Does Diversification Create Value for the Company? · 2010. 6. 7. · GD Geographical Diversification ID Industrial Diversification MTB Market to Book MVE Market Value of Equity Obs

GHENT UNIVERSITY

FACULTY OF ECONOMICS AND BUSINESS

ADMINISTRATION

ACADEMIC YEAR 2008 – 2009

Does Diversification Create Value for the Company? European Evidence.

Master thesis submitted to obtain the degree of

Master in Business Economics

Elke Verstraeten

Maarten Wybaillie

under the guidance of

Dr. Olivier De Jonghe

I

GHENT UNIVERSITY

FACULTY OF ECONOMICS AND BUSINESS

ADMINISTRATION

ACADEMIC YEAR 2008 – 2009


Master thesis submitted to obtain the degree of

Master in Business Economics

Elke Verstraeten

Maarten Wybaillie

under the guidance of

Dr. Olivier De Jonghe

II

CONFIDENTIALITY CLAUSE PERMISSION

The undersigned declare that the contents of this masters’ dissertation can be

used and/or consulted and/or reproduced, provided that the sources are quoted.

Elke Verstraeten Maarten Wybaillie

III

ACKNOWLEDGMENTS

We would like to thank Olivier De Jonghe, Riet De Baets, Koen Berteele, and

our parents and friends for their comments and suggestions.

The research in this paper was conducted while the authors were master

students at the Ghent University.

IV

TABLE OF CONTENTS

Abbreviations Used .................................................................................................... V List of Tables ............................................................................................................. VI Abstract .................................................................................................................... VII 1. Introduction............................................................................................................. 1 2. Literature Review ................................................................................................... 3

2.1. Geographic Diversification ............................................................................... 3 2.2. Industrial Diversification ................................................................................... 6 2.3. Combined View................................................................................................ 8 2.4. European Studies .......................................................................................... 12

3. Sample Selection and Methodology ..................................................................... 15

3.1. Sample Frame and Sample Description ........................................................ 15 3.2. Measures ....................................................................................................... 18 3.3. Descriptive Statistics...................................................................................... 19

4. Method of Analysis ............................................................................................... 24

4.1. Multivariate Analysis ...................................................................................... 24 4.2. Main Results .................................................................................................. 25 4.3. Sensitivity and Robustness Tests .................................................................. 28 4.4. Discussion and Interpretations....................................................................... 37

5. Conclusion............................................................................................................ 39

5.1. Summary ....................................................................................................... 39 5.2. Limitations and Guidelines for Further Investigation ...................................... 39

References .............................................................................................................. VIII List of Appendices ..................................................................................................... XI

V

ABBREVIATIONS USED

BVL Book Value of Liabilities

BVTA Book Value of Total Assets

capex Capital Expenditure

EBIT Earnings Before Interest and Taxes

e.g. example given

EU15 European Union with 15 member states

GD Geographical Diversification

ID Industrial Diversification

MTB Market to Book

MVE Market Value of Equity

Obs. Observations

OLS Ordinary Least Squares

R&D Research & Development

SIC Standard Industrial Classification

U.S. United States of America

U.K. United Kingdom

VI

LIST OF TABLES

Table 1: Concise Summary of Literature Review...................................................... 13 Table 2: Geographical and Industrial Distribution of the Sample .............................. 17 Table 3: Descriptive Statistics of Sample for MTB Value Measures ......................... 22 Table 4: Distribution of MTB Value across Diversification Categories...................... 23 Table 5: Multivariate Test for Diversification Value Impacts ..................................... 27 Table 6: Geographical and Industrial Distribution of the Sample (Sensitivity and Robustness Tests, part 1) ............................................................... 30 Table 7: Multivariate Test for Diversification Value Impacts (Sensitivity and Robustness Tests, part 1) ............................................................... 32 Table 8: Geographical and Industrial Distribution of the Sample (Sensitivity and Robustness Tests, part 2) ............................................................... 34 Table 9: Multivariate Test for Diversification Value Impacts (Sensitivity and Robustness Tests, part 2) ............................................................... 36 APPENDICES: Table 10: Geographical and Industrial Distribution of the Sample ............................ XII Table 11: Regression details for sector, country and year dummies – Model IV ........ XIII Table 12: Regression details for sector, country and year dummies – Model V.........XIV

VII


ABSTRACT

This paper examines the impact of geographical and industrial diversification on

firm value for a sample of 1 921 European companies. During the period 1996 till

2008, this results in 12 427 observations. Confirming the predictions of most theories,

a geographic (industrial) diversification premium (discount) of 10,2% and 7,6%

respectively, is found. Furthermore, the extension of the research model with an

interaction coefficient shows that being doubly diversified has a positive impact on

firm value. In addition, an interesting comparison between diversification within

European firms across European borders and American firms across the U.S.

borders, leads to think that diversification is valued higher in European firms.

1

1. INTRODUCTION

Since the 1980’s, academic and business communities have had substantial

interest in the diversification discount. Lang & Stulz (1994) found that multi-activity

firms trade at an average discount relative to firms that focus on a single activity. This

finding was the starting point of an active debate about the impact of corporate

diversification in both its geographical and industrial dimension on the value of a

company. Theoretical argumentation leads to value-enhancing as well as value

reducing effects, associated with both forms of corporate diversification. The potential

benefits of a firm active in multiple lines of business and/or in different countries

include lower taxes, economies of scale, the possibility to spread risks, a greater debt

capacity, a preference of investors for diversity, and a greater operating efficiency

and flexibility. According to Berger & Ofek (1995), “the potential costs of

diversification include the use of increased discretionary resources to undertake

value-decreasing investments, cross-subsidies that allow poor segments to drain

resources from better-performing segments, and miss-alignment of incentives

between central and divisional managers” (Berger & Ofek, 1995, p.40). There is no

clear prediction about the overall value effect of diversification, and empirical

research in the area of corporate diversification has not led to an univocal opinion

yet. However, the impact of a potential advantage or disadvantage caused by

diversification, is of growing importance nowadays because of increasing

globalization.

Industrial diversification is more often subject of study than geographical

diversification. Different authors such as Bodnar, Tang & Weintrop (1999), and

Barnes & Brown (2006), tried to fill up this gap by making different models that

measure the value impact of both geographic and industrial diversification. Literature

on the value impact of diversification decisions has focused on U.S. and U.K. firms

and has rarely included an interaction coefficient to observe the influence of different

diversification combinations. In addition, there are only a few studies with a European

sample (Moerman (2008), Joliet & Hubner (2008)), but there is no record of any

study which investigates the impact of diversification on the value of European firms.

The initial aim of this paper is to exploit this gap by examining the overall impact

of being doubly diversified, as well as the independent impact of geographical and

industrial diversification on firm value. The research is rooted on methodologies of

2

Bodnar, Tang & Weintrop (1999), and Barnes & Brown (2006). By following a long

tradition in American and U.K. diversification research, their methodologies provide a

model to measure the impact of industrial and geographical diversification on a firm’s

value, measured by Market To Book (MTB).

This paper estimates the value impact of both forms of diversification, for a

sample of European companies over the period 1996-2008, that enhances 1 921

firms which equals 12 427 observations. The MTB analysis of the European market

provides evidence of a significant geographic (industrial) diversification premium

(discount) of approximately 10,2% and 7,6% respectively. In addition, the analysis

shows an important impact on a firm’s value of being doubly diversified: the

interaction coefficient provides a value increase that even compensates the industrial

diversification discount. Furthermore, the impact and sensitivity of various changes in

definitions have been explored. These controls confirm the robustness of the model.

In addition, the impact of diversification outside Europe on the value of European

firms is studied.

The results obtained by this research have important implications for the

literature about diversification. First, this study is one of the rare studies about

diversification with a fully European sample. Second, it measures the value impact of

both geographical and industrial diversification, because of their mutual dependence.

Furthermore, this study introduces an interaction coefficient to measure the impact of

being doubly diversified on a firm’s value. The results of that interaction coefficient

prove the usefulness of investigating the impact of being doubly diversified. An

interesting comparison of the geographical diversification premium between Europe

and the U.S. is made. The results of this paper can help managers in their

diversification decision process.

The remainder of this paper is organized as follows: Section 2 summarizes the

extent literature related to geographic and industrial diversification. Section 3

describes the sample selection and the adopted methodology. Section 4 presents the

results of the main analyses and several robustness checks. The final section

summarizes, states the limitations and concludes.

3

2. LITERATURE REVIEW

The literature on both geographic and industrial diversification is extensive. In

the combined view literature, Bodnar, Tang & Weintrop (1999), and

Barnes & Brown (2006) are some of the most investigated authors. They provide a

relativily complete literature overview of both forms of corporate diversification. In

case of unclear information and missing references, we will try to complete their

literature view.

2.1. Geographic Diversification

Empirical research into the value impact of geographic diversification has a rich

history. Several synonyms for geographic diversification, such as global

diversification or international diversification are used.

In the literature about geographic diversification, four general reasons for a

company to diversify geographically are studied. These reasons are listed by

Bodnar, Tang & Weintrop (1999).

First, geographic diversification has its roots in studies concerning foreign

direct investment: Because of the imperfection of markets, assets cannot be sold

for their internal value (Caves (1971), and Hymner (1976)). Consequently, firms have

to invest abroad to exploit firm-specific assets and to obtain rents on these assets.

Internationalizing the firms can lead to economies of scale of specific assets, such as

marketing and research and development. If so, the incrementing size of the firm’s

activities using these specific assets, will cause a value increase of the firm. The

internalization theory of multinational firms also argues that direct international

investment occurs (so that the value of a firm increases) when markets internalize

their information-related intangible assets with public good properties.

Secondly, geographic diversification can create value through the operational flexibility of a multinational corporate system (Kogut (1983)). An unknown

international environment causes a lot of uncertainty e.g. demand shocks are not

perfectly correlated. Consequently, a geographically diversified network will give the

firm the possibility to exploit market conditions, and this network will add additional

value to the firm. On the contrary, Reeb, Kwok & Back (1998) provide evidence of a

significant positive relationship between systematic risk and international

4

diversification. This means that negative influences, which increase overall firm

volatility of returns, dominate the international diversification benefit of reduced cash

flow correlation which causes an increase of systematic risk. Their study therefore,

suggests a value discount for internationally diversifying firms.

