Determinants of the Voting Premium in

Swedish Listed Shares

Liquidity or Corporate Control

Author: Yu ZHENG

Supervisor: Jens Josephson Master’s Thesis in Financial Economics September 2011

Department of Economics

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Abstract

This thesis investigates the impact of corporate control and liquidity on the voting premium in

Swedish dual class shares. The thesis uses the size of the largest shareholder and the dummy

variables as corporate control variables. The relative trading volume, the relative freely traded

shares and the relative free float are used as liquidity variables. Before the regression, a

Hausman test is performed to choose the panel regression model. The variance inflation factor

(VIF) is applied to show that no multicollinearity presents. Using a panel data set consists of 26

dual class shares listed on NASDAQ OMX Stockholm from 2005 to 2009, a random effects

model and a pooled OLS regression model are used to estimate the voting premium. The main

findings are two. First, the corporate control is a determinant for the voting premium via

ownership structure. The size of the largest shareholder has a negative impact on the voting

premium, and a majority ownership over 40 percent significantly reduces the voting premium.

Second, liquidity is not priced in the voting premium in Sweden.

Keywords: dual class shares, voting premium, corporate control, liquidity, private benefits of

control

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Contents

1 Introduction .......................................................................................................................... 2

2 Theoretical Background ...................................................................................................... 3

2.1 Private Benefits of Control ...................................................................................................... 3

2.2 Liquidity Theories .................................................................................................................... 6

3 Data ........................................................................................................................................ 7

3.1 Sample Selection....................................................................................................................... 7

3.2 Price Data and the Voting Premium ...................................................................................... 8

3.3 Corporate control Variables ................................................................................................. 11

3.4 Liquidity Variables ................................................................................................................ 12

3.5 Summary Statistics ................................................................................................................ 14

4 Empirical Methodology ..................................................................................................... 16

4.1 Regression Model Specification ............................................................................................ 16

4.2 Primary Tests and Choice of Model ..................................................................................... 17

4.3 Correlation and Multicollinearity Analysis ......................................................................... 18

4.4 Correction for Heteroskedasticity and Serial Correlation ................................................. 20

5 Statistical Analysis .............................................................................................................. 20

5.1 Regression Result Analysis .................................................................................................... 20

5.2 Endogenous Problems ........................................................................................................... 26

6 Conclusion and Prospective ............................................................................................... 27

7 Reference ............................................................................................................................. 29

8 Appendix ............................................................................................................................. 32

8.1 List of the Sample Firms ....................................................................................................... 32

8.2 The VIF of Dummy Variables .............................................................................................. 33

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1 Introduction

In the thesis, dual class shares are two classes of common equities issued by a single firm with

the same cash flow right but different voting rights. According to standard asset pricing theory,

in which agency problem and liquidity compensation are not taken into consideration, shares

with equal payoff must have equal value and the same price. However, empirically there is a

price difference between dual class shares. Since the only difference for the two classes of

shares is voting rights, the price difference is defined as a voting premium. The existence of a

voting premium has been known by scholars for years. Lease et al. (1983) record a 5.44 percent

premium in the U.S.. Rydqvist (1987) finds the voting premium ranges between 2 percent and 6

percent from 1975 to 1985 in Sweden. Horner (1988) finds an over 10 percent premium in

Switzerland. Zingales (1994) finds an 81 percent premium in Italy.

Numerous literatures have linked the voting premium with corporate control. The logic behind it

is that the superior voting shares indicate a superior control power in a control contest. If control

is valuable, the superior voting shares would trade at a higher price than the inferior voting

shares. Lease et al. (1983) study 26 dual class firms in the U.S. and find positive and negative

voting premiums across firms. It suggests that there are both benefits and costs of corporate

control. Grossman and Hart (1988) develop a theoretical model to link the value of votes with

the private benefits of control. Following them, Zingales (1995) and Rydqvist (1996) have

derived further models. According to Zingales (1995), the voting premium reflects the

expectation that voting rights become valuable in a control contest, and the premium is a

function of the size of the private benefits of control. He studies the determinants of the value of

voting rights in U.S. corporations from 1984 to 1990. The study shows that the value of votes is

determined by the private benefits obtained from controlling the company, and ownership

structure has a major impact on the value of votes.

While lots of researches relate the voting premium to private benefits of control, fewer papers

relate it to liquidity. Large shareholders tend to have a stable holding of the superior voting

shares compared to the inferior voting shares, which makes the superior voting shares less liquid.

If liquidity is valued in the asset pricing, liquidity would be another determinant for the voting

premium and it would have a price discount effect on the superior voting shares. Evidence from

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Swiss dual class shares shows that both the degree of liquidity and the valuation of corporate

control benefits are major determinants for the relative stock price (Gardiol et al. 1997).

However, evidence from the Danish market from 1991 to 1999 shows that the voting premium

is negative for some firms for a long period and the premium is sensitive to liquidity but not to

corporate control (Neumann, 2003).

Given the fact that many firms in the Swedish stock market are dual class firms, this thesis aims

to investigate the voting premium in Swedish dual class shares, and to test the trade-off between

liquidity and corporate control. Based on a panel data regression model, the thesis provides

empirical evidence showing that whether corporate control and liquidity are valued in asset

pricing in the Swedish stock market.

Compared to other papers, the thesis puts more emphasis on liquidity. In the thesis, I try to use

free float to measure liquidity, a proxy noted by many investment banks in assessing the

acceptability for investment and liquidity of stock market during recent years (Chan et al., 2002).

The remainder of the paper is organized in the following way: In Section 2, a theoretical

background and previous empirical researches on corporate control and liquidity are presented.

Section 3 describes the data and the choice of variables. Descriptive statistics are also reported.

Section 4 explains the methodology and primary tests applied in the paper. Section 5 discusses

the empirical result. Section 6 presents the conclusions of the paper.

2 Theoretical Background

2.1 Private Benefits of Control

By holding company shares one owns the cash flow rights and voting rights. The voting rights

indicate the corporate control power. If control is valuable, the superior voting shares would

trade at a premium. Why is control valuable? The academic discussion starts from the agency

theory works by Berle and Means (1932) and Jensen and Meckling (1976). After that, a majority

literature has linked private benefits of control with the value of voting rights.

Early agency theory works by Berle and Means (1932) and Jensen and Meckling (1976) state

that agency costs exist with the separation of ownership and control. They suggest that the

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agency relationship is a contract under which the principles engage the agents to perform

services. There is a conflict of interest between these two parties. Since both parties are seeking

utility maximization, the agents might have incentive to act in self interest and might not act in

the best interest of the principles. As a result, the agents can acquire personal benefits at the

expense of the principles. The examples of the personal benefits ( so-called private benefits)

include appropriate corporate resources in the form of perquisites, the influence of electing the

Board of Directors, the ability to transfer assets on non-market terms to related parties (Nevona,

2003), self dealing, etc.

