81 32.50(0.00) (0.00) (0.00) (0.00) (0.00)

Largest owner is listed -0.19 -1.01 -9.21 1.62 4.63(0.03) (0.02) (0.13) (0.23) (0.61)

n 1068 1031 731 1061 894R2 0.00 0.00 0.00 -0.00 -0.00

B.1.1.3 Insider ownership

Q RoA5 RoS5 RoA RoS

constant 1.47 9.26 43.91 5.16 31.91(0.00) (0.00) (0.00) (0.00) (0.00)

All insiders 0.03 0.38 0.15 -0.70 5.57(0.81) (0.49) (0.98) (0.67) (0.61)

n 1068 1031 731 1061 894R2 -0.00 -0.00 -0.00 -0.00 -0.00


Q RoA5 RoS5 RoA RoS

constant 1.42 9.11 44.14 5.01 32.28(0.00) (0.00) (0.00) (0.00) (0.00)

Board members 0.65 2.86 -2.25 0.06 9.91(0.00) (0.00) (0.81) (0.98) (0.49)

n 1068 1031 731 1061 894R2 0.02 0.01 -0.00 -0.00 -0.00

Q RoA5 RoS5 RoA RoS

constant 1.46 9.23 42.00 5.14 32.15(0.00) (0.00) (0.00) (0.00) (0.00)

Management team 0.25 2.41 43.87 -2.87 21.46(0.23) (0.02) (0.00) (0.35) (0.28)

n 1068 1031 731 1061 894R2 -0.00 0.00 0.01 -0.00 -0.00

Q RoA5 RoS5 RoA RoS

constant 1.42 9.11 42.03 5.31 31.18(0.00) (0.00) (0.00) (0.00) (0.00)

Primary insiders 0.68 2.74 21.80 -3.57 22.54(0.00) (0.00) (0.04) (0.13) (0.15)

n 1068 1031 731 1061 894R2 0.01 0.01 0.00 0.00 0.00

B.1.1.4 Board characteristicsQ RoA5 RoS5 RoA RoS

constant 1.64 10.71 74.30 0.89 44.40(0.00) (0.00) (0.00) (0.69) (0.00)

ln(Board size) -0.07 -0.61 -16.18 2.38 -6.24(0.38) (0.13) (0.01) (0.05) (0.45)

n 966 937 670 959 812R2 -0.00 0.00 0.01 0.00 -0.00

B.1.1.5 Security design

Q RoA5 RoS5 RoA RoS

constant 0.77 9.48 5.46 12.18 -12.53(0.02) (0.00) (0.77) (0.01) (0.68)

Fraction voting shares 0.74 -0.12 40.16 -7.42 47.35(0.03) (0.94) (0.04) (0.13) (0.14)

n 1053 1016 731 1046 894R2 0.00 -0.00 0.00 0.00 0.00

B.1.1.6 Financial policy

Q RoA5 RoS5 RoA RoS

constant 1.51 9.23 45.72 4.40 33.07(0.00) (0.00) (0.00) (0.00) (0.00)

Dividends to earnings -0.08 0.34 -3.92 2.50 2.42(0.08) (0.12) (0.15) (0.00) (0.59)

n 1039 1003 708 1032 867R2 0.00 0.00 0.00 0.01 -0.00

Q RoA5 RoS5 RoA RoS

constant 1.52 8.95 44.88 4.06 33.29(0.00) (0.00) (0.00) (0.00) (0.00)

Dividends to price -0.03 0.25 -0.50 0.60 -0.11(0.00) (0.00) (0.35) (0.00) (0.90)

n 1068 1031 731 1061 894R2 0.01 0.02 -0.00 0.02 -0.00


B.1.1.7 Competitive markets

Q RoA5 RoS5 RoA RoS

constant 1.47 9.46 41.59 4.93 31.00(0.00) (0.00) (0.00) (0.00) (0.00)

Industrial 0.02 -0.38 6.44 0.28 6.14(0.75) (0.24) (0.12) (0.77) (0.35)

n 1068 1031 731 1061 894R2 -0.00 -0.00 0.00 -0.00 -0.00

Q RoA5 RoS5 RoA RoS

constant 1.49 9.56 43.77 5.03 32.39(0.00) (0.00) (0.00) (0.00) (0.00)

Offshore -0.22 -2.66 2.34 -0.14 9.46(0.04) (0.00) (0.76) (0.93) (0.42)

n 1068 1031 731 1061 894R2 0.00 0.02 -0.00 -0.00 -0.00

Q RoA5 RoS5 RoA RoS

constant 1.61 9.62 46.79 4.51 35.68(0.00) (0.00) (0.00) (0.00) (0.00)

Transport/shipping -0.52 -1.10 -10.43 1.99 -9.80(0.00) (0.00) (0.02) (0.06) (0.16)

n 1068 1031 731 1061 894R2 0.05 0.01 0.00 0.00 -0.00

B.1.1.8 ControlsQ RoA5 RoS5 RoA RoS

constant -0.05 10.76 82.23 -37.90 -45.84(0.88) (0.00) (0.00) (0.00) (0.21)

ln(Firm value) 0.08 -0.07 -1.88 2.15 3.94(0.00) (0.39) (0.13) (0.00) (0.03)

n 1068 1031 731 1061 894R2 0.02 -0.00 0.00 0.07 0.00

Q RoA5 RoS5 RoA RoS

constant 1.73 10.14 30.81 11.74 23.89(0.00) (0.00) (0.00) (0.00) (0.00)

Stock volatility -0.38 -1.42 21.69 -10.30 15.32(0.00) (0.01) (0.00) (0.00) (0.13)

n 949 921 688 944 814R2 0.01 0.00 0.01 0.06 0.00

Q RoA5 RoS5 RoA RoS

constant 1.28 9.23 24.83 5.73 6.65(0.00) (0.00) (0.00) (0.00) (0.09)

Stock turnover 0.35 0.19 27.66 -0.92 41.31(0.00) (0.44) (0.00) (0.16) (0.00)

n 1033 997 728 1027 890R2 0.05 -0.00 0.12 -0.00 0.09

Q RoA5 RoS5 RoA RoS

constant 1.43 9.57 30.56 6.77 26.09(0.00) (0.00) (0.00) (0.00) (0.00)

Stock beta 0.09 -0.32 14.11 -1.42 6.60(0.12) (0.23) (0.00) (0.06) (0.22)

n 947 920 678 940 824R2 0.00 -0.00 0.02 0.00 -0.00


B.1.2 Using voting rights instead of cash flow rights

We provide univariate results which show that the conclusions do not materially differ when we usevoting rights instead of cash flow rights in the calculations of ownership concentration. Table B.1complements table 4.1 in the text by summarizing univariate regressions where the concentrationvariables are in terms of voting rights instead of cash flow rights.

Table B.1 Summary of univariate regressions, voting rights.Q RoA5 RoS5 RoA RoS

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) − + + − +

The table summarizes the estimated signs of univariate relations between a performance measure and an independentvariable (governance mechanism or control variable). Statistical significance is indicated with ∗, ∗∗, and ∗∗∗, whichmeans the relationship is significant at the 5%, 2.5% and 1% level, respectively. Data for firms listed on the OsloStock Exchange, 1989-1997. Variable definitions are in Appendix A.2.

The summary table above shows no differences regarding the significance levels of these regressionsfor Q. Comparing the detailed regression results listed below to the corresponding univariateregressions using cash flow rights in appendix section B.1.1 above, we observe that while some ofthe coefficient estimates show minor numerical differences, the signs and levels of significance aremostly consistent. We therefore do not make further use of the distinction between voting and cashflow 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.

Q RoA5 RoS5 RoA RoS

constant 1.64 9.62 44.59 5.25 34.10(0.00) (0.00) (0.00) (0.00) (0.00)

Herfindahl (voting rights) -1.00 -1.64 -4.26 -1.46 -6.27(0.00) (0.07) (0.74) (0.58) (0.75)

n 1062 1025 731 1055 894R2 0.03 0.00 -0.00 -0.00 -0.00

Q RoA5 RoS5 RoA RoS

constant 1.76 9.82 47.94 5.12 36.95(0.00) (0.00) (0.00) (0.00) (0.00)

Largest owner (voting rights) -0.94 -1.56 -13.90 -0.36 -13.01(0.00) (0.04) (0.19) (0.87) (0.41)

n 1062 1025 731 1055 894R2 0.03 0.00 -0.00 -0.00 -0.00


Q RoA5 RoS5 RoA RoS

constant 1.96 10.24 54.64 5.24 43.64(0.00) (0.00) (0.00) (0.00) (0.00)

1-3 largest owners (voting rights) -1.00 -1.83 -22.96 -0.47 -22.02(0.00) (0.02) (0.02) (0.83) (0.15)

n 1062 1025 731 1055 894R2 0.04 0.00 0.00 -0.00 0.00

Q RoA5 RoS5 RoA RoS

constant 2.10 10.49 59.75 5.53 50.51(0.00) (0.00) (0.00) (0.00) (0.00)

1-5 largest owner (voting rights) -1.09 -1.98 -28.56 -0.90 -30.69(0.00) (0.01) (0.01) (0.70) (0.06)

n 1062 1025 731 1055 894R2 0.04 0.00 0.01 -0.00 0.00

Q RoA5 RoS5 RoA RoS

constant 1.50 9.35 45.59 4.99 33.98(0.00) (0.00) (0.00) (0.00) (0.00)

Largest owner is state (voting rights) -0.28 -0.19 -16.48 0.36 -9.55(0.01) (0.73) (0.01) (0.82) (0.37)

n 1068 1031 731 1061 894R2 0.00 -0.00 0.01 -0.00 -0.00

Q RoA5 RoS5 RoA RoS

constant 1.43 9.20 41.90 5.34 32.02(0.00) (0.00) (0.00) (0.00) (0.00)

Largest owner is individual (voting rights) 0.44 1.30 20.51 -3.04 11.78(0.00) (0.01) (0.00) (0.04) (0.27)

n 1068 1031 731 1061 894R2 0.02 0.00 0.01 0.00 -0.00

Q RoA5 RoS5 RoA RoS

constant 1.47 9.36 44.50 4.91 33.23(0.00) (0.00) (0.00) (0.00) (0.00)

Largest owner is financial (voting rights) 0.01 -0.41 -8.93 1.65 -1.70(0.93) (0.50) (0.29) (0.36) (0.89)

n 1068 1031 731 1061 894R2 -0.00 -0.00 -0.00 -0.00 -0.00

Q RoA5 RoS5 RoA RoS

constant 1.58 9.58 48.31 3.63 36.09(0.00) (0.00) (0.00) (0.00) (0.00)

Largest owner is nonfinancial (voting rights) -0.20 -0.43 -7.90 2.51 -5.32(0.00) (0.16) (0.05) (0.01) (0.39)

n 1068 1031 731 1061 894R2 0.01 -0.00 0.00 0.01 -0.00

Q RoA5 RoS5 RoA RoS

constant 1.49 9.31 43.06 5.10 31.99(0.00) (0.00) (0.00) (0.00) (0.00)

Largest owner is international (voting rights) -0.09 0.21 6.71 -0.66 8.25(0.34) (0.64) (0.26) (0.63) (0.36)

n 1068 1031 731 1061 894R2 -0.00 -0.00 -0.00 -0.00 -0.00


B.1.3 Plots of performance vs explanatory variables

A useful way of gaining some intuition about the univariate relations between performance and itdeterminants is to plot one against the other. This appendix presents a number of plots whichrelate performance (Q) to the various governance mechanisms and controls, one by one. To reducethe visual noise in the pictures we plot grouped means rather than individual observations. Thecompanies are sorted into 20 groups based on the numerical value of the independent variable, andwe plot the mean performance for the group against the mean numerical value of the independentvariable (governance mechanism or control) for the group.

B.1.3.1 Ownership concentration

0.8

1

1.2

1.4

1.6

1.8

2

2.2

2.4

2.6

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9

Q

�

Herfindahl index

Herfindahl index

0.8

1

1.2

1.4

1.6

1.8

2

2.2

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

Q

�

Largest owner

The largest owner

0.8

1

1.2

1.4

1.6

1.8

2

2.2

2.4

0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

Q

�

1-2 largest owners

1-2 largest owners

1

1.2

1.4

1.6

1.8

2

2.2

0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

Q

�

1-3 largest owners

1-3 largest owners


1

1.2

1.4

1.6

1.8

2

2.2

2.4

0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

Q

�

1-4 largest owners

1-4 largest owners

0.8

1

1.2

1.4

1.6

1.8

2

2.2

2.4

0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

Q

�

1-5 largest owners

1-5 largest owners

1

1.2

1.4

1.6

1.8

2

2.2

2.4

0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

Q

�

1-10 largest owners

1-10 largest owners

1.1

1.2

1.3

1.4

1.5

1.6

1.7

1.8

1.9

2

2.1

2.2

0.4 0.5 0.6 0.7 0.8 0.9 1

Q

�

1-20 largest owners

1–20 largest owners

1

1.1

1.2

1.3

1.4

1.5

1.6

1.7

1.8

0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4

Q

�

2nd largest owner

2nd largest owner

1

1.1

1.2

1.3

1.4

1.5

1.6

1.7

1.8

1.9

2

2.1

0 0.02 0.04 0.06 0.08 0.1 0.12 0.14 0.16 0.18 0.2 0.22

Q

�

3rd largest owner

3rd largest owner


1.1

1.2

1.3

1.4

1.5

1.6

1.7

1.8

1.9

2

0 0.02 0.04 0.06 0.08 0.1 0.12 0.14 0.16

Q

�

4th largest owner

4th largest owner

0.9

1

1.1

1.2

1.3

1.4

1.5

1.6

1.7

1.8

1.9

0 0.02 0.04 0.06 0.08 0.1 0.12

Q

�

5th largest owner

5th largest owner

B.1.3.2 Owner type

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

2

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9

Q

�



1

1.1

1.2

1.3

1.4

1.5

1.6

1.7

1.8

1.9

2

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

Q

�



1.1

1.2

1.3

1.4

1.5

1.6

1.7

1.8

1.9

2

2.1

2.2

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8

Q

�



0.8

1

1.2

1.4

1.6

1.8

2

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8

Q

�




1

1.1

1.2

1.3

1.4

1.5

1.6

1.7

1.8

1.9

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

Q

�



1.2

1.3

1.4

1.5

1.6

1.7

1.8

1.9

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9

Q

�



B.1.3.3 Insider ownership

1.1

1.2

1.3

1.4

1.5

1.6

1.7

1.8

1.9

2

0 0.2 0.4 0.6 0.8 1

Q

�

All insiders

All insiders

1.1

1.2

1.3

1.4

1.5

1.6

1.7

1.8

1.9

0 0.2 0.4 0.6 0.8 1

Q

�

Management team

Management team

1.1

1.2

1.3

1.4

1.5

1.6

1.7

1.8

1.9

2

2.1

2.2

0 0.2 0.4 0.6 0.8 1

Q

�

Board members

Board members

1.1

1.2

1.3

1.4

1.5

1.6

1.7

1.8

1.9

2

2.1

0 0.2 0.4 0.6 0.8 1

Q

�

Primary insiders

Primary insiders


B.1.3.4 Board characteristics

1.35

1.4

1.45

1.5

1.55

1.6

1.65

3 4 5 6 7 8 9 10 11

Q

�

Board size

Board size

B.1.3.5 Financial policy

1.1

1.2

1.3

1.4

1.5

1.6

1.7

1.8

1.9

2

2.1

2.2

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

Q

�

Debt to assets

Debt to assets

1.1

1.2

1.3

1.4

1.5

1.6

1.7

1.8

1.9

0 0.5 1 1.5 2 2.5 3 3.5 4 4.5

Q

�



B.1.3.6 Controls

0.9

1

1.1

1.2

1.3

1.4

1.5

1.6

1.7

1.8

1.9

0 1e+10 2e+10 3e+10 4e+10 5e+10 6e+10

Q

�

Firm value

Firm value

1

1.1

1.2

1.3

1.4

1.5

1.6

1.7

0 5 10 15 20 25 30 35 40

Q

�




0.8

1

1.2

1.4

1.6

1.8

2

2.2

2.4

2.6

2.8

0 0.5 1 1.5 2 2.5 3 3.5

Q

�

Stock turnover

Stock turnover

1.3

1.4

1.5

1.6

1.7

1.8

1.9

0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5

Q

�

Stock beta

Stock beta

1.1

1.2

1.3

1.4

1.5

1.6

1.7

1.8

1.9

0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6

Q

�

Stock volatility

Stock volatility


B.2 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. Usingthe 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 indicatorvariables for each year (fixed effects) in an OLS regression.

Table B.2 Multivariate regression relating performance (Q) to ownership concentration and con-trols, following Demsetz and Lehn (1985)Panel A: Year by year OLS regressions

Year1989 1990 1991 1992 1993 1994 1995 1996 1997

constant −0.03 0.84 1.53 −0.35 −0.12 0.60 −1.44 6.94 −2.04(0.97) (0.28) (0.12) (0.69) (0.93) (0.39) (0.26) (0.01) (0.31)

lntrans(1-5 largest owners) −0.08 −0.07 −0.03 −0.03 −0.11 −0.09 −0.15 −0.21 −0.21(0.13) (0.19) (0.64) (0.72) (0.20) (0.05) (0.11) (0.16) (0.09)

Industrial −0.25 −0.25 −0.06 −0.31 −0.32 −0.23 −0.40 −0.61 −0.45(0.03) (0.02) (0.64) (0.05) (0.12) (0.04) (0.02) (0.04) (0.09)

Transport/shipping 0.03 −0.28 −0.23 −0.42 −0.68 −0.56 −0.84 −1.49 −1.37(0.82) (0.02) (0.07) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00)

Offshore −0.10 −0.37 −0.43 −0.48 −0.71 −0.48 −0.69 −0.74 −0.92(0.66) (0.04) (0.01) (0.02) (0.01) (0.01) (0.03) (0.17) (0.02)

Investments over income −0.01 −0.01 0.00 −0.12 −0.08 −0.07 −0.04 −0.19 0.01(0.37) (0.43) (0.82) (0.56) (0.58) (0.21) (0.40) (0.05) (0.89)

ln(Firm value) 0.08 0.03 0.00 0.09 0.09 0.05 0.15 −0.18 0.19(0.04) (0.37) (0.94) (0.03) (0.18) (0.12) (0.01) (0.11) (0.03)

Stock volatility −0.16 −0.14 −0.53 −0.01 0.36 0.08 0.79 −0.95 1.32(0.44) (0.49) (0.03) (0.95) (0.19) (0.67) (0.01) (0.14) (0.04)

n 83 79 77 69 83 108 117 126 163

R2 0.12 0.04 0.14 0.11 0.13 0.19 0.19 0.17 0.13Average (Q) 1.28 1.18 1.10 1.11 1.46 1.33 1.48 2.02 2.04



Constant 0.32 (0.64) 0.61lntrans(1-5 largest owners) -0.18 (0.04) 0.00Industrial -0.44 (0.10) 0.00Transport/shipping -0.84 (0.10) 0.00Offshore -0.74 (0.11) 0.00Investments over income -0.01 (0.01) 0.02ln(Firm value) 0.08 (0.03) 0.00Stock volatility -0.04 (0.17) 0.83n 905Average (Q) 1.53


Constant 0.11 (0.55) 0.84lntrans(1-5 largest owners) -0.16 (0.04) 0.00Industrial -0.38 (0.08) 0.00Transport/shipping -0.77 (0.08) 0.00Offshore -0.68 (0.12) 0.00Investments over income -0.01 (0.01) 0.34ln(Firm value) 0.07 (0.02) 0.00Stock volatility 0.21 (0.14) 0.131990 -0.12 (0.14) 0.401991 -0.16 (0.14) 0.271992 -0.16 (0.15) 0.291993 0.13 (0.14) 0.371994 0.05 (0.13) 0.731995 0.15 (0.13) 0.251996 0.65 (0.13) 0.001997 0.66 (0.12) 0.00n 905

R2 0.22Average (Q) 1.53


B.2 Ownership concentration 119

Table B.3 Multivariate regression relating performance (Q) to ownership concentration and con-trols, using the piecewise linear function of Morck et al. (1988)Panel A: Year by year OLS regressions

Year1989 1990 1991 1992 1993 1994 1995 1996 1997

constant 0.26 0.89 1.36 −0.21 −0.18 0.48 −0.45 23.94 −8.00(1.00) (1.00) (1.00) (1.00) (1.00) (1.00) (0.90) (0.01) (0.08)

Largest owner (0 to 5) −1.42 3.66 4.06 −1.96 4.24 5.93 −18.39 −348.98 139.68(1.00) (1.00) (1.00) (1.00) (1.00) (1.00) (0.78) (0.06) (0.10)

Largest owner (5 to 25) −0.84 0.64 −0.12 0.40 −0.65 0.98 0.20 −2.60 −3.70(0.39) (0.48) (0.89) (0.71) (0.67) (0.25) (0.89) (0.23) (0.06)

Largest owner (25 to 100) −0.44 −0.90 −0.42 −0.56 −0.46 −0.99 −1.02 −0.80 −1.00(0.33) (0.03) (0.30) (0.30) (0.51) (0.00) (0.13) (0.45) (0.31)

Industrial −0.28 −0.25 −0.06 −0.33 −0.32 −0.19 −0.41 −0.54 −0.50(0.02) (0.04) (0.61) (0.04) (0.13) (0.09) (0.02) (0.06) (0.06)


Offshore −0.12 −0.39 −0.45 −0.51 −0.76 −0.46 −0.71 −0.62 −0.75(0.62) (0.04) (0.01) (0.02) (0.01) (0.01) (0.04) (0.24) (0.06)


ln(Firm value) 0.08 0.03 0.00 0.09 0.09 0.04 0.15 −0.15 0.17(0.04) (0.44) (0.96) (0.03) (0.18) (0.20) (0.01) (0.19) (0.06)

Stock volatility −0.21 −0.23 −0.53 −0.04 0.38 −0.00 0.77 −0.81 1.42(0.32) (0.30) (0.03) (0.85) (0.19) (0.99) (0.01) (0.20) (0.03)

n 83 79 77 69 83 108 117 126 163

R2 0.09 −0.10 0.12 0.10 0.07 0.22 0.18 0.20 0.16Average (Q) 1.28 1.18 1.10 1.11 1.46 1.33 1.48 2.02 2.04



Constant 2.45 (3.17) 0.44Largest owner (0 to 5) -38.62 (59.04) 0.51Largest owner (5 to 25) -1.34 (0.72) 0.06Largest owner (25 to 100) -0.76 (0.20) 0.00Industrial -0.44 (0.10) 0.00Transport/shipping -0.86 (0.10) 0.00Offshore -0.75 (0.12) 0.00Investments over income -0.02 (0.01) 0.04ln(Firm value) 0.09 (0.03) 0.00Stock volatility -0.07 (0.17) 0.70n 905Average (Q) 1.53


Constant 0.60 (2.07) 0.77Largest owner (0 to 5) -5.40 (40.75) 0.89Largest owner (5 to 25) -1.54 (0.57) 0.01Largest owner (25 to 100) -0.67 (0.27) 0.01Industrial -0.38 (0.08) 0.00Transport/shipping -0.78 (0.08) 0.00Offshore -0.68 (0.12) 0.00Investments over income -0.01 (0.01) 0.32ln(Firm value) 0.08 (0.02) 0.00Stock volatility 0.19 (0.14) 0.151990 -0.11 (0.14) 0.441991 -0.15 (0.14) 0.301992 -0.14 (0.15) 0.351993 0.14 (0.14) 0.311994 0.06 (0.13) 0.661995 0.18 (0.13) 0.171996 0.67 (0.13) 0.001997 0.68 (0.12) 0.00n 905

R2 0.23Average (Q) 1.53



Table B.4 Multivariate regression relating performance (Q) to ownership concentration, using aquadratic functionPanel A: Year by year OLS regressions

Year1989 1990 1991 1992 1993 1994 1995 1996 1997

constant 0.14 0.92 1.63 −0.28 0.05 0.77 −1.29 7.64 −1.20(0.87) (0.24) (0.10) (0.75) (0.97) (0.25) (0.32) (0.00) (0.57)

Largest owner −0.72 0.76 −0.33 −0.47 −1.53 0.69 −0.08 −4.63 −3.47(0.57) (0.45) (0.70) (0.71) (0.31) (0.40) (0.96) (0.07) (0.11)

Squared (Largest owner) 0.24 −1.73 −0.01 0.27 1.33 −1.49 −0.83 4.40 2.56(0.89) (0.21) (0.99) (0.88) (0.49) (0.12) (0.69) (0.20) (0.39)

Industrial −0.27 −0.25 −0.06 −0.32 −0.35 −0.20 −0.40 −0.61 −0.46(0.02) (0.02) (0.61) (0.04) (0.10) (0.07) (0.02) (0.04) (0.08)


Offshore −0.13 −0.39 −0.45 −0.49 −0.75 −0.48 −0.67 −0.65 −0.87(0.58) (0.02) (0.01) (0.02) (0.01) (0.01) (0.04) (0.22) (0.03)


ln(Firm value) 0.08 0.03 0.00 0.09 0.09 0.04 0.15 −0.18 0.19(0.03) (0.41) (0.96) (0.02) (0.16) (0.21) (0.01) (0.11) (0.04)

Stock volatility −0.21 −0.20 −0.52 0.01 0.38 0.03 0.77 −0.97 1.29(0.30) (0.31) (0.03) (0.95) (0.17) (0.87) (0.01) (0.12) (0.05)

n 83 79 77 69 83 108 117 126 163

R2 0.13 0.08 0.14 0.11 0.12 0.22 0.19 0.19 0.15Average (Q) 1.28 1.18 1.10 1.11 1.46 1.33 1.48 2.02 2.04



Constant 0.66 (0.63) 0.29Largest owner -1.96 (0.69) 0.00Squared (Largest owner) 1.37 (0.77) 0.07Industrial -0.45 (0.10) 0.00Transport/shipping -0.86 (0.10) 0.00Offshore -0.74 (0.11) 0.00Investments over income -0.01 (0.01) 0.04ln(Firm value) 0.09 (0.03) 0.00Stock volatility -0.06 (0.17) 0.71n 905Average (Q) 1.53


Constant 0.43 (0.55) 0.44Largest owner -1.95 (0.62) 0.00Squared (Largest owner) 1.39 (0.80) 0.08Industrial -0.38 (0.08) 0.00Transport/shipping -0.78 (0.08) 0.00Offshore -0.68 (0.12) 0.00Investments over income -0.01 (0.01) 0.32ln(Firm value) 0.08 (0.02) 0.00Stock volatility 0.19 (0.14) 0.151990 -0.11 (0.14) 0.431991 -0.16 (0.14) 0.281992 -0.14 (0.15) 0.341993 0.14 (0.14) 0.341994 0.05 (0.13) 0.721995 0.17 (0.13) 0.191996 0.67 (0.13) 0.001997 0.68 (0.12) 0.00n 905

R2 0.23Average (Q) 1.53



Table B.5 Multivariate regression relating performance (Q) to ownership concentration, withoutcontrols, using the piecewise linear formulation of Morck et al. (1988)Panel A: Pooled OLS regression


Constant 2.14 (1.46) 0.14Largest owner (0 to 5) -6.54 (29.43) 0.82Largest owner (5 to 25) -1.85 (0.56) 0.00Largest owner (25 to 100) -0.68 (0.24) 0.00n 1068

R2 0.03Average (Q) 1.48

Panel B: Year by year OLS regressions

Year1989 1990 1991 1992 1993 1994 1995 1996 1997

constant 0.96 0.95 0.95 0.73 0.84 0.64 0.71 2.36 −3.46(1.00) (1.00) (1.00) (1.00) (1.00) (1.00) (0.83) (0.42) (0.40)

Largest owner (0 to 5) 8.92 3.74 4.98 7.06 14.10 12.46 19.57 4.75 125.71(1.00) (1.00) (1.00) (1.00) (1.00) (1.00) (0.77) (0.94) (0.13)

Largest owner (5 to 25) −0.27 0.61 −0.33 0.18 −0.88 0.70 −1.50 −3.66 −5.54(0.78) (0.44) (0.69) (0.84) (0.48) (0.44) (0.30) (0.11) (0.00)

Largest owner (25 to 100) −0.63 −0.80 −0.48 −0.64 −0.60 −0.81 −0.52 −1.11 −0.53(0.09) (0.02) (0.17) (0.09) (0.20) (0.01) (0.40) (0.30) (0.51)

n 104 94 90 102 107 120 129 141 181

R2 0.01 0.02 −0.00 −0.01 0.00 0.01 0.00 0.02 0.06Average (Q) 1.29 1.16 1.09 1.06 1.38 1.32 1.43 1.98 1.97

Panel A shows results of a pooled regression using data for whole period. Panel B shows results of OLS regressionson a year by year basis. Data for firms listed on the Oslo Stock Exchange, 1989-1997. Variable definitions are inAppendix 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 largestownerPanel A: Pooled OLS regression


Constant 1.88 (0.09) 0.00Largest owner -1.89 (0.55) 0.00Squared (Largest owner) 1.14 (0.66) 0.08n 1068

R2 0.03Average (Q) 1.48


Year1989 1990 1991 1992 1993 1994 1995 1996 1997

constant 1.39 1.09 1.19 1.12 1.63 1.30 1.70 2.76 2.86(0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00)

Largest owner −0.04 1.02 −0.23 0.01 −1.05 0.67 −1.21 −4.06 −4.75(0.97) (0.21) (0.77) (0.99) (0.37) (0.42) (0.40) (0.11) (0.01)

Squared (Largest owner) −0.66 −1.94 −0.27 −0.56 0.46 −1.35 0.58 3.01 3.78(0.59) (0.07) (0.78) (0.59) (0.73) (0.15) (0.74) (0.35) (0.06)

n 104 94 90 102 107 120 129 141 181

R2 0.02 0.05 0.01 0.00 0.02 0.03 0.01 0.03 0.06Average (Q) 1.29 1.16 1.09 1.06 1.38 1.32 1.43 1.98 1.97

Panel A shows results of a pooled regression using data for whole period. Panel B shows results of OLS regressionson a year by year basis. Data for firms listed on the Oslo Stock Exchange, 1989-1997. Variable definitions are inAppendix A.2. This regression underlies figure 5.2.


B.2.2 Alternative concentration measures

This section uses two alternative concentration measures: The Herfindahl index of ownership con-centration and the holdings by the 20 largest owners.

Table B.7 Multivariate regression relating performance (RoA5) to ownership concentration(Herfindahl) and controlsPanel A: Pooled regressions (OLS and GMM)


Constant 13.40 (2.68) 0.00lntrans(Herfindahl index) -0.46 (0.14) 0.00Industrial -1.98 (0.38) 0.00Transport/shipping -2.84 (0.40) 0.00Offshore -3.94 (0.61) 0.00Investments over income -0.08 (0.05) 0.11ln(Firm value) -0.11 (0.12) 0.36Stock volatility -1.76 (0.65) 0.01n 886



Constant 13.40 (3.86) 0.00lntrans(Herfindahl index) -0.46 (0.13) 0.00Industrial -1.98 (0.39) 0.00Transport/shipping -2.84 (0.47) 0.00Offshore -3.94 (0.56) 0.00Investments over income -0.08 (0.04) 0.04ln(Firm value) -0.11 (0.16) 0.47Stock volatility -1.76 (0.95) 0.06n 886Average (RoA5) 9.41


Year1989 1990 1991 1992 1993 1994 1995 1996 1997

constant 6.83 −0.80 11.38 12.50 25.06 22.23 1.67 22.07 32.88(0.33) (0.91) (0.20) (0.08) (0.00) (0.00) (0.80) (0.02) (0.00)

lntrans(Herfindahl index) −0.22 0.02 −0.38 −0.71 −0.63 −0.74 −0.50 −0.11 −0.22(0.59) (0.97) (0.34) (0.15) (0.12) (0.02) (0.16) (0.81) (0.62)

Industrial −1.60 0.24 −0.00 −0.60 −2.04 −2.19 −2.41 −2.52 −3.37(0.11) (0.82) (1.00) (0.65) (0.08) (0.03) (0.01) (0.02) (0.01)


Offshore −5.71 −1.76 −3.09 −2.77 −4.71 −4.55 −3.39 −3.40 −3.98(0.00) (0.30) (0.05) (0.11) (0.00) (0.00) (0.05) (0.10) (0.03)


ln(Firm value) 0.24 0.50 0.05 −0.04 −0.59 −0.59 0.34 −0.45 −0.87(0.45) (0.14) (0.89) (0.90) (0.11) (0.04) (0.27) (0.30) (0.03)

Stock volatility −1.89 0.23 −5.62 −2.67 −4.46 −1.86 2.57 −3.60 −6.44(0.28) (0.90) (0.01) (0.13) (0.01) (0.32) (0.10) (0.12) (0.03)

n 83 79 76 69 82 106 115 119 157

R2 0.04 −0.04 0.17 0.03 0.17 0.15 0.14 0.12 0.12Average (RoA5) 9.82 9.33 9.19 10.16 10.06 8.94 8.77 9.04 9.76

Panel C: OLS fixed (annual) effects regression


Constant 15.44 (2.74) 0.00lntrans(Herfindahl index) -0.46 (0.14) 0.00Industrial -2.04 (0.38) 0.00Transport/shipping -2.90 (0.40) 0.00Offshore -4.13 (0.61) 0.00Investments over income -0.09 (0.05) 0.10ln(Firm value) -0.16 (0.12) 0.20Stock volatility -2.55 (0.68) 0.001990 -0.44 (0.70) 0.531991 -0.30 (0.71) 0.681992 0.69 (0.74) 0.351993 0.25 (0.70) 0.721994 -1.28 (0.66) 0.051995 -1.53 (0.65) 0.021996 -1.34 (0.65) 0.041997 -0.54 (0.61) 0.38n 886


Panel A shows results of pooled regressions using data for the whole period. The left hand table in panel A showsresult for a OLS regression. The right hand table in panel A is a GMM regression. Panel B shows estimates ona year by year basis, and panel C is a (annual) fixed effects regression. In regressions using firm size across yearsthe 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.


Table B.8 Multivariate regression relating performance (RoA5) to ownership concentration (20largest owners) and controlsPanel A: Pooled regressions (OLS and GMM)


Constant 14.87 (2.69) 0.00lntrans(1-20 largest owners) -0.29 (0.18) 0.11Industrial -2.00 (0.39) 0.00Transport/shipping -2.87 (0.40) 0.00Offshore -3.94 (0.61) 0.00Investments over income -0.08 (0.05) 0.13ln(Firm value) -0.11 (0.12) 0.35Stock volatility -1.82 (0.66) 0.01n 886



Constant 14.87 (3.77) 0.00lntrans(1-20 largest owners) -0.29 (0.18) 0.10Industrial -2.00 (0.40) 0.00Transport/shipping -2.87 (0.47) 0.00Offshore -3.94 (0.55) 0.00Investments over income -0.08 (0.04) 0.04ln(Firm value) -0.11 (0.16) 0.47Stock volatility -1.82 (0.96) 0.06n 886Average (RoA5) 9.41


Year1989 1990 1991 1992 1993 1994 1995 1996 1997

constant 7.98 −1.41 12.83 14.21 27.78 24.84 2.90 22.43 33.15(0.25) (0.85) (0.14) (0.05) (0.00) (0.00) (0.66) (0.02) (0.00)

lntrans(1-20 largest owners) −0.40 0.30 0.08 −0.04 −0.46 −0.40 −0.23 −0.19 0.12(0.43) (0.59) (0.88) (0.94) (0.38) (0.34) (0.61) (0.74) (0.83)

Industrial −1.64 0.31 0.19 −0.22 −2.14 −2.28 −2.47 −2.55 −3.37(0.11) (0.77) (0.87) (0.87) (0.07) (0.03) (0.01) (0.02) (0.01)


Offshore −5.77 −1.57 −2.89 −2.27 −4.69 −4.52 −3.42 −3.43 −4.02(0.00) (0.36) (0.06) (0.19) (0.00) (0.01) (0.05) (0.09) (0.03)


ln(Firm value) 0.23 0.51 0.03 −0.05 −0.62 −0.60 0.35 −0.44 −0.86(0.47) (0.13) (0.95) (0.89) (0.10) (0.04) (0.26) (0.31) (0.04)

Stock volatility −1.66 −0.05 −6.06 −3.17 −4.68 −2.22 2.52 −3.54 −6.48(0.35) (0.98) (0.01) (0.07) (0.00) (0.25) (0.11) (0.13) (0.03)

n 83 79 76 69 82 106 115 119 157

R2 0.05 −0.03 0.16 −0.00 0.15 0.12 0.12 0.12 0.12Average (RoA5) 9.82 9.33 9.19 10.16 10.06 8.94 8.77 9.04 9.76



Constant 16.90 (2.75) 0.00lntrans(1-20 largest owners) -0.29 (0.18) 0.10Industrial -2.05 (0.38) 0.00Transport/shipping -2.93 (0.40) 0.00Offshore -4.13 (0.61) 0.00Investments over income -0.08 (0.05) 0.11ln(Firm value) -0.16 (0.12) 0.19Stock volatility -2.62 (0.69) 0.001990 -0.41 (0.71) 0.561991 -0.23 (0.71) 0.751992 0.73 (0.74) 0.331993 0.30 (0.70) 0.671994 -1.25 (0.67) 0.061995 -1.51 (0.65) 0.021996 -1.34 (0.65) 0.041997 -0.52 (0.62) 0.40n 886




Table B.9 Multivariate regression relating performance (Q) to ownership concentration (Herfind-ahl) and controlsPanel A: Pooled regressions (OLS and GMM)


Constant -0.02 (0.56) 0.97lntrans(Herfindahl index) -0.16 (0.03) 0.00Industrial -0.44 (0.08) 0.00Transport/shipping -0.85 (0.08) 0.00Offshore -0.75 (0.13) 0.00Investments over income -0.02 (0.01) 0.17ln(Firm value) 0.08 (0.03) 0.00Stock volatility -0.06 (0.14) 0.67n 905

R2 0.14Average (Q) 1.53


Constant -0.02 (0.66) 0.97lntrans(Herfindahl index) -0.16 (0.03) 0.00Industrial -0.44 (0.10) 0.00Transport/shipping -0.85 (0.10) 0.00Offshore -0.75 (0.11) 0.00Investments over income -0.02 (0.01) 0.03ln(Firm value) 0.08 (0.03) 0.00Stock volatility -0.06 (0.17) 0.73n 905Average (Q) 1.53


Year1989 1990 1991 1992 1993 1994 1995 1996 1997

constant −0.26 0.66 1.42 −0.42 −0.31 0.43 −1.60 6.34 −2.50(0.74) (0.39) (0.15) (0.63) (0.83) (0.54) (0.21) (0.01) (0.21)

lntrans(Herfindahl index) −0.11 −0.08 −0.05 −0.03 −0.09 −0.08 −0.10 −0.23 −0.23(0.02) (0.06) (0.25) (0.64) (0.21) (0.03) (0.14) (0.05) (0.02)

Industrial −0.28 −0.26 −0.07 −0.32 −0.32 −0.23 −0.41 −0.59 −0.44(0.02) (0.02) (0.59) (0.05) (0.12) (0.04) (0.02) (0.04) (0.09)


Offshore −0.14 −0.37 −0.45 −0.48 −0.75 −0.50 −0.68 −0.71 −0.89(0.54) (0.03) (0.01) (0.02) (0.01) (0.00) (0.04) (0.18) (0.02)


ln(Firm value) 0.08 0.03 0.00 0.09 0.09 0.05 0.14 −0.18 0.19(0.03) (0.35) (0.95) (0.02) (0.19) (0.12) (0.01) (0.11) (0.03)


n 83 79 77 69 83 108 117 126 163

R2 0.15 0.07 0.15 0.11 0.12 0.20 0.19 0.18 0.15Average (Q) 1.28 1.18 1.10 1.11 1.46 1.33 1.48 2.02 2.04



Constant -0.23 (0.55) 0.68lntrans(Herfindahl index) -0.15 (0.03) 0.00Industrial -0.38 (0.08) 0.00Transport/shipping -0.77 (0.08) 0.00Offshore -0.69 (0.12) 0.00Investments over income -0.01 (0.01) 0.31ln(Firm value) 0.07 (0.02) 0.00Stock volatility 0.19 (0.14) 0.151990 -0.11 (0.14) 0.431991 -0.15 (0.14) 0.291992 -0.15 (0.15) 0.321993 0.14 (0.14) 0.331994 0.06 (0.13) 0.671995 0.17 (0.13) 0.201996 0.66 (0.13) 0.001997 0.67 (0.12) 0.00n 905

R2 0.23Average (Q) 1.53



Table B.10 Multivariate regression relating performance (Q) to ownership concentration (20largest owners) and controlsPanel A: Pooled regressions (OLS and GMM)


Constant 0.53 (0.57) 0.35lntrans(1-20 largest owners) -0.14 (0.04) 0.00Industrial -0.45 (0.08) 0.00Transport/shipping -0.86 (0.08) 0.00Offshore -0.75 (0.13) 0.00Investments over income -0.01 (0.01) 0.22ln(Firm value) 0.08 (0.03) 0.00Stock volatility -0.06 (0.14) 0.67n 905

R2 0.13Average (Q) 1.53


Constant 0.53 (0.64) 0.41lntrans(1-20 largest owners) -0.14 (0.04) 0.00Industrial -0.45 (0.10) 0.00Transport/shipping -0.86 (0.10) 0.00Offshore -0.75 (0.11) 0.00Investments over income -0.01 (0.01) 0.03ln(Firm value) 0.08 (0.03) 0.00Stock volatility -0.06 (0.17) 0.73n 905Average (Q) 1.53


Year1989 1990 1991 1992 1993 1994 1995 1996 1997

constant 0.06 0.85 1.60 −0.40 0.06 0.61 −1.33 7.23 −1.87(0.94) (0.29) (0.11) (0.65) (0.97) (0.39) (0.30) (0.00) (0.36)

lntrans(1-20 largest owners) −0.08 −0.05 0.02 0.03 −0.07 −0.01 −0.05 −0.15 −0.13(0.19) (0.43) (0.76) (0.72) (0.46) (0.90) (0.59) (0.30) (0.29)

Industrial −0.26 −0.25 −0.04 −0.29 −0.33 −0.23 −0.42 −0.63 −0.45(0.03) (0.03) (0.72) (0.06) (0.11) (0.04) (0.02) (0.03) (0.09)


Offshore −0.10 −0.35 −0.42 −0.45 −0.74 −0.51 −0.66 −0.75 −0.94(0.66) (0.05) (0.02) (0.03) (0.01) (0.01) (0.04) (0.16) (0.02)


ln(Firm value) 0.08 0.03 −0.00 0.09 0.08 0.05 0.15 −0.19 0.19(0.04) (0.35) (0.99) (0.02) (0.21) (0.13) (0.02) (0.10) (0.03)


n 83 79 77 69 83 108 117 126 163

R2 0.11 0.03 0.13 0.11 0.11 0.16 0.17 0.16 0.12Average (Q) 1.28 1.18 1.10 1.11 1.46 1.33 1.48 2.02 2.04



Constant 0.26 (0.56) 0.64lntrans(1-20 largest owners) -0.11 (0.04) 0.00Industrial -0.39 (0.08) 0.00Transport/shipping -0.78 (0.08) 0.00Offshore -0.69 (0.12) 0.00Investments over income -0.01 (0.01) 0.38ln(Firm value) 0.07 (0.02) 0.00Stock volatility 0.18 (0.14) 0.191990 -0.11 (0.14) 0.461991 -0.14 (0.15) 0.351992 -0.14 (0.15) 0.351993 0.15 (0.14) 0.291994 0.07 (0.14) 0.621995 0.17 (0.13) 0.201996 0.66 (0.13) 0.001997 0.68 (0.13) 0.00n 905

R2 0.21Average (Q) 1.53



B.3 Insider ownership

This appendix presents supplementary results to the analyses in chapter 6.