Thirdly, because of its ability to arbitrage institutional restrictions, such as tax

codes and financial restrictions, geographically diversified firms can be more

valuable. In contrast to earlier empirical work, which generally focused on financial

performance rather than value, Errunza & Senbet (1981) were the first authors to

examine the empirical implications of geographic diversification for firm value. Their

research leads to a significant positive relation between excess value and

international activity. Multinational firms which operate in multiple geographic

locations, show remarkable possibilities to make value-maximizing conditional

decisions. As a result, the expected cash flow of diversified firms will increase

compared to the expected cash flow of domestic firms. In 1984, they re-examined

their research question on a larger database and looked into the effect of firm size,

using different measures of international activity. Errunza & Senbet (1984) again find

a positive correlation between geographical diversification and excess value. These

studies cannot provide an estimate of the geographical diversification discount

because they only study multinational firms.

Finally, value from corporate geographic diversification can be created by

investor preferences. Morck & Yeung (1992) confirm the internalization theory in an

event study test. They explain why investors judge diversification to be expensive: to

investors, multinational firms represent a portfolio of geographically spread

companies as a claim on a collection of profit streams from various areas of the

world. Thus, investors should be willing to pay more for shares of global firms for

providing this service. This premium is a cause of the increased value of diversified

firms. In addition Fauver, Houston & Naranjo (2003) argue that, if there is a lack of

shareholder protection in domestic markets and when external financing is difficult,

internal capital markets will be particularly valuable for diversified firms. Under these

circumstances, there can be a positive impact on the value of a company as the

benefits of corporate diversification outweigh the agency-related costs. This premium

5

contrasts the diversification discount among high-income countries where capital

markets are integrated and well developed, so that external funding is not difficult.

This paper will study the impact of diversification on the value of a company.

Several studies already showed that geographic diversification can enhance the

value of a firm. To reflect the benefits which are only available for geographically

diversified firms (for instance economies of scale of specific assets), the value of

these firms should increase. More general, the value of geographically diversified

firms should be higher than the value of domestic firms. Research on this subject by

Kogut & Kulatilaka (1994) concludes that the value of a geographically diversified

firm should be increasing with certain characteristics. These characteristics can be

both global manufacturing and production shifting as flexibility options. Being

operational across different regulatory and consumer markets as well as the volatility

of the environment in which it operates, can lead to a higher value for companies.

Nevertheless, there are also several studies that show a negative impact of

geographic diversification on the firm’s value. The dominant logic behind these

studies is that the principal-agency problem will increase when the organization

becomes more complex. Shareholders seek value maximization in contrast to

managers, who act in their own interest. A common solution to solve this incentive

problem is giving equity stakes to managers. This results in a higher concern about

the firm’s specific risk. As a result, managers will favor geographic diversification

because it reduces the firm specific risk, even if it results in a lower shareholder

value.

A recent study of Doukas & Kan (2006) supports a lower shareholder value of

geographical diversification in a contingent claims framework. By using a database of

355 U.S. acquisitions during the period 1992 till 1997 with 612 observations, they

confirm that more foreign involvement increases bondholder value while it decreases

shareholder value. More precisely, they explain it as follows: “the univariate analysis

results indicate that globally diversified bidders trade at a discount regardless of their

industrial structure” (Doukas & Kan, 2006, p.358). Their multivariate analysis results

also indicate that global diversification harms shareholder value. Furthermore, they

provide strong evidence in support of the risk-reduction hypothesis of global

diversification, which leads to an increase in bondholders’ wealth. Both results are in

line with the contingent claims theory that predicts that global diversification has a

6

positive impact on bondholders' wealth while it has a negative influence on

shareholders’ value, in this case a global diversification discount. They conclude that

geographical diversification does not decrease the overall value of a firm.

Foregoing arguments lead to zero hypothesis 1:

H01: Geographic diversification has no impact on the value of European companies.

For this zero hypothesis, both positive and negative alternative hypotheses will

be tested.

2.2. Industrial Diversification

For many years, it was taken for granted that industrial diversification was the

key to success because of a greater operating efficiency, the possibility to spread

risks, a greater debt capacity, and lower taxes. Since the diversification boom in the

1960s, academics are fascinated by the question of the impact of industrial

diversification on the value of a company. As mentioned in the introduction, the

impact of industrial diversification on a firm’s value has been more thoroughly

examined than the impact of geographic diversification.

The cornerstone of the literature about industrial diversification is a paper written

by Lang & Stulz (1994). They are the first to focus on the value impact of industrial

diversification and demonstrate a negative relation between the value, as measured

by both Tobin’s q and Market To Book, and industrial diversification in the U.S.

Berger & Ofek (1995) use the market value of industrially diversified firms to

measure the overall value impact of industrial diversification. They compare it to the

sum of the imputed value of each industrial segment and register a value loss. The

explanation for this are problems of over-investment in industries with low growth

opportunities and cross-subsidizing of loss generating activities. Such a value loss

will be smaller when the diversified firms stay in the same sector (when they have the

same 2-digit SIC code) and when they take profit out of tax benefits of diversification.

Servaes (1996) conducts his research about the value impact of being active in

different segments during the diversification boom from 1960 till 1980. He finds no

evidence for a premium. On the contrary, he even finds a waning diversification

discount. When the discount was still large during the 1960’s, firms with low insider

7

ownership were more inclined to diversify. This effect reversed during the 1970’s

when the discount declined to zero.

A few years later, Rajan, Servaes & Zingales (2000) modeled the internal power

fights by the allocation of resources between divisions of an industrially diversified

firm. They conclude that funds will be transferred from divisions with poor

opportunities to divisions with good opportunities. Nevertheless, higher levels of

diversification might harm these transfers, leading to inefficient investments. This

misallocation of funds will destroy value through overinvestment in value-destroying

projects (infra, p.10).

Furthermore, Brusco & Panunzi (2000) show that this diversification discount

will not necessarily be eliminated by ex-post allocations of funds. Moreover, they

prove that asymmetries in size and growth prospects increase the diversification

discount.

Graham, Lemmon & Wolf (2002) find no such a discount in their study about

industrial diversification. According to them, the excess value reduction occurs

because of acquiring already discounted business units and not because diversifying

destroys value.

Campa & Kedia (2002) find a strong negative correlation between a firm’s

choice to diversify and its value. In their own words: “firms that choose to diversify

have a higher value than existing firms in their industry and lower value than other

firms in the industry that remain focused” (Campa & Kedia, 2002, p.1759).

Villalonga (2004a) uses a unique new database that covers the whole U.S.

economy and shows a diversification premium, which is robust to variations in

sample, business unit definition and measures of excess value and diversification. In

a second study, Villalonga (2004b) points out that firms do not randomly become

diversified, but rather endogenously choose to do so. Her study shows that

diversified firms trade at a discount prior to becoming diversified. However, when

controlling the self-selection bias in diversified firms, the discount disappears.

In addition, Bohl & Pal (2006) find a diversification premium of 30%, in contrast

with previous findings stressing the agency problem of U.K. conglomerates. Their

sample contains all constituent companies of the FTSE all-share index listed on the

London Stock Exchange over the 1998 – 2003 period and de-listed companies (both

diversified and focused firms) that provide balance-sheet, profit and loss statements

for the selected period. A comparison between U.S. studies and their own U.K. study

8

indicates a major cause for such a diversification premium. While U.S. studies

explain the diversification discount of conglomerates relative to focused firms by firm-

specific characteristics, they find significant macroeconomic effects for the U.K.

conglomerates. More precisely, “less favorable macroeconomic conditions hinder

firms’ growth, decrease their market value and affect positively their decision to

operate as a diversified firm” (Bohl & Pal, 2006, p22).

However, recent findings of Mackey & Barney (2005) tend to support the original

conclusion that unrelated acquisitions can reduce firm value. In their study, a

comparison is made between the diversification decision versus the decision to pay

dividends or repurchase firm stock. They find that diversification destroys value when

compared to alternative payout policies. Their result is robust to the use of

econometric techniques that control self-selection of the diversification decision.

Furthermore Gomes & Livdan (2004), Schoar (2002), and Maksimovic & Philips

(2002) investigate the productivity of conglomerates and stand-alone firms as a result

of industrial diversification. Because these studies do not handle real value creation,

their relevance is limited for the empirical study in this paper.

Hence, zero hypothesis 2 states:

H02: Industrial diversification has no impact on the value of European companies.


be tested.

2.3. Combined View

None of the papers on industrial diversification in the literature review above

consider geographic diversification, nor do any of the papers on geographic

diversification consider industrial diversification. In the literature review above, the

authors could not give an univocal conclusion about the existence of a diversification

premium or a diversification discount. Their conflicting results and interpretations can

be caused by the bias in the estimated effect of diversification on performance across

a large variety of industries (Santalo & Becerra (2008)) on the one hand and the

estimated value impact of industrial diversification for studies about geographic

diversification (and vice versa) if these two phenomena are related on the other

hand. According to Bodnar et al. (1999) “One must consider both forms of

9

diversification simultaneously in order to generate an unbiased estimate of the impact

of industrial diversification on firm value. Such an approach is also necessary to

obtain an unbiased estimate of the value impact of geographic diversification.”

(Bodnar, Tang & Weintrop, 1999, p.8).

In spite of the large amount of studies without a combined view, there are two

leading studies with a combined view on the diversification topic. In a non-published

working paper Bodnar et al. (1999) examined the value impact of diversification using

a framework that controls both forms of corporate diversification. They used the basic

models of Errunza & Senbet (1981) and Lang & Stulz (1994) on a sample of 7000

U.S. firms for the period 1984 to 1997. They report evidence of a 2,7% value

premium for geographic diversification and a 6% value discount for industrial

diversification.

In 2006, Barnes & Brown (2006) exploit the Lang & Stulz (1994),

Berger & Ofek (1995), and Bodnar et al. (1999) methodologies on a sample of U.K.

firms. They control the form of diversification in assessing the value impact on their

U.K. sample for the period 1996-2000, and report evidence of a 14% perverse

geographic discount and no systematic industrial value impact.

The combined view has become a hot topic in today’s research about

diversification. Denis, Denis & Yost (2002) use financial information of U.S. firms

from 1984 till 1997 and their selection results in 44 288 firm-years associated with

7 520 firms. They find a rise in the scope of geographical diversification over time.

