minority expropriation : study on tunneling in norway › globalassets › forskning ›...

Namhee Matheson Heidi Remmen

BI Norwegian School of Management – Thesis

Minority Expropriation : Study on Tunneling in Norway

Hand-in date:

01.09.2010

Campus: BI Oslo

Exam code and name:

GRA 1900 Master Thesis

Supervisor: Professor Øyvind Bøhren

Program:

Master of Science in Financial Economics Master of Science in Business and Economics

“This thesis is a part of the MSc programme at BI Norwegian School of Management. The school takes no responsibility for the methods used, results found and conclusions drawn.”

Tunneling (GRA 1900 Master Thesis) 01.09.2010

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i. Content

I. CONTENT ........................................................................................................................... II

LIST OF TABLES AND FIGURES .................................................................................................... IV

LIST OF APPENDIX ..................................................................................................................... IV

ABSTRACT .................................................................................................................................. V

1 INTRODUCTION ................................................................................................................. 1

1.1 BACKGROUND ......................................................................................................................... 1

1.2 MOTIVATION AND OBJECTIVES.................................................................................................... 1

1.3 RESEARCH QUESTION ................................................................................................................ 4

1.4 THESIS OUTLINE ...................................................................................................................... 4

2 THE THEORETICAL AND EMPIRICAL RESEARCH .................................................................. 5

2.1 MARKET REACTIONS TO TUNNELING ............................................................................................ 5

2.2 PROXIES FOR TUNNELING........................................................................................................... 6

2.2.1 Separation of control and cash flow rights ............................................................... 6

2.2.2 Ownership structure .................................................................................................. 6

2.2.3 Private and Public firms............................................................................................. 7

2.2.4 Legal proxies.............................................................................................................. 8

2.3 DIFFERENT FORMS OF TUNNELING ............................................................................................. 10

3 RESEARCH HYPOTHESIS AND METHODOLOGY .................................................................12

3.1 TUNNELING MECHANISM ........................................................................................................ 12

3.1.1 The Ability................................................................................................................ 12

3.1.2 The Incentive ........................................................................................................... 14

3.1.3 The Discretion .......................................................................................................... 16

3.2 TESTING FOR TUNNELING ........................................................................................................ 17

3.3 DETERMINANT OF TUNNELING .................................................................................................. 21

3.3.1 Divergence of cash flow right .................................................................................. 21

3.3.2 Large Owner’s Insider positions .............................................................................. 22

3.3.3 Second largest shareholder ..................................................................................... 22

3.3.4 Regression ............................................................................................................... 23

3.4 HYPOTHESIS SUMMARY ........................................................................................................... 24

3.4.1 Testing for Tunneling............................................................................................... 24

3.4.2 Determinant of Tunneling ....................................................................................... 24

4 DATA ................................................................................................................................25

4.1 DATA DESCRIPTION ................................................................................................................ 25

4.1.1 Data Source ............................................................................................................. 25


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4.1.2 Population and Filtering process ............................................................................. 25

4.1.3 Family group identification ..................................................................................... 26

4.2 VARIABLE DESCRIPTION ........................................................................................................... 28

4.2.1 Dependent variable ................................................................................................. 28

4.2.2 Independent variable .............................................................................................. 28

4.2.3 Control variables ..................................................................................................... 29

4.3 DESCRIPTIVE STATISTICS .......................................................................................................... 30

5 RESULT AND ANALYSIS .....................................................................................................36

5.1 REGRESSION RESULT: DOES TUNNELING EXIST? ............................................................................ 36

5.2 REGRESSION RESULT: DETERMINANT OF TUNNELING .................................................................... 38

5.3 ROBUSTNESS TEST .................................................................................................................. 40

6 CONCLUSION ....................................................................................................................42

APPENDIX ..................................................................................................................................44

REFERENCES ..............................................................................................................................61

ATTACHEMENT: Preliminary thesis report


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List of Tables and Figures

Figure 1: Insider ownership and profitability of Norwegian listed firms .......................................... 2 Figure 2: Ability and Incentive of Tunneling ................................................................................... 13 Figure 3: Ability and Incentive of Tunneling ................................................................................... 14 Figure 4: Family group tunneling example ...................................................................................... 15 Figure 5: Tunneling Flow in family group ........................................................................................ 16 Table 1: Independent variable description ..................................................................................... 24 Table 2: Conversion from Individual Owner into Family ................................................................. 26 Table 3: Family Group ..................................................................................................................... 27 Table 4: Sub sample description ..................................................................................................... 27 Table 5: Industry shock construction .............................................................................................. 28 Table 6: Corporate Finance Descriptive Statistics (1) ..................................................................... 31 Table 7: Corporate Finance Descriptive Statistics (2) ..................................................................... 31 Table 8: Corporate Governance Descriptive Statistics (1) .............................................................. 32 Table 9: Corporate Governance Descriptive Statistics (2) .............................................................. 32 Table 10: Corporate Governance Descriptive Statistics (3) ............................................................ 33 Table 11: Industry Descriptive statistics ......................................................................................... 33 Table 12: Industry distribution of sample ....................................................................................... 34 Table 13: Regression result –Testing for Tunneling ........................................................................ 36 Table 14: Regression result - Determinant of Tunneling ................................................................ 39 Table 15 : Regression result comparison to Stand Alone ............................................................... 39 Table 16 : Inter corporate investment ............................................................................................ 40 Table 17 : Regression result Robustness test .................................................................................. 41

List of Appendix Appendix 1: Tunneling Example –Aker Solution Case ..................................................................... 44 Appendix 2 : Family group identification process ........................................................................... 45 Appendix 3 : Definition of variable ................................................................................................. 46 Appendix 4: Corporate finance Descriptive statistics ..................................................................... 47 Appendix 5: Corporate governance Descriptive statistics .............................................................. 52 Appendix 6: Industry descriptive statistics - Industry Frequency ................................................... 57 Appendix 7: Industry descriptive statistics-Industry ROA ............................................................... 58 Appendix 8 : Industry descriptive statistics-Industry Asset ............................................................ 59 Appendix 9 : Industry descriptive statistics-Industry Sale .............................................................. 60


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Abstract

In this paper we empirically examine to what extent minority expropriation is

present among family firms in the Norwegian economy. In particular we

investigate a specific type of expropriation known as ‘Tunneling’: controlling

families’ transferring resources from companies where they have few cash flow

rights to ones where they have more cash flow rights. To investigate whether this

phenomenon prevails in the Norwegian economy, we use a general empirical

technique for measuring tunneling developed by Bertrand, Mehta and

Mullainathan (2002) . Based on cross sectional data for 2003, the results suggest a

significant degree of tunneling between firms controlled by common family

owner. The results also suggest that more tunneling prevails the greater the cash

flow rights between two firms diverge and the fewer shares the largest second

shareholder holds.

*Acknowledgement

We would like to thank our supervisor Professor Øyvind Bøhren for invaluable support and

feedback on our thesis as well as patience, guidance and understanding in and for our thesis

process. We would also thank Professor Bøhren for introducing us to the field of Corporate

Governance and awakening our interest and knowledge in this particular area of study. We would

also like to thank the Centre for Corporate Governance Research for providing us with the

necessary data.


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1 Introduction

1.1 Background

One of the biggest media scandals in 2009 was a transaction that occurred

between Aker ASA and Aker Solution. The majority owner of Aker ASA had sold

several firms to Aker Solution, a deal seemingly unfavorable to the minority

owners of Aker Solution. The state, as the biggest minority owner, argued that the

price was well above market value and that the minority shareholders were hurt by

the deal. Investigations of the transactions concluded that the majority owner was

within his legal rights but that the ethical aspects of the deal were questionable.

The Aker Solution case is just one of the many cases where majority owners

perform self-dealing actions that provide private benefits for the majority owner at

the expense of the minority shareholders1 . Conflicts between majority and

minority owners are one of the major themes within the field of Corporate

Governance. According to Shleifer and Vishny (1997:1), corporate governance

deals with the ways in which suppliers of finance to corporations assure

themselves of getting a return on their investment2.

Along with increasing public attention to corporate governance, research on

corporate governance has attempted to answer more fundamental questions “Is

economic value of the firm driven by governance mechanism?” and “If so, what

factors in corporate governance affects the economic value of the firm and how”?

1.2 Motivation and objectives

The theoretical foundation of Corporate Governance is the Agency Cost Theory.

Agency costs are caused by conflicting interests between the firm’s stakeholders.

These conflicts arise because stakeholders, with deviating interests, don’t

internalize the utility and wealth of other stakeholders. The main interest of

corporate governance is to reduce the agency costs and to ultimately avoid value

destructions caused by the agency costs (Jensen et al. 1976; Shleifer and Vishny

1997; Becht, Bolton, and Röell 2002; Tirole 2001).

1 See appendix 1 for relating article by BI Professor Øyvind Bøhren. Another famous International example is the Enron scandal where Thomas (2002) argue that some of the losses sustained by shareholders were as a direct result of related party transactions. 2 There are several definitions available. However the definition by Shleifer and Vishny is most consistent with purpose of our research.


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Villalonga and Amit (2006) decompose the overall agency problem into the first

and second agency problem.3 The first agency problem deals with the traditional

conflicts between outside shareholders and managers, whereas the second agency

problem deals with the conflict between majority and minority stockholders. The

second agency problem arises when a shareholder has majority control over the

firm’s assets and hence has the ability to pursue own interests at the expense of

the minority shareholders. As expressed by Shleifer and Vishny (1997:758):

“Large investors may represent their own interest, which need not coincide with

the interest of other investors in the firm .“

Figure 1 illustrates how the two types of agency costs affect the profitability of

Norwegian listed firms as insider ownership increases.(Bøhren and Ødegaard

2006). It shows that as insider ownership increases until about 40%, the

profitability increases and then decreases again. The graph can be interpreted as

net effect of first agency cost and second agency cost: that is, as inside ownership

increases up to about 40%, inside owner has incentive to monitor the manager and

thus reduce the first agency cost, leading higher profitability. However as

ownership increases beyond 40%, the second agency problem becomes dominant

and causes lower profitability in the firm. Motivated by empirical evidence and

theoretical prediction, our main interest in this study is the effect of second agency

cost on firms’ profitability.

Figure 1: Insider ownership and profitability of Norwegian listed firms *Profitability measured as Tobin’s Q ratio (total market value of firm to total book asset value). Data is from 1989-1997 *Øyvind Bøhren and Bernt Arne Ødegaard. 2006. Governance and performance revisited. I International Corporate Governance after Sarbanes-Oxley 3 Even though we make the distinction between the first and second agency problem there also exists a third agency problem: the potential conflict between owners and creditors. (Shleifer and Vishny 1997) How the majority/minority conflict interlinks with owners/creditors conflicts is mentioned in papers by La Porta et al (2000) , Berkman, Cole and Fu (2007) and Tang (2008).

11,11,21,31,41,51,61,71,8

0 20 40 60 80 100Profi

tabili

ty ( T

obin'

s Q)

Insider Ownership (%)


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There are many ways in which large owners can expropriate minority owners. We

will research on a specific type of expropriation, tunneling, as illustrated in Aker

Solution case. The term ‘Tunneling’ was originally coined to characterize

expropriation of minority shareholders in the Czech Republic. According to

Johnson et al. (2000) and Glaeser, Johnson and Shleifer. (2001) , the expropriation

of minority shareholders through the stripping of the firm’s assets was so common

that it required a new term: ‘tunneling’. The assets disappeared from the firm as if

removed through a hidden tunnel.

Johnson et al. (2000) page 22: defines tunneling as the “transfer of resources out

of a company to its controlling shareholder (who is typically also a top

manager)”. This definition has been adopted in the majority of tunneling

research. This definition is not without weaknesses. Transfer of resources can in

its widest interpretation include dividend, loans, salaries, as well as engaging in

self-dealing transactions at non-market terms. Since it’s a very wide definition;

the interpretation and hence the use of the concept throughout the literature spans

in a variety of directions. Johnsons’ definition also lacks a crucial component of

tunneling; that tunneling expropriates the minority shareholder.

We define tunneling in our paper as:

The transfer of profit or resources by controlling shareholders from companies

where they have few cash flow rights to ones where they have more cash flow

rights through 4related party transactions, which in effect leads to expropriation

of the minority shareholder(Bertrand, Mehta, and Mullainathan 2002)

In our research we will study tunneling in the Norwegian economy. Norwegian

data on non-listed and listed firms extracted from the CCGR database are made

available from the Department of Financial Economics. By Norwegian law all

limited liability firms have to publish an audited annual report. In addition the

company must “publish the identity of its CEO and its directors, and the fraction

of equity held by every owner.” (Berzins, Bøhren, and Rydland 2008:1). Most

other nations do not have these kinds of disclosure requirements for private firms.

4 Transactions between the company and another entity, where one of its’ shareholders/board members/management has ownership stakes, are referred to as “connected transactions” or “related party transactions”.


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The CCGR data base not only contains accounting data, corporate governance

data, but also data on kinship and marriage. The unique data material gives us an

exceptional position to conduct our research on tunneling.

1.3 Research question

To get knowledge about tunneling in the Norwegian economy, we will investigate

following two research questions:

1. To what degree does tunneling exist in Norwegian firm?

2. What are the major determinants for this phenomenon?

With these research questions we wish to investigate how widespread and serious

the minority-majority conflict is within the Norwegian Economy. By investigating

different firm properties and external surroundings we hope to gain more

knowledge about determinants for tunneling.

1.4 Thesis Outline

Chapter 2 introduces the major theoretical and empirical research that is related to

tunneling. Chapter 3 suggest research hypothesis relevant to answering this papers

two research questions. A description of the tunneling mechanism is presented to

give the reader a more systematic orientation to understanding the concept of

tunneling. Based on the understanding of the tunneling mechanism, testing

implications and hypothesis are presented alongside a regression model. Chapter

4 introduces the filtering and the sub sample construction process and illustrate

the observation from descriptive statistics. Chapter 5 provides result of regression

models and includes a discussion of the implications of the previously introduced

hypothesis. A robustness test of the base case is also presented at the end of this

chapter. In conclusion, Chapter 6, summaries this papers main findings and

discuss the limitation with suggestion for future research.


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2 The theoretical and empirical research

The purpose of this chapter is to investigate the existing research conducted on

tunneling and presenting theoretical and empirical insights for our research. The

existing literature aims at identifying tunneling and its impact on firm

performance and firm valuation. The literature on tunneling can be divided into

two subsections:

1. The strand that examines market reactions when a publicly listed firm

announce different types of related party transactions.

2. The strand that measures the degree of minority expropriation indirectly

through the use of proxies for the degree of expropriation.

The sections below give a summary of the two strands of research. The Chapter

concludes with a presentation of the different ways of conducting tunneling

2.1 Market reactions to tunneling

Most of the existing research conducted on tunneling has been done on publicly

traded companies. The main focus of this strand of research is to investigate 1) to

what degree valuation of listed firm is affected by related transaction, and 2)

whether investor takes the effect of related transaction into account ex-ante. By

observing market reactions to announced related party transactions, one can

estimate the impact of such a transaction by investigating movements in the

market value. This assumes that investors are able to predict the implications of

the related party transaction and value the firm accordingly (Cheung, Rau, and

Stouraitis 2006).

It has been argued that the market will demand a discount ex-ante of firms which

they think are more likely to undertake related party transactions. In such a

manner they pay a “fair” price for their stocks given the risk of tunneling. An

empirical study by Cheung et al (2006) did not find evidence that the market

anticipated expropriation ex-ante. Instead they did find that after the

announcement of a related party transaction the company could have a negative

abnormal stock returns up to 12 months later.


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2.2 Proxies for tunneling

Since the different forms of tunneling often is “hidden” in the firm’s regular

operations research on tunneling mainly take use of proxies as indicators of

tunneling. The most common proxies for tunneling are:

1. The use deviation of cash flow from control rights

2. The use of ownership structure

3. Listing status

4. The use of legal system

2.2.1 Separation of control and cash flow rights

This strand of theory and research tries to explain how minority expropriation

takes place using different forms of ownership structure mechanisms that separate

control rights from cash flow rights. The larger the gap between control rights and

cash flow rights the greater is the incentive to expropriate. (Grossman and Hart

1988; la Porta et al. 1998; Claessens et al. 2002) These studies argue that this

distinction can lead to lower shareholder value.

Bebchuk et.al (2000) lists three mechanisms that entrench minority control and

enable expropriation of minority shareholders:, pyramids, dual class shares and

cross-holdings. For Western European countries, a study conducted by Faccio and

Lang(2002) reported that both dual-class shares and pyramids are commonly used.

Yet, a study conducted by La Porta, Lopez-de-Silanes and Shleifer (1999) finds

little use of these mechanisms for Norwegian listed firms. The main rule in

Norway is that every share is granted similar rights in the company, the so called 5one-vote-one-share principle.

2.2.2 Ownership structure

The literature on ownership structure as a proxy for tunneling deals with the

ownership structure in the firm such as the size and distribution of shareholders

and ownership types that is present in the firm. Concentrated ownership is

normally associated with family ownership. When a family has ownership control,

they most of the time also keep insider positions (Claessens, Djankov, and Lang 5 However, the firm can in its articles of incorporation implement dual class shares according to asl § 4-1. Based on media coverage, it seems that dual class shares in private firms are used as a way for the founder to keep control, while at the same time distribute wealth to his children. However, we have yet to find any research relating to what kind of relationship the owners of the two classes of shares have to one another.


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2000; La Porta, Lopez-de-Silanes, and Shleifer 1999) . Almedia and Wolfenzon

(2006) suggest that diverting corporate resources are more likely to take place in

family business groups. Cronquist and Nilsson (2003) supports this view by

arguing that when families are involved in the management of affiliated firms they

have larger discretion of manipulation of corporate wealth.