Rydqvist (1987) suggests that control is valuable because shareholders usually disagree on the

choice of the production plan. A shareholder with majority control could ensure the firm is

efficiently operated according to his point of view, while shareholders with minority control

have to accept the strategy even if they perceive the strategy to be inefficient.

Grossman and Hart (1988), Rydqvist (1996) and Zingales (1994, 1995) develop theoretical

models to link the value of votes with private benefits of control through ownership structure.

Grossman and Hart (1988) try to model the connection between agency problem and the voting

structure. They build a model in which there are two candidates for the management position,

the incumbent team and the rival team. The two candidates would hold a corporate control

contest. Different scenarios are analyzed to show how the structure of voting influences the

outcome of the control contests. Following their model, Zingales (1995) develops a model to

determine the optimal bidding strategy. Eq. (1) is a measurement of private benefits of control

given by Zingales (1995).

( ) is the price of the voting (nonvoting) share, is the private benefits of control

obtained by the winner in the control contest, is the proportion of voting share outstanding.

The equation shows that the voting premium is a function of the relative size of the private

benefits of control. In conclusion, according to Grossman and Hart (1988) and Zingales (1995),

when there is a control contest, the superior voting shares should receive a premium which is a

function of the private benefits of control (as is shown in Eq. (1)). On the other hand, the

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probability of a control contest is influenced by the ownership structure. Therefore, the

ownership structure has an impact on the voting premium.

Similar with the model above, Rydqvist (1987) proposes an oceanic games model to describe

the control power distribution among shareholders. The idea is that in a model of control test,

there are a few major shareholders and an ocean of an infinite number of minor shareholders.

The Shapley value per share of the voting game, called the oceanic power ratio, gives the

probability that small shareholders are pivotal in forming a majority coalition (Rydqvist, 1987).

He suggests that it could predict the voting premium. Following Rydqvist (1987), Zingales

(1994, 1995) and Neumann (2003) use the Shapley ratio as a proxy for control to predict voting

premium.

The existence of the private benefits of control has been shown by many empirical papers.

Previous researches also imply that there is a linkage between private benefits of control and the

voting premium. Lease et al. (1983) test the hypothesis that control is valued by capital market.

They suggest that there are both benefit and cost of corporate control. In the oceanic game

model, Rydqvist (1987) finds that the Shapley value for a control contest could predict the

voting premium in the Stockholm Stock Exchange. Barclay and Holderness (1989) argue that 20

percent premium of NYSE reflects private benefits that accrue exclusively to the block holder.

Rydqvist (1996) finds that the value of voting right depends on the ownership distribution.

Specifically, the voting premium is larger in the firms where two largest shareholders have more

equal shares than in the firms where there is only one dominant shareholder. Interestingly,

Rydqvist (1996) also finds that the voting premium is larger during periods of frequent takeover

activity. Zingales (1994) studies the value of voting rights in the Milan Stock Exchange (MSE)

and finds that the influence of voting rights in Italy is as large as dividend rights. The voting

premium on the MSE is intrinsically related to the value of control via ownership structure,

whereas the liquidity is difficult to judge. Zingales (1995) studies the determinants of the value

of voting rights in US corporations from 1984 to 1990. The study shows that the value of vote is

determined by the private benefit obtained from controlling the company, and the ownership

structure has a major impact on the value of votes. Doidge (2004) finds a lower voting premium

of firms that are cross-listings in the U.S. and he suggests that cross-listing leads to lower

private benefits of control. Nevona (2003) studies 661 dual class firms across 18 countries and

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finds that the value of the control block vote vary among countries. Guadalupe and González

(2010) investigate the impact of competition on private benefits of control in 16 countries,

where private benefits of control are presented as the voting premium. The result shows that

competition significantly reduces the private benefits of control.

2.2 Liquidity Theories

The impact of liquidity on the voting premium for dual class shares is less mentioned than

corporate control in previous studies. However, the impact liquidity to general asset pricing has

been discussed by lots of literatures.

Kyle (1985) builds a theoretical model to demonstrate that information asymmetry is correlated

with liquidity, and the liquidity could capture some pricing features of trading. He suggests that

market liquidity refers to three elements: tightness, depth and resiliency. In Kyle‟s (1985) model,

he use as the liquidity parameter and the model shows is an increasing function of

information asymmetry. Amihud and Mendelson (1986) use bid-ask spreads to measure

liquidity and find that asset returns increase significantly with bid-ask spreads, which indicate

liquidity is priced in the asset return. Liquidity based asset pricing models are developed by an

increasing number of papers. Brennan and Subrahmanyam (1996) research asset pricing on

compensation for illiquidity and find that there is a significant return premium for illiquidity due

to information asymmetry in the NYSE market. Holmström and Tirole (1998) develop an asset

pricing model based on industrial and financial corporations‟ desires for liquidity to fulfill future

cash needs. Acharya and dersen (2005) develop an equilibrium model with liquidity risk. They

find empirical evidence that liquidity risk affects asset pricing through various channels. Liu

(2006) builds a two-factor (market and liquidity) model that successfully describes the stock

returns. He finds empirical evidence that a significant liquidity risk premium exists in the

NASDAQ market.

According to the theories above, if dual class shares have different liquidity features, liquidity

would also be a determinant for the voting premium. Doidge (2003) argues that if the low voting

class is more liquid than the high voting class, it will have a negative impact on voting premium.

He states that, for example, foreign ownership restrictions on the superior voting shares exist in

some countries may reduce the liquidity of the superior voting shares, which leads to a

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downward bias in the voting premium. Neumann (2003) and Gardiol et al. (1997) discuss the

possible interaction of control and liquidity. Neumann (2003) finds that blockholders typically

concentrate their ownership in stock with superior rights. Their holding tends to be stable over

time that shares with inferior voting rights are expected to be more liquid. Gardiol et al. (1997)

argue that if control is really valued by the shareholders, there will be various mechanisms to

prevent control dilution and hostile takeover. Such mechanisms will reduce the liquidity of

shares with superior voting rights. If such liquidity differences occur, it is reasonable to observe

a price discount for the superior voting shares due to liquidity.

Smith and Amoako-Adu (1995) find that both higher liquidity in the restricted share class and

concentrated ownership reduce the price premium in dual class shares in Canada. Doidge (2003)

finds that the liquidity proxy is insignificant in the U.S. cross listing firms. His findings are

similar to Zingales (1994) and Zingales‟s (1995) findings, which state that liquidity doesn‟t have

an impact on the price premium in Italy and the U.S.. However, using a different liquidity

measure (implied liquidity risk estimated over 20 trading days), Neumann (2003) finds a

negative and significant impact in Denmark. Defining liquidity level as the relative proportion

of freely negotiable shares, Gardiol et al. (1997) also find that liquidity has an influence on the

voting premium in Switzerland.