However, they remark that this boost in geographical diversification does not come

from a substitution of geographical by industrial diversification. Furthermore, their

estimation of Ordinary Least Squares (OLS) regressions of excess value on dummy

variables leads to the conclusion that the discounts for both forms of diversification

are approximately the same in size. When looking at the effect of changes in

diversification, they find that increasing the scope of geographical diversification

reduces excess value while a reduction of the scope increases excess value. They

conclude that the gains of geographical diversification are more important than the

costs. Robustness tests prove that the discount associated with geographical

diversification has remained relatively stable over time. By contrast, the value

10

discounts associated with industrial diversification decline over time. Similarly, the

discount for being both industrially and geographically diversified declines.

Fauver, Houston & Naranjo (2004) investigate the value of industrial and

international diversification for more than 3 000 firms in Germany, the U.K., and the

U.S. In line with Lang & Stulz (1994), Berger & Ofek (1995), and Rajan et al. (2000),

they find an industrial diversification discount in the U.K. and the U.S. Furthermore,

they find, just as Denis et al. (2002), that U.S. multinationals trade at a discount

relative to firms operating only in the domestic market. This result is robust to

different specifications and to different benchmarks used to estimate the value of

diversification. On the contrary, they find no such discount for U.K. or German firms.

International diversification has no effect on their firm value. There are two possible

explanations for this result. Maybe the benefits of diversifying overseas are smaller

for U.S. firms and/or the agency and coordination costs of multinational expansion

are larger for U.S. firms. In the robustness test, Fauver et al. (2004) control agency

costs associated with ownership concentration as suggested by Morck et al. (1988)

and Servaes (1990). The effects of ownership concentration are significantly different

for focused and diversified firms, and these effects also vary across the three

countries. These results suggest that ownership concentration and excess value are

linked and that this link varies for focused and diversified firms.

In contrast to the majority of studies about diversification,

Freund, Trahan, and Vasudevan (2007) use a case study to test the impact of

increases in global and industrial diversification on firm value and operating

performance directly. The sample they use represents 194 U.S. firms that acquired

foreign companies between 1985 and 1998. They base their investigation on a recent

trend: “On the one hand, U.S. firms have greatly expanded overseas operations in

the past two decades. But, at the same time, there has been a tendency for firms to

divest unrelated assets and to focus on core businesses, in other words, to reduce

industrial diversification.” (Freund, Trahan, & Vasudevan, 2007, p.159). Their findings

lead to several generalizations. First, announcement period returns are significantly

positive for the acquirers. The stock-price reaction is greater for firms with fewer

growth opportunities and not significant for acquisitions by high-growth firms.

Secondly, acquirer firms with fewer growth opportunities, as measured by Tobin's q,

create more value than do firms with more growth opportunities. And thirdly,

announcement-period returns and changes in operating performance are lower for

11

firms that increase their global, industrial, or both forms of diversification. After cross-

sectional regressions, they conclude that changes in operating performance from

pre- to post-merger are lower for the firms that increase their global, industrial, or

both forms of diversification.

Gao, Ng & Wang (2008) use a database that contains 5 117 public and private

companies worldwide. They make a distinction between single-segment firms and

multi-segment firms on the one hand, and domestic and geographically diversified

firms on the other hand. Nevertheless, in their database, they don’t look for an impact

of industrial diversification on the value of the firms. The reason for this distinction is

a supposed correlation between being geographically diversified and being

industrially diversified. This correlation can cause a bias and that is why their

regression models control both industrial and global diversification. In addition, these

regression models also control other possible determinants of firm valuation. For

example, they include leverage as a proxy for any financing benefits or costs of being

geographically diversified. They also take R&D and advertising expenditure as a

proxy for a firm's proprietary assets. They find that being geographically diversified

affects the value of a firm. Firms with subsidiaries located in different regions of the

United States, in other words, geographically diversified firms, experience a valuation

discount of 6,2%. This geographic diversification discount increases when firms

expand their operations to different regions nationwide. In general they conclude that

the geographic location of a company is an essential component of corporate policies

and that it has important market valuation implications.

Hence, zero hypothesis 3 states:

H03: The combination of geographic diversification and industrial diversification has

no impact on the value of European companies.


be tested.

12

2.4. European Studies

Table 1 gives an overview of the papers discussed above. Almost all studies

about the impact of diversification on the value of a firm, have U.K. or U.S. samples.

There is no record of any study which investigates the impact of diversification on the

value of European firms.

Nevertheless, there are a few studies about diversification with a European

database. One of them is a study of Moerman (2008). In his study, Moerman

examines the impact of the harmonization of fiscal and economic policies within the

European Monetary Union (EMU) on the economies of member countries. He adopts

a mean-variance approach and he finds strong evidence that diversification over

industries yields more efficient portfolios than diversification over countries.

Nevertheless, the study of Moerman has not the same idea of value creation as

Lang & Stulz (1994) or Bodnar et al. (1999) So, further elaboration of his research

has little importance for this study.

Another recent study about diversification with a European sample, is from

Joliet & Hubner (2008). Based on a sample of 598 firms, spread over 9 countries,

they analyze the impact of corporate international diversification on domestic and

world betas through the notion of psychic distance between countries. Because they

do not analyze the impact of diversification on the value of companies, further

elaboration of their research is less important for this study.

13

Table 1: Concise Summary of Literature Review Notes: This table provides a summary of the papers discussed in the Literature Review about geographic and industrial diversification. More precisely, the source, the sample and the impact on the value of a company are given in a concise format. GD stands for Geographical Diversification. A firm is geographically diversified when it is established in more than 1 country or region, depending on the author. ID stands for Industrial Diversification. A firm is industrially diversified when it is operating in more than one sector. The method of stipulating sectors depends on the author.

GD ID

author source sample

prem

ium

disc

ount

prem

ium

disc

ount

Caves (1971), Hymner (1976)

Economica, MIT Press No empirical study x

Errunza & Senbet (1981) Journal of Finance U.S. multinational firms (1968-1977),

236 observations x

Kogut (1983) Sloan Managment Review No empirical study x

Errunza & Senbet (1984) Journal of Finance U.S. multinational firms (1970-1978),

402 observations x

Morck & Yeung (1992)

Journal of International Economics

1 277 U.S. firms (1987) x

Kogut & Kulatilaka (1994)

Management Science No empirical study x

Lang & Stulz (1994)

Journal of Finance, Journal of Political Economy

U.S. firms (1978-1990), 35 518 observations (excl. smaller firms: 17 371 observations)

x

Berger & Ofek (1995)

Journal of Financial Economics

3 659 U.S. firms (1986-1991), 16 181 observations x

Servaes (1996) Journal of Finance 2 593 U.S. firms (1961-1976) (x)

Reeb, Kwok & Back (1998)

Journal of International Business Studies

3 903 public firms (1987-1996) x

Graham, Lemmon & Wolf (2002)

Journal of Finance 356 acquisitions (1978-1995) No discount

Bodnar, Tang & Weintrop (1999)

Unpublished working paper 7 000 U.S. firms (1984-1997) x x

Rajan, Servaes & Zingales (2000)

Journal of Finance U.S. firms (1980-1993), 156 598 observations x

Brusco & Panunzi (2000)

Unpublished working paper No empirical study x

Campa & Kedia (2002) Journal of Finance 8 815 U.S. firms (1978-1996),

58 965 observations x

14

Table 1 (continuation): Concise Summary of Literature Review

GD ID

author source sample

prem

ium

disc

ount

prem

ium

disc

ount

Denis, Denis & Yost (2002) Journal of Finance 7 520 U.S. firms (1984-1997),

44 288 observations x x

Fauver, Houston & Naranjo (2003)

Journal of Financial and Quantitative Analysis

more than 8 000 firms from 35 countries (1991-1995) x

Fauver, Houston & Naranjo (2004)

Journal of Corporate Finance

more than 3 000 firms, Germany, U.K., U.S. (1991-1995)

No impact x

Villalonga (2004a) Journal of Finance 8 937 firms (1978-1997),

60 930 observations No discount

Villalonga (2004b)

Financial Management

U.S. firms (1989-1996), 12 708 observations x

Mackey & Barney (2005)

Unpublished Working Paper No empirical study x

Barnes & Brown (2006)

Journal of Business Finance and Accounting

495 U.K. firms (1996-2000), 1 628 observations

Depends on value metric

No impact

Doukas & Kan (2006)

Journal of International Business Studies

355 U.S. acquisitions (1992-1997), 612 observations

No discount

Bohl & Pal (2006)

Unpublished Working Paper

796 U.K. firms (1998-2003), 2 252 observations x

Freund, Trahan, & Vasudevan (2007)

Financial Management

194 U.S. acquiring industrial firms (1985-1998) x x

Gao, Ng & Wang (2008)

Journal of Corporate Finance

5 117 U.S.-based firms (1993-2003), 23 844 observations x

15

3. SAMPLE SELECTION AND METHODOLOGY

3.1. Sample Frame and Sample Description

The population of this study is defined as all European companies. The sample

is constructed by applying the criteria of different authors to the longest possible

period for which business segment data are available and comparable.

Consequently, in the first place, all listed, non-financial European (EU15) companies

as recorded in Amadeus® are taken.

In line with Campa & Kedia (2002), and Graham, Lemmon & Wolf (2002)

(referencing Ofek & Berger (1995)) non-financial firms are defined as having no

primary or secondary SIC codes in the range of 6 000 to 6 999. Firms with segments

in the financial sector are excluded because the valuation methods used in this paper

have proven to be problematic for these firms. Specifically, earnings before interest

and taxes (EBIT) are not meaningful for financial companies.

The EU15 is defined as “the member countries in the European Union prior to

the accession of ten candidate countries on May 1, 2004” and enhances the following

15 countries: Austria, Belgium, Denmark, Finland, France, Germany, Greece,

Ireland, Italy, Luxembourg, Netherlands, Portugal, Spain, Sweden, United Kingdom

(OECD, Main Economic Indicators, Paris). This leads to 3 441 firms during the period

1996 - 2008.