2.2.3 Private and Public firms

Most 6 of the prior research on tunneling has been conducted on publicly listed

firms. Reasons for this can be that the effect of transactions (tunneling) on public

firms easier can be observed in the company’s market values. Public firms are

also generally subject to stronger transparency requirements of enclosing

company information to the market. This is problematic in a sense that private

firms are actually the dominant form of firm in the economy. The exclusion of

unlisted firms creates bias in terms of ownership structure and valuation. It also

leads to considerable underestimates, as unlisted firms can have direct and indirect

ownership in listed corporations. This can lead to a possible underreporting of the

measures for ultimate ownership and control. (Claessens et al. 2002)

Literature and theories on corporate governance and corporate finance suggest

that private firms (as opposed to the listed firms) generally have a) higher

ownership concentration (which is presumably major source of second agency

problem) and b) are less transparent in conducting transactions, which might have

same effect as low legal protection (Shleifer and Vishny 1997; Berzins, Bøhren,

and Rydland 2008; McConnell, Servaes, and Lins 2008).

Since literally (almost) all research on tunneling have been conducted on listed

firms, we have to question whether the knowledge gained from research on listed

companies can be applicable on unlisted firms. According to Berzins and Bøhren

(2009) the answer is most likely no. They argue that unlisted firms operate under

different external conditions than listed firms. They list the main external

conditions as: financial market, transparency and regulations.

6 Some studies have been conducted on private firms. Gutierrez and Tribo (2008) examines how multiple large shareholders share control and extract private benefits in closely-held corporations in unlisted Spanish firms. Cheung et al.(2008) incorporates tunneling transactions between listed firms and non-listed subsidiaries in their research on connected transactions in China.


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2.2.4 Legal proxies

The majority shareholders opportunity and incentive to conduct tunneling is

expected to be affected by the laws protecting the minority shareholders and the

quality of the enforcement of these laws. Several studies have indicated that there

is a great deal of tunnelling in countries with weak legal systems. (Johnson et al.

2000; Friedman, Johnson, and Mitton 2003; La Porta et al. 1998; La Porta et al.

2000)

To protect the minority shareholder from tunneling, the Norwegian legal system

includes several laws that impose restriction on transactions that can be of conflict

of interests between a particular shareholder, board member or management and

the company. The Norwegian Limited Liability Companies Act, “Aksjeloven”

(asl), and the Norwegian Public Limited Liability Companies Act

‘Allmennaksjeloven’ (asal), are the main laws regulating behavior related to

connected transactions. In addition the Norwegian Accounting Law,

“Regnskapsloven” (rskl), states disclosure and accounting requirements in relation

to connected transactions.

The main paragraphs constructed to restrict minority expropriation are:

o asl. §6-27(1) and asal §6-27(1)

o “ A member of the board of directors may not participate in the

discussion or decision of issues which are of such importance to the

board member in question, or to any connected person 7 of said board

member that the board member must be regarded as having a major

personal or financial special interest in the matter. The same shall

apply for the general manager”(Norge 2009:44)

o asl. § 6-28(1) and asal §6-28(1)

o “The board of directors or other parties who represent the company

[…] must not take any action that confer on certain shareholders or

other parties on unfair advantage at the expense of other shareholders

or the company” (Norge 2009:44)

7 The law considers connected persons to be persons the shareholder has family relations to such as spouse, children, parents and siblings. The parents and siblings (and their spouses) of the shareholder’s spouse are also considered connected persons (Norge 2009).


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Basically, this means that a board member, representing a shareholder, is not

allowed to partake in decisions where the shareholder or the shareholder’s close

family members could have personal interests. This is resolved by having the

board member “leaving the boardroom” when such decisions are discussed.

Some transactions are considered too big to be approved by the board alone.

Transactions which surpass 10 % (AS) or 5% (ASA) of the firms share capital

also require approval from the general meeting to be binding for the company

(Norge 2009:asl/asal §3-8(1)).

Not all decisions involving related party transactions require the same strict

impartiality. The recommendation on the law of the Norwegian Limited Liability

Companies Act states that one should put less weight on conflict of interest when

the company is conducting regular business transactions8. The argument is that a

strict regulation of related party transactions might be out-weighted by the

efficiency needs of the firm (Haagensen and Lie 2004). The efficiency transaction

hypothesis argues that related party transaction can be beneficial to the firm since

they reduces transactions cost, uncertainty and mitigate hold-up problems (Stein

1997; Ryngaert and Thomas 2007).

Even though these regulations are designed to protect the company and its

minority shareholders, they are far from water-tight. The indirect influence of a

partial board member on the other board members can color the boards’ decision.

The notion of what is a “normal business activity” and what is “normal price” are

also very interpretive, as illustrated by the Aker Solution case.

Due to the waste grey area that the majority shareholder can operate under it

should be noted that the high legal costs of contesting such a transaction often can

lead the minority shareholder either to accept the transaction or to exit the firm

instead of confronting. If the minority owner in a listed firm wishes to exit, he

can simply sell the stock. In non-listed firms, he has virtually no easy exit

strategy. In unlisted firms, the minority owner has according to asl §§ 4-24 ,4-25

8The Law defined a regular business transaction as : “ Agreements entered into as part of the normal activities of the company and which are based on a price and other terms and conditions which are normal for such a transaction”(Norge 2009:asl/asal §3-8(1))


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the right to demand to be bought out if there exists a serious and enduring clash of

interests between the shareholders regarding the operations of the company. This

operation requires however both that the minority shareholder institutes legal

proceedings, and that the company conducts a general assembly before the

minority shareholder can exit the company.

The law creates warnings signs and exit strategies for the minority shareholders

when a large shareholder passes certain ownership stakes in the firm. For listed

firms the Norwegian Securities Act (“Verdipapirhandelloven” abbreviated to

“vphl”) makes shareholder obliged to flag ownership when they pass certain

ownership stakes (vphl 4-2). They are also required to make a compulsory bid for

all outstanding shares when they pass 1/3 9of all outstanding votes in the firm

(vphl §6-1). The same requirement for bid happens when the ownership stake

passes 40 % and 50 % (vphl §6-6.).

2.3 Different forms of tunneling

How tunneling is conducted and its impact on the majority owner depends on the

resource being tunneled. Johnson et al (2000) separates tunneling into two forms: 1) the transfer of resources from the firm through self-dealing transactions, and 2)

financial transactions that discriminate against the minorities.

Transfer of resources through self-dealing transactions can include outright theft

and fraud. However, most transactions that are considered tunneling are not

criminal offences but related party transactions where the majority participant

gains at the expense of the minority.

Atanasov, Black and Ciccotello (2008) expands Johnsons et al’s framework by

dividing the first form of tunnelling into two categories: Cash flow tunnelling and

Asset tunnelling. They rename tunnelling through financial transactions as equity

tunnelling. Whether or not a resource transfer falls into the category of Cash flow

tunneling or Asset tunneling depends on how the transaction affects the firm’s

future cash generating capacity.

9 This percentage was lowered from 40% in the 1997 law to 1/3 in the 2007 law.


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Cash flow tunneling removes a portion of the current years’ cash flow (profit), but

it does not significantly affect the firm’s future cash generating capacity. Cash

flow tunneling hence does not directly affect the balance sheet. Examples of cash

flow tunneling are the sale of outputs to an intermediary controlled by insiders for

below-market prices; or purchase of inputs at above-market prices. The purchase

of services by related party transactions also falls within this category. Excessive

executive salaries or perquisites and small-scale sales or purchases of assets also

fall within this category.

Asset tunneling involves the transfer of long-term (tangible or intangible) assets

from or to the firm. They are distinct from cash-flow tunneling because the scale

of the transfer has a permanent effect on the firm's future cash-generating capacity.

Examples of asset tunneling include overpriced/underpriced purchases/sales of

assets, or investments in an affiliated firm on better terms than the affiliate could

obtain on its own.

Equity tunneling increases the controller's share of the firm's value, at the expense

of minority shareholders, but does not directly change the firm’s productive assets.

Examples of equity tunneling are dilutive offerings, freeze-outs of minority

shareholders, loans to insiders (which will not be paid in bad states of the world),

equity-based incentive compensation which exceeds market level and insider

trading.


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3 Research Hypothesis and Methodology

Chapter 3 presents the research hypothesis used to answer the two research

question; 1) does tunneling exist in Norwegian firm? , and 2) what are the major

determinants for this phenomenon? The chapter begins with a description of the

tunneling mechanism to give the reader a systematic understanding of the

tunneling concept. Based on the understanding of tunneling mechanism, we move

on to the testing implication of the first research question, followed by our

hypothesis and regression models. Chapter 3 concludes with a hypothesis

summary and the regression model presented in this chapter.

3.1 Tunneling Mechanism

Having put the various theories in Chapter 2 together, we argue that tunneling is

likely to occur when a combination of three factors prevails. These three factors

are: (1) the large shareholder has sufficient control to expropriate minority

shareholder, (2) the large shareholder has the incentive to tunnel ,that is tunneling

has positive effect on his net worth , and lastly (3) the firm’s environment

provides discretion to make easily hide tunneling. These three factors are the

foundation of the hypothesis and will be referred to as (1) the ability, (2) the

incentive, and (3) the discretion in same order. An explanation of each factor is

presented below.

3.1.1 The Ability

To understand first two factors (the ability and the incentive) conceptually,

consider two firms A and B owned by common shareholder (denoted as ‘Large

Owner’ for the following). In addition assume ‘Large Owner’ has 10ultimate

ownership share (or cash flow right) of X% and Y% respectively in firm A and B.

Also consider the conditions when large owner tunnels 11F amount of resource

from Firm A to B as described in Figure 1.

10 To measure cash flow fraction, we will use ultimate cash flow right (hereafter simply referred to as cash flow right) so that all the possible structures such as pyramid and cross holdings can transform to simple form as figure above. Voting right, cash flow right will be interchangeable in our case. 11 F can be any form described in 2.3. Different forms of Tunneling.

Tunneling (GRA 1900 Master Thesis)

Figure 2: Ability and Incentive of Tunneling

In order to make the tunneling feasible,

owner’ need to have

expropriation is initiated by

firm for his own interest

be either in the form

informal control, which may

or boad seat. We will

the following hypothesis.

For formal cont

definition for the following test

define ‘sufficient control

majority greater than 50%

the following two reasons.

and thus always entails control in the firm

useful to control the effect of first agency problem

which shows that firm’s per

reduction of first agency problem

more than 50%, we can

confusing with

logic descibed

in our study as formal


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: Ability and Incentive of Tunneling

n order to make the tunneling feasible, it is easy to und

’ need to have ‘sufficient control’ in Firm A. This is because

expropriation is initiated by the large shareholder, who can in fact influence the

firm for his own interest. Here the meaning of sufficent contro

be either in the form of formal control given by voting right

nformal control, which may magnify the control right, such as insider ownership

or boad seat. We will further describe how informal control affects on tunneling in

the following hypothesis.

For formal control or voting right, it is of importance to clarify and limit the

definition for the following test because there are various thresholds used to

sufficient control’. Among the various thereshold

majority greater than 50% voting right as a lower bound of the control because of

two reasons. First absolute majority is absolute threshold for

thus always entails control in the firm. Secondly and more importantly it is

to control the effect of first agency problem in light of empirical evidence,

shows that firm’s performance increases up to about 40

reduction of first agency problem (Bøhren and Ødegaard 200

more than 50%, we can therefore test the effect of second agency problem without

the first agency effect. Reflecting on the empirical evidences and

above, we chose ownership concentration greater than

in our study as formal control to ensure the ability of tunneling.

01.09.2010

t is easy to understand that ‘ Large

. This is because the

the large shareholder, who can in fact influence the

Here the meaning of sufficent control is generic. It might

control given by voting right or in the form of

, such as insider ownership

control affects on tunneling in

to clarify and limit the

there are various thresholds used to

various theresholds, we chose absolute

as a lower bound of the control because of

absolute threshold for control

and more importantly it is

light of empirical evidence,

formance increases up to about 40 % due to the

(Bøhren and Ødegaard 2006). By choosing

test the effect of second agency problem without

empirical evidences and

greater than 50% range

control to ensure the ability of tunneling.


3.1.2 The Incentive

Even though sufficient control is a necessary condition for tunneling,

necessarily ensure expropriation

reduce value in Firm A deliverately if his or her net worth is expected to be

unaffected ( or even negative for obvious reason).

can infer that ‘ Large

initiated by tunneling is expected to be strictly positive.

As an example, a

CF right 51% and 100% in Firm A and Firm B respectively. If

is able to tunnel this earning to

increased to $1,000 in firm B, relative to $510 if the earnings were kept in firm A

Because of the

of firm A to firm B.

Figure 3: Ability and Incentive of Tunneling

Extending the example to the general case, ‘Large owner’ would

benefit and thus have an incentive to tunnel

that is (� � �� right in Firm B than Firm A.

type as ‘Low CF firm’ and firm B type as ‘ High CF firm’ from now on.

The setting described so far is most simplistic setting

extended into a more realistic setting. So far the owner type of the ‘Large Owner’

has not been explicitly specified as an individual or any other type of owner.

described in Chapter 2,

business groups


Page 14

The Incentive

Even though sufficient control is a necessary condition for tunneling,

necessarily ensure expropriation. ‘ Large owner’ does not have a clear incentive to


unaffected ( or even negative for obvious reason). In view

can infer that ‘ Large owner’ has incentive to tunnel when his

tunneling is expected to be strictly positive.

As an example, assume that Firm A has $ 1,000 earnings and ‘Large owner

% and 100% in Firm A and Firm B respectively. If

is able to tunnel this earning to from Firm A to Firm B, his benefit would

increased to $1,000 in firm B, relative to $510 if the earnings were kept in firm A

net gain the ‘Large owner’ will look for a way to divert them out

of firm A to firm B.

ty and Incentive of Tunneling

Extending the example to the general case, ‘Large owner’ would

and thus have an incentive to tunnel if tunneling generates positive income

� �� 0 ; � � , when the large owner has more cash flow

in Firm B than Firm A. Reflecting this argument, we will refer to firm A


The setting described so far is most simplistic setting of all.


has not been explicitly specified as an individual or any other type of owner.

in Chapter 2, diverting resources are more likely to take pla

s (Almeida and Wolfenzon 2006) and moreover

01.09.2010

Even though sufficient control is a necessary condition for tunneling, it does not

. ‘ Large owner’ does not have a clear incentive to


view of this argument, we

’ has incentive to tunnel when his net worth effect

earnings and ‘Large owner’ has

% and 100% in Firm A and Firm B respectively. If the ‘Large owner’

irm B, his benefit would be

increased to $1,000 in firm B, relative to $510 if the earnings were kept in firm A.

’ will look for a way to divert them out

Extending the example to the general case, ‘Large owner’ would realize ex post

if tunneling generates positive income

ge owner has more cash flow

Reflecting this argument, we will refer to firm A


. The argument is now


has not been explicitly specified as an individual or any other type of owner. As

diverting resources are more likely to take place in family

moreover family firm is the


most dominant firm type in the Norwegian economy

Rydland 2008). In light of

not individual owner.

A more realistic ownership structure and hence tunneling relationship can also

involve group structures as well as bilateral firm relationship.

as a group owns 4

65%, 75% and 90

in simple setting, tunneling predicts that ‘Large owner’ has incentive to divert

between any combinations of two firms in the group as

between two firms are positive

Figure 4: Family group tunneling example

For example (figure

have incentive to tunnel to all

his cash flow right in firm D is lowest of them all. Likewise

from firm C to firm B and

can infer that the firm with highest cash flow right in the group is

recipient in tunneling relationship. By the same logic, the firm with lowest cash

flow right in the group is only expropriated.

(lowest) cash flow

distinguish from other type of firms in the group (Mid CF firm).

12 Group of personal owners associated based on kinship and marriage.


Page 15

most dominant firm type in the Norwegian economy (Berzins, Bøhren, and

. In light of these arguments, our unit of analysis will be

not individual owner.


involve group structures as well as bilateral firm relationship.

as a group owns 4 firms and let their sum of ultimate ownership

d 90% respectively as described in figure 3. Based on the argument


between any combinations of two firms in the group as long

between two firms are positive.

: Family group tunneling example

For example (figure 4) for the perspective of firm D, the ‘Large Owner’ would

have incentive to tunnel to all the three other firms (A,B,C)

his cash flow right in firm D is lowest of them all. Likewise

C to firm B and A, and from firm B to firm A. From this example, we

can infer that the firm with highest cash flow right in the group is


flow right in the group is only expropriated. The firm in the group with

est) cash flow rights will be referred to as Max (Min) CF firm in order to

h from other type of firms in the group (Mid CF firm).

roup of personal owners associated based on kinship and marriage.

01.09.2010

(Berzins, Bøhren, and

these arguments, our unit of analysis will be family12


involve group structures as well as bilateral firm relationship. Consider family X

firms and let their sum of ultimate ownership in each firm, 55%,

Based on the argument


long as cash flow wedge

the ‘Large Owner’ would

(A,B,C) in the group because

his cash flow right in firm D is lowest of them all. Likewise he would tunnel out

from firm B to firm A. From this example, we

can infer that the firm with highest cash flow right in the group is the only


The firm in the group with highest

as Max (Min) CF firm in order to

h from other type of firms in the group (Mid CF firm).


Figure 5: Tunneling Flow in family group

As minimum cash flow

internalize more due to increase in loss compared to benefit. Therefore b

certain ownership point it is

decreases. Reflecting this argument

flow right in minimum cash flow firm and assume that beyond this point

tunneling does not prevail in

To gather up two factors

above, tunneling is

1. A family owns more than two firms

ownership

2. The maximum ownership in the

minimum ownership.