3 Data

3.1 Sample Selection

The sample of firms in this paper consists of all the dual class firms from Nasdaq OMX

Stockholm except those that lack ownership structure data. In total there are 30 dual class firms

listed in Stockholm OMX, with 26 A/B shares, 3 A/C shares (Hufvudstaden AC, Industrivärden

AC, SEB AC) and 1 A/R share (Stora Enso AR). The basic information (industry sector, size) of

the samples is collected from Nasdaq OMX website1. The price data are collected from

Datastream. The ownership structure data are collected from the book “Ägarna och makten i

Sveriges” (Owners and Power in Sweden‟s Listed Companies) by Fristedt and Sundqvist (2005-

1For more information about the website see http://www.nasdaqomxnordic.com/shares/

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2009). The book publishes annually and provides detailed data of the share holdings of the 25

largest shareholders in Swedish listed firms.

In my sample of firms, I excluded TranscomWorldWide AB, Metro International AB and Stora

Enso AR because of the lack of ownership data. Hufvndstaden AC is excluded because its

voting arrangement is different from other dual class shares. Hufvndstaden C share has a higher

voting right than A share, and it is 1: 100 (10: 1 for others). Besides, the price data of Husqvarna

AB and the ownership data of Midsona AB are only available from 2007.

The 26 firms chosen can be seen in Appendix 8.1. The firms cover 7 industries (Industrials 8,

Financial 7, Consumer discretionary 3, Materials 3, IT 2, Health care 1, Telecommunication 1,

Consumer staples 1) with 19 large firms, 2 mid firms and 5 small firms. The information of

industry classification is collected from the Nordic list on Nasdaq OMX1, and it is in accordance

with the Global Industry Classification Standard (GICS).

The time period is from 2005 to 2009. This time period is chosen because before 2005, there is a

lack of free float data, and ownership structure data of 2010 hasn‟t published in the magazine

used in the study when the research is done. In all, there are 126 observations for an unbalanced

panel.

3.2 Price Data and the Voting Premium

Daily close prices of the firms from 03 January 2005 to 31 December 2009 are collected from

Datastream. The voting arrangements of A share and B2 share are collected from the book by

Fristedt and Sundqvist (2005-2009). The voting rights of the A shares and B shares of the

sample are 10: 1 from 2005 to 2009, where A shares are superior voting shares and B shares are

inferior voting shares3. A shares and B shares of the sample firms have equal cash flow each

1 For more information about the Nordic list see

http://www.nasdaqomxnordic.com/digitalAssets/76/76084_thenordiclistsep232011.xls

2 Note: C shares and R shares are referred as B shares in this paper for simplicity.

3 Before Sweden joined the EC in 1995 the Swedish Companies Act allowed proportions up to 1000 votes‟ a share.

Today, the highest allowed proportion is 10 votes a share (Jonnergård and Larsson, 2009).

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year.

The most common measurement of the voting premium is . The voting premium could

also be simply calculated as the price premium of dual class shares, as done in Lease et al. (1983)

and Neumann (2003):

(2)

Zingales (1995), Doidge (2004) and Guadalupe and González (2010) adjust the simple voting

premium to relative number of votes of an inferior voting share versus a superior voting share:

(3)

where is the price of the superior voting shares and is the price of the inferior voting

shares, and is the relative number of votes of the inferior voting shares compared to the

superior voting shares.

In this thesis, the voting arrangements across companies are constant. The voting premium

could be comparable without adjusting for relative votes. However, I consider it is necessary to

adjust the simple voting premium to the voting arrangement. Because if we need to set up model

with a larger sample, or need to compare the voting premium with that in other countries, the

voting premium adjusted to relative votes would be a more reliable measurement. Following

Zingales (1995), I measure the voting premium as in Eq. (3). Since the ownership structure data

are on an annual basis, I calculate the average of the daily voting premium annually and get the

voting premium on an annual basis.

Figure 3-1 shows the daily average voting premium from 2005 to 2009. From the figure we

could see that the average voting premium of all the samples has a positive value all the time. It

fluctuates at 4 percent level. The fluctuation of the voting premium is small across the years.

Only in the last half of 2008, the voting premium shows a higher fluctuation.

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Figure 3-1 Daily Average Voting Premium 2005 - 2009

Table 3-1 Description Statistics of the Voting Premium

The Voting Premium is calculated as , where ( ) is the price of the

superior (inferior) voting shares, is the relative number of votes of the inferior voting shares compared

to the superior voting shares. Data are based on the daily close price in Datastream. Two outliers (1724

percent from Ortivus 30-31 Oct 2006) are excluded.

Year Equally weighted mean Min Median Max Negative

2005 3.55% -3.68% 1.01% 35.45% 7

2006 3.33% -4.97% 1.33% 12.69% 4

2007 4.92% -2.51% 5.64% 15.42% 4

2008 6.47% -19.23% 4.08% 32.59% 4

2009 3.97% -31.84% 2.22% 36.45% 9

Table 3-1 presents the summary statistics for the annual voting premium of the samples. The

annual voting premium is calculated as the annual average of the daily premium for each firm.

From 2005 to 2009, the equally weighted mean of the voting premium each year remains

positive and is relatively stable. The value of the voting premium is relatively low in 2005 and

2006, and it increases a little in the year 2007 and 2008, but decreases in 2009. On average the

voting premium is quite low in Sweden compared to other countries. The table also shows that

in 2009, 9 out of 26 firms have a negative voting premium, implying a price discount for the

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superior voting shares. The number is 4 from 2006 to 2008, and 7 in 2005.

3.3 Corporate control Variables

In order to investigate the influence the corporate control on the voting premium, several

corporate control variables are used in the thesis. Based on previous theory (Grossman and Hart,

1988), the ownership distribution decides the probability of a control contest, so that the value

of control is linked with ownership structure. I use the size of the largest shareholder, which is

the percentage of votes held by the largest shareholder, as one of the corporate control variables.

The shareholder data are complex because there are cross holdings and family holdings. Before

collecting the corporate control variables, it is necessary to identify the principles for grouping

shareholders into coalition first. We assume one coalition (e.g. Family members) cooperates and

makes the same votes, so their holdings of votes could be added together as the holding of one

shareholder.

Family holdings are considered as the fraction of shares owned by family members or a close

group of individuals who do not belong to the same family like co-founders (Cronqvist and

Nillsson, 2003). Following Rydvist (1987), the holdings of the various members of the family

are added together. Besides, the holdings of the company that is majority controlled by the

family are added into the family holdings. For example, Wallenberg family is the largest family

owners in the sample of firms. The family has a controlling holding for their investment

company Investor. When the Wallenberg family members and Investor have shares of one

company, the family members and Investor will be considered as one coalition. Their votes will

be added together to be the voting power of one shareholder. Other family owners in the sample

of firms include Stenbeck family, Lundberg family and Söderberg family.