In the second place, non-small companies as recorded in Datastream® are

taken. According to Campa & Kedia (2002), Graham, Lemmon & Wolf (2002),

Villalonga (2004b), and Denis et al. (2002) (referencing Ofek & Berger (1995)) ‘non-

small firms’ are defined as having a total sales amount of more than $20 million per

year. This is more or less equal to €20 million per year, following the exchange rate

of 20021. To avoid distortions by ratio calculations from firms with sales or assets

close to zero, firms will be required to have minimum sales of €20 million. From the

3 441 firms of Amadeus®, 435 firms had no full data in Datastream®. After the

restriction of total sales, another 1 085 firms where lost, making for a sample of 1 921

companies. During the period of 1996 till 2008, this results in 12 427 observations.

1 The average exchange rate in 2002 equals $1 = €1,06106.

16

Outliers in the data are modified using the winsorizing technique: all reported

variables are 1% winsorized, which means that all numbers outside the first and the

99th percentile are confined to the 1-respectively 99-percentiel number.

Table 2 reports the number of firms in the sample classified by country and by

industry (at level one SIC code). This information can be used to take an in-depth

look at the geographical and industrial distribution of the sample. When looking at the

total number of observations of the different countries, one can state that the U.K.

(2 631 observations), Germany (2 291 observations) and France (1 874

observations) have most observations. This can be explained by the size and the

higher level of economic activity of these countries. There are ten different sectors,

reported at level one SIC code. It has to be pointed out that the financial sector is

excluded in the sample based on the SIC codes reported in Amadeus®. Furthermore,

looking at the details of the firms2, only Italy has observations in the sector ‘public

administration and other’ (PAO). These two observations come from the company

A2A, which is a listed Italian based conglomerate that provides energy, district

heating, waste management, networks and other services to several countries. The

primary and only sic-code is 9 600, which raised some suspicion and might be due to

differences in reporting standards. Omitting these observations has no impact on the

results. Besides, during the past decennia, agriculture gave way to the tertiary

industry in West Europe. This is well reflected in the sample where the sector

‘agricultural, forestry, and fishery products’ (AFF) counts 14 firms, which is less than

1% of the total observations.

Considering the number of observations, this research has a sufficiently large

dataset to perform cross-country as well as cross-sector analyses. Furthermore, with

this sample, an average of approximately 6,5 observations per company is reached.

The sample implies an attrition rate of 50,2% for individual firms. This attrition rate is

larger than the rate of Barnes & Brown (2006), which is 35%.

(Barnes & Brown, 2006, p.1 514) A possible explanation would be the difference in

period: Barnes & Brown (2006) have a five-year period of observation; this study

covers a thirteen-year period.

2 More details about the geographic and industrial distribution of the firms of this sample, can be found in Appendix I, Table 10.

Table 2: Geographical and Industrial Distribution of the Sample Notes: This table reports the number of observations in the sample classified by country and by broad industry. The last row and column provides the number of firms. Country codes (two letters) are: AT = Austria; BE = Belgium; DK = Denmark; FI = Finland; FR = France; DE = Germany; GR = Greece; IE = Ireland; IT = Italy; LU = Luxembourg; NL = the Netherlands; PT = Portugal; ES = Spain, SE = Sweden; GB = United Kingdom. SIC codes (three letters) are: AFF = agricultural, forestry, and fishery products; MCP = mining and construction products; LMP = light manufactured products; HMP = heavy manufactured products; TCE = transportation, communications, electric, gas, and sanitary service; WTR = wholesale trade; FIR = finance, insurance, and real estate; SER = services; HSE = health services; PAO = public administration and other. In the table, the level one SIC-code are indicated between brackets. Source: The sector data are taken from Amadeus®, the country data are taken from Datastream®.

SECTOR COUNTRY

AT BE DK FI FR DE GR IE IT LU NL PT ES SE GB # Obs.

# Firms

(0) AFF 0 0 0 0 0 4 47 0 12 0 0 0 0 0 18 81 14

(1) MCP 0 0 13 16 46 77 32 13 80 0 54 11 80 15 216 653 104

(2) LMP 53 95 137 139 266 387 201 24 267 0 206 51 155 14 405 2 400 333

(3) HMP 49 131 103 300 467 737 121 8 326 7 146 0 101 84 565 3 145 446

(4) TCE 50 37 48 66 161 193 69 2 164 45 46 11 100 40 241 1 273 196

(5) WTR 17 82 100 64 305 229 111 45 96 0 149 12 50 92 537 1 889 265

(6) FIR 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

(7) SER 9 45 52 104 534 460 27 21 105 5 192 22 54 110 542 2 282 420

(8) HSE 3 13 21 28 95 204 7 9 24 0 0 0 31 160 107 702 146

(9) PAO 0 0 0 0 0 0 0 0 2 0 0 0 0 0 0 2 1

# Obs. 181 403 474 717 1 874 2 291 615 122 1 076 57 793 107 571 515 2 631 12 427

# Firms 28 56 49 101 312 359 148 13 170 6 96 13 73 85 412 1 921

18

3.2. Measures

In this paper, the impact of diversification on the value of a company is

examined.

The value of a firm is expressed by the variable Market To Book (MTB).

Consistent with Lang & Slutz (1994), MTB is defined as follows:

MTBi,t = (MVEi,t + BVLi,t ) / BVTAi,t (1)

In other words, the Market To Book ratio for firm i in year t is equal to the Market

Value of Equity for firm i in year t plus the Book Value of Liabilities for firm i in year t

divided by the Book Value of the Total Assets for firm i in year t.

To observe the impact of diversification, a distinction is made between the two

most common diversification forms. Each company is examined on whether it is

geographically diversified, industrially diversified, none of them or both of them. A

firm is geographically diversified (multinational) when it has one or more subsidiaries

established in a different country than its (registered) European country of origin. A

firm is industrially diversified (multi-activity) when it is operating in more than 1 sector.

Consistent with Bodnar et al. (1999), Denis et al. (2002), Doukas & Kan (2006), and

Gao et al. (2008) different sectors are indicated by their four level SIC code. A firm is

as well industrially and geographically diversified (doubly diversified, fully diversified)

when it satisfies both descriptions above. In the analysis part of this paper, GEOG is

the proxy for geographic diversification for each firm, INDUST is the proxy for

industrial diversification for each firm and GEOGxINDUST is the proxy for fully

diversification for each firm.

The control variables measure size, leverage, profitability, investments, volatility,

sector and country characteristics. Profitability is measured by EBIT and investments

by capex.

19

3.3. Descriptive Statistics

The description of the variables is based on the method elaborated by

Barnes & Brown (2006). To reproduce diversification details, they made a compilation

of the various methods used by, among other, Berger & Ofek (1995) and

Doukas & Kan (2006). In performing this review, medians will be empasized rather

than means, in line with Berger & Ofek (1995), and Barnes & Brown (2006).

Table 3 displays the descriptive statistics for the sample used for the MTB value

measure. There are four different combinations of diversification: two forms of

industrial diversification namely single- and multi-activity, and two forms of

geographical diversification; domestic and multinational companies. There appears to

be an abundance of industrially diversified companies: multi-activity firms represent

71% of the total observations. More specific, multinational, multi-activity firms

dominate the observations with 55,6% of the total observations. Furthermore,

European industrially diversified firms on average are active in four different sectors .

European geographically diversified firms on average are active in ten different

countries.

When zooming in on the impact of diversification, the information about the

variables in Table 3 can be discussed. Keeping in mind companies with sales less

than €20 million per year have already been excluded from the sample, diversification

seems to have a positive correlation with the size of a company, either measured by

total assets or total sales. Especially geographic diversification has a great influence.

In other words, single-activity, multinational companies (YN), (respectively multi-

activity, multinational companies (YY)) have a sales volume of €192 million (€202

million), and total assets medians of €230 million (€208 million). On the contrary,

companies that are not globally diversified (NN, NY) have total assets and sales

volumes that are below €100 million. In addition, the test of differences indicates that

there is no statistically significant difference in size of geographic diversified firms,

whether industrially diversified or not (YN-YY is not statistically significant). Next,

following the co-insurance theory, diversified firms would be more leveraged. By

examining the results, one can conclude that it is the case for geographically

diversified firms: domestic firms are not significantly different (NN-NY is not

statistically significant), and the median value of leverage for multinational firms is

clearly higher even if this effect is not as outspoken as with size. Thirdly, there is a

positive relation between EBIT to sales and geographic diversification. However,

20

industrial diversification does not influence EBIT to sales: the profitability medians of

domestic firms are not significantly different, the same can be said about

multinational firms. Fourthly, capex to sales has a different relationship with

diversification: being industrially diversified (NY, YY) is not statistically different from

having a pure focus (NN). Only single-activity, multinational companies have a

distinct higher level of investments (capex to sales). Furthermore, diversification has

no impact on R&D to sales: the median is zero for all combinations of diversification.

And last but not least, for volatility there is no statistical difference between the

combinations of diversification (NN-YN, NN-NY, YN-NY, and YN-YY are not

statistically significant).

Overall, geographical diversification seems to have a positive impact on the

value of a company. Geographically diversified firms (YN, YY) have a higher sales

volume, higher total assets, a slightly higher leverage, and a better profitability.

Table 4 provides the distribution of the MTB ratio across the four different

combinations of diversification. The results suggest that being geographically

diversified has a positive value impact on the median values of MTB: the MTB value

of multinational firms (YN, YY) is higher than the MTB value of domestic firms (NN,

NY). These results are statistically significant on a 1% level for single- as well as

multi-activity firms. Furthermore, focusing on a single-activity has a positive impact on

the MTB values, although this effect is not significant for multinational companies.

The medians from domestic, multi-activity firms (NY) are below those of focused

firms (NN). As a conclusion, the row and column tests indicate that industrial focus

and geographical diversification are both value enhancing effects.

The diagonal tests, displayed in Table 4, can provide more information on the

relative strengths of these value enhancing effects. First, the diagonal test statistics

on the left indicate that domestic, multi-activity firms are statistically different from

multinational, single-activity firms (NY-YN is statistically significant). Thus, one can

conclude that domestic multi-activity firms (NY) have a lower value than multinational

single-activity firms (YN). This result could have been expected given that domestic

industrial diversified firms combine both value enhancing effects. Second, the

diagonal test statistics on the right indicate that focused firms are statistically different

from doubly diversified firms (NN-YY is statistically significant). Comparing domestic,

single-activity firms with doubly diversified firms results in a significant value surplus

21

for firms that are both industrially and geographically diversified. This result indicates

that, not taking other variables into account, the positive impact on MTB of

geographical diversification outweighs the negative impact of industrial diversification.