3. The minimum ownership in the group exceeds 80%.

From this point

will be referred to

selection criteria

group) which will

3.1.3 The Discretion

Although the combination of

tunnel could be perceived as creating conditions that could increase the risk of

tunneling, theory predicts that

would also have to take into consideration the cost of


Page 16

: Tunneling Flow in family group

minimum cash flow right in the group increases, the large owner is expected to

internalize more due to increase in loss compared to benefit. Therefore b

certain ownership point it is expected that the expropriation sign

Reflecting this argument, we selected a cut-off

flow right in minimum cash flow firm and assume that beyond this point

tunneling does not prevail in the group.

To gather up two factors (ability and incentive) and generalize the arguments

tunneling is likely to occur when:

family owns more than two firms which it has ultimate sum of

ownership greater than 50%.

maximum ownership in the group is strictly greater than the

minimum ownership.

The minimum ownership in the group exceeds 80%.

From this point, a group of firms that fulfills the three conditions mentioned above

will be referred to family group and thus call three conditions as

selection criteria. The three criteria will be used to find the sub sample (

will be discussed in Chapter 4.

Discretion

Although the combination of the incentive and the ability


theory predicts that these condition are not yet sufficient.

would also have to take into consideration the cost of someone else finding out

01.09.2010

increases, the large owner is expected to

internalize more due to increase in loss compared to benefit. Therefore beyond

the expropriation significantly

point of 80% of cash

flow right in minimum cash flow firm and assume that beyond this point, the

and generalize the arguments

which it has ultimate sum of

group is strictly greater than the

The minimum ownership in the group exceeds 80%.

, a group of firms that fulfills the three conditions mentioned above

conditions as family group

the sub sample (family

for ‘ Large owner’ to


t yet sufficient. ‘Large owner’

someone else finding out


Page 17

about the tunneling transaction. In other word, the possibility of tunneling is

intrinsically related to discretion, discretion that others cannot easily restrict

(Dyck and Zingales 2004). Such discretion for ‘Large owner’ to conduct

tunneling depends on the firm environment.

3.2 Testing for Tunneling

Tunneling is likely to occur in subtle and hard-to-detect ways by large

shareholder’s intention (Bertrand, Mehta, and Mullainathan 2002). Due to this

reason, quantifying the extent of tunneling is proven to be a difficult task. This

means that we can only use indirect measures. The only way to measure tunneling

is to investigate an observable effect expected to be the result of tunneling. Rooted

in corporate governance research, our fundamental ground is that corporate

governance mechanism to some extent affects firm’s performance. Corporate

governance variables are expected to affect the potential and degree of tunneling

which again manifests itself in the firm’s performance. To be more precise, it is

expected that ‘Recipient firm’ shows higher performance and ‘Tunneled firm’

shows lower performance relative to the performance that would be expected in

the absence of tunneling.

If one can 13measure correctly the performance in the absence of tunneling for

each firm or ‘the fundamental earning’, then tunneling is simply measured as the

difference between the fundamental earning and the observable earning (earning

diversion for the following): positive for the recipient in a tunneling relationship

and negative for the tunneled firms. Another prediction of tunneling is such that

the negative abnormal earning of tunneled firms causes the positive abnormal

earning of recipient. This can be verified by testing the causal relationship

between abnormal earnings of two group firms.

To combine these arguments, tunneling predicts that when earning diversion is

measured as the difference of observable earning from fundamental earning the

following statements will be observed:

13 It is unrealistic but the purpose of this part is conceptual understanding. We will discuss more possible testing implication later on. For the following discussion we first would like to say that our model is extensively based on the general model for quantifying tunneling suggested by Bertrand, Mehta, and Mullainathan (2002) (denoted as BMM model for the following).


Page 18

1. Positive earning diversion for recipient firms as a group as oppose to

negative earning diversion for tunneled firms as a group.

2. Causal relationship: negative earning diversion for tunneled firm is cause

of resulting positive earning diversion for recipient firm.

While conceptually appealing, the problem for empirical testing arises in a sense

that we cannot measure the fundamental earning perfectly. It is in fact proven as

difficult as measuring diversion. Consequently, one needs to use best proxy to the

fundamental earning. BMM model suggest that one of the good candidates is the

industry movement to which each firm belongs to. A firm’s industry affiliation is

expected to influence the individual firm’s earning to a large extent because

different industries operate under different industry specific conditions and

general economic conditions. For example will industry specific regulations,

macro conditions and competition affect a firm’s earning, and hence the firms in

the same industry will be exposed to similar condition on their earnings. The

BMM model suggests that industry movement is not actual fundamental earning,

but more likely to be a major exposure for the individual firm. The BMM model

calls industry movements as ‘shock’ to the firm’s performance level. Consistent

with the use of term in BMM model, we will also use the term ‘shock’ referring to

industry performance.

The important point to take into account is that this proxy works for aggregate

industry level base not individual firm. For instance individual firm’s performance

can deviate from the industry average performance for numerous reasons.

Observations of significantly low (or higher) performance for the group of

‘Tunneled firm’ (or ‘Recipient firm’), after incorporating control variables, can be

interpreted as the diversion, caused by some systematic factor suspected to be

tunneling. Suppose that the world price of gold rises, causing the gold industry’s

profits to rise on average. In other words, if the rise in gold prices increases profits

in comparable firms by $100, then one can assume that if the reported earning is

$90, then $10, on average, has been diverted away in simple setting. As a measure

of industry performance, we will use industry median ROA (return on asset) and

ROA for firm level performance.


Page 19

To make comparison, family group defined in previous section is first identified

before Max CF firm and Min CF firm are identified among the family groups. In

addition, the sample contains firms where family owners don’t have ownership

stakes in any additional firms. These firms are referred to as ‘Stand_alone’. Our

primary method will be to compare the difference between sub sample groups:

Stand_alone, family group, Max CF firms, and Mid CF firms. If there are

significant differences between groups in the direction expected, we will conclude

that the data support our hypothesis.

With the proxy for fundamental earning and observed earning at hand and

construction of sub groups, the next step is to design the empirical model and

describe the testing hypothesis for our first research question ‘Does tunneling

exist?’

First it is expected that ‘family group’ is less sensitive to industry shock than

‘Stand_alone’ firms. This is because tunneling predicts that earning is to some

degree lost during the transaction between firms in the group while stand alone

firms have no such influence. Consequently ‘Group firm’ is expected to be less

sensitive to industry shock. Therefore our hypothesis states:

Hypothesis A 1): ‘Family group’ is expected to be less sensitive to its own

shock than ‘Stand_alone’.

Let �� be a dummy variable for whether firm i is in a group or not. To test

this hypothesis, the following regression is estimated:

�� = + � �� _�ℎ�� + � �� ∗ �� _�ℎ�� + � �� (1)

, where Controls are other variables that might affect firm performance and

Own_Shock is industry shock measured as industry median. The coefficient b

indicates how sensitive firms are, in general, to industry shock. The interaction

term asks whether group firms are differentially sensitive to industry performance.

If they are less sensitive, as tunnelling would predict, then c should be negative.


Page 20

The first regression tests for reduced sensitivity of ‘family group’ relative to

‘Stand-alone’. We now turn to testing whether, among ‘family group’, ‘Min CF

firm’ show less sensitivity than average group firm and ‘Max CF firm’ is more

sensitive to own shock than average group. Recall the example in figure 2 and 3.

We conclude that Max CF firm is only recipient while other can be somewhat

expropriated. Hence Max CF firm is expected to be more sensitive to its own

shock compared to other in the group. Likewise Min CF firm is only expropriated

and thus less sensitive than average to own shock. Therefore we suggest:

Hypothesis A 2): ‘Max CF firm’ is expected to be more sensitive to its own

shock than the firms in the same group and‘Min CF firm’ is expected to be

less sensitive to its own shock than the firms in the same group.

Let �_�� ( �_�� ) be a dummy variable for whether firm i is in ‘Max CF firm’

(‘Min CF firm’) group or not. We then estimate the following regression for the

sample of ‘family group’ only.

�� = + � �� + � �� ∗ �� +� �_�� ∗ �� _�ℎ�� + � �� (2)

As before, the interaction term measures differential sensitivity. If ‘Max CF firm’

is more sensitive than average, we would expect c to be positive. Likewise if ‘Min

CF firm’ is more sensitive than average, we would expect d to be negative.

The most critical part of test is to verify whether diversion from fundamental

earning for tunnelling pair has causal relationship. For testing we assume that

‘Max CF firm’ ROA responds to industry shock of firms in the group as a whole.

We will refer the average of industry shock of the firms in the group other than

itself as group shock. We would also expect that ‘Min CF firm’ does not respond

to group shock in the group to confirm not only correlation but also the causal

relationship. Thus we suggest that:

Hypothesis A 3): ‘Max CF firm’ is expected to be positively sensitive to

group shock while ‘Min CF firm’ is expected to be insensitive to group

shock.


Page 21

Let �_�� ( �_�� ) be a dummy variable for whether firm i is in ‘Max CF firm’

(‘Min CF firm’) group or not and ��_�ℎ�� as group shock as defined. We

then estimate the following regression for the sample of ‘family group’ only like

the previous test.

�� = + ��_�ℎ�� + � � ∗ ��_�ℎ�� + � �_�� ∗ ��_�ℎ��+ � �_�� ∗ ��_�ℎ�� + � �� (3)

It is worth noting that we control for the firm’s own shock. This control means

that we do not confuse an overlap of industry between firms in the group with the

flow of tunneling. A significant and positive coefficient d would suggest that

‘Max CF firm’ is in fact sensitive to industry shock of other firms in the group. A

further prediction of tunneling is that ‘Min CF firm’ is insensitive to group shock.

If this prediction is true, we would expect c to be insignificant.

3.3 Determinant of tunneling

In this section we suggest several hypotheses to answer our second research

question ‘What are the major determinants for tunneling?

3.3.1 Divergence of cash flow right

As explained in tunneling mechanism, the divergence of cash flow right fraction

in the two firms is main incentive for tunneling. By the same logic we can infer

that the incentive is greater when the divergence increases. This is because the

large shareholder loses relatively less than the gain he achieves from tunneling.

For example in figure 2 and 3, we expect that minimum cash flow firm is more

expropriated than firm B while both firms are expected to be tunneled.

Hence we expect that:

Hypothesis B 1): The incentive of tunneling increases as divergence

increases between the cash flow rights in a firm in the family group and

maximum cash flow firm in the same group.


Page 22

3.3.2 Large Owner’s Insider positions

The previous hypothesis describes how ‘Large owner’s incentive to tunnel

changes. This section describes how the ability side of ‘Large owner’ affects

tunneling. As discussed, ‘sufficient control’ to make tunneling occur can be either

formal control or informal control. We argue that informal control can also affect

on tunneling through the effect on ‘Large owner’s ability to tunnel. One of the

most widely discussed informal controls is insider ownership. Morck et al. (1988)

argue that powerful insiders may entrench themselves and expropriate wealth

from outside owners. Insider positions give discretion over day-to-day business

which particularly increases the risk of cash flow tunneling. Legally, this can be

linked to the relaxation in the regulation of related party transactions concerning

regular business operations. We will examine how insider ownership of the

largest family owner affects on tunneling in the case where family has CEO or

board seats.

Therefore,

Hypothesis B 2): When the large shareholder holds insider positions, greater

degree of tunneling is expected.

3.3.3 Second largest shareholder

The existence of a minority shareholder with a significant ownership stake can

function as a prevention mechanism of tunneling. If the majority shareholder is

monitored it will reduce his or her incentives and/or ability to conduct

expropriation. By the same logic as the tunneling argument, a second

shareholder’s incentive to monitor also depends on the cost-to-benefit ratio he or

she is facing. If a minority shareholder holds a marginal stake in the firm it’s

assumed that the costs of monitoring will be too high to ensure monitoring

relative to the gains. (Jensen and Meckling 1976)(Demsetz and Lehn 1985).

Consequently Pagano and Röell (1998) predict that the size of the equity stake of

the second largest shareholder affects this shareholders incentive to monitor the

majority shareholder, hence affecting the degree of tunneling. While empirical

research suggests the positive effect of size of the equity stake of the second

shareholder, the legal framework indicates minimum share required to contest the

largest shareholder (according to Bloch and Hege (2001) contestability). If a


Page 23

shareholder or a group of shareholder holds more than 10 % of the voting shares

in a limited liability company (5% in a listed company) they can:

− demand an extraordinary general meeting (asl § 5-6)

− demand an inquire regarding specific aspects of the management or the

account (asl § 5-25)

− bring a claim for damages against the management or other owners (asl §

17-4)

Since our unit of analysis is family not individual, we define the second largest

shareholder as a personal owner who is outside of largest family and has largest

share among non-family shareholders. Based on the arguments above we expect

that the ownership share of the second largest shareholder affects the largest

shareholder opportunity to expropriate. Therefore,

Hypothesis B 3): The existence of a large non-controlling family member

shareholder works as a corporate governance mechanisms. Thus reduction in

the ownership share of second largest shareholder increases the possibilities of

tunneling.

3.3.4 Regression

For the hypothesis related to the determinants of tunneling, we will estimate the

following regression. Let �� be the independent variable representing each

hypothesis and the rest of variables consistent with the previous regression

equations. We then estimate the following regression for the sample of ‘family

group’.

�� = + � �� _�ℎ�� + � �� ∗ �� _�ℎ�� + � �� (4)

A significant coefficient c would suggest that the independent variable actually

influence the sensitivity of its own shock. The expected signs of each independent

variable are presented in table 1.


Page 24

Table 1: Independent variable description

Hypothesis Factor Description Abbreviation Expected Sign

B 1 Cash flow wedge Wedge of cash flow right between

respective firm and maximum cash flow firm in the same family group

CF -

B 2

Share of the second largest shareholder

The share of the second largest shareholder(largest personal owner outsider of the controlling family)

Second +

B3 Insider position

CEO Controlling family has CEO. CEO - Board Seat

Controlling family has chair. Board -

3.4 Hypothesis summary

3.4.1 Testing for Tunneling

Hypothesis A 1): ‘Family group’ is expected to be less sensitive to its own

shock than ‘Stand_alone’.

Hypothesis A 2): ‘Max CF firm’ is expected to be more sensitive to its own

shock than the firms in the same group , and ‘Min CF firm’ is expected to be less

sensitive to its own shock than the firms in the same group.

Hypothesis A 3): ‘Max CF firm’ is expected to be positively sensitive to group

shock while ‘Min CF firm’ is expected to be insensitive to group shock.

3.4.2 Determinant of Tunneling

Hypothesis B 1): The incentive of tunneling increases as divergence

increases between the cash flow rights in a firm in the family group and

maximum cash flow firm in the same group.

Hypothesis B 2): When the large shareholder holds insider positions, greater

degree of tunneling is expected.

Hypothesis B 3): The existence of large non-controlling family member

shareholder works as a corporate governance mechanisms. Thus a reduction in

ownership share of second shareholder increases the possibilities of tunneling.


Page 25

4 Data

4.1 Data Description

4.1.1 Data Source

In this thesis we use data from the CCGR database. The database provides

accounting, corporate governance data and ownership data (containing ownership

ID and share in each company) for all Norwegian limited liability firms. With this

data we will do cross sectional data analysis for year 2003. We selected this year

as it appear as the most “neutral year”, avoiding the effects of 9/11 (2001), the tax

reform of 2005 (Norge 2004) ,and any affects associated with the current financial

crisis.

4.1.2 Population and Filtering process

The CCGR database for accounting, corporate governance variable contains a

sample of 155,996 firms in 2003. For a firm to be able to tunnel the firms have to

be active. Hence we exclude non operating firms that 1) do not have any asset

(total asset) and 2) operating income in 2003. We also exclude financial firms

because they are subject to strict regulations which reduce the risk of tunneling.

Since it is critical for our test to use the industry performance, we exclude the

firms with missing industry code. Having filtered non-operating firms and

financial / missing industry firm we are left with 127,306 firms. For ownership

database, the CCGR database in 2003 does not provides ownership ID for all the

reported firms contained in accounting/ corporate governance data: the number of

observed firms in ownership database is 132,670 firms. Hence we further filtered

the firm which miss the ownership data and obtained 111,539 firms. We refer to

this sample as overall economy sample. From the overall economy sample the

sample in our interest is the firms in which largest family sum of ownership

(item_15302 in the database) is strictly greater than 50%. We observed 71,832

firms which meet this criterion as opposed to 39,707 firms for the rest. Further we

exclude 1,897 firms 14we could not find the largest family owner identification.

As a result we get 69,935firms. This is our testing sample and for the following

we refer it as family firms.

14 See the description in Appendix 2: Family group identification process.


Page 26

4.1.3 Family group identification

As described in chapter 3 briefly, we need first to construct sub samples from

family firm: ‘family group’ and ‘Stand_alone’. To identify any links by common

family owner, family identification and its ownership stake in each firm is critical

information for testing. However we could not get the family membership data

regarding which owners construct family groups due to the data restriction. Since

this information is key information for this study, we used indirect measures to

estimate the match between ownership ID and family group membership15. As a

result, we classified individual owner of 80,801 observed in family firm into

53,064 families with its own family ID.

The table 2 shows number of family members in the family we assigned and the

corresponding number of families. The figures below should be used with caution:

they should not be interpreted as the entire family membership since we consider

only family firm sample to construct family membership. The majority of family

firms are composed of only one person, 67% of sample families. Families with

two to seven family members constitute the majority of the remaining sample

while families with more than seven members constitute just few cases.

Table 2: Conversion from Individual Owner into Family

Once family membership is defined, we identify the largest family owner for each

firm (for detail see Appendix 2). Then we grouped the firms according to common

largest family owner. Table 3 presents the overview of the result. We define the

15 See the description in Appendix 2: Family group identification process.

Number of Family Members

Number of Families

% of Total Family

Number of Personal Owners

% of Total Owner

1 35,507 66.9% 35,507 43.9% 2 10,976 20.7% 21,952 27.2% 3 4,050 7.6% 12,150 15.0% 4 1,793 3.4% 7,172 8.9% 5 542 1.0% 2,710 3.4% 6 127 0.2% 762 0.9%

7 to 14 69 0.1% 548 0.7% Total 53,064 100,0% 80,801 100,0%


Page 27

firm which one family owns only one firm as Stand_alone. The rest of firm we

define as Group firm as opposed to Stand_alone. Among Group firm, we identify

Family group according to ‘family group selection criteria’, described in page 16.