Except from family holdings, the holdings of different companies if they are majority controlled

by the same company are combined together. For example, in 2009, SCA has 29.8 percent of

votes held by Industrivärden, 6.4 percent of votes held by SHB pensionsstiftelse. However, SHB

group has majority holdings for Industrivärden. Therefore the holdings of Industrivärden and

SHB pensionsstiftelse are added together as the holdings of the SHB group in the case of SCA.

The SEB group is another shareholder coalition in the sample of firms. The grouping principles

are consistent with the data source by Fristedt and Sundqvist.

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Using the grouping principles above, the percentage of votes held by the largest shareholder is

collected from the book by Fristedt and Sundqvist (2005–2009) from 2005 to 2009. This

corporate control variable is called the size of the largest shareholder. When the size of the

largest shareholder is large, the voting power is concentrated to one shareholder or one

shareholder coalition. The control contest is less likely to occur and the voting rights become

less valuable. However, when the votes are diversely held by different small shareholders,

according to the oceanic theory by Rydqvist (1987) and the competition theory, the private

benefits and the value of voting rights will increase.

In addition, I introduce three dummy variables to describe different levels of corporate control.

The similar methodology is applied by Neumann (2003) and Gardiol et al. (1997). The value of

the dummy D40 (D30, D20) equals one when the largest shareholder holds more than 40 (30, 20)

percent of votes. The smallest dummy size I choose is D20, because for most of the firms in the

sample, there is at least one shareholder that holds more than 10 percent of votes. Dummy

variables are used as a supplement to the size of the largest shareholder, for dummies better

distinct the level of corporate control.

3.4 Liquidity Variables

The liquidity variables I use in the thesis include the daily trading volume and the free float.

Free float is defined as the total shares excluding shares held by strategic investors such as

governments, corporations, controlling shareholders, and board members (Chan et al., 2002). It

is the portion of the listed share capital that freely trades on the market. In asset allocation

literature, market capitalism is a proxy for liquidity. However, in continental European

companies, because of partial privatizations, family controlled companies, cross holding, etc.,

the difference between market capitalism and free float is larger (Hamon and Jacquilat, 1999).

Similar conclusion is found by Gingliner and Hamon (2007). Their findings suggest that the free

float is an effective proxy for liquidity in the continental European market. When a company has

a small free float, the availability of trading capital on the market is small, which suggest a low

level of liquidity. In the case of dual class shares, if the strategic investors tend to have a stable

holding of the superior voting shares, the free float of the superior voting shares would be

smaller than that of the inferior voting shares. I collect the annual free float data from the book

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by Fristedt and Sundqvist (2005-2009) from 2005 to 2009.

I calculate the ratio between the free float of A shares divided by the free float of B shares as

one liquidity variable. The ratio is defined as the relative free float. If the relative free float is

smaller than 1, A shares are less liquid than the B shares.

The free float is the portion of the freely traded shares on the market. In order to get the number

of the shares that is freely traded on the market, I collect the total share outstanding of each dual

class share from the same source as the free float. By multiplying the free float with the total

share outstanding, I obtain the number of the freely traded shares on the market.

I calculate the ratio between the freely traded A shares divided by the freely traded B shares as

another liquidity variable. The ratio is defined as the relative free float*, and it is shown in Eq.

(4)

Similar to the relative free float ratio, a ratio smaller than 1 indicate that there are less freely

traded A shares on the market, and A shares are less liquid than B shares.

However, there are some drawbacks to use the free float as liquidity proxy. Firstly, since the

concept of the strategic shareholder is obscure, the value of the free float may vary with

different definitions for the strategic shareholder. In the thesis, I collect the free float data from

one source to make sure that the definition of the strategic shareholder is constant within the

sample of firms. Secondly, the idea of the free float is related to ultimate owners and ownership

structure, which may infer a potential correlation between the liquidity variable and the

corporate control variable. In the thesis, the correlation between the liquidity variable and the

corporate control variable is tested before the regression.

The trading volume also reflects the liquidity of the shares. I collect the daily trading volume of

both classes of shares for all the sample of firms from Nasdaq OMX from 2005 to 2009.

Following Zingales (1994, 1995), I calculate the average of the daily trading volume of each

class of share annually, and define the ratio between the average trading volume of A shares

divided by the average trading volume of B shares as the relative volume. This is the third

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liquidity variable. When the relative volume is smaller than 1, the trading volume of A shares is

lower than the trading volume of B shares, and A shares are less liquid. The three liquidity

variables will be used respectively as independent variables to estimate voting premium.

3.5 Summary Statistics

Table 3-2 Summary Statistics

The voting Premium is calculated as , where ( ) is the price of the

superior (inferior) voting share, is the relative number of votes of the inferior voting shares compared

to the superior voting shares. The relative volume is the ratio average daily trading volume of A shares

divided by the average daily trading volume of B shares. Relative Free Float* is the ratio the freely

traded A shares divided by the freely traded B shares. Relative Free Float is the ratio the free float of A

shares divided by the free float of B shares. Size Largest shareholder is the percentage of votes held by

the largest shareholder. D40 (30, 20) is a dummy variable that equals 1 if one largest shareholder holds

over 40 (30, 20) percent of votes. Three free float outliers are excluded. Two voting premium outliers

(1724 percent from Ortivus 30-31 Oct 2006) are excluded. The time period is from 2005 to 2009.

Mean Std.Dev Min Max Median Obs

Voting Premium 4.48% 8.53% -31.84% 36.45% 1.72% 126

Relative Volume 10.880 39.713 0.0001 262.100 0.011 126

Relative Free Float* 3.085 9.543 0.000 43.670 0.110 123

Relative Free Float 0.498 0.332 0.050 1.590 0.410 123

Size Largest shareholder 36.73 16.27 14.40 72.80 31.80 126

D40 - - - - - 46

D30 - - - - - 64

D20 - - - - - 115

Table 3-3 presents summary statistics of the panel data set. The median of the annual average of

the daily premium is 1.72 percent. The mean voting premium is 4.48 percent. The level of the

voting premium in the data set for Sweden is comparable to the price premium 5.44 percent in

U.S. estimated by Lease et al. (1983), ranging between 2 percent to 6 percent from 1975 to 1985

in Sweden (Rydqvist, 1987), 5.8 percent in Denmark (Neumann, 2003) and 3.9 percent in

Norway (Doidge, 2004). It is lower than the value 10.47 percent in U.S. (Zingales, 1995), 16.2

percent in Switzerland (Doidge, 2004), 32.8 percent for OECD countries between 1990 and

2003 (Guadalupe and González, 2010), 19.31 percent in Canada between 1988 and 1992 (Smith

15

and Amoako-Adu, 1995) and 81 percent in Italy (Zingales, 1994). Generally the voting premium

in Sweden is lower than that of other countries except Norway.