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

Year1989 1990 1991 1992 1993 1994 1995 1996 1997

constant 0.35 −0.02 −0.16 −0.73 0.48 0.29 −0.51 2.69 0.14(0.48) (0.96) (0.75) (0.08) (0.47) (0.54) (0.54) (0.10) (0.91)

Primary insiders (0 to 5) 4.51 7.83 9.02 1.69 0.71 5.82 6.24 9.69 11.76(0.24) (0.00) (0.00) (0.58) (0.88) (0.06) (0.18) (0.22) (0.07)

Primary insiders (5 to 25) −0.82 −1.85 −2.16 1.45 1.40 0.84 −0.26 3.72 2.85(0.72) (0.10) (0.06) (0.25) (0.47) (0.48) (0.88) (0.21) (0.21)

Primary insiders (25 to 100) 0.05 0.52 0.18 −0.76 −0.68 −0.44 1.86 −1.77 −0.79(0.94) (0.20) (0.60) (0.24) (0.41) (0.38) (0.00) (0.24) (0.52)

Industrial −0.28 −0.19 −0.11 −0.13 −0.28 −0.21 −0.33 −0.44 −0.10(0.02) (0.06) (0.29) (0.24) (0.08) (0.05) (0.04) (0.13) (0.67)

Transport/shipping −0.01 −0.25 −0.24 −0.26 −0.55 −0.47 −0.66 −1.05 −0.84(0.95) (0.02) (0.03) (0.03) (0.00) (0.00) (0.00) (0.00) (0.00)

Offshore −0.04 −0.16 −0.35 −0.33 −0.64 −0.57 −0.51 −1.04 −0.67(0.85) (0.30) (0.03) (0.04) (0.01) (0.00) (0.05) (0.02) (0.04)

Debt to assets 0.05 −0.05 −0.29 0.17 −0.52 −0.55 −0.26 −2.26 −1.99(0.84) (0.83) (0.17) (0.46) (0.19) (0.02) (0.47) (0.00) (0.00)

ln(Firm value) 0.05 0.06 0.07 0.09 0.08 0.07 0.11 0.04 0.14(0.04) (0.01) (0.00) (0.00) (0.02) (0.00) (0.01) (0.67) (0.04)

n 102 91 90 99 107 120 128 140 180

R2 0.01 0.12 0.14 0.19 0.12 0.24 0.27 0.24 0.25Average (Q) 1.29 1.15 1.09 1.03 1.38 1.32 1.42 1.98 1.95



Constant -0.00 (0.31) 0.99Primary insiders (0 to 5) 7.74 (2.08) 0.00Primary insiders (5 to 25) 1.85 (0.97) 0.06Primary insiders (25 to 100) -0.57 (0.40) 0.16Industrial -0.28 (0.09) 0.00Transport/shipping -0.59 (0.07) 0.00Offshore -0.58 (0.10) 0.00Debt to assets -1.10 (0.22) 0.00ln(Firm value) 0.11 (0.02) 0.00n 1057Average (Q) 1.47


Constant 0.41 (0.32) 0.21Primary insiders (0 to 5) 7.48 (1.90) 0.00Primary insiders (5 to 25) 1.44 (0.74) 0.05Primary insiders (25 to 100) -0.39 (0.29) 0.19Industrial -0.24 (0.07) 0.00Transport/shipping -0.56 (0.07) 0.00Offshore -0.54 (0.10) 0.00Debt to assets -0.91 (0.14) 0.00ln(Firm value) 0.08 (0.02) 0.001990 -0.18 (0.12) 0.141991 -0.21 (0.12) 0.091992 -0.18 (0.12) 0.131993 0.08 (0.12) 0.501994 -0.04 (0.12) 0.751995 0.06 (0.11) 0.621996 0.52 (0.11) 0.001997 0.44 (0.11) 0.00n 1057

R2 0.26Average (Q) 1.47


B.3 Insider ownership 127

Table B.12 Multivariate regression relating performance (Q) to insider ownership, ownershipconcentration and controls, using the piecewise linear function of Morck et al. (1988).Panel A: Year by year OLS regressions

Year1989 1990 1991 1992 1993 1994 1995 1996 1997

constant 0.58 0.21 0.08 −0.53 0.95 0.62 −0.11 3.10 0.62(0.25) (0.68) (0.87) (0.24) (0.16) (0.20) (0.89) (0.06) (0.64)

Primary insiders (0 to 5) 3.61 7.45 8.40 1.24 −0.86 3.99 5.11 7.90 10.66(0.34) (0.00) (0.01) (0.68) (0.85) (0.20) (0.27) (0.32) (0.10)



Largest owner −0.52 −0.45 −0.38 −0.24 −0.67 −0.49 −0.66 −1.02 −0.99(0.04) (0.03) (0.08) (0.30) (0.03) (0.02) (0.05) (0.12) (0.04)

Industrial −0.28 −0.19 −0.10 −0.12 −0.21 −0.22 −0.32 −0.41 −0.10(0.02) (0.05) (0.35) (0.29) (0.18) (0.04) (0.05) (0.16) (0.68)


Offshore −0.09 −0.17 −0.36 −0.33 −0.62 −0.54 −0.53 −1.03 −0.66(0.70) (0.25) (0.02) (0.03) (0.01) (0.00) (0.04) (0.02) (0.05)

Debt to assets 0.04 −0.03 −0.31 0.14 −0.64 −0.64 −0.32 −2.17 −1.94(0.87) (0.90) (0.14) (0.55) (0.10) (0.01) (0.38) (0.00) (0.00)

ln(Firm value) 0.05 0.06 0.07 0.09 0.06 0.07 0.10 0.03 0.13(0.05) (0.02) (0.01) (0.00) (0.05) (0.00) (0.01) (0.74) (0.06)

n 102 91 90 99 107 120 128 140 180

R2 0.05 0.15 0.16 0.19 0.16 0.27 0.29 0.25 0.26Average (Q) 1.29 1.15 1.09 1.03 1.38 1.32 1.42 1.98 1.95



Constant 0.45 (0.30) 0.14Primary insiders (0 to 5) 6.31 (2.10) 0.00Primary insiders (5 to 25) 1.90 (0.96) 0.05Primary insiders (25 to 100) -0.42 (0.40) 0.29Largest owner -0.78 (0.14) 0.00Industrial -0.26 (0.09) 0.00Transport/shipping -0.60 (0.07) 0.00Offshore -0.59 (0.10) 0.00Debt to assets -1.12 (0.22) 0.00ln(Firm value) 0.10 (0.01) 0.00n 1057Average (Q) 1.47


Constant 0.85 (0.33) 0.01Primary insiders (0 to 5) 6.08 (1.89) 0.00Primary insiders (5 to 25) 1.49 (0.73) 0.04Primary insiders (25 to 100) -0.24 (0.29) 0.41Largest owner -0.77 (0.14) 0.00Industrial -0.22 (0.07) 0.00Transport/shipping -0.57 (0.07) 0.00Offshore -0.55 (0.10) 0.00Debt to assets -0.93 (0.14) 0.00ln(Firm value) 0.07 (0.02) 0.001990 -0.19 (0.12) 0.121991 -0.22 (0.12) 0.071992 -0.19 (0.12) 0.121993 0.08 (0.12) 0.501994 -0.03 (0.11) 0.781995 0.05 (0.11) 0.681996 0.50 (0.11) 0.001997 0.43 (0.11) 0.00n 1057

R2 0.28Average (Q) 1.47



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

Year1989 1990 1991 1992 1993 1994 1995 1996 1997

constant 0.42 −0.05 −0.01 −0.72 0.53 0.38 −0.47 2.91 0.22(0.39) (0.93) (0.98) (0.08) (0.42) (0.44) (0.56) (0.08) (0.87)

Primary insiders 0.17 0.36 0.54 1.70 1.30 1.80 0.93 5.01 5.09(0.87) (0.61) (0.41) (0.03) (0.21) (0.00) (0.36) (0.01) (0.00)

Squared (Primary insiders) −0.06 −0.04 −0.49 −2.38 −1.71 −1.84 0.74 −5.68 −5.40(0.95) (0.96) (0.48) (0.05) (0.22) (0.02) (0.51) (0.03) (0.01)

Industrial −0.24 −0.18 −0.10 −0.13 −0.28 −0.23 −0.38 −0.44 −0.10(0.05) (0.08) (0.38) (0.25) (0.08) (0.03) (0.02) (0.12) (0.68)


Offshore −0.01 −0.12 −0.33 −0.32 −0.65 −0.50 −0.50 −1.04 −0.69(0.97) (0.45) (0.05) (0.04) (0.01) (0.00) (0.06) (0.02) (0.04)

Debt to assets 0.01 −0.04 −0.29 0.16 −0.52 −0.57 −0.26 −2.33 −2.04(0.97) (0.85) (0.20) (0.49) (0.18) (0.02) (0.47) (0.00) (0.00)

ln(Firm value) 0.05 0.07 0.07 0.09 0.07 0.07 0.11 0.03 0.14(0.06) (0.01) (0.01) (0.00) (0.03) (0.00) (0.01) (0.72) (0.04)

n 102 91 90 99 107 120 128 140 180

R2 0.01 0.04 0.07 0.20 0.13 0.23 0.27 0.23 0.24Average (Q) 1.29 1.15 1.09 1.03 1.38 1.32 1.42 1.98 1.95



Constant 0.04 (0.31) 0.90Primary insiders 2.81 (0.63) 0.00Squared (Primary insiders) -2.64 (0.75) 0.00Industrial -0.29 (0.09) 0.00Transport/shipping -0.59 (0.08) 0.00Offshore -0.56 (0.10) 0.00Debt to assets -1.15 (0.23) 0.00ln(Firm value) 0.11 (0.02) 0.00n 1057Average (Q) 1.47


Constant 0.45 (0.33) 0.17Primary insiders 2.47 (0.41) 0.00Squared (Primary insiders) -2.24 (0.50) 0.00Industrial -0.24 (0.07) 0.00Transport/shipping -0.56 (0.07) 0.00Offshore -0.53 (0.11) 0.00Debt to assets -0.95 (0.14) 0.00ln(Firm value) 0.08 (0.02) 0.001990 -0.17 (0.13) 0.171991 -0.20 (0.13) 0.111992 -0.19 (0.12) 0.131993 0.09 (0.12) 0.471994 -0.03 (0.12) 0.811995 0.07 (0.12) 0.551996 0.53 (0.11) 0.001997 0.45 (0.11) 0.00n 1057

R2 0.25Average (Q) 1.47



Table B.14 Multivariate regression relating performance (Q) to insider ownership, ownershipconcentration and controls, following McConnell and Servaes (1990)Panel A: Year by year OLS regressions

Year1989 1990 1991 1992 1993 1994 1995 1996 1997

constant 0.65 0.20 0.26 −0.52 1.00 0.72 −0.06 3.35 0.70(0.18) (0.70) (0.63) (0.25) (0.14) (0.14) (0.94) (0.04) (0.60)


Squared (Primary insiders) 0.04 0.04 −0.36 −2.40 −1.57 −1.55 0.90 −5.19 −4.75(0.97) (0.96) (0.60) (0.05) (0.25) (0.04) (0.42) (0.04) (0.02)

Largest owner −0.55 −0.48 −0.45 −0.25 −0.66 −0.54 −0.69 −1.15 −1.02(0.03) (0.03) (0.05) (0.26) (0.03) (0.01) (0.04) (0.07) (0.03)

Industrial −0.25 −0.18 −0.08 −0.11 −0.21 −0.22 −0.35 −0.40 −0.09(0.04) (0.07) (0.44) (0.31) (0.18) (0.03) (0.03) (0.16) (0.69)


Offshore −0.06 −0.14 −0.34 −0.33 −0.64 −0.49 −0.52 −1.03 −0.68(0.78) (0.38) (0.04) (0.03) (0.01) (0.00) (0.05) (0.02) (0.04)

Debt to assets 0.01 −0.03 −0.31 0.13 −0.63 −0.67 −0.32 −2.21 −1.99(0.97) (0.91) (0.15) (0.57) (0.10) (0.01) (0.38) (0.00) (0.00)

ln(Firm value) 0.04 0.06 0.06 0.09 0.06 0.07 0.10 0.02 0.13(0.06) (0.02) (0.01) (0.00) (0.06) (0.01) (0.01) (0.81) (0.05)

n 102 91 90 99 107 120 128 140 180

R2 0.05 0.08 0.10 0.20 0.17 0.27 0.29 0.24 0.26Average (Q) 1.29 1.15 1.09 1.03 1.38 1.32 1.42 1.98 1.95



Constant 0.52 (0.31) 0.09Primary insiders 2.56 (0.61) 0.00Squared (Primary insiders) -2.31 (0.73) 0.00Largest owner -0.85 (0.14) 0.00Industrial -0.26 (0.09) 0.00Transport/shipping -0.59 (0.07) 0.00Offshore -0.58 (0.10) 0.00Debt to assets -1.16 (0.22) 0.00ln(Firm value) 0.10 (0.01) 0.00n 1057Average (Q) 1.47


Constant 0.92 (0.33) 0.01Primary insiders 2.22 (0.41) 0.00Squared (Primary insiders) -1.91 (0.50) 0.00Largest owner -0.83 (0.14) 0.00Industrial -0.22 (0.07) 0.00Transport/shipping -0.56 (0.07) 0.00Offshore -0.54 (0.10) 0.00Debt to assets -0.96 (0.14) 0.00ln(Firm value) 0.07 (0.02) 0.001990 -0.18 (0.12) 0.141991 -0.21 (0.12) 0.091992 -0.19 (0.12) 0.111993 0.08 (0.12) 0.481994 -0.02 (0.12) 0.841995 0.05 (0.11) 0.631996 0.52 (0.11) 0.001997 0.44 (0.11) 0.00n 1057

R2 0.27Average (Q) 1.47



Table B.15 Multivariate regression relating performance (Q) to insider ownership, ownershipconcentration, institutional ownership, and controls, following McConnell and Servaes (1990)Panel A: Year by year OLS regressions

Year1989 1990 1991 1992 1993 1994 1995 1996 1997

constant 0.69 0.18 0.30 −0.51 1.00 0.76 −0.09 2.64 0.66(0.15) (0.74) (0.58) (0.26) (0.14) (0.10) (0.91) (0.11) (0.63)


Squared (Primary insiders) −0.29 0.04 −0.35 −2.37 −1.57 −1.37 0.82 −5.13 −4.75(0.79) (0.96) (0.61) (0.06) (0.25) (0.07) (0.45) (0.04) (0.02)

Largest owner −0.73 −0.51 −0.41 −0.23 −0.66 −0.75 −1.11 −1.41 −1.03(0.00) (0.02) (0.08) (0.32) (0.03) (0.00) (0.00) (0.03) (0.03)

Aggregate financial holdings −1.00 −0.18 0.24 0.12 −0.02 −0.87 −1.76 −1.97 −0.10(0.01) (0.64) (0.62) (0.73) (0.97) (0.00) (0.00) (0.02) (0.88)

Industrial −0.25 −0.17 −0.11 −0.12 −0.21 −0.21 −0.23 −0.39 −0.09(0.03) (0.09) (0.37) (0.29) (0.18) (0.03) (0.14) (0.17) (0.69)


Offshore −0.11 −0.14 −0.35 −0.34 −0.64 −0.54 −0.53 −0.98 −0.68(0.62) (0.38) (0.03) (0.03) (0.01) (0.00) (0.04) (0.03) (0.04)

Debt to assets −0.03 −0.01 −0.32 0.12 −0.63 −0.47 −0.03 −1.91 −1.98(0.91) (0.97) (0.14) (0.60) (0.12) (0.05) (0.93) (0.00) (0.00)

ln(Firm value) 0.05 0.06 0.06 0.08 0.06 0.07 0.12 0.07 0.13(0.03) (0.02) (0.02) (0.00) (0.07) (0.00) (0.00) (0.39) (0.06)

n 102 91 90 99 107 120 128 140 180

R2 0.10 0.07 0.09 0.20 0.16 0.32 0.33 0.27 0.26Average (Q) 1.29 1.15 1.09 1.03 1.38 1.32 1.42 1.98 1.95



Constant 0.47 (0.32) 0.14Primary insiders 2.56 (0.61) 0.00Squared (Primary insiders) -2.33 (0.72) 0.00Largest owner -0.89 (0.15) 0.00Aggregate financial holdings -0.24 (0.21) 0.24Industrial -0.26 (0.08) 0.00Transport/shipping -0.60 (0.08) 0.00Offshore -0.58 (0.10) 0.00Debt to assets -1.14 (0.23) 0.00ln(Firm value) 0.11 (0.02) 0.00n 1057Average (Q) 1.47


Constant 0.79 (0.33) 0.02Primary insiders 2.18 (0.41) 0.00Squared (Primary insiders) -1.91 (0.50) 0.00Largest owner -0.93 (0.14) 0.00Aggregate financial holdings -0.63 (0.21) 0.00Industrial -0.20 (0.07) 0.00Transport/shipping -0.59 (0.07) 0.00Offshore -0.54 (0.10) 0.00Debt to assets -0.89 (0.14) 0.00ln(Firm value) 0.08 (0.02) 0.001990 -0.16 (0.12) 0.181991 -0.19 (0.12) 0.121992 -0.16 (0.12) 0.201993 0.14 (0.12) 0.251994 0.03 (0.12) 0.801995 0.10 (0.11) 0.391996 0.57 (0.11) 0.001997 0.52 (0.11) 0.00n 1057

R2 0.28Average (Q) 1.47



Table B.16 Multivariate regression relating performance (Q) to insider ownership, the largestprimary insider, external concentration, and controlsPanel A: Year by year OLS regressions

Year1989 1990 1991 1992 1993 1994 1995 1996 1997

constant 0.91 0.53 0.45 −0.50 1.05 0.63 1.49 4.24 0.98(0.08) (0.33) (0.49) (0.29) (0.20) (0.24) (0.06) (0.01) (0.47)

Primary insiders −0.06 0.16 0.22 4.15 2.34 2.25 2.53 6.64 5.42(0.96) (0.85) (0.76) (0.00) (0.09) (0.00) (0.02) (0.00) (0.00)

Squared (Primary insiders) 0.63 0.48 −0.06 −4.02 −2.36 −1.92 −2.25 −6.84 −5.04(0.61) (0.60) (0.93) (0.00) (0.11) (0.01) (0.05) (0.01) (0.02)

Largest primary insider −0.42 −0.51 0.07 −2.38 −1.32 −0.96 −0.47 −1.66 −1.71(0.40) (0.25) (0.92) (0.00) (0.24) (0.16) (0.53) (0.22) (0.09)

Largest outside owner −0.54 −0.49 −0.47 −0.12 −0.58 −0.51 −0.58 −0.78 −0.72(0.05) (0.02) (0.04) (0.60) (0.07) (0.01) (0.05) (0.20) (0.18)

Industrial −0.27 −0.23 −0.11 −0.12 −0.27 −0.20 −0.22 −0.39 −0.12(0.03) (0.02) (0.36) (0.27) (0.10) (0.05) (0.12) (0.16) (0.60)


Offshore −0.12 −0.30 −0.46 −0.38 −0.73 −0.47 −0.43 −0.70 −0.65(0.63) (0.08) (0.01) (0.02) (0.00) (0.00) (0.08) (0.14) (0.07)

Debt to assets −0.26 −0.20 −0.51 0.11 −0.89 −0.36 −0.64 −2.49 −2.79(0.35) (0.40) (0.05) (0.65) (0.03) (0.16) (0.06) (0.00) (0.00)


ln(Firm value) 0.04 0.05 0.06 0.08 0.07 0.06 0.03 −0.02 0.13(0.10) (0.04) (0.04) (0.00) (0.08) (0.02) (0.43) (0.78) (0.05)

n 94 85 83 94 101 115 119 131 168

R2 0.07 0.14 0.12 0.29 0.21 0.27 0.24 0.28 0.30Average (Q) 1.30 1.16 1.10 1.04 1.39 1.30 1.39 1.96 1.99



Constant 1.08 (0.33) 0.00Primary insiders 3.54 (0.78) 0.00Squared (Primary insiders) -3.35 (0.73) 0.00Largest primary insider -1.10 (0.38) 0.00Largest outside owner -0.73 (0.13) 0.00Industrial -0.29 (0.09) 0.00Transport/shipping -0.60 (0.07) 0.00Offshore -0.61 (0.10) 0.00Debt to assets -1.51 (0.27) 0.00Investments over income -0.00 (0.01) 0.49ln(Firm value) 0.08 (0.01) 0.00n 990Average (Q) 1.47


Constant 1.48 (0.35) 0.00Primary insiders 3.30 (0.48) 0.00Squared (Primary insiders) -3.09 (0.54) 0.00Largest primary insider -1.08 (0.33) 0.00Largest outside owner -0.70 (0.14) 0.00Industrial -0.24 (0.07) 0.00Transport/shipping -0.56 (0.07) 0.00Offshore -0.56 (0.11) 0.00Debt to assets -1.31 (0.15) 0.00Investments over income -0.00 (0.01) 0.76ln(Firm value) 0.05 (0.02) 0.001990 -0.20 (0.12) 0.101991 -0.23 (0.12) 0.061992 -0.22 (0.12) 0.071993 0.05 (0.12) 0.681994 -0.10 (0.11) 0.401995 -0.02 (0.11) 0.851996 0.46 (0.11) 0.001997 0.42 (0.11) 0.00n 990

R2 0.31Average (Q) 1.47



B.3.2 Inside ownership without controls

This section complements figures 6.2 and 6.4 by first showing the underlying regressions, and thenshowing similar figures and tables when RoA5 is the performance measure.

Table B.17 Multivariate regression relating performance (Q) to insider ownership, without con-trols, using the piecewise linear formulation of Morck et al. (1988)Panel A: Pooled OLS regression


Constant 1.29 (0.04) 0.00Primary insiders (0 to 5) 8.56 (2.13) 0.00Primary insiders (5 to 25) 2.28 (0.83) 0.01Primary insiders (25 to 100) -0.99 (0.32) 0.00n 1068

R2 0.06Average (Q) 1.48


Year1989 1990 1991 1992 1993 1994 1995 1996 1997

constant 1.26 1.07 1.02 1.01 1.34 1.22 1.22 1.65 1.58(0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00)




n 104 94 90 102 107 120 129 141 181

R2 −0.03 0.06 0.02 0.00 −0.01 0.05 0.16 0.06 0.10Average (Q) 1.29 1.16 1.09 1.06 1.38 1.32 1.43 1.98 1.97

Panel A shows results of a pooled regression using data for whole period. Panel B shows results of OLS regressionson a year by year basis. Data for firms listed on the Oslo Stock Exchange, 1989-1997. Variable definitions are inAppendix A.2. The tables relate to figure 6.2 in the text.

Table B.18 Multivariate regression relating performance (Q) to insider ownership, without con-trols, using the quadratic specifiction of McConnell and Servaes (1990)Panel A: Pooled OLS regression


Constant 1.35 (0.03) 0.00Primary insiders 3.30 (0.46) 0.00Squared (Primary insiders) -3.37 (0.55) 0.00n 1068

R2 0.05Average (Q) 1.48


Year1989 1990 1991 1992 1993 1994 1995 1996 1997

constant 1.28 1.13 1.07 1.01 1.32 1.24 1.29 1.74 1.68(0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00)


Squared (Primary insiders) 0.11 −0.18 −0.32 −2.85 −2.72 −2.45 0.03 −8.35 −8.74(0.92) (0.81) (0.66) (0.05) (0.06) (0.00) (0.98) (0.00) (0.00)

n 104 94 90 102 107 120 129 141 181

R2 −0.02 −0.00 −0.03 0.02 0.01 0.06 0.15 0.06 0.09Average (Q) 1.29 1.16 1.09 1.06 1.38 1.32 1.43 1.98 1.97

Panel A shows results of a pooled regression using data for whole period. Panel B shows results of OLS regressionson a year by year basis. Data for firms listed on the Oslo Stock Exchange, 1989-1997. Variable definitions are inAppendix A.2. The tables relate to figure 6.4 in the text.


Figure B.1 The relationship between performance (RoA5) and insider ownership in Norwegianfirms, following Morck et al. (1988)

All years

8.5

9

9.5

10

10.5

11

11.5

0 0.2 0.4 0.6 0.8 1

RoA

5

fraction owned

all years

Year by year

8.5

9

9.5

10

10.5

11

11.5

0 0.2 0.4 0.6 0.8 1

RoA

5

fraction owned

all years

The figure shows the implied functional relationship from a piecewise linear regression with RoA5 as the dependentvariable and insider ownership as the independent variable. The figure on the left pools data for all years, the figureon the right shows the results of doing the estimation year by year. The underlying regression, which is detailed inappendix table B.19, includes no controls and no other governance mechanism than insider ownership. Data for firmslisted 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, withoutcontrols, using the piecewise linear formulation of Morck et al. (1988)Panel A: Pooled OLS regression


Constant 8.65 (0.20) 0.00Primary insiders (0 to 5) 37.07 (10.74) 0.00Primary insiders (5 to 25) 3.02 (4.16) 0.47Primary insiders (25 to 100) -1.23 (1.61) 0.44n 1031



Year1989 1990 1991 1992 1993 1994 1995 1996 1997

constant 9.40 8.84 8.67 8.97 9.49 8.39 7.78 7.62 8.82(0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00)

Primary insiders (0 to 5) −2.77 36.78 69.40 53.81 23.35 22.90 51.66 49.79 35.73(0.93) (0.17) (0.03) (0.13) (0.48) (0.44) (0.05) (0.10) (0.30)

Primary insiders (5 to 25) 22.41 −6.44 −14.82 9.93 16.89 2.35 −1.12 10.74 1.18(0.28) (0.56) (0.21) (0.49) (0.22) (0.84) (0.91) (0.35) (0.92)

Primary insiders (25 to 100) −5.08 3.95 −0.17 −12.90 −12.76 8.57 0.98 −6.91 −0.29(0.37) (0.29) (0.96) (0.08) (0.03) (0.08) (0.78) (0.22) (0.96)

n 102 93 89 99 104 117 125 130 172

R2 −0.02 0.01 0.01 0.03 0.03 0.06 0.04 0.05 −0.01Average (RoA5) 9.58 9.44 9.34 9.71 9.98 9.16 8.70 8.72 9.58

Panel A shows results of a pooled regression using data for whole period. Panel B shows results of OLS regressionson a year by year basis. Data for firms listed on the Oslo Stock Exchange, 1989-1997. Variable definitions are inAppendix A.2. The tables relate to figure B.1.


Figure B.2 The quadratic relationship between performance (RoA5) and insider ownership forNorwegian firms, without controls, following McConnell and Servaes (1990).

All years

8.5

9

9.5

10

10.5

11

11.5

12

0 0.2 0.4 0.6 0.8 1

RoA�

fraction owned

all years

Year by year

-4

-2

0

2

4

6

8

10

12

14

16

18

0 0.2 0.4 0.6 0.8 1

RoA�

fraction owned

198919901991199219931994199519961997

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 poolsdata for all years, the figure on the right shows the results of doing the estimation year by year. The underlyingregressions 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, withoutcontrols, using the quadratic specifiction of McConnell and Servaes (1990)Panel A: Pooled OLS regression


Constant 8.90 (0.17) 0.00Primary insiders 10.48 (2.28) 0.00Squared (Primary insiders) -9.94 (2.75) 0.00n 1031



Year1989 1990 1991 1992 1993 1994 1995 1996 1997

constant 9.35 9.16 9.16 9.25 9.62 8.55 8.12 7.97 9.02(0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00)


Squared (Primary insiders) −10.18 −0.40 −6.57 −33.40 −23.29 1.42 −11.31 −23.83 −13.71(0.32) (0.96) (0.34) (0.02) (0.02) (0.85) (0.08) (0.01) (0.21)

n 102 93 89 99 104 117 125 130 172

R2 −0.02 0.00 −0.02 0.03 0.03 0.07 0.03 0.04 −0.00Average (RoA5) 9.58 9.44 9.34 9.71 9.98 9.16 8.70 8.72 9.58

Panel A shows results of a pooled regression using data for whole period. Panel B shows results of OLS regressionson a year by year basis. Data for firms listed on the Oslo Stock Exchange, 1989-1997. Variable definitions are inAppendix A.2. The tables relate to figure B.2.


B.3.3 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)


Constant 11.57 (1.74) 0.00Primary insiders (0 to 5) 35.07 (10.42) 0.00Primary insiders (5 to 25) 0.89 (4.05) 0.83Primary insiders (25 to 100) -0.99 (1.58) 0.53Industrial -1.44 (0.37) 0.00Transport/shipping -1.86 (0.39) 0.00Offshore -3.92 (0.57) 0.00Debt to assets -3.64 (0.81) 0.00ln(Firm value) 0.03 (0.08) 0.73n 1022



Constant 11.57 (2.01) 0.00Primary insiders (0 to 5) 35.07 (9.20) 0.00Primary insiders (5 to 25) 0.89 (4.01) 0.82Primary insiders (25 to 100) -0.99 (1.75) 0.57Industrial -1.44 (0.38) 0.00Transport/shipping -1.86 (0.47) 0.00Offshore -3.92 (0.62) 0.00Debt to assets -3.64 (1.29) 0.00ln(Firm value) 0.03 (0.08) 0.72n 1022Average (RoA5) 9.36


Year1989 1990 1991 1992 1993 1994 1995 1996 1997

constant 6.65 9.69 4.77 9.40 14.01 18.40 10.32 4.50 15.54(0.12) (0.07) (0.38) (0.06) (0.00) (0.00) (0.03) (0.48) (0.02)


Primary insiders (5 to 25) 21.44 −6.25 −16.48 13.87 10.79 −3.66 0.09 11.41 −1.56(0.27) (0.59) (0.16) (0.36) (0.44) (0.75) (0.99) (0.30) (0.89)

Primary insiders (25 to 100) −5.27 1.78 0.01 −13.03 −13.06 8.73 0.49 −6.34 4.41(0.31) (0.67) (1.00) (0.09) (0.03) (0.07) (0.89) (0.25) (0.47)

Industrial −1.13 −0.06 −0.62 0.74 −2.59 −1.52 −1.42 −2.05 −3.09(0.30) (0.95) (0.57) (0.56) (0.03) (0.14) (0.13) (0.06) (0.01)

Transport/shipping −0.15 −0.17 −0.79 1.28 −1.17 −1.83 −3.11 −3.85 −4.89(0.89) (0.88) (0.49) (0.35) (0.34) (0.08) (0.00) (0.00) (0.00)

Offshore −4.87 −3.23 −3.06 −1.74 −3.63 −3.93 −3.10 −5.06 −5.81(0.01) (0.04) (0.05) (0.34) (0.03) (0.02) (0.05) (0.00) (0.00)

Debt to assets −5.81 −3.99 −3.24 −5.22 −0.53 −2.72 −1.11 −0.99 −6.05(0.01) (0.07) (0.14) (0.06) (0.85) (0.26) (0.61) (0.70) (0.02)

ln(Firm value) 0.36 0.09 0.33 0.12 −0.13 −0.36 −0.01 0.29 −0.02(0.09) (0.72) (0.20) (0.61) (0.57) (0.13) (0.97) (0.34) (0.96)

n 100 90 89 97 104 117 124 130 171

R2 0.09 0.01 0.04 0.04 0.06 0.13 0.10 0.13 0.13Average (RoA5) 9.73 9.40 9.34 9.71 9.98 9.16 8.73 8.72 9.64



Constant 11.04 (1.80) 0.00Primary insiders (0 to 5) 36.60 (10.40) 0.00Primary insiders (5 to 25) 1.00 (4.05) 0.81Primary insiders (25 to 100) -1.08 (1.58) 0.50Industrial -1.51 (0.37) 0.00Transport/shipping -1.89 (0.39) 0.00Offshore -4.03 (0.57) 0.00Debt to assets -3.94 (0.82) 0.00ln(Firm value) 0.09 (0.09) 0.301990 -0.38 (0.67) 0.571991 -0.35 (0.68) 0.601992 0.29 (0.67) 0.671993 0.35 (0.65) 0.591994 -0.76 (0.63) 0.231995 -1.22 (0.62) 0.051996 -1.33 (0.62) 0.031997 -0.62 (0.60) 0.29n 1022




Table B.22 Multivariate regression relating performance (RoA5) to insider ownership, ownershipconcentration and controls, using the piecewise linear function of Morck et al. (1988).Panel A: Pooled regressions (OLS and GMM)


Constant 12.60 (1.78) 0.00Primary insiders (0 to 5) 31.67 (10.48) 0.00Primary insiders (5 to 25) 1.01 (4.04) 0.80Primary insiders (25 to 100) -0.60 (1.58) 0.70Largest owner -1.96 (0.79) 0.01Industrial -1.42 (0.37) 0.00Transport/shipping -1.88 (0.39) 0.00Offshore -3.94 (0.57) 0.00Debt to assets -3.73 (0.81) 0.00ln(Firm value) 0.01 (0.08) 0.90n 1022



Constant 12.60 (2.12) 0.00Primary insiders (0 to 5) 31.67 (9.61) 0.00Primary insiders (5 to 25) 1.01 (4.01) 0.80Primary insiders (25 to 100) -0.60 (1.75) 0.73Largest owner -1.96 (0.67) 0.00Industrial -1.42 (0.38) 0.00Transport/shipping -1.88 (0.47) 0.00Offshore -3.94 (0.62) 0.00Debt to assets -3.73 (1.28) 0.00ln(Firm value) 0.01 (0.08) 0.90n 1022Average (RoA5) 9.36


Year1989 1990 1991 1992 1993 1994 1995 1996 1997

constant 7.12 9.40 5.35 11.03 16.60 20.60 11.05 4.32 16.85(0.10) (0.08) (0.33) (0.04) (0.00) (0.00) (0.02) (0.50) (0.02)


Primary insiders (5 to 25) 22.03 −6.22 −16.13 14.89 11.77 −2.33 0.30 11.45 −2.13(0.26) (0.59) (0.17) (0.33) (0.39) (0.84) (0.98) (0.30) (0.85)

Primary insiders (25 to 100) −5.20 1.57 0.31 −12.96 −12.78 9.36 0.69 −6.43 4.83(0.32) (0.71) (0.93) (0.09) (0.03) (0.05) (0.84) (0.24) (0.43)

Largest owner −1.42 0.90 −1.29 −2.17 −4.07 −3.34 −1.26 0.55 −2.42(0.53) (0.70) (0.59) (0.43) (0.07) (0.10) (0.53) (0.82) (0.32)

Industrial −1.18 −0.03 −0.61 0.81 −2.23 −1.57 −1.41 −2.05 −3.08(0.27) (0.97) (0.58) (0.53) (0.06) (0.13) (0.13) (0.06) (0.01)


Offshore −5.01 −3.19 −3.08 −1.85 −3.58 −3.74 −3.09 −5.06 −5.76(0.01) (0.04) (0.05) (0.31) (0.03) (0.02) (0.05) (0.00) (0.00)

Debt to assets −5.82 −4.08 −3.25 −5.44 −1.26 −3.42 −1.27 −1.01 −6.02(0.01) (0.07) (0.14) (0.05) (0.66) (0.16) (0.56) (0.70) (0.02)

ln(Firm value) 0.36 0.10 0.32 0.08 −0.18 −0.39 −0.02 0.29 −0.04(0.09) (0.72) (0.22) (0.75) (0.43) (0.10) (0.93) (0.34) (0.90)

n 100 90 89 97 104 117 124 130 171

R2 0.09 0.00 0.03 0.04 0.08 0.14 0.09 0.13 0.13Average (RoA5) 9.73 9.40 9.34 9.71 9.98 9.16 8.73 8.72 9.64



Constant 12.09 (1.84) 0.00Primary insiders (0 to 5) 33.14 (10.46) 0.00Primary insiders (5 to 25) 1.11 (4.04) 0.78Primary insiders (25 to 100) -0.68 (1.59) 0.67Largest owner -2.00 (0.79) 0.01Industrial -1.49 (0.37) 0.00Transport/shipping -1.91 (0.39) 0.00Offshore -4.05 (0.57) 0.00Debt to assets -4.02 (0.82) 0.00ln(Firm value) 0.07 (0.09) 0.411990 -0.41 (0.67) 0.551991 -0.38 (0.67) 0.571992 0.28 (0.66) 0.681993 0.35 (0.65) 0.591994 -0.74 (0.63) 0.241995 -1.24 (0.62) 0.051996 -1.35 (0.62) 0.031997 -0.62 (0.59) 0.30n 1022




Table B.23 Multivariate regression relating performance (RoA5) to insider ownership, follow-ing McConnell and Servaes (1990)Panel A: Pooled regressions (OLS and GMM)


Constant 11.79 (1.74) 0.00Primary insiders 8.61 (2.23) 0.00Squared (Primary insiders) -8.33 (2.70) 0.00Industrial -1.47 (0.37) 0.00Transport/shipping -1.82 (0.39) 0.00Offshore -3.82 (0.58) 0.00Debt to assets -3.82 (0.82) 0.00ln(Firm value) 0.04 (0.08) 0.68n 1022



Constant 11.79 (1.98) 0.00Primary insiders 8.61 (2.56) 0.00Squared (Primary insiders) -8.33 (3.09) 0.01Industrial -1.47 (0.38) 0.00Transport/shipping -1.82 (0.48) 0.00Offshore -3.82 (0.63) 0.00Debt to assets -3.82 (1.27) 0.00ln(Firm value) 0.04 (0.08) 0.67n 1022Average (RoA5) 9.36


Year1989 1990 1991 1992 1993 1994 1995 1996 1997

constant 6.47 9.66 6.23 9.48 14.60 18.64 10.52 5.41 15.74(0.13) (0.07) (0.26) (0.05) (0.00) (0.00) (0.02) (0.40) (0.02)



Industrial −1.18 0.02 −0.49 0.82 −2.66 −1.62 −1.60 −2.13 −3.08(0.25) (0.98) (0.66) (0.52) (0.02) (0.12) (0.08) (0.05) (0.01)

Transport/shipping −0.19 0.06 −0.78 1.50 −1.06 −1.85 −3.18 −3.82 −4.84(0.86) (0.96) (0.51) (0.26) (0.38) (0.08) (0.00) (0.00) (0.00)

Offshore −4.99 −3.01 −2.86 −1.52 −3.51 −3.56 −2.88 −5.08 −5.82(0.01) (0.05) (0.08) (0.40) (0.04) (0.03) (0.07) (0.00) (0.00)

Debt to assets −5.73 −4.07 −3.25 −5.58 −0.77 −2.73 −1.29 −1.28 −5.97(0.01) (0.07) (0.14) (0.04) (0.78) (0.26) (0.55) (0.62) (0.02)

ln(Firm value) 0.37 0.11 0.28 0.14 −0.15 −0.36 0.00 0.27 −0.03(0.08) (0.68) (0.29) (0.57) (0.53) (0.14) (0.99) (0.38) (0.94)

n 100 90 89 97 104 117 124 130 171

R2 0.10 0.00 −0.01 0.05 0.06 0.13 0.10 0.12 0.13Average (RoA5) 9.73 9.40 9.34 9.71 9.98 9.16 8.73 8.72 9.64



Constant 11.30 (1.81) 0.00Primary insiders 8.92 (2.25) 0.00Squared (Primary insiders) -8.62 (2.72) 0.00Industrial -1.53 (0.37) 0.00Transport/shipping -1.85 (0.39) 0.00Offshore -3.92 (0.58) 0.00Debt to assets -4.11 (0.82) 0.00ln(Firm value) 0.09 (0.09) 0.281990 -0.33 (0.68) 0.621991 -0.32 (0.68) 0.641992 0.24 (0.67) 0.721993 0.34 (0.65) 0.601994 -0.74 (0.64) 0.251995 -1.17 (0.63) 0.061996 -1.29 (0.63) 0.041997 -0.60 (0.60) 0.31n 1022




Table B.24 Multivariate regression relating performance (RoA5) to insider ownership, ownershipconcentration and controls, following McConnell and Servaes (1990)Panel A: Pooled regressions (OLS and GMM)