For example if we observe Group firm where all the firms in the group have same

cash flow right owned by the largest family ,or minimum cash flow right in the

group is greater than 80%, we exclude them from the Family group sample. As a

result the sample is reduced from 26,821 firms in Group firm to 8,879 firms in

Family group. Among Family group, we group the maximum cash flow firm as

Max CF firm and minimum cash flow firm as Min CF firm. The observations for

Max CF firm and Mid CF firm are 2,821 firms and 2,778 firms respectively. Table

4 summaries the sub sample selection and corresponding sample size. For the test

we will use four sub samples: Stand_alone, family group, Max CF, and Min CF.

Table 3: Family Group #of Family

Member 1 2 3 4 5 6 7 to 14 Total % of

Total # of Firm

Owned 1

Stand_alone 30,633 8,653 2,499 1,022 243 46 18 43,114 62% 2 7,062 3,206 1,804 844 316 60 24 13,316 19% 3 2,367 1,290 1,023 519 195 60 42 5,496 8% 4 1,048 564 576 332 104 60 28 2,712 4% 5 555 300 350 210 60 30 15 1,520 2% 6 390 198 180 138 60 24 30 1,020 1%

Over 7 1,210 474 480 204 272 53 64 2,757 4% Group Firm 12,632 6,032 4,413 2,247 1,007 287 203 26,821 38%

Total 43,265 14,685 6,912 3,269 1,250 333 221 69,935 100%

Table 4: Sub sample description

Sample Name Description Observation Filtered population Applied the filters described in page 31 111 539 1. Family Firm Largest family ultimate sum ownership >50% 69 935 2. Stand Alone 1+ Family owns only one firm. 8 879 3. Group Firm 1+ Family owns several firms. 26 821 4. Family Group 3+ Apply Family group selection criteria (p.21) 8 879 5. Max CF 4+ Maximum cash flow firm in the family group 2 821 6. Min CF 4+ Minimum cash flow firm in the family group 2 778


Page 28

4.2 16Variable description

4.2.1 Dependent variable

There are many different measure of performance. In our research most of firms

are un-listed firms, which indicate that the commonly used market based

performance measures such as Tobin’s Q ratio and ROE cannot be used.

Consequently we will use accounting based measure: return on assets (ROA) as

our measure of performance. ROA is extracted from CCGR database and defined

as earning before interest after tax (EBI) divided by total asset.

4.2.2 Independent variable

To measure industry shock, we used median ROA of the industry to which each

firms belong based on NAIC code. The industry median was constructed in family

firm sample as a whole because this is the reference performance of each industry

in the economy. This variable is referred to as ‘Own_Shock’. To measure group

shock, we measured the average of ‘Own_Shock’ of firms in the group except

itself. For example, consider the family group described in Table 5. Since each

firm has its industry code, ‘Own_Shock’ can be easily indentified. Once

‘Own_Shock’ is identified for all the firms, we then compute the average of

‘Own_Shock’ for firm B to firm D as ‘Group_Shock’ for firm A ,and firm A to

firm C as ‘Group_Shock’ for firm D. It is worth nothing that we exclude its own

shock from computing group shock because group shock and own shock is the

distinct independent variables in the regression test.

Table 5: Industry shock construction

Firm Name

Family Share

Industry (Example)

Own shock Group shock

A 55% Service Median ROA of Service Industry

Average of Own_Shock of firm B ,C and D

B 60% Transport Median ROA of Transport Industry

Average of Own_Shock of firm A ,C and D

C 75% Manufacturing Median ROA of Manufacturing Industry

Average of Own_Shock of firm A, B and D

D 90% Construction Median ROA of Construction Industry

Average of Own_Shock of firm A, B and C

16 See the Appendix 3 for details on how each variable is constructed from the data source.


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For hypothesis B 1), CF wedge (‘CF’) is defined as difference between shares in

respective firm’s largest family share and family share of maximum CF firm in

the same group.

We will measure hypothesis B 2) insider positions as two dummy variable ‘CEO’

and ‘Board’. ‘CEO’ variable takes the value of 1 if controlling family has CEO

and 0 otherwise. Likewise ‘Board’ variable takes the value of 1 if controlling

family has board seat.

In our study, the second largest shareholder is defined as the personal owner who

is not a member of the largest family in a given firm and has the highest share

among the non family owners. For hypothesis B 3) we simply measure the

ultimate share of the second largest shareholder in a given firm (‘Second’). While

the other variables were readily available from initial source, this variable is found

by matching ownership data and largest family. For example if the firm has five

personal owners and three of them are the members of controlling family, we then

know that rest two owners are outside of controlling family. If among two outside

owners one has 10% share and 5% share, the former is defined as the second

largest shareholder and his or her share as Second variable.

4.2.3 Control variables

To control for other firm characteristics that can influence the dependent variable

the following control variables is introduced in our regression: firm size (‘Size’),

firm age (‘Age’), leverage (‘Leverage’), firm growth (‘Growth’).

There is a general consensus that firm size affects firm performance. Large firms

have a tendency to be the most successful businesses because expansion (increase

in size) is often a result of profitability. Two proxies are used as indicators of firm

size: total asset and operating revenue. To measure we take log value of total

assets (‘Size (asset)’) and operating revenue (‘Size (sale)’).

The age of a firm is expected to influence the firm’s performance in two ways.

Older firms are expected to have higher ROA because older firms normally have

more sales and hence higher profits. Age might also be linked to performance due

to a self-selection bias: older firms might be presents simply because they are


Page 30

successful (Schulze et al. 2001) . Older firms also generally have depreciated their

assets more than younger firms leading to higher ROA. To control this effect on

ROA, we measure the age of a firm as log of age (‘Age’).

Leverage can both have a positive and negative impact on the firm’s performance.

Jensen (1986) argues that debt will reduce the firms’ potential agency costs by

lowering the available free cash flow in the firm. Leverage can also make

managers more efficient since they must meet debt repayments (Stiglitz 1985).

McConnell and Servaes (1995) argue that the same debt repayments hinder the

managers to invest in profitable investments because of constraints (e.g.,

covenants) associated with the debt. Leverage is expected to correlate negatively

with ROA since more profitable firms can finance more from earnings. We define

leverage as total debt divided by total asset (‘Leverage’).

Firm value may be related to the firm’s investment opportunities. High growth

firms tend to be more profitable than low growth firms (Maury 2006; Cooper,

Gulen, and Schill 2008). We use the proxies for growth as sales growth

( �� : ‘g_Sale’).

4.3 Descriptive statistics

Appendix 4 presents summary statistics of key corporate finance variables for

each sub group (Panel A: Family firm, Panel B: stand-alone firm, Panel C: Family

Group, Panel D: Min CF firm, and Panel E: Max CF firm). Appendix 5 reports

statistics for the corporate governance characteristics. Table 6 to 10 presents

histograms of median values of key variable in order to make the comparison

between sub groups easier.

Table 6 shows that family group tends to be larger than stand alone. A median

family group recorded total asset of 3,6 million NOK in 2003 and sales of 3,0

million NOK. In contrast median stand alone firms recorded total asset of 1,2

million NOK and sales of 1,8 million NOK. On the other hand we did not observe

noticeable size difference in term asset among sub samples of the family group. In

terms of sales the median Max CF firm recorded almost half of sales of the

median Min CF firm.


Page 31

Table 6: Corporate Finance Descriptive Statistics (1)

*A : Total Asset, S: Sales, g_S: growth of sales from 2002 to 2003, D : Total debt and D to A : Leverage ratio

defined as total debt to total asset (for description of variables, see Appendix 3 and for full statistics, see

Appendix 4) *The value shown is million NOK except g_S and D to A as of 2003 * Missing column

represents value of zero.

Table 7: Corporate Finance Descriptive Statistics (2)

Table 7 presents that the median age and ROA. The median family group firm is

one year older than stand alone, while median Max CF firm is oldest among them

all. We will control for these difference with control variables described

previously. Descriptive statistics for ROA will be further discussed in association

with industry sector descriptive statistics.

0,000,501,001,502,002,503,003,504,00

A S g_S D D to A

Family StandAloneGroup

0,00,51,01,52,02,53,03,54,0

A S g_S D D to A

GroupMin CFMax CF

0,00 2,00 4,00 6,00 8,00 10,00 12,00

ROA

Age Max CFMin CFGroupStandAloneFamily


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Table 8: Corporate Governance Descriptive Statistics (1)

* Missing column represents value of zero. * Second largest share has median value of zero for all the sub

samples except Min CF because sum of largest family ultimate share has median value close to 100 for those.

* CF wedge applies only for family group.

As shown in table 8, the median stand alone firms recorded sum of largest family

ultimate share of 100 as opposed to 90 of the median family group. Consistent

with this the second largest share is lower and CEO holding is higher for the

median stand alone firm compared to the median family group. Among the family

group firms, the difference is driven by the method how we classify Min CF and

Max CF.


As illustrated in Table 9 the median number of family members is one in all five

groups. This is with the observation in table 2 and 3 suggesting that majority of

family group consists of one person. Median family group recorded three owners

compared to two owners in median stand alone firms. We expect that this

tendency is somewhat related to the size difference of two groups.

0,0020,0040,0060,0080,00

100,00120,00

Sum of Largest family

ultimate share

Sum of Largest

family direct share

Second Largest share

CEO Holding CF WEDGE

FamilyStand AloneGroupMin CFMax CF

0,000,501,001,502,002,503,003,50

# Family Owner # Family chair # Owner Board Size



Page 33


*The histogram presents the percentage of firms in which controlling family has 1) CEO, 2) Chair, and 3) CEO and Chair both.

Table 10 presents that it is quite common for family to have CEO or board seat.

The firms in which controlling family has both CEO and board seat are majority:

76%, 49%, 41%, and 65% for stand alone, family group, Min CF, and Max CF

respectively. In contrast the firms with neither family CEO nor family chair are

3%, 12%, 14%, and 4% in same order (see Appendix 5). We expect that group

differences reflect higher controlling family ownership in stand alone and Max CF

firms (median value 100%) compared to family group (90%) and Min CF firm

(65%).

Table 11: Industry Descriptive statistics

• Note: The graph shows median of ROA and asset of three groups. The line chart (primary axis)

represents median ROA of the group for corresponding industry sector in horizontal axis. The

histogram (second axis) represents median asset in million NOK. Industry sectors are 1: Agriculture,

forestry, fishing and mining, 2: Manufacturing and chemical products, 3: Energy, 4: Construction, 5:

Service, 7: Trade, 8: Transport and Total for total sample. We classified the industry sector as

reference of (Berzins, Bøhren, and Rydland 2008)For detail industry analysis based NAIC code, see

appendix.

0102030405060708090

100

Family CEO Family Chair CEO and Chair


0,01,02,03,04,05,06,07,08,09,010,0

0,02,04,06,08,0

10,012,0

Asset Family Firm

Asset Stand-Alone

Asset Group


Page 34

In addition to general descriptive statistics for overall industry, we further present

the descriptive statistics by industry. The purpose is first to get snapshot on how

each group performance is difference from industry norm and second to see

whether any group is particularly concentrated on certain industry. Appendix 6 to

9 presents industry distribution of each sub group, ROA by industry, asset by

industry and sales by industry respectively.

Table 11 and table 12 present the summary from those appendixes. Most

interestingly median ROA of family group is lower than those of stand alone

firms in all industry sectors. In previous section simple description on corporate

finance variables showed that median family group is larger and older than

median stand alone firms. The description on control variable predicts that larger

and older firm tends to have higher performance than smaller and younger firm.

Considering these observations and theoretical predictions, lower median ROA of

family group signals that our hypothesis might fit to the data.

Table 12 suggests that there is no noticeable difference in industry distribution

except industry sector 4: construction and 5: service. Stand alone firms are

relatively more concentrated in the construction sector and less so in the service

sector in comparison to family group.

Table 12: Industry distribution of sample

1 2 3 4 5 7 8 Multi Family 2,12 % 7,55 % 0,13 % 9,69 % 48,34 % 22,85 % 4,50 % 4,16 % StandAlone 2,15 % 7,62 % 0,13 % 11,88 % 44,78 % 23,43 % 4,77 % 4,68 % Group 2,31 % 7,31 % 0,23 % 5,67 % 54,96 % 20,79 % 4,72 % 3,21 % Min CF 2,48 % 8,32 % 0,25 % 7,20 % 51,04 % 21,53 % 4,18 % 4,36 % Max CF 1,95 % 5,42 % 0,25 % 5,28 % 56,86 % 19,74 % 3,90 % 5,81 %

• Note: The table shows the percentage of each industry to total observation in respect group sample.

The industry sector is same as Figure 3 and Multi represents multi industry. Max CF is the

maximum CF firm in family group while Min CF is the minimum CF firm in family group

For the regression analysis, we excluded multi industry firms from the sample

because we cannot compute the own shock and group shock variable without

knowing weight on each industry of multi industry firms. Table 12 shows that

multi industry firms are not populated in particular sub group: overall 3,2% to 5,8%

of sample size of each group. By excluding multi industry and unspecified


Page 35

industry firms, the sample reduced from 8,879 firms to 8,522 firms for stand alone

and from 2,778 (2,821) firm to 2,639(2,635) firms for Min CF (Max CF) group.


Page 36

5 Result and analysis

5.1 Regression result: Does tunneling exist?

First we test the first prediction of tunnelling: whether group firms would be less

sensitive to their own industry shocks than Stand_alone. Column (1) in table 13

displays our basic result. The general sensitivity of firms to industry performance

is, as expected, positive and significant. More importantly the interaction term

which captures the differential sensitivity of group firms is negative as tunnelling

predicts. The result can be interpreted as follows. If industry shock (measured as

industry median which each firm belongs to) increases by one unit, it leads to

about 1.19 unit increase in ROA of stand alone firms. For a group firm, the same

degree of shock leads to 0.67 unit smaller increase in ROA, or only 0.52 unit

sensitivity to one unit industry shock. This suggests that some part of profitability

in a group firm is lost. In short, the data supports the first prediction.

Table 13: Regression result –Testing for Tunneling (1) (2) (3) Own_Shock 1,191 (,450)*** ,437 (,154)*** ,664 (,139)*** Group_Shock - - -,365 (,169)*** Group* Own_Shock -,669(,073)*** - - H_CF* Own_Shock - ,532 (,132)*** - L_CF* Own_Shock - ,019 (,130) - H_CF* Group_Shock - - ,479 (,149)*** L_CF* Group_Shock - - -,040 (,148) Size (Asset) 3,215 (1,738)*** 1,715 (,389)*** 1,734 (,391)*** Size( Sale) 8,417 (1,481)*** 1,417 (,334)*** 1,374 (,334)*** Age 4,139( 2,125)** 3,459 (,560)*** 3,516 (,560)*** Leverage 5,470 (,044)*** -2,349 (,086)*** -2,350 (,086)*** Group* Leverage(a) -8,755 (,190)*** - - g_Sale ,001 (,003) ,000 (,000) ,000 (,000) Constant -171,889 (20,774)*** -48,506 (4,934)*** -46,976 (4,961)*** F 1997,403 *** 131,861*** 116,742*** Adjusted R2 ,244 ,110 ,109 # Observation 49,382 8,522 8,522

1. Own_Shock is industry median of the industry each firm belongs to. Group_Shock is average of Own_Shock of the firms in the same family group except itself. Group is binary variable whose value is 1 for family group firms and 0 for stand alone firms. H_CF (L_CF) is binary variable whose value is 1 for maximum (minimum) cash flow position in family group and 0 for otherwise. Size and Age are log value of observation as of 2003. g_Sale is sales growth measured as ratio of sale in 2003 to sale in 2002. Leverage is total debt to total asset.

2. Standard errors are in parentheses. *** indicates that coefficients estimates are significant at the 1% level according to the student test, ** at 5%, and * at 10% level.

3. (a) Add the variable to control the different coefficient sign for two groups.


Page 37

Regarding control variables: Size and Age, the regression result is consistent with

our prediction that larger and older firms tend to perform better on average.

Leverage variable suggests more interesting result: two groups of firms show

opposite coefficient signs. We have already discussed the conflicting effect of

leverage on firm’s performance and inconclusive net effect. Here the result

suggests that for stand alone firm, positive leverage effect prevails while negative

leverage effect is dominant in family group. In our test, growth measure does not

provide significant effect on ROA. These tendencies are consistent throughout the

rest of the tests.

The second prediction provides a more stringent test: within group firms,

Maximum CF firm should show greater sensitivity than average group firm.

Column (2) in table 5 shows that Maximum CF firm is more sensitive to its own

shock than group firm on average, indicated as positive and significant coefficient

in interaction term (H_CF*Group_Shock). The result implies that one unit

increase in industry shock leads to about 0,43 unit increases in ROA for a group

firm on average. For Maximum CF firm, it leads to 0,53 unit greater increase, or

about 0,96 sensitivity to one unit industry shock in total. Combining the finding in

the first test, the result suggests that Maximum CF firm is only slightly less

sensitive to industry shock than stand alone firm (1,19 sensitivity). The coefficient

on L_CF interaction term indicates that Minimum CF position is indifferent from

other position in the group firm when it comes to its own sensitivity. The result

also suggests that for the rest of the group firms one unit increases in own industry

shock leads 0,4 unit increase in ROA, which is slightly less than group as a whole

(0,67) due to the exclusion of Maximum CF firm. Therefore data supports the

second prediction as well.

Column (3) shows the result of third prediction: Maximum CF firm would be

positively sensitive to group shock. The negative coefficient on group shock

indicates that group firm on average is negatively related to each other’s shock.