The mean of the relative volume is 10.88, which indicates that the average trading volume of A

shares are larger than the average trading volume of B shares. It is not in accordance with the

hypothesis that A shares trade less than B shares. However, the median of the relative volume is

0.011. The distribution of relative volume is left skewed. A majority of the observations have a

relative volume smaller than 1. The mean of the relative volume in Sweden is larger than the

value 0.44 in the U.S. (Zingales, 1995), and 0.344 in Danmark (Neumann, 2003). The mean and

the median of the relative free float are 0.498 and 0.41 respectively, which indicates that the

superior voting shares are less liquid than the inferior voting shares. As to the relative freely

traded shares on market, the mean and the median of the relative free float* are 3.085 and 0.11

respectively. This is similar to the relative volume. It indicates that a majority of the sample

have less freely traded A shares on the market. Generally, most of the samples are consistent

with the hypothesis that the superior voting shares are less liquid than the inferior voting shares.

As to the corporate control variables, descriptive statistics show a high ownership concentration

in the firms. The average size of the largest shareholder is 36.73 percent, with the minimum size

14.4 percent, the maximum size 72.8 percent and the median size 31.8 percent. The mean and

the median are comparable to 32.33 and 28.38 in the U.S., while the minimum is much bigger

than 0.77 in U.S. (Zingales, 1995). This result is in accordance with the empirical finding by

Bergström and Rydqvist (1990) that a larger shareholder coalition holds more than 50 percent of

equity in many listed firms in Sweden. In 46 out of 126 observations, the largest shareholder

controls over 40 percent of the votes. Only in 11 out of 126 observations, no shareholder

controls over 20 percent of the votes.

16

4 Empirical Methodology

4.1 Regression Model Specification

To investigate the influence corporate control and liquidity on the voting premium, the

following equations are modeled:

(5)

(6)

The description of the variables is shown in Table 4-1.

Table 4-1 Description of the variables

Notation Variable Description

VP Voting premium

Size.L Size of largest

shareholder The percentage of votes held by the largest shareholder

D40 (30,20) Dummy variables Dummy variables that equals 1 if the largest shareholder holds

more than 40 (30, 20) percent of votes, zero otherwise

FreeFloat Relative free float The free float of A shares divided by the free float of B shares

FreeFloat* Relative free float* The freely traded A shares divided by the free traded B shares

RelVol Relative trading

volume

The average trading volume of A shares divided by the average

trading volume of B share

In Eq. (5) and Eq. (6), Size. L and Dummy variables are the corporate control variables;

FreeFloat, FreeFloat* and RelVol are the liquidity variables. According to the theory, the

coefficient is expected to be negative, because ownership concentration is expected to have a

negative effect on the voting premium. The coefficients in Eq. (5) and in Eq. (6) are

expected to be positive, because the lower value of the liquidity variable, the higher price

discount for A shares, and the lower value of the voting premium.

The dummy variables cannot be estimated together with the size of the largest shareholder,

because there is multicollinearity within the variables. The coefficients would lose their

17

significance when estimated together. The three liquidity variables cannot be used together

because of the same reason.

4.2 Primary Tests and Choice of Model

There are several considerations before proceeding to the regression. First of all, when choosing

between the panel data techniques, the fixed effects model and the random effects model, I

consider the fixed effects model is not appropriate for the analysis. The fixed effects model

allows the intercept to be a group specific term, but it cannot be used to investigate time-

invariant causes of the dependent variables, because time invariant characteristics of the

individuals are collinear with the individual dummies (Torres-Reyna, 2010, Doidge, 2004). In

my regression model, the corporate control variable (the size of the largest shareholder) does not

vary much across time for each firm in most cases, and some of the dummy variables D40 (30,

20) are invariant for one firm across the years. The individual dummies in the fixed effects

model would be collinear with them. The fixed effects model is more suitable to study the

impact of variables that vary over time, which is not the purpose of my study.

The random effects model, however, is able to capture the variation across the entities and

include the time invariant variables. Furthermore, Hausman test between the fixed effects model

and the random effects model suggests that the preferred model is the random effects model.

The null hypothesis of Hausman test is that the unique errors are not correlated with the

regressors. Table 4-2 presents the test statistics of Hausman test. The random effects model

controls unobserved individual effects for each specification.

For a general random effects model:

(7)

where is composite error, , is the idiosyncratic error.

To establish a random effects model, Eq. (5) becomes:

(8)

is a random error term and are uncorrelated with other regressors.

18

Breusch-Pagan Lagrange multiplier (LM) test is performed to test if the variance across entities

is zero ( . If

, there is no presence of an unobserved effect. In this case, OLS

pooled regression is valid as the random effects model (Woolridge, 2002). Breusch-Pagan

Lagrange multiplier (LM) test rejects the null hypothesis , which indicates that the

random effects model would be better than OLS pooled regression. Test statistics are presented

in Table 4-2.

The preference to the random effects method is not in accordance with Zingales‟s (1995) that

presents a fixed effect model for U.S. data, but is similar to Doidge‟s (2004) that uses a random

effects model. Considering that the method of OLS pooled regression has been adopted by many

previous researches (Zingales, 1994 1995; Rydqvist, 1987; Neumann, 2003). I adopt both

pooled OLS model and the random effects model in this thesis to confirm that that result is

robust with the methods of estimation.

Table 4-2 test results from Hausman test and Breusch-Pagan LM test

The tests are based on the equation

(8)

Estimation Eq. (8) P value(Hausman test) P value(Breusch-Pagan LM test)

Size.L & RelVol 0.4004 (accept) 7.72E-04 (reject)

Size.L & Free Float* 0.5257 (accept) 8.22E-04 (reject)

Size.L & Free Float 0.2176 (accept) 7.52E-04 (reject)

Null hypothesis Hausman test: the unique errors are not correlated with the regressors

Null hypothesis Breusch-Pagan LM test: variance across entities is zero (

4.3 Correlation and Multicollinearity Analysis

Table 5-1 shows the Pearson correlation coefficients with the corresponding t values. The voting

premium is negatively correlated to the size of the largest shareholder at 5 percent significant

level, and it is not significantly correlated to the liquidity variables. The correlation between the

size of the largest shareholder and the relative free float* is -0.283 at 0.1 percent significant

level, which means that a concentrate ownership is weakly correlated to a low level of freely

traded A shares on the market. Similar correlation coefficients are found with the size of the

largest shareholder and the other liquidity variables. It has been discussed in the theory part

19

about the interaction between control and liquidity, and now the significant correlation

coefficients have verified this interaction. However, this correlation may lead to a potential

multicollinearity problem.