Constant 12.94 (1.78) 0.00Primary insiders 7.99 (2.23) 0.00Squared (Primary insiders) -7.48 (2.70) 0.01Largest owner -2.24 (0.78) 0.00Industrial -1.44 (0.37) 0.00Transport/shipping -1.85 (0.39) 0.00Offshore -3.85 (0.57) 0.00Debt to assets -3.90 (0.81) 0.00ln(Firm value) 0.01 (0.08) 0.87n 1022



Constant 12.94 (2.08) 0.00Primary insiders 7.99 (2.54) 0.00Squared (Primary insiders) -7.48 (3.08) 0.02Largest owner -2.24 (0.64) 0.00Industrial -1.44 (0.38) 0.00Transport/shipping -1.85 (0.48) 0.00Offshore -3.85 (0.63) 0.00Debt to assets -3.90 (1.26) 0.00ln(Firm value) 0.01 (0.08) 0.87n 1022Average (RoA5) 9.36


Year1989 1990 1991 1992 1993 1994 1995 1996 1997

constant 6.88 9.44 6.97 11.47 17.38 20.81 11.45 5.45 16.99(0.11) (0.08) (0.21) (0.03) (0.00) (0.00) (0.02) (0.40) (0.01)


Squared (Primary insiders) −9.01 −3.90 −6.59 −36.10 −22.65 4.69 −8.15 −17.83 1.38(0.34) (0.62) (0.34) (0.01) (0.02) (0.53) (0.20) (0.06) (0.90)

Largest owner −1.35 0.70 −1.78 −2.67 −4.34 −3.57 −1.66 −0.13 −2.35(0.55) (0.77) (0.46) (0.33) (0.05) (0.07) (0.41) (0.96) (0.33)

Industrial −1.25 0.04 −0.48 0.91 −2.27 −1.63 −1.56 −2.13 −3.07(0.23) (0.97) (0.67) (0.48) (0.05) (0.11) (0.09) (0.05) (0.01)


Offshore −5.14 −2.99 −2.91 −1.67 −3.51 −3.53 −2.89 −5.08 −5.76(0.01) (0.06) (0.07) (0.36) (0.03) (0.03) (0.07) (0.00) (0.00)

Debt to assets −5.72 −4.13 −3.27 −5.83 −1.51 −3.43 −1.48 −1.27 −5.93(0.01) (0.06) (0.14) (0.03) (0.59) (0.15) (0.50) (0.63) (0.02)

ln(Firm value) 0.37 0.11 0.27 0.08 −0.21 −0.39 −0.01 0.27 −0.05(0.08) (0.68) (0.31) (0.74) (0.38) (0.10) (0.95) (0.38) (0.87)

n 100 90 89 97 104 117 124 130 171

R2 0.09 −0.01 −0.01 0.04 0.09 0.15 0.09 0.12 0.13Average (RoA5) 9.73 9.40 9.34 9.71 9.98 9.16 8.73 8.72 9.64



Constant 12.48 (1.84) 0.00Primary insiders 8.27 (2.25) 0.00Squared (Primary insiders) -7.73 (2.73) 0.00Largest owner -2.30 (0.78) 0.00Industrial -1.51 (0.37) 0.00Transport/shipping -1.87 (0.39) 0.00Offshore -3.96 (0.57) 0.00Debt to assets -4.19 (0.82) 0.00ln(Firm value) 0.07 (0.09) 0.411990 -0.37 (0.67) 0.581991 -0.36 (0.68) 0.601992 0.23 (0.67) 0.731993 0.34 (0.65) 0.601994 -0.73 (0.63) 0.251995 -1.21 (0.62) 0.051996 -1.32 (0.62) 0.031997 -0.61 (0.60) 0.31n 1022




Table B.25 Multivariate regression relating performance (RoA5) to insider ownership, ownershipconcentration, institutional ownership and controls, following McConnell and Servaes (1990)Panel A: Pooled regressions (OLS and GMM)


Constant 12.97 (1.79) 0.00Primary insiders 7.98 (2.23) 0.00Squared (Primary insiders) -7.46 (2.71) 0.01Largest owner -2.21 (0.80) 0.01Aggregate financial holdings 0.18 (1.13) 0.87Industrial -1.45 (0.37) 0.00Transport/shipping -1.84 (0.40) 0.00Offshore -3.85 (0.57) 0.00Debt to assets -3.92 (0.82) 0.00ln(Firm value) 0.01 (0.09) 0.91n 1022



Constant 12.97 (2.06) 0.00Primary insiders 7.98 (2.54) 0.00Squared (Primary insiders) -7.46 (3.07) 0.02Largest owner -2.21 (0.67) 0.00Aggregate financial holdings 0.18 (1.05) 0.86Industrial -1.45 (0.37) 0.00Transport/shipping -1.84 (0.48) 0.00Offshore -3.85 (0.63) 0.00Debt to assets -3.92 (1.26) 0.00ln(Firm value) 0.01 (0.08) 0.90n 1022Average (RoA5) 9.36


Year1989 1990 1991 1992 1993 1994 1995 1996 1997

constant 6.85 9.67 7.35 11.38 17.17 20.74 11.50 5.59 16.31(0.11) (0.08) (0.20) (0.03) (0.00) (0.00) (0.02) (0.40) (0.02)



Largest owner −1.45 1.00 −1.49 −2.78 −4.60 −3.38 −0.86 −0.08 −2.49(0.54) (0.68) (0.56) (0.32) (0.04) (0.11) (0.69) (0.97) (0.31)

Aggregate financial holdings −0.66 2.20 2.26 −0.78 −1.77 0.78 3.60 0.37 −1.49(0.87) (0.58) (0.65) (0.85) (0.57) (0.80) (0.30) (0.91) (0.65)

Industrial −1.24 −0.04 −0.73 0.99 −2.26 −1.65 −1.82 −2.13 −3.05(0.24) (0.97) (0.56) (0.47) (0.05) (0.11) (0.06) (0.05) (0.01)


Offshore −5.16 −2.98 −3.04 −1.62 −3.61 −3.49 −2.87 −5.09 −5.84(0.01) (0.06) (0.07) (0.38) (0.03) (0.03) (0.07) (0.00) (0.00)

Debt to assets −5.69 −4.34 −3.37 −5.75 −1.00 −3.60 −2.13 −1.34 −5.87(0.01) (0.06) (0.14) (0.04) (0.73) (0.15) (0.35) (0.62) (0.02)

ln(Firm value) 0.37 0.09 0.24 0.09 −0.19 −0.39 −0.04 0.26 −0.00(0.08) (0.75) (0.38) (0.72) (0.43) (0.10) (0.87) (0.42) (0.99)

n 100 90 89 97 104 117 124 130 171

R2 0.08 −0.02 −0.02 0.03 0.08 0.14 0.10 0.11 0.13Average (RoA5) 9.73 9.40 9.34 9.71 9.98 9.16 8.73 8.72 9.64



Constant 12.61 (1.86) 0.00Primary insiders 8.31 (2.25) 0.00Squared (Primary insiders) -7.73 (2.73) 0.00Largest owner -2.20 (0.80) 0.01Aggregate financial holdings 0.62 (1.17) 0.60Industrial -1.53 (0.37) 0.00Transport/shipping -1.85 (0.40) 0.00Offshore -3.95 (0.57) 0.00Debt to assets -4.27 (0.83) 0.00ln(Firm value) 0.06 (0.09) 0.481990 -0.39 (0.68) 0.561991 -0.38 (0.68) 0.571992 0.19 (0.67) 0.771993 0.28 (0.66) 0.671994 -0.78 (0.64) 0.221995 -1.25 (0.63) 0.051996 -1.38 (0.63) 0.031997 -0.68 (0.61) 0.26n 1022




B.3.4 Alternative insider definitions

We show selected regressions which are comparable to those in the text, but which use alternativeinsider definitions. We define insiders as the alternative categories: All insiders, Board members,and the Management team.

B.3.4.1 All insiders


Table B.26 Multivariate regression relating performance (Q) to insider (all) ownership and con-trols, following Morck et al. (1988)Panel A: Pooled regressions (OLS and GMM)


Constant -0.12 (0.34) 0.73All insiders (0 to 5) 3.95 (1.96) 0.04All insiders (5 to 25) 0.52 (0.59) 0.38All insiders (25 to 100) -0.23 (0.20) 0.25Industrial -0.34 (0.07) 0.00Transport/shipping -0.66 (0.08) 0.00Offshore -0.67 (0.11) 0.00Debt to assets -1.07 (0.15) 0.00ln(Firm value) 0.12 (0.02) 0.00n 1057

R2 0.16Average (Q) 1.47


Constant -0.12 (0.34) 0.72All insiders (0 to 5) 3.95 (1.74) 0.02All insiders (5 to 25) 0.52 (0.56) 0.36All insiders (25 to 100) -0.23 (0.19) 0.23Industrial -0.34 (0.09) 0.00Transport/shipping -0.66 (0.08) 0.00Offshore -0.67 (0.10) 0.00Debt to assets -1.07 (0.23) 0.00ln(Firm value) 0.12 (0.02) 0.00n 1057Average (Q) 1.47


Year1989 1990 1991 1992 1993 1994 1995 1996 1997

constant 0.26 −0.57 −0.40 −0.88 0.36 0.47 −0.56 3.35 0.14(0.61) (0.28) (0.44) (0.05) (0.60) (0.35) (0.52) (0.05) (0.92)

All insiders (0 to 5) 2.18 7.89 9.80 2.48 2.53 1.47 −0.06 0.08 11.91(0.48) (0.00) (0.00) (0.41) (0.56) (0.63) (0.99) (0.99) (0.08)

All insiders (5 to 25) 0.34 −0.83 −1.62 0.37 −0.85 0.48 1.35 1.92 0.33(0.73) (0.30) (0.04) (0.67) (0.52) (0.60) (0.33) (0.45) (0.88)

All insiders (25 to 100) −0.15 0.25 0.24 −0.30 0.18 −0.25 0.71 −1.20 −0.87(0.61) (0.36) (0.32) (0.28) (0.70) (0.43) (0.11) (0.20) (0.23)

Industrial −0.26 −0.21 −0.09 −0.18 −0.31 −0.25 −0.39 −0.47 −0.14(0.03) (0.03) (0.40) (0.08) (0.06) (0.02) (0.02) (0.11) (0.56)


Offshore −0.06 −0.13 −0.30 −0.39 −0.66 −0.55 −0.55 −1.15 −0.77(0.79) (0.37) (0.05) (0.01) (0.01) (0.00) (0.05) (0.01) (0.02)

Debt to assets −0.01 −0.12 −0.30 0.28 −0.52 −0.50 0.31 −2.34 −2.01(0.96) (0.58) (0.15) (0.23) (0.19) (0.05) (0.41) (0.00) (0.00)

ln(Firm value) 0.05 0.09 0.08 0.10 0.08 0.07 0.10 0.02 0.14(0.03) (0.00) (0.00) (0.00) (0.01) (0.01) (0.02) (0.83) (0.04)

n 102 91 90 99 107 120 128 140 180

R2 0.01 0.14 0.18 0.18 0.11 0.17 0.19 0.19 0.20Average (Q) 1.29 1.15 1.09 1.03 1.38 1.32 1.42 1.98 1.95



Constant 0.27 (0.34) 0.43All insiders (0 to 5) 4.70 (1.88) 0.01All insiders (5 to 25) 0.39 (0.56) 0.49All insiders (25 to 100) -0.23 (0.19) 0.21Industrial -0.29 (0.07) 0.00Transport/shipping -0.62 (0.07) 0.00Offshore -0.62 (0.11) 0.00Debt to assets -0.85 (0.15) 0.00ln(Firm value) 0.08 (0.02) 0.001990 -0.16 (0.13) 0.211991 -0.18 (0.13) 0.161992 -0.16 (0.12) 0.211993 0.11 (0.12) 0.351994 0.02 (0.12) 0.891995 0.11 (0.12) 0.371996 0.58 (0.12) 0.001997 0.53 (0.11) 0.00n 1057

R2 0.23Average (Q) 1.47



Table B.27 Multivariate regression relating performance (Q) to insider (all) ownership and con-trols, following McConnell and Servaes (1990)Panel A: Pooled regressions (OLS and GMM)


Constant 0.01 (0.34) 0.99All insiders 0.79 (0.32) 0.01Squared (All insiders) -0.73 (0.37) 0.05Industrial -0.33 (0.07) 0.00Transport/shipping -0.66 (0.08) 0.00Offshore -0.66 (0.11) 0.00Debt to assets -1.08 (0.15) 0.00ln(Firm value) 0.12 (0.02) 0.00n 1057

R2 0.15Average (Q) 1.47


Constant 0.01 (0.33) 0.99All insiders 0.79 (0.33) 0.02Squared (All insiders) -0.73 (0.36) 0.04Industrial -0.33 (0.09) 0.00Transport/shipping -0.66 (0.08) 0.00Offshore -0.66 (0.10) 0.00Debt to assets -1.08 (0.23) 0.00ln(Firm value) 0.12 (0.02) 0.00n 1057Average (Q) 1.47


Year1989 1990 1991 1992 1993 1994 1995 1996 1997

constant 0.35 −0.24 −0.14 −0.72 0.40 0.53 −0.57 3.32 0.50(0.49) (0.65) (0.79) (0.10) (0.55) (0.30) (0.51) (0.05) (0.72)

All insiders 0.24 0.52 0.35 0.21 −0.21 0.43 0.86 0.48 2.23(0.69) (0.26) (0.45) (0.67) (0.79) (0.40) (0.25) (0.72) (0.04)

Squared (All insiders) −0.19 −0.25 −0.21 −0.26 0.26 −0.47 −0.02 −0.89 −2.60(0.76) (0.64) (0.67) (0.66) (0.78) (0.42) (0.98) (0.59) (0.04)

Industrial −0.25 −0.19 −0.10 −0.19 −0.30 −0.25 −0.39 −0.47 −0.10(0.04) (0.06) (0.36) (0.08) (0.06) (0.02) (0.02) (0.11) (0.67)


Offshore −0.04 −0.18 −0.37 −0.37 −0.66 −0.53 −0.54 −1.16 −0.78(0.87) (0.24) (0.02) (0.02) (0.01) (0.00) (0.05) (0.01) (0.02)

Debt to assets −0.01 −0.13 −0.28 0.22 −0.53 −0.50 0.31 −2.34 −2.06(0.98) (0.56) (0.22) (0.34) (0.17) (0.05) (0.41) (0.00) (0.00)

ln(Firm value) 0.05 0.08 0.08 0.09 0.08 0.07 0.10 0.02 0.13(0.04) (0.00) (0.00) (0.00) (0.01) (0.01) (0.02) (0.80) (0.06)

n 102 91 90 99 107 120 128 140 180

R2 0.01 0.06 0.08 0.16 0.12 0.17 0.20 0.19 0.19Average (Q) 1.29 1.15 1.09 1.03 1.38 1.32 1.42 1.98 1.95



Constant 0.41 (0.34) 0.23All insiders 0.78 (0.31) 0.01Squared (All insiders) -0.73 (0.35) 0.04Industrial -0.29 (0.07) 0.00Transport/shipping -0.62 (0.07) 0.00Offshore -0.62 (0.11) 0.00Debt to assets -0.87 (0.15) 0.00ln(Firm value) 0.08 (0.02) 0.001990 -0.15 (0.13) 0.231991 -0.17 (0.13) 0.191992 -0.15 (0.13) 0.221993 0.12 (0.12) 0.341994 0.02 (0.12) 0.881995 0.10 (0.12) 0.381996 0.58 (0.12) 0.001997 0.52 (0.11) 0.00n 1057

R2 0.22Average (Q) 1.47



B.3.4.2 Board members

Table B.28 Multivariate regression relating performance (Q) to insider (board) ownership andcontrols, following Morck et al. (1988)Panel A: Pooled regressions (OLS and GMM)


Constant 0.05 (0.32) 0.87Board members (0 to 5) 5.17 (2.21) 0.02Board members (5 to 25) 2.71 (0.93) 0.00Board members (25 to 100) -0.67 (0.29) 0.02Industrial -0.29 (0.07) 0.00Transport/shipping -0.61 (0.08) 0.00Offshore -0.58 (0.11) 0.00Debt to assets -1.09 (0.15) 0.00ln(Firm value) 0.11 (0.02) 0.00n 1057

R2 0.19Average (Q) 1.47


Constant 0.05 (0.30) 0.86Board members (0 to 5) 5.17 (2.19) 0.02Board members (5 to 25) 2.71 (1.33) 0.04Board members (25 to 100) -0.67 (0.41) 0.10Industrial -0.29 (0.09) 0.00Transport/shipping -0.61 (0.08) 0.00Offshore -0.58 (0.10) 0.00Debt to assets -1.09 (0.22) 0.00ln(Firm value) 0.11 (0.01) 0.00n 1057Average (Q) 1.47


Year1989 1990 1991 1992 1993 1994 1995 1996 1997

constant 0.42 −0.10 −0.17 −0.73 0.37 0.30 −0.38 2.81 0.27(0.40) (0.84) (0.74) (0.08) (0.57) (0.52) (0.65) (0.09) (0.84)

Board members (0 to 5) 3.76 10.27 10.15 2.60 −0.27 8.13 0.26 −2.71 10.19(0.37) (0.00) (0.00) (0.45) (0.96) (0.01) (0.96) (0.76) (0.15)

Board members (5 to 25) 0.00 −2.95 −2.33 0.85 0.40 0.15 4.41 9.39 2.94(1.00) (0.02) (0.09) (0.58) (0.85) (0.91) (0.10) (0.02) (0.26)

Board members (25 to 100) 0.04 0.94 0.22 −0.25 0.20 −0.29 0.21 −2.07 −0.43(0.95) (0.03) (0.56) (0.63) (0.77) (0.52) (0.77) (0.09) (0.64)

Industrial −0.25 −0.20 −0.11 −0.14 −0.30 −0.19 −0.38 −0.53 −0.09(0.05) (0.04) (0.30) (0.22) (0.07) (0.07) (0.02) (0.07) (0.70)


Offshore −0.00 −0.10 −0.29 −0.30 −0.64 −0.59 −0.54 −1.17 −0.73(0.99) (0.48) (0.07) (0.06) (0.01) (0.00) (0.05) (0.01) (0.03)

Debt to assets 0.03 0.01 −0.27 0.17 −0.55 −0.54 −0.08 −2.27 −1.98(0.90) (0.95) (0.19) (0.45) (0.16) (0.03) (0.82) (0.00) (0.00)

ln(Firm value) 0.05 0.06 0.07 0.09 0.08 0.07 0.10 0.04 0.13(0.07) (0.01) (0.00) (0.00) (0.01) (0.00) (0.01) (0.63) (0.04)

n 102 91 90 99 107 120 128 140 180

R2 0.02 0.18 0.16 0.18 0.12 0.25 0.23 0.23 0.24Average (Q) 1.29 1.15 1.09 1.03 1.38 1.32 1.42 1.98 1.95



Constant 0.46 (0.32) 0.16Board members (0 to 5) 5.00 (2.12) 0.02Board members (5 to 25) 2.45 (0.89) 0.01Board members (25 to 100) -0.53 (0.28) 0.06Industrial -0.24 (0.07) 0.00Transport/shipping -0.58 (0.07) 0.00Offshore -0.54 (0.10) 0.00Debt to assets -0.89 (0.14) 0.00ln(Firm value) 0.08 (0.02) 0.001990 -0.17 (0.12) 0.171991 -0.20 (0.12) 0.111992 -0.16 (0.12) 0.181993 0.10 (0.12) 0.401994 -0.02 (0.12) 0.891995 0.10 (0.11) 0.391996 0.56 (0.11) 0.001997 0.47 (0.11) 0.00n 1057

R2 0.25Average (Q) 1.47



Table B.29 Multivariate regression relating performance (Q) to insider (board) ownership andcontrols, following McConnell and Servaes (1990)Panel A: Pooled regressions (OLS and GMM)


Constant 0.08 (0.33) 0.80Board members 2.58 (0.46) 0.00Squared (Board members) -2.31 (0.52) 0.00Industrial -0.28 (0.07) 0.00Transport/shipping -0.61 (0.08) 0.00Offshore -0.57 (0.11) 0.00Debt to assets -1.12 (0.15) 0.00ln(Firm value) 0.11 (0.02) 0.00n 1057

R2 0.18Average (Q) 1.47


Constant 0.08 (0.31) 0.79Board members 2.58 (0.81) 0.00Squared (Board members) -2.31 (0.86) 0.01Industrial -0.28 (0.09) 0.00Transport/shipping -0.61 (0.08) 0.00Offshore -0.57 (0.10) 0.00Debt to assets -1.12 (0.23) 0.00ln(Firm value) 0.11 (0.01) 0.00n 1057Average (Q) 1.47


Year1989 1990 1991 1992 1993 1994 1995 1996 1997

constant 0.47 −0.00 −0.01 −0.71 0.38 0.41 −0.41 2.94 0.34(0.33) (0.99) (0.99) (0.09) (0.56) (0.39) (0.63) (0.08) (0.80)

Board members 0.38 0.37 0.85 0.73 0.46 1.32 2.48 5.28 5.69(0.69) (0.59) (0.23) (0.29) (0.66) (0.04) (0.05) (0.01) (0.00)

Squared (Board members) −0.11 0.16 −0.73 −0.63 −0.28 −1.13 −1.39 −5.02 −5.24(0.91) (0.84) (0.32) (0.46) (0.81) (0.14) (0.30) (0.03) (0.00)

Industrial −0.22 −0.17 −0.08 −0.15 −0.29 −0.22 −0.39 −0.44 −0.09(0.07) (0.11) (0.48) (0.19) (0.07) (0.05) (0.02) (0.12) (0.70)


Offshore 0.02 −0.10 −0.30 −0.32 −0.64 −0.49 −0.55 −1.12 −0.73(0.94) (0.55) (0.07) (0.05) (0.01) (0.00) (0.04) (0.01) (0.03)

Debt to assets −0.00 −0.03 −0.30 0.16 −0.55 −0.54 −0.08 −2.31 −1.99(0.99) (0.90) (0.17) (0.48) (0.16) (0.03) (0.82) (0.00) (0.00)

ln(Firm value) 0.04 0.06 0.07 0.09 0.08 0.07 0.11 0.03 0.13(0.08) (0.02) (0.01) (0.00) (0.01) (0.00) (0.01) (0.71) (0.04)

n 102 91 90 99 107 120 128 140 180

R2 0.02 0.07 0.08 0.18 0.13 0.21 0.23 0.22 0.25Average (Q) 1.29 1.15 1.09 1.03 1.38 1.32 1.42 1.98 1.95



Constant 0.50 (0.33) 0.13Board members 2.51 (0.45) 0.00Squared (Board members) -2.21 (0.50) 0.00Industrial -0.24 (0.07) 0.00Transport/shipping -0.57 (0.07) 0.00Offshore -0.53 (0.11) 0.00Debt to assets -0.91 (0.14) 0.00ln(Firm value) 0.08 (0.02) 0.001990 -0.16 (0.13) 0.201991 -0.19 (0.13) 0.141992 -0.17 (0.12) 0.161993 0.10 (0.12) 0.411994 -0.01 (0.12) 0.921995 0.10 (0.12) 0.371996 0.58 (0.11) 0.001997 0.49 (0.11) 0.00n 1057

R2 0.25Average (Q) 1.47



B.3.4.3 Management team

Table B.30 Multivariate regression relating performance (Q) to insider (management) ownershipand controls, following Morck et al. (1988)Panel A: Pooled regressions (OLS and GMM)


Constant 0.09 (0.33) 0.78Management team (0 to 5) 10.81 (2.34) 0.00Management team (5 to 25) -0.32 (0.98) 0.74Management team (25 to 100) -0.48 (0.39) 0.21Industrial -0.29 (0.07) 0.00Transport/shipping -0.65 (0.08) 0.00Offshore -0.63 (0.11) 0.00Debt to assets -1.11 (0.15) 0.00ln(Firm value) 0.11 (0.02) 0.00n 1057

R2 0.17Average (Q) 1.47


Constant 0.09 (0.30) 0.76Management team (0 to 5) 10.81 (2.68) 0.00Management team (5 to 25) -0.32 (1.24) 0.80Management team (25 to 100) -0.48 (0.35) 0.16Industrial -0.29 (0.09) 0.00Transport/shipping -0.65 (0.08) 0.00Offshore -0.63 (0.10) 0.00Debt to assets -1.11 (0.23) 0.00ln(Firm value) 0.11 (0.02) 0.00n 1057Average (Q) 1.47


Year1989 1990 1991 1992 1993 1994 1995 1996 1997

constant 0.40 −0.03 0.01 −0.51 0.47 0.35 −0.22 2.93 0.34(0.42) (0.96) (0.98) (0.21) (0.46) (0.48) (0.80) (0.08) (0.80)

Management team (0 to 5) 2.62 3.51 5.56 12.77 9.03 7.86 11.80 12.86 11.04(0.56) (0.30) (0.17) (0.00) (0.08) (0.02) (0.06) (0.19) (0.12)

Management team (5 to 25) −3.00 −0.84 −1.65 −3.58 −2.93 −0.29 −2.05 −0.34 3.02(0.49) (0.60) (0.28) (0.02) (0.26) (0.85) (0.40) (0.93) (0.32)

Management team (25 to 100) 0.69 0.36 0.28 −0.12 −0.51 −0.68 0.82 −0.71 −1.76(0.55) (0.45) (0.48) (0.85) (0.78) (0.54) (0.43) (0.90) (0.24)

Industrial −0.26 −0.20 −0.10 −0.17 −0.30 −0.20 −0.32 −0.39 −0.04(0.03) (0.06) (0.34) (0.10) (0.06) (0.07) (0.07) (0.19) (0.88)


Offshore −0.04 −0.17 −0.39 −0.48 −0.76 −0.51 −0.52 −1.05 −0.71(0.87) (0.28) (0.02) (0.00) (0.00) (0.00) (0.07) (0.02) (0.04)

Debt to assets 0.03 −0.07 −0.32 0.16 −0.52 −0.55 0.12 −2.30 −2.17(0.90) (0.77) (0.15) (0.45) (0.18) (0.03) (0.75) (0.00) (0.00)

ln(Firm value) 0.05 0.07 0.07 0.08 0.08 0.07 0.09 0.03 0.14(0.05) (0.01) (0.01) (0.00) (0.02) (0.00) (0.04) (0.71) (0.04)

n 102 91 90 99 107 120 128 140 180

R2 0.00 0.03 0.07 0.26 0.14 0.21 0.15 0.20 0.21Average (Q) 1.29 1.15 1.09 1.03 1.38 1.32 1.42 1.98 1.95



Constant 0.48 (0.33) 0.14Management team (0 to 5) 9.98 (2.25) 0.00Management team (5 to 25) -0.68 (0.94) 0.47Management team (25 to 100) -0.19 (0.37) 0.61Industrial -0.25 (0.07) 0.00Transport/shipping -0.61 (0.07) 0.00Offshore -0.59 (0.11) 0.00Debt to assets -0.90 (0.14) 0.00ln(Firm value) 0.08 (0.02) 0.001990 -0.16 (0.13) 0.211991 -0.17 (0.13) 0.171992 -0.15 (0.12) 0.231993 0.12 (0.12) 0.331994 0.00 (0.12) 0.971995 0.09 (0.12) 0.421996 0.56 (0.12) 0.001997 0.49 (0.11) 0.00n 1057

R2 0.24Average (Q) 1.47



Table B.31 Multivariate regression relating performance (Q) to insider (management) ownershipand controls, following McConnell and Servaes (1990)Panel A: Pooled regressions (OLS and GMM)


Constant 0.07 (0.33) 0.84Management team 2.27 (0.59) 0.00Squared (Management team) -2.30 (0.69) 0.00Industrial -0.32 (0.07) 0.00Transport/shipping -0.65 (0.08) 0.00Offshore -0.62 (0.11) 0.00Debt to assets -1.10 (0.15) 0.00ln(Firm value) 0.12 (0.02) 0.00n 1057

R2 0.16Average (Q) 1.47


Constant 0.07 (0.30) 0.82Management team 2.27 (0.80) 0.00Squared (Management team) -2.30 (0.84) 0.01Industrial -0.32 (0.09) 0.00Transport/shipping -0.65 (0.08) 0.00Offshore -0.62 (0.10) 0.00Debt to assets -1.10 (0.23) 0.00ln(Firm value) 0.12 (0.02) 0.00n 1057Average (Q) 1.47


Year1989 1990 1991 1992 1993 1994 1995 1996 1997

constant 0.40 −0.07 0.00 −0.67 0.40 0.35 −0.20 2.91 0.30(0.41) (0.90) (1.00) (0.11) (0.53) (0.48) (0.82) (0.08) (0.82)

Management team −0.59 −0.05 0.15 0.57 1.39 2.91 0.44 6.58 5.38(0.72) (0.96) (0.88) (0.51) (0.52) (0.04) (0.77) (0.12) (0.01)

Squared (Management team) 0.63 0.34 −0.02 −0.96 −3.34 −4.91 0.45 −13.81 −6.21(0.71) (0.74) (0.98) (0.39) (0.39) (0.06) (0.81) (0.21) (0.02)

Industrial −0.25 −0.19 −0.10 −0.20 −0.30 −0.21 −0.42 −0.44 −0.06(0.03) (0.06) (0.36) (0.07) (0.06) (0.05) (0.02) (0.13) (0.81)


Offshore −0.02 −0.15 −0.34 −0.39 −0.69 −0.49 −0.57 −1.08 −0.76(0.92) (0.35) (0.04) (0.01) (0.00) (0.01) (0.05) (0.02) (0.03)

Debt to assets 0.02 −0.05 −0.28 0.21 −0.52 −0.58 0.15 −2.33 −2.15(0.93) (0.82) (0.20) (0.35) (0.18) (0.02) (0.71) (0.00) (0.00)

ln(Firm value) 0.05 0.07 0.07 0.09 0.08 0.08 0.10 0.04 0.14(0.05) (0.01) (0.01) (0.00) (0.01) (0.00) (0.04) (0.67) (0.03)

n 102 91 90 99 107 120 128 140 180

R2 0.01 0.03 0.07 0.17 0.13 0.20 0.13 0.20 0.21Average (Q) 1.29 1.15 1.09 1.03 1.38 1.32 1.42 1.98 1.95



Constant 0.46 (0.33) 0.17Management team 1.76 (0.57) 0.00Squared (Management team) -1.64 (0.67) 0.01Industrial -0.27 (0.07) 0.00Transport/shipping -0.62 (0.07) 0.00Offshore -0.58 (0.11) 0.00Debt to assets -0.89 (0.15) 0.00ln(Firm value) 0.08 (0.02) 0.001990 -0.15 (0.13) 0.241991 -0.17 (0.13) 0.191992 -0.14 (0.13) 0.251993 0.13 (0.12) 0.311994 0.01 (0.12) 0.941995 0.10 (0.12) 0.401996 0.57 (0.12) 0.001997 0.51 (0.11) 0.00n 1057

R2 0.22Average (Q) 1.47



B.3.5 Insider holdings and outside concentration

To account for the possibility that insiders and large shareholders are double-counted, we haveestimated the outside (external) concentration by the size of the largest owner who is not aninsider. That is, we remove the largest owner if the largest insider owner has the same size as thelargest owner. The results are shown in tables B.32 (MSV approach) and B.33 (McS approach).


Table B.32 Multivariate regression relating performance (Q) to insider ownership, outside (exter-nal) concentration and controls, following Morck et al. (1988)Panel A: Pooled regressions (OLS and GMM)


Constant 0.31 (0.33) 0.35Primary insiders (0 to 5) 6.62 (1.98) 0.00Primary insiders (5 to 25) 1.85 (0.76) 0.01Primary insiders (25 to 100) -0.62 (0.30) 0.04Largest outside owner -0.59 (0.15) 0.00Industrial -0.25 (0.07) 0.00Transport/shipping -0.59 (0.07) 0.00Offshore -0.57 (0.11) 0.00Debt to assets -1.10 (0.15) 0.00ln(Firm value) 0.10 (0.02) 0.00n 1057

R2 0.21Average (Q) 1.47


Constant 0.31 (0.32) 0.33Primary insiders (0 to 5) 6.62 (2.10) 0.00Primary insiders (5 to 25) 1.85 (0.97) 0.06Primary insiders (25 to 100) -0.62 (0.41) 0.13Largest outside owner -0.59 (0.12) 0.00Industrial -0.25 (0.09) 0.00Transport/shipping -0.59 (0.07) 0.00Offshore -0.57 (0.10) 0.00Debt to assets -1.10 (0.22) 0.00ln(Firm value) 0.10 (0.01) 0.00n 1057Average (Q) 1.47


Year1989 1990 1991 1992 1993 1994 1995 1996 1997

constant 0.45 0.12 −0.00 −0.60 1.02 0.60 −0.22 2.85 0.38(0.36) (0.81) (1.00) (0.18) (0.14) (0.22) (0.79) (0.09) (0.78)



Primary insiders (25 to 100) −0.07 0.52 0.12 −0.81 −0.88 −0.51 1.87 −1.76 −0.92(0.91) (0.20) (0.73) (0.22) (0.28) (0.31) (0.00) (0.25) (0.46)


Industrial −0.28 −0.18 −0.10 −0.11 −0.19 −0.21 −0.28 −0.41 −0.08(0.02) (0.07) (0.36) (0.31) (0.23) (0.04) (0.09) (0.16) (0.73)


Offshore −0.08 −0.17 −0.36 −0.33 −0.62 −0.54 −0.48 −1.02 −0.64(0.73) (0.27) (0.02) (0.04) (0.01) (0.00) (0.07) (0.02) (0.05)

Debt to assets 0.02 −0.04 −0.30 0.15 −0.63 −0.62 −0.20 −2.21 −1.96(0.94) (0.86) (0.16) (0.51) (0.10) (0.01) (0.59) (0.00) (0.00)

ln(Firm value) 0.05 0.06 0.07 0.09 0.06 0.07 0.10 0.03 0.13(0.04) (0.02) (0.00) (0.00) (0.07) (0.01) (0.02) (0.70) (0.05)

n 102 91 90 99 107 120 128 140 180

R2 0.03 0.12 0.15 0.18 0.16 0.26 0.28 0.23 0.25Average (Q) 1.29 1.15 1.09 1.03 1.38 1.32 1.42 1.98 1.95



Constant 0.70 (0.33) 0.03Primary insiders (0 to 5) 6.41 (1.90) 0.00Primary insiders (5 to 25) 1.44 (0.73) 0.05Primary insiders (25 to 100) -0.44 (0.29) 0.13Largest outside owner -0.57 (0.14) 0.00Industrial -0.21 (0.07) 0.00Transport/shipping -0.56 (0.07) 0.00Offshore -0.54 (0.10) 0.00Debt to assets -0.91 (0.14) 0.00ln(Firm value) 0.07 (0.02) 0.001990 -0.18 (0.12) 0.141991 -0.21 (0.12) 0.091992 -0.18 (0.12) 0.131993 0.09 (0.12) 0.461994 -0.02 (0.12) 0.841995 0.05 (0.11) 0.641996 0.51 (0.11) 0.001997 0.44 (0.11) 0.00n 1057

R2 0.27Average (Q) 1.47



Table B.33 Multivariate regression relating performance (Q) to insider ownership, outside (exter-nal) concentration and controls, following McConnell and Servaes (1990)Panel A: Pooled regressions (OLS and GMM)


Constant 0.37 (0.33) 0.27Primary insiders 2.53 (0.43) 0.00Squared (Primary insiders) -2.43 (0.51) 0.00Largest outside owner -0.64 (0.15) 0.00Industrial -0.25 (0.07) 0.00Transport/shipping -0.59 (0.07) 0.00Offshore -0.56 (0.11) 0.00Debt to assets -1.14 (0.15) 0.00ln(Firm value) 0.10 (0.02) 0.00n 1057

R2 0.20Average (Q) 1.47


Constant 0.37 (0.32) 0.25Primary insiders 2.53 (0.62) 0.00Squared (Primary insiders) -2.43 (0.74) 0.00Largest outside owner -0.64 (0.12) 0.00Industrial -0.25 (0.09) 0.00Transport/shipping -0.59 (0.07) 0.00Offshore -0.56 (0.10) 0.00Debt to assets -1.14 (0.22) 0.00ln(Firm value) 0.10 (0.02) 0.00n 1057Average (Q) 1.47


Year1989 1990 1991 1992 1993 1994 1995 1996 1997

constant 0.52 0.13 0.17 −0.60 1.05 0.70 −0.17 3.10 0.46(0.28) (0.81) (0.75) (0.18) (0.12) (0.16) (0.84) (0.06) (0.73)




Industrial −0.25 −0.17 −0.08 −0.11 −0.20 −0.22 −0.32 −0.41 −0.08(0.04) (0.09) (0.46) (0.33) (0.22) (0.03) (0.05) (0.15) (0.74)


Offshore −0.06 −0.13 −0.34 −0.33 −0.63 −0.48 −0.48 −1.02 −0.66(0.81) (0.39) (0.04) (0.04) (0.01) (0.00) (0.07) (0.03) (0.05)

Debt to assets −0.02 −0.03 −0.30 0.14 −0.62 −0.64 −0.19 −2.26 −2.01(0.95) (0.89) (0.18) (0.54) (0.11) (0.01) (0.60) (0.00) (0.00)

ln(Firm value) 0.05 0.06 0.07 0.09 0.06 0.07 0.10 0.02 0.13(0.04) (0.02) (0.01) (0.00) (0.08) (0.01) (0.01) (0.77) (0.05)

n 102 91 90 99 107 120 128 140 180

R2 0.02 0.05 0.10 0.20 0.17 0.26 0.28 0.23 0.25Average (Q) 1.29 1.15 1.09 1.03 1.38 1.32 1.42 1.98 1.95



Constant 0.76 (0.33) 0.02Primary insiders 2.19 (0.41) 0.00Squared (Primary insiders) -2.03 (0.50) 0.00Largest outside owner -0.62 (0.14) 0.00Industrial -0.21 (0.07) 0.00Transport/shipping -0.56 (0.07) 0.00Offshore -0.52 (0.10) 0.00Debt to assets -0.94 (0.14) 0.00ln(Firm value) 0.07 (0.02) 0.001990 -0.17 (0.12) 0.161991 -0.20 (0.12) 0.111992 -0.18 (0.12) 0.131993 0.09 (0.12) 0.431994 -0.01 (0.12) 0.911995 0.06 (0.11) 0.581996 0.53 (0.11) 0.001997 0.45 (0.11) 0.00n 1057

R2 0.26Average (Q) 1.47



B.4 Owner type

This appendix presents regressions which supplement the analysis in chapter 7.