More importantly interaction term and group shock coefficient term indicates that

Maximum CF firm is positively associated with group shock: on average 0,1 unit

increase as a response of one unit industry shock in other firm of the group as a

whole. Minimum CF firm shows indifferent sensitivity to group shock, which

confirm the tunnelling flow direction from down to the top of CF hierarchy.


Page 38

5.2 Regression Result: Determinant of Tunneling

The results of first part of the test generally support the prediction of tunnelling.

Base on the empirical support on the existence of tunneling, we move on to the

second part of the test for finding what affects the degree of tunnelling. From

Chapter 4, our hypotheses suggest three factors affecting on the extent of

tunnelling: CF wedge, second largest owner, and insider position of largest family.

Table 14 show both univariate and multivariate test for three independent

variables: column (1) CF wedge, column (2) second largest owner, column (3) / (4)

insider position measured as family CEO / family board seat. The last column (5)

shows the multivariate regression result for three independent variables altogether.

Testing for CF wedge and second largest owner supports our prediction. The

negative interaction term for CF wedge suggests that as more CF right in the

group firm deviates from Maximum CF right in the group, more tunnelling is

prevailed. We can infer that one percent CF wedge change causes 0,005 unit

decreases in its sensitivity to own industry shock. Considering average CF wedge

is about 12% (see Appendix 5: Panel C.), CF wedge on average drives the

sensitivity 0,06unit down for one unit industry shock.

The result for testing second largest owner variable is also consistent with our

hypothesis that as the share of the shareholder outside of largest family increases,

tunneling is less prevailed. The positive coefficient on the interaction term

supports the tunneling prediction: one percent increase in outside shareholder’s

share causes 0,01unit increase in its sensitivity to one unit own industry shock.

Considering average second largest share is about 0,6% in group firm (see

Appendix 5: Panel C.), second largest share on average contributes the sensitivity

increase by 0,11unit for one unit industry shock.

On the other hand insider position does not support our prediction that insider

position increases ability of large family and as a result more tunneling would be

expected when large family has insider position. The result shows that Family

CEO contributes to reduce tunneling while Family board member plays no

significant role. The result might be because the regulations restricting insiders in

conducting related party transactions in Norway is effective. Alternatively it might

be that controlling families have already sufficient control in terms of voting right

and thus whether to have insider position does not particularly make difference.


Page 39

The results in univariate test were all consistent with multivariate test as well. One

noticeable finding is that CF wedge gains more significant and magnitude of the

effect is two times larger than univariate case. Second largest share also shows

slight increase in magnitude.

Table 14: Regression result - Determinant of Tunneling

Table 15 : Regression result comparison to Stand Alone *CF is cash flow wedge between each firm and maximum cash flow firm in the same family group. Second is the share of second largest shareholder. CEO (Board) is binary variable whose value is 1 when family has CEO (Board members) and 0 for otherwise. *Other variables / description are same as in table 10.

To confirm the finding we did similar regression test for Stand alone firm as

shown in Table 15 except for CF wedge. All the test results show no significant

result in sample of stand alone firms. This confirms that our prediction captures

unique variances in group firms.

(1) CF (2) Second (3) CEO (4)Board (5) Multivariate Own_Shock ,675 (,141)*** ,474 (,138)*** ,608(,145)*** ,612(,157)*** ,672 (,178)*** CF *Own_Shock -,005(,003)* - - - -,010 (,004)*** Second* Own_Shock - ,011(,003)** * - - ,016 (,004)*** CEO*Own_Shock - - 5,451(1,049)*** - 5,605 (,1,072)*** Board *Own_Shock - - - -,009(,063)- -,083 (,065) Size (Asset) 1,648(,385)*** 1,881(,387)*** 1,827(,385)*** 1,713(,384)*** 2,005 (,390)*** Size( Sale) 1,309(,334)*** 1,118(,333)*** 1,345(,333)*** 1,234(,331)*** 1,297 (,336)*** Age 3,644(,560)*** 3,849(,558)*** 3,526(,562)*** 3,738(,559)*** 3,558 (,564)*** Leverage -2,355(,086)*** -2,348 (,086)*** -2,349(,086)*** -2,355(,086)*** -2,338 (,086) Growth ,000(,000) ,000(,000) ,012 ,000(,000) ,000(,000) ,000 (,000) Constant -46,33(4,883)*** -47,14(4,889)*** -52,36(5,079)*** -46,21(4,888) -54,46 (5,101)*** F 147,830 *** 149,101 *** 150,670 *** 147,435*** 107,864 *** Adjusted R2 ,108 ,109 ,110 ,108 ,112 # Observation 8,522 8,522 8,522 8,522 8,522

(2)Second (3) CEO (4) Board Own_Shock 1,154 (,539)*** 1,125(,708)*** 1,204 (,521)***,010 CF *Own_Shock - - - Second* Own_Shock ,008 (,017) - - CEO*Own_Shock - ,116(,567) - Board *Own_Shock - - ,295 (,781) Size (Asset) 3,115(2,204) 3,143 (2,207) 3,139 (2,204) Size( Sale) 10,716 (1,844)*** 10,710(1,850)*** 10,771 (1,844)*** Age 3,909(,2,533)* 3,849 (2,529)* 3,815 (2,530)* Leverage 5,472(,047)*** 5,472(,047)*** 5,472 (,047)*** Growth ,008(,017) ,004 (,008) 004 (008) Constant -202,016(24,598)*** -202,439 (24,582)*** -203,140 (24,609)*** F 1,913,802*** 1,913,763*** 1,913,782 Adjusted R2 ,247 ,247 ,247 # Observation 40,860 40,860 40,860


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To summarize, the results gives support for that a large cash flow wedge makes

tunneling more attractive (incentive) and that less contesting by other shareholders

makes tunneling more prominent (discretion), but also that informal control

through insider positions (ability) seems restricted.

5.3 Robustness test

Although the findings in previous section support the predictions of the tunneling

hypothesis, other possible explanations need to be considered. We believe that

alternative explanations that ought to discuss: the dividend earning from the

shares held in each other can be prominent alternative explanation. Hence we did

first part of regression test again considering inter-corporate investment between

group firms.

One might worry that the results merely arise from the possible inter-corporate

investment between the pair of companies resulting in owning shares in each other

(Bertrand, Mehta, and Mullainathan 2002). If this is the case, the sensitivity of

one firm in the pair to the other’s performance would then mechanically arise

through the dividend – income earnings from the shares held. To take into account

this possibility, we first check the group composition which CCGR data provides

(See Appendix 3 for variable description.). Table 16 shows the group identity of

family group sample. It is expected that the cases where maximum CF firm is

parent of the group are what we have to concern. This is because the dividend

payment flow is same direction as tunneling from low cash flow firm ultimately to

maximum CF firm. Hence we found the family group where maximum CF frim is

parent and removed all the family group member firms from the sample in order

to re-test. As table 16 presents, the family group in which Max CF firm is parent

in corporate group is 548 cases. By this process, the sample is reduced to 6,837

firms from initial 8,522 firms.

Table 16 : Inter corporate investment Min CF Max CF Mid CF Group Total Associated 215 35 53 303 Independent 1 461 1 668 822 3 951 JC 12 5 17 Parent 163 548 565 1 276 Subsidiary 788 384 1 803 2 975 Total 2 639 2 635 3 248 8 522


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Table 17 shows the results of regression test for ‘Does tunneling exist?’ with the

reduced sample. The result is robust after applying new sample. The sign and

magnitude of coefficient remains similar to those in base case for all three

regression test.

Table 17 : Regression result Robustness test

(1) (2) (3) Own_Shock 1,200 (,463)*** ,439 (,179)*** ,640 (,163)*** Group_Shock - - -,266 (,199) Group* Own_Shock -,671 (,081)*** - - H_CF* Own_Shock - ,501 (,156)*** - L_CF* Own_Shock - ,079 (,151) - H_CF* Group_Shock - - ,417 (,177)*** L_CF* Group_Shock - - -,026 (,175) Size (Asset) 3,317 (1,818)*** 1,406 (,388)*** 1,376 (,388)*** Size( Sale) 8,746 (1,544)*** 1,875 (,455)*** 1,843 (,458)*** Age 4,160 (2,199)** 3,554 (,648)*** 3,601 (,648)*** Leverage 5,471 (,045)*** -2,441 (,092)*** -2,442 (,092)*** Group* Leverage(a) -8,763 (,194)*** - - g_Sale ,001 (,003) ,000 (,000) ,000 (,000) Constant -177,925 (20,774)*** -50,953(5,751)*** -46,419 (5,775)*** F 1930,196 *** 118,618 *** 104,871*** Adjusted R2 ,244 ,121 ,121 # Observation 47,697 6,837 6,837

1. Own_Shock is industry median of the industry each firm belongs to. Group_Shock is average of Own_Shock of the firms in the same family group except itself. Group is binary variable whose value is 1 for family group firms and 0 for stand alone firms. H_CF (L_CF) is binary variable whose value is 1 for maximum (minimum) cash flow position in family group and 0 for otherwise. Size and Age are log value of observation as of 2003. g_Sale is sales growth measured as ratio of sale in 2003 to sale in 2002. Leverage is total debt to total asset.

2. Standard errors are in parentheses. *** indicates that coefficients estimates are significant at the 1% level according to the student test, ** at 5%, and * at 10% level.


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6 Conclusion

Our research is motivated by stream of corporate government research, pursuing

to expand the knowledge on how corporate governance mechanism affects on

firms’ profitability. In this paper we attempt to understand how controlling owners

expropriates minority owners in the Norwegian economy where private firms

controlled by family are dominant form (Berzins, Bøhren, and Rydland 2008). In

particular we investigated a specific type of expropriation known as ‘Tunneling’:

controlling families’ transferring resources from companies where they have few

cash flow rights to ones where they have more cash flow rights. To explore the

research topic, we investigated two research questions 1) to what degree tunneling

prevails among the family firms, and 2) what are the main determinants of this

phenomenon? We used general empirical technique for measuring tunneling

developed by Bertrand, Mehta and Mullainathan (2002) . With data provided by

CCGR data base, we did a cross sectional analysis for 2003.

Regarding the first question, the results suggest a significant tunneling between

firms controlled by common family owner. Data showed that the family groups

are on average about less sensitive to industry shock than stand alone. On the

other hand, maximum cash flow right firms show on average higher sensitivity

than the average firms in the same family groups. This suggests that, as tunneling

predicts, some part of profitability in a family group is lost, and the lost

profitability is more prevalent in the lower cash flow right firms in the group. The

result also suggests that maximum cash flow right firms on average positively

respond to the group shock as opposed to negative response for the rest of firms in

the group. This confirms that the resources are transferred from the low cash flow

right firms to the high cash flow firms.

Regarding the second research question, the results suggest that the more

tunneling prevails as the greater the cash flow rights between two firms diverge

and the fewer shares the largest second shareholder holds. We however found

little significance for insider positions in terms of family CEO and family chair.

The results gives support for that a large cash flow wedge makes tunneling more

attractive (incentive) and that less contesting by other shareholders makes

tunneling more prominent (discretion), but also that informal control through

insider positions (ability) seems restricted.


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In our knowledge, our findings are with four major weaknesses due to data

limitation and limited scope of the research. First we indirectly identify the family

groups. Consequently we might have underestimated the real size of the family

groups. Second we are considering tunneling through connected transactions

between companies as legal entities and not transactions through between a

company and an individual. This creates a bias in our findings because tunneling

also can occur between a company and a sole proprietorship. Due to lack of data

these transactions remain undetected and will lead to underestimation in our

results. Lastly validity of our finding is highly dependent on how precisely a

firm’s industry is measured. If we are mismeasuring the firm’s industry, this

mismeasurment would lead firm to appear less sensitive to their industry

shock(Bertrand, Mehta, and Mullainathan 2002), particularly problematic for

family group firms. Due to limited resources and data, we could not conduct the

further investigation on this matter. Consequently there is possibility of this

alternative explanation for our findings. Our findings also gives indication that

tunneling occur, but not how tunneling occur. In chapter 2.4 different forms of

tunneling were presented. Since we were unable to directly observe how tunneling

occur in “real life”, large parts of how tunneling occur remains a “dark spot”. We

leave these unsolved questions for the future researchers to investigate further.


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APPENDIX

Appendix 1: Tunneling Example –Aker Solution Case (Bøhren 2009)


Page 45

Appendix 2 : Family group identification process The purpose of this process is first to group individual owners into family, secondly to find the largest ‘family’ owner for each firm and ultimately to find the family group linked by common controlling family owner. To achieve these goals we used the following methods: Step 1: Find the firms in which largest family number of owners (item 15307) is equal to number of personal owners (item 14205). For this type of firms, we are certain that all the personal owners reported are in the same family and at the same time, this family is the controlling family. Step 2: In the firms found in step 1, we assigned the randomly chosen family ID to the group of personal owners, for example 1, 2, 3 and so on. One thing to consider is that ‘largest family number of owners’ does not necessarily represent the complete family. For example consider the real family size is actually 3 (owner A, B, and C). If we observe two firms: one firm’s owner is A and B while the other firm’s owner is B and C, we double count owner B to two different family. To avoid it, we go though one more step: if we find two family IDs for one owner, change family ID of higher order number to lower Number as a whole. In the same example we assigned for example family ID ‘1’ to owner A and B and ‘2’ to owner B and C. Observing owner B has two family ID, we replaced family ID ‘2’ to ‘1’ in the list for all the owners who has family ID ‘2’. As a result we have preliminary list of the ownership ID matched to family ID and match those family ID as a controlling family to each firm. Step 3: Since we already grouped all the personal owners to family and found the largest family for the type of firms in step 1, we now consider the rest of firms whose largest number of owners are not same as number of personal owners. The method we used in this case is to find the owners whose ultimate ownership in a given firm exceeds 100-family sum ultimate ownership. For this type of owners, we are certain that he or she is among the members of controlling family. If there are several personal owners of this type found in a given firm, we can expect that they are in the same family. For example we found that in step 2 owners A, B, and C is in the same family with ID ‘1’. Consider in step 3, we found in a given firm owner C and other owner D which meet the criteria described above. Then we know that owner D is also a member of family ‘1’ and thus assign family ID ‘1’ to owner D. As a result family ‘1’ has 4 members so far. If we do not find any related owners from step 1 and 2, we assigned new family ID. As a result we have updated and expanded the list of the personal ID matched to family ID and found the controlling family owner for the firms which has at least one owner whose ownership exceeds 100- family sum of ultimate ownership.


Page 46

Step 4: For the rest of firms (largest family number of owners ≠ number of personal owner, and no such a owners found described in step 3), we first see that any owners observed in this type of firms are in the list we made from step 1 to step 3. If it is so, we manually found the match between family and owner. For example, consider in one of these firms we found owner D with ownership 20% and family sum of ultimate ownership is reported as 60% while the reported largest family number of owners is 3. Then if we observe 2 more 20% owners (let them be owner E and F), we can expect that owner E and F are same family as owner D. As a result family ‘1’ has 6 members. We also found the controlling family for the corresponding firms. Step 5: We excluded the rest of firms (1,897 firms) from the sample. Appendix 3 : Definition of variable (Berzins, Bøhren, and Rydland 2008)

Variables Abbreviation CCGR item number Operating Earnings after tax NOE 35 Operating Result Operating Revenue R 11 Sum operating Income Total Asset A 63 Fixed Asset + 78 Current Asset Total Debt D 91 Provision + 98 Other long term liabilities + 109 Current liabilities Working Capital WC 78 Current asset -109 Current liabilities Return on Asset ROA 127 ROA Dividends Dividends 41 Dividends Investment I 75 Total Investment Employees Em 13405 Number of Employees Sales S 11 Sum operating Income + 24 Other interest received + 25 Other

financial income Largest family sum ultimate ownership

15302 Largest family sum ultimate ownership

Largest family largest direct holding

15303 Largest family sum ultimate ownership

Largest family has CEO CEO 15304 : 1 for yes, 0 for no Largest family has Chair Board 15305 : 1 for yes, 0 for no Leverage Total Debt / Total Asset Growth of Sales g_Sale S (2003)/S (2002) Growth of Assets g_Asset A (2003)/A (2002) Growth of NOE g_NOE NOE (2003)/NOE (2002) Current Asset CA 78 Current asset Current Debt CD 109 Current liabilities Age Age 13420 Company Age Inter corporate- Investment Group

Group ID 14502

Parent : Inter corporate- Investment Group

Is Parent 14502

Subsidiary : Inter corporate- Investment Group (>50% share)

Is Subsidiary 14502

JV : Inter corporate-Investment Group (Joint Investment)

Is Joint Control

14502

Associated : Inter corporate- Investment Group (>30%)

Is Associated 14502

Independent: Inter corporate- Investment Group

Is Independent

14502

Number of Owners 202 Number of Owners


Page 47

Appendix 4: Corporate finance Descriptive statistics Panel A: Family Firm Total

N Mean Median Mode Std. Deviation Skewness Kurtosis Percentiles

Valid p1 p5 p10 p25 p50 p75 p90 p99 p100

Size Total Asset (A) 69 935 8,40 1,75 0,25 63,87 40,89 2 345,39 0,04 0,15 0,26 0,65 1,75 4,67 12,44 101,63 5 365,41

Sales (S) 69 935 8,12 2,04 0,03 50,02 103,23 16 868,38 0,02 0,09 0,21 0,67 2,04 6,03 16,59 91,09 9 165,75 Employees (Em.) 45 964 7,49 3,00 1,00 23,15 38,97 3 147,06 1,00 1,00 1,00 2,00 3,00 7,00 15,00 64,00 2 463,00

Grow

th Growth of Asset 69 935 1,91 0,00 0,00 98,75 183,44 38 379,24 0,00 0,00 0,00 0,00 0,00 1,00 1,00 8,00 22 133,00