Table 4-3 Correlation Statistics

Premium Size.L Free Float Free Float* Rel.Vol

Premium 1.00 -0.20*(-2.28) 0.12(1.28) 0.04 (0.48) 0.08 (0.90)

Size.L -0.20*(-2.28) 1.00 -0.49 ***(-6.25) -0.28 ***(-3.24) -0.26 ***(-3.05)

Free Float 0.12(1.28) -0.49 ***(-6.25) 1.00 0.57***(7.71) 0.57***(7.71)

Free Float* 0.04 (0.48) -0.28 ***(-3.24) 0.57***(7.71) 1.00 0.94 ***(29.84)

Rel.Vol 0.08 (0.90) -0.26 ***(-3.05) 0.57***(7.71) 0.94 ***(29.84) 1.00

Significant level: * 5% **1% ***0.1%

If multicollinearity exists in a regression, the variance of the coefficients would increase; the

variables would lose their significance even with a significant F value; the sign of the

coefficients would be incorrectly estimated; small changes in the data would produce large

change in the estimation (Belsley et al., 1980; Greene, 1993, O‟Brien, 2007). In order to control

on potential multicollinearity, the variance inflation factor (VIF) is applied to detect

multicollinearity before the regression. The VIF is a statistic to measure the degree of

multicollinearity in a regression model. It shows how much the variance of the coefficient

estimate is being inflated by multicollinearity. Most commonly the rule of 10 associated with

VIF are regarded as a sign of severe multicollinearity (O‟Brien, 2007). Table 4-2 presents the

VIF statistics. From the table, we can see that the VIF statistics range from 1.0 to 1.3, which

means that the variance of the coefficient hasn‟t been inflated by multicollinearity, and there is

no multicollinearity problem between the control variable and liquidity variables. We could

conclude that although there is an interaction between liquidity and control, the correlation is

not high enough to cause multicollinearity. Therefore the regression results are not biased

because of multicollinearity.

The correlation between the two liquidity variables is as high as 0.938 at 0.1 percent significant

level. This confirms that the liquidity variables cannot be estimated together or there would be a

multicollinearity problem. There is no multicollinearity between the dummy variables and the

liquidity variables as well. The VIF statistics of the dummy variables are shown in Appedix 8.2.

20

Table 4-4 test results from the Variance Inflation Factor (VIF)

The tests are based on the equation

(5)

Note: a pooled OLS model is used here, because the vif function is not applicable to the random effects model

VP VIF VIF VIF

Size.L 1.323 1.087 1.075

Free Float 1.323 - -

Free Float* - 1.087 -

Rel.Vol - - 1.075

4.4 Correction for Heteroskedasticity and Serial Correlation

To avoid potential bias in the estimation, I use the robust covariance matrix estimator vcovHC

to account for heteroskedasticity and serial correlation in the regression. Similar to the Newey-

West estimator for time series models, vcovHC, according to Croissant and Milo (2008), is an

estimator for panel models doing White-Arellano covariance matrix (White 1980, Arellano

1987), which is robust with heteroskedasticity and serial correlation (when clustered by groups).

The standard errors of the random effects model in the result part are corrected to account for

heteroskedasticity and serial correlation across the observations of the same firms in different

years.

For the OLS pooled regression model, the standard errors are corrected to account for

heteroskedasticity and serial correlation using Newey-West HAC covariance matrix estimator.

5 Statistical Analysis

5.1 Regression Result Analysis

21

Table 5-1 Random effects regression model

The voting Premium is calculated as , where ( ) is the price of the

superior (inferior) voting share, is the relative number of votes of the inferior voting shares compared

to the superior voting shares. The relative volume is the ratio average daily trading volume of A shares

divided by the average daily trading volume of B shares. Relative Free Float* is the ratio the freely

traded A shares divided by the freely traded B shares. Relative Free Float is the ratio the free float of A

shares divided by the free float of B shares. Size Largest shareholder is the percentage of votes held by

the largest shareholder. D40 (30, 20) is a dummy variable that equals 1 if one largest shareholder holds

over 40 (30, 20) percent of votes. Three free float outliers are excluded. Two voting premium outliers

(1724 percent from Ortivus 30-31 Oct 2006) are excluded. The time period is from 2005 to 2009.

Voting

Premium (1) (2) (3) (4) (5) (6) (7) (8) (9)

Intercept 7.380** 8.247** 6.828‟ 5.599*** 5.115** 8.237‟ 5.715*** 5.442** 8.759‟

(2.697) (2.949) (1.765) (3.724) (2.855) (1.678) (3.818) (2.994) (1.831)

Relative 0.014 - - 0.014 0.018 0.014 - - -

Volume

(0.751) - - (0.751) (0.935) (0.770) - - -

Relative - -0.006 - - - - -0.001 0.009 0.018

Free

Float* - (-0.062) - - - - (-0.016) (0.094) (0.239)

Relative - - 1.602 - - - - - -

Free

Float - - (0.504) - - - - - -

Size.L -0.084‟ -0.109* -0.092 - - - - - -

(-1.667) (-2.068) (-1.569) - - - - - -

D40 - - - -3.667* - - -4.132* - -

- - - (-2.301) - - (-2.550) - -

D30 - - - - -1.693 - - -2.424 -

- - - - (-0.810) - - (-1.164) -

D20 - - - - - -4.290 - - -4.976

- - - - - (-0.904) - - (-1.072)

R Square

1.9

2.4

2.6

3.0

1.2

2.3

3.3

1.1

2.5

(%)

Significant level: „10% *5% **1% ***0.1%

22

Table 5-2 OLS pooled regression model

The voting Premium is calculated as , where ( ) is the price of the

superior (inferior) voting share, is the relative number of votes of the inferior voting shares compared

to the superior voting shares. The relative volume is the ratio average daily trading volume of A shares

divided by the average daily trading volume of B shares. Relative Free Float* is the ratio the freely

traded A shares divided by the freely traded B shares. Relative Free Float is the ratio the free float of A

shares divided by the free float of B shares. Size Largest shareholder is the percentage of votes held by

the largest shareholder. D40 (30, 20) is a dummy variable that equals 1 if one largest shareholder holds

over 40 (30, 20) percent of the votes. Three free float outliers are excluded. Two voting premium outliers

(1724 percent from Ortivus 30-31 Oct 2006) are excluded. The time period is from 2005 to 2009.

Voting

Premium (1) (2) (3) (4) (5) (6) (7) (8) (9)

Intercept 8.125* 8.797** 8.493‟ 5.833** 5.437** 10.757‟ 5.839*** 5.677* 10.962‟

(2.446) (2.632) (1.800)' (3.326) (2.783) (1.822) (3.395) (2.847) (1.927)

Relative 0.006 - - 0.007 0.010 0.007 - - -

Volume

(0.302) - - (0.351) (0.477) (0.397) - - -

Relative - -0.020 - - - - -0.011 -0.004 0.007

Free

Float* - (-0.203) - - - - (-0.112) (-0.040) (0.095)

Relative - - 0.143 - - - - - -

Free

Float - - (0.047) - - - - - -

Size.L -0.101 -0.123‟ -0.118 - - - - - -

(-1.544) (-1.897) (-1.578) - - - - - -

D40 - - - -4.010* - - -4.346* - -

- - - (-2.019) - - (-2.248) - -

D30 - - - - -2.094 - - -2.783 -

- - - - (-0.780) - - (-1.087) -

D20 - - - - - -6.957 - - -7.356

- - - - - (-1.165) - - (-1.279)

R Square 4.1 5.1 5.1 5.6 2.0 5.8 5.9 2.7 6.2

(%)

Significant level: „10% *5% **1% ***0.1%

23

Table 5-1 and table 5-2 present the results from the random effects model and the OLS pooled

regression model1. The standard errors of the random effects model in the result part are

corrected to account for heteroskedasticity and serial correlation across the observations of the

same firms in different years.