B.4 Owner type 151

Table B.34 Multivariate regression relating performance (Q) to ownership concentration, insiderownership, aggregate holdings per owner type, and controlsPanel A: Year by year OLS regressions

Year1989 1990 1991 1992 1993 1994 1995 1996 1997

constant −0.45 −0.34 −0.01 −0.50 1.31 −0.37 −0.77 −1.18 0.34(0.46) (0.63) (0.99) (0.43) (0.14) (0.55) (0.50) (0.61) (0.86)

Herfindahl index −0.70 −0.61 −0.47 −0.33 −0.61 −0.71 −0.85 −0.43 −0.79(0.07) (0.09) (0.26) (0.38) (0.15) (0.01) (0.09) (0.64) (0.21)



Aggregate state holdings 0.72 −0.00 0.04 0.27 −0.20 0.36 −0.01 −0.18 −0.86(0.21) (0.99) (0.95) (0.57) (0.72) (0.39) (0.99) (0.89) (0.43)

Aggregate international holdings 0.82 0.05 0.06 −0.27 0.21 0.51 1.00 1.01 0.20(0.05) (0.90) (0.90) (0.45) (0.68) (0.09) (0.07) (0.24) (0.78)

Aggregate individual holdings 1.36 0.59 0.33 0.22 0.52 1.15 1.19 3.46 0.39(0.01) (0.25) (0.60) (0.60) (0.40) (0.00) (0.06) (0.00) (0.64)

Aggregate nonfinancial holdings 0.98 0.30 0.04 −0.08 −0.51 0.56 0.30 0.79 −0.29(0.01) (0.45) (0.93) (0.83) (0.27) (0.09) (0.64) (0.41) (0.68)

Industrial −0.23 −0.15 −0.07 −0.12 −0.13 −0.15 −0.19 −0.22 −0.04(0.05) (0.15) (0.54) (0.30) (0.42) (0.15) (0.24) (0.43) (0.87)


Offshore −0.10 −0.16 −0.34 −0.31 −0.57 −0.45 −0.52 −0.99 −0.67(0.64) (0.30) (0.04) (0.05) (0.02) (0.01) (0.04) (0.02) (0.05)

Debt to assets −0.05 −0.06 −0.28 0.13 −0.87 −0.59 −0.30 −2.07 −1.98(0.83) (0.78) (0.23) (0.58) (0.03) (0.02) (0.41) (0.00) (0.00)

ln(Firm value) 0.05 0.08 0.07 0.09 0.05 0.09 0.10 0.17 0.14(0.03) (0.01) (0.03) (0.00) (0.16) (0.00) (0.03) (0.09) (0.09)

n 102 91 90 99 107 120 128 140 180

R2 0.09 0.07 0.06 0.20 0.18 0.32 0.34 0.29 0.25Average (Q) 1.29 1.15 1.09 1.03 1.38 1.32 1.42 1.98 1.95



Constant -0.18 (0.43) 0.67Herfindahl index -0.59 (0.17) 0.00Primary insiders 2.05 (0.59) 0.00Squared (Primary insiders) -2.02 (0.71) 0.00Aggregate state holdings -0.40 (0.23) 0.08Aggregate international holdings 0.02 (0.19) 0.92Aggregate individual holdings 0.97 (0.29) 0.00Aggregate nonfinancial holdings -0.15 (0.20) 0.45Industrial -0.19 (0.08) 0.02Transport/shipping -0.50 (0.07) 0.00Offshore -0.52 (0.09) 0.00Debt to assets -1.15 (0.23) 0.00ln(Firm value) 0.12 (0.02) 0.00n 1057Average (Q) 1.47


Constant 0.03 (0.44) 0.95Herfindahl index -0.72 (0.20) 0.00Primary insiders 1.85 (0.42) 0.00Squared (Primary insiders) -1.74 (0.50) 0.00Aggregate state holdings 0.00 (0.29) 0.99Aggregate international holdings 0.39 (0.21) 0.06Aggregate individual holdings 1.05 (0.26) 0.00Aggregate nonfinancial holdings 0.18 (0.22) 0.41Industrial -0.16 (0.07) 0.02Transport/shipping -0.51 (0.08) 0.00Offshore -0.51 (0.10) 0.00Debt to assets -0.93 (0.14) 0.00ln(Firm value) 0.09 (0.02) 0.001990 -0.17 (0.12) 0.171991 -0.18 (0.12) 0.131992 -0.17 (0.12) 0.151993 0.11 (0.12) 0.371994 -0.02 (0.12) 0.861995 0.04 (0.11) 0.741996 0.51 (0.11) 0.001997 0.46 (0.11) 0.00n 1057

R2 0.28Average (Q) 1.47



Table B.35 Multivariate regression relating performance (Q) to ownership concentration, insiderownership, aggregate intercorporate holdings, and controlsPanel A: Year by year OLS regressions

Year1989 1990 1991 1992 1993 1994 1995 1996 1997

constant 0.69 0.20 0.19 −0.59 1.07 0.81 −0.08 3.35 0.49(0.17) (0.71) (0.72) (0.20) (0.12) (0.09) (0.92) (0.04) (0.71)




Aggregate intercorporate holdings 0.19 −0.15 −0.30 −0.29 −0.22 −0.22 −0.38 −0.55 1.17(0.46) (0.54) (0.33) (0.30) (0.56) (0.43) (0.47) (0.60) (0.16)

Industrial −0.25 −0.17 −0.07 −0.10 −0.19 −0.20 −0.34 −0.39 −0.12(0.04) (0.10) (0.50) (0.35) (0.24) (0.05) (0.04) (0.17) (0.61)


Offshore −0.08 −0.15 −0.33 −0.30 −0.64 −0.48 −0.53 −1.07 −0.68(0.73) (0.32) (0.04) (0.06) (0.01) (0.00) (0.04) (0.02) (0.04)

Debt to assets 0.01 −0.07 −0.36 0.11 −0.72 −0.71 −0.34 −2.20 −2.08(0.97) (0.76) (0.10) (0.63) (0.07) (0.00) (0.36) (0.00) (0.00)

ln(Firm value) 0.04 0.06 0.07 0.09 0.06 0.06 0.10 0.01 0.13(0.12) (0.02) (0.01) (0.00) (0.08) (0.01) (0.01) (0.86) (0.05)

n 101 91 90 99 107 120 128 140 179

R2 0.04 0.08 0.10 0.20 0.16 0.29 0.29 0.24 0.26Average (Q) 1.29 1.15 1.09 1.03 1.38 1.32 1.42 1.98 1.96



Constant 0.49 (0.31) 0.12Herfindahl index -0.98 (0.15) 0.00Primary insiders 2.50 (0.61) 0.00Squared (Primary insiders) -2.30 (0.73) 0.00Aggregate intercorporate holdings -0.38 (0.15) 0.01Industrial -0.24 (0.08) 0.00Transport/shipping -0.59 (0.07) 0.00Offshore -0.58 (0.09) 0.00Debt to assets -1.20 (0.23) 0.00ln(Firm value) 0.10 (0.01) 0.00n 1055Average (Q) 1.47


Constant 0.89 (0.33) 0.01Herfindahl index -0.98 (0.17) 0.00Primary insiders 2.20 (0.41) 0.00Squared (Primary insiders) -1.93 (0.50) 0.00Aggregate intercorporate holdings -0.16 (0.18) 0.39Industrial -0.21 (0.07) 0.00Transport/shipping -0.56 (0.07) 0.00Offshore -0.55 (0.10) 0.00Debt to assets -1.01 (0.14) 0.00ln(Firm value) 0.07 (0.02) 0.001990 -0.18 (0.12) 0.151991 -0.21 (0.12) 0.101992 -0.19 (0.12) 0.131993 0.10 (0.12) 0.421994 -0.02 (0.12) 0.891995 0.05 (0.11) 0.641996 0.51 (0.11) 0.001997 0.45 (0.11) 0.00n 1055

R2 0.27Average (Q) 1.47


B.4 Owner type 153

Table B.36 Multivariate regression relating performance (Q) to ownership concentration, insiderownership, largest owner identity, and controlsPanel A: Year by year OLS regressions

Year1989 1990 1991 1992 1993 1994 1995 1996 1997

constant 0.29 0.34 1.07 −0.35 0.97 0.68 0.24 2.53 1.01(0.57) (0.53) (0.07) (0.48) (0.17) (0.17) (0.79) (0.17) (0.49)




Largest owner is state 0.37 −0.22 −0.09 0.38 −0.05 −0.20 −0.70 −0.53 −0.84(0.22) (0.31) (0.63) (0.05) (0.85) (0.27) (0.01) (0.26) (0.05)

Largest owner is individual 0.52 −0.29 −0.60 −0.18 0.42 0.06 −0.16 0.58 −0.54(0.04) (0.17) (0.00) (0.41) (0.24) (0.77) (0.55) (0.23) (0.14)

Largest owner is nonfinancial 0.47 −0.13 −0.29 0.11 0.02 −0.00 −0.39 0.07 −0.47(0.02) (0.37) (0.05) (0.44) (0.90) (0.99) (0.07) (0.84) (0.10)

Largest owner is international 0.39 −0.30 −0.37 0.17 0.43 0.03 −0.24 −0.29 −0.41(0.07) (0.05) (0.03) (0.35) (0.09) (0.86) (0.37) (0.53) (0.27)

Industrial −0.27 −0.16 −0.13 −0.13 −0.16 −0.17 −0.22 −0.34 −0.10(0.03) (0.11) (0.22) (0.26) (0.35) (0.12) (0.19) (0.24) (0.65)


Offshore −0.17 −0.16 −0.31 −0.33 −0.60 −0.49 −0.58 −1.15 −0.72(0.47) (0.34) (0.06) (0.03) (0.01) (0.00) (0.03) (0.01) (0.03)

Debt to assets −0.06 −0.09 −0.53 0.08 −0.54 −0.69 −0.48 −2.32 −1.87(0.82) (0.68) (0.02) (0.75) (0.19) (0.01) (0.19) (0.00) (0.00)

ln(Firm value) 0.04 0.06 0.04 0.07 0.05 0.07 0.10 0.06 0.12(0.07) (0.02) (0.12) (0.00) (0.12) (0.01) (0.01) (0.52) (0.08)

n 102 91 90 99 107 120 128 140 180

R2 0.06 0.10 0.17 0.23 0.19 0.28 0.31 0.24 0.26Average (Q) 1.29 1.15 1.09 1.03 1.38 1.32 1.42 1.98 1.95



Constant 0.60 (0.32) 0.06Herfindahl index -0.86 (0.14) 0.00Primary insiders 2.46 (0.63) 0.00Squared (Primary insiders) -2.25 (0.73) 0.00Largest owner is state -0.43 (0.11) 0.00Largest owner is individual -0.16 (0.16) 0.32Largest owner is nonfinancial -0.23 (0.11) 0.03Largest owner is international -0.28 (0.13) 0.02Industrial -0.23 (0.09) 0.01Transport/shipping -0.57 (0.07) 0.00Offshore -0.59 (0.10) 0.00Debt to assets -1.20 (0.23) 0.00ln(Firm value) 0.10 (0.01) 0.00n 1057Average (Q) 1.47


Constant 0.95 (0.35) 0.01Herfindahl index -0.87 (0.18) 0.00Primary insiders 2.15 (0.43) 0.00Squared (Primary insiders) -1.88 (0.51) 0.00Largest owner is state -0.35 (0.12) 0.00Largest owner is individual -0.12 (0.12) 0.29Largest owner is nonfinancial -0.17 (0.08) 0.04Largest owner is international -0.20 (0.10) 0.06Industrial -0.19 (0.07) 0.01Transport/shipping -0.54 (0.07) 0.00Offshore -0.55 (0.10) 0.00Debt to assets -1.00 (0.14) 0.00ln(Firm value) 0.07 (0.02) 0.001990 -0.19 (0.12) 0.131991 -0.21 (0.12) 0.091992 -0.19 (0.12) 0.131993 0.09 (0.12) 0.471994 -0.02 (0.12) 0.831995 0.05 (0.11) 0.671996 0.49 (0.11) 0.001997 0.43 (0.11) 0.00n 1057

R2 0.28Average (Q) 1.47



Table B.37 Multivariate regression relating performance (Q) to ownership concentration, insiderownership, largest owner being listed, and controlsPanel A: Year by year OLS regressions

Year1989 1990 1991 1992 1993 1994 1995 1996 1997

constant 0.65 0.21 0.21 −0.59 1.10 0.82 −0.01 3.28 0.42(0.19) (0.70) (0.70) (0.20) (0.11) (0.09) (0.99) (0.05) (0.75)




Largest owner is listed −0.02 −0.07 −0.14 −0.10 −0.10 −0.08 −0.15 0.10 0.23(0.86) (0.55) (0.27) (0.41) (0.51) (0.50) (0.48) (0.81) (0.50)

Industrial −0.23 −0.17 −0.06 −0.11 −0.19 −0.21 −0.35 −0.40 −0.09(0.05) (0.09) (0.57) (0.32) (0.23) (0.04) (0.03) (0.17) (0.69)


Offshore −0.07 −0.15 −0.32 −0.31 −0.64 −0.48 −0.54 −1.05 −0.67(0.76) (0.34) (0.05) (0.05) (0.01) (0.00) (0.04) (0.02) (0.04)

Debt to assets −0.01 −0.06 −0.34 0.14 −0.71 −0.71 −0.35 −2.25 −2.04(0.98) (0.80) (0.12) (0.56) (0.07) (0.00) (0.34) (0.00) (0.00)

ln(Firm value) 0.04 0.06 0.06 0.09 0.06 0.06 0.10 0.02 0.13(0.09) (0.02) (0.01) (0.00) (0.09) (0.01) (0.02) (0.83) (0.04)

n 102 91 90 99 107 120 128 140 180

R2 0.03 0.08 0.10 0.20 0.17 0.29 0.29 0.23 0.25Average (Q) 1.29 1.15 1.09 1.03 1.38 1.32 1.42 1.98 1.95



Constant 0.52 (0.31) 0.10Herfindahl index -1.02 (0.15) 0.00Primary insiders 2.52 (0.62) 0.00Squared (Primary insiders) -2.31 (0.74) 0.00Largest owner is listed -0.10 (0.07) 0.17Industrial -0.25 (0.09) 0.00Transport/shipping -0.59 (0.07) 0.00Offshore -0.58 (0.10) 0.00Debt to assets -1.20 (0.23) 0.00ln(Firm value) 0.10 (0.01) 0.00n 1057Average (Q) 1.47


Constant 0.91 (0.33) 0.01Herfindahl index -0.99 (0.17) 0.00Primary insiders 2.21 (0.41) 0.00Squared (Primary insiders) -1.94 (0.50) 0.00Largest owner is listed -0.05 (0.08) 0.55Industrial -0.21 (0.07) 0.00Transport/shipping -0.56 (0.07) 0.00Offshore -0.54 (0.10) 0.00Debt to assets -1.00 (0.14) 0.00ln(Firm value) 0.07 (0.02) 0.001990 -0.19 (0.12) 0.131991 -0.22 (0.12) 0.081992 -0.20 (0.12) 0.111993 0.09 (0.12) 0.471994 -0.03 (0.12) 0.831995 0.05 (0.11) 0.691996 0.51 (0.11) 0.001997 0.44 (0.11) 0.00n 1057

R2 0.27Average (Q) 1.47


B.4 Owner type 155

B.4.2 Alternative performance measure: RoA5.

Table B.38 Multivariate regression relating performance (RoA5) to ownership concentration, in-sider ownership, aggregate holdings per owner type, and controlsPanel A: Pooled OLS regression


Constant 12.82 (2.42) 0.00Herfindahl index -2.74 (1.11) 0.01Primary insiders 7.28 (2.31) 0.00Squared (Primary insiders) -7.00 (2.73) 0.01Aggregate state holdings 1.29 (1.73) 0.46Aggregate international holdings -0.90 (1.13) 0.43Aggregate individual holdings 1.33 (1.45) 0.36Aggregate nonfinancial holdings -0.49 (1.18) 0.68Industrial -1.41 (0.38) 0.00Transport/shipping -1.68 (0.41) 0.00Offshore -3.64 (0.58) 0.00Debt to assets -4.11 (0.82) 0.00ln(Firm value) 0.02 (0.10) 0.86n 1022



Year1989 1990 1991 1992 1993 1994 1995 1996 1997

constant 5.93 10.58 3.90 8.46 17.86 23.22 18.49 3.16 11.80(0.32) (0.16) (0.66) (0.28) (0.01) (0.00) (0.01) (0.73) (0.22)




Aggregate state holdings 3.17 1.00 5.38 4.34 4.55 0.81 −6.21 −0.97 2.33(0.59) (0.86) (0.37) (0.47) (0.30) (0.86) (0.24) (0.86) (0.67)

Aggregate international holdings 3.18 −3.51 −0.71 2.59 5.33 −1.08 −5.70 −1.92 −1.24(0.43) (0.40) (0.88) (0.53) (0.14) (0.73) (0.08) (0.57) (0.72)

Aggregate individual holdings 3.71 −2.05 2.31 5.89 1.62 −0.92 −4.43 0.01 3.47(0.45) (0.70) (0.71) (0.25) (0.72) (0.82) (0.24) (1.00) (0.41)

Aggregate nonfinancial holdings 0.13 0.05 4.02 1.51 1.35 −1.79 −7.62 0.05 0.01(0.97) (0.99) (0.42) (0.72) (0.68) (0.60) (0.05) (0.99) (1.00)

Industrial −1.09 −0.25 −0.36 1.17 −2.30 −1.64 −1.69 −2.12 −3.15(0.30) (0.82) (0.76) (0.38) (0.06) (0.14) (0.08) (0.06) (0.01)

Transport/shipping 0.28 −0.51 −1.41 1.67 −0.98 −1.80 −2.43 −3.67 −4.43(0.81) (0.68) (0.27) (0.23) (0.46) (0.13) (0.02) (0.00) (0.00)

Offshore −4.94 −3.17 −2.87 −1.17 −3.87 −3.28 −2.55 −5.08 −5.47(0.01) (0.05) (0.08) (0.54) (0.03) (0.06) (0.12) (0.00) (0.00)

Debt to assets −4.98 −4.61 −3.42 −5.53 −1.87 −4.09 −3.18 −1.73 −6.17(0.03) (0.04) (0.15) (0.05) (0.54) (0.11) (0.18) (0.53) (0.02)

ln(Firm value) 0.29 0.16 0.34 0.08 −0.34 −0.46 −0.09 0.41 0.16(0.19) (0.60) (0.29) (0.79) (0.20) (0.10) (0.76) (0.30) (0.71)

n 100 90 89 97 104 117 124 130 171

R2 0.08 −0.02 −0.01 0.02 0.09 0.12 0.10 0.09 0.12Average (RoA5) 9.73 9.40 9.34 9.71 9.98 9.16 8.73 8.72 9.64

Panel A shows results of a pooled regression using data for whole period. Panel B shows results of OLS regressionson a year by year basis. Data for firms listed on the Oslo Stock Exchange, 1989-1997. Variable definitions are inAppendix A.2.


Table B.39 Multivariate regression relating performance (RoA5) to ownership concentration, in-sider ownership, aggregate intercorporate holdings, and controlsPanel A: Pooled OLS regression


Constant 13.00 (1.78) 0.00Herfindahl index -2.93 (1.01) 0.00Primary insiders 7.75 (2.24) 0.00Squared (Primary insiders) -7.38 (2.70) 0.01Aggregate intercorporate holdings -1.01 (0.99) 0.31Industrial -1.39 (0.37) 0.00Transport/shipping -1.83 (0.40) 0.00Offshore -3.88 (0.57) 0.00Debt to assets -4.02 (0.82) 0.00ln(Firm value) 0.01 (0.09) 0.92n 1020



Year1989 1990 1991 1992 1993 1994 1995 1996 1997

constant 6.73 8.87 6.97 11.59 17.64 20.92 11.42 5.36 17.15(0.12) (0.10) (0.21) (0.03) (0.00) (0.00) (0.02) (0.41) (0.01)

Herfindahl index −1.49 2.63 −2.75 −3.56 −5.28 −4.17 −2.22 −0.19 −2.86(0.66) (0.47) (0.42) (0.33) (0.05) (0.08) (0.41) (0.95) (0.32)



Aggregate intercorporate holdings −1.46 −3.84 −1.00 −1.77 −3.82 −1.17 −0.64 0.71 −3.14(0.52) (0.14) (0.75) (0.59) (0.16) (0.66) (0.83) (0.85) (0.45)

Industrial −1.12 0.25 −0.44 1.01 −2.06 −1.52 −1.53 −2.13 −3.03(0.29) (0.81) (0.70) (0.44) (0.08) (0.15) (0.10) (0.05) (0.01)


Offshore −5.17 −2.93 −2.93 −1.51 −3.52 −3.46 −2.92 −5.06 −5.84(0.01) (0.06) (0.08) (0.42) (0.03) (0.03) (0.07) (0.00) (0.00)

Debt to assets −5.97 −4.88 −3.48 −6.12 −2.08 −3.50 −1.51 −1.34 −5.94(0.01) (0.03) (0.13) (0.03) (0.46) (0.15) (0.49) (0.61) (0.02)

ln(Firm value) 0.38 0.17 0.27 0.08 −0.20 −0.40 −0.02 0.27 −0.06(0.08) (0.52) (0.31) (0.75) (0.39) (0.09) (0.94) (0.38) (0.85)

n 99 90 89 97 104 117 124 130 170

R2 0.09 0.01 −0.02 0.04 0.10 0.14 0.09 0.11 0.13Average (RoA5) 9.75 9.40 9.34 9.71 9.98 9.16 8.73 8.72 9.65


B.4 Owner type 157

Table B.40 Multivariate regression relating performance (RoA5) to ownership concentration, in-sider ownership, largest owner identity, and controlsPanel A: Pooled OLS regression


Constant 13.07 (1.90) 0.00Herfindahl index -3.15 (1.01) 0.00Primary insiders 8.39 (2.37) 0.00Squared (Primary insiders) -7.89 (2.78) 0.00Largest owner is state 0.19 (0.68) 0.78Largest owner is individual -0.20 (0.66) 0.76Largest owner is nonfinancial 0.06 (0.47) 0.90Largest owner is international 0.16 (0.58) 0.79Industrial -1.45 (0.38) 0.00Transport/shipping -1.87 (0.40) 0.00Offshore -3.89 (0.58) 0.00Debt to assets -4.04 (0.82) 0.00ln(Firm value) -0.00 (0.09) 1.00n 1022



Year1989 1990 1991 1992 1993 1994 1995 1996 1997

constant 6.34 11.83 11.30 11.22 17.30 20.30 10.78 5.46 16.70(0.18) (0.04) (0.07) (0.06) (0.00) (0.00) (0.04) (0.46) (0.03)

Herfindahl index −2.54 −0.38 −4.97 −4.10 −4.83 −4.33 −2.08 0.69 −3.05(0.42) (0.92) (0.15) (0.27) (0.08) (0.08) (0.47) (0.84) (0.31)



Largest owner is state 1.83 −0.27 1.00 1.76 −0.13 0.41 −0.43 −1.52 0.60(0.50) (0.91) (0.63) (0.45) (0.95) (0.82) (0.79) (0.40) (0.79)

Largest owner is individual 1.46 −0.12 −1.87 0.29 −3.27 −1.23 0.24 −0.99 0.32(0.53) (0.95) (0.37) (0.91) (0.21) (0.54) (0.88) (0.59) (0.87)

Largest owner is nonfinancial 0.76 −0.86 −0.10 0.63 −0.65 0.78 0.26 0.08 0.32(0.68) (0.55) (0.95) (0.72) (0.64) (0.54) (0.83) (0.95) (0.83)

Largest owner is international 2.36 −2.62 −2.52 3.05 2.66 1.18 −0.14 −2.38 0.96(0.22) (0.09) (0.15) (0.14) (0.16) (0.44) (0.93) (0.19) (0.62)

Industrial −1.19 −0.01 −0.62 0.81 −2.85 −1.79 −1.49 −2.13 −3.01(0.27) (0.99) (0.58) (0.55) (0.02) (0.10) (0.13) (0.05) (0.01)


Offshore −4.87 −3.34 −3.34 −1.33 −3.88 −3.81 −2.89 −4.91 −5.81(0.02) (0.05) (0.05) (0.47) (0.02) (0.02) (0.07) (0.01) (0.00)

Debt to assets −5.49 −4.19 −4.36 −5.93 −2.12 −2.79 −1.45 −1.71 −6.18(0.01) (0.06) (0.07) (0.04) (0.48) (0.27) (0.52) (0.52) (0.02)

ln(Firm value) 0.33 0.06 0.12 0.04 −0.19 −0.42 0.01 0.29 −0.06(0.13) (0.83) (0.68) (0.89) (0.44) (0.08) (0.98) (0.39) (0.86)

n 100 90 89 97 104 117 124 130 171

R2 0.08 −0.01 0.00 0.04 0.11 0.13 0.07 0.11 0.11Average (RoA5) 9.73 9.40 9.34 9.71 9.98 9.16 8.73 8.72 9.64



B.5 Board characteristics, security design, and financial policy

This appendix supplements chapter 8.



Table B.41 Multivariate regression relating performance (Q) to ownership concentration, insiderownership, board size, and controlsPanel A: Year by year OLS regressions

Year1989 1990 1991 1992 1993 1994 1995 1996 1997

constant 1.47 0.60 0.59 −0.59 1.07 0.71 −0.18 3.24 0.26(0.01) (0.32) (0.31) (0.23) (0.13) (0.16) (0.84) (0.07) (0.86)



Squared (Primary insiders) 0.02 0.57 0.04 −3.23 −1.66 −1.81 1.00 −4.80 −4.55(0.98) (0.51) (0.96) (0.04) (0.24) (0.06) (0.39) (0.08) (0.04)

ln(Board size) −0.81 −0.33 −0.24 0.05 −0.00 0.02 0.17 −0.46 −0.33(0.00) (0.07) (0.18) (0.68) (1.00) (0.88) (0.39) (0.28) (0.18)

Industrial −0.08 −0.10 −0.05 −0.12 −0.22 −0.26 −0.39 −0.34 −0.06(0.51) (0.37) (0.71) (0.32) (0.19) (0.02) (0.02) (0.28) (0.80)


Offshore −0.28 −0.12 −0.35 −0.31 −0.64 −0.48 −0.53 −1.11 −0.82(0.40) (0.55) (0.09) (0.08) (0.01) (0.01) (0.07) (0.03) (0.03)

Debt to assets −0.07 −0.08 −0.45 0.15 −0.62 −0.89 −0.38 −2.28 −2.03(0.79) (0.76) (0.06) (0.55) (0.12) (0.00) (0.34) (0.00) (0.00)

ln(Firm value) 0.08 0.07 0.07 0.08 0.06 0.07 0.09 0.06 0.17(0.00) (0.02) (0.02) (0.00) (0.12) (0.01) (0.04) (0.52) (0.03)

n 89 80 79 92 100 106 117 129 164

R2 0.18 0.12 0.10 0.18 0.17 0.29 0.27 0.22 0.23Average (Q) 1.30 1.17 1.12 1.05 1.39 1.35 1.47 2.03 1.96



Constant 0.63 (0.34) 0.06Herfindahl index -1.01 (0.16) 0.00Primary insiders 2.40 (0.63) 0.00Squared (Primary insiders) -2.15 (0.80) 0.01ln(Board size) -0.24 (0.07) 0.00Industrial -0.25 (0.09) 0.00Transport/shipping -0.58 (0.08) 0.00Offshore -0.62 (0.11) 0.00Debt to assets -1.19 (0.23) 0.00ln(Firm value) 0.11 (0.02) 0.00n 956Average (Q) 1.50


Constant 0.96 (0.35) 0.01Herfindahl index -0.98 (0.19) 0.00Primary insiders 2.00 (0.44) 0.00Squared (Primary insiders) -1.70 (0.54) 0.00ln(Board size) -0.25 (0.08) 0.00Industrial -0.20 (0.07) 0.00Transport/shipping -0.55 (0.08) 0.00Offshore -0.61 (0.12) 0.00Debt to assets -1.02 (0.16) 0.00ln(Firm value) 0.09 (0.02) 0.001990 -0.18 (0.13) 0.191991 -0.19 (0.14) 0.161992 -0.15 (0.13) 0.241993 0.09 (0.13) 0.471994 0.00 (0.13) 1.001995 0.07 (0.12) 0.591996 0.56 (0.12) 0.001997 0.48 (0.12) 0.00n 956

R2 0.27Average (Q) 1.50


B.5 Board characteristics, security design, and financial policy 159

Table B.42 Multivariate regression relating performance (Q) to ownership concentration, insiderownership, security design, and controlsPanel A: Year by year OLS regressions

Year1989 1990 1991 1992 1993 1994 1995 1996 1997

constant 0.56 0.02 −0.19 −0.34 1.17 0.66 −2.47 3.08 −0.64(0.51) (0.97) (0.81) (0.61) (0.27) (0.38) (0.04) (0.22) (0.76)




Fraction voting shares 0.04 0.18 0.38 −0.19 −0.09 0.07 1.99 0.14 0.91(0.94) (0.66) (0.39) (0.63) (0.89) (0.88) (0.01) (0.92) (0.48)

Industrial −0.24 −0.16 −0.05 −0.12 −0.20 −0.24 −0.34 −0.42 −0.08(0.05) (0.12) (0.64) (0.29) (0.23) (0.02) (0.03) (0.15) (0.72)


Offshore −0.07 −0.14 −0.32 −0.33 −0.64 −0.51 −0.46 −1.07 −0.68(0.77) (0.36) (0.05) (0.03) (0.01) (0.00) (0.07) (0.02) (0.04)

Debt to assets −0.00 −0.03 −0.32 0.11 −0.71 −0.75 −0.42 −2.23 −2.03(1.00) (0.87) (0.15) (0.63) (0.07) (0.00) (0.25) (0.00) (0.00)

ln(Firm value) 0.04 0.06 0.06 0.08 0.06 0.07 0.13 0.02 0.14(0.08) (0.03) (0.01) (0.00) (0.10) (0.01) (0.00) (0.80) (0.04)

n 101 90 89 98 106 118 125 137 178

R2 0.03 0.07 0.09 0.19 0.16 0.30 0.32 0.23 0.25Average (Q) 1.29 1.16 1.09 1.03 1.38 1.32 1.43 2.00 1.96



Constant -0.55 (0.43) 0.20Herfindahl index -1.08 (0.16) 0.00Primary insiders 2.52 (0.60) 0.00Squared (Primary insiders) -2.20 (0.72) 0.00Fraction voting shares 0.88 (0.22) 0.00Industrial -0.24 (0.09) 0.01Transport/shipping -0.57 (0.07) 0.00Offshore -0.56 (0.10) 0.00Debt to assets -1.18 (0.22) 0.00ln(Firm value) 0.11 (0.02) 0.00n 1042Average (Q) 1.48


Constant 0.19 (0.49) 0.69Herfindahl index -1.03 (0.18) 0.00Primary insiders 2.18 (0.41) 0.00Squared (Primary insiders) -1.84 (0.50) 0.00Fraction voting shares 0.57 (0.30) 0.06Industrial -0.21 (0.07) 0.00Transport/shipping -0.55 (0.07) 0.00Offshore -0.53 (0.10) 0.00Debt to assets -0.99 (0.14) 0.00ln(Firm value) 0.07 (0.02) 0.001990 -0.18 (0.12) 0.161991 -0.20 (0.12) 0.101992 -0.18 (0.12) 0.141993 0.09 (0.12) 0.441994 -0.01 (0.12) 0.901995 0.05 (0.12) 0.661996 0.52 (0.11) 0.001997 0.44 (0.11) 0.00n 1042

R2 0.27Average (Q) 1.48



Table B.43 Multivariate regression relating performance (Q) to ownership concentration, insiderownership, financial policy and controlsPanel A: Year by year OLS regressions

Year1989 1990 1991 1992 1993 1994 1995 1996 1997

constant 0.55 0.30 0.36 −0.60 0.98 0.81 −0.14 3.03 0.43(0.27) (0.58) (0.58) (0.22) (0.17) (0.09) (0.87) (0.08) (0.75)



Squared (Primary insiders) −0.26 0.20 −0.69 −2.39 −1.63 −1.46 0.77 −4.97 −4.91(0.83) (0.81) (0.34) (0.06) (0.25) (0.06) (0.50) (0.06) (0.02)

Industrial −0.23 −0.16 −0.03 −0.12 −0.20 −0.21 −0.35 −0.40 −0.08(0.06) (0.13) (0.78) (0.28) (0.24) (0.05) (0.03) (0.17) (0.72)


Offshore −0.05 −0.15 −0.38 −0.33 −0.65 −0.47 −0.54 −1.09 −0.73(0.81) (0.35) (0.02) (0.05) (0.01) (0.00) (0.04) (0.02) (0.03)

Debt to assets 0.03 −0.03 −0.45 0.11 −0.70 −0.73 −0.37 −2.24 −2.14(0.89) (0.89) (0.07) (0.64) (0.08) (0.00) (0.31) (0.00) (0.00)

Dividends to earnings −0.04 −0.03 0.09 −0.04 −0.06 0.05 −0.15 −0.19 −0.32(0.41) (0.44) (0.20) (0.54) (0.45) (0.46) (0.53) (0.51) (0.25)

ln(Firm value) 0.05 0.05 0.06 0.09 0.06 0.06 0.11 0.03 0.14(0.07) (0.04) (0.04) (0.00) (0.07) (0.01) (0.01) (0.70) (0.05)

n 99 89 81 95 103 118 127 138 178

R2 0.05 0.07 0.15 0.19 0.17 0.28 0.28 0.24 0.26Average (Q) 1.30 1.16 1.09 1.04 1.39 1.31 1.42 1.99 1.95



Constant 0.50 (0.32) 0.12Herfindahl index -1.02 (0.15) 0.00Primary insiders 2.61 (0.62) 0.00Squared (Primary insiders) -2.32 (0.75) 0.00Industrial -0.25 (0.09) 0.00Transport/shipping -0.59 (0.07) 0.00Offshore -0.59 (0.10) 0.00Debt to assets -1.23 (0.23) 0.00Dividends to earnings -0.06 (0.03) 0.02ln(Firm value) 0.10 (0.02) 0.00n 1028Average (Q) 1.48


Constant 0.92 (0.34) 0.01Herfindahl index -1.00 (0.17) 0.00Primary insiders 2.29 (0.42) 0.00Squared (Primary insiders) -1.96 (0.51) 0.00Industrial -0.21 (0.07) 0.00Transport/shipping -0.57 (0.07) 0.00Offshore -0.55 (0.11) 0.00Debt to assets -1.03 (0.14) 0.00Dividends to earnings -0.04 (0.04) 0.35ln(Firm value) 0.07 (0.02) 0.001990 -0.20 (0.13) 0.121991 -0.21 (0.13) 0.101992 -0.20 (0.12) 0.111993 0.08 (0.12) 0.481994 -0.04 (0.12) 0.751995 0.04 (0.12) 0.751996 0.50 (0.12) 0.001997 0.42 (0.11) 0.00n 1028

R2 0.27Average (Q) 1.48



B.5.2 Alternative performance measure: RoA5

Table B.44 Multivariate regression relating performance (RoA5) to ownership concentration, in-sider ownership, board size, and controlsPanel A: Pooled OLS regression


Constant 12.94 (1.85) 0.00Herfindahl index -2.56 (1.10) 0.02Primary insiders 7.84 (2.35) 0.00Squared (Primary insiders) -7.26 (2.90) 0.01ln(Board size) -0.45 (0.44) 0.30Industrial -1.29 (0.39) 0.00Transport/shipping -1.42 (0.43) 0.00Offshore -3.49 (0.64) 0.00Debt to assets -4.19 (0.86) 0.00ln(Firm value) 0.05 (0.09) 0.60n 929



Year1989 1990 1991 1992 1993 1994 1995 1996 1997

constant 7.77 4.73 8.32 10.27 17.66 18.57 11.11 4.38 15.10(0.12) (0.43) (0.14) (0.07) (0.00) (0.00) (0.02) (0.51) (0.04)

Herfindahl index −3.37 0.15 −1.19 −1.35 −5.32 −3.91 −1.99 0.70 −1.74(0.34) (0.97) (0.77) (0.73) (0.06) (0.11) (0.49) (0.84) (0.60)

Primary insiders 9.78 5.32 10.88 26.09 13.01 −2.96 8.54 14.65 0.28(0.31) (0.46) (0.14) (0.01) (0.08) (0.63) (0.13) (0.04) (0.97)


ln(Board size) −1.05 0.57 −0.53 1.83 −0.47 −0.88 0.20 0.35 −1.59(0.58) (0.74) (0.75) (0.17) (0.68) (0.41) (0.85) (0.82) (0.20)

Industrial −1.31 0.20 0.52 1.48 −1.98 −1.71 −1.71 −2.41 −2.77(0.27) (0.86) (0.66) (0.27) (0.10) (0.08) (0.07) (0.03) (0.02)

Transport/shipping −0.26 0.68 0.01 2.29 −0.69 −1.29 −3.00 −3.81 −4.74(0.83) (0.58) (1.00) (0.11) (0.59) (0.22) (0.00) (0.00) (0.00)

Offshore −3.57 −1.21 −1.15 −0.17 −2.62 −3.02 −3.13 −5.06 −6.71(0.23) (0.53) (0.56) (0.93) (0.15) (0.07) (0.05) (0.01) (0.00)

Debt to assets −5.92 −1.07 −2.90 −6.59 −1.81 −3.74 −2.31 −1.62 −5.95(0.01) (0.66) (0.20) (0.02) (0.54) (0.11) (0.31) (0.56) (0.04)

ln(Firm value) 0.44 0.20 0.18 −0.06 −0.20 −0.20 0.01 0.30 0.17(0.07) (0.49) (0.53) (0.80) (0.44) (0.41) (0.95) (0.38) (0.67)

n 87 79 78 90 97 104 114 121 159

R2 0.04 −0.09 −0.06 0.04 0.05 0.25 0.07 0.10 0.13Average (RoA5) 10.09 9.57 9.50 9.90 10.20 9.67 9.07 8.98 9.79



Table B.45 Multivariate regression relating performance (RoA5) to ownership concentration, in-sider ownership, security design, and controlsPanel A: Pooled OLS regression


Constant 14.92 (2.62) 0.00Herfindahl index -2.88 (1.01) 0.00Primary insiders 7.86 (2.24) 0.00Squared (Primary insiders) -7.62 (2.72) 0.01Fraction voting shares -1.73 (1.64) 0.29Industrial -1.53 (0.38) 0.00Transport/shipping -1.98 (0.40) 0.00Offshore -4.01 (0.58) 0.00Debt to assets -4.02 (0.82) 0.00ln(Firm value) -0.00 (0.09) 0.96n 1007



Year1989 1990 1991 1992 1993 1994 1995 1996 1997

constant 3.17 6.16 4.07 12.97 20.71 23.98 15.66 11.96 19.86(0.67) (0.40) (0.60) (0.09) (0.01) (0.00) (0.03) (0.20) (0.06)




Fraction voting shares 3.61 2.96 2.65 −1.03 −2.66 −2.79 −3.79 −5.06 −2.42(0.51) (0.48) (0.56) (0.82) (0.58) (0.56) (0.39) (0.32) (0.71)

Industrial −1.13 0.19 −0.30 0.92 −2.22 −1.75 −1.79 −2.34 −3.05(0.29) (0.86) (0.80) (0.48) (0.06) (0.09) (0.06) (0.03) (0.01)


Offshore −5.09 −2.87 −2.82 −1.75 −3.61 −3.70 −3.25 −5.48 −5.76(0.01) (0.07) (0.09) (0.34) (0.03) (0.02) (0.04) (0.00) (0.00)

Debt to assets −5.83 −4.07 −3.33 −6.11 −1.87 −3.79 −1.23 −1.07 −6.04(0.01) (0.07) (0.14) (0.03) (0.51) (0.12) (0.58) (0.68) (0.02)

ln(Firm value) 0.38 0.14 0.28 0.05 −0.26 −0.41 −0.04 0.19 −0.09(0.08) (0.62) (0.30) (0.84) (0.30) (0.10) (0.86) (0.56) (0.79)

n 99 89 88 96 103 115 121 127 169

R2 0.09 −0.01 −0.02 0.04 0.08 0.14 0.10 0.12 0.13Average (RoA5) 9.71 9.39 9.33 9.72 10.01 9.21 8.79 8.79 9.66



Table B.46 Multivariate regression relating performance (RoA5) to ownership concentration, in-sider ownership, financial policy and controlsPanel A: Pooled OLS regression


Constant 13.62 (1.81) 0.00Herfindahl index -3.26 (1.00) 0.00Primary insiders 8.16 (2.26) 0.00Squared (Primary insiders) -7.98 (2.75) 0.00Industrial -1.40 (0.38) 0.00Transport/shipping -1.90 (0.40) 0.00Offshore -3.84 (0.58) 0.00Debt to assets -4.09 (0.82) 0.00Dividends to earnings 0.45 (0.22) 0.04ln(Firm value) -0.03 (0.09) 0.73n 994



Year1989 1990 1991 1992 1993 1994 1995 1996 1997

constant 7.72 9.03 11.60 11.90 16.86 21.38 11.42 5.02 17.97(0.08) (0.10) (0.07) (0.04) (0.00) (0.00) (0.01) (0.45) (0.01)

Herfindahl index −2.79 0.77 −4.55 −3.79 −5.31 −4.01 −1.79 −0.64 −3.04(0.37) (0.82) (0.16) (0.31) (0.05) (0.08) (0.49) (0.85) (0.30)



Industrial −1.27 −0.04 −0.40 0.91 −2.27 −1.31 −1.30 −2.33 −3.11(0.24) (0.97) (0.73) (0.50) (0.06) (0.18) (0.14) (0.04) (0.01)


Offshore −5.43 −2.97 −3.47 −1.79 −3.57 −3.20 −2.47 −5.22 −5.42(0.01) (0.06) (0.04) (0.36) (0.03) (0.04) (0.10) (0.00) (0.00)

Debt to assets −5.49 −4.31 −5.30 −6.20 −1.61 −3.71 −0.95 −1.27 −5.73(0.01) (0.06) (0.03) (0.03) (0.58) (0.11) (0.66) (0.63) (0.02)

Dividends to earnings 0.38 −0.13 1.29 −0.12 −0.11 1.16 1.89 0.55 1.41(0.38) (0.76) (0.05) (0.87) (0.85) (0.06) (0.15) (0.61) (0.31)

ln(Firm value) 0.32 0.14 0.10 0.06 −0.20 −0.45 −0.07 0.29 −0.13(0.14) (0.61) (0.75) (0.82) (0.42) (0.05) (0.76) (0.36) (0.70)

n 97 88 80 93 100 115 123 128 170

R2 0.08 −0.01 0.06 0.03 0.08 0.17 0.09 0.12 0.13Average (RoA5) 9.80 9.33 9.33 9.73 9.99 9.13 8.62 8.75 9.66



B.6 A full multivariate model

This appendix supplements chapter 9. Subsection B.6.1 shows the usual year by year, GMM, andfixed 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 explanatoryvariable. Subsection B.6.7 considers outside concentration, and subsection B.6.8 considers votingrights.