Summarized, the description of the variables shows a larger positive impact of

geographical diversification and a smaller positive impact of industrial focus on a

firm’s value.

Table 3: Descriptive Statistics of Sample for MTB Value Measures Notes: The sample comprises 1 921 firms which equates to 12 427 observations. Industrial Segments are the different industries, indicated on level 4 SIC code. Geographical Segments are the different countries a company is active in. The first row of each variable represents the mean, the second row represents the median. Between group significance of the means are tested using the two sample t-test, the medians using the non-parametric Mann-Witney test. The t-statistic (MW p-value) is shown in the first (second) row for each variable. The overall significance level is 5%. Source: The diversification and segment data are taken from Amadeus®, the other data are taken from Datastream®. Single-Activity Multi-Activity Test of differences Total Domestic

(NN) MNC (YN)

Domestic (NY)

MNC (YY)

(NN)-(YN)

(NN)-(NY)

(NN)-(YY)

(YN)-NY)

(YN)-(YY)

(NY)-(YY)

Total Observations 12 427 848 2 748 1 921 6 910

1 1 4,26 4,22 # Industrial Segments 1 1 3 3 1 9,69 1 10,84 # Geographic Segments 1 6 1 6

1 620 167 2 256 272 1 920 ,000 ,000 ,000 ,000 ,020 ,000 Total Sales (million €) 150 57 192 77 202 ,000 ,000 ,000 ,000 ,092 ,000

1 881 307 2 665 373 2 181 ,000 ,185 ,000 ,000 ,003 ,000 Total Assets (million €) 166 72 230 85 208 ,000 ,001 ,000 ,000 ,693 ,000

,163 ,151 ,165 ,175 ,161 ,047 ,002 ,105 ,070 ,348 ,007 Leverage ,105 ,072 ,117 ,092 ,105 ,000 ,118 ,000 ,000 ,011 ,000 ,065 ,051 ,070 ,051 ,069 ,006 ,950 ,007 ,000 ,663 ,000 EBIT/Sales ,066 ,057 ,070 ,052 ,070 ,000 ,349 ,000 ,000 ,625 ,000 ,076 ,088 ,082 ,082 ,071 ,277 ,296 ,001 ,929 ,000 ,003 Capex/Sales ,038 ,037 ,042 ,032 ,039 ,001 ,416 ,104 ,000 ,000 ,000 ,023 ,005 ,029 ,006 ,028 ,000 ,732 ,000 ,000 ,539 ,000 R&d/Sales ,000 ,000 ,000 ,000 ,000 ,000 ,040 ,000 ,000 ,000 ,000

10,24 9,83 10,25 10,13 10,31 ,044 ,182 ,013 ,456 ,624 ,195 Volatility 10 9 10 10 10 ,060 ,292 ,008 ,379 ,186 ,040

Table 4: Distribution of MTB Value across Diversification Categories Notes: The sample comprises 12 427 observations. MTBi,t is the Market To Book Ratio for firm i at time t, MVE i,t is the Market Value of Equity for firm i at time t, BVL i,t is the Book Value of Total Liabilities for firm i at time t, and BVTA i,t is the Book Value of Total Assets for firm i at time t. In addition to the Mean, Q1, Median and Q3 are reported. These core numbers are the first, second, and third quartiles, respectively. N refers to the number of observations of each group. Iseg is the mean number of industrial segments that the firm reports on its financial statement, as reported on the Datastream® industrial segment tape. Gseg is mean number of foreign (non-domestic) geographic locations that the firms reports on its financial statement, as reported in Amadeus®. The overall significance level is 5%. Sources: The diversification data are taken from Amadeus®, and data for the MTB-components are taken from Datastream®.

Geographical diversification Domestic Multinational

(NN) (YN) Row Test Stats (NN)-(YN)

Q1 Median Q3 Q1 Median Q3 1,018 1,276 1,715 1.112 1.460 1.018 [,000] [.000] Mean 1,524 (p = ,000) Mean 1,689 (p = ,000) N = 848 N = 2 748

Single- Activity

Iseg = 1 Gseg = 1 Iseg = 1 Iseg = 1

2-sample t-test p = ,000 Mann-Whitney ,000

(NY) (YY) Row Test Stats (NY)-(YY)

Q1 Median Q3 Q1 Median Q3 ,966 1,190 1,619 1.064 1.381 .966 [,000] [.000] Mean 1,428 (p = ,000) Mean 1,740 (p = ,000) N = 1 921 N = 6 910

Industrial Diversification

Multi- Activity

Iseg = 4,26 Gseg = 1 Iseg = 4.12 Iseg = 4.26


Diagonal Test Stats (NY)-(YN)

Column Test Stats (NN)-(NY)

Column Test Stats (YN)-(YY)

Diagonal Test Stats (NN)-(YY)





24

4. METHOD OF ANALYSIS

4.1. Multivariate Analysis

In line with prior research on the subject, a multiple regression analysis (OLS) is

used to explore the impact of diversification on the value of a firm. The dependent

value measurer is MTB, defined in ‘Measures’ (supra, p.18). Dummies are used to

check geographic and industrial diversification and the established methodology will

be elaborated by including control variables for sector and country. Equation (2)

describes the model:

MTB= α + ΣγiTDi + β1GEOG + β2INDUST + β3GEOGxINDUST + β4size

+ β5leverage+ β6 EBIT/sales + β7capex/sales + β8R&D/sales

+ β9volatility + β10sector + β11country + ε (2)

where, MTB is the Market To Book value measure, TDi is a time-based dummy

variable which equals 1 for year i an 0 otherwise (omitted for Yr 2007), GEOG is a

dummy variable which equals 1 for geographical diversification and 0 otherwise,

INDUST is a dummy variable which equals 1 for industrial diversification and 0

otherwise, GEOGxINDUST is a dummy variable which equals 1 for industrial and

geographical diversification and 0 otherwise, size is the natural logarithm of total

assets of a firm, leverage is a firm’s book value of debt divided by market value of

equity, EBIT to sales is a firm’s earnings before interest and taxes divided by the

sales ratio, capex to sales is a firm’s capital expenditure divided by the sales ratio,

R&D to sales is a firm’s research and development expenditure to sales ratio,

volatility is a firm’s volatility factor defined as the degree of fluctuation in the share

price, sector is a firm’s level 1 SIC code, and country is a firm’s home or listing

country.

25

4.2. Main Results

Table 5 displays the results of the MTB regressions. Five different models are

used to compare the obtained results of a European sample with existent literature

about both forms of corporate diversification (model II and model III) and a

combination of geographic as well as industrial diversification with and without

interaction coefficient (model V and model IV). Model I is a basic model without any

diversification dummies, which makes possible to observe the impact of

diversification on the value of a company.

The results of the various OLS-regression models in Table 5, show the different

impact of geographic and industrial diversification. Model IV is the most interesting

model, because it has the same structure as the model elaborated by

Bodnar et al. (1999), and Barnes & Brown (2006). Therefore, a comparison between

the results of this research and their investigation can be made. The p-value of the F-

test of model IV is lower than 0,01. Consequently, model IV is significant. The R

square of model V is 0,247. This means that 24,7% of the variance in the dependent

variable is explained by all the other variables, which is a normal value compared to

Barnes & Brown (2006), who had an R square of 18,50% and to Bodnar et al. (1999),

who got an R square of 24,7%. Consistent with Bodnar et al. (1999), this model finds

evidence for a premium for geographic diversification. The coefficient for industrial

diversification is not statistically significant in model IV. The reported geographic

diversification premium of 17,3% is comparable with Barnes & Brown (2006)’s

premium of 19% and is much higher than Bodnar et al. (1999)’s premium of 7%.

Therefore, one can conclude that the value impact of geographic diversification is

larger for European firms than for U.S. firms.

When taking into account an interaction coefficient between the two forms of

diversification (model V), the coefficient for industrial diversification becomes

significant on a 5% level. An industrial diversification discount of 7,6% turns up, which

is a normal figure in the literature: Bodnar et al. (1999) also found a discount of 7%.

The obtained premium for geographic diversification decreases to 10,2%. In addition,

being doubly diversified creates a diversification premium of 12,8%. This result is

very interesting because there are few studies, for example, Denis et al. (2002),

which added an interaction coefficient to the regression analysis.

26

Furthermore, the impact of the different control variables on MTB is given. For

model V, leverage and volatility are negative drivers of MTB in Europe. On the

contrary, EBIT on sales and R&D on sales are positive, as could be expected from

theory and previous studies. Size and capex on sales are insignificant. Omitting

these variables has no significant influence on the model.

The results of model II can be compared with other results from different

authors . Regression model II finds evidence for a geographic diversification premium

of 17,3%, which is opposite to the discount found by Doukas & Kan (2006). For

Europe, the four general reasons for a company to diversify geographically, studied

in the literature review, are of some importance and have a positive impact.

The results of model III can not be compared with results from research about

industrial diversification, because the found coefficient is not statistically significant.

Model I has an R square of 0,244 which means that 24,4% of the variance in

MTB is explained by the variables in this model. In comparison with model II, where

the dummy variable GEOG is added, the explanatory power rises to 24,7%. This

means that the GEOG helps explaining the variances in MTB. Evaluating model III in

light of model I, the addition of the dummy variable INDUST does not have an impact

on the explanatory power of the model. Consequently, INDUST does not explain the

variances in MTB any further.

In conclusion, all the zero hypotheses of this research will be rejected.

Diversification does have an impact on the value of European companies: industrial

diversification has a negative impact on the value, but geographically diversified

companies will experience a value boost. Combining the two forms of corporate

diversification provides a value increase that compensates for the industrial

diversification discount.

Table 5: Multivariate Test for Diversification Value Impact Notes: The sample comprises 12 427 observations. The regression model is described by equation (2) and the variables are defined in section 4. ***, ** and * indicate significance at the 1%, 5% and 10% levels respectively. Multivariate test results for year, sector and country dummies for model IV and V are reported in Appendix III, Table 12 and Table 13, respectively. Sources: The diversification data are taken from Amadeus®, and data for the control variables are taken from Datastream®.