Growth of Sale 69 935 14,89 1,00 0,00 709,28 113,36 15 634,07 0,00 0,00 0,00 0,00 1,00 1,00 1,00 31,00 118 794,00 Growth of NOE 69 935 0,35 0,00 0,00 53,92 4,59 3 325,36 -34,00 -6,00 -2,00 0,00 0,00 1,00 4,00 35,00 4 821,00

Asse

t Str

uctu

re

Investment 69 935 0,33 0,00 0,00 9,81 104,92 14 350,41 0,00 0,00 0,00 0,00 0,00 0,00 0,01 3,66 1 624,31 Working Capital(WC) 69 935 0,57 0,08 0,10 17,29 79,16 12 219,51 -6,97 -1,52 -0,71 -0,15 0,08 0,38 1,50 15,20 2 893,21

Asset to Em. 69 935 1,26 0,24 0,00 11,39 43,28 2 669,86 0,00 0,00 0,00 0,00 0,24 0,63 1,44 18,20 971,24 Sale to Em. 69 935 1,01 0,53 0,00 4,86 138,10 27 364,16 0,00 0,00 0,00 0,00 0,53 1,17 2,23 7,94 1 013,66 Current asset to A 69 935 0,61 0,00 0,00 1,66 3,37 11,35 0,00 0,00 0,00 0,00 0,00 0,00 1,00 8,00 9,00 WC to A 69 935 -0,16 0,00 0,00 18,74 -125,31 20 287,10 -9,00 -6,00 -3,00 0,00 0,00 0,00 4,00 9,00 9,00

Capit

al

Struc

ture

Debt (D) 69 935 5,98 1,44 0,00 41,96 49,43 3 700,31 0,01 0,09 0,19 0,53 1,44 3,78 9,62 70,78 4 265,00 Current D to D 69 935 0,72 0,00 0,00 1,45 3,61 14,87 0,00 0,00 0,00 0,00 0,00 1,00 1,00 8,00 9,00 Debt to Asset 69 935 1,00 0,00 0,00 40,36 148,17 27 474,12 0,00 0,00 0,00 0,00 0,00 0,00 1,00 8,00 8 319,00

Pay- out Dividend 69 935 -0,51 0,00 0,00 6,07 -70,84 7 236,89 -6,51 -1,56 -0,80 -0,20 0,00 0,00 0,00 0,00 2,00

DividendPayout 69 935 -0,48 0,00 0,00 10,76 -50,25 5 555,60 -9,00 -2,00 -1,00 0,00 0,00 0,00 0,00 1,00 583,00

Profi

tabili

ty Net Operating Earning (NOE) 69 935 0,47 0,07 0,00 6,91 59,49 6 338,49 -2,19 -0,49 -0,23 -0,03 0,07 0,32 0,93 7,38 953,55

ROA 69 935 10,46 9,00 7,00 556,57 85,37 9 116,63 -167,00

-41,00

-17,00 0,00 9,00 22,00 40,00 88,00 65 800,00

Age 69 935 11,52 8,00 5,00 11,93 3,23 21,09 0,00 1,00 2,00 4,00 8,00 15,00 23,00 67,00 332,00

• Note: Except for employee and ratios, all variables are in million NOK as of 2003.


Page 48

Panel B: Stand Alone


Valid p1 p5 p10 p25 p50 p75 p90 p99 p100 Siz

e Total Asset (A) 43 114 8,40 1,22 0,25 11,42 33,11 1 880,78 0,04 0,13 0,22 0,50 1,22 2,92 6,45 31,56 872,50 Sales (S) 43 114 5,01 1,82 0,03 12,84 17,81 640,65 0,02 0,09 0,20 0,66 1,82 4,70 11,40 48,35 669,65 Employees (Em.) 30 761 5,08 3,00 1,00 10,14 30,54 1 937,65 1,00 1,00 1,00 1,00 3,00 6,00 11,00 35,00 830,00

Grow


Growth of Sale 43 114 6,64 1,00 0,00 280,65 114,76 16 339,63 0,00 0,00 0,00 0,00 1,00 1,00 1,00 19,00 44 788,00 Growth of NOE 43 114 0,35 0,00 0,00 31,84 -28,95 4 168,13 -30,00 -6,00 -2,00 0,00 0,00 1,00 3,00 32,00 1 820,00

Asse

t Str

uctu

re

Investment 43 114 0,14 0,00 0,00 2,45 55,19 3 878,49 0,00 0,00 0,00 0,00 0,00 0,00 0,01 2,26 213,20 Working Capital(WC) 43 114 0,28 0,07 0,10 3,82 18,92 1 997,89 -2,84 -0,90 -0,47 -0,11 0,07 0,27 0,90 6,71 259,60

Asset to Em. 43 114 0,69 0,27 0,00 3,79 41,77 2 400,06 0,00 0,00 0,00 0,00 0,27 0,61 1,24 7,10 299,32 Sale to Em. 43 114 0,92 0,58 0,00 1,75 12,73 323,79 0,00 0,00 0,00 0,00 0,58 1,14 2,04 6,45 79,99 Current asset to A 43 114 0,46 0,00 0,00 1,41 4,13 17,97 0,00 0,00 0,00 0,00 0,00 0,00 1,00 8,00 9,00 WC to A 43 114 -0,13 0,00 0,00 21,41 -125,90 18 759,79 -9,00 -6,00 -3,00 0,00 0,00 0,00 4,00 9,00 9,00

Capit

al

Struc

ture

Debt (D) 43 114 2,45 1,01 0,00 6,73 20,24 773,90 0,01 0,07 0,15 0,40 1,01 2,41 5,20 23,34 396,84 Current D to D 43 114 0,63 0,00 0,00 1,25 4,17 21,65 0,00 0,00 0,00 0,00 0,00 1,00 1,00 8,00 9,00 Debt to Asset 43 114 1,03 0,00 0,00 47,51 141,26 22 951,26 0,00 0,00 0,00 0,00 0,00 0,00 1,00 8,00 8 319,00

Pay- out Dividend 43 114 -0,28 0,00 0,00 1,67 -59,23 5 998,92 -3,63 -1,10 -0,63 -0,20 0,00 0,00 0,00 0,00 0,53

DividendPayout 43 114 -0,45 0,00 0,00 9,71 -55,32 6 692,03 -8,00 -2,00 -1,00 0,00 0,00 0,00 0,00 1,00 409,00

Profi

tabili


ROA 43 114 11,39 10,00 6,00 524,76 88,72 10 109,47 -174,85

-45,00

-20,00 0,00 10,00 24,00 44,00 88,00 65 800,00

Age 43 114 10,94 8,00 5,00 11,15 2,93 13,78 0,00 1,00 1,00 4,00 8,00 15,00 22,00 62,00 161,00


Page 49

Panel C: Family Group

N Mean Median Mode Std.

Deviation Skewness Kurtosis Percentiles Valid p1 p5 p10 p25 p50 p75 p90 p99 p100

Size Total Asset (A) 8 879 24,83 3,58 0,25 149,45 19,71 512,05 0,07 0,23 0,46 1,24 3,58 10,56 33,83 405,43 5 365,41

Sales (S) 8 879 16,70 2,95 0,05 122,90 52,64 3 606,86 0,02 0,10 0,23 0,76 2,95 10,21 30,49 217,07 9 165,75 Employees (Em.) 5 105 13,64 5,00 1,00 48,95 28,16 1 263,09 1,00 1,00 1,00 2,00 5,00 12,00 25,00 147,94 2 463,00

Grow


Growth of Sale 8 879 48,40 1,00 0,00 1 356,76 46,25 2 422,82 0,00 0,00 0,00 0,00 1,00 1,00 2,00 106,00 83 393,00 Growth of NOE 8 879 0,99 0,00 0,00 95,63 14,47 1 033,85 -46,00 -7,00 -3,00 0,00 0,00 1,00 4,00 53,20 4 584,00

Asse

t Str

uctu

re

Investment 8 879 0,82 0,00 0,00 16,20 49,56 3 105,61 0,00 0,00 0,00 0,00 0,00 0,00 0,02 9,49 1 151,14 Working Capital(WC) 8 879 1,46 0,10 0,10 44,06 36,49 2 259,15 -22,71 -3,68 -1,51 -0,27 0,10 0,80 3,46 45,14 2 893,21

Asset to Em. 8 879 3,19 0,20 0,00 26,86 21,93 608,33 0,00 0,00 0,00 0,00 0,20 0,75 2,40 55,86 971,24 Sale to Em. 8 879 1,35 0,40 0,00 6,29 26,53 965,60 0,00 0,00 0,00 0,00 0,40 1,29 2,73 14,14 291,54 Current asset to A 8 879 0,85 0,00 0,00 2,00 2,62 6,22 0,00 0,00 0,00 0,00 0,00 0,00 4,00 9,00 9,00 WC to A 8 879 -0,21 0,00 0,00 15,38 -50,31 2 810,64 -9,00 -6,00 -3,00 0,00 0,00 0,00 4,00 9,00 9,00

Capit

al

Struc

ture

Debt (D) 8 879 16,92 2,81 0,00 101,78 23,54 764,94 0,01 0,15 0,34 0,99 2,81 8,18 24,61 270,81 4 265,00 Current D to D 8 879 0,85 0,00 0,00 1,70 2,98 9,47 0,00 0,00 0,00 0,00 0,00 1,00 2,00 9,00 9,00 Debt to Asset 8 879 1,05 0,00 0,00 26,20 54,39 3 210,82 0,00 0,00 0,00 0,00 0,00 0,00 1,00 8,00 1 704,00

Pay- out Dividend 8 879 -1,23 0,00 0,00 14,89 -35,17 1 541,41 -16,00 -2,88 -1,20 -0,25 0,00 0,00 0,00 0,00 0,10

DividendPayout 8 879 -0,52 0,00 0,00 17,83 -35,92 2 647,23 -9,00 -2,00 -1,00 0,00 0,00 0,00 0,00 0,20 583,00

Profi

tabili


ROA 8 879 10,20 8,00 6,00 704,33 82,18 7 447,43 -154,40

-35,00

-14,00 0,00 8,00 18,00 35,00 83,00 63 500,00

Age 8 879 12,07 9,00 5,00 13,07 3,18 14,40 0,00 1,00 2,00 4,00 9,00 16,00 24,00 75,00 131,00


Page 50

Panel D: Tunneled Firm


Valid p1 p5 p10 p25 p50 p75 p90 p99 p100

Size Total Asset (A) 2 778 8,40 2,68 0,01 65,29 37,78 1 714,43 0,05 0,20 0,36 0,93 2,68 7,52 19,22 129,79 3 055,08

Sales (S) 2 778 13,60 3,38 0,03 68,79 28,28 1 061,70 0,02 0,11 0,27 0,90 3,38 10,23 26,29 158,12 2 834,58 Employees (Em.) 1 776 10,13 5,00 1,00 21,41 12,61 272,34 1,00 1,00 1,00 2,00 5,00 11,00 22,00 100,69 566,00

Grow


Growth of Sale 2 778 19,12 1,00 0,00 390,59 37,19 1 614,17 0,00 0,00 0,00 0,00 1,00 1,00 1,00 74,26 17 906,00 Growth of NOE 2 778 1,65 0,00 0,00 94,07 40,74 2 041,41 -46,00 -7,00 -2,00 0,00 0,00 1,00 4,00 41,21 4 584,00

Asse

t Str

uctu

re

Investment 2 778 0,35 0,00 0,00 9,47 48,36 2 454,61 0,00 0,00 0,00 0,00 0,00 0,00 0,00 3,38 483,94 Working Capital(WC) 2 778 1,31 0,12 0,10 22,48 41,85 2 035,88 -6,94 -1,89 -0,90 -0,15 0,12 0,71 2,47 21,85 1 096,67

Asset to Em. 2 778 1,21 0,26 0,00 5,22 11,31 164,46 0,00 0,00 0,00 0,00 0,26 0,72 1,76 23,58 104,45 Sale to Em. 2 778 1,34 0,56 0,00 4,86 22,19 700,33 0,00 0,00 0,00 0,00 0,56 1,39 2,77 13,34 179,00 Current asset to A 2 778 0,63 0,00 0,00 1,68 3,24 10,24 0,00 0,00 0,00 0,00 0,00 0,00 2,00 8,00 9,00 WC to A 2 778 -0,58 0,00 0,00 24,51 -34,72 1 272,49 -9,00 -6,00 -3,00 0,00 0,00 0,00 5,00 9,00 9,00

Capit

al

Struc

ture

Debt (D) 2 778 7,59 2,15 0,00 39,48 36,34 1 633,63 0,01 0,13 0,27 0,78 2,15 5,83 14,58 92,21 1 827,63 Current D to D 2 778 0,78 0,00 0,00 1,55 3,26 12,46 0,00 0,00 0,00 0,00 0,00 1,00 1,00 8,00 9,00 Debt to Asset 2 778 1,65 0,00 0,00 32,68 37,97 1 578,33 0,00 0,00 0,00 0,00 0,00 0,00 1,00 10,00 1 464,00

Pay- out Dividend 2 778 -0,77 0,00 0,00 15,31 -51,34 2 680,58 -9,00 -2,01 -1,00 -0,25 0,00 0,00 0,00 0,00 0,10

DividendPayout 2 778 -0,17 0,00 0,00 8,05 24,18 997,68 -7,00 -2,00 -1,00 0,00 0,00 0,00 0,00 0,00 312,00

Profi

tabili

ty Net Operating Earning (NOE) 2 778 0,72 0,08 18,29 50,99 2 657,44 -3,90 -1,00 -0,40 -0,06 0,08 0,44 1,33 9,62 953,55

ROA 2 778 18,35 8,00 6,00 1 237,46 48,39 2 498,44 -249,63

-47,10

-21,00 0,00 8,00 19,00 37,00 83,42 63 500,00

Age 2 778 10,54 7,00 3,00 12,14 3,54 17,52 0,00 1,00 1,00 3,00 7,00 14,00 21,00 73,21 107,00


Page 51

Panel E: Recipient Firm


Valid p1 p5 p10 p25 p50 p75 p90 p99 p100

Size Total Asset (A) 2 821 8,40 3,05 0,90 73,54 21,48 627,10 0,06 0,19 0,38 1,07 3,05 8,33 22,93 226,59 2 579,84

Sales (S) 2 821 8,60 1,77 0,03 27,48 11,92 226,42 0,02 0,07 0,16 0,51 1,77 6,38 20,04 114,70 733,35 Employees (Em.) 1 483 8,87 4,00 1,00 20,34 10,50 173,25 1,00 1,00 1,00 1,00 4,00 9,00 19,00 90,12 446,00

Grow


Growth of Sale 2 821 47,45 1,00 0,00 1 357,86 42,60 1 979,72 0,00 0,00 0,00 0,00 1,00 1,00 2,00 99,78 65 688,00 Growth of NOE 2 821 1,77 0,00 0,00 52,46 9,55 334,20 -45,78 -8,00 -3,00 0,00 0,00 1,00 4,00 58,56 1 435,00

Asse

t Str

uctu

re

Investment 2 821 1,23 0,00 0,00 24,88 38,92 1 691,29 0,00 0,00 0,00 0,00 0,00 0,00 0,12 11,19 1 151,14 Working Capital(WC) 2 821 1,00 0,07 0,10 21,79 28,67 1 097,35 -13,42 -2,96 -1,35 -0,29 0,07 0,58 2,59 24,96 898,23

Asset to Em. 2 821 2,99 0,11 0,00 27,21 25,40 764,66 0,00 0,00 0,00 0,00 0,11 0,73 2,53 49,94 887,56 Sale to Em. 2 821 1,09 0,16 0,00 6,22 31,81 1 146,61 0,00 0,00 0,00 0,00 0,16 1,12 2,36 12,11 244,45 Current asset to A 2 821 0,97 0,00 0,00 2,15 2,39 4,83 0,00 0,00 0,00 0,00 0,00 1,00 4,00 9,00 9,00 WC to A 2 821 -0,04 0,00 0,00 4,04 -7,06 135,70 -9,00 -6,00 -4,00 0,00 0,00 0,00 4,00 9,00 9,00

Capit

al

Struc

ture

Debt (D) 2 821 9,73 2,38 0,00 42,36 17,94 473,98 0,01 0,10 0,26 0,82 2,38 6,56 17,21 154,61 1 404,79 Current D to D 2 821 0,86 0,00 0,00 1,75 2,92 9,15 0,00 0,00 0,00 0,00 0,00 1,00 2,00 9,00 9,00 Debt to Asset 2 821 0,65 0,00 0,00 8,71 33,08 1 259,27 0,00 0,00 0,00 0,00 0,00 0,00 1,00 8,00 369,00

Pay- out Dividend 2 821 -1,30 0,00 0,00 15,83 -31,85 1 181,93 -14,65 -3,00 -1,30 -0,34 0,00 0,00 0,00 0,00 0,00

DividendPayout 2 821 -0,30 0,00 0,00 14,25 21,92 1 135,97 -12,00 -2,00 -1,00 0,00 0,00 0,00 0,00 2,00 583,00

Profi

tabili

ty Net Operating Earning (NOE) 2 821 1,27 0,10 0,00 13,43 25,01 747,47 -3,11 -0,65 -0,27 -0,02 0,10 0,50 1,51 19,06 472,23

ROA 2 821 7,42 8,00 7,00 194,43 17,31 1 107,22 -105,00

-23,00 -9,00 2,00 8,00 19,50 36,00 84,00 7 925,00

Age 2 821 13,31 10,00 5,00 13,29 2,88 12,66 0,00 1,00 2,00 5,00 10,00 17,00 27,00 72,78 131,00


Page 52

Appendix 5: Corporate governance Descriptive statistics Panel A: Family Firm Total



Sum of Largest family ultimate share

69 935 91,73 100,00 100,00 14,78 -1,49 0,63 51,00 60,00 65,00 90,00 100,00 100,00 100,00 100,00 100,00