B.6 A full multivariate model 165

Table B.47 Multivariate regression relating performance (Q) to ownership concentration, insiderownership, owner type (largest owner), board characteristics, security design, financial policy, andcontrols (full multivariate model)Panel A: Year by year OLS regressions

Year1989 1990 1991 1992 1993 1994 1995 1996 1997

constant 1.14 0.89 1.88 0.11 1.03 −0.11 −2.25 4.70 0.62(0.26) (0.37) (0.17) (0.91) (0.45) (0.92) (0.17) (0.10) (0.79)


Primary insiders 0.26 −0.00 0.77 3.14 0.46 1.10 0.43 3.18 3.37(0.82) (1.00) (0.40) (0.00) (0.72) (0.17) (0.70) (0.15) (0.03)


Largest owner is state 0.43 −0.12 0.14 0.54 0.02 −0.26 −0.69 −0.80 −0.85(0.21) (0.63) (0.63) (0.02) (0.95) (0.18) (0.02) (0.12) (0.07)



Largest owner is nonfinancial 0.24 −0.14 −0.34 0.11 0.05 −0.10 −0.31 −0.38 −0.66(0.27) (0.37) (0.08) (0.50) (0.81) (0.49) (0.18) (0.30) (0.05)

ln(Board size) −0.73 −0.33 −0.41 −0.04 −0.03 0.03 0.26 −0.49 −0.33(0.00) (0.09) (0.06) (0.79) (0.85) (0.82) (0.22) (0.26) (0.19)

Fraction voting shares 0.42 0.15 0.16 −0.31 −0.06 0.64 2.48 −0.33 0.78(0.56) (0.81) (0.83) (0.54) (0.94) (0.36) (0.01) (0.83) (0.55)

Debt to assets −0.35 −0.26 −0.86 0.10 −0.64 −0.58 −1.29 −2.70 −3.16(0.25) (0.37) (0.01) (0.74) (0.18) (0.05) (0.01) (0.00) (0.00)

Dividend payout ratio 0.06 0.03 0.04 −0.06 −0.10 0.05 −0.27 −0.12 −0.51(0.39) (0.86) (0.65) (0.34) (0.26) (0.45) (0.33) (0.72) (0.09)

Industrial −0.22 −0.15 −0.11 −0.18 −0.24 −0.15 −0.26 −0.30 −0.16(0.15) (0.22) (0.44) (0.16) (0.18) (0.19) (0.15) (0.33) (0.51)


Offshore −0.55 −0.33 −0.45 −0.38 −0.73 −0.43 −0.47 −0.80 −0.80(0.24) (0.21) (0.07) (0.08) (0.01) (0.05) (0.16) (0.16) (0.05)

Investments over income 0.00 −0.01 −0.00 −0.20 −0.00 −0.08 −0.00 −0.05 −0.02(0.92) (0.33) (0.81) (0.15) (0.96) (0.16) (0.98) (0.60) (0.73)

ln(Firm value) 0.07 0.06 0.05 0.07 0.06 0.07 0.11 0.04 0.19(0.01) (0.04) (0.26) (0.01) (0.17) (0.03) (0.03) (0.72) (0.03)

n 81 73 64 83 90 98 108 118 153

R2 0.35 0.31 0.45 0.36 0.39 0.40 0.47 0.36 0.38Average (Q) 1.32 1.18 1.13 1.07 1.41 1.34 1.51 2.04 2.00



Constant 0.20 (0.52) 0.70Herfindahl index -1.00 (0.16) 0.00Primary insiders 2.04 (0.69) 0.00Squared (Primary insiders) -1.61 (0.87) 0.06Largest owner is state -0.42 (0.12) 0.00Largest owner is international -0.28 (0.14) 0.04Largest owner is individual -0.16 (0.19) 0.39Largest owner is nonfinancial -0.29 (0.12) 0.01ln(Board size) -0.22 (0.08) 0.01Fraction voting shares 0.89 (0.25) 0.00Debt to assets -1.62 (0.29) 0.00Dividend payout ratio -0.10 (0.03) 0.00Industrial -0.25 (0.08) 0.00Transport/shipping -0.55 (0.08) 0.00Offshore -0.65 (0.12) 0.00Investments over income -0.00 (0.01) 0.48ln(Firm value) 0.12 (0.02) 0.00n 868Average (Q) 1.52


Constant 0.88 (0.60) 0.14Herfindahl index -0.95 (0.20) 0.00Primary insiders 1.73 (0.47) 0.00Squared (Primary insiders) -1.27 (0.58) 0.03Largest owner is state -0.41 (0.13) 0.00Largest owner is international -0.21 (0.12) 0.06Largest owner is individual -0.18 (0.13) 0.17Largest owner is nonfinancial -0.30 (0.09) 0.00ln(Board size) -0.25 (0.08) 0.00Fraction voting shares 0.64 (0.35) 0.07Debt to assets -1.48 (0.17) 0.00Dividend payout ratio -0.07 (0.05) 0.15Industrial -0.21 (0.07) 0.00Transport/shipping -0.52 (0.08) 0.00Offshore -0.66 (0.13) 0.00Investments over income -0.00 (0.01) 0.77ln(Firm value) 0.09 (0.02) 0.001990 -0.23 (0.14) 0.091991 -0.23 (0.14) 0.111992 -0.13 (0.13) 0.321993 0.07 (0.13) 0.591994 -0.09 (0.13) 0.511995 0.04 (0.13) 0.731996 0.49 (0.12) 0.001997 0.44 (0.12) 0.00n 868

R2 0.34Average (Q) 1.52



Table B.48 Multivariate regression relating performance (Q) to ownership concentration, insiderownership, owner type (aggregate holdings), board characteristics, security design, financial policy,and controls (full multivariate model)Panel A: Year by year OLS regressions

Year1989 1990 1991 1992 1993 1994 1995 1996 1997

constant 1.01 0.01 −0.86 −0.19 0.51 −0.96 −3.49 0.34 −0.64(0.34) (1.00) (0.64) (0.85) (0.75) (0.40) (0.04) (0.92) (0.82)




Aggregate state holdings 0.85 0.19 1.00 0.56 −0.16 0.45 0.05 −0.43 −1.17(0.21) (0.73) (0.23) (0.32) (0.82) (0.38) (0.96) (0.77) (0.36)




ln(Board size) −0.78 −0.29 −0.29 −0.07 −0.05 −0.05 0.42 −0.25 −0.23(0.00) (0.14) (0.22) (0.65) (0.78) (0.69) (0.03) (0.55) (0.38)

Fraction voting shares 0.30 0.31 0.87 −0.11 0.49 0.77 2.65 0.59 1.27(0.67) (0.58) (0.25) (0.83) (0.56) (0.26) (0.00) (0.70) (0.35)

Debt to assets −0.29 −0.28 −0.74 0.05 −0.98 −0.45 −1.03 −2.59 −3.28(0.34) (0.35) (0.03) (0.86) (0.04) (0.13) (0.02) (0.00) (0.00)


Industrial −0.18 −0.13 0.01 −0.15 −0.20 −0.15 −0.21 −0.14 −0.07(0.21) (0.32) (0.97) (0.25) (0.27) (0.19) (0.23) (0.63) (0.77)


Offshore −0.48 −0.39 −0.54 −0.34 −0.69 −0.43 −0.35 −0.75 −0.71(0.30) (0.12) (0.04) (0.13) (0.02) (0.05) (0.27) (0.18) (0.09)


ln(Firm value) 0.08 0.08 0.10 0.08 0.08 0.08 0.08 0.11 0.20(0.01) (0.03) (0.06) (0.01) (0.14) (0.03) (0.14) (0.35) (0.05)

n 81 73 64 83 90 98 108 118 153

R2 0.35 0.30 0.37 0.34 0.34 0.43 0.53 0.40 0.36Average (Q) 1.32 1.18 1.13 1.07 1.41 1.34 1.51 2.04 2.00



Constant -0.94 (0.58) 0.10Herfindahl index -0.68 (0.20) 0.00Primary insiders 1.64 (0.62) 0.01Squared (Primary insiders) -1.37 (0.84) 0.10Aggregate state holdings -0.43 (0.26) 0.10Aggregate international holdings 0.12 (0.24) 0.63Aggregate individual holdings 1.02 (0.32) 0.00Aggregate nonfinancial holdings -0.23 (0.21) 0.25ln(Board size) -0.19 (0.07) 0.01Fraction voting shares 1.14 (0.26) 0.00Debt to assets -1.54 (0.29) 0.00Dividend payout ratio -0.11 (0.03) 0.00Industrial -0.19 (0.09) 0.02Transport/shipping -0.46 (0.07) 0.00Offshore -0.57 (0.11) 0.00Investments over income -0.00 (0.00) 0.95ln(Firm value) 0.14 (0.02) 0.00n 868Average (Q) 1.52


Constant -0.42 (0.68) 0.54Herfindahl index -0.82 (0.24) 0.00Primary insiders 1.43 (0.46) 0.00Squared (Primary insiders) -1.15 (0.56) 0.04Aggregate state holdings 0.01 (0.34) 0.99Aggregate international holdings 0.61 (0.25) 0.01Aggregate individual holdings 1.10 (0.29) 0.00Aggregate nonfinancial holdings 0.10 (0.25) 0.68ln(Board size) -0.23 (0.08) 0.01Fraction voting shares 0.93 (0.35) 0.01Debt to assets -1.36 (0.17) 0.00Dividend payout ratio -0.08 (0.05) 0.13Industrial -0.16 (0.07) 0.03Transport/shipping -0.45 (0.09) 0.00Offshore -0.60 (0.13) 0.00Investments over income -0.00 (0.01) 0.86ln(Firm value) 0.10 (0.02) 0.001990 -0.20 (0.14) 0.151991 -0.20 (0.14) 0.171992 -0.11 (0.13) 0.421993 0.11 (0.13) 0.411994 -0.05 (0.13) 0.681995 0.06 (0.13) 0.641996 0.52 (0.13) 0.001997 0.49 (0.12) 0.00n 868

R2 0.35Average (Q) 1.52



B.6.2 Alternative performance measure: RoA5

Table B.49 Multivariate regression relating performance (RoA5) to ownership concentration, in-sider ownership, type of largest owner, board characteristics, security design, financial policy, andcontrols (full multivariate model)Panel A: Pooled OLS regression


Constant 16.54 (3.22) 0.00Herfindahl index -2.22 (1.17) 0.06Primary insiders 7.80 (2.56) 0.00Squared (Primary insiders) -7.07 (3.11) 0.02Largest owner is state -0.21 (0.73) 0.78Largest owner is international 0.53 (0.63) 0.40Largest owner is individual -0.26 (0.71) 0.71Largest owner is nonfinancial -0.30 (0.50) 0.55ln(Board size) -0.75 (0.45) 0.10Fraction voting shares -1.21 (1.89) 0.52Debt to assets -5.98 (0.95) 0.00Dividend payout ratio 0.35 (0.27) 0.20Industrial -1.39 (0.40) 0.00Transport/shipping -1.33 (0.45) 0.00Offshore -3.06 (0.71) 0.00Investments over income -0.08 (0.06) 0.16ln(Firm value) 0.01 (0.10) 0.89n 851



Year1989 1990 1991 1992 1993 1994 1995 1996 1997

constant −2.34 3.12 −0.57 11.55 28.55 29.49 2.07 19.32 25.96(0.78) (0.75) (0.97) (0.27) (0.00) (0.00) (0.81) (0.11) (0.03)


Primary insiders 10.00 5.33 12.02 32.60 22.95 −2.93 7.14 16.23 −0.91(0.28) (0.51) (0.16) (0.01) (0.01) (0.68) (0.23) (0.06) (0.91)


Largest owner is state 2.04 −1.34 −0.05 1.37 −0.49 0.26 −0.55 −2.25 1.34(0.47) (0.59) (0.98) (0.61) (0.82) (0.88) (0.73) (0.27) (0.57)

Largest owner is international 2.36 −2.51 −2.10 2.97 2.49 1.73 0.35 −2.36 2.10(0.21) (0.12) (0.29) (0.18) (0.21) (0.25) (0.83) (0.28) (0.35)

Largest owner is individual 2.52 −0.64 −1.70 −0.98 −4.03 −0.34 1.80 −2.04 −0.46(0.26) (0.79) (0.52) (0.75) (0.19) (0.88) (0.24) (0.34) (0.82)

Largest owner is nonfinancial 1.05 −1.15 −0.64 0.64 −1.11 0.57 0.29 −0.99 0.16(0.55) (0.45) (0.73) (0.73) (0.47) (0.64) (0.81) (0.50) (0.92)

ln(Board size) −0.91 1.70 1.06 1.82 −0.36 −1.62 0.05 −0.76 −2.34(0.63) (0.37) (0.61) (0.25) (0.77) (0.16) (0.97) (0.64) (0.07)

Fraction voting shares 7.19 2.17 2.20 −3.75 −9.10 −7.35 6.66 −5.50 −2.44(0.21) (0.71) (0.75) (0.52) (0.12) (0.22) (0.19) (0.36) (0.71)

Debt to assets −3.98 −3.10 −4.59 −6.26 −2.78 −2.61 −2.41 −4.22 −10.20(0.11) (0.28) (0.12) (0.06) (0.43) (0.31) (0.34) (0.21) (0.00)

Dividend payout ratio 0.09 0.91 1.29 −0.16 −0.12 0.50 1.85 −0.30 0.54(0.87) (0.56) (0.07) (0.83) (0.85) (0.42) (0.21) (0.82) (0.72)

Industrial −1.22 −0.18 0.79 1.12 −2.94 −1.84 −0.99 −2.49 −2.76(0.32) (0.88) (0.56) (0.44) (0.03) (0.07) (0.31) (0.04) (0.02)

Transport/shipping 0.10 0.43 −0.26 2.46 −0.70 −1.19 −2.24 −4.15 −4.24(0.94) (0.76) (0.86) (0.12) (0.63) (0.28) (0.06) (0.00) (0.01)

Offshore −1.08 0.97 −0.98 −0.94 −4.28 −4.64 −1.96 −3.86 −4.82(0.77) (0.70) (0.67) (0.70) (0.04) (0.01) (0.27) (0.07) (0.02)


ln(Firm value) 0.46 0.19 0.46 −0.00 −0.27 −0.37 0.11 0.08 −0.07(0.05) (0.55) (0.28) (0.99) (0.41) (0.18) (0.68) (0.85) (0.87)

n 80 72 63 82 88 96 106 113 151

R2 0.20 0.15 0.29 0.20 0.24 0.43 0.24 0.21 0.24Average (RoA5) 10.08 9.67 9.30 9.90 10.17 9.60 8.99 9.28 10.08



Table B.50 Multivariate regression relating performance (RoA5) to ownership concentration, in-sider ownership, owner type (aggregate holdings), board characteristics, security design, financialpolicy, and controls (full multivariate model)Panel A: Pooled OLS regression


Constant 17.14 (3.67) 0.00Herfindahl index -1.99 (1.31) 0.13Primary insiders 7.44 (2.49) 0.00Squared (Primary insiders) -6.92 (3.06) 0.02Aggregate state holdings 0.92 (1.97) 0.64Aggregate international holdings 0.70 (1.30) 0.59Aggregate individual holdings 0.53 (1.60) 0.74Aggregate nonfinancial holdings -0.97 (1.33) 0.47ln(Board size) -0.75 (0.45) 0.09Fraction voting shares -0.75 (1.88) 0.69Debt to assets -5.93 (0.96) 0.00Dividend payout ratio 0.32 (0.27) 0.24Industrial -1.37 (0.40) 0.00Transport/shipping -1.09 (0.47) 0.02Offshore -2.97 (0.72) 0.00Investments over income -0.07 (0.06) 0.19ln(Firm value) -0.04 (0.11) 0.69n 851



Year1989 1990 1991 1992 1993 1994 1995 1996 1997

constant −0.64 5.45 −5.90 7.00 36.15 36.62 9.38 25.31 24.14(0.94) (0.63) (0.72) (0.56) (0.00) (0.00) (0.34) (0.08) (0.09)

Herfindahl index 0.10 −1.25 −5.66 0.80 −6.04 −4.26 −3.34 1.40 −0.21(0.98) (0.76) (0.34) (0.88) (0.11) (0.12) (0.31) (0.73) (0.96)

Primary insiders 11.26 6.98 10.00 25.29 18.89 −0.87 10.00 16.13 −1.48(0.23) (0.38) (0.22) (0.04) (0.03) (0.89) (0.08) (0.05) (0.85)


Aggregate state holdings 0.60 −1.50 2.10 3.29 8.31 0.54 −2.60 −2.94 2.24(0.92) (0.81) (0.78) (0.63) (0.11) (0.91) (0.64) (0.66) (0.73)

Aggregate international holdings 1.92 −2.00 −1.21 3.22 8.89 1.08 −1.24 −2.02 1.07(0.63) (0.65) (0.82) (0.48) (0.03) (0.74) (0.74) (0.65) (0.82)

Aggregate individual holdings 2.37 −3.02 0.35 5.56 −0.28 −3.96 −2.43 −5.24 0.80(0.64) (0.59) (0.96) (0.34) (0.96) (0.32) (0.57) (0.29) (0.87)

Aggregate nonfinancial holdings −2.36 −0.46 1.83 1.56 1.96 −2.80 −4.60 −2.29 −0.34(0.54) (0.92) (0.76) (0.74) (0.59) (0.41) (0.28) (0.63) (0.94)

ln(Board size) −1.13 1.26 1.09 1.68 −0.89 −1.42 −0.28 −0.64 −2.27(0.56) (0.51) (0.60) (0.31) (0.49) (0.22) (0.80) (0.70) (0.08)

Fraction voting shares 7.47 0.92 2.48 −1.39 −8.89 −6.23 7.25 −5.82 −2.20(0.18) (0.87) (0.71) (0.81) (0.13) (0.29) (0.15) (0.33) (0.74)

Debt to assets −3.61 −3.58 −4.50 −6.02 −1.51 −3.50 −3.47 −4.71 −9.52(0.14) (0.23) (0.13) (0.08) (0.66) (0.17) (0.18) (0.18) (0.00)

Dividend payout ratio 0.00 1.35 1.32 −0.32 −0.18 0.63 1.69 −0.48 0.59(1.00) (0.40) (0.07) (0.67) (0.78) (0.30) (0.25) (0.70) (0.71)

Industrial −0.95 −0.50 0.98 1.56 −2.66 −2.22 −1.33 −2.51 −2.83(0.41) (0.69) (0.49) (0.29) (0.04) (0.03) (0.17) (0.04) (0.02)

Transport/shipping 0.95 −0.20 −0.81 2.74 −0.94 −0.98 −1.67 −4.21 −4.14(0.48) (0.90) (0.62) (0.09) (0.53) (0.38) (0.17) (0.01) (0.01)

Offshore −1.21 1.06 −0.99 −0.63 −4.86 −5.24 −2.26 −3.88 −4.65(0.74) (0.66) (0.66) (0.80) (0.02) (0.00) (0.21) (0.07) (0.03)


ln(Firm value) 0.41 0.20 0.64 0.03 −0.78 −0.67 −0.09 −0.13 −0.02(0.08) (0.56) (0.16) (0.93) (0.04) (0.04) (0.77) (0.79) (0.96)

n 80 72 63 82 88 96 106 113 151

R2 0.22 0.12 0.28 0.18 0.25 0.44 0.25 0.21 0.23Average (RoA5) 10.08 9.67 9.30 9.90 10.17 9.60 8.99 9.28 10.08



B.6.3 Alternative performance measure: RoA

Table B.51 Multivariate regression relating performance (RoA) to ownership concentration, insiderownership, owner type (largest owner), board characteristics, security design, financial policy, andcontrols (full multivariate model)Panel A: Pooled OLS regression

Dependent variable: RoAcoeff (stdev) pvalue

Constant -29.34 (8.78) 0.00Herfindahl index -0.20 (3.05) 0.95Primary insiders 23.00 (7.02) 0.00Squared (Primary insiders) -35.65 (8.57) 0.00Largest owner is state -1.56 (1.98) 0.43Largest owner is international -0.34 (1.72) 0.85Largest owner is individual -2.75 (1.94) 0.16Largest owner is nonfinancial 2.25 (1.38) 0.10ln(Board size) -0.27 (1.25) 0.83Fraction voting shares -1.52 (5.25) 0.77Debt to assets 6.08 (2.55) 0.02Dividend payout ratio 2.12 (0.75) 0.00Industrial -1.66 (1.09) 0.13Transport/shipping -1.50 (1.24) 0.23Offshore -1.27 (1.98) 0.52Investments over income 0.02 (0.16) 0.90ln(Firm value) 1.65 (0.28) 0.00n 869

R2 0.10Average (RoA) 5.99


Year1989 1990 1991 1992 1993 1994 1995 1996 1997

constant −30.37 7.90 −65.37 −4.00 17.19 −21.59 −2.67 −57.22 −43.57(0.39) (0.68) (0.04) (0.83) (0.22) (0.41) (0.89) (0.03) (0.24)

Herfindahl index −5.76 −17.79 −21.05 −17.59 −12.55 14.78 −2.81 −0.21 14.54(0.70) (0.01) (0.07) (0.02) (0.01) (0.02) (0.66) (0.98) (0.22)

Primary insiders 48.54 4.53 5.75 34.64 14.51 110.33 17.41 −4.64 15.19(0.23) (0.78) (0.79) (0.12) (0.28) (0.00) (0.22) (0.82) (0.54)

Squared (Primary insiders) −39.31 −1.15 −16.74 −33.76 −9.15 −195.65 −37.65 −10.85 −12.15(0.36) (0.95) (0.46) (0.35) (0.58) (0.00) (0.01) (0.69) (0.74)

Largest owner is state −4.45 −1.01 4.22 0.46 −3.73 −0.97 −0.52 −2.01 −1.62(0.71) (0.84) (0.53) (0.92) (0.22) (0.84) (0.89) (0.67) (0.83)

Largest owner is international −3.71 −4.59 6.70 −5.98 −5.78 −2.41 5.58 4.34 0.22(0.65) (0.16) (0.17) (0.14) (0.04) (0.57) (0.15) (0.38) (0.98)

Largest owner is individual −19.75 −2.71 13.07 −3.65 −2.33 −13.33 6.05 7.29 −5.06(0.04) (0.57) (0.05) (0.52) (0.60) (0.02) (0.09) (0.13) (0.42)

Largest owner is nonfinancial −0.60 −0.15 9.07 −0.68 0.09 −0.25 5.07 4.21 −2.56(0.94) (0.96) (0.05) (0.84) (0.97) (0.94) (0.08) (0.21) (0.63)

ln(Board size) −15.38 2.79 −1.99 1.21 −3.58 −3.28 3.20 1.02 1.54(0.06) (0.47) (0.70) (0.68) (0.04) (0.32) (0.22) (0.80) (0.70)

Fraction voting shares −1.39 −4.97 33.29 −4.27 −10.83 1.62 −11.36 3.91 −2.41(0.96) (0.67) (0.05) (0.69) (0.19) (0.93) (0.34) (0.78) (0.91)

Debt to assets 25.22 3.44 −14.59 −5.11 −4.04 9.70 2.33 9.48 5.67(0.02) (0.54) (0.05) (0.41) (0.42) (0.18) (0.68) (0.18) (0.58)

Dividend payout ratio 1.85 8.67 2.04 0.84 0.21 0.62 7.35 3.97 5.88(0.44) (0.00) (0.26) (0.54) (0.82) (0.72) (0.03) (0.18) (0.22)

Industrial 4.89 −1.46 4.03 −2.48 −0.31 −5.49 −0.50 −4.15 −2.32(0.34) (0.56) (0.23) (0.35) (0.87) (0.05) (0.82) (0.14) (0.54)

Transport/shipping 4.29 0.20 −4.23 −1.56 1.15 −4.84 −3.57 −3.71 0.28(0.43) (0.94) (0.26) (0.59) (0.57) (0.12) (0.18) (0.28) (0.95)

Offshore 4.27 −0.31 −4.60 −3.27 −2.97 −6.55 −1.20 −5.90 3.76(0.79) (0.95) (0.43) (0.47) (0.30) (0.21) (0.77) (0.26) (0.56)

Investments over income −0.02 −0.14 0.13 −7.54 −0.22 0.75 −0.22 0.29 −0.12(0.95) (0.61) (0.55) (0.01) (0.71) (0.60) (0.66) (0.76) (0.88)

ln(Firm value) 2.62 0.01 2.28 0.98 0.59 1.35 0.50 2.50 2.09(0.01) (0.99) (0.02) (0.09) (0.20) (0.08) (0.42) (0.01) (0.12)

n 81 73 64 83 90 98 109 118 153

R2 0.34 0.31 0.39 0.34 0.35 0.58 0.30 0.15 0.09Average (RoA) 7.94 7.97 4.66 4.06 6.87 5.77 7.01 6.08 4.46



Table B.52 Multivariate regression relating performance (RoA) to ownership concentration, insiderownership, aggregate holdings per owner types, board characteristics, security design, financialpolicy, and controls (full multivariate model)Panel A: Pooled OLS regression

Dependent variable: RoAcoeff (stdev) pvalue

Constant -26.44 (9.98) 0.01Herfindahl index 4.80 (3.55) 0.18Primary insiders 15.81 (6.82) 0.02Squared (Primary insiders) -28.46 (8.41) 0.00Aggregate state holdings -17.32 (5.02) 0.00Aggregate international holdings -10.73 (3.59) 0.00Aggregate individual holdings -5.35 (4.39) 0.22Aggregate nonfinancial holdings -3.14 (3.68) 0.39ln(Board size) 0.12 (1.24) 0.92Fraction voting shares -3.49 (5.20) 0.50Debt to assets 5.51 (2.56) 0.03Dividend payout ratio 1.97 (0.74) 0.01Industrial -1.34 (1.09) 0.22Transport/shipping -1.47 (1.29) 0.25Offshore -1.05 (1.98) 0.60Investments over income 0.05 (0.16) 0.73ln(Firm value) 1.86 (0.31) 0.00n 869

R2 0.11Average (RoA) 5.99


Year1989 1990 1991 1992 1993 1994 1995 1996 1997

constant −35.36 10.17 −26.88 −5.31 9.84 −22.62 6.15 −28.36 −58.26(0.35) (0.63) (0.51) (0.81) (0.55) (0.44) (0.79) (0.37) (0.19)

Herfindahl index −2.34 −16.26 −11.66 −8.49 −9.37 17.04 7.49 8.21 19.46(0.90) (0.03) (0.42) (0.37) (0.09) (0.04) (0.32) (0.37) (0.14)


Squared (Primary insiders) −23.18 4.27 −24.80 −27.10 −5.70 −177.30 −33.97 −18.05 −9.15(0.61) (0.80) (0.27) (0.46) (0.72) (0.00) (0.02) (0.50) (0.80)

Aggregate state holdings −19.88 −12.22 −19.06 −14.45 −9.79 −11.65 −22.07 −28.58 −23.22(0.42) (0.24) (0.30) (0.22) (0.17) (0.38) (0.07) (0.04) (0.24)

Aggregate international holdings −14.35 −14.54 −19.72 −8.85 −7.93 −4.16 −12.26 −20.63 −19.43(0.43) (0.09) (0.14) (0.29) (0.19) (0.67) (0.16) (0.05) (0.18)

Aggregate individual holdings −21.30 −5.69 −5.09 −3.12 −0.57 −1.56 0.61 −14.47 −8.76(0.35) (0.60) (0.80) (0.77) (0.94) (0.89) (0.95) (0.22) (0.56)

Aggregate nonfinancial holdings −13.87 −2.21 −5.68 −7.39 0.98 −4.03 −11.70 −13.96 −6.80(0.43) (0.79) (0.70) (0.38) (0.85) (0.69) (0.24) (0.21) (0.62)

ln(Board size) −16.62 2.20 −4.28 1.38 −3.43 −3.02 1.37 −0.59 1.85(0.06) (0.55) (0.40) (0.65) (0.07) (0.38) (0.60) (0.88) (0.64)

Fraction voting shares 13.52 −6.63 18.41 −5.09 −11.08 1.87 −14.18 −1.82 −2.72(0.59) (0.53) (0.27) (0.63) (0.20) (0.92) (0.23) (0.90) (0.90)

Debt to assets 30.92 0.23 −16.47 −5.02 −3.13 9.86 −0.94 6.58 4.14(0.00) (0.97) (0.03) (0.42) (0.52) (0.19) (0.87) (0.36) (0.68)

Dividend payout ratio 0.91 7.70 1.86 1.32 0.07 0.62 5.97 2.55 5.61(0.72) (0.01) (0.31) (0.33) (0.94) (0.73) (0.08) (0.39) (0.25)

Industrial 7.98 −1.33 2.35 −2.49 −0.63 −3.95 −0.37 −4.66 −1.98(0.12) (0.58) (0.50) (0.36) (0.74) (0.18) (0.87) (0.09) (0.60)

Transport/shipping 6.39 −1.21 −5.39 −1.41 0.49 −3.38 −1.51 −3.18 −0.69(0.29) (0.67) (0.17) (0.64) (0.82) (0.31) (0.59) (0.38) (0.89)

Offshore 6.80 −0.78 −2.01 −2.57 −2.20 −5.26 −1.92 −5.15 2.91(0.68) (0.87) (0.72) (0.58) (0.48) (0.34) (0.65) (0.32) (0.65)

Investments over income −0.02 −0.11 0.18 −6.69 −0.15 −0.63 −0.07 0.26 −0.17(0.96) (0.69) (0.40) (0.02) (0.81) (0.64) (0.89) (0.78) (0.83)

ln(Firm value) 2.57 0.38 2.12 1.24 0.95 1.45 0.97 2.34 3.12(0.01) (0.54) (0.05) (0.05) (0.08) (0.13) (0.19) (0.03) (0.04)

n 81 73 64 83 90 98 109 118 153

R2 0.28 0.36 0.39 0.32 0.32 0.55 0.30 0.17 0.11Average (RoA) 7.94 7.97 4.66 4.06 6.87 5.77 7.01 6.08 4.46



B.6.4 Alternative performance measure: RoS5

Table B.53 Multivariate regression relating performance (RoS5) to ownership concentration, in-sider ownership, owner type (largest owner), board characteristics, security design, financial policy,and controls (full multivariate model)Panel A: Pooled OLS regression

Dependent variable: RoS5coeff (stdev) pvalue

Constant 99.37 (44.29) 0.02Herfindahl index -16.19 (15.38) 0.29Primary insiders -24.18 (35.03) 0.49Squared (Primary insiders) 76.91 (43.24) 0.08Largest owner is state -15.47 (9.40) 0.10Largest owner is international 0.62 (8.50) 0.94Largest owner is individual 10.70 (9.96) 0.28Largest owner is nonfinancial -11.53 (6.94) 0.10ln(Board size) -17.84 (6.68) 0.01Fraction voting shares 39.27 (24.28) 0.11Debt to assets -63.78 (13.17) 0.00Dividend payout ratio -4.60 (3.44) 0.18Industrial 13.08 (5.62) 0.02Transport/shipping 2.73 (6.29) 0.66Offshore 7.32 (10.36) 0.48Investments over income -0.64 (0.92) 0.49ln(Firm value) -0.90 (1.48) 0.54n 621

R2 0.11Average (RoS5) 44.67


Year1989 1990 1991 1992 1993 1994 1995 1996 1997

constant −18.08 −71.39 −115.70 −42.80 413.49 262.02 115.46 389.92 173.87(0.82) (0.13) (0.02) (0.64) (0.04) (0.03) (0.48) (0.00) (0.06)

Herfindahl index −3.71 8.58 −16.40 17.79 8.23 9.75 −54.20 −35.16 −4.27(0.94) (0.64) (0.38) (0.52) (0.90) (0.79) (0.25) (0.40) (0.88)

Primary insiders −61.86 −14.46 −40.84 −56.55 83.26 −17.16 −257.01 99.16 81.54(0.43) (0.74) (0.18) (0.63) (0.74) (0.87) (0.03) (0.34) (0.22)

Squared (Primary insiders) 43.19 15.12 22.53 94.57 −198.71 73.44 390.64 −117.45 −80.63(0.64) (0.78) (0.52) (0.78) (0.63) (0.55) (0.00) (0.39) (0.39)

Largest owner is state 22.98 −2.53 −18.08 −14.28 −8.14 −41.91 −38.61 −12.22 1.73(0.39) (0.85) (0.10) (0.42) (0.84) (0.04) (0.21) (0.59) (0.92)

Largest owner is international 18.00 1.21 8.50 7.55 93.05 −34.05 29.95 −6.63 −13.01(0.38) (0.90) (0.31) (0.61) (0.02) (0.11) (0.34) (0.79) (0.46)

Largest owner is individual 6.89 3.87 12.99 2.88 17.63 −71.01 92.20 8.08 9.85(0.76) (0.75) (0.25) (0.88) (0.77) (0.04) (0.00) (0.76) (0.57)

Largest owner is nonfinancial 16.02 3.44 10.82 0.63 3.35 −46.46 −20.07 −11.16 −9.21(0.42) (0.69) (0.16) (0.97) (0.92) (0.01) (0.38) (0.50) (0.48)

ln(Board size) −23.08 −2.89 −1.84 10.96 −47.69 −6.04 −17.57 −14.79 −6.91(0.23) (0.78) (0.84) (0.49) (0.17) (0.73) (0.48) (0.45) (0.52)

Fraction voting shares 19.45 9.53 8.22 −13.89 −122.65 −38.63 113.76 −14.13 1.88(0.68) (0.71) (0.75) (0.76) (0.27) (0.62) (0.20) (0.83) (0.97)

Debt to assets −36.69 −7.19 −32.24 5.44 20.05 −100.25 −96.92 −131.90 −105.48(0.19) (0.62) (0.01) (0.81) (0.78) (0.01) (0.04) (0.00) (0.00)

Dividend payout ratio 3.90 −1.74 0.90 1.84 0.78 0.55 −36.99 −3.25 −16.20(0.43) (0.81) (0.71) (0.73) (0.95) (0.94) (0.18) (0.84) (0.18)

Industrial −11.46 −1.86 −0.37 −16.16 3.83 −25.31 56.26 28.52 28.79(0.33) (0.77) (0.95) (0.16) (0.89) (0.11) (0.00) (0.05) (0.01)

Transport/shipping 12.75 14.94 6.22 9.21 −18.72 −24.71 9.80 −3.14 11.02(0.32) (0.03) (0.29) (0.46) (0.54) (0.14) (0.68) (0.86) (0.39)

Offshore −22.59 14.70 −5.62 −5.15 −20.69 −28.03 24.02 12.25 13.59(0.45) (0.45) (0.72) (0.80) (0.62) (0.28) (0.47) (0.64) (0.47)

Investments over income −0.95 −0.71 −3.53 −21.80 −1.87 −8.66 17.83 4.72 18.16(0.18) (0.27) (0.06) (0.32) (0.81) (0.59) (0.01) (0.33) (0.05)

ln(Firm value) 4.47 4.45 7.44 3.16 −8.66 −2.94 −4.57 −10.69 −2.94(0.12) (0.01) (0.00) (0.25) (0.15) (0.42) (0.37) (0.02) (0.38)

n 58 54 43 40 72 71 82 96 105

R2 0.35 0.40 0.71 0.37 0.23 0.34 0.53 0.31 0.34Average (RoS5) 36.85 23.70 22.90 27.29 53.27 38.54 54.21 56.86 54.95



Table B.54 Multivariate regression relating performance (RoS5) to ownership concentration, in-sider ownership, aggregate holdings per owner type, board characteristics, security design, financialpolicy, and controls (full multivariate model)Panel A: Pooled OLS regression

Dependent variable: RoS5coeff (stdev) pvalue

Constant 41.38 (52.42) 0.43Herfindahl index 2.69 (17.67) 0.88Primary insiders -23.81 (34.06) 0.48Squared (Primary insiders) 70.17 (42.65) 0.10Aggregate state holdings -17.52 (25.33) 0.49Aggregate international holdings 18.53 (18.78) 0.32Aggregate individual holdings 63.68 (23.56) 0.01Aggregate nonfinancial holdings -11.12 (19.40) 0.57ln(Board size) -16.01 (6.64) 0.02Fraction voting shares 51.64 (23.94) 0.03Debt to assets -63.63 (13.21) 0.00Dividend payout ratio -4.97 (3.42) 0.15Industrial 15.27 (5.66) 0.01Transport/shipping 8.32 (6.57) 0.20Offshore 9.42 (10.45) 0.37Investments over income -0.44 (0.92) 0.63ln(Firm value) 0.13 (1.68) 0.94n 621

R2 0.12Average (RoS5) 44.67


Year1989 1990 1991 1992 1993 1994 1995 1996 1997

constant 42.63 −78.54 −72.72 −130.27 465.42 251.83 −94.91 274.99 86.46(0.65) (0.14) (0.24) (0.23) (0.06) (0.07) (0.64) (0.08) (0.42)

Herfindahl index −10.28 6.37 −22.28 49.22 2.31 −29.02 −10.60 −18.10 32.80(0.85) (0.74) (0.34) (0.16) (0.98) (0.50) (0.86) (0.67) (0.28)

Primary insiders −48.43 3.49 −28.21 −6.79 95.38 −100.72 −146.78 66.56 74.67(0.54) (0.93) (0.36) (0.95) (0.70) (0.28) (0.20) (0.47) (0.23)

Squared (Primary insiders) 48.59 3.86 26.93 −91.22 −217.86 136.65 285.26 −87.83 −61.59(0.60) (0.94) (0.47) (0.77) (0.59) (0.25) (0.02) (0.48) (0.49)

Aggregate state holdings 14.77 11.82 −24.83 −34.06 −9.15 65.12 −1.32 −6.82 −38.06(0.82) (0.68) (0.45) (0.36) (0.93) (0.31) (0.99) (0.92) (0.40)

Aggregate international holdings −0.70 31.24 24.45 44.67 149.66 79.60 81.16 63.12 −58.28(0.99) (0.22) (0.30) (0.14) (0.10) (0.10) (0.30) (0.22) (0.10)

Aggregate individual holdings −56.11 −0.36 −19.92 43.87 −13.87 40.95 217.96 115.84 67.07(0.29) (0.99) (0.54) (0.35) (0.90) (0.51) (0.03) (0.06) (0.10)

Aggregate nonfinancial holdings −11.68 26.48 15.52 5.28 18.94 31.43 0.29 −16.54 −70.22(0.81) (0.30) (0.55) (0.86) (0.83) (0.52) (1.00) (0.77) (0.04)

ln(Board size) −26.35 −3.00 3.46 21.29 −47.17 −4.95 −13.34 −9.13 0.33(0.18) (0.76) (0.74) (0.17) (0.18) (0.79) (0.61) (0.62) (0.97)

Fraction voting shares 21.57 3.81 −8.25 −8.42 −95.81 −22.83 160.75 18.07 32.70(0.63) (0.87) (0.73) (0.82) (0.42) (0.77) (0.08) (0.77) (0.49)

Debt to assets −31.98 −3.39 −29.01 16.61 8.87 −56.36 −85.78 −128.69 −111.89(0.24) (0.81) (0.02) (0.43) (0.90) (0.18) (0.11) (0.00) (0.00)

Dividend payout ratio 3.75 −0.23 1.25 −0.88 2.08 −0.81 −34.46 10.07 −14.70(0.47) (0.97) (0.63) (0.85) (0.88) (0.92) (0.24) (0.53) (0.20)

Industrial −8.57 −2.75 −4.65 −25.32 −5.30 −20.86 56.69 35.02 31.21(0.43) (0.66) (0.42) (0.02) (0.86) (0.21) (0.01) (0.01) (0.00)

Transport/shipping 15.28 12.26 8.57 12.86 −24.35 −30.42 27.98 12.56 23.64(0.24) (0.08) (0.20) (0.31) (0.47) (0.10) (0.27) (0.47) (0.06)

Offshore −23.76 17.74 0.36 −8.82 −50.33 −26.11 32.27 12.83 26.64(0.42) (0.28) (0.98) (0.63) (0.27) (0.35) (0.37) (0.61) (0.13)


ln(Firm value) 2.90 4.14 5.51 5.20 −12.81 −8.04 −1.02 −9.49 −0.18(0.34) (0.02) (0.00) (0.08) (0.08) (0.09) (0.87) (0.06) (0.96)

n 58 54 43 40 72 71 82 96 105

R2 0.36 0.44 0.70 0.48 0.17 0.28 0.47 0.39 0.42Average (RoS5) 36.85 23.70 22.90 27.29 53.27 38.54 54.21 56.86 54.95



B.6.5 Alternative performance measure: RoS

Table B.55 Multivariate regression relating performance (RoS) to ownership concentration, insiderownership, owner type (largest owner), board characteristics, security design, financial policy, andcontrols (full multivariate model)Panel A: Pooled OLS regression

Dependent variable: RoScoeff (stdev) pvalue

Constant -90.02 (66.23) 0.17Herfindahl index -0.15 (22.53) 0.99Primary insiders 46.70 (52.58) 0.37Squared (Primary insiders) -13.85 (62.82) 0.83Largest owner is state -9.37 (14.11) 0.51Largest owner is international 4.88 (12.49) 0.70Largest owner is individual 11.69 (15.07) 0.44Largest owner is nonfinancial -3.28 (10.04) 0.74ln(Board size) -13.37 (9.51) 0.16Fraction voting shares 57.83 (37.67) 0.12Debt to assets -23.93 (19.07) 0.21Dividend payout ratio 1.12 (5.45) 0.84Industrial 6.32 (8.16) 0.44Transport/shipping -2.87 (9.15) 0.75Offshore 18.20 (15.19) 0.23Investments over income -0.22 (1.13) 0.84ln(Firm value) 5.08 (2.18) 0.02n 743

R2 0.03Average (RoS) 34.55


Year1989 1990 1991 1992 1993 1994 1995 1996 1997

constant −15.25 100.34 −128.33 −394.82 450.75 18.99 −211.82 203.03 −236.83(0.93) (0.24) (0.30) (0.01) (0.10) (0.90) (0.31) (0.38) (0.10)

Herfindahl index −62.70 4.30 80.29 −17.89 −90.72 77.34 −125.92 −56.97 47.08(0.34) (0.90) (0.07) (0.73) (0.31) (0.07) (0.04) (0.45) (0.28)

Primary insiders 59.59 79.27 −96.13 435.98 120.19 93.85 −67.82 −30.32 125.56(0.72) (0.30) (0.24) (0.00) (0.71) (0.47) (0.64) (0.87) (0.21)

Squared (Primary insiders) −74.24 −109.00 109.76 −597.12 −142.68 −166.60 210.06 115.76 −133.19(0.68) (0.20) (0.20) (0.01) (0.79) (0.29) (0.16) (0.64) (0.35)

Largest owner is state 5.49 −6.25 −27.60 48.97 −43.34 25.98 −51.46 −11.11 5.79(0.91) (0.79) (0.28) (0.13) (0.44) (0.35) (0.18) (0.78) (0.83)

Largest owner is international 21.04 −29.29 27.11 18.67 65.20 6.42 −14.41 27.27 9.92(0.53) (0.04) (0.14) (0.48) (0.23) (0.82) (0.72) (0.53) (0.71)

Largest owner is individual −8.31 −24.82 7.41 14.77 −30.25 −37.56 92.63 1.51 16.02(0.84) (0.27) (0.77) (0.69) (0.71) (0.41) (0.01) (0.97) (0.53)

Largest owner is nonfinancial 20.40 −16.05 1.31 23.00 −5.24 −7.07 −36.23 22.49 −21.67(0.52) (0.23) (0.94) (0.31) (0.91) (0.74) (0.20) (0.44) (0.28)

ln(Board size) −45.60 24.65 −23.04 17.95 −26.78 −3.91 1.26 −55.19 14.30(0.20) (0.20) (0.28) (0.38) (0.49) (0.86) (0.96) (0.11) (0.37)

Fraction voting shares −30.31 13.80 −18.27 123.29 −76.73 −10.31 278.31 33.54 94.76(0.77) (0.79) (0.78) (0.13) (0.61) (0.92) (0.02) (0.78) (0.22)

Debt to assets 23.61 −52.96 −67.80 26.20 101.74 0.87 −208.93 −65.96 −21.88(0.63) (0.04) (0.02) (0.50) (0.27) (0.99) (0.00) (0.31) (0.61)