Variable I t-statistic (p-value) II t-statistic

(p-value) III t-statistic (p-value) IV t-statistic

(p-value) V t-statistic (p-value)

GEOG ,173*** 7,515 ,173*** 7,505 ,102*** 2,643 (,000) (,000) (,008) INDUST ,008 -,403 ,002 ,106 -,076* -1,888 (,687) (,916) (,059) GEOGxINDUST ,102** 2,264 (,024) Intercept 1,644*** 22,377 1,656*** 22,573 1,637*** 21,565 1,654*** 21,824 1,701*** 21,634 (,000) (,000) (,000) (,000) (,000) Size ,031** 2,553 ,005 ,429 ,031** 2,553 ,005*** ,430 ,006 ,465 (,011) (,668) (,011) (,000) (,642) Lev -1,866*** -35,412 -1,852*** -35,211 -1,867*** -35,408 -1,853*** -35,197 -1,850*** -35,141 (,000) (,000) (,000) (,000) (,000) EBIT/sales 1,060*** 17,782 1,053*** 17,705 1,060*** 17,781 1,053*** 17,704 1,053*** 17,704 (,000) (,000) (,000) (,000) (,000) Capex/sales -,073 -,964 -,050 -,667 -,071 -,943 -,050 -,661 -,052 -,683 (,335) (,505) (,346) (,508) (,495) R&D/sales 3,751*** 22,365 3,603*** 21,381 3,753*** 22,367 3,603*** 21,376 3,612*** 21,425 (,000) (,000) (,000) (,000) (,000) Volatility -,006*** -3,451 -,006*** -3,745 -,006*** -3,442 -,006*** -3,742 -,006*** -3,727 (,001) (,000) (,001) (,000) (,000) F-test 101,295 100,645 98,822 98,241 96,107 (p-value) (,000) (,000) (,000) (,000) (,000) Adjusted R² ,244 ,247 ,244 ,247 ,248 # observations 12 427 12 427 12 427 12 427 12 427

28

4.3. Sensitivity and Robustness Tests

The choices made when developing this study are determinative for the

obtained results. In this section, the sensitivity of the results to changes in definitions

will be examined.

A) Sensitivity of the Sample

The sample is defined as ‘all listed, non-financial European (EU15), non-small

firms’. As a result of this definition, non economic companies can be included.

However, in order to avoid distortions, Villalonga (2004a) also excludes

non-economic activities, defined by their SIC code. Non-economic companies are

agricultural companies (SIC < 1000), membership companies (SIC 8600), private

household companies (SIC 8800), unclassified companies (SIC 8900), and

government companies (SIC 9000).

Studying the main sample in Table 2 points out that it only contains 81

observations (14 firms) in the agricultural sector. Omitting these observations has no

significant influence on the sample distribution, nor on the regression results.

Furthermore, various authors use different cut-off points to define ‘non small

firms’. Combined with different currencies, this yields quite some volatility for the

minimum sales number to be considered in diversification research. In the main

sample definition, ‘non-small firms’ are defined as having a total sales value of more

than €20 million per year. The choice of this cut-off point could have an impact on the

results.

According to Bodnar et al. (1999), ‘non-small firms' are defined as having a total

sales of more than $30 million per year. Converted3, this is €28 million per year: a

minimum of sales of €30 million is used to test the sensitivity of the sample. The

second row of Table 6 shows the industrial and geographical distribution of the

sample, defining ‘non-small firms’ as having a total sales of more than €30 million per

year. The new sales restriction excludes 1 162 observations during the period 1996-

2008, which brings the total sample on 11 265 observations. The distribution of the

sample for the four different combinations of diversification do not change in terms of

percentages of the total observations. A comparison between the main regression

3 The average exchange rate in 1999 equals $1 = € 0,93917.

29

results and those after the new restriction is made in Table 7. Also for this adaptation

of the sample, the geographical diversification premium is bigger than in the main

analysis. Moreover, the result for the industrial diversification discount became

statistically insignificant. Being doubly diversified has a slightly smaller positive

impact on the value of a company.

According to Barnes & Brown (2006), ‘non-small firms' are defined as having a

total sales of more than £30 million per year. Converted4, this is €44 million per year:

a minimum sales of €50 million will be used to test the sensitivity of the sample. The

third row in Table 6 shows the industrial and geographical distribution of the sample,

defining ‘non-small firms’ as having a total sales of more than €50 million per year.

The new sales restriction excludes 2 807 observations, during the period 1996-2008,

which brings the total sample on 9 620 observations. The distribution of the sample

for the four different combinations of diversification does not change in terms of

percentages of the total observations. A comparison between the main regression

results and those after the new restriction is made in Table 7. The geographical

diversification premium is bigger with the new sample. Furthermore, the result for the

industrial diversification discount becomes statistically insignificant. Being doubly

diversified has a slightly smaller positive impact on the value of a company.

4 The average exchange rate in 2006 equals £1 = €1,46725.

30

Table 6: Geographical and Industrial Distribution of the Sample (Sensitivity and Robustness Tests, part 1)

Notes: The row ‘main sample’ are the original results of the main analysis (supra, Table 3). The row ‘firms €30 million/year’ contains information about the industrial and geographical distribution of the sample with a minimum yearly sales of 30 million. The row ‘firms €50 million/year’ contains information about the industrial and geographical distribution of the sample with a minimum yearly sales of 50 million. The row ‘two level SIC code’ contains information about the industrial and geographical distribution of the sample, looking at industrial diversification at differences between the first two SIC digits. Source: All data are taken from Amadeus®.

B) Sensitivity of Different Measures

Industrially diversified firms are measured as ‘operating in more than 1 sector’.

In the main analysis, different sectors are indicated by their four level SIC code. The

robustness of this measure is examined by changing the definition into ‘different

sectors are indicated by their two level SIC code’, in line with robustness checks

performed by Bohl &Pal (2006), Denis et al. (2002), Gao et al. (2008), and

Bodnar et al. (1999). This indication is cruder and so, the sectors are less detailed.

One may expect an increase in the figure of single-activity firms because of this loss

of detail. Furthermore, this should not have a negative influence on the regression

results.

The fourth row in Table 6 shows the industrial and geographical distribution of

the sample, where the dummy variable INDUST equals 0 if the first two digits of the

primary and secondary SIC codes are equal and 1 otherwise. Comparing the results

to those in Table 3, confirms the expectations: single-activity firms now determine

61,3% of the sample instead of 30%. Multinational, multi-activity firms no longer

dominate the observations. Regression results (not reported) indicate no quantitative

difference when changing the definition of the industrial diversification dummy.

Single-Activity Multi-Activity

Total Domestic (NN)

MNC (YN)

Domestic (NY)

MNC (YY)

Main Sample 12 427 848 2 748 1 921 6 910

firms €30 million/year 11 265 675 2 533 1 640 6 417

firms €50 million/year 9 620 480 2 224 1 229 5 687

two level SIC code 12 427 1 681 5 936 1 088 3 722

31

Secondly, the variable size could be measured differently, especially in light of

the insignificance of size in the main model. The alternative definition used is ‘the

natural logarithm of the sales of a firm’ instead of ‘the natural logarithm of the total

assets of a firm’. The seventh column of Table 7 displays the most important results

of the OLS-regression for model V. Size, as log(sales) is statistically significant on a

1% level. As a result, one can conclude that using log(sales) instead of log(assets) is

perfectly acceptable for researchers looking for an in dept view of the impact of size

on MTB, as there is no loss in explanation power. However, the geographical

diversification premium is less reliable. This indicates that log(assets) is a better

measurer when solely interested in the impact of diversification on MTB.

Thirdly, in the main analysis, different sectors are indicated by their one level

SIC code. The robustness of this measure is examined by indicating different sectors

by their ICB code as found on Datastream®. The ninth column of Table 7 shows the

regression results of this robustness check. The regression results in a geographic

diversification premium of 8,2%, and an industrial diversification discount of 8,7%. In

addition, being doubly diversified remains an additional premium of 9,3%. All these

coefficients are significant at a 5% level. These results give the same sign and are in

the same order of magnitude as the main results reported in Table 5. Consequently,

one can conclude that using ICB codes instead of SIC codes provides quantitatively

the same results.

Fourthly, the dependent variable of the mean OLS-regressions is MTB.

Datastream® has its own version of the Market To Book value, MTBV, defined as

follows: “the market value of the ordinary equity divided by the balance sheet value of

the ordinary equity in the company, at security level”. Not all companies in the

sample have an MTBV value, so 520 observations (40 companies) are dropped from

the sample. The eleventh column of Table 7 reports the results of the regression with

both variants of the dependent variable. First, only 16,8% of the variance in MTBV is

explained by all the other variables, in contrast with 24,7% of the variance in MTB.

Secondly, both industrial and double diversification have statistically insignificant

results. Besides, the control variables have the same impact in the two models.

Consequently, computing MTB instead of using the Datastream® version is very

useful to measure the impact of diversification on a firm’s value.

Table 7: Multivariate Test for Diversification Value Impact (Sensitivity and Robustness Tests, part 1) Notes: The regression model is described by equation (2) and the variables are defined in section 4. ***, ** and * indicate significance at the 1%, 5% and 10% levels respectively. Model V (Table 5) are the original results of the main analysis (supra, Table 5). Model V (€30 million) is the robustness check from model V of the main analysis, with an adaptation of the sales restriction based on Bodnar et al. (1999). Model V (€50 million) is the robustness check from model V of the main analysis, with an adaptation of the sales restriction based on Barnes & Brown (2006). Model V (log(sales)) is the robustness check from model V of the main analysis, with a different definition for size. Model V (ICB-code) is the robustness check from model V of the main analysis, with another method to classify industries. Model V (MTBV), is the robustness check from model V of the main analysis, with MTBV as the dependent variable. Sources: The diversification data are taken from Amadeus®, and data for the control variables are taken from Datastream®.