Sum of Largest family direct share 69 935 66,46 66,00 100,00 34,63 -0,68 -0,72 0,00 0,00 0,00 50,00 66,00 100,00 100,00 100,00 100,00 Second Largest share 69 935 5,24 0,00 0,00 11,82 2,39 5,46 0,00 0,00 0,00 0,00 0,00 0,00 25,00 48,00 50,00 CEO Holding 69 935 49,64 51,00 0,00 41,28 -0,02 -1,61 0,00 0,00 0,00 0,00 51,00 100,00 100,00 100,00 100,00 # Family Owner 69 935 1,60 1,00 1,00 0,95 1,92 4,52 1,00 1,00 1,00 1,00 1,00 2,00 3,00 5,00 10,00 # Family chair 69 935 1,42 1,00 1,00 0,81 1,59 2,98 0,00 1,00 1,00 1,00 1,00 2,00 3,00 4,00 8,00 # Owner 69 935 2,01 2,00 1,00 1,40 4,13 52,75 1,00 1,00 1,00 1,00 2,00 2,00 4,00 7,00 47,00 # Personal Owner 69 935 1,90 2,00 1,00 1,25 3,28 30,31 1,00 1,00 1,00 1,00 2,00 2,00 3,00 6,00 34,00 Board Size 69 895 1,86 1,00 1,00 1,09 1,23 9 986,43 1,00 1,00 1,00 1,00 1,00 3,00 3,00 5,00 10,00 Frequency Percentage Total 0 1 2 0 1 2 Listed 69 935 69 928 7 99,99 0,01 * 1 for listed firm, 0 for unlisted firm Family CEO 69 935 16 869 53 066 24,12 75,88 * 1 : Largest family has CEO, 0 for otherwise Family Chair 69 935 7 939 61 996 11,35 88,65 * 1 : Largest family has board seat, 0 for otherwise CEO and Chair 69 935 3 254 18 300 48

381 4,65 26,17 69,18 * 2 : Largest family has CEO and board seat , 1 : either CEO or board seat

, and 0 for otherwise


Page 53

Panel B: Stand Alone




43 114 91,90 100,00 100,00 14,76 -1,50 0,60 51,00 60,00 65,00 91,00 100,00 100,00 100,00 100,00 100,00

Sum of Largest family direct share 43 114 77,80 90,00 100,00 24,46 -0,67 -0,49 0,00 34,00 50,00 60,00 90,00 100,00 100,00 100,00 100,00 Second Largest share 43 114 5,23 0,00 0,00 11,71 2,19 3,64 0,00 0,00 0,00 0,00 0,00 0,00 27,00 45,00 50,00 CEO Holding 43 114 60,21 65,00 100,00 38,69 -0,45 -1,26 0,00 0,00 0,00 30,00 65,00 100,00 100,00 100,00 100,00 # Family Owner 43 114 1,49 1,00 1,00 0,83 2,05 4,99 1,00 1,00 1,00 1,00 1,00 2,00 3,00 4,00 10,00 # Family chair 43 114 1,37 1,00 1,00 0,75 1,79 3,81 0,00 1,00 1,00 1,00 1,00 2,00 2,00 4,00 7,00 # Owner 43 114 1,85 2,00 1,00 1,20 5,02 96,66 1,00 1,00 1,00 1,00 2,00 2,00 3,00 6,00 47,00 # Personal Owner 43 114 1,75 1,00 1,00 1,07 3,55 44,97 1,00 1,00 1,00 1,00 1,00 2,00 3,00 5,00 34,00 Board Size 43 094 1,71 1,00 1,00 0,98 1,34 43 114,00 1,00 1,00 1,00 1,00 1,00 2,00 3,00 5,00 8,00 Frequency Percentage Total 0 1 2 0 1 2 Listed 43 114 43 113 1 100,00 0,00 * 1 for listed firm, 0 for unlisted firm Family CEO 43 114 7 653 35 461 17,75 82,25 * 1 : Largest family has CEO, 0 for otherwise Family Chair 43 114 3 921 39 193 9,09 90,91 * 1 : Largest family has board seat, 0 for otherwise CEO and Chair 43 114 1 183 9 208 32

723 2,74 21,36 75,90 * 2 : Largest family has CEO and board seat , 1 : either CEO or board seat



Page 54

Panel C: Family group




8 879 83,01 90,00 100,00 18,09 -0,41 -1,47 51,00 52,00 56,00 66,00 90,00 100,00 100,00 100,00 100,00

Sum of Largest family direct share 8 879 43,53 50,00 0,00 37,29 0,16 -1,35 0,00 0,00 0,00 0,00 50,00 70,00 100,00 100,00 100,00 Second Largest share 8 879 10,55 0,00 0,00 15,66 1,50 1,92 0,00 0,00 0,00 0,00 0,00 20,00 34,00 48,00 49,00 CEO Holding 8 879 28,50 0,00 0,00 35,63 0,88 -0,67 0,00 0,00 0,00 0,00 0,00 55,00 100,00 100,00 100,00 CF Wedge 8 879 11,89 0,00 0,00 16,78 0,98 -0,65 0,00 0,00 0,00 0,00 0,00 26,00 40,00 49,00 49,00 # Family Owner 8 879 1,78 1,00 1,00 1,09 1,63 2,92 1,00 1,00 1,00 1,00 1,00 2,00 3,00 5,00 9,00 # Family chair 8 879 1,43 1,00 1,00 0,87 1,31 2,39 0,00 0,00 1,00 1,00 1,00 2,00 3,00 4,00 7,00 # Owner 8 879 2,72 2,00 2,00 1,97 3,36 25,68 1,00 1,00 1,00 1,00 2,00 3,00 5,00 11,00 37,00 # Personal Owner 8 879 2,48 2,00 1,00 1,72 2,93 19,53 1,00 1,00 1,00 1,00 2,00 3,00 4,00 9,00 27,00 Board Size 8 873 2,30 2,00 1,00 1,25 0,80 0,52 1,00 1,00 1,00 1,00 2,00 3,00 4,00 6,00 9,00 Frequency Percentage Total 0 1 2 0 1 2 Listed 8 879 8 875 4 99,95 0,05 * 1 for listed firm, 0 for unlisted firm Family CEO 8 879 3 791 5 088 42,70 57,30 * 1 : Largest family has CEO, 0 for otherwise Family Chair 8 879 1 823 7 056 20,53 79,47 * 1 : Largest family has board seat, 0 for otherwise CEO and Chair 8 879 1 047 3 520 4 312 11,79 39,64 48,56 * 2 : Largest family has CEO and board seat , 1 : either CEO or board seat



Page 55

Panel D: Tunneled Firm




2 778 64,38 65,00 66,00 8,62 0,16 -0,87 51,00 51,00 52,00 58,00 65,00 70,00 77,00 80,00 80,00

Sum of Largest family direct share 2 778 36,08 40,00 0,00 27,35 -0,19 -1,48 0,00 0,00 0,00 0,00 40,00 60,00 66,00 80,00 80,00 Second Largest share 2 778 20,12 20,00 0,00 14,94 0,10 -1,12 0,00 0,00 0,00 7,00 20,00 33,00 40,00 49,00 49,00 CEO Holding 2 778 24,73 20,00 0,00 26,15 0,52 -1,24 0,00 0,00 0,00 0,00 20,00 50,00 65,00 80,00 80,00 CF Wedge 2 778 31,43 34,00 34,00 11,98 -0,59 -0,22 2,00 7,00 15,00 24,00 34,00 40,00 47,00 49,00 49,00 # Family Owner 2 778 1,65 1,00 1,00 0,96 1,88 4,80 1,00 1,00 1,00 1,00 1,00 2,00 3,00 5,00 9,00 # Family chair 2 778 1,33 1,00 1,00 0,73 1,28 2,79 0,00 0,00 1,00 1,00 1,00 2,00 2,00 4,00 6,00 # Owner 2 778 3,48 3,00 2,00 2,17 4,49 40,58 1,00 2,00 2,00 2,00 3,00 4,00 6,00 12,00 37,00 # Personal Owner 2 778 3,03 3,00 2,00 1,95 3,71 27,72 1,00 1,00 1,00 2,00 3,00 4,00 5,00 10,00 27,00 Board Size 2 777 2,56 3,00 3,00 1,23 0,55 1 386,50 1,00 1,00 1,00 2,00 3,00 3,00 4,00 6,00 8,00 Frequency Percentage Total 0 1 2 0 1 2 Listed 2 778 2 776 2 99,93 0,07 * 1 for listed firm, 0 for unlisted firm Family CEO 2 778 1 313 1 465 47,26 52,74 * 1 : Largest family has CEO, 0 for otherwise Family Chair 2 778 718 2 060 25,85 74,15 * 1 : Largest family has board seat, 0 for otherwise CEO and Chair 2 778 385 1 261 1 132 13,86 45,39 40,75 * 2 : Largest family has CEO and board seat , 1 : either CEO or board seat



Page 56

Panel E: Recipient Firm

N Mean Media

n Mode Std. Deviation

Skewness

Kurtosis

Percentiles Valid p1 p5 p10 p25 p50 p75 p90 p99 p100


2 821 95,74 100,00 100,00 9,91 -2,38 4,58 60,00 69,10 80,00 100,0

0 100,0

0 100,0

0 100,0

0 100,0

0 100,00

Sum of Largest family direct share 2 821 65,06 66,00 100,0

0 35,33 -0,58 -0,93 0,00 0,00 0,00 45,00 66,00 100,00

100,00

100,00 100,00

Second Largest share 2 821 3,51 0,00 0,00 10,52 4,16 21,20 0,00 0,00 0,00 0,00 0,00 0,00 0,00 8,00 12,00

CEO Holding 2 821 43,27 40,00 0,00 41,64 0,25 -1,59 0,00 0,00 0,00 0,00 40,00 100,00

100,00

100,00 100,00

# Family Owner 2 821 1,77 1,00 1,00 1,07 1,59 2,73 1,00 1,00 1,00 1,00 1,00 2,00 3,00 5,00 9,00 # Family chair 2 821 1,56 1,00 1,00 0,91 1,37 1,88 0,00 1,00 1,00 1,00 1,00 2,00 3,00 4,78 6,00 # Owner 2 821 2,04 2,00 1,00 1,33 2,20 11,41 1,00 1,00 1,00 1,00 2,00 3,00 4,00 6,00 18,00 # Personal Owner 2 821 1,97 2,00 1,00 1,23 2,04 10,20 1,00 1,00 1,00 1,00 2,00 3,00 4,00 6,00 17,00 Board Size 2 821 1,98 2,00 1,00 1,14 1,09 1,00 1,00 1,00 1,00 2,00 3,00 3,00 5,00 8,00 Frequency Percentage Total 0 1 2 0 1 2 Listed 2 821 2 821 0 100,00 0,00 * 1 for listed firm, 0 for unlisted firm Family CEO 2 821 814 2 007 28,86 71,14 * 1 : Largest family has CEO, 0 for otherwise Family Chair 2 821 311 2 510 11,02 88,98 * 1 : Largest family has board seat, 0 for otherwise CEO and Chair 2 821 125 875 1 821 4,43 31,02 64,55 * 2 : Largest family has CEO and board seat , 1 : either CEO or board seat



Page 57

Appendix 6: Industry descriptive statistics - Industry Frequency

N % of Total N % of Total N % of Total N % of Total N % of Total1. Agriculture and hunting 355 0,53 % 247 0,60 % 28 0,33 % 10 0,38 % 14 0,53 %10. Oil and gas extraction,incl.services 1 0,00 % 1 0,00 % 0,00 % 0,00 % 0,00 %14. Other mining and quarrying 188 0,28 % 105 0,26 % 22 0,26 % 10 0,38 % 2 0,08 %2. Food products and beverages 130 0,20 % 106 0,26 % 6 0,07 % 2 0,08 % 2 0,08 %5. Textile products 806 1,21 % 470 1,15 % 149 1,75 % 47 1,78 % 37 1,40 %

Sum 1 480 2,22 % 929 2,27 % 205 2,41 % 69 2,61 % 55 2,09 %15. Wearing apprel., fur 700 1,05 % 375 0,92 % 99 1,16 % 29 1,10 % 25 0,95 %16. Footwear and leather products 1 0,00 % 0,00 % 0,00 % 0,00 % 0,00 %17. Forestry and logging 169 0,25 % 100 0,24 % 19 0,22 % 8 0,30 % 5 0,19 %18. Wood and wood products 57 0,09 % 36 0,09 % 5 0,06 % 2 0,08 % 2 0,08 %19. Pulp,paper and paper products 19 0,03 % 12 0,03 % 2 0,02 % 0,00 % 1 0,04 %20. Publishing,printing,reproduction 489 0,73 % 319 0,78 % 58 0,68 % 18 0,68 % 15 0,57 %21. Refined petroleum products 37 0,06 % 14 0,03 % 7 0,08 % 5 0,19 % 0,00 %22. Chemicals and chemical products 1 027 1,54 % 698 1,71 % 126 1,48 % 42 1,59 % 37 1,40 %23. Rubber and plasitc products 2 0,00 % 2 0,00 % 0,00 % 0,00 % 0,00 %24. Other non-metallic mineral products 77 0,12 % 46 0,11 % 12 0,14 % 6 0,23 % 2 0,08 %25. Basic metals 176 0,26 % 95 0,23 % 23 0,27 % 11 0,42 % 7 0,27 %27. Fabricated metal products 47 0,07 % 30 0,07 % 6 0,07 % 1 0,04 % 2 0,08 %28. Machinery and equipment n.e.c. 683 1,03 % 432 1,06 % 76 0,89 % 30 1,14 % 12 0,46 %29. Office machinery and computers 612 0,92 % 402 0,98 % 70 0,82 % 28 1,06 % 17 0,65 %30. Electrical machinery and apparatus 12 0,02 % 9 0,02 % 2 0,02 % 0,00 % 0,00 %31. Radio, TV sets, communication equip 166 0,25 % 111 0,27 % 16 0,19 % 9 0,34 % 1 0,04 %32. Instruments, watches and clocks 36 0,05 % 21 0,05 % 6 0,07 % 2 0,08 % 1 0,04 %33. Motor vehicles,trailers, semi-tr 199 0,30 % 134 0,33 % 31 0,36 % 14 0,53 % 10 0,38 %34. Other transport equipment 63 0,09 % 24 0,06 % 8 0,09 % 4 0,15 % 3 0,11 %35. Furniture, manufacturing n.e.c. 324 0,49 % 163 0,40 % 63 0,74 % 18 0,68 % 9 0,34 %36. Recycling 384 0,58 % 264 0,65 % 20 0,23 % 4 0,15 % 4 0,15 %

Sum 5 280 7,93 % 3 287 8,04 % 649 7,62 % 231 8,75 % 153 5,81 %11. Electricity, gas and stream supply 50 0,08 % 29 0,07 % 13 0,15 % 3 0,11 % 5 0,19 %40. Water supply 42 0,06 % 27 0,07 % 7 0,08 % 4 0,15 % 2 0,08 %

Sum 92 0,14 % 56 0,14 % 20 0,23 % 7 0,27 % 7 0,27 %45. Contruction 6 777 10,18 % 5 124 12,54 % 503 5,90 % 200 7,58 % 149 5,65 %

Sum 6 777 10,18 % 5 124 12,54 % 503 5,90 % 200 7,58 % 149 5,65 %37. Fishing,fish farming,incl.services 41 0,06 % 14 0,03 % 9 0,11 % 4 0,15 % 0,00 %41. Motor vehicles services 4 0,01 % 2 0,00 % 0,00 % 0,00 % 0,00 %50. Wholesale trade, commision trade 3 092 4,65 % 1 840 4,50 % 381 4,47 % 120 4,55 % 108 4,10 %55. Retail trade, repair personal goods 2 505 3,76 % 1 440 3,52 % 335 3,93 % 105 3,98 % 95 3,61 %64. Hotels and restaurants 105 0,16 % 67 0,16 % 18 0,21 % 9 0,34 % 1 0,04 %70. Land trandsport, pipeline transport 12 355 18,56 % 4 656 11,40 % 2 442 28,66 % 544 20,61 % 894 33,93 %71. Water transport 676 1,02 % 280 0,69 % 121 1,42 % 33 1,25 % 39 1,48 %72. Air transport 1 527 2,29 % 1 095 2,68 % 195 2,29 % 92 3,49 % 52 1,97 %73. Supporting transport activities 46 0,07 % 32 0,08 % 6 0,07 % 4 0,15 % 1 0,04 %74. Post and telecommunications 9 474 14,23 % 6 998 17,13 % 1 012 11,88 % 370 14,02 % 319 12,11 %80. Financial intermediation, less ins. 435 0,65 % 323 0,79 % 37 0,43 % 20 0,76 % 9 0,34 %85. Insurance and pension funding 1 709 2,57 % 1 352 3,31 % 110 1,29 % 48 1,82 % 35 1,33 %90. Ausiliary financial intermediation 128 0,19 % 72 0,18 % 14 0,16 % 8 0,30 % 2 0,08 %91. Real estate activities 10 0,02 % 2 0,00 % 4 0,05 % 1 0,04 % 0,00 %92. Renting of machinery and equipment 795 1,19 % 517 1,27 % 95 1,11 % 34 1,29 % 19 0,72 %93. Computers and related activities 901 1,35 % 615 1,51 % 101 1,19 % 26 0,99 % 30 1,14 %95. Research and development 1 0,00 % 1 0,00 % 0,00 % 0,00 % 0,00 %

Sum 33 804 50,79 % 19 306 47,25 % 4 880 57,26 % 1 418 53,73 % 1 604 60,87 %51. Other business activities 6 513 9,79 % 4 072 9,97 % 845 9,92 % 312 11,82 % 263 9,98 %52. Education 9 464 14,22 % 6 028 14,75 % 1 001 11,75 % 286 10,84 % 294 11,16 %