Dividend payout ratio 10.96 3.94 7.85 −12.14 4.04 −0.97 −62.90 −14.58 12.58(0.28) (0.78) (0.25) (0.28) (0.83) (0.93) (0.06) (0.62) (0.49)

Industrial 7.00 −4.33 9.17 4.96 −42.09 2.45 34.43 31.07 −10.30(0.75) (0.69) (0.47) (0.78) (0.26) (0.89) (0.15) (0.22) (0.51)

Transport/shipping 65.79 −20.43 −7.33 −6.45 −109.86 −2.11 29.43 −10.30 24.86(0.01) (0.11) (0.61) (0.74) (0.01) (0.92) (0.28) (0.73) (0.19)

Offshore −6.58 73.40 −38.16 4.07 −10.80 2.61 13.35 35.55 51.64(0.92) (0.05) (0.08) (0.89) (0.85) (0.93) (0.74) (0.45) (0.07)


ln(Firm value) 7.25 −5.52 10.25 9.54 −10.40 −0.20 6.19 −2.68 6.53(0.17) (0.07) (0.01) (0.04) (0.22) (0.97) (0.33) (0.74) (0.20)

n 75 66 62 68 77 86 94 102 113

R2 0.32 0.38 0.44 0.31 0.23 0.13 0.45 0.12 0.22Average (RoS) 54.57 −0.64 −13.98 −19.73 122.05 10.33 45.52 56.85 30.64



Table B.56 Multivariate regression relating performance (RoS) to ownership concentration, in-sider ownership, aggregate holdings per owner type, board characteristics, security design, financialpolicy, and controls (full multivariate model)Panel A: Pooled OLS regression

Dependent variable: RoScoeff (stdev) pvalue

Constant -183.51 (75.64) 0.02Herfindahl index 25.84 (26.02) 0.32Primary insiders 23.45 (50.54) 0.64Squared (Primary insiders) -4.42 (61.14) 0.94Aggregate state holdings -2.36 (36.00) 0.95Aggregate international holdings -0.55 (26.15) 0.98Aggregate individual holdings 108.24 (34.41) 0.00Aggregate nonfinancial holdings -2.52 (26.96) 0.93ln(Board size) -12.19 (9.43) 0.20Fraction voting shares 71.19 (37.07) 0.05Debt to assets -19.86 (19.09) 0.30Dividend payout ratio -0.26 (5.41) 0.96Industrial 10.77 (8.14) 0.19Transport/shipping 4.12 (9.48) 0.66Offshore 26.83 (15.23) 0.08Investments over income 0.15 (1.13) 0.89ln(Firm value) 7.65 (2.38) 0.00n 743

R2 0.05Average (RoS) 34.55


Year1989 1990 1991 1992 1993 1994 1995 1996 1997

constant −81.27 185.58 −87.36 −512.03 710.04 4.87 −378.77 −44.53 −329.12(0.63) (0.04) (0.57) (0.00) (0.03) (0.98) (0.10) (0.87) (0.05)

Herfindahl index −130.76 −17.99 126.79 9.14 −132.58 10.09 −64.80 −22.70 76.85(0.08) (0.61) (0.02) (0.89) (0.21) (0.83) (0.38) (0.78) (0.10)

Primary insiders 137.22 66.34 −87.01 329.52 126.77 16.06 50.35 −115.03 154.21(0.41) (0.34) (0.27) (0.02) (0.68) (0.88) (0.71) (0.50) (0.11)

Squared (Primary insiders) −148.92 −94.91 84.41 −463.41 −111.16 −99.02 104.46 173.73 −155.10(0.40) (0.22) (0.32) (0.04) (0.83) (0.48) (0.46) (0.46) (0.26)

Aggregate state holdings 168.03 −10.38 −112.84 84.43 −77.26 226.08 −81.39 −16.73 41.37(0.08) (0.83) (0.11) (0.26) (0.56) (0.00) (0.49) (0.89) (0.55)

Aggregate international holdings 107.81 −88.79 −3.01 11.91 84.85 72.92 42.03 74.29 −80.73(0.13) (0.02) (0.95) (0.82) (0.45) (0.19) (0.63) (0.41) (0.13)

Aggregate individual holdings 61.27 −90.52 −2.88 161.58 −209.46 117.57 209.29 178.63 55.14(0.50) (0.06) (0.97) (0.02) (0.15) (0.10) (0.05) (0.09) (0.39)

Aggregate nonfinancial holdings 122.22 −68.00 −39.72 39.22 −28.06 71.05 −83.25 97.57 −86.64(0.08) (0.06) (0.48) (0.47) (0.77) (0.20) (0.41) (0.32) (0.10)

ln(Board size) −50.68 14.65 −15.98 19.51 −24.58 −16.16 13.22 −44.17 21.57(0.16) (0.42) (0.45) (0.33) (0.54) (0.43) (0.62) (0.20) (0.17)

Fraction voting shares −25.72 11.23 −11.24 136.83 −94.21 −7.09 338.62 40.08 132.55(0.80) (0.80) (0.86) (0.06) (0.54) (0.94) (0.00) (0.73) (0.08)

Debt to assets 39.67 −57.58 −66.06 21.05 86.59 5.26 −218.71 −45.68 −44.28(0.39) (0.02) (0.02) (0.56) (0.34) (0.90) (0.00) (0.49) (0.28)

Dividend payout ratio 14.54 7.58 6.65 −14.64 6.00 −3.15 −55.59 −15.45 1.88(0.15) (0.57) (0.34) (0.16) (0.74) (0.74) (0.10) (0.61) (0.92)

Industrial 7.46 −11.08 6.87 8.93 −59.48 12.07 40.03 37.74 −11.47(0.72) (0.29) (0.60) (0.60) (0.11) (0.48) (0.10) (0.13) (0.46)

Transport/shipping 62.57 −25.59 0.64 1.19 −120.38 0.78 56.99 −6.01 41.36(0.01) (0.04) (0.97) (0.95) (0.00) (0.97) (0.05) (0.85) (0.03)

Offshore −4.98 70.14 −42.38 16.25 −47.26 23.19 30.70 32.89 65.58(0.94) (0.03) (0.05) (0.57) (0.44) (0.44) (0.45) (0.48) (0.02)


ln(Firm value) 6.59 −5.76 8.19 13.22 −19.63 −2.17 8.03 4.04 9.99(0.21) (0.05) (0.05) (0.00) (0.05) (0.69) (0.28) (0.66) (0.07)

n 75 66 62 68 77 86 94 102 113

R2 0.35 0.42 0.44 0.38 0.24 0.25 0.45 0.15 0.27Average (RoS) 54.57 −0.64 −13.98 −19.73 122.05 10.33 45.52 56.85 30.64



B.6.6 Intercorporate ownership

Table B.57 Multivariate regression relating performance (Q) to ownership concentration, insiderownership, aggregate intercorporate ownership by listed firms, board characteristics, security de-sign, financial policy, and controls (full multivariate model)Panel A: Pooled OLS regression


Constant -0.20 (0.59) 0.74Herfindahl index -1.15 (0.21) 0.00Primary insiders 2.15 (0.46) 0.00Squared (Primary insiders) -1.73 (0.58) 0.00Aggregate intercorporate holdings -0.41 (0.21) 0.05ln(Board size) -0.21 (0.09) 0.01Fraction voting shares 1.05 (0.36) 0.00Debt to assets -1.62 (0.18) 0.00Dividend payout ratio -0.10 (0.05) 0.06Industrial -0.25 (0.07) 0.00Transport/shipping -0.57 (0.09) 0.00Offshore -0.64 (0.14) 0.00Investments over income -0.00 (0.01) 0.71ln(Firm value) 0.12 (0.02) 0.00n 866

R2 0.27Average (Q) 1.52


Year1989 1990 1991 1992 1993 1994 1995 1996 1997

constant 1.48 0.62 0.19 −0.39 0.68 −0.34 −3.10 4.47 −0.40(0.11) (0.51) (0.89) (0.66) (0.62) (0.74) (0.04) (0.09) (0.86)


Primary insiders 0.34 −0.30 −0.04 2.23 1.44 1.58 0.84 4.37 3.39(0.76) (0.68) (0.97) (0.03) (0.20) (0.03) (0.43) (0.03) (0.03)


Aggregate intercorporate holdings 0.21 −0.16 −0.31 −0.28 −0.36 −0.38 −0.53 −0.29 2.21(0.46) (0.56) (0.47) (0.38) (0.44) (0.23) (0.34) (0.80) (0.04)

ln(Board size) −0.79 −0.32 −0.26 0.05 −0.02 0.02 0.27 −0.44 −0.33(0.00) (0.09) (0.24) (0.71) (0.92) (0.88) (0.19) (0.32) (0.19)

Fraction voting shares 0.31 0.25 0.58 −0.12 0.23 0.86 2.89 −0.09 0.92(0.64) (0.65) (0.42) (0.81) (0.78) (0.22) (0.00) (0.95) (0.48)

Debt to assets −0.29 −0.28 −0.85 0.07 −0.91 −0.57 −1.08 −2.78 −3.37(0.32) (0.33) (0.01) (0.82) (0.05) (0.05) (0.02) (0.00) (0.00)


Industrial −0.15 −0.16 −0.02 −0.13 −0.27 −0.20 −0.40 −0.30 −0.13(0.28) (0.20) (0.91) (0.31) (0.13) (0.08) (0.03) (0.33) (0.58)


Offshore −0.49 −0.39 −0.56 −0.35 −0.76 −0.39 −0.41 −0.67 −0.77(0.28) (0.11) (0.03) (0.12) (0.01) (0.07) (0.22) (0.23) (0.05)


ln(Firm value) 0.07 0.07 0.08 0.08 0.08 0.07 0.12 0.02 0.20(0.02) (0.03) (0.05) (0.00) (0.08) (0.02) (0.02) (0.83) (0.01)

n 80 73 64 83 90 98 108 118 152

R2 0.35 0.29 0.35 0.29 0.31 0.37 0.43 0.34 0.37Average (Q) 1.32 1.18 1.13 1.07 1.41 1.34 1.51 2.04 2.01



Table B.58 Multivariate regression relating performance (RoA5) to ownership concentration, in-sider ownership, the largest owner being listed, board characteristics, security design, financialpolicy, and controls (full multivariate model)Panel A: Pooled OLS regression


Constant -0.15 (0.59) 0.80Herfindahl index -1.19 (0.21) 0.00Primary insiders 2.19 (0.46) 0.00Squared (Primary insiders) -1.75 (0.58) 0.00Largest owner is listed -0.07 (0.09) 0.42ln(Board size) -0.22 (0.09) 0.01Fraction voting shares 1.04 (0.36) 0.00Debt to assets -1.60 (0.18) 0.00Dividend payout ratio -0.10 (0.05) 0.05Industrial -0.26 (0.07) 0.00Transport/shipping -0.58 (0.09) 0.00Offshore -0.64 (0.14) 0.00Investments over income -0.00 (0.01) 0.68ln(Firm value) 0.12 (0.02) 0.00n 868

R2 0.27Average (Q) 1.52


Year1989 1990 1991 1992 1993 1994 1995 1996 1997

constant 1.26 0.60 0.10 −0.36 0.72 −0.35 −3.15 4.43 −0.76(0.18) (0.52) (0.94) (0.68) (0.60) (0.74) (0.04) (0.10) (0.73)


Primary insiders 0.20 −0.33 −0.03 2.20 1.43 1.57 0.86 4.40 3.57(0.86) (0.65) (0.98) (0.04) (0.21) (0.03) (0.42) (0.03) (0.02)


Largest owner is listed −0.05 −0.11 −0.18 −0.09 −0.14 −0.12 −0.07 0.15 0.68(0.70) (0.45) (0.33) (0.54) (0.41) (0.38) (0.77) (0.74) (0.08)

ln(Board size) −0.76 −0.34 −0.27 0.05 −0.02 −0.01 0.25 −0.51 −0.32(0.00) (0.08) (0.22) (0.72) (0.91) (0.92) (0.22) (0.24) (0.21)

Fraction voting shares 0.40 0.28 0.70 −0.14 0.24 0.88 2.90 −0.14 0.96(0.56) (0.61) (0.34) (0.77) (0.77) (0.21) (0.00) (0.93) (0.46)

Debt to assets −0.30 −0.27 −0.81 0.09 −0.90 −0.57 −1.08 −2.80 −3.38(0.31) (0.34) (0.01) (0.76) (0.05) (0.05) (0.02) (0.00) (0.00)


Industrial −0.14 −0.15 0.01 −0.14 −0.28 −0.20 −0.41 −0.29 −0.11(0.32) (0.21) (0.95) (0.28) (0.12) (0.08) (0.02) (0.35) (0.64)


Offshore −0.48 −0.40 −0.54 −0.37 −0.77 −0.41 −0.41 −0.66 −0.75(0.30) (0.10) (0.03) (0.10) (0.01) (0.05) (0.22) (0.24) (0.06)


ln(Firm value) 0.07 0.07 0.08 0.08 0.08 0.07 0.12 0.03 0.22(0.01) (0.03) (0.06) (0.00) (0.09) (0.02) (0.02) (0.76) (0.01)

n 81 73 64 83 90 98 108 118 153

R2 0.33 0.29 0.35 0.28 0.31 0.37 0.42 0.34 0.36Average (Q) 1.32 1.18 1.13 1.07 1.41 1.34 1.51 2.04 2.00



B.6.7 Outside (external) concentration

Table B.59 Multivariate regression relating performance (RoA5) to outside (external) ownershipconcentration, insider ownership, the type of the largest owner, board characteristics, securitydesign, financial policy, and controls (full multivariate model)Panel A: Pooled OLS regression


Constant 0.12 (0.61) 0.84Largest outside owner -0.55 (0.17) 0.00Primary insiders 2.02 (0.49) 0.00Squared (Primary insiders) -1.69 (0.60) 0.00Largest owner is state -0.47 (0.14) 0.00Largest owner is international -0.30 (0.12) 0.01Largest owner is individual -0.19 (0.14) 0.16Largest owner is nonfinancial -0.30 (0.10) 0.00ln(Board size) -0.20 (0.09) 0.02Fraction voting shares 0.81 (0.37) 0.03Debt to assets -1.56 (0.18) 0.00Dividend payout ratio -0.10 (0.05) 0.05Industrial -0.25 (0.08) 0.00Transport/shipping -0.55 (0.09) 0.00Offshore -0.64 (0.14) 0.00Investments over income -0.00 (0.01) 0.80ln(Firm value) 0.12 (0.02) 0.00n 868

R2 0.27Average (Q) 1.52


Year1989 1990 1991 1992 1993 1994 1995 1996 1997

constant 1.01 0.87 1.74 0.02 0.88 −0.14 −2.14 4.94 0.65(0.33) (0.39) (0.22) (0.99) (0.52) (0.90) (0.19) (0.09) (0.78)




Largest owner is state 0.30 −0.20 0.07 0.53 0.01 −0.29 −0.67 −0.85 −0.89(0.40) (0.42) (0.82) (0.02) (0.96) (0.15) (0.03) (0.10) (0.06)




ln(Board size) −0.70 −0.31 −0.41 −0.03 −0.01 0.05 0.28 −0.49 −0.34(0.00) (0.13) (0.07) (0.84) (0.97) (0.70) (0.19) (0.26) (0.18)

Fraction voting shares 0.31 0.01 0.09 −0.31 −0.03 0.57 2.46 −0.66 0.66(0.68) (0.99) (0.90) (0.54) (0.97) (0.43) (0.01) (0.67) (0.62)

Debt to assets −0.31 −0.24 −0.75 0.16 −0.55 −0.49 −1.16 −2.71 −3.16(0.32) (0.42) (0.02) (0.58) (0.25) (0.10) (0.01) (0.00) (0.00)


Industrial −0.19 −0.15 −0.12 −0.16 −0.26 −0.16 −0.25 −0.32 −0.17(0.21) (0.25) (0.43) (0.22) (0.15) (0.18) (0.17) (0.30) (0.49)


Offshore −0.53 −0.26 −0.43 −0.37 −0.74 −0.45 −0.43 −0.81 −0.80(0.28) (0.32) (0.08) (0.09) (0.01) (0.04) (0.20) (0.16) (0.05)


ln(Firm value) 0.08 0.07 0.06 0.07 0.06 0.07 0.10 0.04 0.19(0.01) (0.03) (0.19) (0.01) (0.15) (0.03) (0.04) (0.70) (0.03)

n 81 73 64 83 90 98 108 118 153

R2 0.31 0.28 0.42 0.36 0.38 0.37 0.47 0.36 0.38Average (Q) 1.32 1.18 1.13 1.07 1.41 1.34 1.51 2.04 2.00



Table B.60 Multivariate regression relating performance (Q) to (outside) ownership concentration,insider ownership, aggregate holdings by owner type, board characteristics, security design, financialpolicy, and controls (full multivariate model)Panel A: Pooled OLS regression


Constant -0.99 (0.69) 0.15Largest outside owner -0.19 (0.20) 0.33Primary insiders 1.58 (0.47) 0.00Squared (Primary insiders) -1.37 (0.58) 0.02Aggregate state holdings -0.75 (0.34) 0.03Aggregate international holdings 0.00 (0.25) 0.99Aggregate individual holdings 1.02 (0.31) 0.00Aggregate nonfinancial holdings -0.38 (0.26) 0.13ln(Board size) -0.17 (0.09) 0.05Fraction voting shares 1.06 (0.36) 0.00Debt to assets -1.52 (0.18) 0.00Dividend payout ratio -0.11 (0.05) 0.03Industrial -0.19 (0.08) 0.01Transport/shipping -0.44 (0.09) 0.00Offshore -0.56 (0.14) 0.00Investments over income 0.00 (0.01) 0.92ln(Firm value) 0.14 (0.02) 0.00n 868

R2 0.28Average (Q) 1.52


Year1989 1990 1991 1992 1993 1994 1995 1996 1997

constant 0.80 −0.00 −1.00 −0.23 0.40 −1.04 −3.18 0.30 −0.66(0.46) (1.00) (0.60) (0.83) (0.81) (0.37) (0.06) (0.93) (0.82)

Largest outside owner −0.59 −0.30 −0.65 −0.34 −0.42 −0.44 −0.86 0.35 0.10(0.15) (0.37) (0.19) (0.33) (0.36) (0.12) (0.06) (0.65) (0.88)



Aggregate state holdings 0.44 −0.14 0.52 0.50 −0.27 0.22 0.14 −1.05 −1.48(0.52) (0.79) (0.52) (0.36) (0.70) (0.68) (0.88) (0.48) (0.25)

Aggregate international holdings 0.31 0.13 0.45 −0.27 0.14 0.66 1.78 1.34 −0.12(0.56) (0.77) (0.47) (0.49) (0.82) (0.10) (0.01) (0.22) (0.90)



ln(Board size) −0.72 −0.26 −0.27 −0.06 −0.02 −0.02 0.44 −0.24 −0.23(0.00) (0.21) (0.27) (0.66) (0.90) (0.87) (0.03) (0.57) (0.37)


Debt to assets −0.20 −0.26 −0.62 0.09 −0.93 −0.38 −0.88 −2.69 −3.32(0.51) (0.39) (0.07) (0.75) (0.05) (0.21) (0.06) (0.00) (0.00)

Dividend payout ratio 0.04 −0.01 0.06 −0.07 −0.12 0.04 −0.12 −0.15 −0.47(0.54) (0.94) (0.48) (0.32) (0.20) (0.53) (0.64) (0.64) (0.13)

Industrial −0.14 −0.11 0.00 −0.13 −0.22 −0.15 −0.19 −0.15 −0.08(0.34) (0.39) (0.98) (0.30) (0.24) (0.21) (0.26) (0.62) (0.75)


Offshore −0.46 −0.36 −0.54 −0.33 −0.70 −0.44 −0.32 −0.80 −0.71(0.34) (0.16) (0.04) (0.13) (0.02) (0.05) (0.31) (0.15) (0.09)

Investments over income 0.00 −0.01 0.00 −0.08 −0.00 −0.07 −0.02 −0.01 −0.01(0.83) (0.68) (0.97) (0.55) (0.99) (0.23) (0.67) (0.94) (0.83)

ln(Firm value) 0.08 0.08 0.10 0.09 0.08 0.09 0.06 0.12 0.20(0.01) (0.02) (0.04) (0.00) (0.13) (0.02) (0.25) (0.29) (0.04)

n 81 73 64 83 90 98 108 118 153

R2 0.30 0.26 0.33 0.34 0.34 0.40 0.53 0.40 0.36Average (Q) 1.32 1.18 1.13 1.07 1.41 1.34 1.51 2.04 2.00



B.6.8 Voting rights

Table B.61 Multivariate regression relating performance (Q) to ownership concentration (votingrights), insider ownership, the type of the largest owner, board characteristics, security design,financial policy, and controls (full multivariate model)Panel A: Pooled OLS regression


Constant 0.38 (0.61) 0.53Herfindahl (voting rights) -0.92 (0.21) 0.00Primary insiders 2.06 (0.49) 0.00Squared (Primary insiders) -1.64 (0.60) 0.01Largest owner is state -0.44 (0.14) 0.00Largest owner is international -0.29 (0.12) 0.02Largest owner is individual -0.16 (0.13) 0.22Largest owner is nonfinancial -0.29 (0.10) 0.00ln(Board size) -0.22 (0.09) 0.01Fraction voting shares 0.69 (0.36) 0.06Debt to assets -1.62 (0.18) 0.00Dividends to earnings -0.10 (0.05) 0.06Industrial -0.25 (0.08) 0.00Transport/shipping -0.55 (0.09) 0.00Offshore -0.66 (0.14) 0.00Investments over income -0.00 (0.01) 0.73ln(Firm value) 0.12 (0.02) 0.00n 868

R2 0.26Average (Q) 1.52


Year1989 1990 1991 1992 1993 1994 1995 1996 1997

constant 1.20 0.98 2.08 0.18 1.19 −0.08 −2.12 5.11 0.83(0.23) (0.32) (0.13) (0.85) (0.39) (0.94) (0.19) (0.08) (0.73)

Herfindahl (voting rights) −1.26 −0.81 −1.34 −0.57 −0.75 −0.76 −0.89 −0.87 −0.51(0.00) (0.03) (0.01) (0.13) (0.07) (0.00) (0.08) (0.31) (0.48)



Largest owner is state 0.44 −0.12 0.13 0.53 −0.00 −0.26 −0.70 −0.81 −0.85(0.19) (0.63) (0.64) (0.02) (0.99) (0.18) (0.02) (0.12) (0.07)




ln(Board size) −0.74 −0.34 −0.41 −0.03 −0.02 0.03 0.26 −0.49 −0.33(0.00) (0.09) (0.06) (0.81) (0.89) (0.83) (0.22) (0.25) (0.19)

Fraction voting shares 0.36 0.08 −0.02 −0.40 −0.26 0.60 2.32 −0.69 0.59(0.61) (0.90) (0.98) (0.42) (0.74) (0.40) (0.01) (0.64) (0.65)

Debt to assets −0.36 −0.26 −0.86 0.10 −0.61 −0.59 −1.29 −2.69 −3.16(0.24) (0.37) (0.01) (0.75) (0.20) (0.05) (0.01) (0.00) (0.00)

Dividends to earnings 0.06 0.02 0.04 −0.06 −0.10 0.05 −0.27 −0.14 −0.51(0.37) (0.88) (0.65) (0.34) (0.25) (0.45) (0.32) (0.67) (0.09)

Industrial −0.21 −0.15 −0.11 −0.19 −0.26 −0.15 −0.27 −0.31 −0.16(0.15) (0.23) (0.44) (0.14) (0.15) (0.19) (0.14) (0.31) (0.50)


Offshore −0.55 −0.33 −0.44 −0.39 −0.75 −0.43 −0.47 −0.83 −0.80(0.23) (0.21) (0.07) (0.07) (0.01) (0.04) (0.16) (0.14) (0.05)


ln(Firm value) 0.07 0.06 0.05 0.07 0.06 0.07 0.11 0.03 0.19(0.01) (0.04) (0.27) (0.01) (0.16) (0.02) (0.03) (0.73) (0.03)

n 81 73 64 83 90 98 108 118 153

R2 0.18 0.10 0.24 0.19 0.24 0.27 0.36 0.26 0.30Average (Q) 1.32 1.18 1.13 1.07 1.41 1.34 1.51 2.04 2.00



Table B.62 Multivariate regression relating performance (Q) to ownership concentration (votingrights), insider ownership, type of owner, board characteristics, security design, financial policy,and controls (full multivariate model)Panel A: Pooled OLS regression


Constant -0.83 (0.69) 0.23Herfindahl (voting rights) -0.60 (0.24) 0.01Primary insiders 1.64 (0.47) 0.00Squared (Primary insiders) -1.38 (0.58) 0.02Aggregate state holdings -0.49 (0.34) 0.16Aggregate international holdings 0.09 (0.25) 0.72Aggregate individual holdings 1.02 (0.30) 0.00Aggregate nonfinancial holdings -0.26 (0.25) 0.30ln(Board size) -0.19 (0.09) 0.03Fraction voting shares 1.01 (0.36) 0.00Debt to assets -1.54 (0.18) 0.00Dividends to earnings -0.11 (0.05) 0.03Industrial -0.20 (0.08) 0.01Transport/shipping -0.46 (0.09) 0.00Offshore -0.57 (0.14) 0.00Investments over income -0.00 (0.01) 0.99ln(Firm value) 0.14 (0.02) 0.00n 868

R2 0.27Average (Q) 1.52


Year1989 1990 1991 1992 1993 1994 1995 1996 1997

constant 1.07 0.09 −0.63 −0.14 0.51 −0.93 −3.31 0.54 −0.48(0.31) (0.93) (0.73) (0.89) (0.76) (0.41) (0.05) (0.88) (0.87)

Herfindahl (voting rights) −1.37 −0.82 −1.38 −0.43 −0.48 −0.80 −0.88 −0.41 −0.36(0.01) (0.04) (0.03) (0.34) (0.34) (0.01) (0.11) (0.66) (0.66)


Squared (Primary insiders) −0.00 1.04 0.25 −2.94 −1.40 −1.55 1.32 −3.37 −2.48(1.00) (0.25) (0.80) (0.09) (0.37) (0.12) (0.24) (0.24) (0.29)

Aggregate state holdings 0.93 0.20 0.98 0.53 −0.25 0.44 −0.05 −0.51 −1.21(0.17) (0.71) (0.23) (0.34) (0.72) (0.39) (0.96) (0.73) (0.34)




ln(Board size) −0.80 −0.30 −0.29 −0.07 −0.04 −0.05 0.42 −0.25 −0.23(0.00) (0.13) (0.21) (0.65) (0.83) (0.69) (0.04) (0.56) (0.37)


Debt to assets −0.30 −0.28 −0.74 0.05 −0.96 −0.45 −1.04 −2.59 −3.29(0.32) (0.34) (0.03) (0.87) (0.05) (0.12) (0.02) (0.00) (0.00)

Dividends to earnings 0.08 0.01 0.05 −0.07 −0.10 0.03 −0.13 −0.11 −0.46(0.28) (0.94) (0.56) (0.32) (0.25) (0.65) (0.61) (0.72) (0.15)

Industrial −0.18 −0.13 0.01 −0.15 −0.22 −0.15 −0.21 −0.15 −0.07(0.20) (0.32) (0.97) (0.24) (0.24) (0.19) (0.22) (0.62) (0.76)


Offshore −0.49 −0.40 −0.54 −0.34 −0.70 −0.43 −0.35 −0.76 −0.71(0.29) (0.11) (0.04) (0.12) (0.02) (0.04) (0.26) (0.16) (0.09)

Investments over income 0.00 −0.01 −0.00 −0.09 0.00 −0.06 −0.01 −0.02 −0.01(0.89) (0.62) (0.95) (0.52) (0.99) (0.25) (0.72) (0.86) (0.84)

ln(Firm value) 0.08 0.08 0.09 0.09 0.08 0.08 0.08 0.11 0.19(0.01) (0.03) (0.06) (0.00) (0.12) (0.03) (0.13) (0.35) (0.05)

n 81 73 64 83 90 98 108 118 153

R2 0.18 0.09 0.14 0.16 0.18 0.30 0.44 0.30 0.28Average (Q) 1.32 1.18 1.13 1.07 1.41 1.34 1.51 2.04 2.00


B.7 Explaining the corporate governance mechanisms with single equation models 181

B.7 Explaining the corporate governance mechanisms with single equation mod-els

This appendix complements section 10.1 of chapter 10. It contains detailed regression results foreach of the summary tables in the text.

B.7.1 Single equation estimates of governance mechanism endogeneity, using aggre-gate 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 mechanismsand controls, using aggregate ownership per type as owner identity proxy

Dependent variable: Herfindahl indexcoeff (stdev) pvalue

Constant -0.09 (0.11) 0.43Primary insiders 0.08 (0.03) 0.00Aggregate state holdings 0.57 (0.05) 0.00Aggregate international holdings 0.24 (0.03) 0.00Aggregate individual holdings -0.02 (0.04) 0.62Aggregate nonfinancial holdings 0.30 (0.03) 0.00Fraction voting shares 0.22 (0.05) 0.00ln(Board size) -0.02 (0.01) 0.17Debt to assets 0.02 (0.03) 0.40Dividends to earnings 0.01 (0.01) 0.03Industrial -0.01 (0.01) 0.29Transport/shipping -0.04 (0.01) 0.00Offshore 0.00 (0.02) 0.89Investments over income -0.00 (0.00) 0.08ln(Firm value) -0.01 (0.00) 0.04Stock volatility 0.02 (0.02) 0.22n 796R2 0.25Average (Herfindahl index) 0.14



Table B.64 Multivariate regression relating primary insider holdings to other mechanisms andcontrols, using aggregate ownership per type as owner identity proxy

Dependent variable: Primary insiderscoeff (stdev) pvalue

Constant -0.31 (0.16) 0.05Herfindahl index 0.16 (0.05) 0.00Aggregate state holdings -0.17 (0.07) 0.02Aggregate international holdings 0.01 (0.05) 0.83Aggregate individual holdings 0.43 (0.06) 0.00Aggregate nonfinancial holdings -0.01 (0.05) 0.79ln(Board size) -0.03 (0.02) 0.10Fraction voting shares -0.09 (0.07) 0.20Debt to assets 0.10 (0.04) 0.00Dividends to earnings 0.00 (0.01) 0.84Industrial -0.01 (0.01) 0.42Transport/shipping -0.04 (0.02) 0.03Offshore -0.06 (0.03) 0.02Investments over income -0.00 (0.00) 0.85ln(Firm value) 0.02 (0.01) 0.00Stock volatility 0.05 (0.03) 0.03n 796R2 0.16Average (Primary insiders) 0.08


Table B.65 Multivariate regression relating aggregate state holdings to other mechanisms andcontrols, using aggregate ownership per type as owner identity proxy

Dependent variable: Aggregate state holdingscoeff (stdev) pvalue

Constant -0.41 (0.09) 0.00Herfindahl index 0.25 (0.03) 0.00Primary insiders -0.08 (0.02) 0.00ln(Board size) 0.03 (0.01) 0.00Fraction voting shares -0.02 (0.05) 0.65Debt to assets 0.03 (0.02) 0.18Dividends to earnings 0.00 (0.01) 0.45Industrial 0.03 (0.01) 0.00Transport/shipping -0.02 (0.01) 0.04Offshore -0.04 (0.02) 0.01Investments over income -0.00 (0.00) 0.90ln(Firm value) 0.02 (0.00) 0.00Stock volatility 0.00 (0.02) 0.99n 796R2 0.21Average (Aggregate state holdings) 0.05



Table B.66 Multivariate regression relating aggregate international holdings to other mechanismsand controls, using aggregate ownership per type as owner identity proxy

Dependent variable: Aggregate international holdingscoeff (stdev) pvalue

Constant -0.74 (0.17) 0.00Herfindahl index 0.02 (0.05) 0.77Primary insiders -0.01 (0.04) 0.74ln(Board size) -0.01 (0.02) 0.58Fraction voting shares -0.02 (0.08) 0.83Debt to assets -0.05 (0.04) 0.21Dividends to earnings -0.02 (0.01) 0.19Industrial -0.00 (0.02) 0.87Transport/shipping -0.07 (0.02) 0.00Offshore 0.06 (0.03) 0.05Investments over income 0.01 (0.00) 0.03ln(Firm value) 0.05 (0.01) 0.00Stock volatility 0.08 (0.03) 0.01n 796R2 0.12Average (Aggregate international holdings) 0.21


Table B.67 Multivariate regression relating aggregate individual holdings to other mechanismsand controls, using aggregate ownership per type as owner identity proxy

Dependent variable: Aggregate individual holdingscoeff (stdev) pvalue

Constant 1.21 (0.11) 0.00Herfindahl index -0.27 (0.03) 0.00Primary insiders 0.25 (0.03) 0.00ln(Board size) -0.02 (0.01) 0.06Fraction voting shares -0.15 (0.05) 0.00Debt to assets -0.05 (0.03) 0.07Dividends to earnings 0.00 (0.01) 0.62Industrial -0.05 (0.01) 0.00Transport/shipping -0.08 (0.01) 0.00Offshore -0.09 (0.02) 0.00Investments over income -0.00 (0.00) 0.00ln(Firm value) -0.04 (0.00) 0.00Stock volatility -0.03 (0.02) 0.18n 796R2 0.36Average (Aggregate individual holdings) 0.18



Table B.68 Multivariate regression relating aggregate financial holdings to other mechanisms andcontrols, using aggregate ownership per type as owner identity proxy

Dependent variable: Aggregate financial holdingscoeff (stdev) pvalue

Constant 0.08 (0.11) 0.46Herfindahl index -0.23 (0.03) 0.00Primary insiders -0.05 (0.03) 0.06ln(Board size) 0.02 (0.01) 0.11Fraction voting shares 0.07 (0.05) 0.16Debt to assets 0.14 (0.03) 0.00Dividends to earnings 0.00 (0.01) 0.60Industrial 0.03 (0.01) 0.00Transport/shipping -0.03 (0.01) 0.00Offshore 0.01 (0.02) 0.51Investments over income -0.00 (0.00) 0.21ln(Firm value) 0.00 (0.00) 0.98Stock volatility -0.11 (0.02) 0.00n 796R2 0.17Average (Aggregate financial holdings) 0.18


Table B.69 Multivariate regression relating aggregate nonfinancial holdings to other mechanismsand controls, using aggregate ownership per type as owner identity proxy

Dependent variable: Aggregate nonfinancial holdingscoeff (stdev) pvalue

Constant 0.96 (0.17) 0.00Herfindahl index 0.24 (0.05) 0.00Primary insiders -0.10 (0.04) 0.01ln(Board size) -0.02 (0.02) 0.26Fraction voting shares 0.08 (0.09) 0.36Debt to assets -0.06 (0.04) 0.16Dividends to earnings 0.00 (0.01) 0.84Industrial 0.01 (0.02) 0.76Transport/shipping 0.21 (0.02) 0.00Offshore 0.06 (0.03) 0.06Investments over income 0.00 (0.00) 0.72ln(Firm value) -0.03 (0.01) 0.00Stock volatility 0.05 (0.03) 0.12n 796R2 0.24Average (Aggregate nonfinancial holdings) 0.38



Table B.70 Multivariate regression relating board size to other mechanisms and controls, usingaggregate ownership per type as owner identity proxy

Dependent variable: ln(Board size)coeff (stdev) pvalue

Constant 0.88 (0.34) 0.01Herfindahl index -0.15 (0.11) 0.17Aggregate state holdings 0.21 (0.15) 0.17Aggregate international holdings -0.12 (0.11) 0.25Aggregate individual holdings -0.28 (0.13) 0.03Aggregate nonfinancial holdings -0.14 (0.11) 0.18Primary insiders -0.13 (0.08) 0.10Fraction voting shares -0.24 (0.15) 0.11Debt to assets 0.06 (0.08) 0.46Dividends to earnings 0.02 (0.02) 0.40Industrial 0.11 (0.03) 0.00Transport/shipping -0.05 (0.04) 0.18Offshore -0.19 (0.06) 0.00Investments over income -0.01 (0.00) 0.04ln(Firm value) 0.06 (0.01) 0.00Stock volatility 0.14 (0.06) 0.01n 796R2 0.17Average (ln(Board size)) 1.84


Table B.71 Multivariate regression relating fraction voting shares to other mechanisms and con-trols, using aggregate ownership per type as owner identity proxy

Dependent variable: Fraction voting sharescoeff (stdev) pvalue

Constant 1.44 (0.06) 0.00Herfindahl index 0.11 (0.03) 0.00Aggregate state holdings -0.04 (0.04) 0.24Aggregate international holdings -0.04 (0.03) 0.10Aggregate individual holdings -0.09 (0.03) 0.00Aggregate nonfinancial holdings -0.03 (0.03) 0.18Primary insiders -0.02 (0.02) 0.32ln(Board size) -0.01 (0.01) 0.25Debt to assets -0.01 (0.02) 0.47Dividends to earnings -0.01 (0.01) 0.22Industrial -0.02 (0.01) 0.03Transport/shipping -0.02 (0.01) 0.01Investments over income -0.00 (0.00) 0.53ln(Firm value) -0.02 (0.00) 0.00Stock volatility -0.03 (0.01) 0.01n 796R2 0.12Average (Fraction voting shares) 0.97



Table B.72 Multivariate regression relating debt to assets to other mechanisms and controls, usingaggregate ownership per type as owner identity proxy

Dependent variable: Debt to assetscoeff (stdev) pvalue

Constant 0.72 (0.15) 0.00Herfindahl index 0.04 (0.05) 0.42Aggregate state holdings -0.14 (0.07) 0.05Aggregate international holdings -0.20 (0.05) 0.00Aggregate individual holdings -0.24 (0.06) 0.00Aggregate nonfinancial holdings -0.21 (0.05) 0.00Primary insiders 0.10 (0.03) 0.00ln(Board size) 0.01 (0.02) 0.44Fraction voting shares -0.04 (0.07) 0.55Industrial 0.03 (0.01) 0.05Transport/shipping 0.09 (0.02) 0.00Offshore 0.01 (0.03) 0.79Investments over income 0.01 (0.00) 0.00ln(Firm value) -0.00 (0.01) 0.78Stock volatility 0.05 (0.02) 0.03n 817R2 0.08Average (Debt to assets) 0.59


Table B.73 Multivariate regression relating dividends to earnings to other mechanisms and con-trols, using aggregate ownership per type as owner identity proxy

Dependent variable: Dividends to earningscoeff (stdev) pvalue

Constant 0.04 (0.57) 0.94Herfindahl index 0.39 (0.18) 0.03Aggregate state holdings 0.07 (0.27) 0.78Aggregate international holdings -0.15 (0.18) 0.40Aggregate individual holdings 0.04 (0.22) 0.84Aggregate nonfinancial holdings -0.03 (0.18) 0.85Primary insiders 0.02 (0.13) 0.88ln(Board size) 0.05 (0.06) 0.44Fraction voting shares -0.36 (0.26) 0.16Industrial -0.00 (0.05) 0.95Transport/shipping 0.10 (0.06) 0.12Offshore -0.12 (0.10) 0.21Investments over income -0.01 (0.01) 0.42ln(Firm value) 0.03 (0.02) 0.19Stock volatility -0.13 (0.09) 0.17n 800R2 0.02Average (Dividends to earnings) 0.29



B.7.2 Single equation estimates of governance mechanism endogeneity, using type oflargest 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 mechanismsand controls, using type of largest owner as owner type proxy

Dependent variable: Herfindahl indexcoeff (stdev) pvalue

Constant -0.36 (0.11) 0.00Largest owner is state 0.13 (0.02) 0.00Largest owner is international 0.08 (0.02) 0.00Largest owner is individual 0.05 (0.02) 0.02Largest owner is nonfinancial 0.07 (0.01) 0.00Primary insiders 0.02 (0.03) 0.50ln(Board size) -0.01 (0.01) 0.48Fraction voting shares 0.31 (0.05) 0.00Debt to assets 0.01 (0.03) 0.68Dividends to earnings 0.02 (0.01) 0.04Industrial 0.00 (0.01) 0.72Transport/shipping -0.01 (0.01) 0.50Offshore 0.02 (0.02) 0.30Investments over income -0.00 (0.00) 0.39ln(Firm value) 0.00 (0.00) 0.24Stock volatility 0.07 (0.02) 0.00n 796R2 0.09Average (Herfindahl index) 0.14


Table B.75 Multivariate regression relating primary insider holdings to other mechanisms andcontrols, using type of largest owner as owner type proxy


Constant 0.05 (0.14) 0.71Herfindahl index 0.03 (0.04) 0.51Largest owner is state -0.05 (0.03) 0.08Largest owner is international 0.00 (0.02) 0.96Largest owner is individual 0.21 (0.02) 0.00Largest owner is nonfinancial 0.02 (0.02) 0.38ln(Board size) -0.02 (0.02) 0.21Fraction voting shares -0.10 (0.07) 0.11Debt to assets 0.11 (0.04) 0.00Industrial -0.02 (0.01) 0.28Transport/shipping -0.06 (0.02) 0.00Offshore -0.08 (0.03) 0.00Investments over income 0.00 (0.00) 0.37ln(Firm value) 0.00 (0.00) 0.46Stock volatility 0.04 (0.02) 0.09n 817R2 0.16Average (Primary insiders) 0.08



Table B.76 Multivariate regression relating board size to other mechanisms and controls, usingtype of largest owner as owner type proxy

Dependent variable: ln(Board size)coeff (stdev) pvalue

Constant 0.71 (0.31) 0.02Herfindahl index -0.07 (0.10) 0.48Largest owner is state 0.08 (0.06) 0.15Largest owner is international -0.04 (0.05) 0.43Largest owner is individual -0.17 (0.05) 0.00Largest owner is nonfinancial -0.09 (0.04) 0.02Primary insiders -0.10 (0.08) 0.17Fraction voting shares -0.25 (0.15) 0.10Debt to assets 0.06 (0.08) 0.44Dividends to earnings 0.02 (0.02) 0.38Industrial 0.10 (0.03) 0.00Transport/shipping -0.04 (0.04) 0.23Offshore -0.18 (0.06) 0.00Investments over income -0.01 (0.00) 0.04ln(Firm value) 0.07 (0.01) 0.00Stock volatility 0.13 (0.05) 0.01n 796R2 0.18Average (ln(Board size)) 1.84


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 sharescoeff (stdev) pvalue

Constant 1.40 (0.05) 0.00Herfindahl index 0.13 (0.02) 0.00Largest owner is state -0.03 (0.01) 0.01Largest owner is international -0.01 (0.01) 0.29Largest owner is individual -0.03 (0.01) 0.01Largest owner is nonfinancial -0.03 (0.01) 0.00Primary insiders -0.03 (0.02) 0.15ln(Board size) -0.01 (0.01) 0.21Debt to assets -0.01 (0.02) 0.57Dividends to earnings -0.01 (0.00) 0.31Industrial -0.01 (0.01) 0.06Transport/shipping -0.02 (0.01) 0.06Investments over income -0.00 (0.00) 0.52ln(Firm value) -0.02 (0.00) 0.00Stock volatility -0.04 (0.01) 0.01n 796R2 0.13Average (Fraction voting shares) 0.97



Table B.78 Multivariate regression relating debt to assets to other mechanisms and controls, usingtype of largest owner as owner type proxy

Dependent variable: Debt to assetscoeff (stdev) pvalue

Constant 0.54 (0.14) 0.00Herfindahl index 0.02 (0.04) 0.70Largest owner is state 0.00 (0.03) 0.96Largest owner is international -0.01 (0.02) 0.59Largest owner is individual -0.06 (0.03) 0.01Largest owner is nonfinancial -0.03 (0.02) 0.14Primary insiders 0.11 (0.04) 0.00ln(Board size) 0.01 (0.02) 0.41Fraction voting shares -0.03 (0.07) 0.65Industrial 0.03 (0.01) 0.04Transport/shipping 0.09 (0.02) 0.00Offshore 0.01 (0.03) 0.77Investments over income 0.01 (0.00) 0.00ln(Firm value) 0.00 (0.00) 0.93Stock volatility 0.03 (0.02) 0.17n 817R2 0.06Average (Debt to assets) 0.59


Table B.79 Multivariate regression relating dividends to earnings to other mechanisms and con-trols, using type of largest owner as owner type proxy

Dependent variable: Dividends to earningscoeff (stdev) pvalue

Constant 0.11 (0.52) 0.84Herfindahl index 0.34 (0.17) 0.04Largest owner is state 0.16 (0.10) 0.10Largest owner is international -0.02 (0.08) 0.80Largest owner is individual 0.08 (0.09) 0.40Largest owner is nonfinancial 0.05 (0.07) 0.42Primary insiders 0.02 (0.13) 0.87ln(Board size) 0.05 (0.06) 0.42Fraction voting shares -0.31 (0.26) 0.23Industrial -0.01 (0.05) 0.87Transport/shipping 0.10 (0.06) 0.11Offshore -0.13 (0.10) 0.19Investments over income -0.01 (0.01) 0.39ln(Firm value) 0.02 (0.02) 0.34Stock volatility -0.14 (0.09) 0.13n 800R2 0.02Average (Dividends to earnings) 0.29




To check on the potential problem of double counting of concentration and insiders we estimate theoutside (external) concentration as the fraction of the company owned by the largest owner whois 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 theconcentration - insider relationship has a negative (but not very significant) sign when using suchan adjusted concentration measure.