In table 5-1, the estimated coefficients of the size of the largest shareholder always have the

expected sign (-0.084, -0.109 and -0.092), and they are significantly different from zero at 10

percent level and 5 percent level in the first two regressions. The result shows that the corporate

control influences the value of votes via ownership structure. The ownership concentration has a

negative effect on the voting premium. The significant negative sign of the size of the largest

shareholder is consistent with the theory of private benefits of control. When control is allocated

to one major shareholder, there is less probability of a corporate control contest, and the value of

control becomes less valuable. This negative relationship has explained the low voting premium

in Sweden in the summary statistics, for Sweden has a higher level of ownership concentration

compared to other countries. This result is similar to Rydqvist‟s (1996) findings that the voting

premium is small when there is only one dominant shareholder in the firm for the Stockholm

Stock Exchange. Zingales‟s (1994) uses the size of the largest shareholder as a proxy for the

private benefits that outside shareholder expect to receive. He finds a similar result that the

coefficient is negatively significant in the Milan Stock Exchange. The coefficient of Size.L from

Zingales (1994) is -0.77, which is more negative than the coefficient of Sweden. It is

understandable because the Italian market has high private benefits of control according to his

argument.

The coefficients of corporate control which are measured by the dummy variables are shown in

regression 4-9. The majority hold dummy D40 is significant (t test = -2.55) at 5 percent level.

The value of the coefficient is -3.667 in regression (4), -4.13 in regression (7). This finding is

the same with the expectation, and is in accordance with the result of the size of the largest

shareholder. It means that when a firm is controlled by one shareholder who has over 40 percent

1 Two voting premium outliers (1724 percent from Ortivus 30-31 Oct 2006) are excluded in the regression.

The free float of A share of Modern Times Group MTG, SWECO and Tele2 are zero in 2009, while that data

are 87.2, 50.5 and 75.5 in 2008. I dropped these three free float observations in 2009.

24

voting power, the voting rights would lose the value at the speed of 4.13 percent. This is because

when there is a majority shareholder, it becomes impossible for small shareholders to make

decisions through the voting rights to peruse private benefits (Gardiol et al., 1997). The

coefficients for dummy variables D30 and D20 are also negative but are insignificant. It

indicates that 40 percent could be a benchmark for the power of control to influence the value of

votes. This result is similar to the finding by Zingales (1994) who finds a majority owned

dummy have a strong negative effect on the voting premium in Italy. However, the coefficient

found in Italy has a larger absolute value -30.2. Gardiol et al. (1997) also find the significant

negative effects from the dummy variables D50, D20 and D10 in Switzerland. The coefficients

have value ranging from -10 to -15 which are also higher than that in Sweden. As it is shown in

the summary statistics, the Swedish listed firms have high levels of ownership concentration.

The largest shareholder holds over 10 percent of votes in all the sample of firms. Compared to

Sweden, the ownership distribution in Switzerland is more dispersed. That may be the reason

why the dominant holding of 10 percent and 20 percent control power has an influence on the

voting premium in Switzerland (Gardiol et al., 1997), but only a dominant holding of over 40

percent of control power has an impact on the voting premium in Sweden. On the other hand,

Neumann (2003) finds corporate control dummy variables don‟t affect the voting premium in

Denmark.

Overall, the coefficients of the corporate control variables infer that corporate control has an

impact on the voting premium via ownership structure in Sweden. This result also confirms the

theoretical model developed by Grossman and Hart (1988), Zingales (1995) that there is a

linkage between the value of votes and private benefits of control. Besides, the level of private

benefits in Sweden is relatively small because the coefficients of the corporate control variables

are relatively low.

The liquidity variable measured as the relative trading volume does not relate to the voting

premium in all the regressions, though all the coefficients have the expected signs. This means

that liquidity with the measurement of relative trading volume is not a determinant for the

voting premium for the sample. Using the same proxy for liquidity, similar insignificant results

are found by Zingale (1994, 1995) and Doidge (2004). A different result is found by Nevona

(2003) that relative turnover is negative and significant. Doidge (2004) argues that it is not a

25

serious concern because there is no strong theoretical prediction about the impact of relative

turnover on the voting premium.

The liquidity variable measured as relative free float*, which is the relative freely traded shares,

is insignificant in all the regressions. However, when corporate control variable is estimated

with relative free float*, the corporate control variable has a more significant t value. Besides,

despite of the insignificancy, the coefficients have the expected signs. The similar insignificant

coefficients are found when using relative free float as liquidity variable, and the coefficients

don‟t have the expected signs.

Overall, all the liquidity variables are not relevant to the voting premium in dual class shares in

Sweden. This is consistent with Zingales‟s (1994, 1995) findings for Italy and U.S. market, and

Doidge‟s (2004) finding for non-US firms listed in U.S. market. However, the result is different

from the findings by Gardiol et al. (1997) for Switzerland, Nevona (2003) for a cross-country

study, and Neumann (2003) for Danmark.

Why are the liquidity variables insignificant in the regressions? Although there is a weak

interaction between the liquidity variables and the control variables, the multicollinearity

analysis has already ruled out the bias caused by this correlation relationship. The insignificance

does not originate from multicollinearity. One explanation is that Nevona (2003) has invented a

different measurement to calculate liquidity which he finds significant. Gardiol et al. (1997)

uses the book value of inferior shares in the capital structure as a proxy for liquidity. Besides,

from the summary statistics we could see that not all the A shares are less liquid than B shares.

The liquidity differences between A shares and B shares are varying across firms. It is possible

that the phenomenon that the large shareholder tends to have a stable holding of the superior

voting shares is not significant in Sweden. Bergström and Rydqvist (1990) show that in Sweden,

largest shareholder coalition often invests in a large number of B shares which add little voting

power. In all, the insignificant liquidity variables demonstrate that liquidity is not a determinant

for the voting premium in Swedish firms.

The OLS pooled regression results in table 5-2 are similar to the random effects model. The

coefficients for the corporate control variables are significant and negative. The significant

levels of the corporate control variables have decreased slightly in the OLS pooled regression,

26

and the coefficients have higher negative values. The liquidity variables are not significantly

relevant to the voting premium in the OLS pooled regression. The result from the OLS pooled

regression has confirmed the robust of the estimation.