Sum 15 977 24,00 % 10 100 24,72 % 1 846 21,66 % 598 22,66 % 557 21,14 %60. Health and social work 1 821 2,74 % 1 417 3,47 % 122 1,43 % 43 1,63 % 41 1,56 %61. Sewage, refuse disposal activities 561 0,84 % 173 0,42 % 181 2,12 % 38 1,44 % 31 1,18 %62. Membership organizations n.e.c. 15 0,02 % 7 0,02 % 5 0,06 % 1 0,04 % 3 0,11 %63. Cultural and sporting activities 753 1,13 % 461 1,13 % 111 1,30 % 34 1,29 % 35 1,33 %

Sum 3 150 4,73 % 2 058 5,04 % 419 4,92 % 116 4,40 % 110 4,17 %66 560 95,17 % 40 860 94,77 % 8 522 95,98 % 2 639 95,00 % 2 635 93,41 %

466 4,16 % 235 0,55 % 72 0,81 % 18 0,65 % 22 0,78 %2 909 0,67 % 2 019 4,68 % 285 3,21 % 121 4,36 % 164 5,81 %

69 935 100,00 % 43 114 100,00 % 8 879 100,00 % 2 778 100,00 % 2 821 100,00 %

Max CF

TotalUnclassified ('88')Multi industry

Total

8

Family Firm Stand Alone Group Firm Min CF

1

2

3

4

5

7


Page 58

Appendix 7: Industry descriptive statistics-Industry ROA

Mean Median Std. Mean Median Std. Mean Median Std.1 4,54 7,00 37,94 2,51 7,00 42,31 6,89 6,50 20,57

10 1,00 1,00 1,00 1,0014 -2,09 8,00 133,77 7,35 10,00 37,57 3,05 7,00 18,032 4,43 7,00 28,50 3,99 7,50 29,29 23,83 8,50 29,035 32,25 0,00 1339,25 54,06 0,00 1751,43 0,85 0,00 28,96

Sum 18,77 2,00 989,51 29,30 2,00 1245,65 2,58 2,00 27,1415 9,23 8,00 160,65 5,50 8,00 29,07 4,41 6,00 23,4216 8,00 8,0017 9,94 9,00 30,11 11,63 9,00 35,88 6,84 7,00 19,2518 85,09 4,00 701,09 132,64 3,00 882,97 -18,60 3,00 47,9019 4,26 7,00 14,04 3,50 4,00 17,19 6,00 6,00 2,8320 7,03 8,00 92,86 7,17 8,00 76,79 6,71 6,00 16,3521 6,03 6,00 19,82 2,29 7,50 28,37 8,43 4,00 15,9522 3,62 8,00 91,54 5,28 9,00 102,20 3,55 6,00 37,9823 8,50 8,50 16,26 8,50 8,50 16,2624 8,55 10,00 34,18 6,85 10,50 27,35 -9,83 0,00 52,0525 7,62 8,00 29,01 8,53 7,00 31,18 8,35 9,00 7,6127 7,51 7,00 15,91 6,90 6,00 15,48 -1,83 6,00 19,1728 13,75 8,00 204,51 18,32 8,00 256,41 3,20 5,50 23,3829 11,08 10,00 84,08 16,43 10,00 86,85 -7,37 5,00 107,9130 -46,17 10,00 137,23 -73,00 7,00 150,26 42,00 42,00 15,5631 -10,75 7,00 144,75 -17,06 7,00 172,95 6,56 5,50 17,0432 7,06 14,00 62,09 3,90 6,00 75,03 25,50 20,50 13,0733 9,77 12,00 40,20 11,91 12,00 36,49 -5,13 7,00 49,1734 9,24 6,00 17,24 9,33 6,50 15,30 1,25 2,50 15,8935 6,10 7,00 32,17 9,09 8,00 30,60 0,51 4,00 35,2736 2,91 7,00 201,35 3,43 6,00 241,03 8,95 13,50 21,28

Sum 8,06 8,00 145,74 9,63 8,00 163,98 2,43 6,00 45,3511 16,34 14,50 33,73 22,59 16,00 25,42 13,00 9,00 36,4440 -5,33 7,00 76,59 -13,22 7,00 94,91 8,86 8,00 11,78

Sum 6,45 9,00 58,08 5,32 9,00 70,09 11,55 8,50 29,7845 8,77 11,00 62,85 8,57 11,00 69,85 8,63 9,00 23,33

Sum 8,77 11,00 62,85 8,57 11,00 69,85 8,63 9,00 23,3337 11,20 14,00 40,23 3,57 4,50 35,65 1,78 13,00 35,5341 31,50 26,00 36,57 25,00 25,00 35,3650 21,84 8,00 1043,14 3,81 9,00 242,24 0,46 8,00 406,7955 -13,25 4,00 233,52 -14,85 5,00 261,66 -0,36 4,00 61,8764 -62,80 10,00 373,82 -20,24 14,00 208,27 -24,28 2,00 125,5170 14,54 8,00 648,27 22,13 8,00 1031,86 11,08 8,00 29,3271 16,71 7,00 223,95 16,87 7,00 260,29 38,42 7,00 347,6972 -6,69 10,00 212,65 -0,70 12,00 157,93 -37,08 5,00 429,8173 9,72 1,00 48,22 7,84 2,00 50,54 31,67 22,00 54,0274 11,04 14,00 666,39 18,15 15,00 738,94 6,27 11,00 74,3280 -0,50 9,00 119,89 5,84 11,00 61,09 -62,19 1,00 364,6185 47,07 27,00 864,99 28,00 30,00 36,61 27,56 23,50 39,3790 9,77 9,00 25,15 14,21 14,00 25,24 4,79 5,00 36,3991 10,60 8,50 21,82 36,00 36,00 19,80 9,25 7,50 22,4492 19,91 7,00 605,31 43,36 8,00 725,40 -12,42 3,00 126,1693 4,50 11,00 165,25 3,00 11,00 194,25 -9,58 9,00 82,9995 10,00 10,00 10,00 10,00

Sum 12,29 9,00 657,68 14,70 11,00 695,31 6,01 8,00 163,5651 10,22 9,00 338,18 13,76 9,00 412,68 -6,81 8,00 202,1752 1,85 8,00 222,44 4,10 9,00 171,03 -9,25 9,00 350,03

Sum 5,26 9,00 275,58 7,99 9,00 293,48 -8,13 8,00 291,7360 6,51 9,00 151,15 5,01 9,00 165,95 16,37 9,00 73,6461 14,50 7,00 357,40 -8,19 8,00 204,69 48,92 7,00 590,4762 5,40 10,00 16,30 0,57 5,00 19,99 8,00 10,00 12,3563 3,94 9,00 155,10 8,15 8,00 150,93 -16,84 10,00 253,88

Sum 7,32 9,00 204,15 7,99 9,00 293,48 21,53 8,00 411,589,81 9,00 513,55 11,67 10,00 537,72 3,52 8,00 205,70

7

8

Total

Family Firm Stand Alone

5

Group Firm

1

2

3

4


Page 59

Appendix 8 : Industry descriptive statistics-Industry Asset


10 0,15 0,15 0,00 0,15 0,15 0,00 0,00 0,00 0,0014 15,97 2,38 54,43 3,21 1,61 4,00 74,34 7,03 137,262 5,09 1,75 16,10 2,52 1,56 2,78 31,26 3,80 64,525 26,00 6,16 76,52 13,03 3,53 24,72 43,94 12,24 84,95

Sum 17,70 3,23 60,76 8,11 2,27 18,53 41,91 8,77 86,9615 14,97 3,68 39,03 6,97 1,97 13,95 32,30 12,57 54,6916 30,43 30,43 0,00 0,00 0,00 0,00 0,00 0,00 0,0017 9,64 2,07 24,97 3,57 1,13 8,37 37,53 9,55 59,7718 4,28 0,92 10,01 1,66 0,47 3,53 1,81 1,33 1,4019 13,02 3,39 24,03 12,36 3,56 25,92 0,29 0,29 0,0520 9,04 2,65 22,31 4,40 1,94 8,48 20,92 8,14 28,8821 201,10 5,21 761,20 5,98 2,07 10,14 15,21 7,57 19,0622 3,84 1,14 11,16 1,90 0,89 3,27 9,05 2,38 22,5323 4,54 4,54 1,38 4,54 4,54 1,38 0,00 0,00 0,0024 23,92 1,86 119,30 3,82 1,42 5,61 23,59 2,05 38,7925 9,61 2,29 26,13 3,97 1,33 9,08 12,17 5,68 14,1927 13,21 4,21 29,11 11,26 2,91 31,85 31,38 15,66 35,1528 6,28 2,10 13,24 3,36 1,42 6,35 15,23 8,10 25,7629 8,81 1,82 41,59 3,63 1,21 18,31 31,97 4,16 108,7230 7,06 0,71 12,68 4,63 0,66 7,33 21,12 21,12 29,1731 5,92 1,39 12,95 3,08 1,02 5,64 16,21 7,31 24,9432 6,22 1,01 12,22 1,01 0,67 0,87 21,12 21,78 15,3833 7,81 1,40 61,89 8,78 1,03 75,25 7,79 2,93 11,4034 11,79 5,33 18,97 6,76 2,67 8,09 12,85 13,49 7,9435 28,87 3,77 149,75 3,76 1,84 4,83 101,03 10,71 326,5736 8,33 1,91 30,28 3,11 1,14 6,66 40,01 10,26 80,21

Sum 11,06 2,01 81,18 3,95 1,32 18,27 28,45 6,03 114,8411 7,57 1,52 18,98 1,59 0,86 1,98 17,48 3,12 30,8040 11,70 2,74 37,63 2,69 1,90 2,57 56,01 7,29 83,04

Sum 9,46 2,11 28,92 2,12 1,21 2,33 30,96 4,31 55,9745 4,48 1,67 17,62 2,63 1,32 5,38 11,42 3,53 50,34

Sum 4,48 1,67 17,62 2,63 1,32 5,38 11,42 3,53 50,3437 14,29 3,59 31,73 6,70 1,86 9,92 33,20 5,28 62,1041 28,36 0,59 55,85 0,59 0,59 0,59 0,00 0,00 0,0050 5,62 2,19 13,51 3,12 1,80 5,40 9,83 3,57 17,7155 3,85 1,24 13,91 2,19 0,91 4,71 9,49 2,08 32,7064 7,96 0,91 52,92 1,46 0,68 2,23 33,92 1,15 126,9870 16,15 4,04 85,37 6,91 2,55 23,27 34,61 6,06 175,6671 7,70 1,81 26,83 3,14 1,12 7,14 14,73 3,03 44,5172 2,01 0,63 11,00 1,12 0,56 1,98 5,47 0,95 28,3173 3,19 0,79 8,71 1,51 0,42 3,32 10,65 1,04 21,7374 4,70 0,78 52,57 1,76 0,67 8,29 19,66 1,61 143,9580 1,80 0,74 4,09 1,18 0,66 1,62 2,31 0,86 2,9885 2,54 1,29 6,32 1,85 1,18 2,40 8,29 2,23 20,9990 10,41 2,97 33,78 3,56 2,02 4,15 10,20 4,25 16,1191 2,42 1,53 2,30 0,51 0,51 0,40 2,53 2,56 2,2292 3,16 0,72 10,02 1,47 0,50 3,39 8,96 3,00 21,8893 1,97 0,81 4,95 1,23 0,65 2,02 3,08 1,27 6,9895 1,94 1,94 0,00 1,94 1,94 0,00 0,00 0,00 0,00

Sum 8,63 1,69 59,52 3,13 1,05 12,91 24,07 3,58 141,8651 6,73 1,67 38,10 3,25 1,22 7,60 15,08 3,36 81,3252 5,64 1,78 77,87 2,66 1,47 5,23 15,76 2,34 198,86

Sum 6,09 1,74 64,68 2,90 1,37 6,30 15,45 2,66 156,4060 3,73 1,63 7,10 2,73 1,47 4,51 6,39 2,90 11,6461 78,90 6,18 267,01 7,46 2,16 17,92 157,04 14,32 402,9362 23,27 3,77 58,97 3,16 0,60 4,16 55,82 16,17 99,0063 8,77 1,66 46,37 4,18 1,18 20,53 18,79 4,26 48,10

Sum 18,42 1,94 118,47 3,46 1,45 11,70 75,34 6,59 275,298,46 1,76 64,15 3,20 1,22 11,59 24,76 3,60 148,46

7

8

Total


5

Group Firm

1

2

3

4


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Appendix 9 : Industry descriptive statistics-Industry Sale


10 0,80 0,80 0,00 0,80 0,80 0,00 0,00 0,00 0,0014 11,16 2,44 31,45 3,44 1,69 4,55 42,88 10,69 75,002 3,83 2,58 4,41 3,36 2,47 3,57 7,58 5,70 6,695 10,02 2,24 26,99 5,40 1,68 9,03 20,28 4,15 44,67

Sum 8,85 2,53 23,41 4,94 2,11 7,70 21,54 4,98 46,0815 26,52 7,18 62,73 14,36 4,62 31,58 56,16 20,16 115,3716 46,39 46,39 0,00 0,00 0,00 0,00 0,00 0,00 0,0017 11,03 2,94 23,92 4,82 1,94 10,60 35,02 8,21 52,3018 7,32 1,47 16,82 2,94 0,60 5,92 2,54 1,58 3,3119 17,25 4,27 37,31 8,56 4,34 15,05 0,33 0,33 0,4120 14,52 4,01 40,60 6,40 3,09 10,41 32,32 13,42 45,9921 98,69 5,61 328,83 12,78 3,36 23,21 26,02 10,83 47,9022 5,59 1,90 13,40 3,27 1,58 4,57 12,31 4,13 26,3023 3,72 3,72 0,66 3,72 3,72 0,66 0,00 0,00 0,0024 23,39 2,22 80,58 6,48 2,23 11,93 38,67 2,57 90,7825 12,71 3,28 28,07 5,41 2,22 10,09 15,98 8,36 17,9527 15,86 4,92 34,13 15,11 4,12 37,16 34,51 21,99 44,1228 10,01 4,13 19,57 5,29 2,79 7,84 21,33 12,08 32,3329 11,93 2,70 72,32 5,47 2,06 22,80 40,37 6,10 201,2730 10,26 1,15 19,05 6,12 0,53 10,22 32,17 32,17 44,7131 9,32 2,89 19,86 5,08 1,77 9,95 12,54 6,14 16,3632 13,69 1,26 29,97 1,67 1,12 1,69 54,10 40,95 49,7833 9,14 2,27 47,83 8,79 2,16 57,16 12,31 2,20 20,4434 21,99 9,45 42,00 9,17 4,26 12,96 22,60 25,46 13,8835 30,13 4,93 79,12 6,59 3,43 9,70 64,76 14,46 126,7836 12,48 2,71 38,19 4,80 1,79 9,11 68,24 19,94 123,72

Sum 14,50 3,33 54,58 6,31 2,29 19,58 33,79 8,60 97,2011 7,31 1,55 25,46 1,44 1,08 1,78 17,82 3,19 48,3140 16,16 0,56 71,43 0,62 0,42 0,92 92,46 1,42 163,41

Sum 11,35 0,97 51,65 1,04 0,53 1,48 43,94 2,85 106,0245 8,83 3,32 31,93 5,26 2,75 9,90 22,87 6,75 90,03

Sum 8,83 3,32 31,93 5,26 2,75 9,90 22,87 6,75 90,0337 14,84 3,64 28,44 7,35 3,15 9,10 38,69 11,71 51,3141 2,67 1,51 3,49 1,37 1,37 0,00 0,00 0,00 0,0050 19,18 8,78 32,85 12,70 6,39 17,65 31,03 13,89 50,4355 6,12 3,03 13,22 3,89 2,41 5,15 13,00 4,77 28,8064 10,62 1,00 57,78 3,60 0,75 7,76 40,79 1,68 137,1570 2,87 0,76 12,36 1,70 0,60 4,75 5,10 1,06 22,2671 4,35 1,32 11,09 3,50 1,12 8,83 5,68 1,79 13,3272 2,36 0,90 6,41 1,73 0,83 3,77 4,04 1,29 7,5273 1,46 0,44 4,95 0,68 0,44 0,92 0,71 0,64 0,6774 2,91 1,02 13,03 2,03 0,97 7,72 6,76 1,51 29,7680 2,96 1,41 5,88 2,03 1,24 2,48 4,06 1,64 6,2585 3,71 2,16 10,46 2,65 2,05 2,78 13,24 3,14 37,1690 10,35 4,60 22,55 5,36 2,95 6,20 14,53 9,80 21,3391 5,09 1,11 7,49 0,95 0,95 0,64 5,76 4,46 6,7792 3,83 1,07 9,29 2,13 0,80 4,82 7,31 2,98 15,6793 3,31 1,81 6,79 2,49 1,70 2,61 5,07 2,87 7,8395 1,79 1,79 0,00 1,79 1,79 0,00 0,00 0,00 0,00

Sum 4,76 1,22 16,25 3,19 1,15 8,51 8,41 1,62 29,2651 13,62 3,10 119,85 7,39 2,43 20,71 34,35 5,89 320,0152 10,68 4,24 38,73 8,57 3,75 15,86 14,04 5,29 36,89

Sum 11,88 3,89 82,13 8,09 3,27 17,99 23,34 5,52 218,3760 6,61 2,66 13,97 4,59 2,34 7,98 14,46 5,62 29,5461 39,31 4,53 203,73 5,35 1,60 16,99 80,61 9,25 342,0562 39,18 2,04 120,70 1,35 0,44 2,16 103,27 13,74 206,0663 9,74 2,71 23,48 7,44 2,29 17,68 15,83 4,67 30,77

Sum 13,34 2,84 88,56 5,28 2,28 11,80 44,46 6,55 228,868,16 2,05 49,94 5,06 1,83 13,03 16,60 2,97 121,53

7

8

Total


5

Group Firm

1

2

3

4


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