Table B.80 Multivariate regression relating outside concentration (Largest outside owner) to othermechanisms and controls

Dependent variable: Largest outside ownercoeff (stdev) pvalue

Constant 0.01 (0.15) 0.92Primary insiders -0.06 (0.03) 0.07Aggregate state holdings 0.73 (0.06) 0.00Aggregate international holdings 0.30 (0.04) 0.00Aggregate individual holdings -0.11 (0.05) 0.05Aggregate nonfinancial holdings 0.38 (0.04) 0.00Fraction voting shares 0.24 (0.06) 0.00ln(Board size) 0.00 (0.02) 0.96Debt to assets 0.10 (0.03) 0.00Dividends to earnings 0.01 (0.01) 0.21Industrial -0.01 (0.01) 0.70Transport/shipping -0.06 (0.02) 0.00Offshore 0.03 (0.02) 0.25Investments over income -0.00 (0.00) 0.20ln(Firm value) -0.01 (0.01) 0.00Stock volatility 0.02 (0.02) 0.34n 796R2 0.31Average (Largest outside owner) 0.24



Table B.81 Multivariate regression relating primary insider holdings to other mechanisms andcontrols, using largest outside owner as concentration measure


Constant -0.37 (0.16) 0.02Largest outside owner -0.08 (0.04) 0.05Aggregate state holdings -0.01 (0.07) 0.86Aggregate international holdings 0.07 (0.05) 0.14Aggregate individual holdings 0.43 (0.06) 0.00Aggregate nonfinancial holdings 0.07 (0.05) 0.13ln(Board size) -0.03 (0.02) 0.09Fraction voting shares -0.01 (0.07) 0.92Debt to assets 0.12 (0.04) 0.00Industrial -0.01 (0.01) 0.39Transport/shipping -0.05 (0.02) 0.00Offshore -0.06 (0.03) 0.03Investments over income 0.00 (0.00) 0.36ln(Firm value) 0.02 (0.01) 0.00Stock volatility 0.05 (0.03) 0.04n 817R2 0.15Average (Primary insiders) 0.08



B.8 Interactions between ownership concentration and insider holdings in asystem of equations

This appendix complements the results of section 10.2. Section B.8.1 contains similar regressions tothe 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. Con-centration and insider holdings are endogenous variables. Controls only as additional explanatoryvariables. Board size and stock volatility are instruments.Panel A. Regression results

Dep.variable Indep.variable coeff (stdev) pvalue

Herfindahl index Primary insiders 0.29 (0.34) 0.39Industrial 0.01 (0.02) 0.72Transport/shipping 0.01 (0.03) 0.80Offshore 0.02 (0.04) 0.57ln(Firm value) 0.01 (0.00) 0.23Stock volatility 0.04 (0.03) 0.14constant -0.02 (0.10) 0.82

Primary insiders Herfindahl index 0.94 (0.63) 0.14ln(Board size) -0.03 (0.02) 0.17Industrial -0.04 (0.02) 0.03Transport/shipping -0.06 (0.02) 0.01Offshore -0.10 (0.03) 0.01ln(Firm value) -0.00 (0.01) 0.66constant 0.10 (0.15) 0.48


Equation n Parms RMSE R2 χ2 p

1 741 6 0.14 -0.16 7.24 0.302 741 6 0.22 -0.44 24.93 0.00

The table complements table 10.4 in the text. It shows results with the same set of endogenous variables, but asmaller set of explanatory variables: Only the controls. The tables shows 3SLS estimates of a system of simultanousequations. Panel A report system estimates. The leftmost column is the dependent variable in that particularequation, which is a function of the variables listed in the next column. Equations are separated by a line. PanelB holds diagnostics. For each equation the diagnostics include n, the number of observations, Parms, the numberof 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. Variabledefinitions 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 ofequations 193

Table B.83 Interactions between governance mechanisms modeled as system of equations. Con-centration and insider holdings are endogenous variables. Controls only as additional explanatoryvariables. Board size and stock turnover are instruments.Panel A. Regression results


Herfindahl index Primary insiders 0.54 (0.38) 0.16Industrial 0.02 (0.03) 0.48Transport/shipping 0.01 (0.03) 0.74Offshore 0.04 (0.04) 0.34ln(Firm value) 0.01 (0.00) 0.23Stock volatility 0.03 (0.03) 0.30Stock turnover -0.07 (0.01) 0.00constant -0.02 (0.12) 0.89

Primary insiders Herfindahl index -0.06 (0.15) 0.72Industrial -0.05 (0.02) 0.00Transport/shipping -0.08 (0.02) 0.00Offshore -0.10 (0.03) 0.00ln(Firm value) 0.00 (0.01) 0.54Stock volatility 0.06 (0.03) 0.06ln(Board size) -0.05 (0.02) 0.02constant 0.11 (0.12) 0.33



1 741 7 0.16 -0.45 58.53 0.002 741 7 0.17 0.05 38.00 0.00



Table B.84 Interactions between governance mechanisms modelled as system of equations. Con-centration and insider holdings are endogenous variables. Controls only as additional explanatoryvariables. Aggregate intercorporate holdings and debt to assets are instruments.Panel A. Regression results


Herfindahl index Primary insiders 0.48 (0.37) 0.20Industrial 0.01 (0.02) 0.68Transport/shipping 0.01 (0.03) 0.69Offshore 0.04 (0.04) 0.38ln(Firm value) 0.00 (0.00) 0.66Aggregate intercorporate holdings 0.26 (0.08) 0.00constant 0.02 (0.12) 0.87

Primary insiders Herfindahl index -1.12 (0.35) 0.00Industrial -0.07 (0.02) 0.00Transport/shipping -0.10 (0.02) 0.00Offshore -0.10 (0.04) 0.01ln(Firm value) -0.01 (0.01) 0.28Debt to assets 0.14 (0.05) 0.01constant 0.33 (0.13) 0.01



1 741 6 0.15 -0.36 19.62 0.002 741 6 0.22 -0.57 31.58 0.00



B.8.2 Outside concentration

This appendix replaces the Herfindahl index used in the main text by the holdings of the largestoutside owner. We estimate outside (external) concentration by removing the largest owner if ithas the same holdings as the largest insider owner.

Table B.85 Interactions between governance mechanisms modeled as system of equations. Con-centration and insider holdings are endogeneous variables. Adjusting for overlap between insidersand large owners. Board size and stock volatility are instruments.Panel A. Regression results

Dep.variable Indep.variable coeff (stdev) pvalueLargest outside owner Primary insiders 0.08 (0.58) 0.89

Aggregate state holdings 0.73 (0.08) 0.00Aggregate international holdings 0.28 (0.05) 0.00Aggregate individual holdings -0.17 (0.26) 0.52Aggregate nonfinancial holdings 0.37 (0.05) 0.00Fraction voting shares 0.25 (0.07) 0.00Debt to assets 0.11 (0.07) 0.13Dividends to earnings 0.01 (0.01) 0.18Industrial -0.01 (0.02) 0.67Transport/shipping -0.06 (0.03) 0.03Offshore 0.03 (0.04) 0.47Investments over income -0.00 (0.00) 0.12ln(Firm value) -0.01 (0.01) 0.21Stock volatility 0.04 (0.04) 0.33constant -0.04 (0.24) 0.86

Primary insiders Largest outside owner 1.29 (1.04) 0.22Aggregate state holdings -1.02 (0.78) 0.19Aggregate international holdings -0.33 (0.32) 0.30Aggregate individual holdings 0.59 (0.16) 0.00Aggregate nonfinancial holdings -0.47 (0.41) 0.25Fraction voting shares -0.38 (0.27) 0.16Debt to assets -0.04 (0.15) 0.77Dividends to earnings -0.01 (0.02) 0.60Industrial -0.01 (0.03) 0.71Transport/shipping 0.04 (0.07) 0.56Offshore -0.09 (0.05) 0.06Investments over income 0.00 (0.00) 0.49ln(Firm value) 0.03 (0.02) 0.10ln(Board size) -0.02 (0.03) 0.38constant -0.21 (0.25) 0.40


Equation n Parms RMSE R2 χ2 p1 741 14 0.15 0.31 345.75 0.002 741 14 0.26 -1.05 60.75 0.00

The tables shows 3SLS estimates of a system of simultanous equations. Panel A report system estimates. Theleftmost column is the dependent variable in that particular equation, which is a function of the variables listed inthe next column. Equations are separated by a line. Panel B holds diagnostics. For each equation the diagnosticsinclude 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 estimatesare performed with Stata’s reg3 3SLS routine. Variable definitions are in Appendix A.2. Data for firms listed on theOslo Stock Exchange, 1989-1997.


Table B.86 Interactions between governance mechanisms modeled as system of equations. Con-centration and insider holdings are endogeneous variables. Adjusting for overlap between insidersand large owners. Board size and stock turnover are instruments.Panel A. Regression results


Aggregate state holdings 0.68 (0.09) 0.00Aggregate international holdings 0.25 (0.05) 0.00Aggregate individual holdings -0.25 (0.25) 0.33Aggregate nonfinancial holdings 0.32 (0.05) 0.00Fraction voting shares 0.30 (0.07) 0.00Debt to assets 0.09 (0.07) 0.23Dividends to earnings 0.01 (0.01) 0.43Industrial -0.00 (0.02) 0.88Transport/shipping -0.05 (0.03) 0.05Offshore 0.04 (0.04) 0.33Investments over income -0.00 (0.00) 0.11ln(Firm value) -0.01 (0.01) 0.19Stock volatility 0.04 (0.04) 0.37Stock turnover -0.04 (0.01) 0.00constant 0.00 (0.24) 1.00

Primary insiders Largest outside owner 0.30 (0.24) 0.22Aggregate state holdings -0.31 (0.19) 0.11Aggregate international holdings -0.05 (0.09) 0.56Aggregate individual holdings 0.46 (0.07) 0.00Aggregate nonfinancial holdings -0.10 (0.11) 0.33Fraction voting shares -0.13 (0.09) 0.16Debt to assets 0.08 (0.05) 0.12Dividends to earnings 0.00 (0.01) 0.81Industrial -0.02 (0.02) 0.21Transport/shipping -0.02 (0.03) 0.40Offshore -0.07 (0.03) 0.02Investments over income 0.00 (0.00) 0.97ln(Firm value) 0.02 (0.01) 0.01ln(Board size) -0.03 (0.02) 0.15Stock volatility 0.04 (0.03) 0.18constant -0.27 (0.18) 0.13


Equation n Parms RMSE R2 χ2 p1 741 15 0.15 0.26 343.25 0.002 741 15 0.17 0.08 137.02 0.00



Table B.87 Interactions between governance mechanisms modelled as system of equations. Con-centration and insider holdings are endogeneous variables. Adjusting for overlap between insidersand large owners. Aggregate intercorporate holdings and debt to assets are instruments.Panel A. Regression results


Aggregate state holdings 0.82 (0.11) 0.00Aggregate international holdings 0.27 (0.07) 0.00Aggregate individual holdings -0.56 (0.20) 0.01Aggregate nonfinancial holdings 0.35 (0.07) 0.00Fraction voting shares 0.32 (0.11) 0.00Dividends to earnings 0.00 (0.02) 0.77ln(Board size) 0.02 (0.03) 0.40Industrial 0.01 (0.02) 0.63Transport/shipping -0.02 (0.03) 0.53Offshore 0.10 (0.05) 0.05Investments over income -0.00 (0.00) 0.49ln(Firm value) -0.03 (0.01) 0.00Aggregate intercorporate holdings 0.23 (0.08) 0.00constant 0.18 (0.22) 0.43

Primary insiders Largest outside owner -0.66 (0.39) 0.09Aggregate state holdings 0.42 (0.30) 0.16Aggregate international holdings 0.25 (0.13) 0.05Aggregate individual holdings 0.35 (0.08) 0.00Aggregate nonfinancial holdings 0.27 (0.16) 0.09ln(Board size) -0.02 (0.02) 0.23Fraction voting shares 0.09 (0.12) 0.47Dividends to earnings 0.02 (0.01) 0.23Industrial -0.03 (0.02) 0.09Transport/shipping -0.08 (0.03) 0.01Offshore -0.05 (0.03) 0.15Investments over income -0.00 (0.00) 0.31ln(Firm value) -0.00 (0.01) 0.94Debt to assets 0.21 (0.07) 0.00constant -0.12 (0.17) 0.50





B.9 Causation between corporate governance and economic performance, gov-ernance driving performance

This appendix lists complementary estimations to those in section 11.1. Section B.9.1 gives theestimations underlying summary table 11.1 in the text. Section B.9.2 contains similar regressions tothe ones in the text with fewer explanatory (exogenous) variables. Section B.9.3 considers outsideconcentration.


This section gives the estimations underlying summary table 11.1 in the text.

B.9 Causation between corporate governance and economic performance, governance drivingperformance 199

Table B.88 Simultaneous systems estimation of the determinants of ownership concentration,insider holdings, and economic performance (Q). The two endogeneous governance mechanismsare independent of performance. Stock volatility and board size are instruments (Model (I) insummary table 11.1)Panel A. Regression results

Dep.variable Indep.variable coeff (stdev) pvalueQ Herfindahl index 29.28 (18.46) 0.11

Primary insiders -31.81 (62.64) 0.61Squared (Primary insiders) 34.69 (71.83) 0.63Aggregate state holdings -20.35 (16.67) 0.22Aggregate international holdings -8.54 (7.37) 0.25Aggregate individual holdings 3.40 (4.48) 0.45Aggregate nonfinancial holdings -11.09 (9.49) 0.24Fraction voting shares -3.19 (2.22) 0.15Debt to assets -3.90 (2.12) 0.07Dividends to earnings -0.72 (0.49) 0.15Industrial 0.16 (0.40) 0.69Transport/shipping 0.52 (0.49) 0.29Offshore -1.05 (1.11) 0.34Investments over income 0.09 (0.08) 0.26ln(Firm value) 0.55 (0.48) 0.26constant -0.41 (4.55) 0.93

Herfindahl index Primary insiders 0.11 (0.02) 0.00Aggregate state holdings 0.55 (0.05) 0.00Aggregate international holdings 0.24 (0.03) 0.00Aggregate individual holdings -0.01 (0.04) 0.88Aggregate nonfinancial holdings 0.29 (0.03) 0.00Fraction voting shares 0.22 (0.05) 0.00Debt to assets 0.05 (0.03) 0.07Dividends to earnings 0.02 (0.01) 0.01Industrial -0.02 (0.01) 0.04Transport/shipping -0.04 (0.01) 0.00Offshore 0.00 (0.02) 0.94Investments over income -0.00 (0.00) 0.07ln(Firm value) -0.01 (0.00) 0.03Stock volatility 0.00 (0.01) 0.99constant -0.13 (0.11) 0.21

Primary insiders Herfindahl index 9.41 (2.75) 0.00Aggregate state holdings -5.19 (1.57) 0.00Aggregate international holdings -2.24 (0.74) 0.00Aggregate individual holdings 0.06 (0.40) 0.88Aggregate nonfinancial holdings -2.75 (0.87) 0.00Fraction voting shares -2.07 (0.72) 0.00Debt to assets -0.45 (0.30) 0.13Dividends to earnings -0.17 (0.08) 0.04Industrial 0.21 (0.12) 0.08Transport/shipping 0.39 (0.18) 0.03Offshore -0.01 (0.17) 0.94Investments over income 0.02 (0.02) 0.12ln(Firm value) 0.07 (0.04) 0.05ln(Board size) 0.00 (0.06) 0.99constant 1.26 (1.04) 0.23


Equation n Parms RMSE R2 χ2 p1 741 15 4.07 -13.54 35.93 0.002 741 14 0.11 0.25 680.02 0.003 741 14 1.06 -34.05 402.45 0.00



Table B.89 Simultaneous systems estimation of the determinants of ownership concentration,insider holdings, and economic performance (Q). The two endogeneous governance mechanismsare independent of performance. Stock turnover and board size are instruments (Model (II) insummary table 11.1)Panel A. Regression results

Dep.variable Indep.variable coeff (stdev) pvalueQ Herfindahl index -8.77 (1.70) 0.00

Primary insiders 19.43 (16.55) 0.24Squared (Primary insiders) -19.39 (19.42) 0.32Aggregate state holdings 5.98 (2.24) 0.01Aggregate international holdings 2.80 (1.04) 0.01Aggregate individual holdings -0.71 (1.26) 0.57Aggregate nonfinancial holdings 3.21 (1.37) 0.02Fraction voting shares 2.11 (1.24) 0.09ln(Board size) -0.21 (0.14) 0.13Debt to assets -1.34 (0.44) 0.00Dividends to earnings 0.06 (0.09) 0.51Industrial -0.17 (0.20) 0.41Transport/shipping -0.61 (0.23) 0.01Offshore -0.22 (0.35) 0.54Investments over income -0.03 (0.02) 0.15ln(Firm value) -0.00 (0.11) 0.98constant -0.65 (1.94) 0.74

Herfindahl index Primary insiders 0.36 (0.02) 0.00Aggregate state holdings 0.54 (0.05) 0.00Aggregate international holdings 0.21 (0.03) 0.00Aggregate individual holdings -0.12 (0.04) 0.00Aggregate nonfinancial holdings 0.27 (0.03) 0.00Fraction voting shares 0.25 (0.05) 0.00Debt to assets 0.02 (0.03) 0.55Dividends to earnings 0.02 (0.01) 0.03Industrial -0.02 (0.01) 0.14Transport/shipping -0.03 (0.01) 0.01Offshore 0.02 (0.02) 0.38Investments over income -0.00 (0.00) 0.07ln(Firm value) -0.01 (0.00) 0.05Stock volatility 0.02 (0.02) 0.37Stock turnover -0.01 (0.01) 0.04constant -0.13 (0.11) 0.24

Primary insiders Herfindahl index 2.11 (0.35) 0.00Aggregate state holdings -1.22 (0.22) 0.00Aggregate international holdings -0.46 (0.12) 0.00Aggregate individual holdings 0.35 (0.10) 0.00Aggregate nonfinancial holdings -0.60 (0.13) 0.00Fraction voting shares -0.52 (0.14) 0.00Debt to assets -0.01 (0.07) 0.92Dividends to earnings -0.03 (0.02) 0.07Industrial 0.03 (0.03) 0.31Transport/shipping 0.06 (0.04) 0.10Offshore -0.05 (0.05) 0.28Investments over income 0.01 (0.00) 0.18ln(Firm value) 0.02 (0.01) 0.03ln(Board size) -0.01 (0.02) 0.58Stock volatility -0.02 (0.05) 0.73constant 0.20 (0.30) 0.49





Table B.90 Simultaneous systems estimation of the determinants of ownership concentration,insider holdings, and economic performance (Q). The two endogeneous governance mechanisms areindependent of performance. Debt to assets and intercorporate investments and are instruments(Model (III) in summary table 11.1)Panel A. Regression results


Primary insiders 178.99 (670.37) 0.79Squared (Primary insiders) -223.64 (803.40) 0.78Aggregate state holdings -24.54 (30.46) 0.42Aggregate international holdings -9.10 (12.83) 0.48Aggregate individual holdings -9.42 (49.22) 0.85Aggregate nonfinancial holdings -10.15 (12.15) 0.40Fraction voting shares -28.95 (84.11) 0.73Dividends to earnings -1.12 (2.12) 0.60ln(Board size) 1.82 (5.88) 0.76Industrial 3.17 (10.67) 0.77Transport/shipping 4.68 (15.26) 0.76Offshore 2.68 (12.83) 0.83Investments over income 0.13 (0.32) 0.70ln(Firm value) -0.34 (2.93) 0.91constant 22.92 (88.37) 0.80

Herfindahl index Primary insiders 0.21 (0.02) 0.00Aggregate state holdings 0.56 (0.05) 0.00Aggregate international holdings 0.23 (0.03) 0.00Aggregate individual holdings -0.06 (0.04) 0.14Aggregate nonfinancial holdings 0.28 (0.03) 0.00Fraction voting shares 0.22 (0.05) 0.00Dividends to earnings 0.02 (0.01) 0.01ln(Board size) -0.02 (0.01) 0.17Industrial -0.02 (0.01) 0.12Transport/shipping -0.04 (0.01) 0.00Offshore 0.00 (0.02) 0.85Investments over income -0.00 (0.00) 0.09ln(Firm value) -0.01 (0.00) 0.02Aggregate intercorporate holdings 0.01 (0.03) 0.75constant -0.06 (0.10) 0.54

Primary insiders Herfindahl index 4.63 (1.01) 0.00Aggregate state holdings -2.59 (0.60) 0.00Aggregate international holdings -1.04 (0.29) 0.00Aggregate individual holdings 0.31 (0.20) 0.12Aggregate nonfinancial holdings -1.30 (0.34) 0.00ln(Board size) 0.08 (0.06) 0.22Fraction voting shares -1.02 (0.31) 0.00Dividends to earnings -0.08 (0.04) 0.04Industrial 0.08 (0.06) 0.18Transport/shipping 0.17 (0.08) 0.03Offshore -0.02 (0.09) 0.83Investments over income 0.01 (0.01) 0.14ln(Firm value) 0.04 (0.02) 0.04Debt to assets 0.01 (0.06) 0.89constant 0.27 (0.51) 0.59





B.9.2 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 mechanismsare 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


Q Herfindahl index -7.90 (7.27) 0.28Primary insiders 36.96 (28.43) 0.19Squared (Primary insiders) -41.96 (34.01) 0.22Industrial 0.34 (0.65) 0.60Transport/shipping 0.22 (0.88) 0.81Offshore 0.41 (0.91) 0.66ln(Firm value) 0.07 (0.07) 0.29constant -0.41 (1.77) 0.82

Herfindahl index Primary insiders 0.23 (0.01) 0.00Industrial 0.01 (0.01) 0.66Transport/shipping 0.00 (0.01) 0.77Offshore 0.02 (0.02) 0.42ln(Firm value) 0.00 (0.00) 0.80Stock volatility 0.01 (0.02) 0.78constant 0.09 (0.09) 0.32

Primary insiders Herfindahl index 4.01 (1.56) 0.01ln(Board size) -0.01 (0.03) 0.83Industrial -0.03 (0.05) 0.61Transport/shipping -0.02 (0.06) 0.69Offshore -0.08 (0.09) 0.39ln(Firm value) -0.00 (0.01) 0.90constant -0.39 (0.38) 0.31



1 741 7 2.70 -5.39 29.17 0.002 741 6 0.14 -0.10 606.61 0.003 741 6 0.55 -8.53 18.60 0.00

The table complements table B.88 by using only controls as additional exogenous variables to the instruments in theestimation. 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 listedin the next column. Equations are separated by a line. Panel B holds diagnostics. For each equation the diagnosticsinclude 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 estimatesare performed with Stata’s reg3 3SLS routine. Variable definitions are in Appendix A.2. Data for firms listed on theOslo Stock Exchange, 1989-1997.


Table B.92 Simultaneous systems estimation of the determinants of ownership concentration,insider holdings, and economic performance (Q). The two endogeneous governance mechanismsare 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


Q Herfindahl index -3.39 (2.99) 0.26Primary insiders 16.62 (17.33) 0.34Squared (Primary insiders) -14.88 (20.85) 0.47Industrial -0.00 (0.43) 1.00Transport/shipping -0.26 (0.59) 0.66Offshore -0.02 (0.60) 0.98ln(Firm value) 0.11 (0.05) 0.04constant -0.91 (1.42) 0.52

Herfindahl index Primary insiders 0.12 (0.03) 0.00Industrial -0.01 (0.01) 0.62Transport/shipping -0.02 (0.01) 0.11Offshore 0.00 (0.02) 0.93ln(Firm value) 0.01 (0.00) 0.15Stock volatility 0.05 (0.02) 0.01Stock turnover -0.06 (0.01) 0.00constant 0.03 (0.08) 0.70

Primary insiders Herfindahl index 0.15 (0.15) 0.34Industrial -0.05 (0.02) 0.00Transport/shipping -0.07 (0.02) 0.00Offshore -0.10 (0.03) 0.00ln(Firm value) 0.00 (0.01) 0.69Stock volatility 0.04 (0.03) 0.16ln(Board size) -0.05 (0.02) 0.01constant 0.12 (0.12) 0.29



1 741 7 1.51 -1.00 136.11 0.002 741 7 0.12 0.08 106.89 0.003 741 7 0.18 0.03 39.18 0.00

The table complements table B.89 by using only controls as additional exogenous variables to the instruments in theestimation. 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 listedin the next column. Equations are separated by a line. Panel B holds diagnostics. For each equation the diagnosticsinclude 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 estimatesare performed with Stata’s reg3 3SLS routine. Variable definitions are in Appendix A.2. Data for firms listed on theOslo Stock Exchange, 1989-1997.


Table B.93 Simultaneous systems estimation of the determinants of ownership concentration, in-sider holdings, and economic performance (Q). The two endogeneous governance mechanisms areindependent of performance. Concentration and insider holdings are endogeneous variables. Aggre-gate intercorporate shareholdings and debt to assets are instruments. Only controls as additionalexplanatory variables.Panel A. Regression results


Q Herfindahl index 54.48 (114.75) 0.64Primary insiders 204.63 (339.38) 0.55Squared (Primary insiders) -239.81 (410.28) 0.56Industrial 4.52 (8.21) 0.58Transport/shipping 6.17 (11.65) 0.60Offshore 5.79 (10.54) 0.58ln(Firm value) -0.01 (0.43) 0.99constant -16.41 (29.65) 0.58

Herfindahl index Primary insiders 0.35 (0.03) 0.00Industrial 0.01 (0.01) 0.56Transport/shipping 0.01 (0.01) 0.57Offshore 0.03 (0.02) 0.19ln(Firm value) 0.00 (0.00) 0.71Aggregate intercorporate holdings 0.14 (0.03) 0.00constant 0.06 (0.07) 0.35

Primary insiders Herfindahl index 0.70 (0.30) 0.02Industrial -0.05 (0.02) 0.00Transport/shipping -0.07 (0.02) 0.00Offshore -0.09 (0.03) 0.00ln(Firm value) -0.00 (0.01) 0.34Debt to assets 0.08 (0.04) 0.06constant 0.08 (0.11) 0.46



1 741 7 14.20 -176.26 4.70 0.702 741 6 0.14 -0.19 188.30 0.003 741 6 0.20 -0.23 35.40 0.00

The table complements table B.90 in the text by only using controls as additional exogenous variables to the instru-ments in the estimation. The tables shows 3SLS estimates of a system of simultanous equations. Panel A reportsystem estimates. The leftmost column is the dependent variable in that particular equation, which is a functionof the variables listed in the next column. Equations are separated by a line. Panel B holds diagnostics. For eachequation the diagnostics include n, the number of observations, Parms, the number of parameters, RMSE, the rootmean squared error, R2, a “pseudo” R squared, χ2, the chi squared of the equation, and p, a p-value for the fit of theequation. 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.



This appendix replaces the Herfindahl index used in the main text by the holdings of the largestoutside owner. To estimate outside concentration we remove the largest owner if it has the sameholdings as the largest insider.


Table B.94 Simultaneous systems estimation of the determinants of ownership concentration,insider holdings, and economic performance (Q). The two endogeneous governance mechanismsare independent of performance. Concentration and insider holdings are endogeneous variables.Adjusting for overlap between insiders and large owners. Board size and stock volatility are instru-ments.Panel A. Regression results

Dep.variable Indep.variable coeff (stdev) pvalueQ Largest outside owner -1.25 (3.37) 0.71

Primary insiders 9.30 (13.14) 0.48Squared (Primary insiders) -10.48 (15.48) 0.50Aggregate state holdings 1.04 (3.37) 0.76Aggregate international holdings 0.79 (1.42) 0.58Aggregate individual holdings 0.52 (1.20) 0.67Aggregate nonfinancial holdings 0.66 (1.87) 0.73Fraction voting shares 0.95 (1.05) 0.36Debt to assets -1.56 (0.61) 0.01Dividends to earnings -0.09 (0.09) 0.36Industrial -0.12 (0.16) 0.45Transport/shipping -0.44 (0.24) 0.07Offshore -0.37 (0.31) 0.24ln(Firm value) 0.09 (0.11) 0.40constant -0.71 (1.60) 0.66

Largest outside owner Primary insiders -0.15 (0.02) 0.00Aggregate state holdings 0.73 (0.06) 0.00Aggregate international holdings 0.30 (0.04) 0.00Aggregate individual holdings -0.06 (0.05) 0.28Aggregate nonfinancial holdings 0.39 (0.05) 0.00Fraction voting shares 0.23 (0.06) 0.00Debt to assets 0.15 (0.03) 0.00Dividends to earnings 0.01 (0.01) 0.11Industrial -0.01 (0.01) 0.29Transport/shipping -0.07 (0.02) 0.00Offshore 0.02 (0.02) 0.51Investments over income -0.00 (0.00) 0.11ln(Firm value) -0.01 (0.01) 0.01Stock volatility 0.00 (0.02) 0.94constant 0.01 (0.15) 0.93

Primary insiders Largest outside owner -6.40 (3.29) 0.05Aggregate state holdings 4.66 (2.47) 0.06Aggregate international holdings 1.96 (1.02) 0.06Aggregate individual holdings -0.36 (0.55) 0.51Aggregate nonfinancial holdings 2.48 (1.29) 0.06Fraction voting shares 1.46 (0.88) 0.10Debt to assets 0.94 (0.48) 0.05Dividends to earnings 0.09 (0.07) 0.21Industrial -0.09 (0.10) 0.33Transport/shipping -0.45 (0.24) 0.06Offshore 0.10 (0.17) 0.57Investments over income -0.02 (0.02) 0.22ln(Firm value) -0.09 (0.06) 0.12ln(Board size) -0.00 (0.02) 0.84constant 0.12 (0.91) 0.90


Equation n Parms RMSE R2 χ2 p1 741 14 1.04 0.05 190.72 0.002 741 14 0.15 0.31 997.74 0.003 741 14 0.94 -26.66 9.20 0.82



Table B.95 Simultaneous systems estimation of the determinants of ownership concentration,insider holdings, and economic performance (Q). The two endogeneous governance mechanismsare independent of performance. Concentration and insider holdings are endogeneous variables.Adjusting for overlap between insiders and large owners. Board size and stock turnover are instru-ments.Panel A. Regression results


Primary insiders 25.04 (20.17) 0.21Squared (Primary insiders) -30.28 (23.70) 0.20Aggregate state holdings 6.23 (3.05) 0.04Aggregate international holdings 3.05 (1.38) 0.03Aggregate individual holdings -0.79 (1.60) 0.62Aggregate nonfinancial holdings 3.60 (1.83) 0.05Fraction voting shares 0.81 (1.48) 0.59ln(Board size) -0.07 (0.16) 0.68Debt to assets -0.52 (0.62) 0.40Dividends to earnings 0.02 (0.11) 0.85Industrial -0.03 (0.24) 0.90Transport/shipping -0.61 (0.28) 0.03Offshore -0.06 (0.44) 0.89Investments over income -0.03 (0.02) 0.24ln(Firm value) -0.06 (0.14) 0.64constant 1.01 (2.37) 0.67

Largest outside owner Primary insiders -0.47 (0.02) 0.00Aggregate state holdings 0.65 (0.06) 0.00Aggregate international holdings 0.29 (0.04) 0.00Aggregate individual holdings 0.07 (0.05) 0.20Aggregate nonfinancial holdings 0.35 (0.05) 0.00Fraction voting shares 0.24 (0.06) 0.00Debt to assets 0.17 (0.03) 0.00Dividends to earnings 0.01 (0.01) 0.10Industrial -0.02 (0.01) 0.09Transport/shipping -0.08 (0.02) 0.00Offshore -0.00 (0.02) 0.94Investments over income -0.00 (0.00) 0.06ln(Firm value) -0.00 (0.01) 0.48Stock volatility 0.07 (0.02) 0.01Stock turnover -0.02 (0.01) 0.02constant -0.22 (0.15) 0.14

Primary insiders Largest outside owner -1.21 (0.32) 0.00Aggregate state holdings 0.79 (0.25) 0.00Aggregate international holdings 0.38 (0.11) 0.00Aggregate individual holdings 0.27 (0.10) 0.01Aggregate nonfinancial holdings 0.46 (0.14) 0.00Fraction voting shares 0.25 (0.13) 0.05Debt to assets 0.26 (0.07) 0.00Dividends to earnings 0.02 (0.01) 0.12Industrial -0.04 (0.02) 0.08Transport/shipping -0.12 (0.03) 0.00Offshore -0.03 (0.04) 0.46Investments over income -0.00 (0.00) 0.15ln(Firm value) 0.00 (0.01) 0.89ln(Board size) -0.01 (0.02) 0.57Stock volatility 0.10 (0.04) 0.01constant -0.39 (0.24) 0.11





Table B.96 Simultaneous systems estimation of the determinants of ownership concentration,insider holdings, and economic performance (Q). The two endogeneous governance mechanismsare independent of performance. Concentration and insider holdings are endogeneous variables.Adjusting for overlap between insiders and large owners. Aggregate intercorporate shareholdingsand debt to assets are instruments.Panel A. Regression results


Primary insiders 6.38 (435.39) 0.99Squared (Primary insiders) -24.85 (508.45) 0.96Aggregate state holdings 29.98 (12.41) 0.02Aggregate international holdings 12.93 (5.34) 0.01Aggregate individual holdings 0.19 (22.83) 0.99Aggregate nonfinancial holdings 15.92 (7.10) 0.03Fraction voting shares 8.10 (45.99) 0.86Dividends to earnings 0.53 (0.74) 0.47ln(Board size) -0.46 (1.75) 0.79Industrial -0.57 (5.11) 0.91Transport/shipping -3.02 (9.06) 0.74Offshore -0.17 (6.08) 0.98Investments over income -0.11 (0.16) 0.48ln(Firm value) -0.50 (1.45) 0.73constant 5.47 (37.13) 0.88

Largest outside owner Primary insiders -0.33 (0.02) 0.00Aggregate state holdings 0.70 (0.06) 0.00Aggregate international holdings 0.29 (0.04) 0.00Aggregate individual holdings -0.01 (0.06) 0.84Aggregate nonfinancial holdings 0.36 (0.04) 0.00Fraction voting shares 0.21 (0.06) 0.00Dividends to earnings 0.01 (0.01) 0.11ln(Board size) -0.01 (0.02) 0.67Industrial -0.01 (0.01) 0.36Transport/shipping -0.06 (0.02) 0.00Offshore 0.00 (0.02) 0.87Investments over income -0.00 (0.00) 0.23ln(Firm value) -0.01 (0.00) 0.00Aggregate intercorporate holdings 0.01 (0.04) 0.86constant 0.16 (0.13) 0.25

Primary insiders Largest outside owner -2.80 (0.81) 0.00Aggregate state holdings 1.96 (0.62) 0.00Aggregate international holdings 0.81 (0.27) 0.00Aggregate individual holdings 0.01 (0.18) 0.97Aggregate nonfinancial holdings 1.02 (0.33) 0.00ln(Board size) -0.02 (0.04) 0.63Fraction voting shares 0.58 (0.26) 0.03Dividends to earnings 0.04 (0.03) 0.12Industrial -0.04 (0.04) 0.31Transport/shipping -0.19 (0.07) 0.01Offshore 0.01 (0.07) 0.92Investments over income -0.01 (0.01) 0.24ln(Firm value) -0.04 (0.02) 0.03Debt to assets 0.07 (0.12) 0.56constant 0.37 (0.37) 0.31




B.10 Two-way causaution between corporate governance and economic performance 209

B.10 Two-way causaution between corporate governance and economic perfor-mance

This appendix lists complementary estimations to those in section 11.2. Section B.10.1 gives theestimations underlying summary table 11.2 in the text. Section B.10.2 contains similar regressionsto the ones in the text with fewer explanatory (exogenous) variables. Section B.10.3 considersoutside concentration.


This section gives the estimations underlying summary table 11.1 in the text.


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 areinstruments. (Model (A) in table 11.1)Panel A. Regression results


Primary insiders -65.74 (74.55) 0.38Squared (Primary insiders) 74.74 (85.20) 0.38Aggregate state holdings -24.59 (21.39) 0.25Aggregate international holdings -10.36 (9.37) 0.27Aggregate individual holdings 5.98 (5.46) 0.27Aggregate nonfinancial holdings -13.60 (12.03) 0.26Fraction voting shares -0.96 (2.31) 0.68Debt to assets -4.59 (2.62) 0.08Dividends to earnings -0.83 (0.63) 0.19Industrial -0.11 (0.37) 0.76Transport/shipping 0.31 (0.59) 0.60Offshore -1.52 (1.22) 0.21Investments over income 0.11 (0.10) 0.28ln(Firm value) 0.78 (0.62) 0.21Stock beta -0.41 (0.39) 0.30constant -4.16 (5.25) 0.43

Herfindahl index Primary insiders 0.05 (0.05) 0.26Q 0.06 (0.06) 0.37Aggregate state holdings 0.59 (0.08) 0.00Aggregate international holdings 0.23 (0.04) 0.00Aggregate individual holdings -0.07 (0.09) 0.44Aggregate nonfinancial holdings 0.31 (0.05) 0.00Fraction voting shares 0.15 (0.10) 0.12Debt to assets 0.16 (0.13) 0.21Dividends to earnings 0.03 (0.01) 0.02Industrial -0.01 (0.02) 0.51Transport/shipping -0.02 (0.03) 0.58Offshore 0.03 (0.04) 0.43Investments over income -0.00 (0.00) 0.08ln(Firm value) -0.01 (0.01) 0.24Stock volatility 0.03 (0.02) 0.15constant -0.13 (0.17) 0.44

Primary insiders Herfindahl index 1.70 (3.64) 0.64Q 1.64 (0.74) 0.03Aggregate state holdings 0.45 (2.50) 0.86Aggregate international holdings -0.32 (0.97) 0.74Aggregate individual holdings -1.91 (1.07) 0.07Aggregate nonfinancial holdings 0.29 (1.36) 0.83Fraction voting shares -2.32 (0.89) 0.01Debt to assets 3.02 (1.52) 0.05Dividends to earnings 0.17 (0.16) 0.29Industrial 0.33 (0.17) 0.05Transport/shipping 0.82 (0.31) 0.01Offshore 0.96 (0.48) 0.04Investments over income 0.00 (0.02) 0.95ln(Firm value) -0.27 (0.15) 0.07ln(Board size) 0.38 (0.18) 0.04constant 2.36 (1.46) 0.11



The tables shows 3SLS estimates of a system of simultanous equations. Panel A report system estimates. Theleftmost column is the dependent variable in that particular equation, which is a function of the variables listed inthe next column. Equations are separated by a line. Panel B holds diagnostics. For each equation the diagnosticsinclude 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 estimatesare performed with Stata’s reg3 3SLS routine. Variable definitions are in Appendix A.2. Data for firms listed on theOslo Stock Exchange, 1989-1997. Appendix table B.100 shows the results from estimating a similar system withonly controls as additional explanatory variables.