Variable V Table 5

V €30 million

t-statistic (p-value)

V €50 million


V log(sales)


V ICB-code


V MTBV


GEOG ,102*** ,132*** 3,149 ,133*** 2,871 ,082** 2,116 ,082** 2,104 ,148** 2,056 (,002) (,004) (,034) (,035) (,040) INDUST -,076* -,068 -1,557 -,064 -1,304 -,078* -1,952 -,087** -2,145 -,035 -,472 (,120) (,192) (,051) (,032) (,637) GEOGxINDUST ,102** ,086* 1,771 ,095* 1,777 ,106** 2,350 ,093** 2,048 ,122 1,456 (,077) (,076) (,019) (,041) (,145) Intercept 1,701*** 1,823*** 21,562 1,873*** 20,406 1,526*** 19,016 2,009*** 26,211 2,035*** 13,991 (,000) (,000) (,000) (,000) (,000) Size ,006 -,018 -1,379 -,028** -1,995 -,009 -,780 ,118*** 5,105 (,168) (,046) (,436) (,000) Size [log(sales)] ,042*** 3,307 (,001) Lev -1,850*** -1,841*** -34,260 -1,848*** -32,875 -1,873*** -35,949 -1,875*** -35,383 -2,548*** -26,084 (,000) (,000) (,000) (,000) (,000) EBIT/sales 1,053*** 1,226*** 18,453 1,468*** 19,056 1,033*** 17,604 ,985*** 16,387 1,027*** 9,266 (,000) (,000) (,000) (,000) (,000) Capex/sales -,052 -,075 -,937 -,119 -1,352 -,024 -,317 ,029 ,329 -,273* -1,917 (,349) (,176) (,751) (,695) (,055) R&D/sales 3,612*** 3,968*** 20,928 4,521*** 20,059 3,623*** 21,575 3,869*** 22,139 3,363*** 10,717 (,000) (.000) (,000) (,000) (,000) Volatility -,006*** -,007*** -4,138 -,009*** -5,275 -,006*** -3,585 -,008*** -5,018 -,011*** -3,450 (,000) (,000) (,000) (,000) (,001) F-test 96.107 93,778 63,811 96,440 84,156 56,866 (p-value) (,000) (,000) (,000) (,000) (,000) (,000) Adjusted R² ,248 ,262 ,287 ,248 ,227 ,168 # observations 12 427 11 265 9 620 12 427 12 427 11 907

33

C) Robustness over Time

First, the expansion of the European Union from 15 countries to 25 countries on

May 2004 might have had an impact on the influence of diversification on a firm’s

value. To control this possible impact, the sample was split up in two periods: 1996-

2004 and 2004-2008. The distribution results or the results of the regression did not

show any impact of the expansion of the European Union on the way diversification

distributes value.

Second, the introduction of the Euro in 1999 might have had an impact on the

influence of diversification on a firm’s value. To control this possible impact, the

sample was split up in two periods: 1996-1999 and 1999-2008. The distribution

results or the results of the regression did not show any impact of introduction of the

Euro on the way diversification distributes value.

34

D) Robustness across the European Border

In this robustness control, Europe is seen as one country by changing the

definition of geographical diversification into ‘a firm is geographically diversified when

it has one or more subsidiaries established outside the EU15’. As a result, a United

Europe is easier to compare with the United Sates or other big countries.

Table 8 displays the distribution of the sample, after applying the new definition

of geographical diversification. As expected, there are less observations that reflect

geographical diversification. Instead of the 9 658 multinational observations (77,7%)

from the main analysis, there are 6 797 multinational observations (54,7%) when

viewing Europe as a single country. This is still a large number, which indicates that

geographically diversified companies have subsidiaries outside the EU15.

Table 8: Geographical and Industrial Distribution of the Sample

(Sensitivity and Robustness Tests, part 2) Notes: The row ‘main sample’ are the original results of the main analysis (supra, Table 3). The row ‘United Europe’ contains information about the industrial and geographical distribution of the sample with a new definition of geographical diversification. Source: All data are taken from Amadeus®.

Table 9 reports the results of the OLS regression, where GEOG’ is the proxy for

the geographic diversification for each firm, after applying the new definition of

geographical diversification. Model IV is used to compare the obtained results of a

European sample with the U.S. study of Bodnar et al. (1999).

Comparing the results from the main analysis (column two) with those from the

robustness check (column three), several conclusions can be made. First, GEOG’ is

statistically significant on a 1% level. So, having subsidiaries outside the EU15,

increases a company’s value with a premium of 12,7%. In comparison with the

Single-Activity Multi-Activity

Total Domestic (NN)

MNC (YN)

Domestic (NY)

MNC (YY)

Main Sample 12 427 848 2 748 1 921 6 910

United Europe 12 427 1 718 1 878 3 912 4 919

35

diversification premium across different European countries, one may suspect that a

part of the premium may come from being geographically diversified outside the

EU15. Moreover, using a different definition for geographical diversification, does not

have an impact on the coefficients of the other variables of the regression.

Comparing the results from the U.S. study of Bodnar et al. (1999) (column five)

with the main results about Europe, some differences catch the eye. First, as well the

European analysis as the U.S. analysis have an R square of 24%. This means that in

both models 24% of the variance in a company’s value is explained by the variables

in this model. Second, both analyses find a geographical diversification premium.

However, for Europe this premium is 12,7%, where the U.S. premium is only 2,7%.

This difference can be caused by the definition of the EU15: only 15 countries are

considered in the definition, but Europe is larger than just those 15 countries. Due to

that definition, it is possible to be active outside the EU15, but still in Europe. This

might cause a higher positive value impact. Furthermore, the dependent variable is

not the same for both models: Bodnar et al. (1999) use the adjusted-value measure.

Probably, this different value measure has an impact on the premium found. Third,

the value for the industrial diversification discount of model IV is not statistically

significant. Fourth, the impact on the control variables is different. In Europe,

leverage is negatively correlated to a firm’s value: The higher the leverage, the

smaller a company’s value. In the U.S., this is not the case: the control variable

leverage has a positive coefficient in the study of Bodnar et al. (1999). Furthermore,

the coefficients of both size and capex to sales are not statistically significant in

model IV of the robustness check. They will not be compared with the results of

Bodnar et al. (1999). The impacts of EBIT to sales and R&D to sales in Europe or in

the U.S. are not the same, but the differences are small.

In general, this robustness check shows that the positive impact of geographical

diversification is bigger for Europe than for the U.S. This means that it is more

interesting for European companies to found subsidiaries outside Europe than it is for

U.S. companies to diversify outside the U.S.

Further investigation about this subject is recommended.

36

Table 9: Multivariate Test for Diversification Value Impact (Sensitivity and Robustness Tests, part 2)

Notes: This table reports the main regression results of the robustness checks performed in section 4.3. The regression model is described by equation (2) and variables are defined in section 4. ***, ** and * indicate significance at the 1%, 5% and 10% levels respectively. Model IV (Europe) the robustness check from model IV of the main analysis, with an adaptation of the definition of geographical diversification. Model IV (Table 5) are the original results of the main analysis (supra, Table 5). Model (Bodnar et al. (1999)) are the original results of the U.S. analysis by Bodnar et al. (1999). Sources: The diversification data are taken from Amadeus®, and data for the control variables are taken from Datastream®. Furthermore, the regression results from Bodnar et al. (1999) are taken from Bodnar, Tang & Weintrop, 1999, p. 33.

Variable IV Table 5

IV Europe


Bodnar et al. (1999)

GEOG’ ,173*** ,127*** 6,314 ,027*** ,000 INDUST ,002 ,002 ,116 -,060*** ,907 GEOG’xINDUST - - - - Intercept 1,654*** 1,719*** 22,358 NA ,000 Size ,005*** ,001 ,095 ,016*** ,925 Leverage -1,853*** -1,855*** -35,215 ,028** ,000 EBIT/sales 1,053*** 1,056*** 17,751 2,093*** ,000 Capex/sales -,050 -,061 -,811 ,645*** ,418 R&D/sales 3,603*** 3,527*** 20,592 2,537*** ,000 Volatility -,006*** -,006*** -3,506 NA ,000 F-test 98,241 97,721 NA (p-value) (,000) (,000) (NA) Adjusted R² ,247 ,246 ,248 # observations 12 427 12 427 31 648

37

4.4. Discussion and Interpretations

The ‘Descriptive Statistics’ (supra, p.19) suggest a positive impact of

geographical diversification and a negative impact of industrial diversification on a

firm’s value. The multivariate analysis of this paper confirms that expectation and

results in a geographical diversification premium of 10,2%, and an industrial

diversification discount of 7,6%. Both results can be explained by the corporate

diversification literature. First, different authors indicate reasons why geographical

diversification will lead to an increase of a firm’s value. Being active in different

countries will create more value because of different institutional restrictions such as

lower taxes, a greater debt capacity, and a greater operating efficiency and flexibility,

according among others to Errunza & Senbet (1984), and Kogut & Kulatilaka (1994).

Second, there are two explanations for the industrial diversification discount.

Villalonga (2004b) argues that lower valued firms choose to diversify industrially and

Berger & Ofek (1995) reason that firms diversify by purchasing lower-valued firms.

However, the literature assumes and proves a causal link between diversification and

value.

In addition, both forms of corporate diversification interact and might create

synergy. To be able to measure the impact of being doubly diversified, an interaction

coefficient is introduced, in line with Denis et al. (2002). The research of this paper

finds a positive value for the interaction coefficient of 10,2%, which means that being

doubly diversified has a positive impact on the value of a firm. In other words, being

geographically as well as industrially diversified, creates an overall diversification

premium of 12,8%. The negative impact of industrial diversification on a firm’s value,

is canceled by the advantages of being active in multiple industries and in different

countries. The introduction of this interaction coefficient is a real strength for the

analysis.

The robustness checks indicate that the sample is robust and not very sensitive

to changes in the main definition. Furthermore, changing the definitions of variables,

even the dependent variable, leads to results which are fundamentally the same. The

main results are not skewed by the introduction of the Euro in 1999 nor by the

expansion of the EU in 2004. Changing the definition of geographical diversification

to reflect a United Europe, considering Europe as one country, still leads to a

geographic diversification premium. A comparison between the U.S. analysis by

38

Bodnar et al. (1999) and the United Europe study shows that geographic

diversification creates a higher value for European companies than for American

companies.