5.2 Endogenous Problems

One of the potential biases in my regression result is that the explanatory variables are likely to

be endogenous. According to Wooldridge (2002), endogeneity usually arises in one of the three

ways: Omitted Variables, Measurement Error and Simultaneity. In my regression model:

(5)

The voting premium probably has an impact on liquidity and control variables, because the

prices of the dual class shares could affect the trading volume and the holding of shares by the

shareholders. This is endogeneity caused by simultaneity. Moreover, we would worry that

control and liquidity variables are correlated from unobserved factors. As is shown in Breusch-

Pagan LM test, there are presents of unobserved effects in the panel data. Possibly that both

control and liquidity variables are correlated with legislation and the takeover bid rules, which

also affects the value of votes. This is endogeneity caused by omitted variables. The control

variable and the liquidity variable are weakly correlated to each other as well. At last,

measurement error in control and liquidity is always a possibility.

The random effects model employed may capture the unobserved effects and could deal with the

endogeneity caused by omitted variables for panel data. However, it is necessary to note that the

random effects model is a panel technique that is in developing and it may not be capable of

capturing all the unobserved effects. Another way to enhance the model is to take more

variables that may lead to the unobserved effects into the regression. These variables include

dummies for legislation, size of the firm, conversion rights, etc. A more robust regression model

could be estimated by testing these variables together with the control and liquidity variables.

Regarding the fact that the liquidity variables are weakly correlated with the control variables, a

better liquidity proxy that is uncorrelated with control variable may improve the estimation.

Instrumental variables method, such as two-stage least squares method (2SLS) regression, could

be used to deal with Endogeneity caused by omitted variables as well.

27

Endogeneity caused by simultaneity is a more complicated econometric problem, because all the

variables (dependent and independent) may influence on each other. Wooldridge (2002)

proposed simultaneity equation models (SEMs) to deal with such endogenous. In SEMs, two or

more variables are jointly determined by a system of equations. The equations system could be

estimated by 2SLS or 3SLS. However, Wooldridge (2002) states that system procedures are

efficient if all equations are correctly specified, but single-equation methods are more robust.

6 Conclusion and Prospective

This thesis investigates the impact of corporate control and liquidity on the voting premium in

the Nasdaq OMX Stockholm from 2005 to 2009. This thesis uses the size of the largest

shareholder and the dummy variables as corporate control variables. The relative trading volume,

the relative freely traded shares and the relative free float are used as liquidity variables. The

thesis finds that the Swedish listed dual class shares have an average voting premium of 4.48

percent, which is relatively low compared to other countries. The voting power is quite

concentrated to one largest shareholder across the firms. For most of the firms, A shares are less

liquid than B shares. The Pearson correlation shows that there is a weak negative interaction

between corporate control and liquidity, but the correlation would not produce multicollinearity.

The estimations of the random effects regression and the pooled OLS regression show that

corporate control is a determinant for the voting premium in Swedish listed shares via

ownership structure. The size of the largest shareholder has a negative impact on the voting

premium. The intuition behind it is that the concentration of control power reduces the

probability of a control contest, which makes the value of votes (value of control) less valuable.

This negative coefficient has explained that the low voting premium in Sweden is a result of the

concentrated ownership structure. Besides, a majority ownership over 40 percent significantly

reduces the voting premium while the majority ownership below 30 percent is not recorded to

be significant. The empirical findings in Sweden confirm the theoretical models developed by

Grossman and Hart (1988) and Zingales (1995) that there is a linkage between the value of votes

and private benefits of control. The level of private benefits in Sweden is relatively small

because the coefficients of the corporate control variables are relatively low compared to other

empirical papers (Zingales 1994, Gardiol et al. 1997).

28

On the other hand, the thesis finds that liquidity is not a determinant for the voting premium in

Sweden. All the liquidity variables I use do not have an impact on the voting premium. This

may result from the tendency of stable holding of the superior voting shares varies across the

firms in the Swedish market. As suggest by Bergström and Rydqvist (1990) that in Sweden,

largest shareholder coalition often invests in a large number of B shares which add little voting

power.

There is a potential endogenous problem that may influence the regression results. I used the

random effect model to control for the endogeneity caused by omitted variables for panel data.

However, the endogeneity caused by simultaneity (the voting premium has an impact on control

and liquidity) is not controlled in the thesis. To enhance the model, simultaneity equation

models estimated by 2SLS could be built for the further research. Other dynamic liquidity

proxies could be tried to estimate the voting premium. Another possible way to enhance the

model is to include more independent variables such as legislation and takeover bid rules to

capture the unobserved effects.

Acknowledgement

I would like to thank Jens Josephson and Bo Larsson, for helpful suggestions and insightful

comments.

29

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8 Appendix

8.1 List of the Sample Firms

Sources: http://www.nasdaqomxnordic.com/digitalAssets/76/76084_thenordiclistsep232011.xls the

Nordic list, 2011, 3rd

Jan

Name Sector Size Voting Rights(A : B)

ACAP Invest AB Industrials SMALL 10 : 1

Atlas Copco AB Industrials LARGE 10 : 1

Electrolux AB Consumer Discretionary LARGE 10 : 1

Ericsson AB IT LARGE 10 : 1

Holmen AB Materials LARGE 10 : 1

Industrial Financial Systems AB IT MID 10 : 1

Industrivärden, AB Financials LARGE 10 : 1

Investor AB Financials LARGE 10 : 1

Kinnevik AB Financials LARGE 10 : 1

Midway Holding AB Industrials SMALL 10 : 1

Modern Times Group MTG AB Consumer Discretionary LARGE 10 : 1

NCC AB Industrials LARGE 10 : 1

Ortivus AB Health Care SMALL 10 : 1

Ratos AB Financials LARGE 10 : 1

SCA AB Industrials LARGE 10 : 1

SCANIA AB Materials LARGE 10 : 1

SEB AB Financials LARGE 10 : 1

SKF AB Industrials LARGE 10 : 1

SSAB AB Materials LARGE 10 : 1

Svenska Handelsbanken Financials LARGE 10 : 1

Svolder AB Financials SMALL 10 : 1

SWECO AB Industrials MID 10 : 1

Tele 2 AB Telecommunication

Services

LARGE 10 : 1

Volvo AB Industrials LARGE 10 : 1

Hufvudstaden AB Consumer Discretionary LARGE 10 : 1

Midelfart Sonesson AB Consumer Staples SMALL 10 : 1

33

8.2 The VIF of Dummy Variables

Table 4-1 test results from the Variance Inflation Factor (VIF)

The tests are based on the equation

(6)

Note: a pooled OLS model is used here, because the vif function is not applicable to the random effects model

VP VIF VIF VIF VIF VIF VIF

D40 1.055 - - 1.044 - -

D30 - 1.093 - - 1.084 -

D20 - - 1.021 - - 1.046

Free Float* 1.055 1.093 1.021 - - -

Rel.Vol - - - 1.044 1.084 1.046