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 areinstruments. (Model (B) in table 11.1)Panel A. Regression results


Primary insiders 11.38 (11.18) 0.31Squared (Primary insiders) -12.35 (13.11) 0.35Aggregate state holdings 2.53 (1.44) 0.08Aggregate international holdings 1.40 (0.68) 0.04Aggregate individual holdings 0.35 (0.97) 0.72Aggregate nonfinancial holdings 1.39 (0.87) 0.11Fraction voting shares 1.32 (1.02) 0.20ln(Board size) -0.23 (0.14) 0.10Debt to assets -1.50 (0.33) 0.00Dividends to earnings -0.02 (0.08) 0.77Industrial -0.17 (0.15) 0.26Transport/shipping -0.54 (0.18) 0.00Offshore -0.44 (0.26) 0.09Investments over income -0.01 (0.02) 0.44ln(Firm value) 0.07 (0.08) 0.39Stock beta 0.11 (0.12) 0.37constant -0.66 (1.66) 0.69

Herfindahl index Primary insiders -0.02 (0.08) 0.82Q 0.16 (0.12) 0.17Aggregate state holdings 0.55 (0.10) 0.00Aggregate international holdings 0.19 (0.06) 0.00Aggregate individual holdings -0.20 (0.16) 0.21Aggregate nonfinancial holdings 0.26 (0.06) 0.00Fraction voting shares 0.09 (0.15) 0.54Debt to assets 0.34 (0.22) 0.12Dividends to earnings 0.03 (0.02) 0.07Industrial 0.01 (0.03) 0.72Transport/shipping 0.03 (0.05) 0.63Offshore 0.09 (0.07) 0.21Investments over income -0.00 (0.00) 0.12ln(Firm value) -0.03 (0.02) 0.16Stock volatility 0.04 (0.03) 0.19Stock turnover -0.08 (0.03) 0.00constant 0.01 (0.26) 0.97

Primary insiders Herfindahl index 5.43 (1.79) 0.00Q 1.15 (0.36) 0.00Aggregate state holdings -1.96 (0.96) 0.04Aggregate international holdings -1.17 (0.54) 0.03Aggregate individual holdings -1.32 (0.68) 0.05Aggregate nonfinancial holdings -0.97 (0.56) 0.08Fraction voting shares -2.58 (0.87) 0.00Debt to assets 1.92 (0.66) 0.00Dividends to earnings 0.04 (0.08) 0.62Industrial 0.32 (0.15) 0.03Transport/shipping 0.77 (0.26) 0.00Offshore 0.70 (0.30) 0.02Investments over income 0.01 (0.02) 0.42ln(Firm value) -0.19 (0.08) 0.01ln(Board size) 0.35 (0.17) 0.04Stock volatility -0.22 (0.21) 0.30constant 2.76 (1.49) 0.06


Equation n Parms RMSE R2 χ2 p1 741 17 1.14 -0.14 181.71 0.002 741 16 0.18 -1.00 112.00 0.003 741 16 1.17 -41.28 15.51 0.49

The tables shows 3SLS estimates of a system of simultanous equations. Panel A report system estimates. Theleftmost column is the dependent variable in that particular equation, which is a function of the variables listed inthe next column. Equations are separated by a line. Panel B holds diagnostics. For each equation the diagnosticsinclude 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 estimatesare performed with Stata’s reg3 3SLS routine. Variable definitions are in Appendix A.2. Data for firms listed on theOslo Stock Exchange, 1989-1997. Appendix table B.98 shows the results from estimating a similar system with onlycontrols as additional explanatory variables.


Table B.99 Simultaneous systems estimation of the determinants of economic performance (Q),ownership concentration, and insider holdings. Debt to assets, intercorporate shareholdings andstock beta are instruments. (Model (C) in table 11.1)Panel A. Regression results


Primary insiders 401.13 (1113.58) 0.72Squared (Primary insiders) -478.09 (1329.20) 0.72Aggregate state holdings -8.92 (45.48) 0.84Aggregate international holdings -3.55 (21.09) 0.87Aggregate individual holdings -30.25 (90.44) 0.74Aggregate nonfinancial holdings -1.14 (18.07) 0.95Fraction voting shares -47.09 (140.72) 0.74Dividends to earnings -0.83 (2.86) 0.77ln(Board size) 3.49 (11.46) 0.76Industrial 5.33 (15.89) 0.74Transport/shipping 7.28 (22.79) 0.75Offshore 6.19 (19.03) 0.74Investments over income 0.13 (0.56) 0.82ln(Firm value) -2.01 (6.24) 0.75Stock beta 4.10 (13.82) 0.77constant 53.68 (164.21) 0.74

Herfindahl index Primary insiders 0.10 (0.03) 0.00Q -0.03 (0.01) 0.02Aggregate state holdings 0.53 (0.05) 0.00Aggregate international holdings 0.25 (0.03) 0.00Aggregate individual holdings 0.06 (0.05) 0.25Aggregate nonfinancial holdings 0.27 (0.03) 0.00Fraction voting shares 0.25 (0.05) 0.00Dividends to earnings 0.01 (0.01) 0.06ln(Board size) -0.03 (0.01) 0.03Industrial -0.03 (0.01) 0.01Transport/shipping -0.06 (0.02) 0.00Offshore -0.02 (0.02) 0.29Investments over income -0.00 (0.00) 0.07ln(Firm value) -0.00 (0.00) 0.85Aggregate intercorporate holdings 0.10 (0.03) 0.00constant -0.18 (0.11) 0.11

Primary insiders Herfindahl index 4.76 (2.20) 0.03Q 1.20 (0.50) 0.02Aggregate state holdings -1.61 (1.39) 0.25Aggregate international holdings -1.08 (0.65) 0.10Aggregate individual holdings -1.41 (0.84) 0.10Aggregate nonfinancial holdings -0.81 (0.79) 0.30ln(Board size) 0.33 (0.18) 0.07Fraction voting shares -2.45 (0.91) 0.01Dividends to earnings 0.06 (0.11) 0.57Industrial 0.32 (0.16) 0.05Transport/shipping 0.76 (0.30) 0.01Offshore 0.72 (0.37) 0.05Investments over income 0.01 (0.02) 0.55ln(Firm value) -0.17 (0.10) 0.07Debt to assets 2.00 (0.97) 0.04constant 2.18 (1.41) 0.12



The tables shows 3SLS estimates of a system of simultanous equations. Panel A report system estimates. Theleftmost column is the dependent variable in that particular equation, which is a function of the variables listed inthe next column. Equations are separated by a line. Panel B holds diagnostics. For each equation the diagnosticsinclude 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 estimatesare performed with Stata’s reg3 3SLS routine. Variable definitions are in Appendix A.2. Data for firms listed on theOslo Stock Exchange, 1989-1997. Appendix table B.102 shows the results from estimating a similar system withonly controls as additional explanatory variables.


B.10.2 Controls, instruments and endogenous mechanisms, only

This section complements the analysis of section 11.2, (detailed in appendix section B.10.1), usingestimation 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


Q Herfindahl index -6.13 (6.17) 0.32Primary insiders 20.25 (26.74) 0.45Squared (Primary insiders) -23.31 (31.97) 0.47Industrial -0.06 (0.61) 0.92Transport/shipping -0.34 (0.82) 0.68Offshore -0.17 (0.85) 0.84ln(Firm value) 0.07 (0.07) 0.28Stock beta 0.13 (0.20) 0.53constant 0.20 (1.52) 0.90

Herfindahl index Primary insiders 0.06 (0.04) 0.10Q -0.05 (0.04) 0.17Industrial -0.03 (0.02) 0.15Transport/shipping -0.06 (0.03) 0.11Offshore -0.04 (0.03) 0.24ln(Firm value) 0.01 (0.01) 0.13Stock volatility 0.03 (0.02) 0.11constant 0.06 (0.08) 0.46

Primary insiders Herfindahl index 9.00 (3.30) 0.01Q 1.25 (0.63) 0.05ln(Board size) 0.26 (0.28) 0.35Industrial 0.57 (0.32) 0.08Transport/shipping 1.17 (0.62) 0.06Offshore 0.96 (0.59) 0.11ln(Firm value) -0.14 (0.09) 0.11constant -1.24 (1.02) 0.23



1 741 8 1.70 -1.54 22.04 0.002 741 7 0.13 -0.04 7.85 0.353 741 7 1.58 -76.13 10.72 0.15

The table complements table B.97. It shows results with the same set of endogenous variables, but a smaller set ofexplanatory 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, whichis 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 forthe fit of the equation. The estimates are performed with Stata’s reg3 3SLS routine. Variable definitions are inAppendix A.2. Data for firms listed on the Oslo Stock Exchange, 1989-1997.


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


Q Herfindahl index -0.43 (2.48) 0.86Primary insiders 23.36 (12.00) 0.05Squared (Primary insiders) -27.27 (14.42) 0.06Industrial 0.04 (0.31) 0.91Transport/shipping -0.19 (0.41) 0.64Offshore -0.07 (0.43) 0.87ln(Firm value) 0.07 (0.05) 0.14Stock beta 0.19 (0.14) 0.16constant -0.73 (1.09) 0.51

Herfindahl index Primary insiders -0.04 (0.05) 0.47Q 0.10 (0.07) 0.16Industrial 0.03 (0.03) 0.36Transport/shipping 0.05 (0.05) 0.40Offshore 0.06 (0.05) 0.28ln(Firm value) -0.00 (0.01) 0.72Stock volatility 0.07 (0.03) 0.01Stock turnover -0.10 (0.02) 0.00constant 0.04 (0.11) 0.70

Primary insiders Herfindahl index 7.73 (3.38) 0.02Q 1.63 (0.65) 0.01Industrial 0.70 (0.34) 0.04Transport/shipping 1.52 (0.66) 0.02Offshore 1.29 (0.63) 0.04ln(Firm value) -0.23 (0.11) 0.03Stock volatility -0.30 (0.30) 0.32ln(Board size) 0.56 (0.31) 0.08constant -0.32 (1.18) 0.79



1 741 8 1.65 -1.38 50.59 0.002 741 8 0.16 -0.51 58.34 0.003 741 8 1.77 -95.86 6.72 0.57

The table complements table B.98 in the text. It shows results with the same set of endogenous variables, but asmaller set of explanatory variables: Only the controls. The tables shows 3SLS estimates of a system of simultanousequations. Panel A report system estimates. The leftmost column is the dependent variable in that particularequation, which is a function of the variables listed in the next column. Equations are separated by a line. PanelB holds diagnostics. For each equation the diagnostics include n, the number of observations, Parms, the numberof 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. Variabledefinitions are in Appendix A.2. Data for firms listed on the Oslo Stock Exchange, 1989-1997.


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


Q Herfindahl index 112.73 (140.11) 0.42Primary insiders 340.87 (398.51) 0.39Squared (Primary insiders) -410.85 (480.75) 0.39Industrial 7.49 (9.36) 0.42Transport/shipping 10.28 (13.01) 0.43Offshore 9.17 (11.66) 0.43ln(Firm value) -0.33 (0.70) 0.64Stock beta 2.94 (4.78) 0.54constant -26.93 (32.72) 0.41

Herfindahl index Primary insiders 0.06 (0.03) 0.04Q -0.03 (0.01) 0.04Industrial -0.02 (0.01) 0.08Transport/shipping -0.04 (0.02) 0.02Offshore -0.02 (0.02) 0.29ln(Firm value) 0.00 (0.00) 0.49Aggregate intercorporate holdings 0.13 (0.03) 0.00constant 0.13 (0.07) 0.04

Primary insiders Herfindahl index 6.25 (1.95) 0.00Q 1.08 (0.31) 0.00Industrial 0.42 (0.17) 0.01Transport/shipping 0.83 (0.27) 0.00Offshore 0.76 (0.30) 0.01ln(Firm value) -0.10 (0.04) 0.01Debt to assets 1.78 (0.59) 0.00constant -1.92 (0.85) 0.03



1 741 8 24.79 -539.10 2.30 0.972 741 7 0.13 0.04 28.38 0.003 741 7 1.21 -44.60 14.80 0.04

The table complements table B.99 in the text. It shows results with the same set of endogenous variables, but asmaller set of explanatory variables: Only the controls. The tables shows 3SLS estimates of a system of simultanousequations. Panel A report system estimates. The leftmost column is the dependent variable in that particularequation, which is a function of the variables listed in the next column. Equations are separated by a line. PanelB holds diagnostics. For each equation the diagnostics include n, the number of observations, Parms, the numberof 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. Variabledefinitions are in Appendix A.2. Data for firms listed on the Oslo Stock Exchange, 1989-1997.


B.10.3 Outside concentration

This appendix replaces the Herfindahl index used in the main text as a concentration measureby the ownership by the largest outside owner. To estimate outside concentration we remove thelargest owner if it has the same holdings as the largest insider owner.


Table B.103 Simultaneous systems estimation of the determinants of ownership concentration,insider holdings, and economic performance (Q). Adjusting for overlap between insiders and largeowners. Board size, stock beta and stock volatility are instruments.Panel A. Regression results


Primary insiders 50.34 (110.48) 0.65Squared (Primary insiders) -58.94 (130.84) 0.65Aggregate state holdings 17.44 (43.92) 0.69Aggregate international holdings 7.52 (18.12) 0.68Aggregate individual holdings -4.58 (13.33) 0.73Aggregate nonfinancial holdings 9.53 (23.84) 0.69Fraction voting shares 1.86 (4.50) 0.68Debt to assets 1.25 (7.63) 0.87Dividends to earnings 0.24 (0.91) 0.79Industrial 0.05 (0.68) 0.94Transport/shipping -1.17 (2.10) 0.58Offshore 0.58 (2.66) 0.83Investments over income -0.07 (0.18) 0.71ln(Firm value) -0.44 (1.38) 0.75Stock beta 0.49 (1.04) 0.64constant 4.67 (14.33) 0.74

Largest outside owner Primary insiders -0.05 (0.05) 0.36Q 0.02 (0.07) 0.81Aggregate state holdings 0.74 (0.09) 0.00Aggregate international holdings 0.29 (0.04) 0.00Aggregate individual holdings -0.14 (0.11) 0.21Aggregate nonfinancial holdings 0.38 (0.06) 0.00Fraction voting shares 0.23 (0.11) 0.04Debt to assets 0.16 (0.15) 0.27Dividends to earnings 0.02 (0.01) 0.21Industrial -0.01 (0.02) 0.66Transport/shipping -0.06 (0.04) 0.10Offshore 0.03 (0.05) 0.50Investments over income -0.00 (0.00) 0.10ln(Firm value) -0.01 (0.01) 0.30Stock volatility 0.04 (0.02) 0.09constant -0.05 (0.19) 0.79

Primary insiders Largest outside owner -2.16 (5.70) 0.70Q 1.67 (0.75) 0.03Aggregate state holdings 3.00 (4.09) 0.46Aggregate international holdings 0.73 (1.75) 0.68Aggregate individual holdings -2.17 (1.19) 0.07Aggregate nonfinancial holdings 1.62 (2.15) 0.45Fraction voting shares -1.49 (1.96) 0.45Debt to assets 3.45 (1.36) 0.01Dividends to earnings 0.24 (0.14) 0.08Industrial 0.27 (0.23) 0.23Transport/shipping 0.61 (0.62) 0.32Offshore 1.01 (0.50) 0.04Investments over income -0.01 (0.03) 0.70ln(Firm value) -0.32 (0.14) 0.02ln(Board size) 0.32 (0.23) 0.15constant 2.31 (1.77) 0.19





Table B.104 Simultaneous systems estimation of the determinants of ownership concentration,insider holdings, and economic performance (Q). Adjusting for overlap between insiders and largeowners. Board size, stock turnover and stock volatility are instruments.Panel A. Regression results


Primary insiders 11.38 (11.18) 0.31Squared (Primary insiders) -12.35 (13.11) 0.35Aggregate state holdings 2.53 (1.44) 0.08Aggregate international holdings 1.40 (0.68) 0.04Aggregate individual holdings 0.35 (0.97) 0.72Aggregate nonfinancial holdings 1.39 (0.87) 0.11Fraction voting shares 1.32 (1.02) 0.20ln(Board size) -0.23 (0.14) 0.10Debt to assets -1.50 (0.33) 0.00Dividends to earnings -0.02 (0.08) 0.77Industrial -0.17 (0.15) 0.26Transport/shipping -0.54 (0.18) 0.00Offshore -0.44 (0.26) 0.09Investments over income -0.01 (0.02) 0.44ln(Firm value) 0.07 (0.08) 0.39Stock beta 0.11 (0.12) 0.37constant -0.66 (1.66) 0.69

Herfindahl index Primary insiders -0.02 (0.08) 0.82Q 0.16 (0.12) 0.17Aggregate state holdings 0.55 (0.10) 0.00Aggregate international holdings 0.19 (0.06) 0.00Aggregate individual holdings -0.20 (0.16) 0.21Aggregate nonfinancial holdings 0.26 (0.06) 0.00Fraction voting shares 0.09 (0.15) 0.54Debt to assets 0.34 (0.22) 0.12Dividends to earnings 0.03 (0.02) 0.07Industrial 0.01 (0.03) 0.72Transport/shipping 0.03 (0.05) 0.63Offshore 0.09 (0.07) 0.21Investments over income -0.00 (0.00) 0.12ln(Firm value) -0.03 (0.02) 0.16Stock volatility 0.04 (0.03) 0.19Stock turnover -0.08 (0.03) 0.00constant 0.01 (0.26) 0.97

Primary insiders Herfindahl index 5.43 (1.79) 0.00Q 1.15 (0.36) 0.00Aggregate state holdings -1.96 (0.96) 0.04Aggregate international holdings -1.17 (0.54) 0.03Aggregate individual holdings -1.32 (0.68) 0.05Aggregate nonfinancial holdings -0.97 (0.56) 0.08Fraction voting shares -2.58 (0.87) 0.00Debt to assets 1.92 (0.66) 0.00Dividends to earnings 0.04 (0.08) 0.62Industrial 0.32 (0.15) 0.03Transport/shipping 0.77 (0.26) 0.00Offshore 0.70 (0.30) 0.02Investments over income 0.01 (0.02) 0.42ln(Firm value) -0.19 (0.08) 0.01ln(Board size) 0.35 (0.17) 0.04Stock volatility -0.22 (0.21) 0.30constant 2.76 (1.49) 0.06


Equation n Parms RMSE R2 χ2 p1 741 17 1.14 -0.14 181.71 0.002 741 16 0.18 -1.00 112.00 0.003 741 16 1.17 -41.28 15.51 0.49



Table B.105 Simultaneous systems estimation of the determinants of ownership concentration,insider holdings, and economic performance (Q). Adjusting for overlap between insiders and largeowners. Aggregate intercorporate holdings, debt to assets and stock beta are instruments.Panel A. Regression results


Primary insiders -156.93 (337.46) 0.64Squared (Primary insiders) 183.12 (392.92) 0.64Aggregate state holdings 7.61 (11.28) 0.50Aggregate international holdings 2.88 (4.77) 0.55Aggregate individual holdings 9.11 (19.45) 0.64Aggregate nonfinancial holdings 2.43 (5.99) 0.69Fraction voting shares 20.06 (36.08) 0.58Dividends to earnings -0.06 (0.66) 0.93ln(Board size) -0.83 (1.72) 0.63Industrial -1.92 (3.61) 0.59Transport/shipping -4.09 (6.56) 0.53Offshore -2.21 (4.33) 0.61Investments over income -0.05 (0.14) 0.72ln(Firm value) 0.67 (1.41) 0.63Stock beta -1.43 (2.95) 0.63constant -16.22 (32.52) 0.62

Largest outside owner Primary insiders 0.02 (0.04) 0.62Q -0.07 (0.02) 0.00Aggregate state holdings 0.68 (0.07) 0.00Aggregate international holdings 0.31 (0.05) 0.00Aggregate individual holdings -0.01 (0.07) 0.92Aggregate nonfinancial holdings 0.34 (0.05) 0.00Fraction voting shares 0.32 (0.07) 0.00Dividends to earnings 0.00 (0.01) 0.70ln(Board size) -0.02 (0.02) 0.36Industrial -0.03 (0.02) 0.10Transport/shipping -0.10 (0.02) 0.00Offshore -0.02 (0.03) 0.54Investments over income -0.00 (0.00) 0.17ln(Firm value) -0.00 (0.01) 0.60Aggregate intercorporate holdings 0.11 (0.04) 0.01constant -0.07 (0.16) 0.65

Primary insiders Largest outside owner 4.29 (5.16) 0.41Q 1.94 (0.98) 0.05Aggregate state holdings -1.53 (3.40) 0.65Aggregate international holdings -1.18 (1.62) 0.47Aggregate individual holdings -1.74 (1.28) 0.17Aggregate nonfinancial holdings -0.73 (1.81) 0.69ln(Board size) 0.38 (0.29) 0.19Fraction voting shares -3.34 (2.30) 0.15Dividends to earnings 0.18 (0.15) 0.22Industrial 0.39 (0.29) 0.18Transport/shipping 1.15 (0.76) 0.13Offshore 1.02 (0.61) 0.10Investments over income 0.01 (0.03) 0.76ln(Firm value) -0.26 (0.15) 0.07Debt to assets 3.11 (1.50) 0.04constant 2.39 (2.18) 0.27




220 LIST OF TABLES

List of Tables

2.1 Mechanism interaction and mechanism–performance causality in empirical corporate governance research 14

3.1 Summary of descriptive statistics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20

3.2 Correlations between performance measures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23

3.3 Governance variables, controls, and performance measures used in the regression models . . . . . . . . 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, followingDemsetz and Lehn (1985) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31

5.2 Multivariate regression relating performance (RoA5) to ownership concentration and controls accordingto Demsetz and Lehn (1985), but using year–by–year OLS, GMM, and fixed effects OLS techniques . 33

5.3 Multivariate regression relating performance to ownership concentration and controls, using Q ratherthan RoA5 as performance measure in the Demsetz and Lehn (1985) approach . . . . . . . . . . . . . 35

5.4 Multivariate regression relating performance (Q) to ownership concentration and controls, using thepiecewise linear function of Morck et al. (1988) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36

5.5 Multivariate regression relating performance (Q) to ownership concentration and controls, using aquadratic function . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38

6.1 Multivariate regression relating performance (Q) to insider ownership and controls, following Morcket al. (1988) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41

6.2 Multivariate regression relating performance (Q) to insider holdings, ownership concentration andcontrols, using the piecewise linear function of Morck et al. (1988). . . . . . . . . . . . . . . . . . . . . 42

6.3 Multivariate regression relating performance (Q) to insider ownership and controls, following Mc-Connell and Servaes (1990) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45

6.4 Multivariate regression relating performance (Q) to insider ownership, ownership concentration andcontrols, following McConnell and Servaes (1990) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45

6.5 Multivariate regression relating performance (Q) to insider holdings, ownership concentration, institu-tional ownership and controls, following McConnell and Servaes (1990) . . . . . . . . . . . . . . . . . . 46

6.6 Multivariate regression relating performance (Q) to insider ownership, the holdings of the largestinsider, ownership concentration, and controls . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47

7.1 Multivariate regression relating performance (Q) to ownership concentration, insider holdings, aggre-gate holdings per owner type, and controls . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49

7.2 Multivariate regression relating performance (Q) to ownership concentration, insider holdings, aggre-gate intercorporate holdings, and controls . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50

7.3 Multivariate regression relating performance (Q) to ownership concentration, insider holdings, largestowner identity, and controls . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50

7.4 Multivariate regression relating performance (Q) to ownership concentration, insider holdings, largestowner being listed, and controls . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51

8.1 Multivariate regression relating performance (Q) to ownership concentration, insider ownership, boardsize, and controls . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53

8.2 Multivariate regression relating performance (Q) to ownership concentration, insider ownership, secu-rity design, and controls . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54

8.3 Multivariate regression relating performance (Q) to ownership concentration, insider ownership, finan-cial policy, and controls . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55

9.1 Multivariate regression relating performance (Q) to ownership concentration, insider holdings, ownertype (identity of largest owner), board characteristics, security design, financial policy, and controls . . 57

9.2 Multivariate regression relating performance (Q) to ownership concentration, insider holdings, ownertype (aggregate holding per type) , board characteristics, security design, financial policy, and controls 58

9.3 Multivariate regression relating performance (Q) to ownership concentration, insider holdings, ownertype (aggregate holding per type) , board characteristics, security design, financial policy, and controls 61

LIST OF TABLES 221

10.1 Summary of the single–equation regressions for governance mechanism endogeneity, using the aggregateholding per type as owner identity proxy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 66

10.2 Summary of the single–equation regressions for governance mechanism endogeneity, using the type ofthe largest investor as owner type proxy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68

10.3 Estimating the determinants of the largest owner type using a multinomial logit model . . . . . . . . 70

10.4 Interactions between governance mechanisms modeled as a system of equations. Concentration andinsider holdings are endogeneous variables. Stock volatility and board size are used as instruments . . 74

10.5 Interactions between governance mechanisms modeled as a system of equations. Concentration andinsider holdings are endogeneous variables. Stock turnover and board size are used as instruments . . 76

10.6 Interactions between governance mechanisms modeled as a system of equations. Concentration andinsider holdings are endogeneous variables. Intercorporate investments and financial leverage are usedas instruments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 78

11.1 Summary of estimations of the simultaneous determinants of economic performance (Q), ownershipconcentration, and insider holdings, using three alternative sets of instruments. Only the two gover-nance mechanisms enter the system endogeneously. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82

11.2 Summary of estimations of the simultaneous determinants of economic performance (Q), ownershipconcentration, and insider holdings, using three alternative sets of instruments. . . . . . . . . . . . . . 84

B.1 Summary of univariate regressions, voting rights. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 110

B.2 Multivariate regression relating performance (Q) to ownership concentration and controls, followingDemsetz and Lehn (1985) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 118

B.3 Multivariate regression relating performance (Q) to ownership concentration and controls, using thepiecewise 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, usingthe 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 con-trols . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 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) andcontrols . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 125

B.11 Multivariate regression relating performance (Q) to insider ownership and controls, following Morcket al. (1988) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 126

B.12 Multivariate regression relating performance (Q) to insider ownership, ownership concentration andcontrols, using the piecewise linear function of Morck et al. (1988). . . . . . . . . . . . . . . . . . . . . 127

B.13 Multivariate regression relating performance (Q) to insider ownership and controls, following Mc-Connell and Servaes (1990) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 128

B.14 Multivariate regression relating performance (Q) to insider ownership, ownership concentration andcontrols, following McConnell and Servaes (1990) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 129

B.15 Multivariate regression relating performance (Q) to insider ownership, ownership concentration, insti-tutional 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 thepiecewise linear formulation of Morck et al. (1988) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 132

B.18 Multivariate regression relating performance (Q) to insider ownership, without controls, using thequadratic specifiction of McConnell and Servaes (1990) . . . . . . . . . . . . . . . . . . . . . . . . . . . 132

B.19 Multivariate regression relating performance (RoA5) to insider ownership, without controls, using thepiecewise linear formulation of Morck et al. (1988) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 133

B.20 Multivariate regression relating performance (RoA5) to insider ownership, without controls, using thequadratic specifiction of McConnell and Servaes (1990) . . . . . . . . . . . . . . . . . . . . . . . . . . . 134

B.21 Multivariate regression relating performance (RoA5) to insider ownership and controls, following Morcket al. (1988) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 135

222 LIST OF TABLES

B.22 Multivariate regression relating performance (RoA5) to insider ownership, ownership concentrationand controls, using the piecewise linear function of Morck et al. (1988). . . . . . . . . . . . . . . . . . . 136

B.23 Multivariate regression relating performance (RoA5) to insider ownership, following McConnell andServaes (1990) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 137

B.24 Multivariate regression relating performance (RoA5) to insider ownership, ownership concentrationand controls, following McConnell and Servaes (1990) . . . . . . . . . . . . . . . . . . . . . . . . . . . 138

B.25 Multivariate regression relating performance (RoA5) to insider ownership, ownership concentration,institutional ownership and controls, following McConnell and Servaes (1990) . . . . . . . . . . . . . . 139

B.26 Multivariate regression relating performance (Q) to insider (all) ownership and controls, followingMorck et al. (1988) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 141

B.27 Multivariate regression relating performance (Q) to insider (all) ownership and controls, following Mc-Connell and Servaes (1990) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 142

B.28 Multivariate regression relating performance (Q) to insider (board) ownership and controls, followingMorck et al. (1988) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 143

B.29 Multivariate regression relating performance (Q) to insider (board) ownership and controls, follow-ing McConnell and Servaes (1990) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 144

B.30 Multivariate regression relating performance (Q) to insider (management) ownership and controls,following Morck et al. (1988) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 145

B.31 Multivariate regression relating performance (Q) to insider (management) ownership and controls,following McConnell and Servaes (1990) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 146

B.32 Multivariate regression relating performance (Q) to insider ownership, outside (external) concentrationand controls, following Morck et al. (1988) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 148

B.33 Multivariate regression relating performance (Q) to insider ownership, outside (external) concentrationand controls, following McConnell and Servaes (1990) . . . . . . . . . . . . . . . . . . . . . . . . . . . 149

B.34 Multivariate regression relating performance (Q) to ownership concentration, insider ownership, ag-gregate holdings per owner type, and controls . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 151

B.35 Multivariate regression relating performance (Q) to ownership concentration, insider ownership, ag-gregate intercorporate holdings, and controls . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 152

B.36 Multivariate regression relating performance (Q) to ownership concentration, insider ownership, largestowner identity, and controls . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 153

B.37 Multivariate regression relating performance (Q) to ownership concentration, insider ownership, largestowner being listed, and controls . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 154

B.38 Multivariate regression relating performance (RoA5) to ownership concentration, insider ownership,aggregate holdings per owner type, and controls . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 155

B.39 Multivariate regression relating performance (RoA5) to ownership concentration, insider ownership,aggregate intercorporate holdings, and controls . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 156

B.40 Multivariate regression relating performance (RoA5) to ownership concentration, insider ownership,largest owner identity, and controls . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 157

B.41 Multivariate regression relating performance (Q) to ownership concentration, insider ownership, boardsize, and controls . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 158

B.42 Multivariate regression relating performance (Q) to ownership concentration, insider ownership, secu-rity design, and controls . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 159

B.43 Multivariate regression relating performance (Q) to ownership concentration, insider ownership, finan-cial policy and controls . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 160

B.44 Multivariate regression relating performance (RoA5) to ownership concentration, insider ownership,board size, and controls . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 161

B.45 Multivariate regression relating performance (RoA5) to ownership concentration, insider ownership,security design, and controls . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 162

B.46 Multivariate regression relating performance (RoA5) to ownership concentration, insider ownership,financial policy and controls . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 163

B.47 Multivariate regression relating performance (Q) to ownership concentration, insider ownership, ownertype (largest owner), board characteristics, security design, financial policy, and controls (full multi-variate model) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 165

B.48 Multivariate regression relating performance (Q) to ownership concentration, insider ownership, ownertype (aggregate holdings), board characteristics, security design, financial policy, and controls (fullmultivariate model) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 166

LIST OF TABLES 223

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 multi-variate model) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 167

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) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 168

B.51 Multivariate regression relating performance (RoA) to ownership concentration, insider ownership,owner type (largest owner), board characteristics, security design, financial policy, and controls (fullmultivariate model) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 169

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) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 170

B.53 Multivariate regression relating performance (RoS5) to ownership concentration, insider ownership,owner type (largest owner), board characteristics, security design, financial policy, and controls (fullmultivariate model) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 171

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) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 172

B.55 Multivariate regression relating performance (RoS) to ownership concentration, insider ownership,owner type (largest owner), board characteristics, security design, financial policy, and controls (fullmultivariate model) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 173

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) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 174

B.57 Multivariate regression relating performance (Q) to ownership concentration, insider ownership, aggre-gate intercorporate ownership by listed firms, board characteristics, security design, financial policy,and controls (full multivariate model) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 175

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 (fullmultivariate model) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 176

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) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 177

B.60 Multivariate regression relating performance (Q) to (outside) ownership concentration, insider own-ership, aggregate holdings by owner type, board characteristics, security design, financial policy, andcontrols (full multivariate model) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 178

B.61 Multivariate regression relating performance (Q) to ownership concentration (voting rights), insiderownership, the type of the largest owner, board characteristics, security design, financial policy, andcontrols (full multivariate model) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 179

B.62 Multivariate regression relating performance (Q) to ownership concentration (voting rights), insiderownership, type of owner, board characteristics, security design, financial policy, and controls (fullmultivariate model) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 180

B.63 Multivariate regression relating concentration (Herfindahl index) to other mechanisms and controls,using aggregate ownership per type as owner identity proxy . . . . . . . . . . . . . . . . . . . . . . . . 181

B.64 Multivariate regression relating primary insider holdings to other mechanisms and controls, usingaggregate ownership per type as owner identity proxy . . . . . . . . . . . . . . . . . . . . . . . . . . . 182

B.65 Multivariate regression relating aggregate state holdings to other mechanisms and controls, usingaggregate ownership per type as owner identity proxy . . . . . . . . . . . . . . . . . . . . . . . . . . . 182

B.66 Multivariate regression relating aggregate international holdings to other mechanisms and controls,using aggregate ownership per type as owner identity proxy . . . . . . . . . . . . . . . . . . . . . . . . 183

B.67 Multivariate regression relating aggregate individual holdings to other mechanisms and controls, usingaggregate ownership per type as owner identity proxy . . . . . . . . . . . . . . . . . . . . . . . . . . . 183

B.68 Multivariate regression relating aggregate financial holdings to other mechanisms and controls, usingaggregate ownership per type as owner identity proxy . . . . . . . . . . . . . . . . . . . . . . . . . . . 184

B.69 Multivariate regression relating aggregate nonfinancial holdings to other mechanisms and controls,using aggregate ownership per type as owner identity proxy . . . . . . . . . . . . . . . . . . . . . . . . 184

B.70 Multivariate regression relating board size to other mechanisms and controls, using aggregate ownershipper type as owner identity proxy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 185

224 LIST OF TABLES

B.71 Multivariate regression relating fraction voting shares to other mechanisms and controls, using aggre-gate ownership per type as owner identity proxy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 185

B.72 Multivariate regression relating debt to assets to other mechanisms and controls, using aggregateownership per type as owner identity proxy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 186

B.73 Multivariate regression relating dividends to earnings to other mechanisms and controls, using aggre-gate ownership per type as owner identity proxy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 186

B.74 Multivariate regression relating concentration (Herfindahl index) to other mechanisms and controls,using type of largest owner as owner type proxy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 187

B.75 Multivariate regression relating primary insider holdings to other mechanisms and controls, using typeof largest owner as owner type proxy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 187

B.76 Multivariate regression relating board size to other mechanisms and controls, using type of largestowner as owner type proxy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 188

B.77 Multivariate regression relating fraction voting to other mechanisms and controls, using type of largestowner as owner type proxy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 188

B.78 Multivariate regression relating debt to assets to other mechanisms and controls, using type of largestowner as owner type proxy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 189

B.79 Multivariate regression relating dividends to earnings to other mechanisms and controls, using type oflargest owner as owner type proxy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 189

B.80 Multivariate regression relating outside concentration (Largest outside owner) to other mechanismsand controls . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 190

B.81 Multivariate regression relating primary insider holdings to other mechanisms and controls, usinglargest outside owner as concentration measure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 191

B.82 Interactions between governance mechanisms modeled as system of equations. Concentration andinsider holdings are endogenous variables. Controls only as additional explanatory variables. Boardsize and stock volatility are instruments. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 192

B.83 Interactions between governance mechanisms modeled as system of equations. Concentration andinsider holdings are endogenous variables. Controls only as additional explanatory variables. Boardsize and stock turnover are instruments. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 193

B.84 Interactions between governance mechanisms modelled as system of equations. Concentration andinsider holdings are endogenous variables. Controls only as additional explanatory variables. Aggregateintercorporate holdings and debt to assets are instruments. . . . . . . . . . . . . . . . . . . . . . . . . 194

B.85 Interactions between governance mechanisms modeled as system of equations. Concentration andinsider holdings are endogeneous variables. Adjusting for overlap between insiders and large owners.Board size and stock volatility are instruments. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 195

B.86 Interactions between governance mechanisms modeled as system of equations. Concentration andinsider holdings are endogeneous variables. Adjusting for overlap between insiders and large owners.Board size and stock turnover are instruments. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 196

B.87 Interactions between governance mechanisms modelled as system of equations. Concentration andinsider holdings are endogeneous variables. Adjusting for overlap between insiders and large owners.Aggregate intercorporate holdings and debt to assets are instruments. . . . . . . . . . . . . . . . . . . 197

B.88 Simultaneous systems estimation of the determinants of ownership concentration, insider holdings,and economic performance (Q). The two endogeneous governance mechanisms are independent ofperformance. Stock volatility and board size are instruments (Model (I) in summary table 11.1) . . . . 199

B.89 Simultaneous systems estimation of the determinants of ownership concentration, insider holdings,and economic performance (Q). The two endogeneous governance mechanisms are independent ofperformance. Stock turnover and board size are instruments (Model (II) in summary table 11.1) . . . 200

B.90 Simultaneous systems estimation of the determinants of ownership concentration, insider holdings,and economic performance (Q). The two endogeneous governance mechanisms are independent ofperformance. Debt to assets and intercorporate investments and are instruments (Model (III) insummary table 11.1) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 201

B.91 Simultaneous systems estimation of the determinants of ownership concentration, insider holdings,and economic performance (Q). The two endogeneous governance mechanisms are independent ofperformance. Concentration and insider holdings are endogeneous variables. Board size and stockvolatility are instruments. Only controls as additional explanatory variables. . . . . . . . . . . . . . . 202

B.92 Simultaneous systems estimation of the determinants of ownership concentration, insider holdings,and economic performance (Q). The two endogeneous governance mechanisms are independent ofperformance. Concentration and insider holdings are endogeneous variables. Board size and stockturnover are instruments. Only controls as additional explanatory variables. . . . . . . . . . . . . . . . 203

LIST OF TABLES 225

B.93 Simultaneous systems estimation of the determinants of ownership concentration, insider holdings,and economic performance (Q). The two endogeneous governance mechanisms are independent ofperformance. Concentration and insider holdings are endogeneous variables. Aggregate intercorporateshareholdings and debt to assets are instruments. Only controls as additional explanatory variables. . 204

B.94 Simultaneous systems estimation of the determinants of ownership concentration, insider holdings,and economic performance (Q). The two endogeneous governance mechanisms are independent ofperformance. Concentration and insider holdings are endogeneous variables. Adjusting for overlapbetween insiders and large owners. Board size and stock volatility are instruments. . . . . . . . . . . 206

B.95 Simultaneous systems estimation of the determinants of ownership concentration, insider holdings,and economic performance (Q). The two endogeneous governance mechanisms are independent ofperformance. Concentration and insider holdings are endogeneous variables. Adjusting for overlapbetween insiders and large owners. Board size and stock turnover are instruments. . . . . . . . . . . . 207

B.96 Simultaneous systems estimation of the determinants of ownership concentration, insider holdings,and economic performance (Q). The two endogeneous governance mechanisms are independent ofperformance. Concentration and insider holdings are endogeneous variables. Adjusting for overlapbetween insiders and large owners. Aggregate intercorporate shareholdings and debt to assets areinstruments. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 208

B.97 Simultaneous systems estimation of the determinants of ownership concentration, insider holdings, andeconomic performance (Q). Board size, stock beta and stock volatility are instruments. (Model (A) intable 11.1) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 210

B.98 Simultaneous systems estimation of the determinants of economic performance (Q), ownership con-centration, and insider holdings. Board size, stock beta and stock turnover are instruments. (Model(B) in table 11.1) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 211

B.99 Simultaneous systems estimation of the determinants of economic performance (Q), ownership con-centration, and insider holdings. Debt to assets, intercorporate shareholdings and stock beta areinstruments. (Model (C) in table 11.1) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 212

B.100Simultaneous systems estimation of the determinants of ownership concentration, insider holdings, andeconomic performance (Q). Controls only as additional explanatory variables. Board size, stock betaand stock volatility are instruments. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 213

B.101Simultaneous systems estimation of the determinants of ownership concentration, insider holdings,and economic performance (Q). Controls only as additional explanatory variables. Board size, stockturnover and stock volatility are instruments. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 214

B.102Simultaneous systems estimation of the determinants of ownership concentration, insider holdings, andeconomic performance (Q). Controls only as additional explanatory variables. Aggregate intercorpo-rate holdings, debt to assets and stock beta are instruments. . . . . . . . . . . . . . . . . . . . . . . . 215

B.103Simultaneous systems estimation of the determinants of ownership concentration, insider holdings, andeconomic performance (Q). Adjusting for overlap between insiders and large owners. Board size, stockbeta and stock volatility are instruments. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 217

B.104Simultaneous systems estimation of the determinants of ownership concentration, insider holdings, andeconomic performance (Q). Adjusting for overlap between insiders and large owners. Board size, stockturnover and stock volatility are instruments. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 218

B.105Simultaneous systems estimation of the determinants of ownership concentration, insider holdings,and economic performance (Q). Adjusting for overlap between insiders and large owners. Aggregateintercorporate holdings, debt to assets and stock beta are instruments. . . . . . . . . . . . . . . . . . 219

226 LIST OF FIGURES

List of Figures

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) . . . . . . . . . . . . . . . . . . . . . . . . . . 36

5.2 The quadratic relationship between performance (Q) and the holdings of the largest owner . . . . . . 37

6.1 Relating performance (Q) to insider ownership using a piecewise linear function. US data . . . . . . . 406.2 The piecewise linear relationship between performance (Q) and insider ownership in Norwegian firms,

following Morck et al. (1988) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 406.3 The quadratic relationship between performance (Q) and insider ownership in the US . . . . . . . . . 436.4 The quadratic relationship between performance (Q) and insider ownership, following McConnell and

Servaes (1990) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44

B.1 The relationship between performance (RoA5) and insider ownership in Norwegian firms, followingMorck 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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