39

5. CONCLUSION

5.1. Summary

Extent literature on the impact of corporate diversification on a company’s value

reflects many insights but gives no univocal answer. A first remark is that the

literature has mainly focused on U.S. and U.K. firms. Secondly, most papers examine

the impact of either industrial or geographical diversification. However, the

importance of the impact of both forms of corporate diversification is emphasized by

different authors, among others Bodnar, Tang & Weintrop (1999), and

Barnes & Brown (2006).

This paper tried to complete international evidence by investigating the impact

of geographic and industrial diversification in 1 921 European firms over the period

1996-2008, which equals to 12 427 observations. On the other hand, the research in

this paper examines the impact of both forms of industrial diversification and also the

interaction between both industrial and geographical diversification. The initial

results, using MTB as a proxy for value, suggest an industrial (geographical)

diversification discount (premium) of 7,6% and 10,2% respectively, in line with the

predictions of most theories in the literature. Being doubly diversified results in a

positive impact for a company’s value of 12,8%, measured by the interaction

coefficient.

This study does not only confirm a geographical diversification premium for

European firms, but it also states that geographical diversification may create a

higher value for European companies than for U.S. companies.

5.2. Limitations and Guidelines for Further Investigation

Because the data used in this research are taken from the databases

Datastream® and Amadeus®, the internal validity of this study is quite high. The

detailed sample description makes it possible to redo this research with the same

companies and with the same data. The external validity is lower, because the

sample is limited to listed companies with a sales amount higher than €20 million per

year. The conclusions are not generalizable to small companies and/or companies

that are not listed.

40

The first limitation of this study is caused by the data selection done to identify

the sectors and countries where each company of the sample is active in. The main

intention was to use information from Worldscope® for those variables, and to

construct the database for this research like Barnes & Brown (2006),

Fauver, Houston, Naranjo (2004), and Joliet & Hubner (2008) did. Worldscope® has

product and geographic segment data on more than 8 000 firms in 49 countries.

Unfortunately, the Ghent University does not have access yet. Contacts with other

Flemish universities, the University of Maastricht and the University of Geneva, did

not provide a solution. Therefore, Amadeus® information about subsidiaries is used

for the dummy variables GEOG and INDUST. The major shortcoming of these data is

that they are not available for different years, with as a consequence that a firm

specific fixed effect is not included in the OLS-regression (equation (2)).

A second limitation is the restriction to one OLS regression. The main intention

was to do a second regression with the Adjusted Value Metric as dependent variable

instead of MTB in equation (2), following Bodnar et al. (1999), and

Barnes & Brown (2006). The data to construct this variable are the same detailed

Worldscope® information and are not accessible yet for the Ghent University.

Consequently, the overall results are less applicable than they could have been.

Thirdly, in Europe, accounting standards and reporting of information about

sectors and subsidiaries, differs across countries. This study introduces country

dummies, but probably not all country specific effects will have been eliminated.

Further investigation on the impact of diversification on the value of European

companies with the detailed Worldscope® data are recommended. The authors of

this paper even suggest to redo the study, with the segment data of Worldscope®.

VIII

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IX

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XI

LIST OF APPENDICES Appendix I: Sample Description................................................................................ XII Appendix II: Multivariate Analysis ............................................................................ XIII

Appendix I: Sample Description Table 10: Geographical and Industrial Distribution of the Sample Notes: This table reports the number of firms in the sample classified by country and by broad industry. The last row and column provide the number of observations. Country codes (two letters) are: AT = Austria; BE = Belgium; DK = Denmark; FI = Finland; FR = France; DE = Germany; GR = Greece; IE = Ireland; IT = Italy; LU = Luxembourg; NL = the Netherlands; PT = Portugal; ES = Spain, SE = Sweden; GB = United Kingdom. SIC codes (three letters) are: AFF = agricultural, forestry, and fishery products; MCP = mining and construction products; LMP = light manufactured products; HMP = heavy manufactured products; TCE = transportation, communications, electric, gas, and sanitary service; WTR = wholesale trade; FIR = finance, insurance, and real estate; SER = services; HSE = health services; PAO = public administration and other. In the table, the level 1 SIC code are indicated between brackets. Source: The sector data are taken from Amadeus®, the country data are taken from Datastream®.

SECTOR COUNTRY

AT BE DK FI FR DE GR IE IT LU NL PT ES SE GB # Firms

# Obs.

(0) AFF 0 0 0 0 0 1 10 0 1 0 0 0 0 0 2 14 81

(1) MCP 0 0 1 3 8 13 13 1 10 0 5 1 11 3 35 104 653

(2) LMP 5 12 13 19 47 45 47 2 38 0 26 7 16 2 54 333 2 400

(3) HMP 9 16 9 40 70 109 27 1 55 1 19 0 13 10 67 446 3 145

(4) TCE 6 8 5 6 28 30 14 1 25 4 6 1 14 5 39 192 1 273

(5) WTR 3 9 9 9 51 37 26 4 15 0 14 1 5 15 67 265 1 889

(7) SER 4 6 9 19 87 85 8 3 20 1 26 3 7 21 121 420 2 282

(8) HSE 1 5 3 5 21 39 3 1 5 0 0 0 7 29 27 146 702

(9) PAO 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 2

# Firms 28 56 49 101 312 359 148 13 170 6 96 13 73 85 412 1 921

# Obs. 181 403 474 717 1 874 2 291 615 122 1 076 57 793 107 571 515 2 631 12 427

XIII

Appendix II: Multivariate Analysis Table 11: Regression details for sector, country and year dummies – Model IV Notes: The sample comprises 12 427 observations. Country codes (two letters) are: AT = Austria; BE = Belgium; DK = Denmark; FI = Finland; FR = France; DE = Germany; GR = Greece; IE = Ireland; IT = Italy; LU = Luxembourg; NL = the Netherlands; PT = Portugal; ES = Spain, SE = Sweden; GB = United Kingdom. SIC codes (three letters) are: AFF = agricultural, forestry, and fishery products; MCP = mining and construction products; LMP = light manufactured products; HMP = heavy manufactured products; TCE = transportation, communications, electric, gas, and sanitary service; WTR = wholesale trade; FIR = finance, insurance, and real estate; SER = services; HSE = health services; PAO = public administration and other. In the table, the level 1 SIC code are indicated between brackets. The constant sector dummy is ‘Services’, the constant country dummy is ‘United Kingdom’, and the constant Year dummy is ‘2007’. Source: Data for the dummy variables are taken from Datastream®.

Sector IV t-statistic (p-value) Country IV t-statistic

(p-value) Year IV t-statistic (p-value)

(0) AFF -,247 -2,255 AT -,296 -4,010 2008 -,324 -5,529 (,024) (,000) (,000) (1) MCP -,042 -,969 BE -,039 -,750 2007 Constant (,332) (,453) (2) LMP ,096 3,581 DK -,119 -2,483 2006 ,038 1,105 (,000) (,013) (,269) (3) HMP Constant FI -,106 -2,590 2005 ,014 ,386 (,010) (,699) (4) TCE ,265 7,638 FR -,141 -4,613 2004 -,064 -1,764 (,000) (,000) (,078) (5) WTR ,164 5,653 DE -,188 -6,732 2003 -,142 -3,817 (,000) (,000) (,000) (6) FIR - - GR -,023 -,514 2002 -,265 -7,074 - (,607) (,000) (7) SER ,383 14,104 IE -,111 -1,248 2001 -,116 -2,986 (,000) (,212) (,003) (8) HSE ,503 12,271 IT -,195 -5,396 2000 ,279 6,821 (,000) (,000) (,000) (9) PAO ,289 ,428 LU -,394 -3,055 1999 ,349 8,101 (,668) (,002) (,000)

NL -,037 -,933 1998 ,178 3,953 (,351) (,000) PT -,278 -2,923 1997 ,074 1,575 (,003) (,115) ES -,089 -1,981 1996 -,007 -,137 (,048) (,891) SE ,128 2,696 (,007) GB Constant

XIV

Table 12: Regression details for sector, country and year dummies – Model V Notes: The sample comprises 12 427 observations. Country codes (two letters) are: AT = Austria; BE = Belgium; DK = Denmark; FI = Finland; FR = France; DE = Germany; GR = Greece; IE = Ireland; IT = Italy; LU = Luxembourg; NL = the Netherlands; PT = Portugal; ES = Spain, SE = Sweden; GB = United Kingdom. SIC codes (three letters) are: AFF = agricultural, forestry, and fishery products; MCP = mining and construction products; LMP = light manufactured products; HMP = heavy manufactured products; TCE = transportation, communications, electric, gas, and sanitary service; WTR = wholesale trade; FIR = finance, insurance, and real estate; SER = services; HSE = health services; PAO = public administration and other. In the table, the level 1 SIC code are indicated between brackets. The constant sector dummy is ‘HMP’, the constant country dummy is ‘United Kingdom’, and the constant Year dummy is ‘2007’. Source: Data for the dummy variables are taken from Datastream®.

Sector V t-statistic (p-value) Country V t-statistic

(p-value) Year V t-statistic (p-value)

(0) AFF -,236 -2,158 AT -,294 -3,979 2008 -,326 -5,560 (,031) (,000) (,000) (1) MCP -,039 -,906 BE -,041 -,790 2007 Constant (,365) (,430) (2) LMP ,097 3,640 DK -,121 -2,527 2006 ,038 1,108 (,000) (,012) (,268) (3) HMP constant FI -,106 -2,589 2005 ,014 ,387 (,010) (,699) (4) TCE ,268 7,724 FR -,139 -4,525 2004 -,064 -1,768 (,000) (,000) (,077) (5) WTR ,168 5,789 DE -,178 -6,697 2003 -,142 -3,826 (,000) (,000) (,000) (6) FIR - - GR -,017 -,373 2002 -,266 -7,082 - (,709) (,000) (7) SER ,385 14,155 IE -,106 -1,195 2001 -,116 -2,988 (,000) (,232) (,003) (8) HSE ,504 12,306 IT -,194 -5,389 2000 ,280 6,831 (,000) (,000) (,000) (9) PAO ,309 ,458 LU -,391 -3,029 1999 ,350 8,126 (,647) (,002) (,000)

NL -,040 -1,018 1998 ,179 3,969 (,309) (,000) PT -,279 -2,939 1997 ,075 1,600 (,003) (,110) ES -,091 -2,030 1996 -,006 -,111 (,042) (,024) SE ,126 2,659 (,008) GB constant

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